OCS Study

BOEM 2016-059

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

Review of the Long-term Monitoring 

Program and the Associated Data of the 

Long-term Monitoring Program at the

Flower Garden Banks 

National Marine Sanctuary
OCS Study

BOEM 2016-059

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico OCS Region

New Orleans, LA

August 2016

Review of the Long-term Monitoring

Program and the Associated Data of the

Long-term Monitoring Program at the

Flower Garden Banks

National Marine Sanctuary

Authors

Hawai’i International Environmental Services, Inc.

Prepared under BOEM Contract

M14PD00048

by

Hawai’i International Environmental Services, Inc.

70 Kihapai Street

Kailua, Hawai’i 96734
DISCLAIMER

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

(BOEM) and Hawai’i International Environmental Services, Inc. This report has been 

technically reviewed by BOEM, and it has been approved for publication. Approval does not 

necessarily signify that the contents reflect the views and policies of BOEM, nor does mention of 

trade names or commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

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

the Interior, Bureau of Ocean Energy Management, Environmental Studies Program Information 

System website and search on OCS Study BOEM 2016-059.

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

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

U.S. Department of Commerce

National Technical Information Service

5301 Shawnee Rd.

Springfield, Virginia 22312

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

Fax: (703) 605-6900

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

CITATION

Hawai’i International Environmental Services. 2016. Review of the long-term monitoring 

program and associated data of the long-term monitoring program at the Flower Garden 

Banks National Marine Sanctuary. U.S. Dept. of the Interior, Bureau of Ocean Energy 

Management, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study BOEM 

2016-059. 311 p.
i

CONTENTS

Chapter 1.0: General Program Evaluation ...................................................................................1

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

1.2 Historical Overview and Timeline .....................................................................................1

1.2.1 Program History .........................................................................................................2

1.2.2 Timelines....................................................................................................................8

1.3 Program Evaluation .........................................................................................................14

1.3.1 Introduction ..............................................................................................................14

1.3.2 Program Objectives ..................................................................................................15

1.3.3 Sampling Design.......................................................................................................18

1.3.4 Variables Measured ..................................................................................................20

1.3.5 Sampling Methodology.............................................................................................25

1.3.6 Data Handling...........................................................................................................29

1.3.7 Quality Assurance and Quality Control.....................................................................30

1.4 Discerning among Drivers of Change ..............................................................................31

1.5 Conclusions and Recommendations.................................................................................36

1.6 References Cited .............................................................................................................38

Chapter 2.0: Technique Evaluation............................................................................................48

2.1 Introduction.....................................................................................................................48

2.2 Methods Evaluation.........................................................................................................49

2.2.1 Benthic Cover...........................................................................................................49

2.2.2 Coral Growth............................................................................................................58

2.2.3 Coral Health .............................................................................................................67

2.2.4 Coral Recruitment.....................................................................................................69

2.2.5 Urchins, Lobsters, and Lionfish ................................................................................72

2.2.6 Fish Surveys.............................................................................................................73

2.2.7 Water Quality ...........................................................................................................77

2.2.8 Perimeter Surveys and Qualitative Observations.......................................................81

2.3 100 m by 100 m Study Site..............................................................................................82

2.4 Conclusions and Recommendations.................................................................................84

2.5 References Cited .............................................................................................................86

Chapter 3.0: Sampling Evaluation .............................................................................................97

3.1 Introduction.....................................................................................................................97
ii

3.2 Sampling Evaluation .....................................................................................................101

3.2.1 Benthic Cover.........................................................................................................101

3.2.2 Coral Growth..........................................................................................................135

3.2.3 Coral Health ...........................................................................................................152

3.2.4 Coral Recruitment...................................................................................................164

3.2.5 Sea Urchins, Lobsters, and Lionfish........................................................................173

3.2.6 Fish Surveys...........................................................................................................176

3.2.7 Water Quality .........................................................................................................202

3.3 Conclusions and Recommendations...............................................................................220

3.4 References Cited ...........................................................................................................223

Appendix A: Anderson-Darling Normality Test Results......................................................228

Appendix B: Outlier Calculation for EFGB Transect Coral Data .........................................231

Chapter 4.0: Evaluation of Analyses........................................................................................233

4.1 Introduction...................................................................................................................233

4.2 Analysis Evaluation.......................................................................................................234

4.2.1 Benthic Cover.........................................................................................................234

4.2.2 Coral Growth..........................................................................................................243

4.2.3 Urchins, Lobsters, and Lionfish ..............................................................................250

4.2.4 Fish Surveys...........................................................................................................251

4.2.5 Water Quality .........................................................................................................254

4.3 Evaluation of Bank Comparisons...................................................................................261

4.4 Conclusions and Recommendations...............................................................................262

4.5 References Cited ...........................................................................................................264

Chapter 5.0: Recommendations...............................................................................................271

5.1 Introduction...................................................................................................................271

5.2 Program Design.............................................................................................................271

5.3 Methodology and Techniques........................................................................................275

5.3.1 Benthic Cover.........................................................................................................276

5.3.2 Coral Growth..........................................................................................................278

5.3.3 Coral Health ...........................................................................................................279

5.3.4 Coral Recruitment...................................................................................................280

5.3.5 Sea Urchins, Lobsters, Lionfish ..............................................................................281

5.3.6 Qualitative Observations.........................................................................................281

5.3.7 Fish ........................................................................................................................282
iii

5.3.8 Water Quality .........................................................................................................282

5.4 Analyses........................................................................................................................284

5.5 Conclusions and Final Recommendations......................................................................287

5.6 References Cited ...........................................................................................................291
iv

LIST OF TABLES

Table 1.1 Discoveries and Events Timeline .................................................................................8

Table 1.2 Long-Term Monitoring Program Methods and Methods Changes Timeline ...............10

Table 1.3 Comparing Variables Measured at Flower Garden Banks with Other Long-term 

Monitoring Programs ................................................................................................................21

Table 1.4 Data collection methods for each variable and comparison to other LTM programs...26

Table 1.5 Potential Drivers of Ecosystem Change at FGB .........................................................33

Table 2.1 Random transect total areas .......................................................................................55

Table 2.2 FGBLTM Night Transect Widths and Total Area Surveyed .......................................73

Table 3.1 Equivalent Annual Trend Values for Net Trend Values..............................................99

Table 3.2 Benthic Cover Transects Current Sample Design Power Results..............................103

Table 3.3 Benthic Cover Transects Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................105

Table 3.4 Benthic Cover Transects Biennial Sampling Power Results .....................................108

Table 3.5 Benthic Cover Transects Biennial Sampling Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................109

Table 3.6 Benthic Cover Transects Triennial Sampling Power Results....................................110

Table 3.7 Benthic Cover Transects Triennial Sampling Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................111

Table 3.8 Benthic Cover Transects Biannual Sampling Power Results ....................................113

Table 3.9 Benthic Cover Transects Biannual Sampling Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................115

Table 3.10 Comparison of Power Analyses for Benthic Cover Transect Data ..........................116

Table 3.11 Benthic Cover Quadrats Current Sample Design Power Results.............................119

Table 3.12 . Benthic Cover Quadrats Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................120

Table 3.13 Benthic Cover Quadrats Biennial Sampling Power Results....................................122

Table 3.14 Benthic Cover Quadrats Biennial Sampling Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................123

Table 3.15 Benthic Cover Quadrat Triennial Sampling Power Results.....................................125

Table 3.16 Benthic Cover Quadrats Triennial Sampling Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................126

Table 3.17 Benthic Cover Quadrats Biannual Sampling Power Results ...................................127

Table 3.18 Benthic Cover Quadrats Biannual Sampling Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................128

Table 3.19 Comparison of Power Analyses for Benthic Cover Quadrat Data ...........................130
v

Table 3.20 Benthic Cover Deep Quadrats Current Sample Design Power Results....................131

Table 3.21 Benthic Cover Deep Quadrats Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes....................................................................................................132

Table 3.22 Benthic Cover Deep Quadrats Alternate Sampling Design Power Results..............133

Table 3.23 Benthic Cover Deep Quadrats Alternative Sample Designs Minimum Detectable 

Trends and Minimum Sample Sizes ........................................................................................134

Table 3.24 Lateral Growth Current Sampling Design Power Results (Ideal Sample Size)........138

Table 3.25 Lateral Growth Current Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes (Ideal Sample Size)............................................................................................139

Table 3.26 Lateral Growth Current Sampling Design Power Results (Realistic Sample Size) ..140

Table 3.27 Lateral Growth Current Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes (Realistic Sample Size)......................................................................................141

Table 3.28 Lateral Growth Biennial Sampling Design Power Results (Ideal Sample Size).......143

Table 3.29 Lateral Growth Biennial Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes (Ideal Sample Size)............................................................................................144

Table 3.30 Lateral Growth Triennial Sampling Design Power Results (Ideal Sample Size) .....145

Table 3.31 Lateral Growth Triennial Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes (Ideal Sample Size)............................................................................................146

Table 3.32 Lateral Growth Biannual Sampling Design Power Results (Ideal Sample Size)......147

Table 3.33 Lateral Growth Biannual Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes (Ideal Sample Size)............................................................................................148

Table 3.34 Sclerochronology Current Sampling Design Power Results (ANOVA)..................150

Table 3.35 Sclerochronology Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes (ANOVA) .........................................................................................150

Table 3.36 Sclerochronology Current Sampling Design Power Results (Simple Regression)...151

Table 3.37 Sclerochronology Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes (Simple Regression) ..........................................................................152

Table 3.38 Coral Size Current Sampling Design Power Results...............................................154

Table 3.39 Coral Size Current Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................155

Table 3.40 Coral Size Biennial Sampling Design Power Results. ............................................157

Table 3.41 Coral Size Biennial Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................158

Table 3.42 Coral Size Triennial Sampling Design Power Results ............................................159

Table 3.43 Coral Size Triennial Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................160

Table 3.44 Coral Size Biannual Sampling Design Power Results ............................................160
vi

Table 3.45 Coral Size Biannual Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................162

Table 3.46 Coral Size Biennial Sampling Design Power Results for Double the Sample Size ..163

Table 3.47 Coral Size Biennial Sample Design Minimum Detectable Trends for Double the 

Sample Size ............................................................................................................................164

Table 3.48 Coral Recruits Current Sampling Design Power Results ........................................166

Table 3.49 Coral Recruits Current Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................167

Table 3.50 Coral Recruits Biennial Sampling Design Power Results.......................................167

Table 3.51 Coral Recruits Biennial Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................168

Table 3.52 Coral Recruits Triennial Sampling Design Power Results......................................169

Table 3.53 Coral Recruits Triennial Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................169

Table 3.54 Coral Recruits Biannual Sampling Design Power Results ......................................170

Table 3.55 Coral Recruits Biannual Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................171

Table 3.56 Coral Recruits Biennial Sampling Design Power Results for Double the Sample Size

...............................................................................................................................................172

Table 3.57 Coral Recruits Biennial Sample Design Minimum Detectable Trends for Double the 

Sample Size ............................................................................................................................172

Table 3.58 Sea Urchin Current Sample Design Power Results.................................................174

Table 3.59 Sea Urchin Current Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................174

Table 3.60 Sea Urchin Alternative Sample Design Power Results ...........................................175

Table 3.61 Sea Urchin Alternative Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................176

Table 3.62 Fish Point Count Current Sample Design Power Results........................................178

Table 3.63 Fish Point Count Current Sample Design Minimum Detectable Trends and Minimum 

Sample Sizes...........................................................................................................................181

Table 3.64 Fish Point Count Biennial Sample Design Power Results.......................................183

Table 3.65 Fish Point Count Biennial Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................186

Table 3.66 Fish Point Count Triennial Sample Design Power Results .....................................188

Table 3.67 Fish Point Count Triennial Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................190

Table 3.68 Fish Point Count Biannual Sample Design Power Results......................................191
vii

Table 3.69 Fish Point Count Biannual Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................195

Table 3.70 Comparison of Power Analyses for Fish Point Counts ...........................................196

Table 3.71 Comparison of Power Analyses for Fish Point Counts ...........................................197

Table 3.72 Fish Belt Transect Current Sample Design Power Results......................................198

Table 3.73 Fish Belt Transect Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................199

Table 3.74 Fish Belt Transect Biannual Sampling Power Results ............................................201

Table 3.75 Fish Belt Transect Biannual Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes...........................................................................................................201

Table 3.76 Nutrient Water Quality Current Sampling Power Results (ND Replaced with Zero)

...............................................................................................................................................204

Table 3.77 Nutrient Water Quality Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes (ND Replaced with Zero) ..................................................................206

Table 3.78 Nutrient Water Quality Current Sampling Power Results (ND Replaced with DL).206

Table 3.79 Nutrient Water Quality Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes (ND Replaced with DL) ....................................................................208

Table 3.80 Nutrient Water Quality Current Sampling Power Results (Non-parametric Rank Data)

...............................................................................................................................................208

Table 3.81 Nutrient Water Quality Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes (Non-parametric Ranked Data)..........................................................210

Table 3.82 Nutrient Water Quality Monthly Sampling Power Results (Non-parametric Rank 

Data).......................................................................................................................................211

Table 3.83 Nutrient Water Quality Monthly Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes (Non-parametric Ranked Data)..........................................................212

Table 3.84 Nutrient Water Quality Quarterly Sampling Power Results for Double the Sample 

Size (Non-parametric Rank Data)............................................................................................213

Table 3.85 Nutrient Water Quality Quarterly Sampling Design with Double the Sample Size 

Minimum Detectable Trends (Non-parametric Ranked Data) ..................................................214

Table 3.86 Temperature and Salinity Time Series Current Sampling Power Results (ANOVA)

...............................................................................................................................................216

Table 3.87 Temperature and Salinity Time Series Current Sampling Design Minimum Detectable 

Trends.....................................................................................................................................218

Table 3.88 Temperature and Salinity Time Series Current Sampling Power Results (Simple 

Regression) .............................................................................................................................218

Table 3.89 Temperature and Salinity Time Series Current Sampling Design Minimum Detectable 

Trends.....................................................................................................................................219

Table 4.1 Logistic Regression Results for TKN Concentrations 2009 to 2013 .........................257
viii

Table 5.1 Rotating Panel Sample Designs ...............................................................................274
ix

LIST OF ABBREVIATIONS

AGRRA Atlantic and Gulf Rapid Reef Assessment

AIMS LTM Australia Institute of Marine Science Long-Term Monitoring

ANCOVA Analysis of Covariance

ANOSIM Analysis of Similarity

ANOVA Analysis of Variance

BLM Bureau of Land Management

BOEM U.S. Department of the Interior, Bureau of Ocean Energy Management

CAP Canonical Analysis of Principal Coordinates

CCA Crustose coralline algae

Chl a Chlorophyll a

cm Centimeter

CPCe Coral Point Count with Microsoft® Excel® extensions

CRAMP Coral Reef Assessment and Monitoring Program

CRCP Coral Reef Conservation Program

CRTR Coral Reef Targeted Research

CSA Continental Shelf Associates

CTB CCA, fine turf algae, bare rock

CV Coefficient of variance

db-RDA Distance-based Redundancy Analysis

DO Dissolved oxygen

EFGB East Flower Garden Banks

EPA Environmental Protection Agency

FGB Flower Garden Banks

FGBNMS Flower Garden Banks National Marine Sanctuary

HDS Honest Significant Difference

I&M Inventory and Monitoring

IA Interagency Agreement

IndVal Indicator Value Analysis

LTM Long-term monitoring

m Meters

MANOVA Multivariate Analysis of Variance

MDS Multidimensional Scaling

mm Millimeter

MMS U.S. Department of the Interior, Minerals Management Service

NOAA National Oceanic and Atmospheric Administration

np Non-parametric

NPS U.S. Department of the Interior, National Park Service

OCS Outer continental shelf

PACN Pacific Islands Network
x

PAH Polycyclic aromatic hydrocarbons

PAR Photosynthetically active radiation

PBS&J PBS&J Ecological Services

PCB Polychlorinated biphenyls

PCoA Principal Coordinates Analysis

ppt Parts per thousand

QA Quality Assurance

QC Quality Control

SC Species complex

SD Standard deviation

SFCN South Florida and Caribbean Network

SIMPER Similarity percentages

SOP Standard operating procedure

SPAW Special Protected Areas and Wildlife

SPMD Semi-permeable membrane devices

TAMU Texas A&M University

TKN Total Kjeldahl Nitrogen

USFWS U.S. Fish and Wildlife Service

USGS U.S. Geological Society

WFGB West Flower Garden Banks
xi

SUMMARY

This report provides an evaluation and analysis of the Flower Garden Banks (FGB) Long-term 

Monitoring (LTM) Program. A brief history of the program includes a timeline of major events 

and methodology changes that have occurred since the first long-term surveys began in 1988. 

The program design, objectives and documentation are then evaluated and compared to other 

LTM programs from around the world. We find that the FGB program needs a mission 

statement, clear and achievable objectives, improved data management, and finalized standard 

operating procedures (SOPs) for each method. We also suggest that the single 100 meter (m) by 

100 m study site at each bank lacks sufficient replication and habitat coverage to sufficiently 

monitor long-term changes across the entire coral cap. 

The methods used in this monitoring program are individually evaluated. This includes 

reviewing the variables chosen, the methods used to collect data for those variables, and 

determining if they are acceptable for LTM and answer the questions being asked by the FGB

program. We also made comparisons to other LTM programs and to other surveys conducted 

within the Flower Garden Banks National Marine Sanctuary (FGBNMS). 

Some of the recommendations are as follows: the focus of permanent quadrats should return to 

recording process variables rather than state variables, which are collected with random 

transects; the lateral (encrusting) growth method should be terminated due to continual problems 

with this method; the frequency of sclerochronology samples should be reduced; one fish survey 

method should be chosen to conserve program resources; and photosynthetically active radiation, 

hydrocarbons and metals relevant to oil and gas drilling should be included in water quality 

sampling. 

Power analyses are conducted for each method using the current annual sampling schedule and 

the following alternatives: biannual, biennial, and triennial sampling. We found that most 

methods lack sufficient power to detect a cumulative change of 20% after five or 10 years with a 

confidence of 0.8. Statistical power is found to be very similar for biannual and triennial 

sampling in many methods. We suggest that additional study sites be added to the program that 

are sampled on a less frequent basis, but improve total study area and sample size.

The statistical methods used for analyzing data within each sampling methods since 2010 are 

evaluated and compared to other LTM programs. We consistently found that conclusions are 

being made based on qualitative comparisons and apparent trends, without sufficient statistical 

analyses to support the claims. Additionally, most analyses only encompass two consecutive 

surveys and lack the long-term perspective that is the goal of a LTM program. Most methods 

would also benefit from increasing the use of non-parametric testing when appropriate, and 

verifying that assumptions are met when using parametric tests. Several alternative statistical 

tests that would improve power and interpretability of results are recommended.

Finally, we summarize recommendations from each evaluation into an overall program 

modification scheme that includes cost considerations for each change. The most pertinent 

recommendations are that the program should develop clear and achievable objectives, standard 

operating procedures for each method, and that the sampling design should be expanded to 

include additional study sites at each bank. A rotating panel sampling schedule is suggested, 
xii

which would not greatly increase program costs once new sites were established. Further cost 

control measures include discontinuing unsuccessful or unnecessary methods such as lateral 

growth and one of the fish survey methods.
1

CHAPTER 1.0: GENERAL PROGRAM EVALUATION

1.1 Introduction

The Flower Garden Banks (FGB) are two underwater diapirs (salt domes) that host coral reef 

ecosystems; they are located along the continental shelf of the Gulf of Mexico. These reefs are 

among the healthiest coral reefs in the Gulf and Caribbean region (Johnston et al. 2013; Jackson 

et al. 2014). East FGB (EFGB) and West FGB (WFGB) are located 193 and 172 kilometers (km) 

offshore of Galveston, Texas, respectively (Johnston et al. 2013). The reef caps of EFGB and 

WFGB are 19 km apart (Gittings et al. 1993). The coral caps rise to approximately 15 meters (m) 

below the surface and descend to a depth of 61–150 m between banks. Concerns regarding the 

impacts of offshore oil and gas development in the vicinity of the banks led to numerous 

scientific studies in the 1970s and 1980s and to the establishment of the FGB Long-Term 

Monitoring (LTM) program in 1988. The LTM program has undergone numerous changes 

through its history from program inception through the current methods.

Coral reef degradation resulting from numerous stressors, including overexploitation of marine 

resources, coastal development, and climate change is well-documented worldwide (Burke et al.

2011; Jackson et al. 2014). Remarkably, in the more than 25 years of continuous monitoring by 

the LTM program, no significant long-term changes in coral cover have been detected at FGB. 

Warning signs of ecosystem decline have been perceived in recent years; they include increased 

incidence of bleaching events, presence of invasive species, and reduction in fish densities, but 

statistically significant trends have not been identified (Johnston et al. 2013).

Data collected through the LTM program may provide an important baseline for survey sites 

where impacts of various acute and chronic stressors could be quantitatively compared. 

However, concerns have been raised over the years if the methods and analyses employed by the 

program are adequate to detect community changes and discriminate among causal factors 

(Dokken et al. 1999; BOEM 2014b). Until now, the FGBLTM program has not been reviewed 

by an independent, third party to evaluate performance and efficacy (BOEM 2014b). The 

Bureau of Ocean Energy Management (BOEM) (BOEM 2014b) contracted an environmental 

engineering and consulting firm to conduct a thorough review of the specific techniques, 

sampling strategies, and analyses. The results of Part I of this review, the General Program 

Evaluation, are presented in this chapter.

1.2 Historical Overview and Timeline

In this section, we provide a discussion of the investigative history at FGB and timelines 

detailing the program history and events of interest to the LTM program since the 1970s. A 

comprehensive timeline is provided in Table 1.1, and a more focused timeline detailing changes 

to the LTM program methods is provided in Table 1.2.
2

1.2.1 Program History

Although FGB first became of interest to policy makers in the oil and gas industry during the 

1970s, the banks had been known to fisherman much earlier. Fisherman long-line fished in the 

area since the late 1800s (Bright et al. 1985). In 1936, following a hydrographic survey of a large 

portion of the Gulf, the official discovery of the banks was first recorded by the U.S. Coast and 

Geodetic Survey (now part of the National Oceanic and Atmospheric Administration [NOAA]) 

(Bright et al. 1985; Jackson et al. 2014). In 1953 the presence of coral at the banks was first 

proven by Stetson (Stetson 1953; Bright et al. 1985), and subsequently, the first diving 

exploration at the banks occurred in 1960 (Jackson et al. 2014 and references therein).

In the 1960s and early 1970s, driven by an interest in documenting the unique biota on the banks 

and in developing a conceptual model of coral reef growth on a terrigenous shelf, Texas A&M 

University (TAMU) conducted numerous diving explorations and mapping efforts (Bright et al.

1985; Gittings et al. 1993). The first assessment of coral cover was conducted on WFGB in 

1972, followed by the first accurate positioning of the reefs and the first comprehensive coral 

reef assessment on the Banks in 1974 (Bright and Pequegnat 1974; Bright et al. 1985). During 

this assessment, Bright and Pequegnat (1974) documented 253 invertebrate species and 103 fish 

species at WFGB.

In the mid-1970s, research goals shifted due to interest in the petroleum resources in the area. 

TAMU performed dozens of cruises under funding from the Bureau of Land Management 

(BLM), Mineral Management Service (MMS) [now BOEM] (Rezak et al. 1982). Research 

focused on providing a biological and geological baseline to the MMS to assist in developing 

policy and protective regulations with regard to oil and gas drilling operations near the banks 

(Gittings et al. 1993). Research included collection of sediment and rock samples and the use 

side-scan sonar, sub-bottom seismic profiles, and hydrographic measurements in mapping the 

banks and surrounding areas. These explorations led to numerous discoveries in the region 

including the discovery of a nepheloid layer surrounding all banks in the area in 1975 and the 

discovery of the Brine Lake at EFGB in 1976. Numerous biological assessments were performed 

expanding the list of species known to the banks and highlighting the relatively pristine state of 

the ecosystems compared to other reefs in the Caribbean and Gulf (Bright et al. 1985). 

In 1974, BLM implemented protective lease stipulations around the banks and others in the 

region. A “No Activity Zone” was established out to 100 m depth at each bank. A “One-Mile 

Zone” beyond the “No Activity Zone” required shunting within 10 m of the seafloor and 

required monitoring studies to be conducted at the reefs in conjunction with the drilling. A FourMile Zone required shunting within this buffer, but no monitoring (Gittings et al. 1992).

In 1975, Mobil Oil Corporation drilled the first exploratory well in the vicinity of FGB, 

installing two temporary wells in Lease Block A398. Continental Shelf Associates (CSA) studied 

the effects of this activity and found no observable adverse effects on the coral reefs (Bright and 

Rezak 1976). In 1977, another exploratory well was drilled in the vicinity of the banks with no 

apparent effect on the reefs (CSA 1978). In 1981, the first production platform in the area, Mobil 

HI-A389-A, was installed approximately midway between the two exploratory drilling sites. The 

platform is about 1.5 km southeast of the nearest upper coral reef habitat at EFGB and 0.75 km 
3

from the nearest living benthic features (Boland et al. 1983). This was the first production 

platform in the US to operate close to a documented coral reef (Boland et al. 1983).

BLM continued to fund reef monitoring studies until 1982. From 1982 through 1988, required 

monitoring studies were funded by oil companies conducting exploratory drilling near the banks 

(Gittings et al. 1992). CSA performed numerous biological monitoring studies, eventually 

leading to the establishment of the study sites used in the current FGBLTM program (CSA 1989; 

Gittings et al. 1993). 

By this time it was well understood that the FGB were unique; they are some of the northernmost 

high-diversity coral reefs and the most pristine natural reef systems in the Gulf. Coral cover was 

high, near 50 to 60 percent, and species diversity was high compared to other northern reefs (but 

lower than Caribbean coral reefs to the south). Although the common species at the FGB were 

similar to other reefs in the Caribbean and Gulf, octocorals and scleractinian corals of the genus 

Acropora were observed to be absent from the reefs, likely due to the latitude being at the 

northernmost limit of the coral distribution range (Bright et al. 1985; CSA 1989). 

In 1983 and 1984, a mass die-off of the sea urchin species Diadema antillarum occurred across 

the Caribbean and Gulf (Mumby et al. 2006). The die-off apparently affected the urchins at FGB

(Dokken et al. 1999), but no quantitative assessment of the urchin populations prior to the event 

had been conducted. There is no baseline information for D. antillarum population densities at 

FGB (D. antillarum population surveys became part of the LTM program in 1996).

In the summer of 1988, CSA established a 100 m by 100 m LTM study site at WFGB. The study 

site included 134 permanent lateral growth stations, 40 permanent photo quadrat stations, 32 

permanent accretionary growth stations, and videotaped transects of two study site 

perimeters.These permanent stations were monitored, and 20 random transects were 

photographed (CSA 1989). Under funding by MMS, in late 1988 through 1989, TAMU 

established a 100 m by 100 m LTM study site at EFGB, which was comparable to the CSA site 

at WFGB. The EFGB study site included 120 permanent lateral growth stations, 40 permanent 

photo quadrat stations, 30 permanent accretionary growth stations, and two videotaped 100 m 

perimeter transects (Gittings et al. 1993).

The establishment of these study sites marked the start of the FGBLTM program. In 1989, 

funded by MMS, TAMU conducted the first semi-annual LTM survey on both banks. Lateral 

growth at 120 permanent stations (60 Psuedodiploria strigosa [formerly Diploria strigosa] and

60 Montastrea annularis [species complex which included Orbicella annularis, O. faveolata, 

and O. franksi]), accretionary growth at 30 permanent stations and in two coral cores, benthic 

cover at 40 permanent photo quadrat stations and 20 random photo transects, and qualitative reef 

status on two 100 m video transects, and three 360 degree corner videos were monitored at each 

bank within the 100 m by 100 m study sites. Water quality parameters, including dissolved 

oxygen (DO), salinity, temperature, and ambient light, were also measured using shallow and 

deep grab samples and in situ instrumentation. Thermographs were also deployed for continuous 

temperature monitoring (Gittings et al. 1993). TAMU surveyed the stations semi-annually, in the 

late winter to early spring and late summer to early fall, from 1989 through 1991 (Gittings et al.

1993). Results of the surveys suggested no change in long-term trends of stability in the coral 

reef community in comparison to prior research at the banks. Coral disease was documented and 
4

postulated to be a potentially significant source of coral mortality. During the surveys in 1991, 

the first mass coral spawning was observed at the banks (Gittings et al. 1993). 

Physical reef damage caused by anchoring on the FGB was recognized as a primary threat to the 

reefs. Although large vessels had historically anchored on the reefs, after the area was listed as 

Protected on navigational charts in 1986, large vessel anchoring may have been reduced. Small 

vessel anchoring, particularly related to commercial and recreation vessels, remained a source of 

reef damage. As a result, 12 mooring buoys were installed in 1990 (Gittings et al. 1992, 1993).

The FGB National Marine Sanctuary (FGBNMS) was established in 1992. As part of the 

designation as a US National Marine Sanctuary, the following actions became prohibited or 

restricted at the Banks: 

 anchoring or mooring of all vessels within the sanctuary boundaries

 discharge of any material or matter within the sanctuary boundaries

 any alteration of the seabed within the sanctuary boundaries

 any injury or removal or attempt of injury or removal of any living or non-living 

sanctuary resource

 taking of marine mammals and sea turtles

 possessing or using within the sanctuary boundaries any fishing gear except conventional 

hook and line gear, and possessing or using explosives within the sanctuary boundaries or 

releasing electrical charges within the sanctuary boundaries.

From 1992 onward, the LTM was conducted annually in the late summer and early fall. TAMU 

conducted the survey in fall 1992, no surveys were conducted in 1993, and CSA conducted the 

LTM surveys in 1994 and 1995 and analyzed the data from 1992 through 1995 (MMS 1996). 

The results of the 1992 through 1995 data suggested no significant change in long-term stability. 

Difficulties with the permanent accretionary and lateral growth stations were found during this 

period, and as a result, most of the stations were replaced. Masonry expansion bolts were used to 

replace the accretionary growth spikes, because many of the spikes had become loose between 

annual surveys. The number of random photo transects was reduced from 20 per bank to 14 per

bank, the lateral growth stations were reduced to 60 Psuedodiploria strigosa (no Orbicella 

species) per bank, water profiles (temperature, salinity, DO, and ambient light) were collected, 

and light meters were installed at the banks and the nearby Mobil oil platform to continuously 

monitor photosynthetic active radiation (PAR) range light (MMS 1996).

Starting in 1996, funding for the LTM program was shared by MMS and NOAA (FGBNMS). 

Also in 1996, TAMU resumed the task of completing the LTM surveys under competitive 

contract. During the 1996 to 1997 contract period, the focus of the effort to measure 

accretionary growth at the study sites was narrowed to only O. faveolata (one species of the 

Montastrea annularis species complex [SC]). Previously, no specific species of the species 

complex was targeted because the three species that make up the complex were considered 

subspecies. Semi-permeable membrane devices (SPMDs) were added to the water quality 

methods to measure polycyclic aromatic hydrocarbons (PAHs), pesticides, and polychlorinated 

biphenyls (PCBs). Diadema antillarum surveys were added to the LTM program and video 

transect surveys of the deeper areas outside the study sites were performed (Dokken et al. 1999). 
5

Following the recommendations of Dokken et al. (1999), the accretionary growth spike method 

was discontinued after 1997. 

TAMU monitored the study sites annually from 1998 through 2001. In 2001, semi-permanent

platforms were installed near each study site, and YSI multi-parameter data sondes were 

deployed on those platforms to continuously monitor pressure, temperature, salinity, PAR, DO, 

pH, and turbidity. Surveys outside the study areas, consisting of three random photo transects, 

were performed from 1998 to 2001 to determine if the study sites were representative of the coral 

reef cap (Dokken et al. 2002, 2003). In 2001, the International Maritime Organization designated 

FGBNMS as a no-anchor zone to protect the coral reef habitats there (NOAA 2008).

As in previous years, results of TAMU’s monitoring from 1996 through 2001 revealed no 

change in long-term trends. Studies of the deep reef areas outside the study sites showed no 

significant inconsistencies between the study sites and the reef areas outside (Dokken et al. 2002,

2003). Dokken et al. (1999) discussed weaknesses of the LTM program including that the 

program design was unequipped to assess cause-and-effect relationships of ecosystem dynamics.

From 2002 through 2008, PBS&J Ecological Services (PBS&J) was contracted to perform the 

annual LTM surveys (Precht et al. 2006, 2008a; Zimmer et al. 2010). In 2002, some of the 

changes recommended in previous reports with respect to sampling methodology were adopted, 

including adding deep permanent repetitive quadrat stations (nine stations) at EFGB (Precht et al.

2006). Stationary visual fish surveys were also added to the LTM program in 2002. Before that, 

fish surveys had been conducted at the reefs occasionally, but these studies were independent of 

the LTM program. The water quality methods were also changed at this time. Near-surface, midwater, and near-bottom water samples were collected and analyzed for chlorophyll a (Chl a), 

total phosphorus, mercury, chromium, total Kjeldahl nitrogen (TKN), ammonia, nitrate, and 

nitrite. YSI data sondes and thermographs continued to be deployed, but SPMDs for PAH, 

pesticide, and PCB analysis and light meters were no longer used. In 2004, after a comparison 

study, PBS&J began using digital videography for the random transect surveys. Four coral cores 

were collected per bank each contract period starting in 2003. Also, in 2004 the water sample 

analyses changed slightly to include Chl a, TKN, ammonia, nitrate, nitrite, and soluble reactive 

phosphorous. In 2008, SeaBird MicroCAT instruments were deployed with the YSI data sondes 

to collect continuous temperature and conductivity measurements (Zimmer et al. 2010).

Two hurricanes passed close to the banks during PBS&J’s seven years of conducting the LTM 

surveys (Precht et al. 2008b; Zimmer et al. 2010). Hurricane Rita, a Category 3 hurricane, passed 

within 50 miles of the EFGB on September 23, 2005. Soon after the storm, FGBNMS staff 

visually assessed the reefs condition and noted significant damage. In November 2005, a team 

from PBS&J visited the 100 m by 100 m study site at EFGB to assess the hurricane impact using 

the 40 repetitive quadrats, 9 permanent deep stations, 2 permanent 100 m video transects, and 

water quality data sondes. Approximately 1.5% of the coral colonies in the repetitive quadrat 

stations were found to be missing during the post-hurricane assessment. Colonies were 

dislodged, toppled, and relocated. Overall, the coral cover remained similar to previous studies, 

but the corals suffered from a severe bleaching event (about 10% of colonies were bleached). 

Hurricane damage was less at the deep stations, but missing colonies and bleaching were 

documented. Elevated water temperatures were recorded for 50 days during that year, and 
6

temperatures were lower during the passage of the hurricane, so the bleaching event may not 

have been related to the hurricane (Precht et al. 2008b).

Hurricane Ike, also a Category 3 hurricane, passed directly over EFGB on September 18, 2008. 

The 2008 annual monitoring was delayed until November 2008, when hurricane damage was 

assessed. The full suite of LTM surveys were performed at both banks in November 2008 as a 

combined annual survey and post-hurricane assessment (Zimmer et al. 2010). About 2.3 m2of 

coral were missing at the banks when permanent stations were compared between September 

2007 and November 2008. Psuedodiploria strigosa colonies accounted for most of the loss. 

Despite the hurricane damage, no long-term changes in coral community and fish populations 

were detected at the banks between 2002 and 2008 (Precht et al. 2006; Zimmer et al. 2010). 

Other important events also occurred during the same period. Tubastraea coccina, a potentially 

aggressive invasive species of coral originally from the Indo-Pacific (Riul et al. 2013), was first 

observed at EFGB in 2002. However, the species was not observed during the LTM surveys until 

2009 (Johnston et al. 2013). One living colony of Acropora palmata was discovered at WFGB in 

2003, outside of the LTM study site. Another living colony of Acropora palmata was discovered 

at EFGB in 2005, also outside of the LTM study site (Zimmer et al. 2006). Those discoveries led 

to exploratory research and the discovery of sub-fossil A. palmata and A. cervicornis underlying 

the banks in 2006 (Zimmer et al. 2010; Precht et al. 2014). Prior to these discoveries it was 

thought that the FGB were too far north for Acroporids to survive. Both Acroporid species are 

listed as listed as Threatened under the Code of Federal Regulations (50 CFR 223, Threatened 

marine and anadromous species). 

In 2009, NOAA’s FGBNMS staff assumed responsibility for performing the annual LTM 

monitoring under an interagency agreement (IA) (MMS 2009). The decision to move to an IA

rather than a competitive contract with a third party was made in part due to the purchase of the 

R/V Manta in 2008 (Johnston et al. 2013; Jackson et al. 2014). An IA is in place through 2016. 

The LTM program remains dual-funded by BOEM and the FGBNMS with both agencies 

supporting approximately 50% of the program.

In 2009, methods changes included switching from film still photography to digital still 

photography for the repetitive quadrats and replacing the use of YSI data sondes with seabird 

MicroCATs (Johnston et al. 2013). YSI data sondes were used for opportunistic collection of 

temperature and salinity profile during the cruises through 2013. In 2010, digital still 

photography was also adopted for the random transect surveys, which had been performed using 

digital videography since 2004.

The Deepwater Horizon oil spill occurred between April and July 2010 at a location 

approximately 520 km west northwest from EFGB (Hsing et al. 2013; Johnston et al. 2013). 

Approximately 4.1 million barrels of crude oil was released into the Gulf at 1,520 m depth, 

during the three months between the blowout and when the well was capped (Hsing et al. 2013). 

This resulted in a massive deep-water plume of oil and gas as documented by Camilli et al.

(2010), which threatened coral communities (Fisher et al. 2014). The spill is known to have 

impacted sea turtles, birds, marine mammals, oysters, marine plants, deep-sea corals, shallow 

water corals, marine fish, zooplankton, and shorelines and marshes around the Gulf (NOAA 

2010, Ocean Conservancy 2014). SPMDs were deployed at the banks during 2010 to test for 
7

hydrocarbons from the spill. No hydrocarbon signatures were found in the SPMDs, and no 

visible oil or oil-related impacts were observed during the 2010 LTM surveys (Johnston et al.

2013). To date no measureable affects have been reported at FGB in relation to the spill (NOAA 

2014).

In 2011, the first observation of the invasive Pacific red lionfish (Pterois volitans) was made at 

the Banks (Nuttall et al. 2014). This predator has had negative effects on reefs across the 

Caribbean and Western Atlantic since it first became established in 2009; it causes reef fish 

decline, resulting in community phase shifts with loss of herbivores (Munoz et al. 2011; Nuttall 

et al. 2014).

From 2010 through 2013, NOAA’s Coral Reef Conservation Program (CRCP) funded an 

extensive three year study at the FGBNMS independent of the LTM program (Clark et al. 2014). 

The study included mapping the benthic habitats, mapping fish densities, and documenting the 

condition of benthic and fish communities in the coral reef zone and mid-, lower, and upper 

mesophotic reef zones. The effort covered significantly more reef area than previous studies, and 

it provides important baseline information on the various zones examined. Results of this 

monitoring period were similar to those found by the FGBLTM of a stable coral community with 

high coral cover.

In 2012, FGBNMS was listed under the Special Protected Areas and Wildlife (SPAW) Method

of the Cartagena Convention (Jackson et al. 2014), an international environmental treaty among 

25 countries in North and South America and the Caribbean to protect their coastal and marine 

resources in a sustainable manner (CEP 2014). The SPAW method assists with improving 

management and conservation of areas that require protection and threatened or endangered 

species. Between 2006 and 2015 NOAA listed 22 coral species as threatened and three coral 

species listed as endangered under the Endangered Species Act. These listings affected Orbicella 

and Acropora species at FGB, all of which was now considered threatened (NOAA 2015). 

Also in 2012, permanent deep quadrat sites were added to EFGB and WFGB to increase the 

number of sites to a total of 11 at EFGB and 12 at WFGB. In 2014, several updates were made to 

LTM program methods, as documented in the IA (BOEM 2014a). Sea urchin and lobster surveys 

were increased in number from two per bank to three per bank and lionfish counts were added to 

that survey. Belt transects (16 per bank) were added to the fish survey methods. Lateral growth 

measurements are now only required for 30 Psuedodiploria strigosa stations, and 16, rather than 

14, random transects are now required per bank. Additionally, coral size and health surveys and 

coral recruitment surveys were added to the LTM program. Results from the 2014 LTM surveys 

will be combined with the 2013 results and documented in a formal report.
8

1.2.2 Timelines

Table 1.1 Discoveries and Events Timeline

Major events relevant to the FGBLTM program are listed by year. The contractors or agencies that 

performed the research or monitoring work are indicated in the Contractor column.

Year Contractor Events and Discoveries

1972 TAMU 

discovery 

and 

research 

cruises 

funded by 

TAMU, 

MMS, and 

BLM

Fist percent coral cover assessment 

on WFGB

1973

1974 First accurate positioning of FGB 

consistent with OCS platform 

positioning

Bright & Pequant conduct first coral reef 

assessment; report 253 invertebrate and 

103 fish species as WFGB

1975 Mobil Oil installs first drilling 

platforms near FGB

Nepheloid layer discovered at FGB

1976 Brine lake discovered at FGB

1977 EFGB cruise, biological monitoring 

initiated

1978

1979

1980 WFGB cruise, biological monitoring 

initiated

LGL conducts reef fish survey 

comparing Banks to drilling platforms

1981

1982 Biological 

monitoring 

by CSA 

funded by 

oil 

companies

1983 Mass mortality of Diadema 

anitllarum (1983–1984)

1984

1985 Recreational diver charters begin at 

FGB

1986

1987

1988 TAMU 

conducts 

LTM

CSA establishes 100m by 100m 

study site on WFGB

TAMU establishes 100m by 100m study 

site on EFGB

1989 LTM surveys begin: Random 

transects, Repetitive Quadrats; 

Accretionary Growth; Encrusting 

Growth; Sclerochronology; Video 

Perimeter Transects; Water Quality 

& Light

1990 Permanent mooring buoys installed

1991 First FGB coral spawning observed

1992 FGB becomes National Marine 

Sanctuary

1993

1994 CSA 

conducts 

LTM

LTM surveys become annual

1995

1996 TAMU 

conducts 

LTM

1997 New species discovered–Mardi 

Gras Wrasse (Halichoeres burekae)

1998 Bleaching event

1999

2000 LSU conducts coral recruitment and 

genetic connectivity study at FGB
9

Year Contractor Events and Discoveries

2001 FGBNMS is designated as a noanchor zone by the International 

Maritime Organization

ROV and sub surveys expand studies in 

deep portions of sanctuary

2002 PBS&J 

conducts 

LTM

Tubastraea coccina first observed at 

FGB

2003 Acropora palmata colony discovered 

at WFGB

2004

2005 Hurricane Rita; post-hurricane 

damage assessment surveys

First documented coral disease 

outbreak at FGB; A. palmata colony 

discovered at EFGB

2006

2007

2008 Hurricane Ike Bleaching event; Manta vessel begins 

service

2009 NOAA 

conducts 

LTM

2010 NOAA conducts independent study 

(2010–2013)

Deepwater Horizon spill

2011 Pterois volitans or miles first 

observed at FGB

2012 FGBNMS listed under the Special 

Protected Areas and Wildlife 

(SPAW) Method of the Cartagena 

Convention

2013

2014 NOAA proposes to list coral as 

endangered species

2015 As of 2015, 22 coral species are 

listed as threatened and 3 coral 

species listed as endangered under 

the Endangered Species Act
10

Table 1.2 Long-Term Monitoring Program Methods and Methods Changes Timeline

Brief descriptions and LTM methods are listed by method. Methods remain the same throughout the LTM history until changes are indicated.

Year Contractor LTM Methods and Changes

Random 

Coral

Transects

Accretionary 

Growth

Encrusting

and Lateral 

Growth

Repetitive 

Quadrats

Water 

Quality

Perimeter 

Videography

Deep 

Coral 

Reef 

Surveys

Sea Urchin 

Survey

Fish 

Survey

1988 TAMU 20/bank, 

film still 

photograp

hy

Growth 

spikes (30 

O. annularis 

& 30 

P. strigosa) 

and sclerochronology (2 

cores/bank)

60 O. 

annularis, 60 

P. strigosa

/bank

8m2

quadrats, 

40/bank, 

film 

photograp

hy

Shallow & 

deep grab 

samples 

DO, salinity, 

temp, 

ambient 

light

100m along 

boundary 

lines, 

2/bank

1989

1990

1991

1992

1993 No Surveys

1994 CSA 14/bank Masonry 

expansion 

bolts instead 

of spikes

60 P. 

strigosa only

PAR light 

meters 

added at 

Banks and 

Mobile 

platform

1995

1996 TAMU O. faveolata 

only (30 

bolts/bank & 

1 core/bank)

SPMD 

analysis 

added

Added 3600

rotational 

view at 

corners

Video 

transects

Diadema 

surveys 1.5 

hrs after 

sunset

1997

1998

1999

2000

*
11

Year Contractor LTM Methods and Changes

Random 

Coral

Transects

Accretionary 

Growth

Encrusting

and Lateral 

Growth

Repetitive 

Quadrats

Water 

Quality

Perimeter 

Videography

Deep 

Coral 

Reef 

Surveys

Sea Urchin 

Survey

Fish 

Survey

2001 Semi-perm. 

platforms 

and YSIs 

installed

2002 PBS&J Growth 

spikes 

removed 

from 

methods

Mid-water 

grab added, 

No SPMD, 

Analyses: Chl 

a, total 

Phosphorus, 

Hg, Cr, TKN, 

NH3, NO3

-

, 

NO2

-

8m2

repetitive 

quadrats, 

9 at 

EFGB 

only

Stationary 

visual 

surveys 

added (16 

/bank)

2003 Number of 

cores 

increased to 

4 per bank

2004 Digital 

videography

Total 

Phosphorus 

changed to 

soluble 

reactive 

Phosphorus, 

Cr and Hg 

removed

2005

2006 Size 

estimate 

used to 

calculate 

biomass

2007

2008 MicroCAT 

delployed
12

Year Contractor LTM Methods and Changes

Random 

Coral

Transects

Accretionary 

Growth

Encrusting

and Lateral 

Growth

Repetitive 

Quadrats

Water 

Quality

Perimeter 

Videography

Deep 

Coral 

Reef 

Surveys

Sea Urchin 

Survey

Fish 

Survey

2009 NOAA YSI no 

longer 

deployed, 

Seabirds 

deployed in 

place, 

opportunistic YSI 

temp and 

salinity 

profiles 

collected 

during cruise

2010 Digital still 

photography

Hydrocarbon

s analyzed in 

water 

samples for 

2010 only in 

response to 

Deepwater

Horizon spill

2011

2012 Two 

more 

quadrats

installed 

at EFGB 

and 12 

installed 

at 

WFGB
13

Year Contractor LTM Methods and Changes

Random 

Coral

Transects

Accretionary 

Growth

Encrusting

and Lateral 

Growth

Repetitive 

Quadrats

Water 

Quality

Perimeter 

Videography

Deep 

Coral 

Reef 

Surveys

Sea Urchin 

Survey

Fish 

Survey

2013 16/bank, 

include 

coral 

recruitment and 

size 

surveys 

on 8/bank

Increased 

to 6/site 

and include 

P. volitans 

/miles

Belt 

transects 

added 

(16/site)

2014 No 

opportunistic 

YSI 

measurements 

required
14

1.3 Program Evaluation

1.3.1 Introduction

Environmental LTM programs are directed toward providing data for effective management of a 

particular resource. As such, the goals of the resource managers must be at the forefront of 

planning a program and will ultimately drive the scale of the effort required, and the level of 

scientific rigor, necessary in the program design (Hill and Wilkinson 2004). The FGBLTM 

program is managed by BOEM and NOAA, who aim to monitor and preserve the sensitive coral 

reef ecosystems located at FGB from anthropogenic environmental stressors (NOAA 2012; 

BOEM 2014a). Successfully preserving the coral reef ecosystems will depend on early detection 

of environmental impacts and identification of the causes of those impacts. With this in mind, the 

LTM program should be rigorous enough to detect small changes in various parameters, identify 

the cause of the changes, and direct management actions to prevent further impacts.

One primary aspect of a successful LTM program design is careful attention to statistical and 

quantitative problems, such as statistical power of a survey design, methodological bias and 

uncertainty, and meaningful analysis of data (Gitzen and Millespaugh 2012). Lack of attention to 

these issues when planning program design is a common cause for program failure and 

ineffectiveness. For monitoring to produce information that is useful in making management 

decisions, the program should be based on well-defined, measurable questions and a rigorous 

statistical design (Lindenmayer and Likens 2009; Williams et al. 2009).

Gitzen and Millespaugh (2012) provide the following list of indicators that a monitoring program 

has adequately addressed quantitative issues:

 An explicit summary of the past and ongoing roles, and plans for future involvement, of 

quantitative experts involved in addressing these components

 Specific monitoring objectives and clear definitions of the target populations of interest 

and the sample frame (in time and space)

 Quantified sampling objectives regarding focal parameters of interest, with clear practical 

justification for how these were selected

 Tied to these objectives, specific description of selected high-priority analyses that are 

expected to be routinely conducted

 Careful conceptual identification of major relevant sources of variance and bias for the 

study at hand

 Quantitative estimates of variance components and measures of the uncertainty of these 

estimates

 A specific plan for addressing expected sources of bias or at worst periodically assessing 

their current magnitude, or very strong justification for ignoring them

 Quantitative assessments of expected performance of candidate survey designs relative to 

primary sampling objectives

 Justification for choice of design and very clear documentation of the design

 Identification of areas of flexibility and inflexibility for change

 Ongoing records of study implementation and data on necessary covariates and 

subpopulations of interest
15

 Explicit specification of how major assumptions related to the methodology and about 

potential sources of bias were addressed during data analysis 

Although it is apparent that some of these considerations were addressed in the FGBLTM 

program, the biggest shortcoming of the current program design is a lack of documentation. 

There is currently no cohesive monitoring method document for the FGB program. Monitoring 

reports and IAs have been used to document the study design, methodologies, and analyses year 

after year. The current IA (BOEM 2014a), for example, states “In general, survey techniques are 

listed and described in detail in monitoring reports from previous reporting periods” (Johnston et 

al. 2013). However, the level of detail in these documents is not enough to address quantitative 

issues or to ensure that the methods and analyses are carried out consistently year to year.

Standard operating procedures (SOPs) are currently under development for FGBLTM program 

(M. Johnston, FGBNMS, personal communication November 11, 2015).

Development of detailed monitoring methods is a key component of quality assurance for any 

LTM program (Oakley et al. 2003; Fancy and Bennetts 2012). Such methods should document 

how data are collected, managed, analyzed, and reported with enough detail that all aspects of 

the program can be understood and reproduced by various observers. Without this 

documentation, it is difficult to determine if changes detected by the monitoring program are a 

true measure of environmental changes or observer bias induced by slight methodology changes 

among observers (Oakley et al. 2003). The FGBLTM program has been carried out by several 

different contractors and agencies over the 25 years that the program has been running, and it is 

evident the level of quality assurance is quite variable and not well documented. This raises 

concern as to the comparability and quality of data across the years. 

The key issue of method documentation is examined in several sections of this report. This and 

other aspects of good quantitative program design are evaluated in relation to the 12 indicators 

listed above by Gitzen and Millespaugh (2012).

1.3.2 Program Objectives

Program objectives and goals are the foundation of a LTM program, informing program design, 

chosen methodology and objective assessment of program success (Hill and Wilkinson 2004;

Mumby et al. 2014). A careful approach to program design, determined by the ultimate program 

goals, will increase usefulness of the monitoring data while reducing unnecessary expenditure in 

the form of wasted effort or resources. 

The overall objective of the FGBLTM program is to monitor the biological health of the coral 

reef communities at EFGB and WFGB to detect any significant effects from natural and/or 

human-induced factors that could potentially threaten the coral communities (BOEM 2014a). 

Initially, the program was designed with the following specific objectives: 

1. provide relevant and timely environmental data to guide development of policies 

concerning oil and gas exploration and development in the vicinity;

2. document long-term changes in coral and associated communities at the Banks caused by 

petroleum exploration and development or other human activities; and 
16

3. document long-term natural variation in reef-building and associated communities on the 

banks (Gittings et al. 1993).

Over the years, objectives defined in the monitoring reports have become more generalized. By 

2006 the stated objectives were to: 

1. document long-term changes in community composition of reef-building coral and 

associated communities;

2. document long-term natural variation in reef growth;

3. stimulate ancillary research efforts and coordinate monitoring activities by other agencies 

and institutions; and 

4. evaluate and synthesize monitoring data and other research in order to assess the impact 

of change and provide recommendations to managers (Precht et al. 2006). 

In more recent monitoring reports, specific monitoring objectives are not at all clearly outlined

(Johnston et al. 2013).

Because the FGBLTM program is dual-funded by BOEM and NOAA, it is imperative that 

objectives are clearly stated to ensure that the interests of both agencies are being addressed by 

the program. According to the FGBNMS 2012 Management Plan, NOAA’s interest in the LTM 

program is examining the health of the reef as an early warning system for sanctuary 

management so that steps can be taken to prevent future loss of health if detected (NOAA 2012). 

It is noted that the water quality measurements could aid sanctuary management in making 

informed decisions on the surrounding offshore reefs. According to the current IA between 

BOEM and NOAA, BOEM’s interest in the LTM project is to gain information on 

environmental impacts on the marine and coastal environmental of the Outer Continental Shelf 

(OCS) that may be affected by oil and gas development. The Agreement also states that the 

program should provide an adequate baseline to discriminate among drivers of change within the 

ecosystem (BOEM 2014a). This ability to determine causes of change seems to be a key point 

for both agencies. To meet these needs, it is critical that the LTM program monitor the 

community dynamics at FGB and provide information that will allow for discrimination among 

drivers of change, natural or anthropogenic. This will enable NOAA to make informed 

management decisions to prevent future loss and BOEM to steer policy if oil and gas production 

is identified as a driver of community health decline.

Evaluation of all program elements, from design to interpretation, will be informed by stated 

program objectives. Based on previous technical reports, where objectives are more explicitly 

enumerated, and the current IA, the objectives of this LTM will be considered the following:

1. assess the health status of coral reefs through the evaluation of changes in living coral 

and other benthic community cover, coral growth rates, reef fish population dynamics, 

water quality and other indices of reef vitality;

2. document long-term changes in community composition of reef-building coral and 

associated communities caused by petroleum exploration and development or other 

anthropogenic activities;

3. document long-term changes in community composition of reef-building coral and 

associated communities caused by natural variation in the reef community;
17

4. differentiate among drivers of community change to inform management decisions and 

enable action to alleviate anthropogenic drivers;

5. maintain an adequate baseline to discriminate among the drivers of variation within the 

ecosystem of the northern Gulf.

Gitzen and Millespaugh (2012) stress the importance of specific, unambiguous monitoring 

objectives that are measureable, achievable, results-oriented, and applied over a specific relevant 

time-frame (s.m.a.r.t., as defined by Williams et al. 2009). Currently, the FGBLTM program 

does not have documented s.m.a.r.t objectives. Such objectives are immensely important to 

ensuring that management goals underlying the program are met. The objectives of the 

FGBLTM should specify: 

 the attributes of the resource to be monitored (coral percentage cover, coral species 

diversity, fish biomass, etc.);

 the spatial and temporal frame to be monitored;

 the type of change the program is designed to detect;

 the magnitude of change that should be detectable;

 the required precision for estimating change; and

 the level of error that is acceptable (Reynolds 2012).

These specific objectives should be developed in collaboration among scientists and subject 

matter experts who will be leading the program and performing the work, the managers and 

stakeholders who will use the data generated, and quantitative experts. Ideally, these objectives 

would be in place at the beginning of the LTM program; however, to evaluate the success of the 

program up to this point and to ensure future success, we recommend that specific objectives be 

developed and documented promptly (prior to the next IA or contract).

For an example, the general objective “to assess the health status of coral reefs through the 

evaluation of changes in living coral and other benthic community cover, coral growth rates, reef 

fish population dynamics, water quality and other indices of reef vitality” should be separated

into several, more specific objectives. One appropriate specific objective might be to monitor 

changes in scleractinian coral cover to achieve 80% likelihood of detecting 10% change in the 

communities at the 100 m by 100 m study sites after five years of monitoring. The details of such 

an objective should be adjusted to meet the needs of the adaptive management programs of 

BOEM and NOAA in regards to the FGB.

The National Park Service (NPS) Inventory and Monitoring (I&M) Program provides an 

excellent example of well-defined monitoring objectives. Each method within the program 

defines realistic, specific, and measureable goals (NPS 2012). For the Pacific Island Network 

program, general management objectives are defined as determining spatial and temporal 

patterns in benthic marine cover and community distribution. Then specific monitoring 

objectives are defined, such as annually measuring the abundance of sessile benthic marine 

macroinvertebrates (including corals) and algal assemblages at randomly-selected hard bottom 

sites between 10 and 20 meters depth. Sampling objectives are also defined, such as achieving 

80% likelihood of detecting a 25% change in coral cover after 10 years of monitoring (Brown et 

al. 2011). The regional method for benthic marine monitoring was carefully designed around 
18

these specific monitoring objectives (Brown et al. 2011), alleviating uncertainty about what data 

need to be collected and how the data will be used (NPS 2012).

1.3.3 Sampling Design

A well-planned sampling design is also central to meeting a program’s specific monitoring 

objectives. The sampling design determines where samples are located within a study site, and 

consequently, what valid scientific inferences can be made from data collected. A representative 

sample is one that portrays the characteristics of the population, in that data collected from the 

sample directly relate to the population. Achieving a representative sample is necessary if data 

are to be used to make inferences over a population or community. Limitations and bias in the 

sampling design must be considered when interpreting and analyzing data (McDonald 2012; 

Olsen et al. 2012).

Sampling design has two primary levels of planning: spatial and temporal. Due to the inherent 

necessity of most population surveys to collect data only from a subset of the entire population, 

study sites are placed throughout the study area to collect data that are statistically representative 

of the population, such that valid, defensible inferences can be made from the samples to the 

entire population. Spatial design encompasses where and how study sites are placed within the 

area of interest (Reynolds 2012). The methods used for spatial design can range from simple 

random to various non-probability designs, such as choosing areas based on professional 

judgment or ease of access (Olsen et al. 2012). Once the spatial design has been determined, the 

temporal design addresses the frequency of sampling at each site. This can range from each site 

being sampled every time to only being sampled once in the lifetime of the monitoring program 

(Urquhart 2012). 

There are many considerations that go into creating optimal spatial designs (Gitzen et al. 2012). 

For estimates of variance derived from sample data to be statistically valid, they must be 

representative of the variance across the entire population. The optimal way to ensure unbiased 

variance estimates is to incorporate a stochastic element to the design. This is usually introduced 

by random placement of sample sites, ensuring that all units of potential sampling area have an 

equal likelihood of being selected. This randomness reduces or eliminates the bias introduced by 

other selection methods. Non-random site selection methods, such as professional judgment or 

convenience of access, are problematic because they include sampling uncertainty, in the form of 

unknown bias, that cannot be statistically accounted for. It is crucial to reduce bias as much as 

possible or inferences to the entire population from sample data will be inaccurate and 

potentially misleading.

A second crucial component of spatial design is site replication. This is necessary for collecting 

data that are a representative sample of the entire population (Greenwood and Robinson 2006). 

As stated by Green (1979) in his Ten Principles, “differences among can only be demonstrated 

by comparison to differences within.” This helps eliminate natural variance and makes the 

detection of causal variance and potential trends more robust. 

The temporal aspect of sampling design determines the sampling frequency of each site. 

Urquhart (2012) ran thousands of iterations on many different temporal sampling designs and 

found that re-visitation greatly increases the power of a program to detect changes. However, 
19

Urquhart notes that re-visiting the same site every year is not necessary for this level of power 

and that yearly site visits could actually cause the variation being detected, in the form of site 

damage. Urquhart found that for a design to have high statistical power, it should either have 

many replications or be extended over numerous years. 

In an examination of the sampling design employed at FGB, the program objectives must be 

referenced to documents describing the program. The FGBLTM program states that the areas of 

interest are the entirety of the coral caps on each bank (BOEM 2014a). With this as the study 

area, the current spatial and temporal design for each bank is a single, un-replicated study site, 

with professional judgment used for site placement and a yearly sampling frequency over the 

course of many years (Gittings et al. 1993; Johnston et al. 2013). Based on the above discussion, 

this is clearly not an optimal design for gathering accurate and meaningful data to achieve the 

objectives of this LTM program.

Evaluation of 100 m by 100 m Study Site Design

The FGBLTM program measures community dynamics with two 100 m by 100 m study areas, 

one on each bank. Each study area was chosen using professional judgment asserting that it was 

representative of the coral reef zone on each reef (CSA 1989; Gittings et al. 1993). No 

additional statistical or methodological support for this assertion was provided in the program 

documentation at the time of program inception. Gittings et al. (1993) points out that the LTM 

program was not designed “to characterize bank-level assemblages. The study was designed to 

monitor changes within specified study sites.” However, the conclusions of recent monitoring 

reports do not discuss this focus. Rather, conclusions state “the coral reefs of the Flower Garden 

Banks National Marine Sanctuary have maintained high levels of coral cover” (Johnston et al.

2013) and other similarly broad conclusions. For such statements to be justified based on 

conclusions of measurements made in the study sites, the study sites must be representative of 

the whole bank area. As discussed in the NPS I&M monitoring method for Atlantic coral reefs 

(Miller et al. 2007), when multiple variables are being sampled, as is the case with most longterm monitoring, the sample sites must be defensibly representative of all measured variables, 

not just some. No comparative studies have assessed the representativeness of the 100 m by 100 

m study sites for fish, sea urchins, coral recruitment and disease, or lionfish, and only minimal 

comparison has been done for benthic cover. 

To test if the study area is truly representative, three transects consisting of 14 non-overlapping 

images each were sampled in 1998, 1999, 2000, and 2001 outside of the 100 m by 100 m sites

(Dokken et al. 2003). These photos were analyzed using the same methods as the random 

transects conducted annually within the study area and were compared to the random transect 

data collected during the 1998–2001 study period. No significant differences in percent cover of 

the dominant coral taxa were found; however, some minor coral taxa and non-coral taxa 

exhibited significant differences in the analysis of 1998 and 1999 data as reported by Dokken et 

al. 2001. When all four years (1998–2001) were analyzed by Dokken et al. (2003), no significant 

differences in percent cover or major taxa (both coral and non-coral) were identified between 

transects outside and inside the study areas, but no attempt was made to statistically evaluate 

differences in minor taxa. Unfortunately, the statistical analysis used to make this determination 

is not well documented in either report, and no rationale is provided for the analysis in either 

Dokken et al. (2001) or Dokken et al. (2003). Dokken et al. (2001) does not mention what 
20

statistical test was performed. Dokken et al. (2003) used a non-parametric Mann-Whitney U 

test, but the reports fail to state exactly which datasets were used in the analyses or if the key 

assumptions of the test were considered. As a result, it is unclear if the analyses are appropriate 

for the given data set. Furthermore, it is unlikely that three transects per year for a four year 

period could provide enough spatial coverage or statistical power to account for the range and 

variation in community variables over the entire coral cap, spatially and temporally, especially 

given that the 100 m by 100 m areas only account for about 0.5% of coral cap at EFGB and 2.0% 

of the coral cap at WFGB (M. Nuttall, FGBNMS, personal communication).

If the FGBLTM program is to continue to make inferences about the status of the entire coral 

reef zone at the banks using these 100 m by 100 m study areas, it is imperative that an extensive 

assessment be made to determine if the study areas are representative of the coral cap in general. 

Fortunately, NOAA has recently conducted a thorough study of community parameters across 

the banks, including mapping the reef areas and performing benthic quadrat surveys (Clark et al.

2014). If sufficient information can be drawn from this study to compare the LTM study areas to 

the rest of the coral reef zone for all measured variables, then a dedicated study may not be 

necessary.

1.3.4 Variables Measured

After the spatial and temporal designs have been determined, the next decision in program 

design is to determine the variables of a sample population to measure. The variables chosen 

must have a known relationship to the total population for meaningful inferences to be made 

(Reynolds 2012). Likewise, how data are to be quantitatively handled must be considered so 

potential biases are accounted for during analysis and interpretation. The choosing of variables 

can be a two stage process; first, a number of variables can be chosen that will help answer the 

questions posed by the program objectives. This choice must also include quantitative 

considerations about the types of analyses and assumptions that will be used to gather useful 

information from the variables (Green 1979). From this set, the variables that will provide the 

most robust and meaningful data, and be feasibly collected, will determine the final set of 

variables chosen. 

The current FGBLTM program samples for the following monitoring variables: 

 benthic percent cover;

 coral growth;

 coral health (bleaching and disease);

 coral recruitment;

 sea urchin, lobster, and lionfish population density;

 fish population density, species diversity and biomass; and

 water quality (salinity, temperature, ammonia, Chl a, nitrate, nitrite, TKN and 

phosphorous)

Table 1.3 compares the variables measured by the FGBLTM to those from other LTM programs 

with similar objectives of detecting community change and identifying potential causes of 

ecosystem change or decline.
21

Table 1.3 Comparing Variables Measured at Flower Garden Banks with Other Long-term Monitoring Programs

Variable

FGB AGRRA CRTR CREMP AIMS LTM NPS I&M 

SFCN CRAMP NPS I&M PACN

Flower 

Garden 

Banks

Caribbean Tropics Florida Keys 

NMS Australia Virgin Islands Hawai’i Pacific

(Lang et 

al. 2010)

(Van 

Woesik et 

al. 2009)

(Ruzicka et al.

2009)

(Hill and 

Wilkinson 

2004)

(Miller et al.

2007)

(Brown et al.

2004)

(Brown et al.

2011)

Benthic 

Cover x x x x x x x x

Coral Health x x x x x x x

Coral 

Recruitment x x x x x

Coral Size x x x

Coral Growth x x x

Macroinvertebrates x x x x x

Fish x x x x x x x

Water quality x x x

Rugosity x x x x

Sediment x

Bioeroding 

Sponge x

AGRRA = Atlantic and Gulf Rapid Reef Assessment

CRTR = Coral Reef Targeted Research

CREMP = Coral Reef Evaluation and Monitoring Project

AIMS LTM = Australia Institute of Marine Science Long-term Monitoring

NPS I&M SFCN = National Park Service Inventory and Monitoring Program South Florida and Caribbean Network

CRAMP = Coral Reef Assessment and Monitoring Program

NPS I&M PACN = National Park Service Inventory and Monitoring Program Pacific Islands Network
22

Notably, every LTM program shown in Table 1.3 includes measurement of benthic percent 

cover, which is arguably one of the most important metrics. Percent cover of corals, algae, 

sponges, and other benthic taxa can often be correlated with variation in environmental 

parameters such as water quality and wave exposure, and stressors such as pollutant 

concentration and sedimentation (Cooper et al. 2009). Coral cover, specifically, is a widely used 

indicator of ecosystem health (Cooper et al. 2009). Due to the sessile and slow growing nature of 

coral, coral populations are sensitive to environmental changes resulting in reduced light, 

nutrient enrichment, and pollutant input. Corals are also the building blocks of the coral reef 

ecosystem, and reductions of coral cover negatively affect fish and invertebrate populations 

(Jones et al. 2004).

Because of the importance of corals to overall reef health, other aspects of coral ecology are 

often measured in addition to percent cover. Coral health surveys, including coral bleaching, 

disease prevalence and mortality rates, are also included in most of the LTM programs described 

in Table 1.3, including FGBLTM. Worldwide coral bleaching and disease are among the top 

threats to coral reef ecosystems (Wilkinson 2008; Jackson et al. 2014). Major bleaching events 

have been documented at FGB in 1998, 2005, and 2008 (Jackson et al. 2014). Although the 

incidence of coral disease at FGB has been documented as low, compared to other reefs in the 

Caribbean and Western Atlantic, at times disease contributed to more coral mortality than 

bleaching at FGB (Gittings et al. 1993), and it was perceived that disease is one of the most 

serious threats to the reefs at FGBNMS (Gittings et al. 1993; Zimmer et al. 2010). More recent 

information suggests that some of the previously documented “diseases” may be damage related 

to fish bites rather than to pathogens (M. Johnston, FGBNMS, personal communication), yet 

coral diseases are present in low numbers at the banks, as documented by several reports 

(Borneman and Wellington 2005; Hickerson et al. 2008; Johnston et al. 2013; Johnston et al.

2015). Because of the potential significance of coral disease to reef health at FGB, it is crucial 

that coral disease be well-documented by the LTM program.

Although at the time it was not a regular part of the LTM program, in situ coral disease 

assessments have been conducted at FGB (Hickerson et al. 2008; Zimmer et al. 2010). In 2005, 

the first widespread coral disease event was observed at both EFGB and WFGB. A white plaguelike disease affected at least seven coral species and was observed from 2005 through at least 

2008 (Hickerson et al. 2008; Zimmer et al. 2010). Notably, the LTM program random transect, 

repetitive quadrat, and perimeter video methods did not detect any instances of the plague-like 

disease between 2004 and 2008, and in situ coral health surveys conducted simultaneously with 

the 2007 LTM surveys detected a few affected colonies (Zimmer et al. 2010). Prevalence of the 

disease was higher in the winter months (Hickerson et al. 2008) indicating seasonal variability in 

coral health. With the current LTM program design, monitoring is only performed in the summer

and early fall. Coral bleaching events are likely to be at their peak around that time of the year 

due to higher sea water temperatures; however, because the time of the surveys may vary by a 

few months depending on weather, bleaching events could be missed (Johnston et al. 2015). Data 

collected also likely excludes documentation of potentially major disease events that occur in the 

winter and may be impacting overall health of the reefs. The FGBLTM program should consider 

adding a seasonal component to the monitoring design. Adding semiannual in situ coral health 

surveys to document coral health status near the maximum and minimum water temperature 
23

periods should be considered. This survey is not required to be as robust as the LTM surveys and 

could potentially be done in conjunction with routine water monitoring cruises.

An additional variable that the FGBLTM program added in 2014 is coral colony size. Both 

AGRRA and CRTR include this variable in their monitoring methods, and it provides important 

information on community dynamics and processes (Van Woesik et al. 2009). Coral colony sizedistributions can describe important community-level long-term changes on reefs. For example, 

using coral-size distribution data across the Caribbean, Green et al. (2008) showed that the 

increase in coral cover observed in shallow reefs between 1974 and 2003 was the result of 

increasing numbers of relatively young, small colonies of the weedy species Porites astreoides in 

the place of older corals that were dying as a result of various stressors. Coral size information 

could also be easily derived from the repetitive quadrats and random photographic transects 

performed at FGB with no added field effort. However, many corals would extend beyond the 

borders of the photos, so size estimates would be limited to colonies fully encompassed by the 

photo, which could result in biases toward smaller colonies. The LTM program has chosen to 

combine the coral size survey with the coral health surveys, which is an acceptable method and 

can be useful in characterizing disease dynamics (Raymundo et al. 2008).

Coral recruitment is a related variable that was added to the FGBLTM program in 2014 and is 

also measured by AGRRA, CRTR, Australia Institute of Marine Science (AIMS) and the NPS 

I&M PACN program. The presence and absence and abundance of certain genera or families of 

coral recruits on a reef can help to explain adult coral community dynamics and predict longterm recovery from catastrophic events (Van Woesik et al. 2009; Mumby et al. 2007). Though 

coral cover is high at FGB compared to the region (Johnston et al. 2013), the reef is occasionally 

hit by hurricanes, which have caused observable damage to the reef, toppling and displacing 

coral heads (Precht et al. 2008b; Robbart et al. 2009). A reef cannot recover from this type of 

damage without healthy recruitment of new corals, which can be dependent on a supply of 

recruits from distant reefs that may be degrading independently from the subject reefs (RitsonWilliams et al. 2009). Due to the distance of FGB from other coral reefs and the rapid rate at 

which recruitment on oil and gas platforms declines with increasing distance from FGB, it is 

likely that the banks are the only consistent source of coral larvae in the area (Sammarco 2013). 

It may be important to monitor recruitment rates of the invasive coral Tubastraea coccinea, an 

opportunistic pioneer species found in high abundance on nearby oil and gas platforms that has 

recently established at FGB (Sammarco et al. 2006; Johnston et al. 2013).

After the widespread drop in Diadema antillarum abundance in early 1980s (Carpenter 1990a, 

1990b; Mumby et al. 2006) FGBLTM added night transects to each bank to quantify 

populations. Later in the program, lobsters were included in the night surveys. D. antillarum is 

considered a keystone species due to its important ecological role of grazing on macroalgae, 

keeping substratum available for coral recruitment and growth (Carpenter 1981, 1986). Longterm data on D. antillarum populations provides beneficial information on reef health and 

diversity and could document causative effects if phase shifts from coral to algal dominated reefs

occur. Lobsters are important in commercial fisheries in other regions of the Gulf, and were 

added to the FGBLTM because little was known of their populations at FGB (Precht et al. 2006). 

Lionfish (Pterois volitans and miles) were also added to the night surveys, because they are a 

voracious, invasive predator that has been observed at FGB since 2011 (Nuttall et al. 2014). 
24

Monitoring the occurrence of this species will provide managers with pertinent ecosystem health 

information, and could also be useful if shifts in fish populations are observed.

Not surprisingly, surveys of fish populations are also included in almost all of the LTM programs 

listed in Table 1.3, including at FGB. The health of fish populations has an obvious importance 

to local human populations in most coastal areas due to dependency on fisheries for subsistence 

and commercial food sources. Historically, overexploitation by commercial fisheries in the 

1950s led to low densities of red snapper on the banks (Boland et al. 1983). With the 

establishment of the National Marine Sanctuary, all fishing except hook and line is now 

prohibited at the Banks, but fishing pressure within the sanctuary has not been well quantified 

(NOAA 2012). As is true with many protected areas, FGB could serve as a source for nearby 

fisheries (Office of National Marine Sanctuaries 2008). Lionfish populations are also 

documented through the the general fish population surveys.

Herbivorous fish, in conjunction with D. antillarum, also play a major part in controlling 

macroalgae growth on coral reefs. Herbivorous fishes are key to maintaining healthy coral 

populations through removing excess macroalgae that may overgrow corals and hard substrate 

causing mortality and reducing coral recruitment (Mumby et al. 2007; Mumby and Steneck 

2008). With the loss of D. antillarum, the role of grazers, like parrotfish, is even more important. 

For these reasons the AGRRA (Lang et al. 2010) and CRTR (Van Woesik et al. 2009) programs 

also include focused herbivory surveys. Based on information provided by the stationary fish 

surveys at FGB, the banks have relatively healthy herbivorous fish populations (Johnston et al.

2013). These healthy populations have likely contributed to the good health status of the reefs in 

the absence of Diadema antillarum.

The continuance of macroalgae control at FGB by herbivorous fish could be threatened with the 

recent introduction of Pterois volitans. This invasive species could cause significant declines in 

herbivorous fish populations (Albins and Hixon 2011), as they are known to be generalist 

piscivores (Munoz et al. 2011). Their ability to reduce herbivorous fish stocks, resulting in 

immediate phase shifts to algal-dominated reefs, has been documented (Lesser and Slattery 

2011). Lionfish are generalist predators and eat a wide variety of species of a certain size, and 

newly-recruited reef fish populations can be heavily impacted by their presence when compared 

to the impact of a native predator (Albins and Hixon 2008; Albins 2013). Removing new 

recruits from the population has clear implications for future fish stocks. It is justified and 

important that fish populations are monitored by the FGBLTM, and the program should consider 

adding herbivory and recruitment surveys to monitor the effects of the lionfish invasion on these

important reef interactions. This information combined with information from the other LTM 

surveys such as benthic photo transect to discuss related changes in parameters such as algal 

cover.

Water quality parameters are the final variables measured by the FGBLTM. Other programs that 

include water quality in their method are I&M South Florida and Caribean Network (SFCN) and 

I&M PACN. Water quality measurements give context to the biological variables collected by 

quantifying attributes of the physical environment at the time of sampling. Particularly in the 

case of FGBLTM, where program objectives aim to discern between shifts due to OCS 

development and natural causes like climate change, it is necessary to sample these variables. 
25

Though sea temperatures could arguably provide evidence of community changes correlated 

with seasonal weather patterns or effects of climate change, none of the water quality parameters 

currently collected relate to OCS development. If a program objective remains to detect changes 

due to OCS development, we recommend at the very least that sampling for aromatic 

hydrocarbons be resumed. Sampling for the presence of these molecules will provide a continual 

baseline and a comparative parameter to test a hypothesis of causation should reef decline be 

detected. 

1.3.5 Sampling Methodology

Once it has been decided which variables to measure, the next decision is how to measure them. 

As with every level of program design, program objectives and quantitative considerations 

should direct the choice of sampling methods. The quantitative shortcomings of chosen 

methods, in the form of data bias, should be acknowledged and planned for prior to actual 

sampling. Table 1.4 shows what methods are used by FGBLTM and compares to other LTM 

programs. 
26

Table 1.4 Data collection methods for each variable and comparison to other LTM programs

Element

FGB AGRRA CRTR CREMP AIMS LTMP NPS I&M 

SFCN CRAMP NPS I&M PACN

Flower Garden 

Banks Caribbean Tropics Florida Keys NMS Australia Virgin Islands Hawai’i Pacific

(Lang et al.

2010)

(Van Woesik 

et al. 2009)

(Ruzicka et 

al. 2009)

(Hill and 

Wilkinson 

2004)

(Miller et al.

2007)

(Brown et al.

2004)

(Brown et al.

2011)

Benthic 

Cover

-Random photo 

transects

-Permanent 

repetitive photo 

quadrats

-Line intercept 

survey

-Algal height 

measurements

-Random 

photo 

transects

-Permanent 

photo 

quadrats

-Permanent 

video 

transects

-Manta tow 

survey

-Permanent 

photo 

transects

-Permanent 

video 

transects

-Permanent 

video 

transects

-Permanent 

photo 

quadrats

-Rapid 

assessment 

technique

-Permanent 

photo transects

-Random 

photo transects

Coral Health

-Point intercept 

transects 

-Permanent 

repetitive quadrats

-Belt transects

-Coral size

-Belt 

transects

-Colony 

tagging

-Timed 

inventory

-Permanent 

belt 

transects

-Permanent 

belt 

transects

-Permanent 

photo transects

-Random 

photo transects

Coral 

Recruitment

-Random belt 

transects

-In situ quadrat 

survey

-In situ

permanent 

quadrat 

survey

-Settlement 

tiles

-Permanent 

belt 

transects

-Settlement 

tiles

Coral Size -Point intercept 

transects

-Belt transects -Random 

photo 

transects

-Permanent 

photo 

quadrats
27

Element

FGB AGRRA CRTR CREMP AIMS LTMP NPS I&M 

SFCN CRAMP NPS I&M PACN

Flower Garden 

Banks Caribbean Tropics Florida Keys NMS Australia Virgin Islands Hawai’i Pacific

(Lang et al.

2010)

(Van Woesik 

et al. 2009)

(Ruzicka et 

al. 2009)

(Hill and 

Wilkinson 

2004)

(Miller et al.

2007)

(Brown et al.

2004)

(Brown et al.

2011)

Coral Growth -Permanent 

repetitive quadrats

-Lateral growth 

stations

-Sclerochronology 

(accretionary 

growth)

-Permanent 

photo 

quadrats

-Measuring 

tagged 

colonies

Macroinvertebrate

-Permanent belt 

transects, night

-Belt transects -Timed 

inventory

-Manta tow -Permanent 

belt 

transects

Fish

-Stationary point 

surveys

-Belt transects

-Lionfish belt 

transect, night

-Belt transect

-Lionfish belt 

transect

-Optional 

roving diver 

census

-Optional 

herbiv. survey

-Belt 

transects

-Optional 

stationary 

survey

-Permanent 

belt 

transects

-Timed belt 

transects

-Belt 

transects

-Permanent 

belt transects

-Random belt 

transects

Water Quality

-Time series

-Quarterly discreet 

measure

-Quarterly 

and in situ

-Quarterly and 

in situ

Rugosity -Maximum 

relief measure

-Chain and 

tape

-Chain and 

tape

-Chain and 

tape

Sediment

-Haphazard 

samples

-Sieving for 

grain size

Bioeroding 

Sponge

-Permanent 

belt 

transects
28

Spatial Layout of Sampling Methods

Data within each 100 m by 100 m study area are collected at 40 permanent quadrat stations, 30 

permanent lateral growth stations, two permanent video transect stations, three permanent sea 

urchin, lobster, and lionfish survey stations, 24 and 16 random fish survey stations (two different 

methods), and 16 random transect locations during each monitoring period. Water quality is 

monitored at semi-permanent moorings throughout the year and water samples are collected 

quarterly (BOEM 2014a). Of the various stations sampled, only the stationary point count fish 

survey stations are truly random and expected to provide a representative sample of the study 

area communities. As discussed previously, no assertions can currently be made for areas 

outside the study area.

The random transects, used to collect benthic cover, coral recruitment and coral growth data, use 

a spatial layout that is a stratified random sampling plan. Random locations are chosen in even 

numbers in four quadrants of the study area. This allows for a more spatially balanced random 

sample than distributing points across the study area, which would result in less consistent 

coverage of the study area (McDonald 2012). One potential problem with the benthic transects is 

that they are not truly random. The first transect of a survey dive always begins by swimming 

from a point on the boundary, and the start point of subsequent transects are dependent upon the 

end point of the previous transect. This, by definition, is haphazard, and can introduce statistical 

issues (Lewis 2004). To correct for this, we recommend that the starting point and the direction 

of transects in each quarter be truly randomly generated and unrelated to the boundary or 

previously conducted transects. Otherwise, this set of transects should be representative of the 

study area as long as the number of transects and stations is sufficient to capture the population 

variance (discussed in Chapter 3).

The 40 permanent quadrat stations were established as “premium monitoring sites with high 

coral cover” (Johnston et al. 2013). Coral cover at the permanent quadrat stations averages 

around 72%, whereas at the random transect stations cover is closer to 55% (Johnston et al.

2013). These permanent stations are not representative of the study sites or the banks, and data 

cannot be used to make inferences about the status of the study area as a whole. It could be 

argued that these high coral cover locations are priority habitats for monitoring, but data 

interpretation should only be applied to the specific areas that are sampled. To improve the 

sampling design, the FGBLTM program should consider using permanent quadrat stations that 

are randomly placed throughout the study site rather than sites determined a priori as high 

quality. Details of current quadrat placement will be further discussed in Chapter 2.

The permanent lateral growth stations were selected haphazardly, attempting to avoid harming 

live coral when installing the stations, and initially consisted of 120 stations, including 60 from 

each of two species per bank (Gittings et al. 2013; M. Johnston, FGBNMS, personal 

communication). Due to difficulty maintaining the lateral growth stations and recognition that 

the lateral growth stations had a limited lifespan, the number of stations was reduced to 60 

stations from one species in 1994 (MMS 1996), and now the IA only requires 30 stations to be 

maintained and surveyed annually (BOEM 2014a). Due to their haphazard placement and the 

loss of many stations, the lateral growth stations also cannot be considered representative of the 

study sites or the banks without further evaluation. Without too much effort, an evaluation could 

be performed to determine average growth rates and the variance of growth rates across the 
29

entire study site using a truly representative sampling plan over a limited time-scale, and this 

information could be compared to that collected at the current stations to evaluate the 

representativeness. This would involve randomly selecting colonies across the study site to set up 

stations and measuring growth rates at those stations simultaneously to the current stations for a 

couple years. However, this effort is likely unnecessary, as the discontinuation of the lateral 

growth method will be recommended in Chapter 2.

The two permanent video transects are conducted on study site boundary lines. They are a 

convenience sample, chosen based on ease of access (McDonald 2012), and the usefulness of the 

data collected there is very limited. Essentially, the perimeter transect data is used to determine if 

the conditions at the site boundary and beyond are changing, but because the relationship for 

community composition between those lines and the areas outside the study area are unknown, 

no inferences can be made as to how changes observed at the perimeter line relate to changes in 

other areas. The sea urchin, lobster, and lionfish night-time surveys also use the boundary lines 

as transects and are subject to the same limited interpretation that cannot infer beyond the 

transects themselves. 

The placement of fish survey stations uses the same stratified random sampling plan as is used 

for the random benthic transects. The same conclusions apply: these samples should be 

representative of the study area, provided that the locations are truly randomly generated and the 

sample size has sufficient power to adequately detect population variance (discussed in Chapter 

3). 

A detailed analysis of each method and how appropriate it is for the variable being collected will 

be found in Chapter 2.

1.3.6 Data Handling

The purpose of any monitoring program is to collect data, and so it goes without saying that 

careful data handling is an important component of any monitoring program. To be useful, data 

must be entered accurately and stored in a format that can be easily accessed and analyzed for 

immediate and future purposes (Hill and Wilkinson 2004). Most LTM programs use a database 

to store data collected, often they choose to have one database per monitoring method (e.g.,

Brown et al. 2011) or one database for the whole program (e.g., Van Woesik et al. 2009). 

Microsoft® Excel® and Microsoft® Access® are commonly used to store this information (Hill 

and Wilkinson 2004).

The FGBLTM program data handling system has improved in recent years, but still needs 

improvement. Due to the long history of the FGBLTM program, some of the program data was 

not digitized initially, though FGBNMS has been working to digitize all available data. Further, 

data was not always provided to BOEM at the end of a contract period. These facts have led to 

wide data gaps in the digital data records for the program. All of the digital data currently 

available has been collected by the FGBNMS, who has inventoried the information and 

organized it by contract period. A quick review of the data that was available in September 2014 

reveals that most information is stored in Microsoft® Excel® spreadsheets, and that the data 

format has rarely remained consistent from contractor to contractor. In its current state, the data 

would require extensive reformatting before comparisons could be made across the length of the 
30

LTM program. Also, data from each monitoring method has a completely unique data format. 

There is no standardization among the metadata provided (i.e., site, location, date, observer, etc.).

We recommend that resources be allocated to create a database for the LTM program and to 

reorganize and import all of the digital data into the database. An effort should be made to locate 

and digitalize all of the data that is currently missing from the program directories. NOAA 

already has data management systems in place for delivering raw data, and this infrastructure 

should be used to benefit the FGBLTM program. Dedicated annual funding for data 

management, perhaps in the form of a part-time data manager position is recommended to 

accomplish creating the database and maintaining it during future monitoring periods.

1.3.7 Quality Assurance and Quality Control

An important part of ensuring that a LTM program is meeting its objectives is establishing a 

quality assurance (QA) and quality control (QC) plan that addresses the potential problems with 

data quality (Shampine 1992). QA is increasingly important when multiple agencies are involved 

in data collection and management for a given monitoring program (Vaughan et al. 2001). 

Human error is a concern for LTM programs in data collection, data handling, and data analysis

(Shampine 1992; Hill and Wilkinson 2004; Beninger et al. 2012). To reduce the amount of 

human error in data collection, quality control training and testing must be part of the program 

design and performed regularly (Hill and Wilkinson 2004). QC issues in data handling and 

analysis can be addressed through establishment of SOPs (NPS 2012).

FGBLTM currently has no QA and QC plan. The only documentation of QC measures lies in 

guidance provided by monitoring reports and IAs, and this information is extremely limited. The 

current IA does require a QA and QC plan (BOEM 2014a); however, past agreements did not 

include this requirement and a plan has not yet been written. We recommend that a QA and QC 

plan be written, evaluated, and approved immediately. This process is currently underway (M. 

Johnston, FGBNMS, personal communication), and we recommend that a plan be approved 

before the next contract period.

Example QA and QC plans can be found at several federal agencies. The NPS I&M program 

requires that each monitoring method include a QA and QC plan (NPS 2012). QA plans are also 

available from the Environmental Protection Agency [EPA] (EPA 2002), Fish and Wildlife 

Service [USFWS] (USFWS 2013), and the US Geological Survey (Atkinson et al. 2004). 

The federal Information Quality Act (Section 515 of Public Law 106-554) was established to 

ensure the quality, objectivity, utility, and integrity of information disseminated by federal 

agencies. The Act requires agencies to establish quality guidelines (Bisong 2008). In response to 

the Act, the Department of Interior under which BOEM falls, and the Department of Commerce 

under which NOAA falls, have both issued quality guidelines. BOEM has additional guidance 

for information quality. These guidance documents should be addressed in the FGBLTM QA 

plan when established.

It is also recommended that a separate method document be written for each monitoring method. 

Method documents provide the backbone for a program, detailing the specifics of each method, 

explaining how data are to be collected, managed, analyzed, and reported, and ensuring a degree 
31

of QA as long as the method is reviewed and followed by each field personnel (Oakley et al. 

2003). Oakley et al. (2003) recommends that methods consist of:

1. a narrative that provides background information and rationale for methodologies; 

2. a series of SOPs that provide the specific step-by-step instructions for each method; and 

3. supplementary materials that include example databases, maps, and additional supporting 

information.

Many of the methodologies used by the FGBLTM are the same or similar to those from other 

LTM programs such as AGRRA. The method SOPs from these other programs could be adapted 

to the FGBLTM program where appropriate. For example, AGGRA uses belt transects for 

macroinvertebrates and fish, as well as includes a separate lionfish belt survey (Lang et al. 2010). 

If no similar method is found, new SOPs should be developed and reviewed. NPS (2012) 

recommends similar strategies for developing methods for the I&M program. The Florida Reef 

Resilience Program also provides training modules that may be useful to FGBLTM when 

considering how to train personnel.

1.4 Discerning among Drivers of Change

LTM studies routinely examine variables such as coverage of benthic organisms and abundance 

of reef fish, which give an indication of the health of the reef ecosystem; yet few studies 

successfully address the processes that drive community change (Van Woesik et al. 2009). 

Though addressing these processes is necessary to meet the needs of both funding agencies at 

FGB, the current LTM program may not fully address this need.

The first step in developing a monitoring program that can discriminate among specific drivers 

of change is determining what drivers are likely in the community of interest and what the 

potential impacts of those drivers are on that community (Fabricius and De’Ath 2004). From this 

list, the LTM program can focus on specific drivers of interest, but all drivers of change must be 

considered in determining causality for ecosystem change. 

The FGBLTM reports discuss various potential drivers of change. Gittings et al. (1993) 

discussed the potential impacts of OCS development and other oil and gas activities on the coral 

reefs of FGB. Spills from oil tankers, discharges of mud and drill cuttings during oil and gas 

exploration and production, noise from seismic surveys, and accidents on platforms leading to 

spills were identified as potential threats. Gittings et al. (1993) considered leaks caused by 

equipment failure to be the largest potential threat in regard to development of pipelines and 

platforms near FGB. 

Gittings et al. (1993) also discussed the potential threat that coral diseases posed for the coral 

communities at the banks. During the initial LTM surveys (1989–1991), coral disease was

identified as greatest source of coral tissue mortality. Gittings et al. (1993) warned that coral 

disease might be the greatest threat to coral health at the banks. Although the urgency of the 

threat seems to have dissipated in more recent years, with the acknowledgement that coral 

disease prevalence is low, the potential for coral disease outbreaks remains and coral diseases 

remain a minor point of discussion when assessing community threats at the banks (Johnston et 

al. 2013, 2015).
32

Coral bleaching is another well-recognized potential threat to the reefs. Since the inception of the 

LTM program, coral bleaching has been closely monitored (Gittings et al. 1993). Bleaching 

events have been observed at the reefs nearly annually during the summer temperature maxima, 

but have typically resulted in little tissue loss (Johnston et al. 2015). Because bleaching mainly 

occurs near the summer temperature maximum and the survey time may vary by a few months 

depending on weather, the LTM data may not be a good record of bleaching (Johnston et al.

2015).

Historically, overfishing has been a threat to fish populations at FGB. In the 1950s 

overexploitation of red snapper led to the collapse of populations across the Gulf and at FGB

(Boland et al. 1983). Since 1992 at the establishment of FGBNMS, protective fishing regulations 

have prohibited commercial fishing with bottom long-liners, traps, nets, and bottom trawls, 

greatly reducing the potential threat of fishing pressure on the reefs. However, hook and line 

fishing remains unregulated at the banks and could be a potential source of recently-observed 

declines in fish density (Johnston et al. 2015), though fishing pressure is not currently well 

quantified. 

Impacts of invasive species on the FGB are a growing threat to reef community health. 

Tubastrea coccina, Thecacera pacifica, and Pterois volitans and miles, the latter of which have 

had devastating effects of other reefs in the Caribbean and Gulf, have been observed on the reefs 

(Johnston et al. 2015).

Hurricane damage is another recognized driver of community dynamics at FGB. Precht et al.

(2008b) and Zimmer et al. (2010) discuss the landscape changes and coral damage that resulted 

from Hurricane Rita in 2005 and Hurricane Ike in 2008. For example, large coral colonies that 

had been part of the LTM data were missing after hurricane events. 

Table 1.5 provides a list of the most relevant potential drivers of ecosystem change for FGB, 

along with widely recognized potential impacts of those drivers on coral reef communities. 

BOEM’s main interest in the FGBLTM program is in discriminating the effects of OCS 

development (hydrocarbon pollution) from the other potential drivers of change (BOEM 2014b); 

however, the impacts of each of these potential drivers must be considered in determining 

causality for an observed change. 
33

Table 1.5 Potential Drivers of Ecosystem Change at FGB

Drivers Acute or Chronic Potential Impacts

Hydrocarbon pollution

Chronic Reduced coral growth

Reduced coral reproduction

Acute (Oil Spills) Coral colony mortality

Anchoring damage Acute Coral breakage

Coral tissue loss

Overexploitation of resources 

(overfishing) Chronic

Reduction in size, abundance and 

diversity of affected species

Community phase shifts

Invasive species Chronic or Acute

Impacts dependent on species, but 

frequently include reduced abundance of 

native species and phase shifts

Climate change Chronic Increased coral bleaching

Reduced coral growth

Hurricanes Acute

Broken, dislodged corals

Coral tissue loss due to abrasion

Coral bleaching

Coral disease and bleaching Acute Coral tissue loss

Coral colony mortality

Eutrophication Chronic

Coral disease and bleaching

Coral colony mortality

Reduced coral growth

Reduce coral reproduction

Algal overgrowth

Algal blooms

Recreational use Chronic

Coral tissue loss

Coral colony mortality

Coral breakage

Discriminating among potential drivers of observed ecological change is often very difficult. 

Misinterpretation and misuse of technical concepts such as probability and causality as well as 

the complexities of interactions in target ecosystems that are not well understood are some of the 

main issues surrounding understanding causality (Fabricius and De’Ath 2004). Traditionally, 

change is detected through a series of hypothesis tests; however, this has proven to be 

problematic, because failure to reject a null hypothesis is often misinterpreted as evidence of “no 

change.” Alternatively, development of an ecosystem models including several drivers of change 

based on estimation is recommended, but this is not without its shortcomings. Inclusion of all 

possible drivers could greatly reduce the ability to detect change, while exclusion of some 

important drivers may cause unwanted biases. 

In addition to these difficulties, statistical analyses, which are often used to determine if a change 

has occurred, do not usually favor interpretation of causality. Statistics usually caution against 

attributing cause to an observed change, as statistical significance alone is not enough to allow 

interpretation (Fabricius and De’Ath 2004). This is especially true in ecology, where the system 

being studied is often continually dynamic, and a true control system is not available for 

comparison. For resource managers; however, the cause of ecosystem decline is an important 

factor in making decisions that will prevent or reverse negative impacts. To this end, it has been 

suggested that the use of rigorous epidemiological criteria for causality be applied to ecosystem 

studies (Fabricius and De’Ath 2004). The main criteria as defined by Hill (1965) are:
34

 The relationship between dose and response should be monotonic

 The association between dose and response should be strong

 The association should be specific to the cause

 The time sequence of events should be logical, i.e., response follows dose

 There should be consistent results across populations and other studies

 The observations should agree with known biological facts

Though not all criteria need to be met to determine causality (Fabricius and De’Ath 2004), this 

list demonstrates the difficulty in identifying OCS development as a cause of ecosystem decline, 

particularly when hydrocarbon exposure is at a low-level and over a long time period in the 

midst of numerous other stressors that may have similar impacts on the reefs.

At FGB no significant change has been detected through hypothesis testing (Johnston et al.

2013), and so no real attempt has been made to determine causality of changes at the reefs. In 

Chapter 3, we will discuss the appropriateness of statistical approaches used and the validity of 

this interpretation. However, here we discuss whether the cause of any changes detected through 

the hypothesis testing-based model could be discriminated as OSC development or attributed to 

other acute or chronic stressors.

Acute stressors, such as hurricanes, disease outbreaks, and other catastrophic events, can be 

determined by comparing LTM data before and after the event. The photography of repetitive 

quadrats has proven effective at monitoring gradual and acute changes in individual colonies, 

particularly monitoring causes of coral tissue mortality and the effect and nature of coral disease 

and bleaching (Gittings et al. 1993). Hurricane damage has been successfully assessed by 

comparing LTM data to post-hurricane survey data (Robbart et al. 2009; Zimmer et al 2010).

The current LTM design of complete annual surveys is sufficient to discuss major changes 

caused by acute events. Definitively assigning causality will be limited by the power of each 

sampling design, the magnitude of the changes caused by the event, and the length of time 

between surveys.

For changes caused by chronic stressors and long-term trends, one primary issue in determining 

causality with the FGBLTM program is the lack of information collected on the variation in 

potential drivers of change related to OCS development. Water nutrients are measured quarterly, 

providing information on some key water quality parameters, but the method lacks any 

measurement of hydrocarbon pollution or other constituents involved in petroleum related 

activities in the Gulf (BOEM 2014a). From 1996 through 2001 hydrocarbons were measured 

using SPMDs and none were detected. The method was discontinued, and since then 

hydrocarbons have only been measured in 2010 after the Deepwater Horizon oil spill (Johnston 

et al. 2013). However, we recommend that hydrocarbons be more regularly monitored to detect 

any unknown sources of hydrocarbon pollution. Without a firm grasp on the dynamics of 

hydrocarbon pollution at the Banks, it is impossible to determine whether hydrocarbon pollution 

has impacted the reefs if change is detected in the ecosystem parameters. Other OCS 

development-related parameters such as measures of vessel traffic and drilling and production 

should be monitored and discussed annually in monitoring reports.
35

Aside from release of hydrocarbons during drilling, production and transportation, water 

produced during drilling can effect water quality and can have both acute and chronic impacts on 

marine organisms (Holdway 2002). Produced waters can contain elevated levels of total 

dissolved solids, radium, petroleum hydrocarbons, and heavy metals such as iron, lead, barium, 

and cadmium, and often elevated levels of lead and barium are found in sediment near drilling 

areas (Trefry et al. 1995). Hydrocarbons are quickly degraded in seawater, but metals remain 

longer and could have harmful impacts on coral ecosystems. It may be beneficial for FGBLTM 

to monitor metal concentrations as part of the water quality monitoring. 

Using the cores collected in 1990 as part of the LTM program, Gittings et al. (1993) assessed 

barium and calcium ratios in the corals’ skeletons as a potential indication of petroleum impacts. 

No significant correlations were found between petroleum activities and the barium to calcium 

ratios; however, ratios were greater in skeleton formed after the 1970s. To date, this potential 

indicator has not been revisited at FGB, but analysis of metal ratios in coral cores could give 

valuable information on historic influence of produced waters on the FGB. This could be 

analyzed on cores that have been already collected as the analysis does not require a special 

preservation method.

The physical and biological characteristics of FGB also present difficulties for determining if 

OCS development is causing ecosystem change. Early hydrology studies at FGB showed that 

deep water currents stay parallel to the isobaths, going around the banks instead of up and over, 

and that water mixing remained minimal even during catastrophic events such as heavy storms 

(McGrail et al. 1982). This indicated that impacts from OCS drilling activity such as release of 

drill cuttings were unlikely, at least for the shallow coral caps where LTM studies have been 

focused. This conclusion has been supported by sampling for aromatic hydrocarbons in the water 

column and tissues of the American thorny oyster, Spondylus americanus (Giam et al. 1982), 

though S. americanus has since been shown to be a poor indicator of hydrocarbon presence 

through tissue bioaccumulation (Neff et al. 2001). Nevertheless, all sampling that has been 

reported to date in the FGBLTM technical reports has shown little evidence of hydrocarbon 

pollution. 

The potential for determining OCS development as a driver of change is further confounded by 

the effects of oil and gas related activities on one of the most common corals found at FGB,

Psuedodiploria strigosa (Johnston et al. 2015). Knap (1987; also Knap et al. 1983) conducted 

studies for more than three years investigating the effects of an oil spill on Psuedodiploria 

strigosa. Though initially the coral responded to oil and dispersant exposure by steep reduction 

in photosynthesis (up to 85%), normal levels were restored within 5–24 hours after exposure. 

Also, long-term exposure was in general sub-lethal, recovery was quick, and it did not ultimately 

affect growth or survival within the timeframe of the experiment either in the laboratory or the 

field. This finding is supported by Bak (1987) who found that P. strigosa was still abundant 

after more than 60 years of pollution exposure near an oil refinery. Orbicella annularis and 

other coral species in this same area, a once thriving reef, were either no longer present or 

severely reproductively impacted. 

This resilience to oil exposure by Psuedodiploria strigosa is not common for many other 

Caribbean coral species. Orbicella annularis, also common at FGB (Johnston et al. 2015),

responds more negatively to oil exposure (Dodge 1982; Bak 1987). As shown with the oil 
36

refinery study, long-term exposure causes loss of this species in polluted areas (Bak 1987). 

Other studies have shown that even during shorter-term exposure O. annularis is impacted by oil 

with reduced growth and three-dimensional skeletal relief, affecting its ability to slough 

sediment, causing further damage and decline (Dodge 1982). Other Caribbean species such as 

Siderastrea siderae and Mancinia areolata have been shown to have marked reproductive 

decline after exposure to oil (Peters et al. 1981; Guzman and Holst 1993). Also, coral studies in 

the Red Sea and other areas where oil production is frequent have shown clear negative impact 

from both drilling and accidental releases (Loya and Rinkevich 1980). Nevertheless, the specific 

responses to hydrocarbon exposure-reduced fecundity, less recruitment, lowered growth, death, 

are similar to responses from other drivers of change such as other pollution sources and climate 

change. This makes pinpointing the exact cause of observed ecosystem change very difficult, 

particularly when data on OCS exposure are not being collected, as has been discussed 

previously in this document.

Similarly, if causality is to be determined, other measures related to potential drivers of change 

must be part of the LTM program. Temperature, salinity, and other water quality parameters that 

are currently measured can be used to determine the influence certain climate change-related 

factors, such as increasing sea temperatures and changes in water body movement. 

Measurements of invasive species populations and coral disease and bleaching dynamics are also 

important to continue. These parameters, along with those related to OSC development, should 

be used to create an ecosystem model on which causality can be assessed.

With the current program design, the FGBLTM program would not be able to discriminate 

among drivers of long-term change. Due to the insufficient information collected about OSC 

development, an accurate ecosystem model cannot currently be built to correlate the reef status 

parameters with any aspect of OSC development. 

1.5 Conclusions and Recommendations

Research at the banks in the 1970s and 1980s and oil exploration in the vicinity of the banks led 

to the establishment of the LTM program in 1988. Throughout the program’s more the 25 year 

history, multiple groups and organizations have been responsible for carrying out the monitoring 

work (TAMU, CSA, PBS&J and NOAA) and funding sources have shifted from MMS or 

BOEM to dual funding from BOEM and NOAA. The program’s history is riddled with 

methodological changes, some of which have had significant impacts of the data collected by the 

program.

A thorough review of the program’s objectives, sampling design, measured variables, sampling 

methodology, data representativeness, data handling, and QA and QC strategies reveals

numerous strengths and weaknesses in the current program design. The program suffers from a 

lack of thorough documentation of methods and SOPs. The program objectives were found to be 

lacking the s.m.a.r.t. characteristics (Gitzen and Millespaugh 2012) that provide the baseline for 

strong, defensible monitoring programs. The sampling design, which entails one 100 m by 100 m 

study area per bank, was found to be currently indefensible, because there is insufficient data to 

determine the representativeness of the study areas for the coral caps on each bank. The variables 

measured by the LTM program were found to be on par with other LTM programs around the 

world, though some improvements may be necessary to the specific methodologies employed to 
37

increase the effectiveness of the program. To ensure data representativeness, changes to the site 

selection methods are necessary. Data handling, QA, and QC procedures were found to be in 

need of significant improvement.

The FGBLTM program is designed such that impacts of acute events like hurricanes can be 

discriminated from long-term variation in reef community variables; although, it is recognized 

that these are event-driven and may require additional field effort. However, it was found that the 

program design is insufficient to discriminate among impacts from various chronic stressors and 

OCS development. 

In summary, based on this General Program Evaluation, the following are recommendations to 

improve the FGBLTM program:

1. Specific, s.m.a.r.t. objectives should be developed and documented promptly (before the 

next IA or contract). These specific objectives should be developed in collaboration 

among scientists and subject matter experts who will be leading the program and 

performing the work, the managers and stakeholders who will utilize the data generated, 

and quantitative experts. Because the FGBLTM program is dual-funded by BOEM and 

NOAA, it is imperative that objectives are clearly stated to ensure that the interests of 

both agencies are being addressed by the program. After developing these objectives, the 

program design should be refined to ensure that the objectives are met and spatial and 

temporal sampling efforts are optimized.

2. If the FGBLTM program is to continue to make inferences about the status of the entire 

coral reef zone at the Banks using these 100 m by 100 m study areas, i an extensive 

assessment should be made to determine whether the study areas are truly representative. 

It may be possible to make this assessment by comparing to recent data collected by 

NOAA at FGBNMS.

3. Adding semiannual in situ coral health surveys to document coral health status near the 

maximum and minimum water temperature periods is suggested. 

4. The FGBLTM program should consider adding fish herbivory and recruitment surveys to 

the program in order to monitor the effects of the lionfish invasion on these important 

reef interactions.

5. Regarding the ability of the program to discriminate among drivers of change, we 

recommend that, at the very least, sampling for aromatic hydrocarbons be resumed. This

will provide a continual baseline and a comparative parameter to test a hypothesis of 

causation should reef decline be detected. Monitoring levels of other constituents of 

drilling fluids should also be considered. 

6. Regarding sampling locations, the start and end points of each random transect in each 

quarter should be truly randomly generated and dive plans for data collection be 

determined by transect placement rather than vice versa.

7. Resources should be allocated to create a database for the LTM program and to 

reorganize and import all of the digital data into the database. An effort should also be 

made to locate and digitize all of the data that is currently missing from the program 

directories. 

8. A QA and QC plan should be finalized and approved immediately.

9. A separate method document should be completed for each monitoring method.
38

1.6 References Cited

Albins MA. 2013. Effects of invasive Pacific red lionfish Pterois volitans vs. a native predator 

on Bahamian coral-reef fish communities. Bio Invas. 15: 29-43.

Albins MA, Hixon MA. 2008. Invasive Indo-Pacific lionfish Pterois volitans reduce recruitment 

of Atlantic coral-reef fishes. Mar Eco Prog Ser. 367: 233-238.

Albins MA, Hixon MA. 2011. Worst case scenario: potential long-term effects of invasive 

predatory lionfish (Pterois volitans) on Atlantic and Caribbean coral-reef communities. 

Environ Biol Fish. 96(10-11): 1151-1157.

Atkinson AJ, Trenham PC, Fisher RN, Hathaway SA, Johnson BS, et al. 2004. Designing and 

monitoring programs in an adaptive management context for regional multiple species 

conservation plans. Sacramento (CA): USGS. 74 p.

Bak RMP. 1987. Effects of chronic oil pollution on a Caribbean coral reef. Mar Poll Bull. 18 

(10): 534-539. 

Beninger PG, Boldina I, Katsanevaskis S. 2012. Strengthening statistical usage in marine 

ecology. J Exp Mar Biol Ecol. 426-427: 97-108.

Bisong SM. [Internet]. 2015. Albuquerque (NM): Modrall Sperling. [updated 2008 Mar 26, cited 

2015 Jan 14]. Available from: http://corporate.findlaw.com/law-library/federal-agenciessubject-to-data-quality-act.html. 

Bohnsack JA, Bannerot SP. 1986. A stationary visual census technique for quantitatively 

assessing community structure of coral reef fishes. U.S. Department of Commerce, 

National Ocean and Atmospheric Administration, National Marine Fisheries Service. 

Technical Report NMFS 41. 21 p. 

Boland GS, Gallaway BJ, Baker JS, Lewbel GS. 1983. Ecological effects of energy development 

on reef fish of the Flower Garden Banks. Final Report. Bryan (TX): National Marine 

Fisheries Service. 499 p.

Bright TJ, McGrail DW, Rezak R, Boland GS, Trippett AR, et al. 1985. The Flower Gardens: a 

compendium of information. U.S. Department of the Interior, Mineral Management 

Service, Gulf of Mexico OCS Regional Office. OCS Studies/MMS 85-0024.

Bright TJ, Pequegnat LH. 1974. Biota of the West Flower Garden Bank. Houston (TX): Gulf 

Publishing Co. 435p.

Bright TJ, Rezak R. 1976. A biological and geological reconnaissance of selected topographical 

features on the Texas Continental Shelf. Final Report to U.S. Department of the Interior, 

Bureau of Land Management, Contract No. 08550-CT4. 377 p.
39

Brown E, Cox E, Jokiel P, Rodgers K, Smith W, Tissot B, Coles SL, Hultquist J. 2004. 

Development of benthic sampling methods for the Coral Reef Assessment and 

Monitoring Program (CRAMP) in Hawaii. Pacific Science 58(2): 145-158.

Brown E, Minton D, Daniel R, Klasner F, Basch L, Snyder A, Criag P, Dicus G, DeVerse K, 

Jones T. 2011. Pacific Islands Network benthic marine community monitoring method. 

Fort Collins (CO): National Park Service. Natural Resources Report NPS/PACN/NRR2011/339. 376 p.

[BOEM] Bureau of Ocean Energy Management. 2014a. Interagency Agreement M14PG00020 

between the Bureau of Ocean Energy Management and the National Oceanic and 

Atmospheric Administration National Ocean Service Office of National Marine 

Sanctuaries Flower Garden Banks National Marine Sanctuary for long term coral reef 

monitoring at the Flower Garden Banks, Gulf of Mexico: 2014–2016. 28 p.

[BOEM] Bureau of Ocean Energy Management. 2014b. Review of the long-term monitoring 

program and the associated data of the long-term monitoring program at the Flower 

Garden Banks. Task Order No. M14PD00048. 21 p.

Burke L, Reytar K, Spalding M, Perry A. 2011. Reefs at risk revisited. Washington (DC): World 

Resources Institute. 130 p.

Camilli R, Reddy CM, Yoerger DR, Van Mooy BAS, Jakuba MV, et al. 2010. Tracking 

hydrocarbon plume transport and biodegradation at Deepwater Horizon. Science. 330: 

201-204.

[CEP] Caribbean Environment Programme [Internet]. 2014. [cited 22 Dec 2014]. Available 

from: http://www.cep.unep.org/cartagena-convention/spaw-method.

Carpenter RC. 1981. Grazing by Diadema antillarum (Philippi) and its effects on the benthic 

algal community. J Mar Res. 39:749-765. 

Carpenter RC. 1986. Partitioning herbivory and its effects on coral reef algal communities. Ecol 

Monogr. 56(4): 345-363. 

Carpenter RC. 1990a. Mass mortality of Diadema antillarum I. Long-term effects on sea urchin 

population dynamics and coral reef algal communities. Mar Bio. 104:67-77. 

Carpenter RC. 1990b. Mass mortality of Diadema antillarum II. Effects on population densities 

and grazing intensity of parrotfishes and surgeonfishes. Mar Bio. 104:79-86.

Clark RJ, Taylor JC, Buckel CA, Kracker LM, editors. 2014. Fish and benthic communities of 

the Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): NOAA Technical Memorandum NOS NCCOS 179. 

317 p.
40

[CSA] Continental Shelf Associates. 1978. Monitoring program for wells #3 and #4, lease OCSG2759, Block A-389, High Island Area, East Addition, South Extension, near East 

Flower Garden Bank. Tech. Rep. for Mobil Oil Corp., Sept. 14–Dec. 15, 1977. Vol. I. 

Jupiter (FL): CSA. 163 p.

[CSA] Continental Shelf Associates. 1989. Environmental monitoring program for exploratory 

well #1, lease OCS-G 6264 High Island Area, South Extension, East Addition, Block A401 near the Flower Garden Bank. Final Report. Jupiter (FL): CSA. 96 p.

Cooper TF, Gilmour JP, Fabricius KE. 2009. Bioindicators of change in water quality on coral 

reefs: review and recommendations for monitoring programmes. Coral Reefs. 28: 589-

606.

Dodge RE. 1982. Effects of drilling mud on the reef-building coral Montastrea annularis. Mar 

Bio. 71:141-147.

Dokken QR, MacDonald IR, Tunnell JW, Beaver CR, Boland GS, et al. 1999. Long-term 

monitoring of the East and West Flower Garden Banks, 1996–1997. New Orleans, (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 99-0005. 101 p.

Dokken QR, MacDonald IR, Tunnell JW, Wade T, Beaver CR, et al. 2001. Long-term 

monitoring of the East and West Flower Garden Banks, 1998–1999. New Orleans (LA):

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 2001-101. 120 p.

Dokken QR, MacDonald IR, Tunnell JW, Wade T, Withers K, et al. 2003. Long-term monitoring 

at the East and West Flower Garden Banks, 1998–2001: final report. New Orleans (LA):

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 2003-031. 90 p.

Eckert R, Embesi JA, Hickerson EL, Johnston MA, Nuttal MF, et al. 2013. Ciguatera fish 

poisoning in the northwestern Gulf of Mexico. Galveston (TX): NOAA Flower Garden 

Banks National Marine Sanctuary. 1 p.

[EPA] Environmental Protection Agency. 2002. Guidance for quality assurance project plans. 

EPA QA/G-5. Washington (DC): EPA Office of Environmental Information. 111 p. 

Fabricius KE, De’Ath G. 2004. Identifying ecological change and its causes: a case study on 

coral reefs. Ecol Appl. 14(5): 1448-1465.

Fancy SG, Bennetts RE. 2012. Institutionalizing an effective long-term monitoring program in 

the US National Park Service. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, 

editors. Design and analysis of long-term ecological monitoring studies. New York (NY): 

Cambridge University Press. p. 481-497.

Fisher CR, Hsing P, Kaiser CL, Yoerger DR, Roberts HH, et al. 2014. Footprint of Deepwater 

Horizon blowout impact to deep-water coral communities. PNAS 111: 11744-11749.
41

Giam C, Ray L, Murray H, Allen D. 1982. High molecular weight hydrocarbons in Spondylus 

americanus. In: Rezak R, McGrail DW, Herrig SC. Environmental studies at the flower 

gardens and selected banks: northwestern Gulf of Mexico, 1979–1981. Final Report. 

College Station (TX): Department of Oceanography, Texas A&M University. Technical 

Report No. 82-7-T. p. 227-252.

Green DH, Edmunds PJ, Carpenter RC. 2008. Increasing relative abundance of Porites 

astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar Ecol 

Prog Ser 359: 1-10. 

Green RH. 1979. Sampling design and statistical methods for environmental biologists. New 

York (NY): John Wiley & Sons. 272 p.

Greenwood JD, Robinson RA. 2006. Principles of sampling. In: Sutherland WJ, editor. 

Ecological census techniques. Second edition. Cambridge: Cambridge University Press. 

p. 11-86. 

Gittings SR, Bright TJ, Hagman DK. 1992. Protection and monitoring of reefs on the flower 

garden banks, 1972–1992. In: Ginsburg RN, compiler. Proc. Coll. on global aspects of 

coral reefs: healths, hazards and history. Miami (FL). p. 181-187.

Gittings SR, Boland GS, Deslarzes KPJ, Hagman DK, Holland BS. 1993. Long-term monitoring 

at the East and West Flower Garden Banks. New Orleans (LA): U.S. Department of the 

Interior, Minerals Management Service, Gulf of Mexico OCS Regional Office. OCS 

Study MMS 92-0006. 206 p. 

Gitzen RA, Millspaugh J. 2012. Ecological monitoring. In: Gitzen RA, Millspaugh JJ, Cooper 

AB, Licht DS, editors. Design and analysis of long-term ecological monitoring studies. 

New York (NY): Cambridge University Press. p. 3-22.

Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. 2012. Design and analysis of longterm ecological monitoring studies. New York (NY): Cambridge University Press. 590 p.

Guzman HM, Holst I. 1993. Effects of chronic oil-sediment pollution on the reproduction of the 

Caribbean reef coral Siderastrea siderea. Mar Poll Bull. 26(5): 276-282. 

Hickerson EL, Schmahl GP, Robbart M, Precht WF, Caldow C. 2008. State of coral reef 

ecosystems of the Flower Garden Banks, Stetson Bank, and other banks in the 

northwestern Gulf of Mexico. In: Waddell JE, Clarke AM, editors. NOAA Technical 

Memorandum NOS NCCOS 73. Silver Springs (MD): NOAA/NCCOS Center for 

Coastal Monitoring and Assessment’s Biogeography Team. p. 189-217.

Hill AB. 1965. The environment and disease: association and causation. Proc R Soc Med. 58: 

295-300.

Hill J, Wilkinson C. 2004. Methods for ecological monitoring on coral reefs. Townsville (MC): 

Australian Institute of Marine Science. 123 p.
42

Holdway DA. 2002. The acute and chronic effects of wastes associated with offshore oil and gas 

production on temperate and tropical marine ecological processes. Marine Poll. Bull. 44: 

185-203.

Hsing P, Fu B, Larcom EA, Berlet SP, Shank TM, Govindarajan AF, Lukasiewicz AJ, Dixon 

PM, Fisher CR. 2013. Evidence of lasting impact of the Deepwater Horizon oil spill on a 

deep Gulf of Mexico coral community. Elem. Sci. Anth. [Internet]. [cited 2015 Jan 6]; 

1:000012. Available from: 

http://elementascience.org/article/info:doi/10.12952/journal.elementa.000012

Jackson JBC, Donovan MK, Cramer KL, Lam VV. 2014. Status and trends of Caribbean coral 

reefs: 1970–2012. Gland (Switzerland): Global Coral Reef Monitoring Network. 306 p.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2013. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2009–2010. 

Volume 1: Technical Report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2013-214. 

202 p.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2015. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2011–2012. Draft 

Volume 1: Technical Report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2015-027. 

205 p.

Jones GP, McCormick MI, Srinivasan M, Eagle JV. 2004. Coral decline threatens fish diversity 

in marine reserves. Proc Natl Acad Sci USA. 101(21): 8251-8253.

Knap AH, Sleeter TD, Dodge RE, Wyers SC, Frith HR, Smith SR. 1983. The effects of oil spills 

and dispersant use on corals a review and multidisciplinary experimental approach. Oil 

Petrochemical Poll. 1(3): 157-169.

Knap AH. 1987. Effects of chemically dispersed oil on the brain coral, Diploria strigosa. Mar 

Poll Bull. 18(3): 119-122.

Lang JC, Marks KW, Kramer PA, Kramer PW, Ginsburg RN. 2010. AGRRA Protocols Version 

5.4. Atlantic and Guld Rapid Reef Assessment, Miami, FL. 31 p.

Lesser MP, Slattery M. 2011. Phase shift to algal dominated communities at mesophotic depths 

associated with lionfish (Pterois volitans) invasion on a Bahamian coral reef. Biological 

Invasions 13: 1855-1868.

Lewis JB. 2004. Has random sampling been neglected in coral reef faunal surveys? Coral Reefs. 

23: 192-194.

Lindenmayer DB, Likens GE. 2009. Adaptive monitoring: a new paradigm for long-term 

research and monitoring. Trends Ecol Evol. 24(9): 482-486.
43

Loya Y, Rinkevich B. 1980. Effects of oil pollution on coral reef communities. Mar Ecol Prog 

Ser. 3: 167-180.

McDonald T. 2012. Spatial sampling designs for long-term ecological monitoring. In: Gitzen 

RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of long-term 

ecological monitoring studies. New York (NY): Cambridge University Press. p. 101-125.

McGrail D, Carnes M, Horne D, Cecil T, Hawkins J, et al. 1982. Water and sediment dynamics 

at the Flower Garden Banks. In: Rezak R, McGrail DW, Herrig SC. Environmental 

studies at the flower gardens and selected banks: northwestern Gulf of Mexico, 1979–

1981. Final Report. College Station (TX): Department of Oceanography, Texas A&M 

University. Technical Report No. 82-7-T. p. 103-226.

Miller WJ, Rogers C, Atkinson A, Muller A, Davis A, et al. 2007. Coral Reef Monitoring 

Method. Miami (FL): National Park Service. Natural Resource Report 

NPS/SER/SFCN/NRTR-2007/003.

[MMS] Minerals Management Service. 1996. Long-term monitoring at the East and West Flower 

Garden Banks. Prepared by Continental Shelf Associates, Inc. New Orleans (LA): U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS Region. 

OCS Study MMS 96-0046. 77 p.

[MMS] Minerals Management Service. 2009. Long-term monitoring program at the Flower 

Garden Banks, Gulf of Mexico. Inter-Agency Agreement No. M09PG00011. 24 p.

Mumby PJ, Flower J, Chollet I, Box SJ, Bozec Y, et al. 2014. Towards reef resilience and 

sustainable livelihoods: a handbook for Caribbean coral reef managers. Exeter (UK): 

University of Exeter. 172 p.

Mumby PJ, Hastings A, Edwards HJ. 2007. Thresholds and resilience of Caribbean coral reefs. 

Nature. 450(1): 98-101.

Mumby PJ, Hedley JD, Zychaluk K, Harborne AR, Blackwell PG. 2006. Revisiting the 

catastrophic die-off of the urchin Diadema antillarum on Caribbean coral reefs: fresh 

insights on resilience from a simulation model. Ecol Monogr. 196: 131-148.

Mumby PJ, Steneck S. 2008. Coral reef management and conservation in light of rapidly 

evolving ecological paradigms. Trends Ecol Evol. 23(10): 555-563. 

Munoz RC, Currin CA, Whitfield PE. 2011. Diet of invasive lionfish on hard bottom reefs of the 

Southeast USA: insights from stomach contents and stable isotopes. Mar Ecol Prog Ser. 

432: 181-193.
44

Neff JM, Sauer TC, Hart AD. 2001. Monitoring polycyclic aromatic hydrocarbons (PAH) 

bioavailability near offshore produced water discharge. In: Greenberg BM, Hull RN, 

Roberts, Jr. MH, Gensemer RW, editors. Environmental toxicology and risk assessment: 

science, policy and standardization- implications for environmental decisions. Tenth 

volume. West Conshohocken (PA): American Society for Testing and Materials. 358 p. 

[NPS] National Park Service. 2012. Guidance for designing an integrated monitoring program. 

National Resource Report NPS/NRSS/NRR—2012/545. Fort Collins (CO): NPS 

Inventory and Monitoring Division. 58 p.

[NOAA] National Oceanic and Atmospheric Administration. 2008. Flower Garden Banks 

National Marine Sanctuary Condition Report 2008. Available from: 

http://flowergarden.noaa.gov/document_library/scidocs/fgbnmsconditionreport.pdf.

[NOAA] National Oceanic and Atmospheric Administration. 2010. Gulf science monitor sharing 

science from the Gulf oil spill response. Issue 8. 3 p. Available from: 

http://www.restorethegulf.gov/sites/default/files/documents/pdf/GulfScienceMonitor_Iss

ue8-December172010.pdf.

[NOAA] National Oceanic and Atmospheric Administration. 2012. Flower Garden Banks 

National Marine Sanctuary final management plan. Silver Spring (MD): NOAA Office of 

National Marine Sanctuaries. 136 p.

[NOAA] National Oceanic and Atmospheric Administration [Internet]. 2014. National Ocean 

Service; [cited 2015 Jan 6]. Available from: 

http://flowergarden.noaa.gov/education/oilspill.html.

[NOAA] National Oceanic and Atmospheric Administration [Internet]. 2015. NOAA Fisheries 

Service; [cited 2015 Nov 8]. Available from: 

http://www.nmfs.noaa.gov/pr/species/invertebrates/corals.htm. 

Nuttall MF, Johnston MA, Eckert RJ, Embesi JA, Hickerson EL, et al. 2014. Lionfish (Pterois 

volitans [Linnaeus, 1758] and P. miles [Bennett, 1828]) records within mesophotic depth 

ranges on natural banks in the northwestern Gulf of Mexico. BioInvasions Records. 3(2): 

in press.

Oakley KL, Thomas LP, Fancy SG. 2003. Guidelines for long-term monitoring methods. 

Wildlife Soc B. 31(4): 1000-1003.

Ocean Conservancy. 2014. Four years after the BP Deepwater Horizon oil disaster. Ocean 

Conservatory [cited 2015 Jan 6]. Available from: 

http://oceanconservancy.org/places/gulf-of-mexico/pdf-4-years-after-bp.pdf. 

Office of National Marine Sanctuaries. 2008. Flower Garden Banks National Marine Sanctuary 

Condition Report 2008. Silver Springs (MD): National Oceanic and Atmospheric 

Administration. 49 p.
45

Olsen AR, Kincaid TM, Payton Q. 2012. Spatially balanced survey designs for natural resources. 

In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of 

long-term ecological monitoring studies. New York (NY): Cambridge University Press. 

p. 126-150.

Peters EC, Meyers PA, Yevich PP, Blake NJ. 1981. Bioaccumulation and histopathological 

effects of oil on a stony coral. Mar Poll Bull. 12(10): 333-339.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Murdoch TJT,et al. 2006. Long-term 

monitoring at the East and West Flower Garden Banks, 2002–2003: final report. New 

Orleans (LA): U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region. OCS Study MMS 2006-035. 182 p.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Evans DJ, et al. 2008a. Long-term 

monitoring at the East and West Flower Garden Banks, 2004- 2005 - Interim report. 

Volume I: Technical report. New Orleans (LA): U.S. Department of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region. OCS Study MMS 2008-

027. 123 p.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Zimmer B, et al. 2008b. Post-hurricane 

assessment at the East Flower Garden Bank long-term monitoring site: November 2005. 

OCS Study MMS 2008-019. New Orleans (LA): U.S. Department of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region. 39 p.

Precht WF, Deslarzes KJP, Hickerson EL, Schmahl GP, Nuttall MF, et al. 2014. Back to the 

future: The history of acroporid corals at the Flower Garden Banks, Gulf of Mexico, 

USA. Marine Geology 349: 152-161.

Raymundo LJ, Couch CS, Harvell CD. 2008. Coral disease handbook: guidelines for assessment, 

monitoring, and management. Melbourne (Australia): Currie Communications. 124 p.

Reynolds JH. 2012. An overview of statistical considerations in long-term monitoring. In: Gitzen 

RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of long-term 

ecological monitoring studies. New York (NY): Cambridge University Press. p. 23-53.

Rezak R, McGrail DW, Herrig SC. 1982. Environmental studies at the flower gardens and 

selected banks: northwestern Gulf of Mexico, 1979–1981. Final Report. Technical Report 

No. 82-7-T. College Station (TX): Department of Oceanography, Texas A&M 

University. 392 p.

Ritson-Williams R, Arnold SN, Fogarty ND, Steneck RS, Vermeij MJA, et al. 2009. New 

perspectives on ecological mechanisms affecting coral recruitment on reefs. Smithsonian 

Contrib Mar Sci. 38: 437-457.

Riul P, Targino CH, Júnior LAC, Creed JC, Horta PA, et al. 2013. Invasive potential of the coral 

Tubastrea coccinea in the southwest Atlantic. Mar Ecol Prog Ser. 480: 73-81. 
46

Robbart ML, Aronson RB, Deslarzes KJP, Precht WF, Duncan L, et al. 2009. Post-hurricane 

assessment of sensitive habitats of the Flower Garden Banks vicinity. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 2009-032. 160 p.

Ruzicka R, Semon K, Colella M, Brinkhuis V, Kindey J, et al. 2009. Coral reef evaluation 

monitoring report. NOAA/NOS MOA-2001-683 (Amendment No. 004)/7477. 110p.

Sammarco PW, Atchison AD, Boland GS. 2006. Geographic expansion and limits of corals in 

the NW Gulf of Mexico: colonization of offshore oil and gas platforms. Proceedings of 

the 10th International Coral Reef Symposium, Okinawa. 453-459.

Sammarco PW. 2013. Corals on oil and gas platforms near the Flower Garden Banks: population 

characteristics, recruitment, and genetic affinity. New Orleans (LA): U.S. Department of 

the Interior, Bureau of Ocean Energy Management, Gulf of Mexico OCS Region. OCS 

Study BOEM 2013-216. 106 p.

Shampine WJ. 1992. Quality assurance and quality control in monitoring programs. Environ 

Monit Assess. 26: 143-151.

Stetson HC. 1953. The sediments of the western Gulf of Mexico, Part I-The continental terraces 

of the western Gulf of Mexico: its surface sediments, origin, and development. 

Cambridge and Woods Hole (MA): Massachusetts Institute of Technology/Woods Hole 

Oceanographic Institute. Papers in Physical Oceanography and Meteorology 12(4):120 p.

Trefry JH, Naito KL, Trocine RP, Metz S. 1995. Distribution and bioaccumulation of heavy 

metals from produced water discharges to the Gulf of Mexico. Wat Sci Tech. 32(2): 31-

36.

[USFWS] United States Fish and Wildlife Service. 2013. 7-year inventory and monitoring plan.

Fort Collins (CO): USFWS National Wildlife Refuge System. 50 p.

Urquhart NS. 2012. The role of monitoring design in detecting trend in long-term ecological 

monitoring studies. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design 

and analysis of long-term ecological monitoring studies. New York (NY): Cambridge 

University Press. p. 151-173.

Van Woesik R, Gilner J, Hooten AJ. 2009. Standard operating procedures for repeated measures 

of process and state variables of coral reef environments. Melbourne (Australia): Currie 

Communications. 36 p.

Vaughan H, Brydges T, Fenech A, Lumb A. 2001. Monitoring long-term ecological changes 

through the ecological monitoring and assessment network: science-based and policy 

relevant. Environ Monit Assess. 67: 3-28.

Wilkinson, C. 2008. Status of coral reefs of the world: 2008. Townsville (Australia): Global 

Coral Reef Monitoring Network and Reef and Rainforest Research Centre. 296 p.
47

Williams BK, Szaro RC, Shapiro CD. 2009. Adaptive management: the U.S. Department of 

Interior technical guide. Washington (DC): Adaptive Management Working Group. 84 p.

Zimmer B, Duncan L, Aronson RB, Deslarzes KJP, Deis D, et al. 2010. Long-term monitoring at 

the East and West Flower Garden Banks, 2004–2008. Volume I: Technical Report. New 

Orleans (LA): U.S. Department of the Interior, Bureau of Ocean Energy Management, 

Regulation, and Enforcement, Gulf of Mexico OCS Region. OCS Study BOEMRE 2010-

052. 310 p.
48

CHAPTER 2.0: TECHNIQUE EVALUATION

2.1 Introduction

This chapter includes an analysis of all monitoring techniques, also referred to as methods , used 

at FGB since the beginning of the LTM program in 1988 through to the current IA. The chapter

is divided into seven sections, one for each monitoring variable identified as part of the 

FGBLTM program design:

1. benthic percent cover;

2. coral growth;

3. coral health;

4. coral recruitment;

5. sea urchin, lobster and lionfish population density;

6. fish populations; and

7. water quality.

The qualitative observations and qualitative data obtained from the perimeter video technique are 

also discussed. 

Some variables include multiple techniques; each one is evaluated independently. We evaluated 

techniques first for consistency through the years. Consistency is vitally important to a LTM

program (Beard et al. 1999). Failure to thoroughly define methods can lead to results being 

incomparable between monitoring periods. The FGBLTM program does not currently have 

proper documentation for its techniques (no SOPs, QA and QC plans, or method documents), 

and we discuss this further in this chapter in the context of individual methods.

Also, we compared each technique to peer-reviewed monitoring guidance and methods used in 

other LTM programs worldwide. Using these sources, the appropriateness of each technique for 

an LTM program is evaluated, along with whether the techniques are carried out correctly. The 

data gleaned from each method is also compared to other publications from the FGB region. 

Discussion focuses on whether there is consistency among results from the FGBLTM program 

and those of other programs and projects.

Finally, the 100 m by 100 m study sites that are used to collect the majority of the data for the 

FGBLTM program are evaluated. Whether focus on these small areas (relative to the size of the 

FGB coral caps) can provide data that is relevant on multiple spatial and temporal scales is 

discussed. 
49

2.2 Methods Evaluation

2.2.1 Benthic Cover

2.2.1.1 Technique Consistency

As part of the FGBLTM program, benthic cover is quantified through two techniques: random 

transects and repetitive quadrats. Both techniques have been part of the program since its 

initiation in 1988, and have assessed benthic cover within the 100 m by 100 m study sites. In 

2003, the repetitive quadrat method was expanded to include deep quadrats outside the study 

sites.

Random Transects

The random photo transects provide percent cover data for corals, algae (macroalgae, fine turf

algae, crustose coralline algae [CCA]), sponges, sand, and bare rock, and allow the study sites to 

be characterized annually on the basis of percent coral cover, coral species dominance, diversity 

and evenness. Occurrences of coral bleaching and disease have also been quantified with this 

method. Random transect data are used for yearly temporal comparisons and spatial comparison 

between EFGB and WFGB.

Multiple changes in the random transect methods have occured throughout the program, in field 

methods and post hoc data collection. In the first monitoring period, divers collected data with 

consecutive, non-overlapping photographs at 20 transects per bank (Gittings et al. 1993a). 

Divers were dropped in the water randomly and then swam random directions and distances to 

the start point of the first transect. The end point of the first transect served as the reference 

point for determining the start point of the second transect. Seventeen photographs produced one 

10 m transect. Transects were photographed with a Nikonos III or V film camera using a 28 

millimeter (mm) lens, 36 exposure color film and two Ikelite 150 watt-second strobes mounted 

on a stainless steel frame that holds the camera 1 m above the substrate (distance from substrate 

not reported until Dokken et al. 2003). The frame was padded to reduce coral damage and 

encompassed a 60 centimeters (cm) by 85 cm area of seafloor. The photograph area was reported 

as 44 cm by 63 cm. Transects were placed randomly throughout the study site except where 

large sand flats were avoided. For post hoc data collection, areal coverage was considered the 

downward projection of a colony onto two-dimensional substrate. Total coverage was calculated 

for all coral species, leafy algae, sponges and reef rock. A digitizing planimeter was used to 

outline boundaries of each benthic category and total cover was automatically calculated with a 

graphics calculator. Percent cover, number of coral species, relative dominance of corals, and 

species diversity (Shannon-Weaver Index) and evenness (Pielou’s Index) were also calculated. 

In the 1994–1995 monitoring period, the number of transects per bank was reduced from 20 to 

14 (MMS 1996). No other changes were made to this technique. During the 1996–1997 

monitoring period, minor equipment changes (from Ikelite to Nikonos strobes, aluminum camera 

frames) were made (Dokken et al. 1999). Field collection methods did not change. Equipment 

used to calculate benthic percent cover was changed, but the general approach to calculations 

and categories of benthic cover were the same. Incidence of “obvious” coral bleaching was also 

quantified as a number of bleached coral colonies and percent bleached colonies per transect.
50

During the 1998–2001 monitoring periods, divers began swimming to a pre-assigned quadrant of 

the study site (northeast, northwest, southeast or southwest) before swimming random distances

and directions to reach the starting point of the first transect (Dokken et al. 2003). This ensured 

that each quadrant of the study site was surveyed at least once. Camera set-up and photography 

methods remained the same. Three additional transects consisting of 14 consecutive photos were 

conducted outside the study area during each survey year during from 1998–2001. These outside 

transects were compared to the study site to determine if the study area was representative of the 

coral cap at each bank. Methods of calculating areal coverage were similar to previous 

monitoring periods, but the benthic categories were expanded in 2000 to include: filamentous 

algae, CCA, Dictyota sp., FA: Serpulidae, other, and artifacts.

In the 2002–2003 monitoring period, transect data were collected at 14 transects using the same 

photographic methods as in previous monitoring periods (Precht et al. 2006). The same transects 

were also collected using videography and in situ point intercept transects to determine if 

videography could replace the traditional photography methods. Transects were placed in a 

haphazard trapline pattern with the beginning of the next transect starting approximately 10 m 

and at an obtuse angle from the end point of the previous transect. During this study period, 

transects were evenly divided among the four quadrants of the study site. Precht et al. (2006) 

notes that in previous technical reports the photographs are reported to cover an area of 44 cm by

63 cm, but the true area of each photograph was 78 cm by 52 cm. All benthic categories had 

historically been evaluated as percent coverage, so the relationships among categories remained 

relevant. However, it should be noted that if Precht et al. (2006) are correct, absolute measures 

of area for each benthic category before 2002 are incorrect, and the actual area surveyed was 

4056 cm2per transect rather than the previously reported 2772 cm2. 

For the videography methods, a 10 m transect tape was laid out and a diver swam slowly along 

the transect with a digital video camera fitted with a wide angle lens and underwater video lights 

(Precht et al. 2006). The camera was held at a perpendicular angle 40 cm above the substrate, 

measured by a depth gauge and scaling bar attached to the camera housing. The camera captured 

a 40 cm wide area of substrate along the 10 m transect, for a total area of 4 m2per transect. Each 

video frame measured 40 cm by 27 cm (1080 cm2), smaller than the still photographs, which was 

necessary to capture sufficient detail to identify corals down to 3 cm in diameter. Each transect 

consisted of 42 overlapping frames. The 20 even numbered, non-overlapping frames were used 

for benthic analysis. It should be noted that while the total transect covered an area of 4 m

2

, only 

2.16 m2 were analyzed. To calculate percent cover, an image of 25 random dots was overlaid 

onto each frame and the benthic category lying under each point was identified, for a total for 

500 dots per transect. The final transect method in 2002–2003 was the linear point intercept, in 

which a diver swam along the 10 m transect tape and identified what was underlying every 10 

cm, for 100 points per transect. These data were used to determine if in situ identification would 

differ from either photography or videography methods.

Benthic categories used in analysis were coral to species, sponges, macroalgae greater than 3 mm 

to species, CTB (grouping of CCA, fine turf algae, bare rock), and sand or other inanimate 

objects (Precht et al. 2006). In a comparison of methods it was determined that digital 

videography was equal in power and accuracy to still photography. This caused a change in 

transect methods to videography for future monitoring.
51

During the 2004–2008 monitoring periods, the number of transects increased to 16 per bank, and 

were divided evenly among the four quadrants at each study site (Zimmer et al. 2010). Within a 

quadrant transects were randomly placed by swimming from the quadrant boundary a random 

number of fin kicks to the starting point of the first transect. The transects were laid in the 

direction of a randomly determined heading. The diver would then continue on this heading a 

second random number of kicks to the start point of the next transect, where another random 

heading would determine the direction of that transect. If the transect would encounter a large 

sand patch, the trajectory was slightly altered to avoid the sand. Videography methods and 

benthic analyses were the same as the previous monitoring period, except that starting in 2006 

sponges were identified to species, and in response to higher observation rates, cyanobacteria 

was added as a category. Observations of coral bleaching, paling and disease, and concentrated 

and isolated fish biting were also recorded for each transect. Data were compiled into project 

specific files, which Zimmer et al. (2010) state is a refinement of previous FGBLTM program 

data management methods. Data management is not described in prior technical reports.

In 2009, the same videography methods were used as in the previous monitoring period. In 

2010, the transition to digital still photography was made due to higher resolution that digital 

images could provide (Johnston et al. 2013). The spatial design remained 16 transects evenly 

distributed among the four study site quadrants, but divers were assigned a random point along a 

quadrant perimeter from which they swam a random distance in a random direction to a transect 

start point, rather than swimming from where the quadrant perimeter was first encountered. A 

digital camera in an Ikelite underwater housing with a 28 mm equivalent wet mount lens was 

attached to an aluminum t-frame that held the camera 65 cm from the substrate. A photo was 

taken every 55.88 cm (22 inches) along the transect tape for a total of 19 non-overlapping 

photographs per transect, each capturing an 80 cm by 55 cm area, for a total area of 8.36 m2per 

transect. During analysis a total of 500 random points was distributed evenly among the 19 

photographs to calculate an estimate of benthic cover. Benthic categories remained the same as 

the previous monitoring period, including the additional analysis of coral bleaching, paling, 

disease and concentrated and isolated fish biting.

Transect photography and analysis methods did not change in the 2011–2012 monitoring period 

(Johnston et al. 2015). In the current IA, the number of transects required is now 16 per site 

(BOEM 2014a), though researchers have endeavored to complete this many for multiple 

monitoring periods, the contractual obligation was 14 per site before this IA (Johnston et al.

2015). In the most recent field seasons, the starting point of each transect is generated randomly 

before entering the water (M. Johnston, FGBNMS, personal communication, November 11, 

2015). 

Repetitive Quadrats

Repetitive quadrats were conducted by the FGBLTM program to monitor benthic cover and 

temporal changes in individual coral colonies. Permanent posts were installed in non-living 

coral skeleton at WFGB in 1998 by CSA for an OCS development monitoring study prior to the 

establishment of the LTM program (CSA 1989). Similar stations were installed throughout the 

study site at EFGB in 1989 during the first monitoring period (Gittings et al. 1993a). Forty (40) 

quadrat stations were established at each site. In the first monitoring year, quadrats were 

photographed in an overlapping mosaic, but considerable problems with this method caused it to 
52

be abandoned by the second year (Gittings et al. 1993a). In 1990, single photographs were taken 

of each quadrat with a Nikonos V camera, 15 mm lens and two 225 watt-second strobes. An 

aluminum angle post ensured the camera was consistently held 2 m from the substrate. A 

compass heading of 0o was used so the orientation of the photographs was the same from year to 

year, and a bubble level ensured that the photographs were taken at the same angle. Each 

photograph encompassed an 8 m2area. 

The optimal size of the quadrats was determined to be 8 m2based on species accumulation 

curves, though no individual species cover or abundance analyses were originally intended to be 

calculated from the quadrat photographs, which were designed to record changes in growth of 

individual coral colonies and total coral cover (Gittings et al. 1993a). Gittings et al. (1993a) also 

state that the large area of the quadrats did not provide the detail required to fully assess the 

cause of coral colony changes or, at times, exact measurements of the magnitude of an observed 

change. To quantify these changes, the visible boundaries of all coral colonies were traced to 

produce templates that were aligned with the subsequent years’ photograph and changes in living 

and dead coral tissue were quantified. Changes in coral tissue were categorized into one of the 

following: growth, disease, bleaching, algae mediated or algae and sediment mat mediated 

retreat, unexplained mortality, and mechanical damage. Overall results of coral tissue change 

were categorized as growth, mortality, recovery, or no effect. Percent total coral cover was also 

calculated by overlaying an image of 100 random points on the photograph. Any point covering 

live coral tissue was counted. This was done three times for each quadrat and results were 

averaged. 

In 1994–1995 the same quadrats were photographed and new station markers and tags were 

installed (MMS 1996). The same photographic equipment and methods were used. Analysis 

changed to exclude unexplained mortality and mechanical damage from the potential categories. 

Percent bleaching was quantified in addition to live coral using the same 100 point overlay 

method. Only one overlay was used per quadrat (no justification provided). In 1996–1997 some 

quadrat stations were refurbished or replaced (Dokken et al. 1999). Dokken et al. (1999) did not 

state how many new stations were installed. Photograph and analysis methods did not change 

from the previous monitoring period, except that percent occurrence of coral disease was also 

quantified in quadrats where disease was observed. Three overlays per quadrat were averaged 

for percent coral cover, bleaching, and disease.

During the 1998–2001 monitoring periods, photographic equipment and field collection methods 

remained the same (Dokken et al. 2003). Analyses changed from outlining each coral colony 

and comparing perimeter changes from year to year, to using the same point intercept method 

used to quantify percent coral cover, averaging the results of three, 100 point overlays. 

Categories of tissue change causes were paling, bleaching, disease, replacement, other, and 

growth. Percent coral cover, bleaching, and disease were also estimated. No discussion is 

provided on why the method of tissue change analysis was modified.

In 2002, 31 quadrats were photographed at EFGB and 34 at WFGB (Precht et al. 2006). Stations 

were refurbished or replaced as necessary in 2003 so the total number of quadrats equaled 40 at 

each bank. Stations were also renumbered, 1–40 at EFGB and 40–80 at WFGB. In 2003, all 

quadrats that were encountered in the field were photographed regardless if they were new, 

original, or refurbished sites. Quadrats photographed in 2003 equaled 41 at EFGB and 44 at 
53

WFGB, indicating that quadrats that had been assumed missing had been overlooked in the 

previous monitoring year. The photograph equipment and field collection methods remained the 

same as in previous monitoring periods. Because the same quadrats were not necessarily 

photographed in both 2002 and 2003, only 20 were suitable for comparison between the two 

years at EFGB and 31 at WFGB. Within these photograph pairs, areal measurements were made 

using digital planimetry analysis to assess tissue changes in four to six select coral colonies of 

large reef building species located towards the center of the photograph where boundaries could 

be easily determined. In the 2001–2002 comparison, seven coral colony matches were made at 

EFGB and nine at WFGB. In the 2002–2003 comparison, 16 matches were made at EFGB and 

27 at WFGB. There was no categorization of causes of tissue change. Benthic cover analyses 

also changed so that percent cover was calculated for individual coral species, algae, bleaching, 

paling, disease, concentrated fish biting and isolated fish biting by averaging three overlays of 

100 random points.

During the 2004–2008 monitoring periods, the number of quadrats photographed ranged from 38 

to 41 at EFGB and 23 to 44 at WFGB (Zimmer et al. 2010). From 2004–2007, equipment and 

photography methods were the same as previous monitoring periods, but in 2007, a subset of 

quadrats were also sampled with digital photography to determine if this method could replace 

film photography. The film and digital photographs were compared and necessary changes were 

made to the equipment setup to acquire the appropriate resolution. In 2008, digital photography 

was used for all quadrats. Camera setup included underwater housing with a wide angle lens and 

two strobes, a compass, and bubble level. The camera was positioned 2.15 m above the 

substrate, which positioned the camera lens 2 m above the substrate, allowing the picture to 

encompass the necessary 8 m2. For coral tissue change comparisons, reef building species from 

each quadrat pair were chosen. Colonies were traced and analyzed using planimetry analysis to 

determine total area, and then compared to the area measurements from the previous year. The 

number of quadrats that could be compared in 2003–2008 ranged from 32 to 38 at EFGB and 20 

to 38 at WFGB. The number of colonies compared within each quadrat is not stated. Only 

changes in total area were calculated. Causes of tissue loss were not analyzed. Benthic percent 

cover was calculated using the point intercept method with three (3) random overlays of 100 

points. Categories of benthic cover were expanded to include sponges and CTB. A separate 

analysis of coral bleaching, paling, disease and concentrated and isolated fish biting was also 

conducted using the same point overlay method.

During the 2009–2010 monitoring period, camera equipment changed each year (no justification 

provided), but included a wide angle lens, compass and bubble level (Johnston et al. 2013). 

Distance from the substrate was 2 m in 2009 and 1.5 m in 2010, due to a different camera lens. 

Johnston et al. (2013) state that in 2009 the photographs encompassed an area that measured 251 

cm by 194 cm, which calculates to 4.87 m2. In 2010 the photographed area measured 384 cm by 

239 cm, or 9.12 m2. Percent cover was calculated for the same categories as the previous 

monitoring period. Only one, 100 point overlay was used, as statistical comparisons to previous 

analyses showed that three replicates were not necessary. Planimetry analysis was used to 

compare tissue changes in specific coral colonies whose boundaries were entirely contained in 

the quadrat images. Twenty-four quadrats were compared at EFGB and 37 at WFGB. 

roportional growth or loss of tissue in the selected colonies was calculated by dividing the total 

area (measured in pixels) of live and dead tissue in 2010 by the total area in 2009. Causes of 

tissue change were not categorized or evaluated. The 2011–2012 monitoring period used the 
54

same methods of field collection and analysis. Equipment was changed to a different digital 

camera with a 16 mm lens and dome port housing. The camera was held 2 m from the substrate 

and an area of 384 cm by 239 cm (9.2 m2) was captured in each image. The current IA requires 

an area of 5 m2in each quadrat photo (BOEM 2014A).

Deep Quadrats

Changes in the deep coral communities outside of the 100 m by 100 m study area have been 

monitored since 2003 with nine deep permanent quadrats, established at EFGB at 32–40 m depth 

(Precht et al. 2006). In 2012, the number of deep quadrats at EFGB was increased to 12 

(Johnston et al. 2015), and 12 additional deep stations were established at WFGB. Quadrat size 

(8 m2), photograph area, methods of data collection, and analysis are the same for the deep 

quadrats as for the repetitive quadrats within the study sites, with the primary focus being benthic 

percent cover analysis. Limited planimetry comparisons are also conducted on selected corals 

that had clear images and were fully contained within the quadrat. 

2.2.1.2 Appropriateness of Method for Long-Term Monitoring

Random Transects

Transects are a well-accepted and frequently used method of community assessment, as can be 

seen by the fact that many well-established LTM programs use some form of transect for benthic 

cover assessment (i.e., Brown et al. 2004; Hill and Wilkinson 2004; Miller et al. 2007; Ruzicka 

et al. 2009; Van Woesik et al. 2009; Lang et al. 2010; Brown et al. 2011). When comparing the 

transect methods used by FGBLTM program to other established LTM programs, transects are 

commonly 10, 25 or 50 m long, with the shorter lengths being more appropriate in areas where 

spatial heterogeneity is high (Miller et al. 2007). As discussed in a NPS I&M Virgin Islands 

method (Miller et al. 2007), the 10 m transect length used in the FGBLTM program is considered 

an appropriate length for monitoring coral dominated substrates. The total area of each 

photograph at FGBLTM program is also within range of other programs. For example, the 

CRTR program uses 1 m x 1 m area photos along a 50 m belt transect for a total of 50 m2per 

transect (Van Woesik et al. 2009), while the NPS I&M Pacific program uses 25 photos 

measuring 0.5 m x 0.36 m for a total of 4.5 m2per transect (Brown et al. 2011). The total area 

surveyed by the FGBLTM program has varied through the years due to changing methods. 

Table 2.1 shows how the changes in random transect methods have affected the total area being 

sampled at each bank. 
55

Table 2.1 Random transect total areas

A comparison of total areas surveyed among years. 

Year new 

method

established

Number 

transects per 

site

Number 

photos/frames 

per transect

Area of each 

photo/video 

frame (m2)

Total area per 

transect

(m2)

Total area per 

site

(m2)

1989 20 17 0.41

(0.28)*

7.0

(4.7)

139.4

(94.2)

1994 14 17 0.41

(0.28)**

7.0

(4.7)

97.6

(65.8)

2002 14 20 0.11 2.2 30.2

2004 16 20 0.11 2.2 34.6

2010 16 19 0.44 8.4 133.8

2013*** 16 17 0.44 7.5 119.7

*

parenthetical values are as reported in the technical reports, which Precht et al. 2006 assert are incorrect

**photo area not stated in technical report; assumed from identical camera setup to previous study period

***from current IA (BOEM 2014a)

The largest total area sampled is 4.5 times greater than the smallest total area sampled, and this 

should be considered when comparing data across years and making conclusions regarding

benthic community changes (see Table 2.1). Precht et al. (2006) note that differing total areas of 

transect coverage do not affect the ability to compare transect data from year to year because all 

data are compiled into percent coverages for various taxa. Though it is correct that the data are 

all presented as percent cover for comparison purposes, it should be noted that when the sampled 

proportion of the total population increases, the power of the sample data to accurately represent 

the total population also increases and overall variance decreases (Olsen et al. 2012; Reynolds 

2012). There is a point at which the amount of resources expended will outweigh the increased 

data quality, but the technical reports do not discuss if any assessment of the appropriate total 

transect area has been considered. (This is further evaluated in the Chapter 3 power analyses.) In 

addition to these considerations, until very recently the method used to place transects was more 

haphazard than random (Lewis et al. 2004). As this can have quantitative implications when 

comparing current data with historical data, it would be prudent to evaluate if each potential 

sampling unit had an equal likelihood of being sampled. Using at least 500 points per transect to 

evaluate percent cover could be more than necessary, as Carleton and Done (1995) found that 

550 points for a 1 m by 200 m video transect had the best power and precision, while 

maximizing efficiency.

Repetitive Quadrats

Permanent quadrats are appropriate for LTM and have been used successfully in conjunction 

with transects, as is seen in the Coral Reef Assessment and Monitoring Program (CRAMP)

(Brown et al. 2004) and CRTR LTM program (Van Woesik et al. 2009). Permanent stations 

have inherently less variance from year to year, so they are useful for increasing the statistical 

power of the survey design (Brown et al. 2011; Urquhart 2012). Random stations provide more 

spatial coverage and can ensure a more representative sample, because a new random sample is 

collected during each monitoring event (Urquhart 2012). As is described in the CRTR Standard 

Operating Procedure (Van Woesik et al. 2009), random sampling methods should be used to 

document “state variables” such as benthic cover, size frequency distributions, and more. 

Permanent stations are more suitable for identifying and evaluating the processes that drive state 
56

variables. This is crucial information in a successful LTM program and management plan 

because understanding of the mechanisms of community dynamics cannot be gained from a 

program that “merely documents” changes such as abundance or percent cover (Hughes 1996).

The size of the FGBTLM quadrats (8 m2) is large when compared to other monitoring programs. 

CRTR uses 4 m2quadrats, collected as a series of four 1 m2photographs (Van Woesik et al.

2009). CRAMP uses 1 m2quadrats (Brown et al. 2004), and Hughes (1996) used 1 m2quadrats, 

photographed as four quadrants. Smaller quadrats are more commonly used; however, if the 

resolution of the FGBLTM photographs is sufficient to meet analysis requirements, then no 

change is recommended. The current method of using a single overlay of 100 points to 

determine percent cover is supported by Johnston et al. (2013) who conducted statistical 

comparisons (one-way ANOVA) between this method and using a total of 300 points and found 

no significant difference or justification of the added analysis. No evaluation of the relationship 

between the number of points used and the standard deviation of the parameters was reported. 

Such an evaluation is necessary to determine the optimal number of points that should be used. 

The spatial layout of the repetitive quadrats should be revised (as discussed in Chapter 1). 

Johnston et al. (2013) states that quadrat stations were installed in areas of “premium” coral 

cover to maximize observations of changes in individual coral colonies. This use of professional 

judgment for spatial layout poses quantitative issues if any inferences are to be made to the larger 

population (McDonald 2012), because the samples are not representative of the study area or the 

larger bank. We recommend that additional quadrats be added throughout the study site to 

improve the statistical robustness of this method. As current quadrats are in areas of high coral 

cover, additional quadrats could be stratified by habitat type, which is the recommended 

sampling method for FGB by Buckel et al. (2014a). The spatial limitations of the 100 m by 100 

m study site may not allow sufficient sample size within strata. Further evaluation of the 

necessary quantity of quadrats will be outlined in Chapter 3. 

The original intent of the repetitive quadrats, as stated by Gittings et al. (1993a), was to monitor 

changes in individual colonies and the causes of those changes. In the initial monitoring period, 

percent coral cover was calculated, but no other benthic cover analyses were done (Gittings et al.

1993a). All living and dead corals within a quadrat were traced and assessed for growth, 

mortality, recovery or no change. If mortality did occur the cause was categorized if possible. 

By the 2002–2003 monitoring period, the purpose of the repetitive quadrats was stated as, “to 

monitor changes in coral reef community structure” (Precht et al. 2006). In these analyses, only 

select colonies of dominant reef building species were chosen for tissue change comparison, and 

this precedent has continued through the most recent monitoring period (Johnston et al. 2013). 

The sets of quadrat pairs are not the same from one comparison period to the next, reducing the 

consistency of data. Also, the causes of observed coral tissue retreat are no longer assessed, 

further reducing the benefit of this technique in gathering data on the drivers of change. 

The collection of process variables is dependent upon the repetitive measurement of the same 

sites and organisms over time (Van Woesik et al. 2009). Though assessing state changes in 

overall benthic cover is a good indicator of reef health, it merely catalogues changes. In contrast, 

detailed analysis of coral tissue growth or loss and their potential causes (temperature, water 

quality, physical and chemical stressors, predation, recruitment, etc.) provide information on the 

drivers of observed changes. Because one objective of the FGBLTM is to determine causation in 
57

order to proactively manage the FGBNMS, retaining data collection methods that look for 

potential drivers of change is paramount. Photograph quality and quadrat size posed issues early 

in the FGBLTM for determining cause of coral tissue loss, but current digital technology should 

allow for the more detailed analyses required. We recommend that the repetitive quadrat analysis 

be re-focused to primarily observe and quantify coral tissue changes and causation, particularly 

with the recommendation to remove the lateral growth stations. 

Deep Quadrats

The analysis of deep repetitive quadrats also focuses on percent cover measurements. This focus 

on percent cover is more appropriate for the deep quadrats than those within the study site 

because there are no complimentary deep random transects to collect these data. The temporal 

comparisons of individual colonies are similar to the study site quadrats in that they are not 

necessarily of the same corals from year to year, making the data less consistent. No information 

is provided in the technical reports on how the placement of the deep quadrats was chosen. 

Assuming placement is a stratified random design within a habitat of interest, namely deep coral 

communities, these quadrats should be representative of the area in which they are placed. The 

total area covered by these quadrats at each bank is fairly small, making the interpretive power of 

these quadrats limited. This will be further evaluated in Chapter 3.

2.2.1.3 Benthic Cover Conclusions

The general conclusions of benthic transect and quadrat data from the FGBLTM have 

consistently found a healthy reef community with high coral cover, lower species diversity than 

comparative Caribbean reefs, and low algal cover (Gittings et al. 1993a; MMS 1996; Dokken et 

al. 1999, 2003; Precht et al. 2006; Zimmer et al. 2010; Johnston et al. 2013, 2015). An AGRRA

survey at FGB, covering more area than the study sites used by FGBLTM, also found high coral 

cover (53.9% at WFGB, 48.8% at EFGB) with large corals, low incidence of disease and 

bleaching and very low macroalgal cover (less than 10% of total algal cover, Pattengill-Semmens 

et al. 2000). The first spatially comprehensive study of the benthic community found similar 

results of high coral and low algal cover, but found significant community changes with depth 

and recommended that benthic surveys be expanded to encompass the whole reef (Caldow et al.

2009). The most recent, and most extensive benthic study at FGB found comparable results of 

high coral cover, low algal cover and low incidence of coral bleaching and disease (Buckel et al.

2014). However, this study also demonstrated the high species diversity and abundance of deep 

(33.5 m to 45 m) reefs at FGB, which are underrepresented in the current FGBLTM method. 

Expanding upon the current deep quadrats and adding random transects to this area is 

recommended to further the program objective of monitoring reef communities at FGB.

One notable difference between FGBLTM results and those of Buckel et al. (2014a) is sponge 

percent cover. Johnston et al. (2013) reports sponge cover of 0.13 to 0.25%, which Buckel et al.

(2014a) reports 0.2 to 3.2%, a difference of 1.5 to 13 times higher. Though sponges are defined 

as a category of benthic cover in FGBLTM surveys, the technical reports acknowledge the 

inaccuracy of their reported values by stating that the encrusting sponges found at FGB tend to 

be on the underside of substrate and therefore are not observed in the current benthic 

quantification methods (Johnston et al. 2013). Though sponges are not typically a focus of most 

monitoring programs, increasing evidence is indicating their importance in overall reef health. 
58

The role of sponges as bioeroders is relatively well known (e.g. Glynn 1997), but sponges are 

also important in reef accretion, structural stability, binding rubble to enable coral recruitment,

water filtration, and other functions (Diaz and Rützler 2001; Wulff 2001; Rützler 2002; Bell 

2008). There is evidence that an increase in bioeroding sponge abundance can be indicative of 

overall reef health decline, particularly due to climate change induced stress (Schonbert and 

Ortiz 2008; Fang et al. 2013). Adding method to more accurately quantify sponge cover similar 

to the methods seen in Clark et al. (2014) may require extensive expenditure of time and other 

resources that would not be reasonable within the scope and methods of the current FGBLTM. 

However, because sponges are a calculated category of benthic cover, resource managers should 

remain aware that currently reported values do not reflect actual sponge cover and a separate 

survey of sponges within the LTM study sites could provide beneficial data. 

2.2.2 Coral Growth

2.2.2.1 Technique Consistency

Since the start of the FGBLTM in 1988, three separate techniques have been used to monitor 

coral health through measurements of coral growth. Lateral or encrusting coral growth is 

measured using permanent photo stations, accretionary growth is measured with permanent 

growth spikes, and accretionary growth is also measured using a sclerochronology method on 

coral cores. In addition to these three coral growth methods, colony size has been 

opportunistically compared in selected colonies from the repetitive quadrats planimetry. The 

techniques used to make these measurements has remained fairly consistent over the years; 

however, the integrity of permanent stations was poor, requiring frequent replacement of the 

lateral growth and accretionary growth spike stations (MMS 1996; Dokken et al. 1999; Dokken 

et al. 2001, Dokken et al. 2003; Precht et al. 2006) and reduction in the number of stations (MMS 

1996; BOEM 2014a).

Lateral and Encrusting Growth

For the lateral growth method, 60 permanent Pseudodiploria strigosa and 60 permanent 

Montastrea annularis (now divided into three Orbicella species) photo stations were initially 

established and monitored as described by Gittings et al. (1993a). Two nails 10 cm long and 0.3 

cm in diameter were used to mark the location where the camera frame was placed, and care was 

taken to ensure that the colony margin crossed the frame near the center. Plastic numbered tags 

were used to mark the stations for relocation on future visits, and maps of the locations were 

used for navigation. Still photographs were taken of the coral colony margin that lay within the 

frame using a Nikonos III or V film camera with a 28 mm lens, underwater housing and strobes. 

Photographs were analyzed by projecting sequential photographs onto a surface using a map 

projector and matching the margins. Growth and retreat rates, and net growth, were measured 

between each photograph pair using planimetry. Rates (cm/six months or cm/year) were 

calculated by dividing planimetric measures of change area (cm2) by the total analyzed border 

length of the station (cm). 

Some changes were made in the specific technique used to capture and analyze the photographs. 

From 1994 through 1995, planimetry measurements of growth and retreat were made with an 

electronic digitizing planimeter after tracing coral margins onto mylar sheets (MMS 1996). The 
59

photo analysis was further modernized in 1996. Photographs from the lateral growth stations 

were digitized and stored on CD-ROM. Images from sequential years were compared and 

margins were traced using a Wacom serial-based graphics tablet and Jandel Scientific SigmaScan® Pro 4.0 software (Dokken et al. 1999). In 2009, digital images were overlaid and the 

margins were compared in Adobe Photoshop to calculate growth and retreat rates (Johnston et al.

2013). Although comparisons between these methods of tracing and measuring the changes were 

not made in the reports, it is not expected that the accuracy would change significantly with these 

modernizations, but likely made the process more efficient and repeatable.

Another change was the value reported. The initial growth and retreat rates (cm/year) (percent

change in area divided by total margin length) reported were replaced by percent changes in area 

(%) beginning in 1998 (Dokken et al. 2001). In 2009 and 2010, using ImageJ open-source 

software, the length of each growing, retreating, or stable section of the margin was measured, 

and values reported were the proportion of the margin length that was growing, retreating or 

remaining stable rather than a percentage of change of the area within the frame (Johnston et al.

2013). The technical reports do not clearly state why these changes were made, but as a result of 

the changes, lateral growth values from earlier reports are not directly comparable to more 

recently reported values.

In 2007, in addition to film photography, several stations were photographed with two different 

digital camera setups to assess if it was suitable to switch to digital photography (Zimmer et al.

2010). Zimmer et al. (2010) determined that further trials with digital photography would be 

necessary before making the switch. The two digital camera setups failed to meet the target of 

less than two to three percent difference in measurements from still photography. In 2009, lateral 

growth stations continued to be photographed with the original Nikonos V film camera setup. 

However, due to mechanical failure with the Nikonos camera partway through the fieldwork, a 

Canon G6 digital camera was used to complete the fieldwork (Johnston et al. 2013). In 2010, 

rather than returning to a film camera, a new digital Canon Power Shot G11 in Fisheye Fix 

housing, was tested and used for monitoring (Johnston et al. 2013). No comparison was reported 

between the Nikonos camera setup and either of the Canon digital camera setups, so it is unclear 

if this method change affected the results reported. However, because the parameter reported was 

also changed to the proportion of the margin length that was growing, retreating or remaining 

stable during the same time frame, a direct comparison with past reported values is not possible.

More substantial to the efficacy of the lateral growth method than the minor methods changes 

and differing units for reporting values, has been the lack of station integrity. In 1994, five years 

after the original lateral growth stations were established, the stations were determined to be 

unusable by CSA (MMS 1996). As a result, the stations were replaced with new stations. The 

rationale behind this determination was that one of the following had occurred at the stations: 

 The coral border had shifted by either growth or recession beyond the area viewed in the 

repetitive photographs.

 The border had become three dimensional, or plateau-like, due to bioerosion of the reef 

rock area or accretionary growth, or both, of the coral tissue. In this case, the growth 

border became perpendicular to the film plane and very difficult to view and determine 

minute changes in growth and recession in station photographs.
60

 Spikes used during the previous study were missing or had become loose in the reef rock.

 The station was not located in the area designated on site maps made during the previous 

monitoring study.

The 60 P. strigosa stations were replaced in 1994, but Monastrea annularis stations were not 

replaced and this species was eliminated from the method (MMS 1996). The new P. strigosa

colonies were selected to have relatively planar surfaces and colonies with diseases or that were 

in border competition with other corals were avoided. Expansion bolts replaced 10 cm nails to 

mark the location for the camera frame (MMS 1996). It was thought that using expansion bolts 

would help improve station integrity; however, from the subsequent monitoring results, any 

improvement in integrity was not evident.

In 1996 and 1997 many of the lateral growth stations established in 1994 were found to be 

unusable (Dokken et al. 1999). Twenty of 60 stations, in one case, were replaced or refurbished. 

In 1998, many of the lateral growth stations were again found to be unusable due to loss of pins 

or advance or retreat of coral tissue beyond the boundaries of the photo frame (Dokken et al.

2001). As a result 14 new stations were established at each bank in 1998. Of the stations 

photographed, only a handful of photographs were suitable for comparison at EFGB and WFGB 

(50 and 47 photographs in 1999, 13 and 25 photographs in 2000, 29 and 24 photographs in 2001, 

and 18 and 11 photographs in 2002, respectively) (Dokken et al. 2003; Precht et al. 2006). Each 

year new stations were established to replace the stations that were lost. In 2003, a more 

complete site rehabilitation was performed for the lateral growth stations. Twelve new stations 

were established at EFGB and 48 old stations were refurbished with new pins and new tags.

Seventeen new stations were established at WFGB and 43 old stations were refurbished with 

new pins and tags (Precht et al. 2006). As a result of the new and refurbished stations, only 8 and 

4 stations were suitable for comparison in 2003.

In 2004 and 2007, bad weather reduced the number of sites that could be photographed. In other 

years (2005–2006 and 2008–2010) during these monitoring periods researchers attempted to visit 

and photograph up to 60 stations per bank, sometimes photographing old stations that were no 

longer numbered when encountered (Zimmer at al. 2010; Johnston et al. 2013). This resulted in 

some of the 60 photographs not being comparable from year to year. Photographer error in 

camera placement, missing pins, and advance or retreat beyond the boundaries of the camera 

frame also contributed to low numbers of stations suitable for comparison for year to year. 

Between 2004 and 2007, few photographs were suitable for comparison with the previous years’ 

photographs at EFGB and WFGB (24 and 21 photographs in 2004, 30 and 25 photographs in 

2005, 35 and 50 photographs in 2006, and 39 and 15 photographs in 2007, respectively) (Zimmer 

et al. 2010). No comparisons were attempted between the 2008 and 2009 photographs, and 5 and 

9 stations were found to be suitable for comparison between 2009 and 2010 (Johnston et al.

2013).

Accretionary Growth by Growth Spikes

The growth spike method employed for measuring accretionary growth initially included spikes 

implanted in 30 M. annularis SC colonies per bank (Gittings et al. 1993a). The spikes were 20 

cm stainless steel and were driven into the live colonies using a modified air hammer. Each spike 

had 10 cm exposed above the coral’s surface. Plastic numbered tags were used to identify the
61

stations. Growth values were measured using calipers to the nearest mm. At the inception of the 

LTM program, it was recognized that this technique presented several problems:

1. nail height measurements could be difficult to make accurately because of the irregular 

shapes and textures of a corals surface;

2. contact with the corals tissue is necessary during measurement and this repetitive contact 

could cause tissue necrosis and affect accretionary growth;

3. the tissue damage inflicted during implantation of growth spikes sometimes led to infection 

by filamentous algae

4. occasional loss of or loosening of some growth spikes was expected and would result in the 

loss of data; and

5. the presence of the spike could alter growth patterns.

Keeping these issues in mind data from the growth spikes were closely reviewed and any data 

affected by these issues were removed from the analysis. In the 1989 to 1991 period, 52% of the 

growth spike measurements were suitable for analysis. Growth rates were reported in mm per 

year (Gittings et al. 1993a).

Most of the accretionary growth spike stations were found to be unusable in 1994. MMS (1996) 

lists the following reasons:

 The growth spikes had become loose within the coral.

 Coral tissue around the growth spikes had receded due to damage caused by implanting 

the spike.

 Placement of the growth spike had altered normal coral growth patterns.

 The spike was lost or the station was not found in the area designated on site maps.

To replace the original 30 growth spikes, 20 new stations per bank were established at M.

annularis SC colonies (no rationale was given for the reduction in station number). Rather than 

using the stainless steel spikes, expansion bolts were used because they were designed to hold 

more securely to the coral skeleton and the method of implantation was less damaging to the 

corals. A second expansion bolt was placed nearby in order to secure a plastic numbered tag to 

aid in relocating the stations. Accretionary growth was measured with calipers in the same 

manner as the original method (MMS 1996).

Of the 20 stations, data from 18 and 15 stations on EFGB and WFGB were suitable for analysis 

in 1995 (MMS 1996). No attempt was made to compare measurements between 1995 and 1996, 

and six new stations were installed at WFGB in 1996 (Dokken et al. 1999). In 1997, ten stations 

on FGB and eight stations on WFGB were replaced. Of the measurements collected only five on 

EFGB and nine on WFGB were suitable for analysis (Dokken et al. 1999). On recommendation 

of Dokken et al. (1999), the growth spike method for measuring accretionary growth was 

discontinued after the 1997 field season (Dokken et al. 2001).
62

Accretionary Growth by Sclerochronology

Accretionary growth was also measured using sclerochronology, beginning with cores collected 

in 1990 (Gittings et al. 1993a). Two cores were extracted from M. annularis SC coral heads at 

each bank at approximately 23 m depth. No mention was made of the diameter of the cores or 

the type of drilling system used. The cores were cut into slabs 3 to 4 mm thick and x-rayed to 

view the high and low density bands. One pair of high and low density bands was considered one 

annual growth increment. Planimetry was used to determine the area of the annual growth band 

pair after tracing the bands and microstructures onto acetate overlays. Annual accretionary 

growth rates were calculated for 1910 through 1989 (Gittings et al. 1993a).

After the initial collection, cores were collected approximately once per contract period (every 

two to three years). In 1995 cores were shorter, encompassing the years 1973 through 1995, and 

each core was 2.5 cm in diameter. The holes that resulted from the core extraction were plugged 

with a concrete cap. The method by which growth rate was calculated during this contract period 

was not described in detail (MMS 1996). In 1997, even shorter cores were extracted, 

encompassing at least 10 cm which is approximately 10 years of growth, and each core was 6 cm 

in diameter (Dokken et al. 1999). By this time the species M. annualris SC had been divided into 

three species and the core extractions were performed on Orbicella faveolata only. Due to the 

fact that the growth form of O. faveolata is more favorable for sclerochronology compared to 

other two species of the M. annularis SC (Smith et al. 2006; Dokken et al. 1999), it is likely that 

cores extracted prior to 1997 were predominantly of this species. However, no attempt was made 

to determine whether this assumption is correct or assess the growth rate differences between the 

three species at FGB. Any trends in sclerochronology growth results before 1997 must be 

interpreted with caution given this knowledge gap.

Growth rate was calculated by measuring the linear distance between two band pairs at three 

locations per band. In 1999 only one core per bank was extracted, and in 2001 two cores per 

bank were extracted. Cores from 1999 and 2001 were again 6 cm in diameter and at least 10 cm 

in length and were analyzed in the same manner as the 1997 cores (Dokken et al. 2003). In 2003, 

2005, and 2007, four cores were collected from each bank, and each core was 3 cm in diameter 

and at least 5 cm long (Precht et al. 2006; Zimmer et al. 2010). In 2010, four 3.5 cm diameter 

and at least 5 cm long cores were extracted from each bank. These cores were transported to the 

lab in ethanol, cut into 8 mm thick slabs, scanned with a digital x-ray, and analyzed using 

CoralXDS software (Johnston et al. 2013).

2.2.2.2 Appropriateness of Technique for Long-Term Monitoring

Coral growth rate is a suitable parameter for measuring long-term changes in tropical marine 

environments (Cooper et al. 2009). Coral growth is known to be influenced by irradiance, water 

temperature and thermal stress, ocean acidification, nutrient availability, and water quality 

(Rogers 1990; Bak et al. 2009; Cooper et al. 2009; Cantin et al. 2010). Though coral growth rates 

are not as quick to respond to water quality changes as some other indicators, they can provide 

and important record of historical environmental changes in water quality (Cooper et al. 2009). It 

is cautioned, however, that drivers of coral growth are not fully understood and the influence of 

one stressor may affect that of another. For example, Edinger et al. (2000) found that massive 

coral growth rates were poor indicators of ecosystem health in the Java Sea, where the effects of 
63

eutrophication combined with sedimentation led to normal coral growth rates in massive corals, 

despite the declining health of the reefs in other parameters such as reduction in coral cover and 

coral species diversity. When combined with other parameters such as these, coral growth 

measurements can be an important part of a LTM program. The use of a single coral species to 

measure coral growth will provide only a limited ability to detect changes in coral growth across 

the reef, as drivers of change may vary widely for difference species and growth forms (Weber et 

al. 1975).

Not all measurement methods for coral growth are reliable or useful. At FGB, three methods 

have been employed for measuring coral growth rate: repetitive photographs of lateral growth 

stations, repetitive measurements of accretionary growth at growth spike stations, and 

sclerochronological analysis of coral cores. Rogers et al. (1994) briefly discusses similar 

methods to each of these. Photographic methods like the lateral growth method and in situ

measurements like the growth spike method are simple and could provide useful information in 

some situations when carried out correctly (Rogers et al. 1994). Measuring growth in coral cores 

is a more complex process, but provides one of the most reliable ways to measure growth in 

massive corals (Rogers et al. 1994; Lough 2008).

At the inception of the FGBLTM program, the lateral growth method was based on methodology 

used by Bright et al. (1987), which had been used to successfully study coral growth after the 

freighter grounding at Molasses Reef in the Florida Keys (Gittings et al. 1993a). These methods 

are also described in Gittings et al. (1988) and are consistent with the original methods used at 

FGB in 1989 through 1991 (Gittings et al. 1993a). Repetitive photographs of the colony 

perimeters of M. annularis (now divided into three Orbicella species) colonies were used to 

successfully measure advance and retreat rates to assess damage and recovery of coral colonies 

after three vessel groundings (Gittings et al. 1988; Gittings et al. 1993b). Although these three 

studies were successful, the lateral growth method has proven to be problematic for FGBLTM 

(Johnston et al. 2013). Each of the vessel grounding studies was relatively short, five years, 

compared to the more than 25-year LTM program at FGB. Perhaps the method is more suitable 

for short term studies than it is for LTM.

Other repetitive photographic methods have proven successful at monitoring coral growth. One 

method for measurement of coral growth in two species, M. annularis (now divided into three 

Orbicella species) and Porites astreoides, was assessed by Gustafson (2006). Gustafson (2006) 

took short videos of permanent quadrat stations using a camera and framer and then selected the 

“best” frame from the video image, i.e., the frame with optimum lighting and focus to measure 

basal area. Basal area was then measured and compared across years to successfully compare 

growth at 54 sites per year for three years. Vago et al. (1994) also reported a successful 

application of a photographic growth assessment of two coral species, Favia favus and 

Stylophora pistillata. The methods of Vago et al. (1994) involved using a permanently installed 

frame that insured proper camera placement. The methods of both Vago et al. (1994) and 

Gustafason (2006) are not vastly different from those of the FGBLTM program and the 

conclusions of both papers suggest that the methods are suitable for LTM. However, these 

studies were again much shorter term, 18 months to three years rather than 25 years, and station 

integrity seems to be the main issue at FGB.
64

Johnston et al. (2013) discusses the various issues the lateral growth method has encountered, 

primarily the extensive effort expended to locate, repair and replace sites. Frequent repair and 

replacement of sites was necessary due to the lack of site integrity discussed above, and the 

amount of time allotted for these tasks has not been enough to correct the problem (Johnston et 

al. 2013). One major consequence of continuously replacing the lateral growth stations is that 

repeated measures type statistical analyses of the data are not appropriate for analyzing across 

more than the few years when sequential images are available for a particular station (Dokken et 

al. 2003; Zimmer et al. 2010). Another consequence is that sample sizes are extremely variable 

from year to year. This limits the comparison of lateral growth across the length on the LTM 

program to cursory comparisons of mean growth rates based on unequal samples, which means 

that the cause of the widely variable growth rates and large standard deviations (MMS 1996; 

Johnston et al. 2013) cannot be definitively determined. These variations could be representative 

of long-term ecological variability, response to environmental factors, or the result of the 

variable sample size. The statistical implications of the lateral growth method design will be 

further discussed in Chapters 3 and 4. 

The accuracy of the lateral growth method is also questionable. Measurements are only made on 

a very small portion of the coral perimeters (19.7 cm maximum), while tissue advance and 

retreat rates can be extremely variable within a single colony. The coral may be growing 

normally in one location, and rapidly retreating in another (Johnston et al. 2013). Because the 

perimeter areas for the lateral growth stations are selected so that competition with other species 

and coral diseases are avoided (MMS 1996), these possible sources of coral retreat are neglected 

and the net growth values cannot be used to predict overall lateral growth in the area. With that 

in mind, statements such as “Net lateral growth of P. strigosa was positive during the 2009 and 

2010 study period” (Johnston et al. 2013) are not meaningful, because it could very well be that 

the margins monitored were growing, while other colonies were retreating due to diseases and 

other stressors.

One of the most important aspects of a successful LTM program method is that it should be 

repeatable year after year. The lateral growth methods have proven to be quite unreliable in this 

aspect. Although carefully placed pins are used to mark the location where the camera frame is 

placed (Gittings et al. 1993a; Johnston et al. 2013), every monitoring period photos are discarded 

due to incorrect camera angles (Johnston et al. 2013). If after more than 25-years of using this 

method, issues with camera placement have not yet been resolved, creating a situation where the 

method is not adequately repeatable from year to year, it seems that the method is fundamentally 

inappropriate for LTM. It is apparent that the lateral growth method is not a successful part of the 

FGBLTM, and we recommend that this method be discontinued.

From 1989 through 1997, accretionary coral growth was measured at FGB using a growth spike 

method. Little justification was given for the method by Gittings et al. (1993a), and the issues 

with the method were numerous. Similar to the lateral growth method, the growth spike stations 

were difficult to maintain. Loose nails and lost nails accounted for many unusable stations 

(MMS 1996). In addition to the site integrity issues, the growth spike method also introduced 

growth anomalies caused by the nail placement and algal growth due to damage caused by 

implanting the spike (Gittings et al. 1993a; MMS 1996).
65

Goreau and Macfarlane (1990) used essentially the same method to measure growth in O.

annularis at Discovery Bay, and ran into similar problems with the method (loose nails and algal 

growth). Despite these problems, Goreau and Macfarlane (1990) were able to measure changes 

in coral growth caused by the 1987–1988 bleaching event. The success of this study suggests that 

the growth spike method might be suitable for short-term monitoring (on the order of a few 

years). However, the low site integrity seen in both the Goreau and Macfarlane (1990) study and 

the FGBLTM studies demonstrate that it is a poor method for LTM. Sites were lost after just a 

few months in both studies and site loss only increased with time at FGB, making it impossible 

to measure growth over more than a few years’ time at a given station (MMS 1996). 

Discontinuation of this method in 1997 is justified.

The third coral growth method used at FGB is sclerochronology. This is a commonly used 

method for measuring long-term growth patterns in coral reefs worldwide (Lough and Barnes 

2000; Lough 2008; Cooper et al. 2009). In general, the method involves extracting cylindrical 

cores from the vertical growth axis of massive coral colonies and using one of several methods 

(x-ray, CT scan, visible observation of bands, etc.) to measure the width of seasonal density 

bands. One pair of a high and a low density band are usually interpreted as one year’s growth. 

This method is effective for LTM because a single core can give information of coral growth 

patterns for the entire life of the coral, sometimes more than a century’s worth of data (Lough 

and Barnes 1997; Lough 2008). However, sclerochronology is not a fail proof method. Many 

considerations must be made, and this method may not be appropriate in all situations (Lough et 

al. 1997). Among the considerations are: 1) the mechanisms behind skeletal accretion are 

complex and not fully understood (Cohen and McConnaughey 2003; Carricart-Ganivet 2011). 

Some records show corals which do not display seasonal (winter and summer) density pattern, 

bands that do not represent exactly one years’ time, or in the case of extremely stressed corals 

entire annual bands may be missing (Carricart-Ganivet 2011). 2) The location on the colony at 

which the core is extracted has a significant effect on the growth measurements. It is extremely 

important to take the core measurements as close to parallel with the vertical growth axis as 

possible. 3) There is considerable variation in growth reported between and among species even 

on a single reef (Weber et al. 1975; Prouty et al. 2014). And it is important that the natural 

variation in growth across a reef be considered when determining sample size and interpreting 

data.

These considerations can be discussed using the FGBLTM data from past reports. First, it can be 

assumed that the O. faveolata corals studied at FGB were displaying annual density banding. The 

fidelity of annual density banding in O. faveolata in the Caribbean and Western Atlantic is 

supported in scientific literature (Smith et al. 2006; Carricart-Ganivet 2007; Halley and Hudson 

2007 and references therein), and the long cores collected in 1989 showed clear density banding 

that indicated a similar growth rate (6.6 mm per year) to the growth spike estimates and cores 

collected at other reefs (Gittings et al. 1993a). There was also little variation in annual growth 

rate in the initial cores (the standard deviation was less than 2% of the mean), suggesting that 

annual density bands were consistently accreted.

Second, the FGBLTM has collected from the vertical axis according to the methods reported 

(Gittings et al. 1993a; MMS 1996; Dokken et al. 2003; Precht et al. 2006; Zimmer et al. 2010; 

Johnston et al. 2013). Dokken et al. (1999) does note an exception to this. Cores collected during 
66

the 1996 to 1997 contract period were not collected from the apex of the colony and were noted 

as potentially not being representative of the maximum accretionary growth.

Third, there is a noticeable amount of natural variation in growth among the corals collected at 

FGB. It is unclear what the source of the variation is, because metadata for the coral, including 

depth and size of the coral colony, are not reported. Although average growth rates were within 

the expected range based on the growth spike data and that of previous studies, average annual 

growth rates for growth between 1979 and 1995 was calculated as 7.6 ± 1.6 mm per year for the 

cores collected in 1995, ranging from 6.3 ± 0.7 to 8.8 ± 1.6 mm per year for individual cores and 

from 5.3 to 11.7 mm per year for individual measurements within the cores (MMS 1996, 

Appendix B). As discussed above, the annual growth variation calculated in the 1989 cores was 

much less (6.6 ± 0.1 mm per year; Gittings et al. 1993a). Because no colony metadata is 

presented with the 1995 cores, the source of these differences cannot be determined. We 

recommend that metadata including colony depth and size be included and discussed in 

analyzing future sclerochronology results at FGB.

Although collecting growth measurements through sclerochronology is appropriate for LTM, 

FGB should consider some adjustments to the current sampling strategy to increase efficiency. 

Collecting very short cores as is done at FGB (70 to 100 mm cores) is not a common strategy. It 

would be more efficient to collect longer cores (perhaps 50 to 70 cm) less frequently (perhaps 

every 5 years or more). This would allow for growth in one colony to be measured for longer 

periods of time (more than 50 years) and allow for simpler analysis of long-term trends, 

potentially with less variation. To assess long-term trends with the shorter core strategy, data 

from numerous sampling periods must be combined. Because the data are coming from different 

cores each 10–15 year period, the data must be treated as independent measurements. With the

longer core strategy, repeated measures analyses could be applied to 50 or more year datasets, 

which would increase the statistical power of the analyses. Collecting longer cores would take 

more field time per collection; however, because the datasets would be longer, cores could be 

collected with much less frequency. This would also reduce the number of coral colonies 

exposed to potential damage caused by drilling, and longer cores, as long as they were plugged, 

would not have any more significant effect on the colony health than shorter cores, because only 

the thin tissue layer on a colony is living. Therefore, we recommend that longer cores be 

collected with less frequency at FGB. Core records should be archived in a manner similar to 

those at St. Petersburg Coastal and Marine Science Center's Core Archive Portal (Reich et al. 

2012).

Beyond the scope of the FGBLTM program, cores collected for the sclerochronology growth 

measurements can provide additional information on the environmental history at FGB. By 

measuring elemental ratios such as Ba/Ca, Sr/Ca, and Fe/Ca (the molar ratios of barium, 

strontium, and iron to calcium) and isotopes like the stable oxygen isotope δ

18O from within 

coral skeletons, coral are used as proxies for the environmental conditions from sea surface 

temperature and salinity to sediment input and heavy metals pollution (Quinn and Sampson 

2002; Fairbanks et al. 1997). This information could be easily gleaned from the cores already 

collected at FGB through other project funds, if desired, providing insight into historic influence 

of the oil and gas industry’s presence near FGB, other pollution sources, and climate change. For 
67

example, petroleum biomarkers including PAHs have been successfully detected in coral skeletal 

tissue (Poulsen et al 2005; Sabourin et al. 2013).

2.2.2.3 Coral Growth Conclusions

Outside of the LTM program, coral growth rates have not been assessed at FGB. The three coral 

growth methods that have been applied at FGB have provided some valuable information about 

lateral and accretionary growth at the banks. However, a lack of site integrity at the lateral 

growth and accretionary growth spike method sites along with other methodological issues has 

greatly reduced the amount of usable data collected each year from these methods. This has 

resulted in the accretionary growth spike method being discontinued after 1997, and we 

recommend that the lateral growth method also be discontinued. Accretionary growth 

measurements collected through sclerochronology have been a more reliable method and is a 

commonly used strategy for monitoring long-term trends in coral growth. We recommend that 

the sclerochronology method be continued and consideration be made to increasing core lengths 

and decreasing sampling frequency.

2.2.3 Coral Health

2.2.3.1 Technique Consistency

Coral colony size and condition (coral health) surveys are a new addition to the FGBLTM 

program design, added in 2014. A report documenting the exact methods has not been composed 

yet; however, the current IA describes the required aspects of this method (BOEM 2014a). Line 

intercept transect surveys are to be conducted in situ at eight of the sixteen random transects. 

Colony size of all living corals that touch (or fall beneath) the transect line are to be identified 

and measured. The condition of each colony is to be described including percent mortality, 

percent bleaching, percent paling, and percent disease-affected. Before this addition, coral 

colony size was not directly measured as part of the LTM program and coral health was only

quantitatively addressed in the random transects and permanent quadrat surveys as a secondary 

objective (measuring benthic cover was the primary objective). In those methods percent 

“coverage” of coral bleaching, coral disease, and concentrated fish biting was calculated within 

the photos or video frames collected (Zimmer et al. 2010; Johnston et al. 2013). Coral health data 

collected using the new method will not be directly comparable to that collected as a percent 

coverage measure, because area covered by line intercept transects is dependent on colony size 

rather than a pre-determined quadrat size, so this method does not provide a consistent area for 

comparison between sites (Raymundo et al. 2008). To ensure that the coral colony size and 

condition surveys are conducted in a consistent and correct manner, a method document 

including detailed SOPs and QA and QC measures should be written as soon as possible.

2.2.3.2 Appropriateness of Technique for Long-Term Monitoring

Healthy corals are an essential part of a healthy coral reef ecosystem, and coral bleaching and 

disease are threatening coral health worldwide (Wilkinson 2008; Jackson et al. 2014). Coral 

diseases can have negative effects on coral reproduction rates, growth rates, community 

structure, and species diversity (Raymundo et al. 2008). An increase in coral disease incidence in 

recent years in the Caribbean region is well-documented (Porter et al. 2001; Bruckner 2002; 
68

Weil 2004; Weil et al. 2006). Although, coral disease incidence has been reported to be lower in 

general at FGB than other reefs in the Caribbean and Gulf of Mexico (Borneman and Wellington 

2005), coral diseases has been suggested as one of the most serious threats to the reefs at

FGBNMS (Gittings et al. 1993a; Zimmer et al. 2010). Outbreaks of a white plague-like disease 

were reported at FGB in 2005 through 2008 (Hickerson et al. 2008) and several other novel 

diseases were found at FGB by Borneman and Wellington (2005). Major bleaching events have 

been reported at FGB in 1998, 2005, and 2008 through the LTM program (Jackson et al. 2014), 

and other coral health afflictions such as predation by fish are also reported (Johnston et al.

2013).

Monitoring coral health and identifying specific syndromes and their causes can be a key 

component of managing coral reef systems (Bruckner 2002; Raymundo et al. 2008). Targeted 

coral health surveys such as those added to the FGBLTM, combined with monitoring of other 

parameters such as benthic organisms, fish, water quality, and human factors is the ideal 

approach to understanding diseases on a given reef (Raymundo et al. 2008). Coral health 

monitoring should focus on the following questions:

 Are there coral diseases present and which diseases are present?

 What species are affected by coral diseases?

 Are some reef areas within the target population more affected by diseases than others?

Raymundo et al. (2008) discusses ten potential methods for monitoring coral diseases. Among 

those is the line intercept transect method indicated for use at FGB in the IA (BOEM 2014a). 

This method can provide detailed disease prevalence information, and provides rapid assessment 

of size structure, colony density and coral cover. Disadvantages of this method include the 

inability to provide a comparable assessment of disease area if coral sizes vary among sites. 

Gittings et al. (1993a) suggested that colony sizes were different between EFGB and WFGB. If 

the intent is to compare data between EFGB and WFGB or different areas within each bank (i.e.,

inside and outside the study sites), choosing a method that does provide comparable area 

coverage should be considered. Another disadvantage to the line intercept method is that percent 

mortality is estimated, and this may introduce observer bias. This potential source of error should 

be addressed in the method document that should be written (refer to Section 1.3.7).

The methods described in the IA are consistent with those described as part of the AGRRA 

methods in references from the early 2000s (Bruckner 2002; Hill and Wilkinson 2004); however, 

AGRRA now suggests a belt transect to assess coral size and condition (Lang et al. 2010).

Although the line intercept method required in the IA is a recognized method for coral health 

assessment, FGBLTM should consider standardizing the method with other programs in the 

region such as AGRRA.

Belt transects are commonly used for coral size and health assessments worldwide (Weil et al.

2002; Hill and Wilkinson 2004; Willis et al. 2004; Page and Willis 2006; Williams et al. 2010). 

Belt transects provide similar information to the line intercept method, but do assess consistent 

areas, increasing comparability across sites (Hill and Wilkinson 2004; Raymundo et al. 2008). In 

general, the belt transect method involves measuring the size of all colonies within a certain 

width belt along a transect line and assessing the percent mortality, bleaching, and disease. 

AGRRA uses a 1 m wide belt transect (Lang et al. 2010), while some other methods use up to 2 
69

m wide belts (Hill and Wilkinson 2004; Raymundo et al. 2008). One major disadvantage to belt 

transects is that they can be time intensive, sometime requiring more than one dive per transect 

(Raymundo et al. 2008). This should be considered when determining the feasibility of aligning 

the coral size and condition surveys with other regional methods.

It is also important that personnel performing the assessments be well-trained in coral disease 

identification. Borneman and Wellington (2005) suggest that many of the coral diseases 

identified in the FGBLTM reports may have been erroneous. Predation, competition, and other 

conditions may have been mistaken for coral diseases, while some diseases may have gone 

undetected by the methods used. Borneman and Wellington (2005) also point out that disease 

nomenclature used in the monitoring reports (i.e., Dokken et al. 1999; 2001; and 2003) is nonspecific and non-standard. Inconsistent nomenclature is has been a common problem in the study 

of coral diseases and is a major hindrance to the understanding of coral diseases worldwide 

(Bruckner 2002). Raymundo et al. (2008) provides a guide to identification of common diseases 

and other health afflictions of the Western Atlantic, using standard nomenclature and 

terminology. We recommend that this terminology be used to describe coral health conditions at 

FGB.

The FGBLTM has recently added dedicated coral size and condition assessments. Results and 

conclusions are not yet available to compare with other published studies from FGB. Information 

from random transects and repetitive quadrates, which were used to provide some measure of 

coral health in the past, suggest low incidence of bleaching and disease compared with other 

reefs in the region (Johnston et al. 2013). These results are consistent with other studies at FGB 

(Borneman and Wellington 2005); however, these methods failed to detect a disease outbreak in 

2005 that was reported in Hickerson et al. (2008).

Coral Health Conclusions

The methods suggested for the dedicated coral size and condition surveys are standard and can 

produce useful results if carried out correctly and consistently. However, to increase the ability to 

compare EFGB and WFGB, with areas outside the current studies, and with other regional 

programs, it is suggested that FGB consider adopting a belt transect method for assessing coral 

size and conditions similar to the methods proposed by Lang et al. (2010). Furthermore, once the 

methods are determined, we recommend that a detailed SOP and QA and QC documents be 

approved as soon as feasible in order to ensure consistency in future years.

2.2.4 Coral Recruitment

2.2.4.1 Technique Consistency

Historically, coral recruitment has not been measured as part of the FGBLTM program. 

Photographic quadrats and videographic transects have not provided the resolution necessary for 

detecting tiny coral recruits. In 2014, the requirement for a coral recruitment survey was added to 

the IA (BOEM 2014a). Coral recruits (corals less than 4 cm in diameter) are to be measured 

along eight of the 16 random transects along with the coral colony size and condition surveys. 

Recruits are to be identified within a belt along the transect line (M. Johnston, FGBNMS, 

personal communication, November 11, 2015). As suggested with the coral size and condition 
70

assessments, to ensure that coral recruitment surveys are conducted in a consistent and correct 

manner, a method document including detailed SOP and QA and QC measures should be 

approved as soon as possible.

2.2.4.2 Appropriateness of Technique for Long-Term Monitoring

Coral recruitment is a key process in reef succession (Hughes and Tanner 2000). Without new 

recruits to replace corals that have died, a reef is not able to recover from mortality events Coral 

recruits may be sourced from nearby reefs or remote locations depending on local currents 

(Roberts 1997; Treml et al. 2008). This creates the potential for poor reef management on a 

remote source reef to have major influence on the resilience of the reefs that are dependent on 

the coral larvae source (Jones et al. 2009; McCook et al. 2009). The presence or absence and 

abundance of certain genera or families of coral recruits on a reef can help to explain adult coral 

community dynamics and predict long-term recovery from catastrophic events (Van Woesik et 

al. 2009; Mumby et al. 2007). Coral recruitment is an appropriate and important parameter for 

long-term coral reef monitoring programs. An additional consideration that should be made at 

FGB is recruitment of invasive coral species. In particular, Tubastraea coccinea is found in high 

abundance on nearby oil and gas platforms and has become established at FGB and is a concern 

at FGB (Sammarco et al. 2006; Johnston et al. 2013).

Due to the lack in clarity in the description of FGBLTM method for coral recruitment surveys 

reported in BOEM (2014a), the appropriateness of this particular method for LTM cannot be 

specifically discussed. Instead, general guidance for LTM of coral recruitment is discussed here. 

Coral recruitment methods reported in literature range from in situ quadrat surveys and belt 

transects to use of recruitment tiles or settlement plates (Van woesik et al. 2009). Each method 

provides different advantages and disadvantages and the overall objectives will determine the 

most appropriate method. In situ quadrat and belt surveys assess post-settlement survival of 

recruits (Cooper et al. 2009; Van woesik et al. 2009). If post-settlement predation or mortality is 

high in situ survey may not be indicative of coral larval supply (Richmond and Hunter 1990; 

Acosta et al. 2011; Penin et al. 2011), suitable recruitment substrate is absent (Rogers 1990), or 

recruitment cues are reduced (Doropoulos et al. 2012). Recruitment tiles may be useful to 

determine presence or absence of coral larval supply in the absence of suitable recruitment 

substrate or in environments with high mortality due to sedimentation (Cooper et al. 2009; Van 

woesik et al. 2009). FGBLTM has chosen an in situ method for measurement of coral 

recruitment, which is appropriate because sedimentation rates are not common (Zimmer et al. 

2010) and suitable recruitment substrates (coralline algae and hard substrates) are present 

(Johnston et al. 2013).

AGRRA monitors coral recruitment using in situ quadrat surveys along the same transects used 

to evaluate coral size and condition. It may be beneficial for FGBLTM to use similar methods to 

those described by AGRRA (Lang et al. 2010). These methods have already been peer-reviewed 

and would allow FGB to compare data to other regional datasets. Briefly, the AGRRA coral 

recruit survey involves tallying and identifying all recruits in five, 25 cm by 25 cm quadrats 

placed at 2 m intervals along 10 m transects after first clearing the quadrat of sediment and algae 

that might obstruct visual observation of the recruits.
71

It is important to standardize the size that is considered a coral recruit (Van woesik et al. 2009). 

BOEM (2014a) requires that a coral recruit is considered any colony smaller than 4 cm. AGRRA 

defines a recruit as colonies less than 2 cm. Other papers define maximum size for recruits to be 

up to 5 cm (Moulding 2005; Cooper et al. 2009). BOEM (2014a) specifies that all recruits are to 

be measured. If recruits are defined as colonies less than 4 cm and all size measurements are 

recorded, the dataset should be easily adapted to the AGRRA method standard by selecting only 

data less than 2 cm. 
72

2.2.4.3 Coral Recruitment Conclusions

The addition of coral recruitment surveys to the FGBLTM will add an important dataset for 

understanding the successional processes occurring at FGB. The method has just been added in 

2014 and little information is available about coral recruitment at the banks, so it is not possible 

to compare the conclusions gleaned from this methodology to other published literature. We 

recommend that the FGBLTM coral recruitment methods be more clearly defined in a thorough 

method document as soon as possible. While developing these methods, standardization with 

other methods in the region, such as Lang et al. (2010), should be considered.

2.2.5 Urchins, Lobsters, and Lionfish

2.2.5.1 Technique consistency

Nighttime transects to survey Diadema antillarum (the Caribbean long-spined sea urchin) began 

during the 1996–1997 monitoring period (Dokken et al. 1999). Dokken et al. states that these 

surveys were included in the LTM method to determine if D. antillarum populations were 

recovering at FGB after a mass die-off in 1983–1984 decimated urchin populations across the 

Caribbean. In the absence of D. antillarum, its importance as a reef herbivore became obvious 

throughout the Caribbean as reefs began to be overgrown by macroalgae (Carpenter 1990a, 

1990b). Macroalgae has had a historically low abundance at FGB compared to other Caribbean 

reefs (Pattengill-Semmons and Gittings 2003 and references therein), but increased to 13 percent 

of total species abundance after the D. antillarum die-off (Gittings 1998 and references therein). 

Macroalgae has since dropped back to historical levels with the increased abundance of 

herbivorous fish. 

Surveys were conducted approximately 1.5 hours after sunset, with the 100 m study site 

perimeter boundaries serving as transect lines. Urchins within 1 m of either side of the perimeter 

were documented, for a total area of 200 m2 per transect. The number of transects and total area 

surveyed has changed through the history of FGBLTM (see Table 2.2). Three transects were 

completed at WFGB in 1996, and eight were conducted at each bank in 1997. Three transects 

were conducted in 1999 at EFGB. In 2000 and 2001, four transects were conducted per bank 

(Dokken et al. 2003). The technical report for this period states that transects covered 100 m2

and reports results in density proportions, so it is unclear if this was a methods change or

reporting error. In 2002–2003, spiny lobsters (Panulirus argus) were added to the night surveys 

(Precht et al. 2006). Two transects were conducted at each bank, on the north and west 

perimeters in 2002 and the south and east perimeters in 2003. Transects were 1 m wide, for a 

total of 100 m2per transect. The 14 random transects were also used to conduct sea urchin and 

lobster surveys, increasing the total area surveyed per year to 340 m2at each bank. In 2004–

2008, methods changed to 2 m wide belt transects that were conducted on the north and east 

boundaries of EFGB and the south and west boundaries at WFGB during all four years. This 

was a total of 400 m2per bank each year. Methods remained the same in the 2009–2010 and 

2011–2012 survey periods. In the current IA (BOEM 2014a) the number of transects required 

has increased to 3 per bank per year, and the pacific lionfish (Pterois volitans and miles) has 

been added to the surveys. 
73

Table 2.2 FGBLTM Night Transect Widths and Total Area Surveyed

A comparison of changes in total area surveyed among years.

Year Transect Width

(m)

Bank - Number of 

Transects per Bank

Total Area Surveyed 

per Bank

(m2)

1996 2 WFGB – 3 600

1997 2 Both - 8 1600

1998 - - -

1999 2 EFGB - 3 600

2000–2001 1 Both - 4 400

2002–2003 1, included 

randomtransects

Both - 2 (plus 14 

random transects)

340

2004–2012 2 Both - 2 400

2013 (IA) 2 Both - 3 600

2.2.5.2 Appropriateness of Technique for Long-Term Monitoring

Night time surveys are appropriate for quantifying nocturnal species (Cox et al. 1997; Tuya et al.

2004; Morris and Akins 2009), and belt transects are frequently used to survey sea urchin, 

lobster and fish populations (i.e., Cox et al. 1997; Tuya et al. 2004; NOAA 2011), but the 

usefulness of the FGBLTM transect surveys is limited due to their spatial coverage and design. 

Recorded sea urchin and lobster densities have been consistently too low to conduct statistical 

analyses, and because the spatial design of the transect layout is a convenience sample 

(McDonald 2012), little statistically useful information could be gathered even if encounter rates 

were higher. It is also likely that the power of these samples is very low with the limited area 

covered (further discussed in Chapter 3). Program managers should consider adding urchin, 

lobster, and lionfish searches to the random transect method, coral recruit surveys or the fish belt 

transects. With little added effort, divers could search each transect, increasing the total survey 

area for these species as well as improving statistical robustness. 

2.2.5.3 Urchin, Lobsters, and Lionfish: Conclusions

When FGBLTM results are compared to other surveys at FGB, similarly low densities are found 

in much larger sample sizes. Caldow et al. (2009) found one each of D. antillarum and P. argus

in 95 survey sites, and Buckel et al. (2014a) found 17 D. antillarum and three P. argus in 225 

sites. Also, Buckel et al. (2014a) did not observe lionfish on any of 291 fish survey sites in 2010 

or 2011, though individuals were observed off transect. However, Buckel et al. (2014a) did 

observe lionfish in 25 of 291 survey sites in 2012. This demonstrates the likelihood that this 

invasive species is increasing in abundance, and highlights the importance of including this 

species in the FGBLTM survey methods. 

2.2.6 Fish Surveys

2.2.6.1 Technique Consistency

Fish populations have been assessed in some capacity of FGBLTM since the first monitoring 

period. Initially the perimeter video surveys footage was analyzed for readily visible, large 

bodied species (Gittings et al. 1993a). Dedicated fish surveys became part of the LTM method
74

during the 2002–2003 monitoring period (Precht et al. 2006). Stationary point counts similar to 

those described in Bohnsack and Bannerot (1986) began in 2002. A surveyor would remain 

stationary and observe all fish that swam within an imaginary 7.5 m cylinder around them for the 

five minutes (Precht et al. 2006). After five minutes the surveyor would then quantify the 

abundance and total length in cm (minimum, maximum, average) of every species that had been 

observed in the first five minutes. Depth, visibility, temperature and survey location were also 

recorded for each point count. A single point count took on average 15 minutes to complete. 

Point count surveys began in the early morning and proceeded until dusk. 

No changes in the execution of the point count technique have been documented through the 

monitoring periods. However, the number of point counts has changed from an average of 16 

per bank in the first monitoring period (Precht et al. 2006) to 21.5 beginning in 2004 (Precht et 

al. 2008) to 24 or more beginning in 2009 (Johnston et al. 2013). Initially placement of point 

counts was completely random, with locations randomly generated prior to sampling (Precht et 

al. 2006). Starting in 2009, a random distance and direction were begun once a quadrant 

perimeter was encountered (Johnston et al. 2013). Subsequent points were determined by 

swimming a random distance and direction from the last point count. As discussed in the 

random transect method discussion (Section 2.1) this point placement is considered haphazard 

(Lewis et al. 2004) and has the same potential quantitative issues. Though point placement in the 

most recent surveys has been randomly generated before entering the water (M. Johnston, 

FGBNMFS, personal communication, November 3, 2015), it is suggested that the randomness of 

the point count patterns before this is tested to ensure accurate long-term data comparisons. 

The latest IA had included an additional 16 random belt transects as described in a NOAA 

method (2011) to begin in the current monitoring period (BOEM 2014a). The NOAA (2011) 

method for fish belt transects requires 25 m long and 4 m wide transects that are given a single 

pass. Divers should record fish seen in holes, under ledges and in the water column, and are 

allowed to move from the center transect line to search for fish, but not outside the 4 m diameter. 

Divers are allowed to look ahead within the 25 m of the transect area, but not beyond it, and are 

not allowed to look behind to areas that have already been surveyed. Lionfish are also surveyed 

during the nighttime D. antillarum transects (BOEM 2014a). Similar considerations of 

convenience sampling and limited interpretability as discussed with the sea urchin surveys will 

apply to the lionfish data gathered from the same transects. Ensuring that the belt transects are 

random will be necessary to maximize the applicability of data. 

2.2.6.2 Appropriateness of Technique for Long-Term Monitoring

Marine fish have been identified as “high priority vital signs” for NPS monitoring programs 

(Brown et al. 2011) and coral reef fish play essential roles in reef health and stability, with 

herbivores shaping the structure and species composition of the benthic plant community and 

predators shaping the abundance and distribution of prey species (McCauley et al. 2010). Larval 

fish will recruit to their natal habitat, particularly on islands or other isolated reefs (Jones et al.

1999; Swearer et al.1999). No studies have been done characterizing the sources of fish larvae 

recruitment to FGB, but given the isolation of the banks, it is possible that a significant 

proportion of larval fish recruits to FGB were born at FGB, making monitoring the fish 

populations a critical part of the FGBLTM.
75

Very few LTM programs use both stationary point counts and belt transects to survey fish. Belt 

transects are the more common survey method (i.e., Brown et al. 2004; Hill and Wilkinson 2004; 

Miller et al. 2007; Ruzicka et al. 2009; Van Woesik et al. 2009; Lang et al. 2010; Brown et al.

2011), with programs such as AGRRA (Lang et al. 2010) and CRTR (Van Woesik et al. 2009) 

including optional herbivory surveys. The NOAA Pacific Reef Assessment and Monitoring 

Program currently uses point counts for LTM of fish populations (Ayotte et al. 2011), and has 

historically used a combination of point counts, transects and towed diver surveys to cover large 

survey areas (NOAA n.d.). The NOAA Center for Coastal Monitoring and Assessment outlines 

methods for belt transects and stationary point counts, but states that points counts are only 

continued for comparability to historical data taken with that method (NOAA 2011). 

Point counts and transects are typically considered equivalent methods with unique strengths and 

weaknesses, and a program will select one method based on available resources and purpose of 

the surveys (i.e., Bortone et al. 1989; Watson and Quinn 1997; Samoileys and Carlos 2000; 

Willis 2001; Calvocoresses and Acosta 2007). Colvocoresses and Acosta (2007) found that 

transects cover more area and yield higher abundance estimates (1.5 times more than point 

counts), but point counts yield much higher density estimates (three times higher than transects). 

Typically higher estimates are considered more accurate, as most survey techniques are thought 

to underestimate populations (Samoileys and Carlos 2000). No reviewed study comparing these 

visual census techniques suggested the use of both point counts and transects when conducting 

surveys (i.e., Bortone et al. 1989; Watson and Quinn 1997; Samoileys and Carlos 2000; Willis 

2001; Calvocoresses and Acosta 2007), but given the complementary strengths and weaknesses 

of each, we recommend that data from the current sampling period be evaluated to determine if 

the effort expended on two methods is worthwhile to FGBLTM. For example, both techniques 

typically underestimate cryptic reef fish (Willis 2001), but with the NOAA (2011) method divers 

are allowed to search within the transect boundaries, which may increase the likelihood of 

encountering these species. Many LTM programs, such as AGGRA (Lang et al. 2010) and NPS 

I&M SFCN (Miller et al. 2007) in the Caribbean, use belt transects for their fish surveys, and 

adding this method to FGBLTM would allow comparisons to other studies in the region. 

Observer experience is more important than which method is used to survey fish (Harvey et al.

2004; Williams et al. 2006), and continual verification that size and distance estimates are correct 

is critical (Bell et al. 1985; Bohnsack and Bannerot 1986; Samoileys and Carlos 2000). Bell et 

al. (1985) suggest training in underwater length estimates prior to conducting surveys, to ensure 

that fish size estimates are accurate, and re-training if more than a week has passed since the last 

survey. Inaccuracies in size estimates will affect analyses such as biomass calculations and size 

frequency distributions. Any errors in estimating the 7.5 m radius of the stationary point count 

are squared when calculating the volume of the observation cylinder, creating compounded error 

in density estimates, which are based on observation area (Samoileys and Carlos 2000). 

Observers should be trained in distance estimates, and sampling area should be measured 

periodically throughout the sampling process. Bohnsack and Bannerot (1986) found that their 

point count method was robust for an error of approximately 0.5 m in observation radius. There 

are currently no QA and QC documents or details in the FGBLTM technical report for training 

individuals in size or distance estimation prior to collecting data. The current IA does not 

specify belt transect methodology, but references a NOAA method (NOAA 2011; BOEM 

2014a). Explicit SOP and QA and QC documents are crucial to a successful LTM program 

(Beets et al. 2011), and these documents should be created for the FGBLTM to allow 
76

replicability of methods that are independent of who is conducting the surveys for a monitoring 

period. 

2.2.6.3 Fish Survey Conclusions

Pattengill-Semmens et al. (2000) surveyed the fish communities of FGB using AGGRA method

in 1999. They characterized 117 species in a healthy reef fish community with low diversity 

compared to other Caribbean reefs, but high density and large body size for many species, 

including apex carnivores. Fish populations were not discussed in the technical report from this 

time period (Dokken et al. 2001). The first spatially comprehensive surveys of the coral reef 

zone (17–33.5 m) at FGB were conducted by Caldow et al. (2009) from 2006–2007. This study 

found similar results of lower reef-wide diversity than Caribbean reefs, but higher per survey 

diversity, as well as higher abundance and body size with 117 species in 37 families. Labridae 

were the most abundant family, and Serranidae dominated biomass. Invertivores and 

zooplanktivores had the highest abundances of any trophic guild, while piscivores had the 

highest biomass. The FGBLTM results from that time period (Zimmer et al. 2010) found a total 

of 85 species in a mean of 20 families, with Labridae as the most abundant family. Herbivores 

were the most abundant trophic guild; however, only herbivores and piscivores were analyzed. 

A more intensive, spatially comprehensive fish survey was conducted in 2010–2012 by Buckel et 

al. (2014b). This study surveyed the coral reef zone and deeper communities (to 45 m). Results 

were a total of 129 species in 36 families with the highest species richness, density and biomass 

in the deep zone at WFGB. Pomacentridae and Labridae were the most abundant families. 

Planktivores and invertivores were the most abundant trophic guilds, with piscivores having the 

highest biomass. The FGBLTM results from the same time period (Johnston et al. 2013) found a 

total of 78 species in 29 families. Pomacentridae and Labridae were the most abundant families. 

In 2010, invertivores and piscivores were the most abundant trophic guilds, and serranidae had 

the highest biomass. Trophic guild biomass differed between banks, with herbivores the highest 

at EFGB and piscivores as WFGB. The overall conclusions and fish community 

characterizations are similar among the different studies when compared to FGBLTM results. 

But the higher species richness of the independent studies demonstrates the limited applicability

of the FGBLTM data, likely due to the limited sampling area of the 100 m by 100 m study site.

One result stressed in Caldow et al. (2009) and Buckel et al. (2014b) is the importance of habitat 

type and structural relief to associated fish communities. Buckel et al. (2014b) had a stratified 

sampling design and found that habitat relief was the second most important factor (following 

depth) in determining associated fish assemblage at FGB. One purpose of the surveys conducted 

by Caldow et al. (2009) was to determine an optimal sampling design for surveying reef fish at 

FGB, and they emphasize the importance of stratifying by habitat to decrease variance and 

increase power within each sampling strata. While the current study sites are encompassed 

within what is considered high relief habitat, very little information is given on if habitat data are 

gathered at each sampling point. The limited area of the 100 m by 100 m study site may negate 

stratification due to insufficient sample size within each habitat. Nevertheless, more detailed 

habitat data for sampling units (points and transects) should be collected, and has been recently 

adopted into the fish survey method in the most recent field season (M. Johnston, FGBNMS, 

personal communication, November 3, 2015). Additionally, the adequacy of the number of 

points and transects is not discussed in the LTM technical reports, so it is not known if any 
77

consideration of statistical power has been used to determine the appropriate sampling size for 

either survey type. This will be further evaluated in Chapter 3. 

2.2.7 Water Quality

2.2.7.1 Technique Consistency

The FGBLTM water quality method has consisted of several different methods throughout the 

program history. These methods have included water quality grab samples, deployment of 

SPMDs, water profiles with in situ instrumentation, and extended deployments of data loggers 

and multi-parameter probes. This method has not been consistent from year to year, and most 

methods changes do not provide the ability to compare results among contract periods.

During the initial monitoring period (1989–1991), daily water samples were collected with 

Niskin bottles from 1 m below the surface and 1 m above the bottom at each bank for DO and 

salinity analysis (Gittings et al. 1993a). DO concentrations were determined by the Winkler 

technique and salinity was determined using an Autosal Laboratory Salinometer to measure 

direct conductivity. Daily grab samples were not reported in the next few monitoring reports. In 

2002, grab water samples were reintroduced to the routine. Four samples per depth at each bank 

were collected near-surface, mid-water, and near-bottom approximately once per quarter from 

2002 through 2004. Samples were analyzed for Chl a, total Phosphorus, Mercury, Chromium, 

TKN, ammonia, nitrate, and nitrite using standard United States EPA methods (Precht et al.

2006). In 2004, the target constituents for the grab sample analysis were changed to include 

soluble reactive Phosphorus, Chl a, TKN, ammonia, nitrate, and nitrite, and sampling frequency 

continued to be approximately quarterly through 2014 (Zimmer et al. 2010; Johnston et al. 2013, 

2015; BOEM 2014a). The number of water samples collected varied between three and nine per 

bank per sampling event. In May 2010, total petroleum hydrocarbons were also measured in the 

grab samples in response to the Deepwater Horizon oil spill (Johnston et al. 2013).

The datasets resulting from these various grab sample routines provide information for 

comparison with future water sample results; however, results from the earlier grab samples 

(Gittings et al. 1993a) cannot be compared with those of the more recent collections (Precht et al.

2006; Zimmer et al. 2010; Johnston et al. 2013) because the constituents measured are not the 

same. As discussed above, DO and salinity were initially (1989–1991) analyzed through grab 

samples, but later (2002 and beyond) those parameters were measured instrumentally and P, Chl 

a, TKN, ammonia, nitrate, and nitrite were chemically analyzed in grab samples. Trend analyses 

have not been attempted, but can likely be appropriately applied to the 2002 through 2014 grab 

sample results for those parameters which were measured consistently. Because the exact EPA 

methods used to measure the constituent concentrations were not specified, this information must 

be determined and considered for future samples, as EPA has several standard methods for some 

constituents and accuracy and precision may vary among techniques.

In 1996, SPMDs were added to the method. These were deployed at each bank for approximately 

six month periods, and samples were analyzed for pesticides, PAHs, and PCBs by standard gas 

chromatography methods (Dokken et al. 1999). SPMDs were used through 2001 (Dokken et al.

2003), and for this time period the SPMD method remained consistent.
78

In addition to the grab samples and SPMDs, various parameters in real-time at FGB were 

recorded using in situ instrumentation. During the initial monitoring period (1989–1991), water 

temperature was measured with a thermometer at near-surface and near-bottom depths as well as 

on deck of the research vessel, and light was measured with a QSP 200L Submersible Quantum 

Scaler irradiance meter in the same three locations. In 1994–1995, a Hydrolab Surveyor III and 

Licor 185B light meter were used to collect daily water profiles including temperature, salinity, 

DO and ambient light measurements at near-surface and near-bottom depths (MMS 1996). In 

2009 through 2012, YSI data sondes were used to collect opportunistic temperature and salinity 

profiles (Johnston et al. 2013, 2015). These datasets were recorded with variable instrumentation 

and were not a consistent part of the LTM program, so they are not useful for analyzing longterm trends. But they do provide isolated records of environmental conditions that may be useful 

for interpreting monitoring results from other methods conducted during the same time frames 

(i.e., bleaching events, fish abundance, etc).

Extended deployments of water quality sondes and loggers were used to collect continuous 

datasets. In 1990, a Ryan TempMentor thermograph was installed at each bank to record bottom 

water temperature every two hours continuously until the data were retrieved during the next 

monitoring trip (Gittings et al. 1993a). In 1994–1995, the Ryan TempMentor thermographs 

continued to record temperature every two hours, and HOBOtemp loggers were also deployed at 

the same locations, with the same recording frequency (MMS 1996). Licor light meters were 

installed in 1995 to record PAR at each bank and at the Mobile HI 384A platform on the surface 

as a reference. Data were reported as total daily irradiance and percent light transmission. In 

1996, light meters and thermographs (HOBOtemp only) continued to be deployed though 

sampling frequency was increased to every 20 minutes for the thermographs (Dokken et al.

1999). In 1998, anti-algae collars were added to the light meters to reduce fouling by algal 

growth (Dokken et al. 2001). Light meters and thermographs continued to be in use with little 

change to the methodology through 2001 (Dokken et al. 2003). In 2001, semi-permanent 

platforms were installed at each bank to allow for extended deployment of YSI data sondes. 

Sondes were installed and recorded pressure, temperature, salinity, PAR light, DO, pH, and 

turbidity on a 30 minute interval (Dokken et al. 2003). YSI data sondes were retrieved and 

replaced on an approximately quarterly basis (Precht et al. 2006). HOBOtemp thermographs 

were used only as a backup temperature recorder for the YSI data sondes in 2002 and beyond, so 

sampling frequency was reduced from 20 minutes to one hour intervals from 2002 through 2004 

(Precht et al. 2006), and 30 minute intervals were used from 2004 through 2010 (Zimmer et al.

2010; Johnston et al. 2013). In 2008, Sea-bird MicroCAT conductivity and temperature loggers 

were added to the semi-permanent platforms (Zimmer et al. 2010) and the extended deployment 

of YSI data sondes was discontinued by 2009 (Johnston et al. 2013). 

The use of various instruments recording different sets of parameters for extended deployments

reduces the possibility of analyzing trends across the entire monitoring program. However, in 

most cases there were periods of overlap when both new and old instrumentation were deployed. 

This provides data that can be used to assess the correlation between data collected from each 

instrument and development of a model that can be used to compare the datasets. The 

replacement of instrument types generally represented movement toward improved technology 

and resources. The instruments deployed changed from single parameter loggers (Ryan 

TempMentor and HOBOtemp) to multi-parameter sondes (YSI, Sea-bird MicroCAT).
79

2.2.7.2 Appropriateness of Technique for Long-Term Monitoring

Coral health and the health of coral reef organisms are inherently linked to water quality. Water 

quality changes related to urban runoff, sedimentation, pesticide use, sewage outflows, and 

climate change are linked to reef declines worldwide (Burke et al. 2011; Jackson et al. 2014). 

Water quality, when measured alongside other coral reef surveys, give context to the biological 

variables collected by quantifying attributes of the physical environment at the time of sampling 

(Raymundo et al. 2008; Brown et al. 2011).

Water quality monitoring is a major component of many long-term coral reef monitoring 

programs (Miller et al. 2007; Cooper et al. 2009; Brown et al. 2011; Briceño and Boyer 2012). 

Parameters such as water temperature, salinity, DO and pH are monitored with multi-parameter 

sondes and loggers to provide long-term datasets describing gross changes in water quality, 

while concentrations of nutrients such as ammonia, nitrate, nitrite and phosphorus are measured 

at certain points in time to monitor influence of point and non-point pollution sources that might 

affect reef organisms. The current FGBLTM water quality methods are consistent with this 

general approach (BOEM 2014a). However, the appropriateness of the water quality program for 

determining long-term water quality changes at FGB is dependent on choosing the most 

appropriate parameters for analysis, i.e., the parameters that are most likely to impact these coral 

reefs.

Federal and state government programs regulate many activities influencing water quality

changes in nearshore environments through water quality standards. The Federal Water Pollution 

Control Act (Clean Water Act) and National Coastal Water Program Gulf of Mexico Program 

aim to restore and protect the chemical, physical, and biological integrity of the waters of the 

Gulf which included setting water quality standards and QA programs for their measurement 

(EPA 2015a and 2015b). The U.S. Fish and Wildlife Service has also published recommended 

water quality requirements for listed aquatic threatened and endangered species for the state of 

Texas, the state to which FGB is closest (White et al. 2006). Among the water quality 

parameters, those most relevant to marine waters are DO, pH, salinity, temperature, turbidity, 

ammonia, nitrate, nitrite, TKN, soluble reactive phosphorus, trace metals (mercury and

selenium), pesticides, petroleum hydrocarbons, pharmaceuticals and other organic wastewater 

contaminants, PBCs, dioxins, furans, PAHs, and radiological substances. Many of these 

parameters such as pharmaceuticals may not be relevant at FGB because FGB is located far 

enough offshore that these pollutants would not likely reach the area in harmful concentration. 

All of the water quality parameters measured at FGB currently, except for Chl a, are listed 

among those in White et al. (2006), providing some justification for their selection. However, a 

method document providing full justification for each parameter and discussion of other 

parameters considered should be written to ensure the method design is defensible. According to 

Johnston et al. (2013 and references therein) potential sources of nutrient influx at FGB include 

near shore waters, sediments, or benthic and planktonic organisms. The nutrient parameters 

currently monitored (soluble reactive Phosphorus, Chl a, TKN, ammonia, nitrate, and nitrite) are 

a common suite of parameters for water quality monitoring on coral reefs (Rogers et al. 2001; 

Hoeke et al. 2009; Jones et al. 2011), and should provide a good baseline for assessing nutrients. 

Elevated levels of phosphorus and nitrogen are typically indicative of land-based pollution like 
80

urban run-off, agricultural activities, and sewage (White et al. 2006; Carpenter et al. 1998). 

Soluble reactive phosphorus, TKN, ammonia, nitrate, and nitrite represent some of the most 

biologically relevant forms of phosphorus and nitrogen in aquatic systems. High levels of these 

constituents can lead to harmful algal blooms and hypoxic or anoxic (oxygen depleted) zones 

(Carpenter et al. 1998). Chl a, which was not included in the White et al. (2006) list, is an 

important indicator and early warning signal for algal blooms (Bell 1992; Boyer et al. 2009). 

In addition to land-based pollution sources, the location of FGB in close proximity to petroleum 

activities in the Gulf raises concern of influence of hydrocarbon pollution from drilling and 

associated activities in the area (Gittings et al. 1993a; Dokken et al. 1999). As discussed in 

Section 1.3.4, we recommend that constituents such as metals and hydrocarbons that are 

commonly associated with drilling and related activities be monitored routinely at FGB.

White et al. (2006) was written before the listing of 66 coral species as threatened (NOAA 

2014a), so threatened or endangered fish and other aquatic species were the major target of the 

assessment. One additional parameter that should be considering along with the effects of water 

quality coral health is the effect of light (Raymundo et al. 2008). Most scleractinian coral species 

are dependent on single-celled photosynthetic organisms called zooxanthellae that live within the 

corals tissues. An appropriate amount of PAR light is a requirement of coral survival and should 

be monitored along with water quality. This parameter was dropped from the FGBLTM program 

methods in 2001 with the introduction of extended deployments of YSI multi-parameter sondes 

(Precht et al. 2006). The sondes measured turbidity, which would give an indirect indication of 

light availability. However, the YSI sondes were replaced with SeaBird MicroCATs, which do 

not record light or turbidity, in 2009. FGBLTM should consider reintroducing PAR as a 

monitoring parameter.

Another consideration should be whether to define and expand the spatial coverage of the water 

quality grab samples. Grab samples are useful at getting a “snapshot” of the water quality at 

certain points in time (Hoeke et al. 2009); however, these snapshots are of little value without 

knowledge of the spatial variability. Several short and LTM studies (Hoeke et al. 2009; Jones et 

al. 2011) use nutrient analysis of grab samples and water profiles (parameters such as 

temperature, salinity, and dissolved oxygen) to monitor spatial variation. These are 

complimented with extended-deployment data loggers to monitor temporal variation. FGBLTM 

is collecting sufficient information from the SeaBird MicroCATs to explore temporal trends in 

temperature and conductivity, and opportunistic YSI temperature and salinity profiles that have 

been collected could be used to evaluate some spatial variation. However, grab samples for the 

nutrient analyses are only taken at one location per bank per quarter (Johnston et al. 2013) and it 

is unclear from the monitoring reports if the location of the water samples is consistent. Without 

much additional effort, FGBLTM could take water samples from multiple locations across each 

bank, following a random or stratified sampling plan, and evaluate spatial variability in nutrient 

concentrations. Diurnal changes in water chemistry should also be considered when analyzing 

the data. Metadata collected for the water samples should always include the time of day the 

sample was collected to ensure that this is possible.
81

2.2.7.3 Water Quality Conclusions

Water quality is an important LTM monitoring parameter that has been included in the FGBLTM 

program since the beginning. Inconsistencies in the method methodologies will make assessment 

of certain parameters (i.e., nutrient grab samples) across all LTM years impossible. However, the 

current parameters measured are a good basis for detecting temporal trends in general water 

quality. With the addition of some constituents such as hydrocarbons and metals that may relate 

to petroleum production, the program will have the added benefit of detecting potential pollution 

from the oil and gas platforms in the region. A more complete spatial strategy is recommended, 

including multiple sampling locations per bank per year, if spatial trends are to be evaluated 

effectively.

2.2.8 Perimeter Surveys and Qualitative Observations

2.2.8.1 Technique Consistency

Video footage of the study site perimeters has been included since the beginning of FGBLTM. 

The intent of the video perimeter surveys was to have repeated transects covering a large area 

with the ability to gather detailed data (Gittings et al. 1993a). It was anticipated that fish, 

macroinvertebrates and gross changes in community components could all be quantified from 

these surveys. Problems with replication, video resolution and applicability of video surveys to 

quantify anticipated taxa have caused the overall focus of this method to change over time, as 

discussed below. 

The execution of perimeter video surveys has been very consistent throughout the FGBLTM 

program, and very little has changed in the methodology. Video is taken of two 100 m 

perimeters at each bank with a width of 3.5 m for a total of 350 m2per transect. Surveys begin at 

the NW corner at EFGB and proceed east and south, ending at the SE corner of the 100 m by 100 

m study site. At WFGB the surveys begin at the SE corner and proceed west and north to end at 

the NW corner of the study site. The camera is kept 2 m above the substrate and at a 450angle. 

The only change in data collection method throughout the history of the FGBLTM program has 

been to add a 3600view at each corner post, which began in 1996 (Dokken et al. 1999). 

The purpose of the perimeter transects has changed during the LTM program. Initially they were 

intended to collect large-scale, long-term changes in “some detail” (Gittings et al. 1993a), but 

limited quality of the video footage necessitated analysis modifications from the start. It was 

expected that a complete quantitative census of fish and motile invertebrates would be possible, 

but speed and quality of footage required data be limited to large body fish. Quantitative 

analyses of coral growth and changes in percent cover were also limited to gross habitat changes 

for similar reasons. Also, bottom currents moved the 100 m perimeter line during the video 

survey, confounding temporal comparisons because the exact same reef area was not videoed 

from year to year. The line movement issue was alleviated by the installation of an eye bolt at 

the center of each perimeter to hold the line in place in 2012 (Johnston et al. 2015). The 

perimeter videos have provided a qualitative review of the study site boundary edges, with 

limited information about overall coral health including disease and predation occurrence, and

limited fish population data. Very little statistical analysis has been attempted on perimeter data 

throughout LTM history due to the qualitative nature of the data.
82

2.2.8.2 Appropriateness of Technique for Long-Term Monitoring

The same spatial layout issues that have been discussed with other methods are also applicable to 

the perimeter surveys. The placement is a convenience sample that is not random, and cannot be 

considered representative of the study site (McDonald 2012). The limited data gathered from 

these surveys cannot be used to draw conclusions for the study site or the population of interest 

(the coral cap on each bank). Considering the added pressure of recent invasive species 

introductions, as well as climate change and continued OCS drilling development, we 

recommend that FGBLTM program managers re-evaluate the usefulness of these perimeter 

surveys. For the effort expended in both collecting and analyzing these data, it could be argued 

that little value is returned. Consistent, long-term datasets are rare and valuable (Magurran et al.

2010), but aforementioned complications with filming the same perimeter area from year to year 

weaken the benefit of consistency in these data. Now that fish and coral health surveys have been 

added to the FGBLTM (in 2002 and 2014, respectively), the perimeter surveys provide few 

relevant data that are not being collected in a separate method. Therefore, this method is not 

considered appropriate for the ultimate goals of the FGBLTM program, and we recommend that 

program managers consider expending available resources on other quantitative analyses that 

would better achieve the program objectives and goals. For example, the addition of fish 

herbivory and recruitment surveys, especially with the recent introduction of the Pacific red 

lionfish, may be more beneficial to the program. As an alternative to removing this method from 

the program entirely, it could be kept as an opportunistic sample that could be performed as time 

allowed. This would keep a continued visual record of a part of the study area that has been 

filmed since 1989. Footage could be also archived without being analyzed to save time. Any 

significant observations made while filming could be catalogued with the other qualitative 

observations now collected during the course of other LTM methods.

2.2.8.3 Qualitative Observations and Conclusions

The documentation of additional relevant observations in the LTM technical reports began in 

2004 (Zimmer et al. 2010). These observations are opportunistic and though not quantitative, 

provide important data to the FGBLTM. Events such as the first observed coral spawning, 

encountering large animals such as manta rays and sea turtles, and the discovery of the coral 

species Acropora palmata, previously thought not to exist at FGB, have all been part of the 

qualitative observation data (Zimmer et al. 2010; Johnstonet al. 2013). These observations 

provide resource managers with meaningful information about the overall health of FGBNMS 

and are made as the surveyors conduct other sampling methods, so do not require additional field 

effort. These qualitative observations should continue to be documented. 

2.3 100 m by 100 m Study Site

The FGBLTM program measures community dynamics with two 100 m by 100 m study areas, 

one at each bank. Using professional judgment, the study sites were established in areas that 

were presumed to be representative of the coral reef zone on each reef with little justification of 

how this decision was made (CSA 1989; Gittings et al. 1993a). Some effort was expended from 

1998 through 2001 to determine if the sites were truly representative; however, the information 

collected was on too limited a scale to make this determination (see Section 1.1.1). FGBLTM 

aims to detect spatial and temporal trends in various community parameters on the coral caps at 
83

EFGB and WFGB. To successfully relate information collected at the study sites to the entire 

coral cap area, it is necessary that the study sites be as representative of the whole coral cap 

community as possible, and that the relationship between the community and study site be 

known (Gitzen and Millespaugh 2012). Spatial trends are evaluated by comparing replicate study 

sites, and temporal trends are evaluated by comparing results from multiple sampling events 

through time.

With a current spatial sampling design of two un-replicated study sites, the only spatial 

comparison that can be made is between the EFGB and WFGB, and this is only true if the study 

sites are representative of the whole coral cap, otherwise the comparison is limited to the EFGB 

study site area compared to the WFGB study site area. This spatial design does not allow 

comparison across different areas on a single bank. If, for example, a hurricane devastated the 

more windward parts of the banks or an invasive benthic species began spreading from one area 

of the bank, the events may go undetected.

A study by Caldow et al. (2009) determined coral cover on the reefs at EFGB and WFGB using 

in situ quadrat surveys at sites distributed over the entire shallow coral cap areas (above 33.5 m). 

The results showed 58% coral cover at EFGB and 59% coral cover at WFGB for the high relief 

coral reefs, which made up most of the coral cap area. Low relief coral reef zones had lower 

coral cover (32%) and overall the coral cover at the two banks was 48%. The FGBLTM program 

coral cover measurements are generally consistent with those of Caldow et al. (2009). Although 

there is variation from year to year, the coral cover estimates from the FGBLTM random 

transects are typically between 50 and 60% (Gittings et al. 1993a; Dokken et al. 1999; Dokken et 

al. 2003; Precht et al. 2006; Zimmer et al. 2010; Johnston et al. 2013). In the most recent LTM 

report, coral cover was reported as 54.49% ± 3.69 at the EFGB and 65.95% ± 2.85 at the WFGB 

for 2010 (Johnston et al. 2013).

In 2010 through 2012, NOAA conducted benthic community surveys using in situ quadrats at 

EFGB and WFGB across the coral caps. Coral cover estimates for the high relief coral cap areas 

were 59% for EFGB and 54% for WFGB. These results were again similar to those of the 

FGBLTM program.

Caldow et al. (2009) found that the reef areas were generally homogeneous in coral cover and no 

consistent spatial patterns were found. If this homogeneity is true, it is possible that the LTM 

study sites are capturing the variation of the population and may be representative of the coral 

caps. However, with no spatial component for the sampling design, spatial trends would still be 

impossible to assess using the FGBLTM program data.

The temporal design of the LTM program is much stronger. Data are collected annually at each 

study site at both fixed quadrats and growth stations and random transects. This provides data 

that are comparable across the history of the LTM program, except for instances of technique 

inconsistencies discussed in the preceding sections. The use of fixed 100 m by 100 m study sites 

reduces the amount of variation that could be introduced by distributing random sites over a wide 

area of reef.

A more optimal sampling design that would allow for both spatial and temporal comparisons 

would be randomly distributed sites throughout the coral cap (fixed or temporary) within a 

spatially stratified sampling design. Caldow et al. (2009) recommended a stratified random 
84

sampling design measuring depth and benthic habitat as covariates to fish indices. Clark et al.

(2014) also used a stratified sampling design in the most recent comprehensive survey of the 

banks. To create a stratified design, the study sites are divided into natural groupings and sample 

points are divided among the groups, or strata (McDonald 2012). A stratified design enables 

sampling intensity, area, and design to vary among strata depending on the needs of the 

monitoring program. Another benefit of a stratified design is that samples within strata are more 

likely to be similar to each other than samples among strata, eliminating a significant source of 

variance from resulting estimates (Johnson 2012). However, if the boundaries are based on a 

definition that could change with time or disturbance, such as habitat type, serious quantitative 

issues can arise if those boundaries begin to blur. Moving boundaries to “chase” habitats can

cause further issues, so it is best to choose more concrete boundary definitions. In the case of 

FGB depth may be a suitable strata definition, as habitat, and therefore other populations of 

interest, are closely tied to this physical parameter (Clark et al. 2014). 

2.4 Conclusions and Recommendations

A technique evaluation of the FGBLTM program was performed as documented in this chapter, 

which presents a review of each monitoring technique used through the history of the FGBLTM, 

outlining methods, equipment and analysis changes and the effects of these changes on the 

integrity program. Techniques were then compared to those used in other LTM programs to 

assess if they are appropriate for LTM and executed correctly. 

Some techniques were found to have many changes through the history of the program, causing 

the value of a long-term dataset to be reduced for these datasets. Other techniques have been 

executed with a high degree of integrity from year to year. One result that was continually found 

to be an issue for most methods was the lack of clear, detailed SOP and QA and QC documents 

for each method. This deficiency should be addressed as soon as possible to ensure that future 

monitoring efforts are carried out in a repeatable and consistent way.

 

Data collected from the FGBLTM were compared to similar, independent studies conducted at 

FGB to determine if results were consistent. Individual results varied from study to study, for 

example, the fish species richness and abundances reported by FGBLTM are much less than 

other, more spatially comprehensive studies. Despite these quantitative differences, the general 

conclusions found by all reviewed studies were very similar, namely a healthy coral reef with 

stable communities and abundant populations. This comparison was followed by an evaluation 

of the 100 m by 100 m study site at each bank. The limitations of the size and design of the 

single, un-replicated study site were discussed. 

 

In summary, based on this technique evaluation, the following are recommended to improve the 

FGBLTM program:

1. Detailed SOP and QA and QC documents should be finalized for each LTM method

currently used by the FGBLTM program.

2. The overall program should be evaluated using a s.m.a.r.t assessment (specific, 

unambiguous monitoring objectives that are measureable, achievable, results-oriented, 

and applied over a specific relevant time-frame as defined by Williams et al. 2009) in 

order to refine the objectives of FGBLTM and re-design, as necessary, the technical 
85

approaches used. This would be particularly useful in identifying and prioritizing 

potential drivers of change (marine pollution, direct reef damage, global warming, etc.) 

and then incorporating appropriate measurements and sample collection into the LTM 

program.

3. The total area surveyed in the random transect methods varies considerably from year to 

year, and total photograph area has potentially been reported incorrectly. These issues 

should be considered when making comparisons across multiple monitoring periods.

4. The focus of repetitive quadrats has shifted through the history of the program from 

collecting process variable data, to collecting state variable data that is similar to the 

information collected by transect data. Comparisons are not the same from year to year, 

compromising the integrity of the long-term dataset. Focus of repetitive quadrats should 

return to primarily collecting process variable data in the form of coral tissue growth and 

retreat and the potential causes for these changes. 

5. Repetitive quadrats are currently placed only in areas of high coral cover. To increase the 

representativeness and robustness of these samples, additional quadrat stations should be 

added to the study site in a stratified design if possible. 

6. The lateral and encrusting growth station method should be terminated due to problems 

with replicability, station integrity and questionable interpretability of data.

7. Sclerochronology method modifications should reduce samples to once every five years 

and take longer sample cores that collect more years per sample.

8. Program managers should consider changing the coral health method from point 

intercept to belt transects to improve comparability to other LTM programs in the Gulf 

region.

9. Surveys for D. antillarum, P. argus and P. volitans are quantitatively limited due to 

convenience sampling within a small area. Surveys should be expanded into random 

transects or fish belt transects. This would increase the total area surveyed and increase 

statistical robustness of these samples with little added effort.

10. Video perimeter data provides little to no quantitative data. Consider reducing this 

method to opportunistic surveys that are not archived and not analyzed post hoc.

11. Using both point count and belt transect surveys is not typical for LTM programs; the 

value of maintaining both for the FGBLTM program should be evaluated after the first 

belt transect data are available.

12. Water quality samples should be expanded to include PAR, hydrocarbons, and metals 

associated with oil or natural gas drilling. Water samples should also be collected 

randomly at multiple locations across each bank for added interpretability of data.

13. The 100 m by 100 m study site should be expanded to increase sample sites in a random, 

stratified design using depth and habitat to inform stratification boundaries. 
86

2.5 References Cited

Acosta A, Duenas LF, Pizarro V. 2011. Review on hard coral recruitment (Cnidaria: 

Scleractinia) in Colomobia. Universitas Scientiarum. 16(3): 200-218.

Ayotte P, McCoy K, Williams I, Zamzow J. 2011. Coral Reef Ecosystem Division standard 

operating procedures: data collection for Rapid Ecological Assessment fish surveys. 

Honolulu (HI): Pacific Islands Fisheries Science Center, National Marine Fisheries 

Service, NOAA. Pacific Islands Fisheries Science Center Administrative Report H-11-08. 

24 p.

Bak PM, Nieuwland G, Meester EH. 2009. Coral growth rates revisited after 31 years: What is 

causing lower extension rates in Acropora palmata? Bull Mar Sci. 84(3): 287-294.

Beard GR, Scott WA, Adamson JK. 1999. The value of consistent methodology in long-term 

environmental monitoring. Environ Monit Assess. 54: 239-258.

Bell JD, Craik GJS, Pollard DA, Russell BC. 1985. Estimating length frequency distributions of 

large reef fish underwater. Coral Reefs. 4(1): 41-44.

Bell JJ. 2008. The functional roles of marine sponges. Estuar Coast Shelf S. 79: 341-353. 

Bell PR. 1992. Eutrophication and coral reef—some examples in the Great Barrier Reef Lagoon. 

Wat Res. 26(5): 553-568.

Bohnsack JA, Bannerot SP. 1986. A stationary visual census technique for quantitatively 

assessing community structure of coral reef fishes. NOAA Technical Report NMFS 41: 

1-15.

Borneman EH, Wellington GM. 2005. Pathologies affecting reef corals at the Flower Garden 

Banks, Northwestern Gulf of Mexico. Gulf Mex Sci. 2005(1): 95-105.

Bortone SA, Kimmel JJ, Bundrick CM. 1989. A comparison of three methods for visually 

assessing reef fish communities: time and area compensated. Northeast Gulf S. 10(2): 85-

96.

Boyer JN, Kelble CR, Ortner PB, Rudnick DT. 2009. Phytoplankton bloom status: Chlorophyll a 

biomass as an indicator of water quality condition in the southern estuaries of Florida, 

USA. Ecol Indic. 95: s56-s67.

Briceno HO, Boyer JN. 2012. 2012 Annual report of the water quality monitoring project for the 

water quality protection program of the Florida Keys National Marine Sanctuary. 

Southeast Environmental Research Center Technical Report T-628. 79 p.

Bright TJ, Gittings SR, Choi A, Barnett RR. 1987. Coral recovery following the grounding of the 

freighter M/V WELLWOOD on Molasses Reef, Key Largo National Marine Sanctuary.

Final Report. Washington (DC): NOAA Office of Coastal and Estuarine Systems. 

Contract No. NA85AA-H-CZ015. 202 p.
87

Brown E, Beets J, Brown P, Craig P, Friedlander A, et al. 2011. Pacific Islands Network marine 

fish monitoring method (Version 1.0). Fort Collins (CO): National Park Service. Natural 

Resource Report NPS/PACN/NRR—2011/421. 282 p.

Brown E, Cox E, Jokiel P, Rodgers K, Smith W, et al. 2004. Development of benthic sampling 

methods for the Coral Reef Assessment and Monitoring Program (CRAMP) in Hawai’i. 

Pac Sci. 58(2): 145-158.

Brown E, Minton D, Daniel R, Klasner F, Basch L, et al. 2011. Pacific Islands Network benthic 

marine community monitoring method. Fort Collins (CO): National Park Service. Natural 

Resources Report NPS/PACN/NRR—2011/339. 376 p.

Bruckner AW. 2002. Priorities for effective management of coral diseases. U.S. Department of 

Commerce NOAA National Marine Fisheries Service. NOAA Technical Memorandum 

NMFS-OPR-22. 57 p.

Buckel CA, Viehman TS, Edwards K, Tester PA, Piniak GA, et al. 2014a. Benthic communities 

of the coral reef. In: Clark R, Taylor JC, Buckel CA, Kracker LM , editors. Fish and 

benthic communities of the Flower Garden Banks National Marine Sanctuary: science to 

support sanctuary management. Silver Springs (MD): National Oceanographic and 

Atmospheric Administration. NOAA Technical Memorandum NOS NCCOS 179. p. 22-

74.

Buckel CA, Munoz RC, Whitfield PE, Degan BP. 2014b. Fish communities of the coral reef. In: 

Clark R, Taylor JC, Buckel CA, Kracker LM , editors. Fish and benthic communities of 

the Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): National Oceanographic and Atmospheric 

Administration. NOAA Technical Memorandum NOS NCCOS 179. p. 75-200.

[BOEM] Bureau of Ocean Energy Management. 2014a. Interagency Agreement M14PG00020 

between the Bureau of Ocean Energy Management and the National Oceanic and 

Atmospheric Administration National Ocean Service Office of National Marine 

Sanctuaries Flower Garden Banks National Marine Sanctuary for long-term coral reef 

monitoring at the Flower Garden Banks, Gulf of Mexico: 2014-2016. 28 p.

[BOEM] Bureau of Ocean Energy Management. 2014b. Review of the long-term monitoring 

program and the associated data of the long-term monitoring program at the Flower 

Garden Banks. Task Order No. M14PD00048. 21 p.

Burke L, Reytar K, Spalding M, Perry A. 2011. Reefs at risk revisited. Washington (DC): World 

Resources Institute. 130 p.
88

Caldow C, Clark R, Edwards K, Hile S, Menza C, et al. 2009. Biogeographic characterization of 

fish communities and associated benthic habitats within the Flower Garden Banks 

National Marine Sanctuary: Sampling design and implementation of scuba surveys on the 

coral caps. NOAA Technical Memorandum NCCOS 81. 152 p.

Cantin NE, Cohen AL, Karnaiskas KB, Tarrant AM, McCirkle DC. 2010. Ocean warming slows 

coral growth in the central Red Sea. Science. 329: 322-325.

Carleton JH, Done TJ. 1995. Quantitative video sampling of coral reef benthos: large-scale 

application. Coral Reefs. 14: 35-46.

Carpenter RC. 1990a. Mass mortality of Diadema antillarum I. long-term effects on sea urchin 

population-dynamics and coral reef algal communities. Mar Bio. 104: 67-77.

Carpenter RC. 1990b. Mass mortality of Diadema antillarum II. effects on population densities 

and grazing intensity of parrotfishes and surgeonfishes. Mar Bio. 104: 79-86. 

Carpenter SR. Caraco NF, Correll DL, Howarth RW, Sharpley AN, et al. 1998. Nonpoint 

pollution of surface waters with phosphorus and nitrogen. Ecol Appl. 8(3): 559-568.

Carricart-Ganivet JP. 2007. Annual denisty banding in massive coral skeletons: result of growth 

strategies to inhabit reefs with high microborers’ activity? Mar Biol. 153: 1-5.

Carricart-Ganivet JP. 2011. Coral skeletal extension rate: an environmental signal or a subject to 

inaccuracies? J Exp Mar Biol Ecol. 405: 73-79.

Clark R, Taylor JC, Buckel CA, Kracker LM , editors. 2014. Fish and benthic communities of 

the Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): National Oceanic and Atmospheric Administration. 

NOAA Technical Memorandum NOS NCCOS 179. 317 p.

Cohen AL, McConnaughey TA. 2003. Geochemical perspectives on coral mineralization. Rev 

Mineral Geochem. 54: 151-187.

Colvocoresses J, Acosta A. 2007. A large-scale field comparison of strip transect and stationary 

point count methods for conducting length-based underwater visual surveys of reef fish 

populations. Fish Res. 85: 130-141.

Cooper TF, Gilmour JP, Fabricius KE. 2009. Bioindicators of change in water quality on coral 

reefs: review and recommendations for monitoring programmes. Coral Reefs. 28: 589-

606.

Cox C, Hunt JH, Lyons WG, Davis GE. 1997. Nocturnal foraging of the Caribbean spiny lobster 

(Panulirus argus) on offshore reefs of Florida, USA. Mar Freshwater Res. 48: 671-679.
89

[CSA] Continental Shelf Associates. 1989. Environmental monitoring program for exploratory 

well #1, lease OCS-G 6264 High Island Area, South Extension, East Addition, Block A401 near the West Flower Garden Bank. Huston (TX): Union Oil Company of California. 

35 p.

Diaz MC, Rützler K. 2001. Sponges: an essential component of Caribbean coral reefs. Bull Mar 

Sci. 69(2): 535-546.

Dokken QR, MacDonald IR, Tunnell JW, Beaver CR, Boland GS, et al. 1999. Long-term 

monitoring of the East and West Flower Garden Banks, 1996–1997. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 99-0005. 101 p.

Dokken QR, MacDonald IR, Tunnell JW, Wade T, Beaver CR,et al. 2001. Long-term monitoring 

of the East and West Flower Garden Banks, 1998–1999. New Orleans (LA): U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS Region. 

OCS Study MMS 2001-101. 120 p.

Dokken QR, MacDonald IR, Tunnell JW, Wade T, Withers K, et al. 2003. Long-term monitoring 

at the East and West Flower Garden Banks, 1998–2001: final report. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 2003-031. 90 p.

Doropoulos C, Ward S, Diaz-Pulido G, Hoegh-Guldberg O, Mumby PJ. 2012. Ocean 

acidification reduces coral recruitment by disrupting intimate larval-algal settlement 

interactions. Ecol Lett. 15(4): 338-346.

Edinger EN, Limmon GV, Janaluddin J, Widjatmoko W, Heikoop JM, et al. 2000. Normal coral 

growth rates on dying reefs: are coral growth rates food indicators of reef health. Mar 

Poll Bul. 40(5): 404-425.

Fairbanks RG, Evans MN, Rubenstone JL, Mortlock RA, Broad K, et al. 1997. Evaluating 

climate indices and their geochemical proxies measured in corals. Coral Reefs. 16: S93-

S100.

Fang JKH, Mello-Athayde MA, Schonburg CHL, Kline DI, Hoegh-Guldberg O, et al. 2013. 

Sponge biomass and bioerosion rates increase under ocean warming and acidification. 

Global Change Biol. 19: 3581-3591. 

Gittings SR. 1998. Reef community stability on the Flower Garden Banks, northwest Gulf of 

Mexico. Gulf Mexico S. 2:161-169. 

Gittings SR, Bright TJ, Choi A, Barnett RR. 1988. The recovery process in a mechanically 

damaged coral reef community: recruitment and growth. Proceedings of the Sixth 

International Coral Reef Symposium, 225-230.
90

Gittings SR, Boland GS, Deslarzes KPJ, Hagman DK, Holland BS. 1993a. Long-term 

monitoring at the East and West Flower Garden Banks. New Orleans (LA): U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Regional Office. OCS Study MMS 92-0006. 206 p. 

Gittings SR, Bright TJ, Hagman DK. 1993b. The M/V Wellwood and other large vessel 

groundings: coral reef damage and recovery. Case Histories for the Colloquium and 

Forum on Global Aspects of Coral Reefs: Health, Hazard and History. p. 174-180.

Gitzen RA, Millspaugh J. 2012. Ecological monitoring. In: Gitzen RA, Millspaugh JJ, Cooper 

AB, Licht DS, editors. Design and analysis of long-term ecological monitoring studies. 

New York (NY): Cambridge University Press. p. 3-22.

Glynn PW. 1997. Bioerosion and coral reef growth: a dynamic balance. In: Birkelund C, editor. 

Life and death of coral reefs. Berlin (Germany): Springer-Verlag. p. 69-98.

Goreau TJ, Macfarlane AH. 1990. Reduced growth rate of M. annularis following the 1987–

1988 coral bleaching event. Coral Reefs. 8: 211-215.

Gustafson SK. 2006. Assessment of image analysis as a measure of scleactinian coral growth 

[dissertation]. University of South Florida. 62 p.

Halley RB, Hudson JH. 2007. Fidelity of annual growth in Montastraea faveolata and the 

recentness of coral bleaching in Florida. In: Aronson RB, editor. Geological approaches 

to coral reef ecology. Berlin (Germany): Springer. p. 161-177

Harvey E, Fletcher D, Shortis M, Kendrick G. 2004. A comparison of underwater visual distance 

estimates made by SCUBA divers and a stereo-video system: implications for underwater 

visual census of reef fish abundance. Mar Freshwater Res. 55: 573:580.

Hickerson EL, Schmahl GP, Robbart M, Precht WF, Caldow C. 2008. State of coral reef 

ecosystems of the Flower Garden Banks, Stetson Bank, and other banks in the 

northwestern Gulf of Mexico. In: Waddell JE, Clarke AM, editors. NOAA Technical 

Memorandum NOS NCCOS 73. Silver Springs (MD): NOAA/NCCOS Center for 

Coastal Monitoring and Assessment’s Biogeography Team. p. 189-217.

Hill J, Wilkinson C. 2004. Methods for ecological monitoring on coral reefs. Townsville (MC): 

Australian Institute of Marine Science. 123 p.

Hoeke RK, Grove JM, Smith E, Fisher-Pool P, Lammers M, et al. 2009. Coral reef ecosystem 

integrated observing system: in situ oceanographic observations at the US Pacific Islands 

and atolls. J Oper Oceanogr. 2(2): 3-14.

Hughes TP. 1996. Demographic approaches to community dynamics: a coral reef example. 

Ecology. 77(7): 2256-2260.

Hughes TP, Tanner JE. 2000. Recruitment failure, life histories, and long-term decline of 

Caribbean Corals. Ecology. 81(8): 2250-2263.
91

Jackson JBC, Donovan MK, Cramer KL, Lam VV. 2014. Status and trends of Caribbean coral 

reefs: 1970–2012. Gland (Switzerland): Global Coral Reef Monitoring Network. 306 p.

Johnson DH. 2012. Monitoring that matters. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht 

DS, editors. Design and analysis of long-term ecological monitoring studies. New York 

(NY): Cambridge University Press. p. 54-73.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2013. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2009–2010. 

Volume 1: Technical Report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2014A214. 202 p.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2015. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2011–2012. Draft 

Volume 1: Technical Report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2015-027. 

205 p.

Jones GP, Almany GR, Russ GD, Sale PF, Steneck RR, et al. 2009. Larval retention and 

connectivity among populations of corals and reef fishes: history, advances and

challenges. Coral Reefs. 28: 307-325.

Jones GP, Millicich MJ, Emslie MJ, Lunow C. 1999. Self-recruitment in a coral reef population. 

Nature. 402: 802-804.

Jones T, DeVerse K, Dicus G, McKay D, Farahi A, et al. 2011. Water quality vital signs 

monitoring method for the Pacific Island Network: Volume 1; Version 1.0. Natural 

Resource Report NPS/PACN/NRR—2011/418. Fort Collins (CO): National Park Service.

112 p.

Lang JC, Marks KW, Kramer PA, Kramer PW, Ginsburg RN. 2010. AGRRA Protocols Version 

5.4. Atlantic and Guld Rapid Reef Assessment, Miami, FL. 31 p.

Lewis JB. 2004. Has random sampling been neglected in coral reef faunal surveys? Coral Reefs. 

23: 192-194.

Lough JM. 1997. Understanding growth mechanisms: the key to successful extraction of proxy 

climate records from corals. Proc. 8th Int Coral Reef Sym. 2: 1697-1700.

Lough JM. 2008. Coral calcification from skeletal records revisited. Mar Ecol Prog Ser. 373: 

257-264.

Lough JM, Barnes DJ. 1997. Several centuries of variation in skeletal extension, density, and 

calcification in massive Porites colonies from the Great Barrier Reef: a proxy for 

seawater temperature and a background of variability against which to identify unnatural 

change. J Exp Mar Biol Ecol. 211: 29-67.
92

Lough JM, Barnes DJ. 2000. Environmental controls on growth of the massive coral Porites. J 

Exp Mar Biol Ecol. 245: 255-243.

Magurran AE, Baillie SR, Buckland ST, Dick JM, Elston DA, et al. 2010. Long-term datasets in 

biodiversity research and monitoring: assessing change in ecological communities 

through time. Trends Ecol Evol. 25(10): 574-582.

McCauley DJ, Micheli F, Young HS, Tittensor DP, Brumbaugh DR, Madin EMP, Holmes KE, 

Smith JE, Lotze HK, DeSalles PA, et al. 2010. Acute effects of removing large fish from 

a near-pristine coral reef. Mar Bio. 157(12): 2739-2750.

McCook LJ, Almany GR, Berumen ML, Day JC, Green AL, Jones GP, Leis JM, Planes S, Russ 

GR, Sale PF, et al. 2009. Management under uncertainty: guide-lines for incorporating 

connectivity into the protection of coral reefs. Coral Reefs. 28: 353-366.

McDonald T. 2012. Spatial sampling designs for long-term ecological monitoring. In: Gitzen 

RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of long-term 

ecological monitoring studies. New York (NY): Cambridge University Press. p. 101-125.

Miller WJ, Rogers C, Atkinson A, Muller A, Davis A, et al. 2007. Coral Reef Monitoring 

Method. Miami (FL); National Park Service. Natural Resource Report 

NPS/SER/SFCN/NRTR-2007/003. 

[MMS] Minerals Management Service. 1996. Long-term monitoring at the East and West Flower 

Garden Banks. Prepared by Continental Shelf Associates, Inc. New Orleans (LA): U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS Region. 

OCS Study MMS 96-0046. 77 p.

Morris JA, Akins JL. 2009. Feeding ecology of invasive lionfish (Pterois volitans) in the 

Bahamian archipelago. Environ Biol Fish. 86: 389-398.

Moulding AL. 2005. Coral recruitment patterns in the Florida Keys. Rev Biol Trop. 25:75-82.

Mumby PJ, Harborne AR, Williams J, Kappel CV, Brumbaugh DR, et al. 2007. Trophic cascade 

facilitates coral recruitment in a marine reserve. PNAS. 104(20): 8362-8367.

[NOAA] National Oceanographic and Atmospheric Administration. n.d. Pacific Islands Fisheries 

Science Center; [cited 2015 March 03]. Available from: 

http://www.pifsc.noaa.gov/cred/survey_methods.php#new_stationary_point_count

[NOAA] National Oceanographic and Atmospheric Administration. 2011. Center for Coastal 

Monitoring and Assessment. Detailed methods for characterization and monitoring of 

coral reef ecosystems and associated biological communities; [cited 2015 March 03]. 

Available from: http://ccma.nos.noaa.gov/ecosystems/coralreef/reef_fish/methods.aspx
93

[NOAA] National Oceanographic and Atmospheric Administration. 2014. Endangered and 

threatened wildlife and plants: final listing determinations on proposal to list 66 reefbuilding coral species and to reclassify elkhorn and staghorn corals. 50 CFR Part 223. 

Docket No, 0911231415-4826-04. 1104 p.

Olsen AR, Kincaid TM, Payton Q. 2012. Spatially balanced survey designs for natural resources.

In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of 

long-term ecological monitoring studies. New York (NY): Cambridge University Press.

p. 126-150. 

Page C, Willis B. 2006. Distribution, host range and large-scale spoatial variability in black band 

disease prevalence on the Great Barrier Reef, Australia. Dis. Aquat. Org. 69: 41-51.

Penin L, Michonneau F, Carroll A, Adjeroud M. 2011. Effects of predators and grazers exclusion 

on early post-settlement coral mortality. Hydrobiologia. 663: 259-264.

Pattengill-Semmens C, Gittings SR. 2003. A rapid assessment of the Flower Garden Banks 

National Marine Sanctuary (stony corals, algae and fishes). In: Lang JC , editor. Status of 

the coral reefs in the western Atlantic: results of initial surveys, Atlantic and Gulf rapid 

reef assessment (AGGRA) program. Atoll Res Bull. 496: 500-511.

Pattengill-Semmens C, Gittings SR, Shyka T. 2000. Flower Garden Banks National Marine 

Sanctuary: a rapid assessment of coral, fish and algae using the AGGRA method. Marine 

Sanctuaries Conservation Series MSD-00-3. Silver Spring (MD): U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, Marine Sanctuaries 

Division. 15 p. 

Penin L, Michonneau F, Carroll A, Adjeroud M. 2011. Effects of predators and grazers exclusion 

on early post-settlement coral mortality. Hydrobiologia. 663: 259-264.

Porter JW, Dustan P, Jaap WC, Patterson KL, Kosmynin V, et al. 2001. Patterns of spread of 

coral disease in the Florida Keys. Hydrobiologia. 460: 1-24.

Poulsen A, Burns K, Lough J, Brinkman D, Delean S. 2005. Trace analysis of hydrocarbons in 

coral cores from Saudi Arabia. Adv Org Geochem. 37(12): 1913-1930.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Murdoch TJT, et al. 2006. Long-term 

monitoring at the East and West Flower Garden Banks, 2002–2003: final report. New 

Orleans (LA): U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region. OCS Study MMS 2006-035. 182 p.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Evans DJ, et al. 2008. Long-term 

monitoring at the East and West Flower Garden Banks, 2004–2005 - Interim report. 

Volume I: Technical report. New Orleans (LA): U.S. Department of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region. OCS Study MMS 2008-

027. 123 p.
94

Prouty NG, Storlazzi CD, McCutcheon AL, Jenson JW. 2014. Historic impact of watershed 

change and sedimentation to reefs along west-central Guam. Coral Reefs. 33(3): 733-749.

Quinn TM, Sampson DE. 2002. A multiproxy approach to reconstructing sea surface conditions 

using coral skeleton geochemistry. Marine Science Faculty Publications Paper 111.

Raymundo LJ, Couch CS, Harvell CD. 2008. Coral disease handbook: guidelines for assessment, 

monitoring, and management. Melbourne (Australia): Currie Communications. 124 p.

Reynolds JH. 2012. An overview of statistical considerations in long-term monitoring. In: Gitzen 

RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of long-term 

ecological monitoring studies. New York (NY): Cambridge University Press. p. 23-53.

Reich C, Streubert M, Dwyer B, Gidbout M, Muslic A, Umberger D. 2012. St. Petersburg 

Coastal and Marine Science Center’s Core Archive Portal. U.S. Geological Survey Data 

Series 626. Available from: http://pubs.usgs.gov/ds/626/.

Richmond RH, Hunter CL. 1990. Reproduction and recruitment of corals: comparisons among 

the Caribbean, the tropical Pacific, and the Red Sea. Mar Ecol Prog Ser. 60: 185-203.

Roberts CM. 1997. Connectivity and management of Caribbean coral reefs. Science. 278: 1454-

1458.

Rogers CS. 1990. Responses of coral reefs and reef organisms to sedimentation. Mar Ecol Prog 

Ser. 62: 185-202.

Rogers CS, Garrison G, Grober R, Hillis Z, Franke MA. 1994. Coral reef monitoring manual for 

the Caribbean and Western Atlantic. National Park Service Virgin Islands National Park. 

114 p.

Rogers CS, Garrison G, Grober R, Hillis Z, Franke MA. 2001. Coral reef monitoring manual for 

the Caribbean and Western Atlantic. National Park Service Virgin Islands National Park. 

114 p.

Rützler K. 2002. Imapact of Crustose Clionid sponges on Caribbean reef corals. Acta Geologica 

Hispanica. 37(1): 61-72.

Ruzicka R, Semon K, Colella M, Brinkhuis V, Kindey J, et al. 2009. Coral reef evaluation 

monitoring report. NOAA/NOS MOA-2001-683 (Amendment No. 004)/7477. 110p.

Sabourin DT, Silliman JE, Strychar KB. 2013. Polycyclic aromatic hydrocarbon contents of 

coral and surface sediments off the south Texas coast of the Gulf of Mexico. Int J Biol. 

5(1): E-ISSN 1916-968X.

Sammarco PW, Atchison AD, Boland GS. 2006. Geographic expansion and limits of corals in 

the northwest Gulf of Mexico: colonization of offshore oil and gas platforms. 

Proceedings of the 10th International Coral Reef Symposium, Okinawa. p. 453-459.
95

Samoileys MA, Carlos G. 2000. Determining methods of underwater visual census for 

estimating the abundance of coral reef fishes. Environ Bio Fishes. 57: 289-304.

Schonberg CHL, Ortiz JC. 2008. Is sponge bioerosion increasing? Proceedings of the Eleventh 

International Coral Reef Symposium, Ft. Lauderdale. 16: 520-523.

Smith JM, Quinn TM, Helmle KP, Halley RB. 2006. Reproducibility of geochemical and 

climatic signals in the Atlantic coral Montastraea faveolata. Paleoceanography. 21: PA 

1010.

Swearer SE, Caselle JE, Lea DW, Warner RR. 1999. Larval retention and recruitment in an 

island population of a coral-reef fish. Nature. 402: 799-802.

Treml EA, Halpin PN, Urban DL, Pratson LF. 2008. Modeling population connectivity by ocean 

currents, a graph-theoretic approach for marine conservation. Landscape Ecol. 23: 19-36.

Tuya F, Martin JA, Luque A. 2004. Patterns of nocturnal movement of the long-spined sea 

urchin Diadema antillarum (Philippi) in Gran Canaria (the Canary Islands, central East 

Atlantic Ocean). Helgol Mar Res. 58: 26-31.

[EPA] United States Environmental Protection Agency. 2015a. Clean water act. EPA Agriculture 

Center; [cited 2015 March 03]. Available from: 

http://www.epa.gov/agriculture/lcwa.html#Summary

[EPA] United States Environmental Protection Agency. 2015b. Gulf of Mexico Program. EPA 

Agriculture Center; [cited 2015 March 03]. Available from: http://www.epa.gov/gmpo

Urquhart NS. 2012. The role of monitoring design in detecting trend in long-term ecological 

monitoring studies. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design 

and analysis of long-term ecological monitoring studies. New York (NY): Cambridge 

University Press. p. 151-173.

Vago R, Vago E, Achituv Y, Ben-Zion M, Dubinsky Z. 1994. A non-destructive method for 

monitoring coral growth affected by anthropogenic and natural long-term changes. Bull 

Mar Sci. 55(1): 126-132.

Van Woesik R, Gilner J, Hooten AJ. 2009. Standard operating procedures for repeated measures 

of process and state variables of coral reef environments. Melbourne (Australia): Currie 

Communications. 36 p.

Watson RA, Quinn II TJ. 1997. Performance of transect and point count underwater visual 

census methods. Ecol Model. 104: 103-112.

Weber JN, White EW, Weber PH. 1975. Correlation of density banding in reef coral skeletons 

with environmental parameters: the basis for interpretation of chronological records 

preserved in the corolla of corals. Paleobiology. 1(2): 137-149.
96

Weil E. 2004. Coral reef diseases in the wider Caribbean. In: Rosenberg E and Loya Y, editors. 

Coral Health and Disease. Berlin (Germany): Springer-Verlag. p. 35-68 

Weil E, Urreiztieta I, Garzón-Ferreira J. 2002. Geographic variability in the incidence of coral 

and octocoral diseases in the wider Caribbean. Proceedings of the Ninth Coral Reef

Symposium, Bali. 2: 123-1237.

Weil E, Smith GW, Gil-Agudelo DL. 2006. Status and progress in coral reef disease research. 

Dis Aquat Organ. 69: 1-7.

White JA, Giggleman CM, Connor PJ. 2006. Recommended water quality for federally listed 

species in Texas. United States Fish and Wildlife Technical Report. 117 p.

Wilkinson, C. 2008. Status of coral reefs of the world: 2008. Townsville (Australia): Global 

Coral Reef Monitoring Network and Reef and Rainforest Research Centre. 296 p.

Williams BK, Szaro RC, Shapiro CD. 2009. Adaptive management: the U.S. Department of 

Interior technical guide. Washington (DC): Adaptive Management Working Group. 84 p.

Williams GJ, Aeby GS, Cowie ROM, Davy SK. 2010. Predictive modeling of coral disease 

distribution within a reef system. Plos One. 5(2): e9264-9274.

Williams ID, Walsh WJ, Tissot BN, Hallacher LE. 2006. Impact of observers’ experience level 

on counts of fishes in underwater visual surveys. Mar Eco Prog Ser. 310: 185-191.

Willis BL, Page CA, Dinsdale EA. 2004. 3. Coral disease on the Great Barrier Reef. In: 

Rosenberg E, Loya Y, editors. Coral health and disease. Berlin (Germany): SpringerVerlag. p. 69-104.

Willis TJ. 2001. Visual census methods underestimate density and diversity of cryptic reef 

fishes. J Fish Bio. 59: 1408-1411.

Wulff J. 2001. Assessing and monitoring coral reef sponges: why and how? Bull Mar Sci. 69(2): 

831-846.

Zimmer B, Duncan L, Aronson RB, Deslarzes KJP, Deis D, et al. 2010. Long-term monitoring at 

the East and West Flower Garden Banks, 2004–2008. Volume I: Technical Report. New 

Orleans (LA): U.S. Department of the Interior, Bureau of Ocean Energy Management, 

Regulation, and Enforcement, Gulf of Mexico OCS Region. OCS Study BOEMRE 2010-

052. 310 p.
97

CHAPTER 3.0: SAMPLING EVALUATION

3.1 Introduction

In this chapter, each of the sampling strategies for the monitoring methods currently conducted

as part of the FGBLTM program is evaluated for statistical power to detect change. The 

implications of reducing or increasing sampling frequency are evaluated, and alternative 

sampling strategies that may improve the ability of the method to detect community change or 

optimize the sampling strategy in terms of effort expended for statistical power achieved are 

discussed for each method. The chapter is divided into seven sections, one for each monitoring 

variable identified as part of the FGBLTM program design:

1. benthic percent cover;

2. coral growth;

3. coral health;

4. coral recruitment;

5. sea urchin, lobster and lionfish population density;

6. fish populations; and

7. water quality

The qualitative observations and qualitative data obtained from the perimeter video technique are 

not discussed here as data collected through these methods are not statistically evaluated as part 

of the FGBLTM program.

What is Statistical Power?

The most common types of statistical analysis with ecological data (i.e., analysis of variance 

[ANOVA], linear regression, t-tests, etc.) involve hypothesis testing (Sokal and Rohlf 1995). 

Relationships between datasets are not proven, but rather hypothesis are tested and rejected. 

Failure to reject a hypothesis is as close to proving a relationship as hypothesis testing can get. 

With this type of statistical analysis two types of error exist. Type I error is rejecting a null 

hypothesis that is in fact true. The probability of Type I error that is determined to be acceptable 

by the analyst is called a. Hypotheses are typically rejected when p-values are less than or equal 

to a. Type II error is failing to reject a null hypothesis when it is in fact false. The probability of 

Type II error is called β. Statistical power is 1-β and is the probability of correctly rejecting a 

null hypothesis when the null hypothesis is false (Steidl et al. 1997).

In LTM programs, statistical analyses are typically centered on determining if parameter values 

are changing over time. A typical null hypothesis would be that mean values from two or more 

datasets representing communities at different points in time are equal, meaning that no change 

in mean values have occurred over time. Statistical power in this instance would represent the 

probability of correctly detecting changes over time (rejecting the null hypothesis of no change).

Power analyses can be performed before data collection or post-hoc (Steidl et al. 1997; Lewis 

2006). Power analyses in the planning phase of a research program, before data collection, are 

used to determine the effective sample size for detecting a biologically meaningful effect size or

trend. This type of power analysis is important in planning for LTM programs to prevent wasted 
98

time and effort (Gerrodette 1987; Steidl et al. 1997). In many situations, researchers 

retrospectively perform power analyses after a non-significant result is achieved to demonstrate 

whether there would have been adequate power to detect a meaningful result (Steidl et al. 1997; 

Lewis 2006). This use of power analysis can be misleading because data are tested on the 

observed effect size and results do not represent true power (Steidl et al. 1997). Retrospective 

power analyses can be more effectively used to determine the sample size that would have been 

necessary to detect specific effect sizes (Steidl et al. 1997). In this chapter, power analyses are 

performed on recent FGBLTM data to determine if current sampling strategies are adequate to 

detect specific effect sizes both to provide a basis for evaluating the monitoring report 

conclusions (in Chapter 4) and to make recommendations for improving on the current sampling 

strategy for future FGBLTM efforts.

Statistical Power for Management of FGB

In resource management, both Type I and Type II errors are of great importance. A Type I error 

(rejecting a null hypothesis when there was actually no change) would result in a false alarm. A 

false alarm could lead to unnecessary expenditure on resource management. A Type II error 

(failing to reject the null hypothesis when a change has occurred) would result in failure to detect 

a change (Zorn et al. 2004). Detecting change is typically the primary objective of an LTM 

program. If an undesirable change goes undetected, management strategies will not be properly 

deployed and resources may be unnecessarily lost. It is in the best interest FGBNMS that both 

Type I and Type II errors be considered in the design of the FGBLTM program.

To date, few assessments of statistical power have been made for certain FGBLTM data. 

Random video transect data from 2002–2003 were evaluated. Video transects temporarily 

replaced photo transects for the random transect method. Aronson et al. (2005) report that a 7.4% 

change in percent coral cover or a 7.4% difference in coral cover between the two banks could be 

detected after two years with a power of 0.8 at a 95% confidence level (α = 0.05). Considerations

of statistical power are mentioned in a few other places throughout the FGBLTM reports (Precht 

et al. 2006; Zimmer et al. 2010; Johnston et al. 2013, 2015), but no other specific power analysis 

results are reported.

The power evaluation reported in this chapter is the most comprehensive power evaluation of the 

FGBLTM data to date. The analysis is limited by the quantity and quality of data available, 

especially where significant adjustments have been made to methods over time. These 

limitations will be discussed for individual methods as appropriate in the sections that follow.

General Methods for Current Power Evaluation

Statistical power is dependent upon the chosen a value, effect size or trend value, variation in the 

parameter measured, and sample size (Steidl et al. 1997; Zorn et al. 2004). Each of these has 

been considered in the current evaluation.

A standard scientific α value is 0.05, which is also referred to as a 95% confidence level (Zorn et 

al. 2004). All statistical assessments reported in the most recent FGBLTM report used this 

confidence level (Johnston et al. 2015). No reason has been found to dispute the appropriateness 
99

of this confidence level, so 0.05 will be considered the target α value for the current power 

evaluation, except where noted in the individual sections. 

The effect size or trend value is the amount of change that has occurred per period of time. For 

this evaluation 10, 20 and 50% net changes will be evaluated for most methods at two year, five 

year, and 10 year intervals. These change values are considered to be biologically relevant for 

benthic and fish datasets (Brown et al. 2004; Seavy and Reynolds 2007; Brown et al. 2011a, 

2011b; Starcevich 2013). Several monitoring programs reviewed used detection of 10% changes 

as their target for power to monitor coral and fish communities (Sweatman 1997; Brown et al. 

2004; Stoddart et al. 2005; Sweatman et al. 2008). Houk and Van Woesik (2006) targeted 

detection of 20% changes for benthic assemblages and Darnell et al. (2012) targeted detection of 

20% changes in nutrients and sediment loads on the Great Barrier Reef. The time intervals were 

chosen to provide power results for short term changes potentially caused by acute events (two 

years) and longer term changes potentially caused by more chronic stressors (five and 10 years). 

Table 3.1 shows the relationship between net trend values and annual trend values. Minimum 

annual trend values will also be evaluated to demonstrate detection limitations when power is 

insufficient to meet monitoring objectives.

Table 3.1 Equivalent Annual Trend Values for Net Trend Values

Annual trend values are provided, corresponding to net trend values of ± 10, 20 and 50% after two, five, 

and 10 years of monitoring. The initial year sample is at time zero, the second year sample is at time one, 

etc.

Monitoring Period Annual Trend Values (%)

-50% -20% -10% 10% 20% 50%

2 years -50.0 -20.0 -10.0 10.0 20.0 50.0

5 years -15.9 -5.4 -2.6 2.4 4.7 10.7

10 years -7.4 -2.4 -1.1 1.1 2.0 4.6

The variation in the parameters measured is calculated from pilot study data. Because the power 

to detect future changes in LTM parameters is of interest, the most recent available data were 

considered to be the relevant “pilot data”. A pilot study for ecological data typically includes one 

to five years of data (Zorn et al. 2004). For most methods data from 2013 and 2014 are not yet 

available. The 2011 and 2012 report has not been finalized at the time of this review (Johnston et 

al. 2015), but for most methods data are available. Numerous method changes occurred between 

2008 and 2009 (see Chapter 2) making some data incomparable prior to 2009. For these reasons, 

the pilot data was limited to the years 2009 through 2012 for most methods. Any deviations will 

be discussed by method in the sections that follow.

Sample sizes for the FGBLTM methods are described in the IA (BOEM 2014). They have been 

adjusted over time for most methods (see Chapter 2). Specific sample size adjustments will be 

discussed as relevant, but the current sample size will be used to make power determinations for 

the current sampling strategy. Sample size is an easily adjusted aspect of the LTM sampling 

strategy that can be used to improve statistical power. For this reason, the minimum sample size 

that will achieve the desired power objectives will be reported both when the current sampling 

strategy does not provide adequate power and to demonstrate how much the sample size could be 

reduced and still maintain desired power.
100

Also, at least three alternate sampling strategies are evaluated for each, when relevant. These 

alternative sampling strategies are presented to demonstrate the effect of reducing or increasing 

survey effort on the power to detect change. For each method, a sampling frequency of one 

survey every other year (biennial sampling), one survey every third year (triennial sampling), 

and two surveys per year (biannual sampling) are evaluated and compared to the current annual 

sampling strategy. Some methods, such as sclerochronology and water quality, are not currently 

sampled annually, so different alternative strategies are evaluated as appropriate.

The current power evaluation will be conducted in MONITOR (Gibbs and Ene 2010), a free 

software program that was designed to estimate power for ecological monitoring programs. This 

software has been used in numerous peer-reviewed publications (Flesch and Steidl 2006; Lewis 

2006; Pollock 2006), and features the ability to customize time intervals, which is necessary for 

evaluating the ability to detect long-term trends and adjusting for alternative sampling 

frequencies. 

Input values in MONITOR include a start value; a total variation or partitioned variation value; 

and information of survey design including sampling intervals, design type (simple regression, 

one-way ANOVA, two-way ANOVA with replication, etc.), and the number of samples per 

survey interval (surveys/plot/interval). Unless specified elsewhere, for this evaluation start values 

were equal to the sample mean of the pilot data for a given method, and total variation was equal 

to the sample standard deviation. Because available data were limited to two to four years with 

small sample sizes for most methods, partitioning variation to sample and process variation 

would have been inaccurate. Design type and the number of samples per interval varied for each 

method based on the information given in the IA. These variables are specified for each method

in the sections that follow.

The MONITOR user must specify the range and number of trends that the program should 

simulate, the mean to variance relationship (constant coefficient of variance [CV] or constant 

standard deviation [SD]), the number of Monte Carlo iterations that should be performed, the 

significance (α), and the desired power level. The range and number of trends was adjusted on a 

simulation by simulation basis to allow for estimation of the desired net trend values given the 

survey intervals and time frame. Constant CV is recommended for ecological datasets because it 

most accurately simulates the behavior of ecological data (Gibbs and Ene 2010; Meyer et al.

2010). With constant CV, the variance is related linearly to the mean value. Constant CV was 

used for the current evaluation unless otherwise specified. Monte Carlo iterations were set to 

1000 for all analyses in the current evaluation. 500 to 1000 iterations are usually sufficient for 

power simulations (Gibbs and Ene 2010). As discussed above, α was set to 0.05 as a standard 

significance level for ecological studies. The power level used in this analysis to determine

whether sample designs would meet desired program goals in detecting changes was set at 0.8. 

This is also a standard power level for ecological studies (Zorn et al. 2004) and is used as an 

acceptable benchmark for other established long-term studies (i.e., Brown et al. 2004; Brown et 

al. 2011a, 2011b; Starcevich 2013). Di Stefano (2003) cautions that target a and power levels 

should be determined based on the specific management goals and the implications that 

associated errors have on costs and resources, rather than using one standard for all ecological 

studies. FGBNMS may wish to raise the target power level to 0.9 if the risk of type II errors is of 

particular concern to the managers. In this evaluation report power values are given for all 

analyses so the current data will be valuable even if the desired power level is increased.
101

3.2 Sampling Evaluation

3.2.1 Benthic Cover

Random transects have been the primary method for collecting benthic cover data since the 

beginning of FGBLTM in 1989 (Gittings et al. 1993). Methods of data collection have changed 

multiple times through the years, from still photography, to video, to digital photography and 

with differing total areas photographed along each transect. The resulting data have been 

consistently reported as percent cover, though the benthic categories of cover have changed 

through the program. The categories reported in the more recent technical reports from Johnston 

et al. (2013, 2015) are cover for coral, sponge, encrusting sponge, macroalgae, substrate and 

sand. These categories and two dominant corals of interest, Pseudodiploria strigosa and 

Montastrea annularis SC were used in power analyses. The current sample size is 16 transects 

per bank per year.

Permanent quadrats were initially included in the LTM methods to identify the causes of benthic 

retreat and growth dynamics over time (Gittings et al. 1993), but have changed in focus as the 

LTM progressed to also collect percent benthic cover data similar to the random transects. Deep 

quadrats were installed at EFGB to assess changes in benthic cover at 32–40 m depth in 2003 

(Precht et al. 2006). In the most recent technical reports, from which pilot data were gleaned, 

percent benthic cover for all quadrats is reported in the same manner as for random transects and 

in the same categories: coral, sponge, encrusting sponge, macroalgae, substrate and sand 

(Johnston et al. 2013, 2015). These categories and the two corals, P. strigosa and M. annularis 

SC, were included in power analyses. The current sample size is 40 quadrats per bank per year 

within the study sites and 11 deep quadrats at EFGB and 12 deep quadrats at WFGB per year.

Large areas of sand have been avoided when placing transects during each sampling period 

(Gittings et al. 1993), and likewise permanent quadrat placement. The resulting data have had 

very low occurrence of sand in each transect or quadrat. Sponges at FGBNMS are mostly found 

growing on the undersides of substrate (Johnston et al. 2013) and the photographic data 

collection methods have consistently under-sampled these benthic categories (sponge and 

encrusting sponge). Due to these collection methods, data for these three categories primarily 

consisted of zeros and had standard deviations that are larger than the corresponding mean 

percent cover. To alleviate this, these data were analyzed as presence or absence data, using the 

Chi square test option in program Monitor (Gibbs and Ene 2010) for single bank analyses, and 

the Cochran’s option for between bank comparisons to account for location (McDonald 2014).

Coral, macroalgae and substrate had sufficient occurrence to be analyzed as mean percent cover 

with standard deviation.

Power analyses for coral, macroalgae, substrate, P. strigosa, and M. annularis SC were evaluated 

as one-way ANOVAs for within bank comparisons, and two-way ANOVAs with replication for 

between bank comparisons and comparisons between transect and quadrat data within the same 

bank (further referred to as mixed model comparisons). Normality for random transect and 

quadrat data was tested using the Anderson-Darling test (Appendix A) and frequency 

histograms. Anderson-Darling is unreliable as a test of normality for very small sample sizes 

(Razali and Wah 2011), so normality of deep quadrat data was assessed with frequency 

histograms only. Transect raw data mostly had normal distributions, or could be transformed to 
102

comply with normality assumptions. The permanent quadrat raw data mostly violated normality 

assumptions. Coral percent cover for EFGB pilot data had one outlier removed prior to analysis 

to comply with the normality assumption (outlier calculations in Appendix B). In these cases a 

square root transformation resulted in a normal distribution. The coral data for both banks was 

bi-modal or very non-normal, and transformations did not improve normality, so these data,

where analyzed, were done so without transformation. Of the parametric statistical tests, 

ANOVAs have been found to be robust against error due to violations of the normality 

assumption (Schmider et al. 2010), so the power analyses presented here should be considered 

valid. 

3.2.1.1 Random Transects

Current Sampling Design Power Evaluation

The benthic categories sponge, encrusting sponge and sand were treated as presence and absence 

data to alleviate quantitative issues as discussed previously in this section. Analyses for presence

and absence random transect data were performed on the benthic category that had the highest 

mean percent cover on each bank and for between bank comparisons. These were sponge for 

EFGB and EFGB compared with WFGB, and sand for WFGB. Results showed very low power 

and minimum annual detectable trend of 100% or greater, or could not be determined (Tables 3.2 

and 3.3). Minimum sample size to detect a 20% change in five years at desired power was 

greater than 1000. Because of these results, analyses were not run on the remaining presence or 

absence data, which had lower means and would have yielded similar, non-determinative results. 

Further discussion of results will address only the remaining analyses of coral, macroalgae,

substrate, P. strigosa and M. annularis SC.

The results for both EFGB and WFGB were similar with the current sampling strategy of 16 

random transects per bank, sampled once a year (BOEM 2014). Most of the analyses met or 

exceeded the desired power level of 0.8 for a net change of 50% after two, five and 10 years. 

Less than half of analyses also exceeded the detection threshold for a net change of 20% across 

all time frames, while only the coral category mixed model analyses did so for a change of 10%. 
103

Table 3.2 Benthic Cover Transects Current Sample Design Power Results

Power analysis results for benthic cover transect data with the current design of annual sampling.

Location or Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Coral* 55.3 13.2 0.973 0.439 0.150 0.157 0.608 1.000

Sponge+ 0.3 N/A 0.176 0.166 0.182 0.177 0.197 0.188

Macroalgae** 1.5 0.2 0.965 0.866 0.420 0.489 0.917 1.000

Substrate 11.3 4.6 0.860 0.191 0.074 0.076 0.172 0.864

P. strigosa^ 2.8 1.3 0.678 0.152 0.093 0.085 0.159 0.708

M. annularis SC** 0.6 0.4 0.361 0.142 0.154 0.145 0.155 0.402

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 0.931 0.237 0.083 0.082 0.237 0.922

M. annularis SC (mixed model) 4.9/6.6 5.8/9.3 0.721 0.135 0.065 0.060 0.138 0.724

Coral (mixed model) 55.3*/67.1 13.2/16.9 1.000 1.000 0.770 0.796 1.000 1.000

West

Coral 58.5 13.0 0.995 0.501 0.142 0.154 0.683 1.000

Macroalgae 28.1 9.6 0.921 0.238 0.101 0.088 0.256 0.943

Substrate** 1.0 0.2 0.999 0.656 0.189 0.201 0.686 1.000

Sand+ 0.5 N/A 0.174 0.177 0.170 0.168 0.154 0.173

P. strigosa^ 3.0 1.3 0.806 0.161 0.098 0.091 0.186 0.820

M. annularis SC** 0.5 0.4 0.284 0.162 0.166 0.156 0.156 0.343

P. strigosa (mixed model) 9.5/8.9 8.9/9.4 0.943 0.236 0.075 0.089 0.236 0.937

M. annularis SC (mixed model) 4.6/8.1 7.6/12.5 0.561 0.134 0.054 0.049 0.121 0.568

Coral (mixed model) 58.5/68.3 13/14.7 1.000 1.000 0.871 0.886 1.000 1.000

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 0.993 0.585 0.564 0.996 1.000

Sponge+ 0.3/0.3 N/A 0.071 0.063 0.066 0.088 0.091 0.112

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.804 0.275 0.243 0.818 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.691 0.173 0.197 0.690 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.558 0.168 0.162 0.590 1.000

M. annularis SC** 0.6/0.5 0.4/0.4 0.868 0.239 0.124 0.112 0.239 0.938

After Five Years

East

Coral* 55.3 13.2 0.990 0.380 0.120 0.110 0.566 1.000

Sponge+ 0.3 N/A 0.061 0.034 0.029 0.034 0.043 0.051

Macroalgae** 1.5 0.2 0.991 0.991 0.360 0.437 0.959 0.990

Substrate 11.3 4.6 0.956 0.137 0.061 0.059 0.179 0.654

P. strigosa^ 2.8 1.3 0.808 0.101 0.048 0.063 0.103 0.420

M. annularis SC** 0.6 0.4 0.338 0.065 0.045 0.068 0.066 0.208

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 0.983 0.182 0.057 0.051 0.121 0.720

M. annularis SC (mixed model) 4.9/6.6 5.8/9.3 0.873 0.112 0.057 0.044 0.078 0.447

Coral (mixed model) 55.3*/67.1 13.2/16.9 1.000 1.000 0.774 0.612 0.998 1.000

West

Coral 58.5 13 1.000 0.514 0.104 0.123 0.649 1.000

Macroalgae 28.1 9.6 0.994 0.208 0.086 0.083 0.235 0.845

Substrate** 1.0 0.2 1.000 0.600 0.162 0.122 0.508 1.000

Sand+ 0.5 N/A 0.074 0.056 0.044 0.035 0.044 0.056

P. strigosa^ 2.8 1.3 0.925 0.145 0.046 0.064 0.096 0.546

M. annularis SC** 0.6 0.4 0.305 0.052 0.050 0.043 0.069 0.173

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 0.991 0.199 0.072 0.043 0.135 0.737

M. annularis SC (mixed model) 4.9/6.6 5.8/9.3 0.667 0.070 0.036 0.037 0.071 0.294

Coral (mixed model) 55.3*/67.1 13.2/16.9 1.000 1.000 0.894 0.731 1.000 1.000
104

Location or Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 1.000 0.481 0.388 0.988 1.000

Sponge+ 0.3/0.3 N/A 0.006 0.020 0.019 0.025 0.034 0.021

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.773 0.209 0.162 0.635 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.636 0.163 0.179 0.498 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.543 0.113 0.114 0.330 0.999

M. annularis SC** 0.6/0.5 0.4/0.4 0.985 0.187 0.076 0.074 0.135 0.789

After Ten Years

East

Coral* 55.3 13.2 1.000 0.456 0.160 0.200 0.660 1.000

Sponge+ 0.3 N/A 0.075 0.048 0.037 0.047 0.047 0.060

Enc. Sponge+ 0.2 N/A - - - - - -

Macroalgae** 1.5 0.2 0.999 0.961 0.502 0.590 0.930 0.998

Substrate 11.3 4.6 0.989 0.151 0.072 0.071 0.167 0.768

Sand+ 0.3 N/A - - - - - -

P. strigosa^ 2.8 1.3 0.950 0.120 0.056 0.050 0.085 0.524

M. annularis SC** 0.6 0.4 0.515 0.063 0.058 0.041 0.053 0.227

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 1.000 0.203 0.063 0.079 0.144 0.809

M. annularis SC (mixed model) 4.9/6.6 5.8/9.3 0.955 0.116 0.047 0.056 0.077 0.497

Coral (mixed model) 55.3*/67.1 13.2/16.9 1.000 1.000 0.865 0.775 1.000 1.000

West

Coral 58.5 13 0.999 0.597 0.128 0.161 0.646 1.000

Sponge+ 0.3 N/A - - - - - -

Enc. Sponge+ 0.2 N/A - - - - - -

Macroalgae 28.1 9.6 1.000 0.270 0.065 0.078 0.297 0.887

Substrate** 1.0 0.2 1.000 0.762 0.143 0.135 0.56 1.000

Sand+ 0.5 N/A 0.089 0.039 0.041 0.054 0.044 0.050

P. strigosa^ 2.8 1.3 0.985 0.152 0.069 0.055 0.150 0.660

M. annularis SC** 0.6 0.4 0.407 0.057 0.053 0.047 0.050 0.176

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 1.000 0.224 0.059 0.065 0.157 0.849

M. annularis SC (mixed model) 4.9/6.6 5.8/9.3 0.846 0.078 0.047 0.062 0.066 0.365

Coral (mixed model) 55.3*/67.1 13.2/16.9 1.000 1.000 0.918 0.923 1.000 1.000

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 1.000 0.796 0.755 0.995 1.000

Sponge+ 0.3/0.3 N/A 0.000 0.007 0.004 0.004 0.006 0.006

Enc. Sponge+ 0.2/0.20 N/A - - - - - -

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.889 0.300 0.315 0.700 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.777 0.166 0.171 0.634 1.000

Sand+ 0.3/0.5 N/A - - - - - -

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.618 0.120 0.106 0.485 1.000

M. annularis SC** 0.6/0.5 0.4/0.4 1.000 0.193 0.070 0.066 0.181 0.865

*one outlier removed for normality ^square root transformed for normality

**log transformed for normality +Presence/Absence Data

Values in red are above the desired power level of 0.80
105

Table 3.3 Benthic Cover Transects Current Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover transect data with the current 

design of annual sampling.

Location or Category Pilot Data Min Annual Trend (%)

Min Sample 

Size for

20% Net 

Change after 

5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Coral* 55.3 13.2 ±28 ±8 ±3 28 38

Sponge+ 0.3 N/A ±>100 ±>100 ±>100 >1000 >1000

Macroalgae** 1.5 0.2 ±16 ±5 ±2 10 12

Substrate 11.3 4.6 ±47 ±13 ±5 80 110

P. strigosa^ 2.8 1.3 -59/+56 ±16 ±7 117 158

M. annularis SC** 0.6 0.4 >-100/+81 -28/+22 ±9 256 326

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 ±42 ±12 ±5 260 342

M. annularis SC

(mixed model) 4.9/6.6 5.8/9.3 ±54 ±16 ±7 418 589

Coral (mixed model) 55.3*/67.1 13.2/16.9 ±11 ±4 ±2 15 21

West

Coral 58.5 13 ±26 ±7 ±3 24 32

Macroalgae 28.1 9.6 ±41 ±11 ±5 52 69

Substrate** 1 0.2 ±24 ±7 ±3 20 27

Sand+ 0.5 N/A ±>100 ±>100 ±>100 >1000 >1000

P. strigosa^ 2.8 1.3 ±50 ±14 ±6 90 118

M. annularis SC** 0.6 0.4 +86/>-100 +23/-37 +10/-13 295 372

P. strigosa (mixed model) 8.3/10.7 8.1/11.1 ±42 ±12 ±5 234 325

M. annularis SC

(mixed model) 4.9/6.6 5.8/9.3 ±65 ±19 ±8 617 848

Coral (mixed model) 55.3*/67.1 13.2/16.9 ±10 ±3 ±2 12 16

East compared with West

Coral 55.3/58.5 13.2/13.0 ±13 ±4 ±2 14 19

Sponge+ 0.3/0.3 N/A ±>100 ±>100 ±>100 >1000 >1000

Macroalgae 33.1/28.1 12.3/9.6 ±21 ±6 ±3 34 44

Substrate 11.3/12.1 4.6/5.0 ±23 ±7 ±3 42 56

P. strigosa^ 2.8/3.0 1.3/1.3 ±26 ±7 ±3 52 70

M. annularis SC** 0.6/0.5 0.4/0.4 +40/-44 ±12 ±5 140 176

*one outlier removed for normality ^square root transformed for normality

**log transformed for normality +Presence/Absence Data

Values in red are above the desired power level of 0.80

At EFGB coral analyses were above desired power levels for a net 50% for all time frames, and 

did not exceed desired power levels for 10% or 20% change. The minimum detectable annual 

trend for coral was 28% per year after two years, 8% after five years, and 3% after 10 years. The 

minimum number of transects required to detect a 20% net change in coral cover after five years 

was 28 for a 20% decrease, and 38 for a 20% increase. The mixed model coral analysis exceeded 

the desired power threshold for both an increase and decrease of 20% in all timeframes, as well 

as for a decrease of 10% after 10 years. P. strigosa exceeded a power of 0.8 for a decrease of 
106

50% after five and 10 years, while the mixed model was above 0.8 for both increases and 

decreases of 50% for all time frames except for an increase of 50% after five years. Neither 

transect or mixed model analyses exceeded 0.8 for M. annularis SC for any analyses except the 

mixed model for a decrease of 50% after five and 10 years. Macroalgae was above 0.8 power for 

20% net change after two, five and 10 years. The minimum detectable annual trend for 

macroalgae was 16% after two years, 5% after five years, and 2% after 10 years. The number of 

transects could be reduced to 10 to detect a decrease of 20% in macroalgae cover after five years, 

and 12 to detect a 20% increase. Substrate analyses were above the detection threshold for 50% 

net change after two years, but dropped below detection limits for a 50% increase after five and 

10 years. The minimum detectable annual trend for substrate was 47% after two years, 13% 

after five years, and 5% after 10 years. The minimum number of transects were determined to be 

80 to detect a net 20% decrease in five years, and 110 to detect a 20% increase.

At WFGB coral, macroalgae and substrate all exceeded desired power levels for a net change of 

50% in all timeframes, and did not exceed limits for 20% or 10% change (Table 3.2). Both P. 

strigosa and the P. strigosa mixed model were above desired power limits for changes of 50% 

except for an increase of 50% after five years. The M. annularis SC analyses did not exceed 0.8 

for any analyses except for a decrease of 50% after ten years in the mixed model. The coral 

mixed model exceeded 0.8 for both an increase and decrease of 10% in all timeframes except for 

an increase of 10% after five years. For coral transect data the minimum detectable annual trend 

was determined to be 26% in two years, 7% in five years, and 3% in 10 years (Table 3.3). The 

minimum number of transects to detect a 20% change in five years was 24 for a 20% decrease 

and 32 for a 20% increase. Macroalgae minimum annual detectable trends were 41% for two 

years, 11% for five years, and 5% for 10 years. The minimum number of transects were 52 to 

detect a 20% decrease in five years, and 69 to detect a 20% increase in five years. The minimum 

detectable annual trends for substrate were 24% in two years, 7% in five years, and 3% in 10 

years. The minimum number of transects were 20 to detect a 20% increase, and 27 to detect a 

20% decrease after five years.

Power results for comparisons between EFGB and WFGB were mixed, with some comparisons 

having higher power than analyses for individual banks, and others having similar or lower 

power (Table 3.2). Coral analyses were above detectable limits at 20% net change for all time 

frames analyzed, but did not exceed limits to detect 10% change in any analyses. The minimum 

annual detectable trends for between bank comparisons of coral cover were 13% in two years, 

4% in five years, and 2% in 10 years (Table 3.3). The minimum number of transects necessary to 

detect a 20% change in five years was 14 to detect a decrease and 19 to detect an increase. Both 

P. strigosa and M. annularis SC were above the desired power level of 0.8 for changes of 50%, 

except for an increase of 50% after five years for M. annularis SC. Mixed model, between bank 

comparisons are not possible in the statistical program Monitor. Changes in macroalgae cover 

between banks were above the detection limit of 0.8 for a change of 20% after two years. After 

five years, changes of 50% were above the threshold; and after 10 years a decrease of 20% and 

increase of 50% were above the detection limit. Minimum annual detectable trends for 

macroalgae were 21% after two years, 6% after five years, and 3% after 10 years. The minimum 

number of transects needed to detect a 20% change in five years was 34 for a 20% decrease, and 

44 for an increase. Substrate was only above the detection threshold for a change of 50% in all 

timeframes analyzed. The minimum detectable annual trend for substrate was 23% after two 
107

years, 7% after five years, and 3% after 10 years. The minimum number of transects was 42 to 

detect a 20% decrease after five years, and 56 to detect a 20% increase in the same time frame.

Alternative Sampling Design Power Evaluation

Sampling Every Other Year

Analyzing transect and quadrat data as a mixed model was considered to be an alternate design, 

as this type of analysis is not performed in any FGBLTM technical reports or the IA, so these 

analyses were only included in the current sample design power evaluations. For all other 

analyses, changing to a biennial sampling design had very little effect overall on the results of 

individual analyses, or which analyses exceeded the desired power threshold (Table 3.4). At 

EFGB the pattern of analyses that exceeded a power of 0.8 was very similar to that for an annual 

sampling design. Differences between the designs were that the substrate analysis did not exceed 

the detection threshold for a 50% increase after three years, and macroalgae did not exceed the 

threshold for 20% increase or decrease in most tests, unlike in the annual design. The minimum 

annual detectable trends and minimum number of transects needed to detect a change of 20% 

after five years at EFGB were also very similar to an annual sampling design (Table 3.5). 

These similarities were also evident for WFGB, with analyses that exceeded the power threshold 

identical to an annual sampling design except P. strigosa did not exceed 0.8 for an increase of 

50% after three years in the biennial design (Table 3.4). The minimum annual detectable trend 

for five and 11 years, and the minimum number of transects needed to detect a 20% change in 

five years were also very similar between the two designs (Table 3.5). This pattern was 

continued in the between bank comparisons, with results in the biennial design very similar to 

those for the annual design. 

Sampling Every Third Year 

Sampling every third year analyses yielded results similar to those for annual and biennial 

sampling (Tables 3.6 and 3.7). Those that exceeded the desired power threshold were nearly 

identical in all sampling designs across both banks and in between bank comparisons. EFGB 

results were nearly identical to the biennial sampling design after five and 10 years. For WFGB, 

macroalgae did not exceed the detection threshold for a net increase of 50% after 10 years in the 

sampling every third year design, while it did in the biennial and annual sampling designs. In the 

between bank comparative analyses, M. annularis SC did exceed 0.8 after four years in the every 

third year design, and macroalgae did not exceed detection threshold for a net decrease of 20% 

after 10 years as it did in the biennial and annual sampling designs. 

The minimum annual detectable trends for sampling every third year are very similar to those for 

sampling annually and biennially, with no clear pattern of superior detection in any of the 

sampling designs. The minimum number of transects needed to detect a net change of 20% after 

four years has a more clear pattern, with most analyses requiring fewer transects than annual and 

biennial designs require after five years. The notable exception to this pattern is macroalgae at 

WFGB, where biennial and triennial sampling designs have similar results that are more than 

twice the number of transects required in the annual design.
108

Table 3.4 Benthic Cover Transects Biennial Sampling Power Results

Power analysis results for benthic cover transect data with the alternative design of biennial sampling.

Location or Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

Coral* 55.3 13.2 0.996 0.415 0.156 0.243 0.612 1.000

Sponge+ 0.3 N/A 0.220 0.212 0.196 0.190 0.187 0.186

Macroalgae** 1.5 0.2 0.960 0.850 0.289 0.296 0.792 1.000

Substrate 11.3 4.6 0.941 0.212 0.079 0.082 0.224 0.768

P. strigosa^ 2.8 1.3 0.804 0.173 0.092 0.097 0.158 0.572

M. annularis SC 0.6 0.4 0.440 0.149 0.149 0.162 0.141 0.300

West

Coral 58.5 13.0 1.000 0.599 0.185 0.227 0.539 1.000

Macroalgae 28.1 9.6 0.986 0.301 0.096 0.101 0.261 0.880

Substrate** 1.0 0.2 1.000 0.753 0.191 0.190 0.691 1.000

P. strigosa^ 3.0 1.3 0.890 0.196 0.104 0.108 0.194 0.739

M. annularis SC** 0.5 0.4 0.370 0.171 0.151 0.151 0.148 0.328

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 0.994 0.609 0.566 0.997 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.868 0.269 0.246 0.763 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.744 0.200 0.229 0.605 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.681 0.186 0.147 0.577 1.000

M. annularis SC** 0.6/0.5 0.4/0.4 0.964 0.238 0.111 0.111 0.240 0.877

After Fifth Year

East

Coral* 55.3 13.2 0.980 0.347 0.106 0.132 0.310 0.900

Macroalgae** 1.5 0.2 0.989 0.670 0.236 0.186 0.603 0.981

Substrate 11.3 4.6 0.935 0.160 0.052 0.068 0.188 0.656

P. strigosa^ 2.8 1.3 0.800 0.112 0.054 0.053 0.075 0.444

M. annularis SC 0.6 0.4 0.370 0.075 0.040 0.051 0.067 0.200

West

Coral 58.5 13.0 1.000 0.544 0.125 0.133 0.638 1.000

Macroalgae 28.1 9.6 0.988 0.279 0.077 0.099 0.199 0.806

Substrate** 1.0 0.2 1.000 0.652 0.153 0.171 0.562 0.998

P. strigosa^ 3.0 1.3 0.897 0.146 0.072 0.049 0.097 0.549

M. annularis SC** 0.5 0.4 0.293 0.082 0.049 0.052 0.061 0.205

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 0.993 0.521 0.523 0.993 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.771 0.192 0.139 0.727 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.648 0.150 0.177 0.429 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.593 0.107 0.129 0.451 0.999

M. annularis SC** 0.6/0.5 0.4/0.4 0.949 0.212 0.072 0.078 0.159 0.758

After Eleventh Year

East

Coral* 55.3 13.2 0.990 0.317 0.087 0.098 0.542 0.995

Macroalgae** 1.5 0.2 0.991 0.798 0.224 0.160 0.606 0.995

Substrate 11.3 4.6 0.993 0.117 0.064 0.068 0.196 0.678

P. strigosa^ 2.8 1.3 0.877 0.119 0.065 0.048 0.071 0.403

M. annularis SC 0.6 0.4 0.384 0.073 0.046 0.063 0.061 0.163

West

Coral 58.5 13.0 1.000 0.524 0.110 0.135 0.598 1.000

Macroalgae 28.1 9.6 0.998 0.194 0.060 0.066 0.237 0.801
109

Location or Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Substrate** 1.0 0.2 1.000 0.643 0.128 0.163 0.741 1.000

P. strigosa^ 3.0 1.3 0.986 0.125 0.058 0.062 0.101 0.545

M. annularis SC** 0.5 0.4 0.349 0.061 0.054 0.051 0.054 0.153

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 0.996 0.687 0.384 0.988 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.890 0.203 0.202 0.749 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.748 0.147 0.132 0.600 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.540 0.136 0.094 0.378 0.999

M. annularis SC** 0.6/0.5 0.4/0.4 0.987 0.194 0.068 0.072 0.161 0.794

*one outlier removed for normality +Presence/Absence Data

**log transformed for normality ^square root transformed for normality

Values in red are above the desired power level of 0.80

Table 3.5 Benthic Cover Transects Biennial Sampling Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover transect data with the 

alternative design of biennial sampling.

Location or 

Category

Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

East

Coral* 55.3 13.2 ±15 ±8 ±3 29 39

Macroalgae** 1.5 0.2 ±10 ±5 ±3 21 23

Substrate 11.3 4.6 ±23 ±13 ±5 88 110

P. strigosa^ 2.8 1.3 ±29 ±16 ±6 120 159

M. annularis SC 0.6 0.4 -50/+42 -30/+22 -11/+9 264 326

West

Coral 58.5 13.0 ±13 ±7 ±3 23 29

Macroalgae 28.1 9.6 ±21 ±12 ±4 50 68

Substrate** 1.0 0.2 ±12 ±7 ±3 20 26

P. strigosa^ 3.0 1.3 ±26 ±14 ±6 92 124

M. annularis SC** 0.5 0.4 -65/+42 -37/+23 -13/+9 320 386

East compared with West

Coral 55.3/58.5 13.2/13.0 ±7 ±4 ±2 16 18

Macroalgae 33.1/28.1 12.3/9.6 ±11 ±6 ±3 34 44

Substrate 11.3/12.1 4.6/5.0 ±12 ±7 ±3 42 56

P. strigosa^ 2.8/3.0 1.3/1.3 ±13 ±8 ±3 53 70

M. annularis SC** 0.6/0.5 0.4/0.4 -22/+20 ±12 ±5 142 185

*one outlier removed for normality +Presence/Absence Data

**log transformed for normality ^square root transformed for normality
110

Table 3.6 Benthic Cover Transects Triennial Sampling Power Results

Power analysis results for benthic cover transect data with the alternative design of triennial sampling.

Location or Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Four Years

East

Coral* 55.3 13.2 0.985 0.444 0.155 0.137 0.312 0.995

Macroalgae** 1.5 0.2 1.000 0.766 0.490 0.397 0.798 1.000

Substrate 11.3 4.6 0.963 0.140 0.071 0.074 0.139 0.742

P. strigosa^ 2.8 1.3 0.869 0.175 0.094 0.092 0.131 0.555

M. annularis SC 0.6 0.4 0.491 0.171 0.149 0.144 0.151 0.291

West

Coral 58.5 13.0 1.000 0.542 0.171 0.216 0.631 1.000

Macroalgae 28.1 9.6 0.992 0.278 0.095 0.098 0.293 0.883

Substrate** 1.0 0.2 1.000 0.714 0.202 0.258 0.721 1.000

P. strigosa^ 3.0 1.3 0.952 0.224 0.103 0.111 0.185 0.670

M. annularis SC** 0.5 0.4 0.412 0.188 0.148 0.133 0.166 0.264

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 1.000 0.604 0.618 0.995 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.873 0.250 0.237 0.729 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.735 0.232 0.167 0.659 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.658 0.181 0.161 0.577 1.000

M. annularis SC** 0.6/0.5 0.4/0.4 0.967 0.264 0.14 0.116 0.254 0.861

After Ten Years

East

Coral* 55.3 13.2 0.982 0.410 0.127 0.120 0.500 0.998

Macroalgae** 1.5 0.2 0.993 0.727 0.298 0.357 0.654 0.987

Substrate 11.3 4.6 0.931 0.143 0.071 0.065 0.135 0.648

P. strigosa^ 2.8 1.3 0.855 0.087 0.055 0.052 0.085 0.419

M. annularis SC 0.6 0.4 0.389 0.092 0.048 0.066 0.064 0.187

West

Coral 58.5 13.0 1.000 0.574 0.145 0.198 0.509 1.000

Macroalgae 28.1 9.6 0.995 0.239 0.070 0.085 0.138 0.757

Substrate** 1.0 0.2 1.000 0.607 0.166 0.185 0.443 0.997

P. strigosa^ 3.0 1.3 0.939 0.136 0.070 0.068 0.114 0.576

M. annularis SC** 0.5 0.4 0.299 0.062 0.063 0.050 0.067 0.166

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 0.998 0.544 0.540 0.979 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.747 0.250 0.250 0.607 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.669 0.124 0.123 0.497 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.502 0.138 0.129 0.420 0.988

M. annularis SC** 0.6/0.5 0.4/0.4 0.98 0.173 0.069 0.058 0.161 0.742

Values in red are above the desired power level of 0.80
111

Table 3.7 Benthic Cover Transects Triennial Sampling Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover transect data with the 

alternative design of Triennial sampling.

Location or 

Category

Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 4 yrs 10 yrs -20% 20%

East

Coral* 55.3 13.2 ±10 ±4 28 32

Macroalgae** 1.5 0.2 ±5 ±2 17 19

Substrate 11.3 4.6 ±16 ±6 72 87

P. strigosa^ 2.8 1.3 ±20 ±8 99 132

M. annularis SC 0.6 0.4 -34/+27 -14/+11 228 259

West

Coral 58.5 13.0 ±9 ±4 23 30

Macroalgae 28.1 9.6 ±14 ±5 53 68

Substrate** 1.0 0.2 ±8 ±3 20 25

P. strigosa^ 3.0 1.3 ±17 ±7 81 98

M. annularis SC** 0.5 0.4 -44/+29 -17/+11 303 254

East compared with West

Coral 55.3/58.5 13.2/13.0 ±5 ±2 14 16

Macroalgae 33.1/28.1 12.3/9.6 ±7 ±3 28 36

Substrate 11.3/12.1 4.6/5.0 ±8 ±3 36 48

P. strigosa^ 2.8/3.0 1.3/1.3 ±9 ±4 44 58

M. annularis SC** 0.6/0.5 0.4/0.4 ±14 ±6 120 146

*one outlier removed for normality +Presence/Absence Data

**log transformed for normality ^square root transformed for normality

Sampling Twice a Year

Biannual sampling results showed some decline in the number analyses that exceeded the power

threshold after two years (Table 3.8). After 10 years, however, there were overall improvements 

in detection above the 0.8 limit compared to other sampling designs. At EFGB coral analyses 

were above the detection limit for a net change of 50% across all timeframes. The minimum 

detectable annual trend for coral was 30% after two years, 7% after five years, and 3% after 10 

years (Table 3.9). The minimum number of transects needed to detect a net 20% change in coral 

after five years was 24 for a decrease and 33 for an increase. P. strigosa exceeded 0.8 for a 

decrease of 50% after five and 10 years, while M. annularis SC did not exceed 0.8 for any 

analyses. Macroalgae analyses were above the detection threshold for 50% change in all 

timeframes, and also exceeded detection limits for a decrease of 20% after five years, and an 

increase and decrease of 20% after 10 years. The minimum detectable annual trend for 

macroalgae was 24% after two years, 6% after five years, and 2% after 10 years. The minimum 

number of transects needed was 16 to detect a 20% increase in five years, and 18 for a 20% 

decrease. Substrate was below the detection limit for all analyses after two years, and above for a 

decrease of 50% after five years. After 10 years, both a 50% increase and decrease in substrate 

cover were above desired detection limits. The minimum detectable annual trend for macroalgae 

at EFGB was 52% after two years, 12% after five years, and 5% after 10 years. A minimum of 

71 transects would be needed to detect a decrease of 20% after five years, while 95 would be 

needed to detect a 20% increase in the same timeframe. 
112

At WFGB coral, macroalgae and substrate followed the same pattern after two and five years 

(Table 3.8). Analyses for all three benthic categories were above the detection threshold for a 

net change of 50% in these timeframes. Coral analyses were also above the detection threshold 

for a decrease of 20% after 10 years. The minimum annual detectable trend for coral was 28% 

after two years, 7% after five years, and 3% after 10 years. The minimum number of transects 

needed to detect a 20% change in coral cover after five years was 21 for a decrease and 28 for an 

increase (Table 3.9). P. strigosa exceeded 0.8 for a decrease of 50% after five years, and both an 

increase and decrease of 50% after 10 years. The M. annularis SC analyses did not exceed 0.8 in 

any timeframe. Macroalgae results remained the same for all timeframes, with detection above 

the designated threshold only for net changes of 50%. The minimum annual detectable trend for 

macroalgae was 44% and 46% for an increase and decrease respectively after two years, 10% 

after five years, and 4% after 10 years. The minimum number of transects necessary were 44 to 

detect a 20% decrease in five years, and 64 to detect a 20% increase in the same timeframe. 

Substrate analyses were above the detection threshold for both an increase and decrease of 20% 

after 10 years. The minimum annual detectable trend for substrate was 26% in two years, 6% in 

five years, and 2% in 10 years. Seventeen transects would be needed to observe a 20% decrease 

in substrate cover over five years, and 23 to observe a 20% increase. 

In the between bank comparative analyses, power of analyses was higher than for individual 

banks for some benthic categories, and lower or the same for others (Table 3.8). Coral 

comparison analyses were above the detection threshold for a net change of 20% in all 

timeframes, and for a net change of 10% after 10 years. The minimum detectable annual trend 

for coral comparison analyses in a biannual sampling design was 15% in two years, 3% in five

years, and 2% in 10 years. The minimum sample size for coral was 12 to detect a 20% increase 

in five years, and 16 to detect a 20% decrease (Table 3.9). Both P. strigosa and M. annularis SC 

were above 0.8 for net changes of 50% in all timeframes, and P. strigosa also exceeded 0.8 for a 

decrease of 20% after 10 years. Macroalgae comparisons were above the detection threshold for 

a net change for 50% for all timeframes, a decrease of 20% after five years, and an increase and 

decrease of 20% after 10 years. The minimum detectable annual trends for macroalgae 

comparisons were 22% after two years, 6% after five years, and 2% after 10 years. Comparisons 

of macroalgae would require 28 transects to detect a 20% decrease after five years, and 38 to 

detect a 20% increase. Comparison analyses of substrate were above the detection limit for a 

50% increase or decrease after two and 5 years, and included detection of a 20% decrease after 

10 years. The minimum annual yearly trend for substrate comparisons was 25% after two years, 

6% after 5 years, and 3% after 10 years. To detect a change of 20% after five years, 36 transects 

would be needed for a decrease in substrate and 48 for an increase.
113

Table 3.8 Benthic Cover Transects Biannual Sampling Power Results

Power analysis results for benthic cover transect data with the alternative design of biannual sampling.

Location or 

Category

Pilot Data Power for Percent Net Changes

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Coral* 55.3 13.2 0.959 0.372 0.117 0.122 0.393 1.000

Macroalgae** 1.5 0.2 0.969 0.701 0.263 0.430 0.674 1.000

Substrate 11.3 4.6 0.786 0.128 0.061 0.060 0.158 0.768

P. strigosa^ 2.8 1.3 0.558 0.101 0.052 0.048 0.093 0.588

M. annularis SC 0.6 0.4 0.246 0.065 0.042 0.044 0.071 0.279

West

Coral 58.5 13.0 0.992 0.456 0.121 0.145 0.572 1.000

Macroalgae 28.1 9.6 0.900 0.192 0.065 0.073 0.187 0.890

Substrate** 1.0 0.2 1.000 0.567 0.144 0.175 0.577 1.000

P. strigosa^ 3.0 1.3 0.696 0.123 0.085 0.067 0.132 0.720

M. annularis SC** 0.5 0.4 0.196 0.053 0.045 0.033 0.054 0.263

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 0.984 0.456 0.479 0.991 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.710 0.185 0.194 0.733 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.557 0.161 0.146 0.607 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.432 0.132 0.122 0.466 0.999

M. annularis SC** 0.6/0.5 0.4/0.4 0.846 0.149 0.083 0.071 0.195 0.906

After Five Year

East

Coral* 55.3 13.2 1.000 0.438 0.096 0.107 0.484 1.000

Sponge+ 0.3 N/A 0.074 0.049 0.043 0.045 0.040 0.055

Macroalgae** 1.5 0.2 0.991 0.878 0.383 0.257 0.722 1.000

Substrate 11.3 4.6 0.991 0.169 0.062 0.059 0.181 0.743

P. strigosa^ 2.8 1.3 0.917 0.110 0.047 0.044 0.074 0.527

M. annularis SC 0.6 0.4 0.437 0.069 0.061 0.061 0.067 0.239

West

Coral 58.5 13.0 1.000 0.634 0.111 0.113 0.737 1.000

Macroalgae 28.1 9.6 1.000 0.220 0.080 0.083 0.160 0.894

Substrate** 1.0 0.2 1.000 0.756 0.148 0.181 0.598 1.000

P. strigosa^ 3.0 1.3 0.975 0.129 0.056 0.072 0.114 0.691

M. annularis SC** 0.5 0.4 0.374 0.070 0.063 0.059 0.056 0.191

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 1.000 0.674 0.534 0.997 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.876 0.233 0.210 0.742 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.770 0.187 0.137 0.647 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.659 0.139 0.090 0.456 1.000

M. annularis SC** 0.6/0.5 0.4/0.4 0.995 0.217 0.076 0.071 0.143 0.855

After Ten Years

East

Coral* 55.3 13.2 1.000 0.665 0.124 0.178 0.700 1.000

Sponge+ 0.3 N/A 0.072 0.054 0.044 0.041 0.049 0.059

Macroalgae** 1.5 0.2 1.000 0.971 0.456 0.261 0.980 1.000

Substrate 11.3 4.6 0.970 0.177 0.079 0.059 0.206 0.965

P. strigosa^ 2.8 1.3 0.996 0.106 0.048 0.064 0.097 0.709

M. annularis SC 0.6 0.4 0.682 0.064 0.059 0.054 0.063 0.307

West

Coral 58.5 13.0 1.000 0.823 0.185 0.255 0.762 1.000
114

Location or 

Category

Pilot Data Power for Percent Net Changes

Mean SD -50% -20% -10% 10% 20% 50%

Macroalgae 28.1 9.6 1.000 0.340 0.064 0.076 0.249 0.994

Substrate** 1.0 0.2 1.000 0.917 0.227 0.233 0.810 1.000

P. strigosa^ 3.0 1.3 0.999 0.180 0.058 0.063 0.125 0.868

M. annularis SC** 0.5 0.4 0.559 0.064 0.046 0.034 0.056 0.255

East compared with West

Coral 55.3/58.5 13.2/13.0 1.000 1.000 0.811 0.846 1.000 1.000

Macroalgae 33.1/28.1 12.3/9.6 1.000 0.997 0.412 0.405 0.885 1.000

Substrate 11.3/12.1 4.6/5.0 1.000 0.909 0.312 0.147 0.761 1.000

P. strigosa^ 2.8/3.0 1.3/1.3 1.000 0.815 0.123 0.148 0.654 1.000

M. annularis SC** 0.6/0.5 0.4/0.4 1.000 0.261 0.068 0.089 0.198 0.978

*one outlier removed for normality +Presence/Absence Data

**log transformed for normality ^square root transformed for normality

Values in red are above the desired power level of 0.80
115

Table 3.9 Benthic Cover Transects Biannual Sampling Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover transect data with the 

alternative design of biannual sampling.

Location or Category Pilot Data Min Annual Trend (%)

Min Sample 

Size for

20% Net 

Change after 

5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Coral* 55.3 13.2 ±30 ±7 ±3 24 33

Sponge+ 0.3 N/A - >100 >100 >1000 >1000

Macroalgae** 1.5 0.2 ±24 ±6 ±2 16 18

Substrate 11.3 4.6 ±52 ±12 ±5 71 95

P. strigosa^ 2.8 1.3 -65/+63 ±15 ±6 - -

M. annularis SC 0.6 0.4 >±100 -25/+20 ±9 - -

West

Coral 58.5 13.0 ±28 ±7 ±3 21 28

Macroalgae 28.1 9.6 -46/+44 ±10 ±4 44 64

Substrate** 1.0 0.2 ±26 ±6 ±2 17 23

P. strigosa^ 3.0 1.3 -56/+54 ±13 ±5 73 101

M. annularis SC** 0.5 0.4 >±100 -29/+21 -10/+8 - -

East compared with West

Coral 55.3/58.5 13.2/13.0 ±15 ±3 ±2 12 16

Macroalgae 33.1/28.1 12.3/9.6 ±22 ±6 ±2 28 38

Substrate 11.3/12.1 4.6/5.0 ±25 ±6 ±3 36 48

P. strigosa^ 2.8/3.0 1.3/1.3 ±28 ±7 ±3 44 58

M. annularis SC** 0.6/0.5 0.4/0.4 -48/+43 ±11 ±4 114 140

*one outlier removed for normality

**log transformed for normality
116

Random Transects Summary

As shown in Table 3.10, there is very little difference between sampling annually or biennially to 

detect changes in the benthic categories coral, macroalgae or substrate. The trends become less 

consistent for sampling every third year, with increases in the minimum detectable annual trend, 

particularly after five years, but becoming very similar to the other sample designs after 10 years. 

Trends are less clear in the minimum number of transects needed to detect a change of 20% after 

five years. Within each benthic category trends differ both at individual banks and in the between 

bank comparison. There is also not a consistent trend among benthic categories at the same bank 

or in the between bank comparison. For coral analyses, the required number of transects varies 

by five or fewer, regardless of sampling frequency. Macroalgae and Substrate have a larger 

magnitude of difference between annual sampling and alternate designs, the largest difference 

being 33 for macroalgae at WFGB between annual and every third year sampling designs.

Patterns among sampling designs are similar for the individual coral analyses, though overall 

power is lower for both than for the overall coral category. Mixed model analyses show some 

increase in minimum detectable trends. 

Table 3.10 Comparison of Power Analyses for Benthic Cover Transect Data

Comparisons of the minimum detectable annual trends and minimum number of transects necessary to 

detect 20% net changes in percent cover after about 5 years for each sampling design. 5 year data are 

used for annual, biennial and biannual sampling and 4 year data are used for triennial sampling.

Sampling Strategy Min Annual Trend (%) Min # Transects

~5 Yrs ~10 Yrs -20% +20%

Coral Cover

East

Annual ±8 ±3 28 38

Biennial ±8 ±3 29 39

Triennial ±10 ±4 28 32

Biannual ±7 ±3 24 33

West

Annual ±7 ±3 24 32

Biennial ±7 ±3 23 29

Triennial ±9 ±4 23 30

Biannual ±7 ±3 21 28

East compared with West

Annual ±4 ±2 14 19

Biennial ±4 ±2 16 18

Triennial ±5 ±2 14 16

Biannual ±3 ±2 12 16

Macroalgae Cover

East

Annual ±5 ±2 10 12

Biennial ±5 ±2 21 23

Triennial ±5 ±2 17 19

Biannual ±6 ±2 16 18

West

Annual ±7 ±3 20 27

Biennial ±12 ±4 50 68

Triennial ±14 ±5 53 68
117

Sampling Strategy Min Annual Trend (%) Min # Transects

~5 Yrs ~10 Yrs -20% +20%

Biannual ±10 ±4 44 64

East compared with West

Annual ±6 ±3 34 44

Biennial ±6 ±3 34 44

Triennial ±7 ±3 28 36

Biannual ±6 ±2 28 38

Substrate Cover

East

Annual ±13 ±5 80 110

Biennial ±13 ±5 88 110

Triennial ±16 ±6 72 87

Biannual ±12 ±5 71 95

West

Annual ±7 ±3 20 27

Biennial ±7 ±3 20 26

Triennial ±8 ±3 20 25

Biannual ±6 ±2 17 23

East compared with West

Annual ±4 ±2 42 56

Biennial ±7 ±3 42 56

Triennial ±8 ±3 36 48

Biannual ±6 ±3 36 48

3.2.1.2 Permanent Quadrats

Current Sampling Design Power Evaluation

The benthic categories sponge, encrusting sponge and sand were treated as presence and absence 

data to alleviate quantitative issues as discussed previously in this section. Presence and absence 

analyses for permanent quadrat data were performed for the benthic category that had the highest 

mean percent cover on each bank and for between bank comparisons. These were sponges for 

EFGB and sand for WFGB and EFGB compared with WFGB. Results showed very low power 

and minimum annual detectable trend of 100% or greater, or could not be determined (Tables

3.11 and 3.12). The minimum sample size needed to detect a 20% change in five years was 

greater than 1000. Because of these results, analyses were not run on the remaining presence or 

absence data, which had lower means and would have yielded similar, non-determinative results. 

Similarly, quadrat data for P. strigosa and M. annularis SC had standard deviations that 

exceeded the means, except for P. strigosa as WFGB, resulting in very low power. Further 

discussion of results will address only the remaining categories of coral, macroalgae, substrate, 

as well as P. strigosa WFGB, and between bank comparisons for both individual corals.

The results for both EFGB and WFGB were similar with the current sampling strategy of 40 

permanent quadrats per bank, revisited once a year (BOEM 2014). Most of the analyses met or 

exceeded the desired power level of 0.8 for a net change of 50% after two, five and 10 years 

(Table 3.11). Approximately half of analyses also exceeded the detection threshold for a net 

change of 20% across all time frames, while none did so for a change of 10%. 
118

At EFGB coral analyses were above desired detection limits for a net 20% change except for an 

increase of 20% after five years, which dropped just below the threshold (power 0.766) (Table 

3.11). The minimum detectable annual trend for coral was 18% per year after two years, 5% after 

five years, and 2% after 10 years (Table 3.12). A sample size reduction to 32 quadrats would be 

sufficient to detect a change of 20% after five years, while 42 would be required to detect a 20% 

increase. Macroalgae was above 0.8 power for only a 50% net change in all timeframes 

analyzed. The minimum detectable annual trend for macroalgae was 23% after two years, 7% 

after five years, and 3% after 10 years. The minimum number of transects required would be 54 

and 72 to detect a 20% increase and decrease respectively in five years. Substrate analyses were 

above desired detection limits for a net 20% change except for an increase of 20% after five

years, which dropped just below the threshold (power 0.734). A minimum change of 19% could 

be detected after two years, 5% after five years and 2% after 10 years. To detect a net change of 

20% after five years at the desired power level, 33 quadrats would be needed for a decrease in 

substrate, and 48 would be needed for an increase.

Coral and macroalgae analysis results at WFGB were very similar to those for EFGB. The 

pattern of analyses that exceeded the detection threshold in all timeframes was identical at both 

banks, and the minimum detectable annual trends were also very similar after five and 10 years 

(Tables 3.11 and 3.12). The minimum number of transects needed to detect a 20% change in 

five years was similar for coral between the two banks, but to detect changes in macroalgae at 

WFGB, almost twice as many transects would be needed as compared to EFGB, with 94 to 

detect a decrease, and 124 to detect an increase. P. strigosa exceeded 0.8 power for a decrease of 

50% in all timeframes, as well as an increase of 50% after two years. Analyses for substrate were 

the most dissimilar between the two banks. Those at WFGB did not exceed the detection limit 

for an increase of 20% in all timeframes. The minimum number of transects needed at WFGB 

were 38 to detect a 20% decrease in five years, and 50 to detect a 20% increase.

Power results for comparisons between EFGB and WFGB were mixed, with most comparisons 

having higher power than analyses for individual banks, and others having similar power (Tables 

3.11 and 3.12). Coral analyses were above detectable limits at 20% net change for all time 

frames analyzed. The minimum annual trends above the power threshold for between bank 

comparisons of coral cover were 12% in two years, 4% in five years, and 3% in 10 years. The 

minimum number of transects necessary to detect a 20% change in five years at the desired 

power was 14 to detect a decrease and 18 to detect an increase. P. strigosa analyses exceeded 0.8 

for changes of 50% in all timeframes, while M. annularis SC analyses exceeded 0.8 for an 

increase of 50% after two years, and a decrease of 50% after five and 10 years. Changes in 

macroalgae cover between banks were above the detection limit of 0.8 for a change of 20% after 

two years. After five years, a decrease of 20% and increase of 50% were above the threshold; 

and after 10 years changes of 20% were above the power level. Minimum annual detectable

trends for macroalgae were 20% after two years, 6% after five years, and 3% after 10 years. The 

minimum number of transects needed to detect a 20% change in five years was 36 for a 20% 

decrease, and 48 for an increase. Substrate was above the detection threshold for a change of 

10% in all timeframes except for an increase of 10% after five years. The minimum detectable 

annual trend for substrate was 10% after two years, 3% after five years, and 2% after 10 years. 

The minimum number of transects needed was 18 to detect a 20% decrease after five years, and 

24 to detect a 20% increase in the same time frame.
119

Table 3.11 Benthic Cover Quadrats Current Sample Design Power Results

Power analysis results for benthic cover quadrat data with the current design of annual sampling.

Location &

Category

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Coral 67.1 16.9 1.000 0.899 0.305 0.290 0.890 1.000

Sponge+ 0.3 N/A 0.151 0.143 0.164 0.143 0.120 0.200

Macroalgae* 4.4 1.5 1.000 0.631 0.191 0.160 0.664 1.000

Substrate* 3.3 0.9 1.000 0.853 0.297 0.280 0.873 1.000

P. strigosa 10.7 11.1 - - - - - -

M. annularis SC 6.6 9.4 - - - - - -

West

Coral 68.3 14.7 1.000 0.977 0.431 0.451 0.961 1.000

Macroalgae* 3.6 1.6 0.999 0.422 0.130 0.121 0.410 0.998

Substrate* 3.6 1.0 1.000 0.838 0.274 0.268 0.789 1.000

Sand+ 1.7 N/A 0.306 0.150 0.149 0.144 0.150 0.326

P. strigosa** 0.8 0.5 0.899 0.232 0.122 0.127 0.204 0.890

M. annularis SC 8.1 12.5 - - - - - -

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 0.995 0.662 0.675 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.851 0.271 0.296 0.842 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.874 0.862 1.000 1.000

Sand+ 0.2/1.7 0.8/7.2 0.053 0.047 0.039 0.045 0.053 0.038

P. strigosa 10.7/8.9 11.1/9.4 0.980 0.326 0.100 0.110 0.314 0.979

M. annularis SC 6.6/8.1 9.4/12.5 0.758 0.177 0.065 0.078 0.183 0.804

After Five Years 

East

Coral 67.1 16.9 1.000 0.904 0.236 0.188 0.766 1.000

Sponge+ 0.3 N/A 0.103 0.039 0.069 0.043 0.057 0.083

Macroalgae* 4.4 1.5 1.000 0.633 0.115 0.141 0.575 1.000

Substrate* 3.3 0.9 1.000 0.833 0.234 0.180 0.734 1.000

West

Coral 68.3 14.7 1.000 0.920 0.317 0.313 0.770 1.000

Macroalgae* 3.6 1.6 1.000 0.383 0.099 0.073 0.325 0.967

Substrate* 3.6 1.0 1.000 0.849 0.179 0.175 0.773 1.000

Sand+ 1.7 N/A 0.390 0.074 0.042 0.042 0.059 0.161

P. strigosa** 0.8 0.5 0.986 0.183 0.070 0.068 0.135 0.645

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.563 0.557 0.996 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.868 0.188 0.194 0.789 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.873 0.711 1.000 1.000

Sand+ 0.2/1.7 0.8/7.2 0.030 0.030 0.037 0.035 0.024 0.027

P. strigosa 10.7/8.9 11.1/9.4 0.998 0.246 0.064 0.083 0.160 0.849

M. annularis SC 6.6/8.1 9.4/12.5 0.919 0.121 0.059 0.053 0.101 0.515

After Ten Years

East

Coral 67.1 16.9 1.000 0.984 0.343 0.195 0.830 1.000

Sponge+ 0.3 N/A 0.106 0.049 0.052 0.041 0.058 0.079

Macroalgae* 4.4 1.5 1.000 0.791 0.174 0.101 0.515 1.000

Substrate 3.3 0.9 1.000 0.956 0.322 0.314 0.832 1.000
120

Location &

Category

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

West

Coral 68.3 14.7 1.000 0.999 0.480 0.485 0.950 1.000

Macroalgae* 3.6 1.6 1.000 0.484 0.106 0.104 0.290 0.989

Substrate* 3.6 1.0 1.000 0.987 0.271 0.266 0.744 1.000

Sand+ 1.7 N/A 0.527 0.069 0.046 0.053 0.071 0.194

P. strigosa** 0.8 0.5 0.998 0.167 0.069 0.067 0.138 0.780

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.772 0.782 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.937 0.263 0.257 0.836 1.000

Substrate 3.3/3.6 0.9/1.0 1.000 1.000 0.967 0.839 1.000 1.000

Sand+ 0.2/1.7 0.8/7.2 0.010 0.009 0.013 0.018 0.019 0.018

P. strigosa 10.7/8.9 11.1/9.4 1.000 0.281 0.067 0.057 0.204 0.899

M. annularis SC 6.6/8.1 9.4/12.5 0.971 0.120 0.066 0.047 0.109 0.615

*square root transformed for normality ** log transformed for normality

+presence or absence data

Values in red are above the desired power level of 0.80

Table 3.12 . Benthic Cover Quadrats Current Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover quadrat data with the current 

design of annual sampling.

Location &

Category

Pilot Data Min Annual Trend (%) Min Sample Size for 20% 

Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Coral 67.1 16.9 ±18 ±5 ±2 32 42

Sponge+ 0.3 N/A ±>100 -68/+32 -26/+13 325 440

Macroalgae* 4.4 1.5 ±23 ±7 ±3 54 72

Substrate* 3.3 0.9 ±19 ±5 ±2 33 48

West

Coral 68.3 14.7 ±15 ±5 ±2 32 44

Macroalgae* 3.6 1.6 ±30 ±8 ±4 94 124

Substrate* 3.6 1.0 ±19 ±6 ±3 38 50

Sand+ 1.7 N/A ±92 -26/+31 ±12 735 1000

P. strigosa** 0.8 0.5 ±45 ±13 ±5 184 251

East compared with West

Coral 67.1/68.3 16.9/14.7 ±12 ±4 ±2 14 18

Macroalgae* 4.4/3.6 1.5/1.6 ±20 ±6 ±3 36 48

Substrate* 3.3/3.6 0.9/1.0 ±10 ±3 ±2 18 24

Sand+ 0.2/1.7 0.8/7.2 ±>100 N/A N/A >1000 >1000

P. strigosa 10.7/8.9 11.1/9.4 ±36 ±11 ±4 260 360

M. annularis SC 6.6/8.1 9.4/12.5 -51/+50 ±15 ±6 520 725

*square root transformed for normality ** log transformed for normality

+presence or absence data

Values in red are above the desired power level of 0.80
121

Alternative Sampling Design Power Evaluation

Sampling Every Other Year

Changing to a biennial sampling design yielded mixed results. EFGB analyses showed both 

reduced and increased power levels; WFGB and between-bank comparisons had increased power 

for some analyses. The minimum detectable trends and minimum sample sizes necessary to 

detect a 20% change in five years at the desired power level for both banks and between bank 

comparisons were very similar to results for an annual design and so will not be further discussed 

here. 

At EFGB coral analyses were above the desired power level for both an increase and decrease of 

20% after three years, and a decrease of 20% after five and 11 years (Table 3.13). These results 

show reduced power after 11 years when compared to the annual design. Macroalgae analyses 

were the same as the annual design, with only 50% changes above the desired power level. And 

substrate analysis results were above the desired power level for decreases of 20% in all 

timeframes, and an increase of 20% after two years, showing similar power to an annual design.

At WFGB coral analysis results were above the desired power level for changes of 20% in all 

timeframes, including an increase after five years, an increase in power from the annual design 

(Table 3.13). P. strigosa analyses exceeded 0.8 for a decrease of 50% in every timeframe as well 

as an increase of 50% after three years. Macroalgae analyses were above the desired power level 

for changes of 50% across all timeframes, similar to the annual design. And substrate analyses 

were above the desired power level for changes of 50% in all timeframes and a decrease of 20% 

after five and 11 years, an improvement in power from the annual design.

Between-bank comparisons also showed a general improvement in power with coral analyses 

being above the desired power level for changes of 10% in all timeframes (Table 3.13). Analyses 

for P. strigosa exceeded 0.8 for a net change of 50% in all timeframes, and M. annularis SC 

analyses exceeded 0.8 for a decrease of 50% in all timeframes. Macroalgae analyses were above 

the desired power level for changes of 20% in all timeframes, including an increase of 20% after 

five years, an improvement over the annual design. Substrate analyses were above desired power 

levels for decreases of 10% in all timeframes, but only an increase of 10% after three years, a 

reduction in power over the annual design.

Sampling Every Third Year

Changing the sampling design to a frequency of every third year generally increased the number 

of analyses that were above the desired power level when compared to an annual design, with the 

exception being analyses at EFGB after 10 years. The minimum annual trends detectable above 

the desired power level were marginally higher than those for an annual design, but the minimum 

sample sizes required to detect a 20% change in four years at the desired power are smaller than 

for an annual design.
122

Table 3.13 Benthic Cover Quadrats Biennial Sampling Power Results

Power analysis results for benthic cover quadrat data with the alternative design of biennial sampling.

Location &

Category

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

Coral 67.1 16.9 1.000 0.955 0.297 0.331 0.906 1.000

Macroalgae* 4.4 1.5 1.000 0.729 0.212 0.205 0.648 1.000

Substrate* 3.3 0.9 1.000 0.907 0.290 0.323 0.847 1.000

West

Coral 68.3 14.7 1.000 0.986 0.596 0.435 0.925 1.000

Macroalgae* 3.6 1.6 1.000 0.493 0.114 0.128 0.401 0.989

Substrate* 3.6 1.0 1.000 0.912 0.277 0.263 0.819 1.000

P. strigosa** 0.8 0.5 0.957 0.260 0.138 0.108 0.214 0.833

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.940 0.945 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.999 0.517 0.537 0.986 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.895 0.888 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 0.995 0.364 0.115 0.104 0.327 0.936

M. annularis SC 6.6/8.1 9.4/12.5 0.877 0.201 0.057 0.079 0.141 0.660

After Fifth Year

East

Coral 67.1 16.9 1.000 0.887 0.286 0.291 0.739 1.000

Macroalgae* 4.4 1.5 1.000 0.640 0.174 0.107 0.477 1.000

Substrate* 3.3 0.9 1.000 0.852 0.271 0.234 0.742 1.000

West

Coral 68.3 14.7 1.000 0.970 0.412 0.252 0.873 1.000

Macroalgae* 3.6 1.6 1.000 0.396 0.127 0.082 0.250 0.956

Substrate* 3.6 1.0 1.000 0.836 0.226 0.184 0.651 1.000

P. strigosa** 0.8 0.5 0.928 0.223 0.137 0.123 0.208 0.771

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.943 0.842 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.992 0.489 0.352 0.947 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.855 0.783 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 0.995 0.247 0.088 0.075 0.185 0.851

M. annularis SC 6.6/8.1 9.4/12.5 0.874 0.131 0.071 0.059 0.118 0.497

After Eleventh Year

East

Coral 67.1 16.9 1.000 0.894 0.312 0.204 0.758 1.000

Macroalgae* 4.4 1.5 1.000 0.626 0.128 0.142 0.422 1.000

Substrate* 3.3 0.9 1.000 0.888 0.267 0.192 0.710 1.000

West

Coral 68.3 14.7 1.000 0.973 0.368 0.362 0.922 1.000

Macroalgae* 3.6 1.6 1.000 0.339 0.087 0.077 0.247 0.960

Substrate* 3.6 1.0 1.000 0.833 0.201 0.194 0.676 1.000

P. strigosa** 0.8 0.5 0.988 0.171 0.064 0.068 0.118 0.671

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.930 0.928 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.997 0.449 0.485 0.974 1.000

Substrate* 3.2/3.6 0.9/1.0 1.000 1.000 0.802 0.785 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 0.999 0.249 0.080 0.064 0.168 0.875

M. annularis SC 6.6/8.1 9.4/12.5 0.954 0.148 0.061 0.045 0.084 0.481

*square root transformed ** log transformed Values in red are above the desired power level of 0.80
123

Table 3.14 Benthic Cover Quadrats Biennial Sampling Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover quadrat data with the 

alternative design of biennial sampling.

Location &

Category

Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 5 yrs

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

East

Coral 67.1 16.9 ±9 ±5 ±3 33 44

Macroalgae* 4.4 1.5 ±12 ±7 ±3 56 75

Substrate* 3.3 0.9 ±10 ±6 ±2 35 49

West

Coral 68.3 14.7 ±8 ±5 ±2 23 31

Macroalgae* 3.6 1.6 ±16 ±9 ±4 95 125

Substrate* 3.6 1.0 ±10 ±6 ±3 38 51

P. strigosa** 0.8 0.5 ±23 -14/+12 ±5 191 246

East compared with West

Coral 67.1/68.3 16.9/14.7 ±5 ±3 ±2 14 19

Macroalgae* 4.4/3.6 1.5/1.6 ±7 ±4 ±2 37 48

Substrate* 3.3/3.6 0.9/1.0 ±6 ±3 ±2 20 26

P. strigosa 10.7/8.9 11.1/9.4 ±19 ±11 ±4 264 364

M. annularis SC 6.6/8.1 9.4/12.5 ±26 ±15 ±6 544 733

*square root transformed for normality ** log transformed for normality

At EFGB coral analyses were above the desired power level for a net change of 20% after four 

years, and a decrease of 20% and increase of 50% after 10 years (Table 3.15). The minimum 

annual trends above the desired power level were 6% after four years and 3% after 10 years 

(Table 3.16). Minimum sample sizes to achieve detection above the desired power level were 27 

quadrats for a decrease of 20% after four years and 35 for an increase of 20%. Macroalgae 

analyses yielded the same results as in the annual and biennial sampling designs, with changes of 

50% above the desired power level. The minimum annual trends were 6% after four years and 

4% after 10 years. Minimum sample sizes to detect a change of 20% after four years with a 

power of 0.8 were 46 for a 20% decrease, and 63 for a 20% increase. Substrate analyses were 

above the desired power level for changes of 20% after four years and only a decrease of 20% 

after 10 years. The minimum annual trends above the desired power level were 7% after four 

years and 3% after 10 years. The minimum sample sizes to detect an increase or decrease 20% in 

four years were 30 and 39, respectively.

Coral analyses at WFGB were above the desired power level for changes of 20% after four and 

10 years (Table 3.15). Minimum annual trends were 6% after four years and 2% after 10 years. 

The minimum sample size was 21 to detect a 20% decrease after four years at the desired power 

level and 25 for a 20% increase (Table 3.16). Analyses of P. strigosa exceeded 0.8 only for 

decreases of 50% in all timeframes. Macroalgae analyses followed the same pattern as in other 

sampling designs of 50% changes being above the desired power level. Minimum annual trends 

above the desired power level were 11% after four years and 4% after 10 years. Minimum 

sample sizes were 82 for a 20% decrease, and 102 for a 20% increase. Substrate analyses were 

above desired power levels for a decrease of 20% and increase of 50% after both four and 10 

years. Minimum annual trends were 6% after four years and 3% after 10 years. Minimum sample 
124

sizes were 32 to detect a 20% decrease at the desired power level and 41 to detect an increase of 

the same magnitude.

In between-bank comparison analyses coral results were above the desired power level for 

changes of 10% after four and 10 years (Table 3.15). Minimum annual trends above 0.8 were 3% 

after four years and 2% after 10 years (Table 3.16). Minimum sample sizes for detecting a 20% 

change in four years with a power of 0.8 were 12 for a 20% decrease and 16 for a 20% increase. 

Analyses for P. strigosa exceeded 0.8 for changes of 50% in all timeframes, while those for M. 

annularis SC only exceeded 0.8 for decreases of 50% in all timeframes. Macroalgae analyses 

were above the desired power level for changes of 20% after four and 10 years. Minimum annual 

trends were 5% after four years and 2% after 10 years. Minimum sample sizes were 32 to detect 

a 20% decrease in four years with desired power and 40 to detect a 20% increase. Substrate 

analyses were above the 0.8 threshold for changes of 10% after four years, and a decrease of 

10% and increase of 20% after 10 years. Minimum annual trends above 0.8 were 7% after 4 

years and 3% after 10 years. To detect a 20% change in substrate after four years with the 

desired power, minimum required sample sizes would be 29 for a 20% decrease and 38 for a 

20% increase.
125

Table 3.15 Benthic Cover Quadrat Triennial Sampling Power Results

Power analysis results for benthic cover quadrat data with the alternative design of triennial sampling.

Location &

Category

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Fourth Year

East

Coral 67.1 16.9 1.000 0.946 0.375 0.299 0.877 1.000

Macroalgae* 4.4 1.5 1.000 0.745 0.211 0.170 0.626 1.000

Substrate* 3.3 0.9 1.000 0.893 0.357 0.290 0.810 1.000

West

Coral 68.3 14.7 1.000 0.985 0.511 0.405 0.962 1.000

Macroalgae* 3.6 1.6 1.000 0.491 0.152 0.125 0.388 0.978

Substrate* 3.6 1.0 1.000 0.946 0.368 0.275 0.794 1.000

P. strigosa** 0.8 0.5 0.985 0.271 0.115 0.141 0.227 0.781

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.970 0.925 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.999 0.616 0.533 0.989 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.857 0.855 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 0.997 0.366 0.102 0.084 0.296 0.950

M. annularis SC 6.6/8.1 9.4/12.5 0.931 0.176 0.063 0.068 0.13 0.655

After Tenth Year

East

Coral 67.1 16.9 1.000 0.885 0.224 0.218 0.692 1.000

Macroalgae* 4.4 1.5 1.000 0.633 0.137 0.127 0.375 0.999

Substrate* 3.3 0.9 1.000 0.843 0.260 0.157 0.726 1.000

West

Coral 68.3 14.7 1.000 0.977 0.294 0.396 0.851 1.000

Macroalgae* 3.60 1.60 1.000 0.358 0.095 0.092 0.224 0.952

Substrate* 3.60 0.99 1.000 0.807 0.155 0.185 0.745 1.000

P. strigosa** 0.8 0.5 0.965 0.169 0.077 0.076 0.130 0.647

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.890 0.904 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.997 0.400 0.391 0.948 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.887 0.731 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 0.996 0.251 0.067 0.062 0.187 0.816

M. annularis SC 6.6/8.1 9.4/12.5 0.910 0.124 0.056 0.052 0.091 0.470

*square root transformed for normality ** log transformed for normality

Values in red are above the desired power level of 0.80
126

Table 3.16 Benthic Cover Quadrats Triennial Sampling Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover quadrat data with the 

alternative design of triennial sampling.

Location & 

Category

Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 5 yrs

Mean SD 4 yrs 10 yrs -20% 20%

East

Coral 67.1 16.9 ±6 ±3 27 35

Macroalgae* 4.4 1.5 ±8 ±4 46 63

Substrate* 3.3 0.9 ±7 ±3 30 39

West

Coral 68.3 14.7 ±6 ±2 21 25

Macroalgae* 3.6 1.6 ±11 ±4 82 102

Substrate* 3.6 1.0 ±6 ±3 32 41

P. strigosa** 0.8 0.5 ±16 ±6 163 214

East compared with West

Coral 67.1/68.3 16.9/14.7 ±3 ±2 12 16

Macroalgae* 4.4/3.6 1.5/1.6 ±5 ±2 32 40

Substrate* 3.3/3.6 0.9/1.0 ±4 ±2 16 22

P. strigosa 10.7/8.9 11.1/9.4 ±13 ±6 220 306

M. annularis SC 6.6/8.1 9.4/12.5 ±18 ±7 458 611

*square root transformed for normality ** log transformed for normality

Sampling Twice a Year

Sampling biannually generally resulted in analyses with higher or equal power to the annual 

sampling design. After two years, the minimum annual trends above the desired power threshold 

were generally higher in a biannual design versus the annual design, but became nearly identical 

after five and 10 years; these results will not be discussed below. The minimum sample sizes 

needed to detect a 20% change after five years at the desired power level were smaller for most 

analyses in the biannual design when compared sampling annually.

Coral analyses at EFGB exceeded the designated power threshold for changes of 20% in all 

timeframes analyzed (Table 3.17). The minimum sample size to detect a 20% change in five

years at the desired power was 27 to detect a decrease and 36 for an increase in coral cover

(Table 3.18). Macroalgae analyses were above the power level for a change of 50% after two and 

five years, and a 20% decrease and 50% increase after 10 years. Minimum sample sizes were 

found to be 45 for a 20% decrease and 61 for a 20% increase after five years. Substrate was 

above the desired power level for changes of 50% after two years and 20% after five and 10 

years. Minimum sample sizes were 29 to detect a 20% decrease and 38 to detect a 20% increase 

after five years with the desired power.

At WFGB coral analyses were above the 0.8 power threshold for changes of 20% across all 

timeframes, similar to EFGB (Table 3.17). The minimum sample size needed to detect a 20% 

change in coral over five years at the desired power were 19 for a 20% decrease and 24 for a 

20% increase (Table 3.18). P. strigosa analyses exceeded 0.8 for a decrease of 50% after five 

years, and both an increase and decrease of 50% after 10 years. Macroalgae at WFGB were the 

only analyses that remained the same across all sampling designs, with changes of 50% above 
127

the power level for all timeframes. The minimum sample size required to detect a 20% change in 

five years with the designated power was 78 to detect a decrease, and 103 to detect an increase. 

Substrate analyses increased in power within each subsequent timeframe. Changes of 50% were 

above power levels after two years; a decrease of 20% was also above power levels after five 

years, and both an increase and decrease of 20% were above the power level after 10 years. The 

minimum sample size needed to detect a 20% change after five years at the desired power were 

31 for a 20% decrease and 42 for a 20% increase.

For between bank comparisons coral analyses were above the desired power level for changes of 

10% in all timeframes (Table 3.17). The minimum required sample size to observe a 20% 

change in 5 years with the desired power was 12 to detect an increase and 16 to detect a decrease 

(Table 3.18). Analyses for P. strigosa exceeded 0.8 for changes of 50% in all timeframes, while 

M. annularis analyses exceeded 0.8 for a decrease of 50% after five and 10 years. Macroalgae 

analyses were above the desired power level for changes of 20% in all timeframes, including an 

increase of 20% after five years, an improvement over the annual design. Sample sizes for 

observing a 20% change in five years with the desired power level were 30 for an increase and 

40 for a decrease in percent cover. Substrate analyses exceeded the power threshold for changes 

of 20% after two years, and 10% after five and 10 years. The minimum required sample size was 

16 to observe a 20% decrease and 20 to observe a 20% increase after five years with the desired 

power.

Table 3.17 Benthic Cover Quadrats Biannual Sampling Power Results

Power analysis results for benthic cover transect data with the alternative design of biannual sampling.

Location &

Category

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Coral 67.1 16.9 1.000 0.844 0.236 0.256 0.822 1.000

Macroalgae* 4.4 1.5 1.000 0.511 0.141 0.134 0.566 1.000

Substrate* 3.3 0.9 1.000 0.782 0.249 0.204 0.772 1.000

West

Coral 68.3 14.7 1.000 0.939 0.327 0.325 0.935 1.000

Macroalgae* 3.6 1.6 0.992 0.317 0.108 0.088 0.300 0.992

Substrate* 3.6 1.0 1.000 0.699 0.190 0.216 0.718 1.000

P. strigosa** 0.8 0.5 0.767 0.085 0.063 0.068 0.132 0.718

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.948 0.934 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.988 0.483 0.523 0.982 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.774 0.767 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 0.952 0.232 0.080 0.078 0.218 0.963

M. annularis SC 6.6/8.1 9.4/12.5 0.692 0.105 0.070 0.055 0.137 0.694

After Five Years

East

Coral 67.1 16.9 1.000 0.957 0.316 0.185 0.830 1.000

Macroalgae* 4.4 1.5 1.000 0.721 0.148 0.147 0.509 1.000

Substrate* 3.3 0.9 1.000 0.959 0.227 0.291 0.817 1.000

West

Coral 68.3 14.7 1.000 1.000 0.450 0.279 0.960 1.000

Macroalgae* 3.6 1.6 1.000 0.434 0.095 0.087 0.256 0.988

Substrate* 3.6 1.0 1.000 0.917 0.244 0.138 0.732 1.000

P. strigosa** 0.8 0.5 0.998 0.194 0.060 0.048 0.104 0.783
128

Location &

Category

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.989 0.921 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 0.999 0.581 0.575 0.991 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.932 0.806 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 1.000 0.277 0.079 0.092 0.165 0.934

M. annularis SC 6.6/8.1 9.4/12.5 0.952 0.148 0.059 0.044 0.097 0.589

After Ten Years

East

Coral 67.1 16.9 1.000 0.998 0.333 0.324 0.957 1.000

Macroalgae* 4.4 1.5 1.000 0.916 0.162 0.141 0.765 1.000

Substrate* 3.3 0.9 1.000 0.997 0.451 0.400 0.954 1.000

West

Coral 68.3 14.7 1.000 1.000 0.506 0.524 0.997 1.000

Macroalgae* 3.6 1.6 1.000 0.589 0.095 0.096 0.379 1.000

Substrate* 3.6 1.0 1.000 0.990 0.442 0.266 0.916 1.000

P. strigosa** 0.8 0.5 1.000 0.231 0.060 0.062 0.167 0.930

East compared with West

Coral 67.1/68.3 16.9/14.7 1.000 1.000 0.998 0.996 1.000 1.000

Macroalgae* 4.4/3.6 1.5/1.6 1.000 1.000 0.689 0.685 1.000 1.000

Substrate* 3.3/3.6 0.9/1.0 1.000 1.000 0.998 0.895 1.000 1.000

P. strigosa 10.7/8.9 11.1/9.4 1.000 0.414 0.071 0.072 0.273 0.986

M. annularis SC 6.6/8.1 9.4/12.5 0.999 0.169 0.058 0.051 0.126 0.744

*square root transformed for normality ** log transformed for normality

Values in red are above the desired power level of 0.80

Table 3.18 Benthic Cover Quadrats Biannual Sampling Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover quadrat data with the 

alternative design of biannual sampling.

Location &

Category

Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Coral 67.1 16.9 ±20 ±5 ±2 27 36

Macroalgae* 4.4 1.5 ±27 ±7 ±3 45 61

Substrate* 3.29 0.87 ±21 ±5 ±2 29 38

West

Coral 68.3 14.7 ±17 ±5 ±2 19 24

Macroalgae* 3.6 1.6 ±35 ±8 ±3 78 13

Substrate* 3.6 1.0 ±22 ±6 ±2 31 42

P. strigosa** 0.8 0.5 -40/+33 ±12 ±5 154 204

East compared with West

Coral 67.1/68.3 16.9/14.7 ±6 ±2 ±1 12 16

Macroalgae* 4.4/3.6 1.5/1.6 ±10 ±4 ±2 30 40

Substrate* 3.3/3.6 0.9/1.0 ±11 ±3 ±1 16 20

P. strigosa 10.7/8.9 11.1/9.4 ±40 ±10 ±4 218 292

M. annularis SC 6.6/8.1 9.4/12.5 -57/+55 ±13 ±5 440 606

*square root transformed for normality ** log transformed for normality
129

Permanent Quadrat Summary

As shown in Table 3.19, there is very little difference in the power analysis results between 

sampling annually or biennially to detect changes in coral, macroalgae or substrate. The trends 

become less consistent for sampling every third year, with increases in the minimum detectable 

annual trend. This is particularly evident after five years, but becomes very similar to the other 

sample designs after 10 years. The patterns in the minimum number of quadrats needed to detect 

a change of 20% after five years at the desired power are also similar for all three benthic 

categories. Annual and biennial designs have very similar sample size requirements, with the 

largest difference in sample size at WFGB for coral analyses, at 13 for an increase of 20%. The 

largest sample size difference between the annual and biennial designs for other coral analyses, 

as well as macroalgae and substrate is three. In all three benthic categories, minimum sample 

size is smaller for sampling every third year and sampling twice a year when compared to annual 

and biennial designs. This is despite the fact that the minimum annual trend detectable at the 

desired power level is generally higher after five years when sampling every third year compared 

to all other designs. Analyses for individual corals had much higher variance and lower power 

than the overall coral category, but followed similar trends to the benthic categories.
130

Table 3.19 Comparison of Power Analyses for Benthic Cover Quadrat Data

Comparisons of the minimum detectable annual trends and minimum number of quadrats for each 

sampling design.

Sampling Strategy Min Annual Trend (%) Min # Transects

~5 Yrs ~10 Yrs -20% 20%

Coral Cover

East

Annual ±5 ±2 32 42

Biennial ±5 ±3 33 44

Triennial ±6 ±3 27 35

Biannual ±5 ±2 27 36

West

Annual ±5 ±2 32 44

Biennial ±5 ±2 23 31

Triennial ±6 ±2 21 25

Biannual ±5 ±2 19 24

East compared with West

Annual ±3 ±2 14 19

Biennial ±4 ±2 16 18

Triennial ±3 ±2 12 16

Biannual ±2 ±1 12 16

Macroalgae Cover

East

Annual ±7 ±3 54 72

Biennial ±7 ±3 56 75

Triennial ±8 ±4 46 63

Biannual ±7 ±3 45 61

West

Annual ±8 ±4 94 124

Biennial ±9 ±4 95 125

Triennial ±11 ±4 82 102

Biannual ±8 ±3 78 103

East compared with West

Annual ±6 ±3 36 48

Biennial ±4 ±2 37 48

Triennial ±5 ±2 32 40

Biannual ±4 ±2 30 40

Substrate Cover

East

Annual ±5 ±2 33 48

Biennial ±6 ±2 35 49

Triennial ±7 ±3 30 39

Biannual ±5 ±2 29 38

West

Annual ±6 ±3 38 50

Biennial ±6 ±3 38 51

Triennial ±6 ±3 32 41

Biannual ±6 ±2 31 42

East compared with West

Annual ±3 ±2 18 24

Biennial ±3 ±2 20 26

Triennial ±4 ±2 16 22

Biannual ±3 ±1 16 20
131

3.2.1.3 Deep Quadrats

The pilot data used to estimate means and standard deviations for power analyses were based on 

nine deep quadrats established in 2003 (Precht et al. 2006). Power analyses for EFGB were run 

using the current IA (BOEM 2014) sample size of 11 quadrats. Deep stations have been 

established at WFGB (Johnston et al. 2013), but data were not available for power analysis. 

Power results for P. strigosa and M. annularis SC did not exceed the 0.8 power threshold and are 

not discussed here. Because of the very low power for the benthic categories sponge, encrusting 

sponge and sand in the random transect and shallow permanent quadrat data, these categories 

were not analyzed for deep quadrat data.

Current Sampling Design Power Evaluation

Power for the current sampling design of 11 quadrats sampled once a year showed that coral 

analyses were above the desired power level for a change of 50% cover after two, five and 10 

years, and a decrease of 20% after 10 years (Table 3.20). The minimum annual detectable trend 

at the desired power level was 23% after two years, 6% after five years, and 3% after 10 years

(Table 3.21). The minimum sample size necessary to detect a 20% change after five years at the 

desired power level would be 13 for a decrease of 20% and 17 for an increase. Macroalgae 

analyses were above the desired power level for a change of 50% after two years, and only a 

decrease of 50% after five and 10 years. The minimum annual trend detectable at the desired 

power level was 46% in two years, 13% after five years, and 6% after 10 years. Minimum 

sample size to detect a 20% change after five years was 51 for a net decrease and 69 for a net 

increase. Substrate did not exceed the desired power level in any analysis. The minimum 

detectable annual trend was 79% after two years, 22% after five years and 9% after ten years. 

Minimum sample size to detect a 20% change in five years was 144 for an increase and 195 for a 

20% decrease.

Table 3.20 Benthic Cover Deep Quadrats Current Sample Design Power Results

Power analysis results for benthic cover deep quadrat data with the current design of annual sampling.

Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

Coral 77.2 12.1 1.000 0.721 0.193 0.272 0.701 1.000

Macroalgae* 1.1 0.4 0.859 0.218 0.067 0.063 0.207 0.864

Substrate 6.7 3.7 0.423 0.084 0.047 0.050 0.085 0.376

After Five Years

Coral 77.2 12.1 1.000 0.709 0.135 0.139 0.479 1.000

Macroalgae* 1.1 0.4 0.974 0.152 0.057 0.055 0.124 0.676

Substrate 6.7 3.7 0.489 0.060 0.049 0.041 0.066 0.213

After Ten Years

Coral 77.2 12.1 1.000 0.830 0.190 0.178 0.721 1.000

Macroalgae* 1.1 0.4 0.996 0.174 0.062 0.066 0.141 0.767

Substrate 6.7 3.7 0.668 0.080 0.058 0.050 0.072 0.230

*Log transformed for normality

Values in red are above the desired power level of 0.80
132

Table 3.21 Benthic Cover Deep Quadrats Current Sample Design Minimum 

Detectable Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover deep quadrat data with the 

current design of annual sampling.

Category Pilot Data Min Annual Trend (%) Min Sample Size for 20%

Net Change after 5 yrs 

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

Coral 77.2 12.1 ±23 ±6 ±3 13 17

Macroalgae* 1.1 0.4 ±46 ±13 ±6 51 69

Substrate 6.7 3.7 ±79 ±22 ±9 144 195

*Log transformed for normality

Alternative Sampling Design Power Evaluation

As seen in Table 3.22, coral analyses were above the desired power level for changes of 50% in 

all timeframes for sampling every other year and every third year. For the biannual design, 

power improved to include a decrease of 20% after five years, and a 20% increase and decrease 

after 10 years. Macroalgae analyses were above the desired power level for only a decrease of 

50% in all timeframes and sampling designs except for an increase of 50% after 10 years in the 

biannual design. Substrate analyses did not exceed the desired power level for any sampling 

design except for a decrease of 50% after 10 years in the biannual design. Minimum annual 

trends and sample sizes to detect a 20% change can be seen in Table 3.23.

Deep Quadrat Summary

In general the biannual design shows greater statistical power than the other designs analyzed, 

though there are differences among the other designs as well. The current sampling design has a 

greater number of analyses overall that exceed the desired power threshold than either design 

with longer sampling intervals (every other or every third year). But the biannual design exceeds 

the annual design in number of analyses that meet the desired power level for all three benthic 

categories. The minimum annual trend detectable at the desired power for each benthic category 

is similar across all four sampling designs. The biannual design has sufficient power to detect the 

smallest yearly percent changes, particularly for substrate analyses, but is closely matched by the 

biennial design, followed by the annual and then every third year designs. For then minimum 

number of samples needed to detect a 20% change after five years, the biannual design requires 

the least number of samples, followed by every third year, annual and then biennial. 
133

Table 3.22 Benthic Cover Deep Quadrats Alternate Sampling Design Power 

Results

Power analysis results for biennial, triennial, and biannual sampling.

Category Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Biennial Sampling - After Third Year

Coral 77.2 12.1 1.000 0.703 0.228 0.194 0.707 1.000

Macroalgae* 1.1 0.4 0.949 0.214 0.077 0.085 0.173 0.785

Substrate 6.7 3.7 0.531 0.105 0.054 0.047 0.081 0.368

Biennial Sampling - After Fifth Year

Coral 77.2 12.1 1.000 0.689 0.210 0.140 0.575 0.999

Macroalgae* 1.1 0.4 0.961 0.176 0.062 0.053 0.122 0.651

Substrate 6.7 3.7 0.521 0.069 0.046 0.051 0.059 0.212

Biennial Sampling - After Eleventh Year

Coral 77.2 12.1 1.000 0.745 0.139 0.142 0.525 1.000

Macroalgae* 1.1 0.4 0.984 0.150 0.051 0.069 0.112 0.672

Substrate 6.7 3.7 0.551 0.074 0.046 0.045 0.048 0.183

Triennial Sampling - After Fourth Year

Coral 77.2 12.1 1.000 0.740 0.209 0.170 0.673 1.000

Macroalgae* 1.1 0.4 0.974 0.217 0.085 0.084 0.183 0.768

Substrate 6.7 3.7 0.559 0.097 0.044 0.054 0.087 0.326

Triennial Sampling - After Tenth Year

Coral 77.2 12.1 1.000 0.703 0.173 0.128 0.474 0.998

Macroalgae* 1.1 0.4 0.981 0.197 0.068 0.072 0.101 0.585

Substrate 6.7 3.7 0.503 0.078 0.052 0.044 0.060 0.197

Biannual Sampling - After Two Years

Coral 77.2 12.1 1.000 0.687 0.172 0.156 0.574 1.000

Macroalgae* 1.1 0.4 0.877 0.166 0.069 0.069 0.104 0.712

Substrate 6.7 3.7 0.365 0.066 0.046 0.051 0.066 0.251

Biannual Sampling - After Five Years

Coral 77.2 12.1 1.000 0.876 0.156 0.096 0.709 0.999

Macroalgae* 1.1 0.4 0.995 0.185 0.054 0.076 0.144 0.769

Substrate 6.7 3.7 0.642 0.085 0.053 0.049 0.072 0.237

*Log transformed for normality

Values in red are above the desired power level of 0.80
134

Table 3.23 Benthic Cover Deep Quadrats Alternative Sample Designs Minimum 

Detectable Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for benthic cover deep quadrat data with the 

alternative designs of biennial, triennial, and biannual sampling.

Biennial Sampling

Category Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 5 yrs 

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

Coral 77.2 12.1 ±11 ±7 ±2 14 18

Macroalgae* 1.1 0.4 ±23 ±12 ±5 52 67

Substrate 6.7 3.7 ±39 ±22 ±9 145 195

Triennial Sampling

Category Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 5 yrs 

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

Coral 77.2 12.1 ±16 ±6 ±2 10 14

Macroalgae* 1.1 0.4 ±34 ±10 ±5 40 55

Substrate 6.7 3.7 ±58 ±19 ±7 111 151

Biannual Sampling

Category Pilot Data Min Annual Trend (%) Min Sample Size for 20% 

Net Change after 5 yrs 

Mean SD 4 yrs 10 yrs -20% 20%

Coral 77.2 12.1 ±7 ±3 12 15

Macroalgae* 1.1 0.4 ±16 ±6 45 56

Substrate 6.7 3.7 ±27 ±10 126 163

*Log transformed for normality

3.1.1.1 Benthic Cover Conclusions

The overall result for both the benthic transect and shallow permanent quadrat analyses was that 

there is little difference between sampling every year versus every other year to detect trends at 

the desired power level of 0.8. The general patterns of analyses that were above the desired 

power level for the magnitudes of change analyzed (10%, 20% and 50%) were very similar 

between the two designs. The minimum annual detectable trends above the desired power level 

were also very similar, as was the minimum sample size needed to detect a change of 20% after 

approximately five years. This pattern of similarity between the two sampling designs was 

consistent for all benthic categories analyzed. Sampling every third year or biannually in general 

did not improve power overall, and in some cases caused a decline in power when compared to 

the current annual design. Though for permanent quadrats, the sample sizes needed to detect a 

20% change at the desired power level after five years were smaller for the alternative designs 

when compared to the annual and biennial designs. 

In assessing the power to detect percent cover changes with the current and biennial designs, 

notable differences could be seen among the different benthic categories. For the random 

transect data, macroalgae had the most results above the desired power level for 20% changes in 

cover across all timeframes, while the other benthic categories were above desired power mostly 

for changes of 50% across all timeframes. In permanent quadrat data, coral and substrate cover 

were more often above the desired power level for changes of 20% and macroalgae was only 

above the desired power level for 50% changes in cover across all timeframes. In both transect 
135

and quadrat datasets, between bank comparisons generally had higher power to detect smaller 

changes (10% and 20%) than either bank individually.

Only a few readily available examples of coral reef LTM programs have performed power 

analyses for their program designs (Brown et al. 2004). The National Park Service CRAMP in 

Hawai’i is one of these (Brown et al. 2011b). CRAMP estimated the statistical power of their 

mixed panel design initially was 0.4 to detect a 25% change in benthic overall benthic cover, but 

would improve to a power of 0.8 after sampling for several years and modifying parameters in 

accordance with observations. The Australian Institute of Marine Science Great Barrier Reef 

LTM has also conducted power analyses (Sweatman 1997). This program uses a significance 

level of α = 0.1, and power analyses estimate that a change in coral cover of less than 10% can 

be detected at 0.8 power after a year. Though results cannot be directly compared due to 

differences in data collection and sampling design, these power estimates show that the 

FGBLTM program, with most analyses able to detect changes in the 20–50% range with the 

desired power, has similar to lower power than other LTM programs, particularly in the longer 

term analyses, where power does not improve greatly compared to short term analyses. With the 

minimal differences between annual and biennial designs, FGBLTM managers should consider 

shifting to a biennial design and using unexpended resources on increases sample size, reducing 

variance and improving program power. 

3.2.2 Coral Growth

Coral growth is currently measured through two monitoring methods as part of the FGBLTM 

program, the lateral growth and sclerochronology methods. Both methods have been part of the 

LTM program since its inception in 1988 and have undergone relatively few technique 

adjustments, but sampling strategies including the number of replicates measured has varied over 

the years (discussed in Chapter 2). The sampling strategies of each of these methods are 

discussed separately below.

3.2.2.1 Lateral Growth

The sampling strategy of the lateral growth method initially included 60 permanent P. strigosa

and 60 permanent M. annularis SC photo stations per bank that were visited semi-annually until 

1991 and then annually thereafter (Gittings et al. 1993; MMS 1996). In the ideal situation, each 

visit would yield a single growth and retreat value per station; however, low site integrity and 

issues with comparability between photographs led to many fewer values in practice (See 

Chapter 2, Section 2.2.1). In 1994, the number of stations was reduced to 60 P. strigosa stations 

only (no Monastrea annularis stations) (MMS 1996). Although the same site integrity and 

photograph comparability issues remained a problem, reducing the number of actual growth 

values obtained, measurements on 60 stations per bank per year remained the sampling goal 

through 2012 (Dokken et al. 2001; Dokken et al. 2003; Precht et al. 2006; Zimmer et al. 2010; 

Johnston et al. 2013). The sampling strategy in the current IA, which includes 2013 and 2014, 

specifies that only 30 lateral growth stations should be maintained and utilized (BOEM 2014). 

Comparison of lateral growth between years and banks should be obtainable by conducting a 

repeated measures ANOVA or similar (BOEM 2014). However, low sampling sizes due to the 

reasons mentioned above (and described in Chapter 2) have prevented this type of statistical 
136

analysis in recent years (Johnston et al. 2013, 2015). No statistical tests were conducted on 2009 

and 2010 data (Johnston et al. 2013). A two-tailed t-test was used to test for differences in 

growth and retreat between banks on 2011 and 2012 data, but no analysis was performed to 

determine whether growth and retreat measurements had changed over time (Johnston et al.

2015). Here the power of the current sampling strategy is evaluated to detect change over time 

using one- and two-way ANOVA in two situations: in an ideal world where every sample 

resulted in usable measurements; and in a more realistic situation where on average seven 

samples per banks are useable for analysis. This second situation mimics the 2009-2010 sample 

period, which was typical of other sampling periods, when a total of 14 samples were useable 

between the two banks.

Due to low usable sample size during our pilot period (2009–2010), data were pooled for 2009 

through 2012 for the lateral growth method and were used to calculate sample mean and variance 

for the lateral growth parameters. The power to detect change at EFGB and WFGB are evaluated 

separately in addition to evaluating the power to detect change at FGB in general. The 

parameters measured and compared under this method are the proportion of the coral margin that 

progressed (“Growth”) and the proportion of the coral margin that regressed (“Retreat”) in one 

years’ time. 

Before calculating mean and standard deviation values for the power analyses, data were tested 

for normality using the Anderson-Darling normality test. All resulting p-values were larger than 

0.05 (Appendix A) indicating failure to reject the null hypothesis that the data are normal. This 

was considered sufficient evidence to assume the datasets were satisfactorily normal to meet the 

assumptions of the power analysis.

Current Sampling Strategy Power Evaluation

Power simulations predicted that, in general, the current sampling strategy is insufficient to 

detect relatively small changes in coral growth and retreat at FGB. Even in the ideal situation, 

when the sample size is considered to be 30 measurements per bank per year, 10 and 20% net 

changes in growth and retreat values were not detected at the desired level of power after two, 

five or 10 years of sampling (Table 3.24). 50% net changes (increases or decreases) in growth 

and retreat were detected with sufficient power after two years of sampling when data were 

combined for both banks. Detecting a change when combining data for both banks in this type of 

statistical evaluation indicates the change would have occurred at both banks. A 50% net decline 

in growth was detected with the desired power after five years of sampling for EFGB and WFGB 

when the banks were analyzed separately. A 50% decline in retreat was detected with the desired 

power only after 10 years of sampling for EFGB and WFGB when the banks were analyzed 

separately. After 10 years of sampling, power remained insufficient to detect 50% increases in 

growth and retreat values at the individual banks. Minimum detectable annual trends ranged 

from 38% to 72% after two years, 11% to 19% after five years, and 4% to 8% after 10 years

(Table 3.25).

Power to detect change was lower across all analyses when the sample size was reduced to a 

more realistic value of seven samples per bank per year (Tables 3.26 and 3.27). 10, 20 and 50% 
137

changes in growth and retreat could not be detected with sufficient power under any of the 

scenarios tested with a sample size of seven per bank.

The low power values obtained from the power simulations for lateral growth and retreat are the 

result of the large variation predicted by pilot study data. The magnitude of the standard 

deviation values was 71 to 104% of the respective mean growth and retreat values. This is likely 

in part due to the small sample size of the pilot study data. Although four years of data were 

compiled, the total sample size for the pilot period was only 22 due to various issues with the 

stations and photographs. Although this small sample size may have contributed to the high 

variation of the pilot data, the useable sample number has been unpredictable and low throughout 

the entire history of the LTM program, so this pilot sample is considered representative of the 

variation that is present from year to year in the lateral growth data.

With the high predicted variation, power simulations indicated that a sample size of 200 or 

greater would be required to produce adequate power to detect a 20% net change in lateral 

growth and retreat after five years of sampling (Table 3.25). This sample size is unrealistically 

large, and it becomes even less achievable when one considers that less than 50% of the stations 

visited or photographs taken have resulted in useable data most years.
138

Table 3.24 Lateral Growth Current Sampling Design Power Results (Ideal Sample 

Size)

Power analysis results for lateral growth data with the current design of annual sampling and 30 stations 

per bank per year.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Growth 55.9 36.8 0.770 0.168 0.066 0.058 0.150 0.776

Retreat 33.7 35.2 0.481 0.101 0.082 0.059 0.093 0.498

West

Growth 51.1 38.6 0.656 0.141 0.063 0.057 0.146 0.642

Retreat 30.8 30.7 0.496 0.116 0.071 0.071 0.111 0.501

East and West Combined

Growth 51.3 36.6 0.966 0.304 0.101 0.095 0.294 0.963

Retreat 34.5 32.3 0.854 0.208 0.079 0.072 0.216 0.870

East compared with West

Growth 55.9/51.1 36.8/38.6 0.972 0.308 0.089 0.104 0.287 0.960

Retreat 33.7/30.8 35.2/30.8 0.837 0.157 0.071 0.093 0.162 0.826

After Five Years

East

Growth 55.9 36.8 0.910 0.155 0.069 0.056 0.092 0.531

Retreat 33.7 35.2 0.611 0.101 0.050 0.065 0.074 0.273

West

Growth 51.1 38.6 0.809 0.107 0.054 0.063 0.109 0.427

Retreat 30.8 30.7 0.652 0.097 0.064 0.060 0.080 0.284

East and West Combined

Growth 51.3 36.6 0.998 0.218 0.079 0.062 0.174 0.827

Retreat 34.5 32.3 0.965 0.167 0.069 0.051 0.130 0.662

East compared with West

Growth 55.9/51.1 36.8/38.6 0.998 0.303 0.083 0.082 0.165 0.863

Retreat 33.7/30.8 35.2/30.8 0.960 0.130 0.071 0.069 0.102 0.604

After Ten Years

East

Growth 55.9 36.8 0.928 0.174 0.069 0.055 0.115 0.641

Retreat 33.7 35.2 0.802 0.082 0.047 0.048 0.071 0.327

West

Growth 51.1 38.6 0.945 0.132 0.044 0.069 0.083 0.572

Retreat 30.8 30.7 0.812 0.084 0.060 0.064 0.068 0.334

East and West Combined

Growth 51.3 36.6 1.000 0.226 0.088 0.078 0.181 0.937

Retreat 34.5 32.3 0.999 0.256 0.071 0.077 0.132 0.769

East compared with West

Growth 55.9/51.1 36.8/38.6 1.000 0.197 0.059 0.054 0.175 0.937

Retreat 33.7/30.8 35.2/30.8 0.999 0.163 0.069 0.065 0.120 0.738

Values in red are above the desired power level of 0.80
139

Table 3.25 Lateral Growth Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (Ideal Sample Size)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for lateral growth data with the current design of 

annual sampling and 30 stations per bank per year.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Growth 55.9 36.8 ±52 ±15 ±6 180 >200

Retreat 33.7 35.2 ±72 ±19 ±7 >200 >200

West

Growth 51.1 38.6 ±58 ±15 ±6 >200 >200

Retreat 30.8 30.7 ±68 ±19 ±7 >200 >200

East and West Combined

Growth 51.3 36.6 ±38 ±11 ±4 200 >200

Retreat 34.5 32.3 ±45 ±13 ±5 >200 >200

East compared with West

Growth 55.9/51.1 36.8/38.6 ±38 ±11 ±9 100 140

Retreat 33.7/30.8 35.2/30.8 ±48 ±13 ±10 170 >200
140

Table 3.26 Lateral Growth Current Sampling Design Power Results (Realistic 

Sample Size)

Power analysis results for lateral growth data with the current design of annual sampling and seven 

stations per bank per year.

Parameter Pilot Data Power after Two Years

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Growth 55.9 36.8 0.181 0.069 0.047 0.056 0.066 0.154

Retreat 33.7 35.2 0.114 0.053 0.054 0.059 0.058 0.078

West

Growth 51.1 38.6 0.135 0.059 0.058 0.058 0.057 0.131

Retreat 30.8 30.7 0.096 0.063 0.054 0.051 0.059 0.107

East and West Combined

Growth 51.3 36.6 0.344 0.103 0.052 0.058 0.088 0.348

Retreat 34.5 32.3 0.271 0.082 0.057 0.05 0.081 0.224

East compared with West

Growth 55.9/51.1 36.8/38.6 0.355 0.091 0.055 0.051 0.085 0.345

Retreat 33.7/30.8 35.2/30.8 0.227 0.069 0.047 0.053 0.076 0.192

After Fiver Years

East

Growth 55.9 36.8 0.209 0.066 0.051 0.063 0.069 0.117

Retreat 33.7 35.2 0.119 0.055 0.051 0.045 0.056 0.090

West

Growth 51.1 38.6 0.153 0.068 0.059 0.06 0.05 0.098

Retreat 30.8 30.7 0.114 0.058 0.054 0.055 0.057 0.066

East and West Combined

Growth 51.3 36.6 0.418 0.067 0.049 0.049 0.057 0.178

Retreat 34.5 32.3 0.278 0.058 0.051 0.046 0.058 0.150

East compared with West

Growth 55.9/51.1 36.8/38.6 0.434 0.082 0.071 0.059 0.067 0.196

Retreat 33.7/30.8 35.2/30.8 0.264 0.064 0.058 0.054 0.058 0.112

After Ten Years

East

Growth 55.9 36.8 0.263 0.056 0.045 0.052 0.061 0.102

Retreat 33.7 35.2 0.127 0.064 0.039 0.057 0.052 0.079

West

Growth 51.1 38.6 0.197 0.064 0.052 0.052 0.061 0.095

Retreat 30.8 30.7 0.156 0.057 0.055 0.057 0.061 0.084

East and West Combined

Growth 51.3 36.6 0.592 0.069 0.063 0.052 0.087 0.201

Retreat 34.5 32.3 0.377 0.07 0.055 0.058 0.065 0.149

East compared with West

Growth 55.9/51.1 36.8/38.6 0.559 0.069 0.06 0.059 0.061 0.222

Retreat 33.7/30.8 35.2/30.8 0.307 0.073 0.041 0.051 0.054 0.136
141

Table 3.27 Lateral Growth Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (Realistic Sample Size)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for lateral growth data with the current design of 

annual sampling and seven stations per bank per year.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Growth 55.9 36.8 >100 ±37 ±13 180 >200

Retreat 33.7 35.2 >100 ±60 ±20 >200 >200

West

Growth 51.1 38.6 >100 ±44 ±16 >200 >200

Retreat 30.8 30.7 >100 ±62 ±19 >200 >200

East and West Combined

Growth 51.3 36.6 ±92 ±23 ±9 200 >200

Retreat 34.5 32.3 >100 ±29 ±11 >200 >200

East compared with West

Growth 55.9/51.1 36.8/38.6 ±90 ±24 ±9 100 140

Retreat 33.7/30.8 35.2/30.8 >100 ±31 ±12 170 >200
142

Alternative Sampling Strategy Power Evaluation

Three alternative sampling frequencies were simulated: 

1. reducing the sampling frequency to every other year, 

2. reducing the sampling frequency to every third year, and 

3. increasing the sampling frequency to biannual samples. 

Each alternative sampling frequency was tested using the ideal sample number of 30 per bank. 

Due to the power simulations resulting in unacceptably low power for the ideal sample number, 

performing further simulations with the more realistic sample number of seven per bank was 

considered unnecessary, because those simulations would undoubtedly result in even lower 

power results.

Reducing the sampling frequency to every other year had minimal effects of power values in the 

simulated data (Tables 3.28 and 3.29). With 30 samples per bank after the second year (two

sampling events), 50% net declines in growth were detectable with greater than 0.80 power when 

data were combined for both banks and when data for each bank was evaluated separately. 50% 

increases in growth and 50% declines in retreat were also detectable with the desired power 

when data were combined for both banks. 10 and 20% increases and decreases in growth and 

retreat were not detectable with the desired power even after 11 years of sampling (six sampling 

events). Minimum detectable annual trends after five years of sampling were within 1% of those 

predicted with annual sampling. Contrary to the annual sampling strategy, 50% declines in 

retreat values at the individual banks were not detected with the desired power even after 11 

years.

Reducing sampling frequencies to every third year also had only minimal effects on power to 

detect changes (Tables 3.30 and 3.31). With 30 samples per bank, 50% net declines in growth 

were detectable with greater than 0.80 power for all cases after the fourth year (second sampling 

event). 10 and 20% increases and decreases in growth and retreat were not detectable with the 

desired power after 10 years (four sampling events). Minimum detectable annual trends after 10 

years of sampling were within 1% of those predicted with annual sampling. Similarly to 

sampling every other year, but contrary to the annual sampling strategy, 50% declines in retreat 

values at the individual banks were not detected with the desired power even after 10 years.
143

Table 3.28 Lateral Growth Biennial Sampling Design Power Results (Ideal Sample 

Size)

Power analysis results for lateral growth data with the alternative design of biennial sampling and 30 

stations per bank per year.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

Growth 55.9 36.8 0.858 0.141 0.093 0.066 0.174 0.692

Retreat 33.7 35.2 0.600 0.127 0.059 0.069 0.117 0.401

West

Growth 51.1 38.6 0.814 0.196 0.072 0.07 0.121 0.581

Retreat 30.8 30.7 0.658 0.128 0.071 0.057 0.129 0.376

East and West Combined

Growth 51.3 36.6 0.99 0.319 0.104 0.088 0.301 0.935

Retreat 34.5 32.3 0.947 0.206 0.086 0.071 0.162 0.777

East compared with West

Growth 55.9/51.1 36.8/38.6 0.993 0.367 0.114 0.082 0.305 0.924

Retreat 33.7/30.8 35.2/30.8 0.936 0.214 0.090 0.076 0.173 0.764

After Fifth Year

East

Growth 55.9 36.8 0.892 0.147 0.071 0.069 0.115 0.512

Retreat 33.7 35.2 0.620 0.107 0.065 0.064 0.082 0.281

West

Growth 51.1 38.6 0.812 0.114 0.065 0.049 0.096 0.449

Retreat 30.8 30.7 0.626 0.094 0.058 0.063 0.073 0.301

East and West Combined

Growth 51.3 36.6 0.994 0.265 0.098 0.064 0.179 0.826

Retreat 34.5 32.3 0.944 0.215 0.074 0.070 0.166 0.578

East compared with West

Growth 55.9/51.1 36.8/38.6 0.997 0.279 0.073 0.078 0.209 0.841

Retreat 33.7/30.8 35.2/30.8 0.943 0.158 0.066 0.067 0.130 0.601

After Eleventh Year

East

Growth 55.9 36.8 0.950 0.140 0.060 0.073 0.112 0.538

Retreat 33.7 35.2 0.696 0.082 0.037 0.059 0.082 0.290

West

Growth 51.1 38.6 0.903 0.110 0.060 0.069 0.103 0.366

Retreat 30.8 30.7 0.718 0.070 0.051 0.047 0.082 0.248

East and West Combined

Growth 51.3 36.6 1.000 0.228 0.071 0.053 0.201 0.791

Retreat 34.5 32.3 0.991 0.152 0.092 0.066 0.150 0.505

East compared with West

Growth 55.9/51.1 36.8/38.6 1.000 0.222 0.086 0.075 0.165 0.861

Retreat 33.7/30.8 35.2/30.8 0.985 0.151 0.050 0.058 0.137 0.622

Values in red are above the desired power level of 0.80
144

Table 3.29 Lateral Growth Biennial Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (Ideal Sample Size)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for lateral growth data with the alternative design 

of biennial sampling and 30 stations per bank per year.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

East

Growth 55.9 36.8 ±26 ±15 ±6 170 >200

Retreat 33.7 35.2 ±36 ±20 ±7 >200 >200

West

Growth 51.1 38.6 ±29 ±16 ±6 >200 >200

Retreat 30.8 30.7 ±36 ±19 ±7 >200 >200

East and West Combined

Growth 51.3 36.6 ±19 ±11 ±4 >200 >200

Retreat 34.5 32.3 ±23 ±13 ±5 >200 >200

East compared with West

Growth 55.9/51.1 36.8/38.6 ±19 ±10 ±9 100 140

Retreat 33.7/30.8 35.2/30.8 ±25 ±13 ±11 170 >200
145

Table 3.30 Lateral Growth Triennial Sampling Design Power Results (Ideal 

Sample Size)

Power analysis results for lateral growth data with the alternative design of triennial sampling and 30 

stations per bank per year.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Fourth Year

East

Growth 55.9 36.8 0.898 0.172 0.067 0.076 0.151 0.626

Retreat 33.7 35.2 0.680 0.116 0.078 0.063 0.119 0.398

West

Growth 51.1 38.6 0.873 0.172 0.069 0.066 0.159 0.560

Retreat 30.8 30.7 0.686 0.102 0.058 0.062 0.092 0.408

East and West Combined

Growth 51.3 36.6 0.996 0.405 0.097 0.105 0.292 0.897

Retreat 34.5 32.3 0.977 0.247 0.080 0.080 0.169 0.732

East compared with West

Growth 55.9/51.1 36.8/38.6 0.998 0.369 0.087 0.087 0.245 0.917

Retreat 33.7/30.8 35.2/30.8 0.947 0.209 0.097 0.073 0.194 0.724

After Tenth Year

East

Growth 55.9 36.8 0.912 0.209 0.058 0.048 0.138 0.422

Retreat 33.7 35.2 0.642 0.100 0.049 0.053 0.085 0.268

West

Growth 51.1 38.6 0.826 0.135 0.078 0.063 0.135 0.410

Retreat 30.8 30.7 0.667 0.091 0.048 0.049 0.084 0.264

East and West Combined

Growth 51.3 36.6 0.999 0.239 0.070 0.062 0.150 0.806

Retreat 34.5 32.3 0.986 0.191 0.088 0.068 0.111 0.625

East compared with West

Growth 55.9/51.1 36.8/38.6 1.000 0.218 0.053 0.064 0.142 0.829

Retreat 33.7/30.8 35.2/30.8 0.944 0.147 0.056 0.078 0.105 0.554

Values in red are above the desired power level of 0.80
146

Table 3.31 Lateral Growth Triennial Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (Ideal Sample Size)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for lateral growth data with the alternative design 

of triennial sampling and 30 stations per bank per year.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 4 yrs

Mean SD 4 yrs 10 yrs -20% 20%

East

Growth 55.9 36.8 ±18 ±6 140 200

Retreat 33.7 35.2 ±24 ±9 >200 >200

West

Growth 51.1 38.6 ±20 ±7 200 >200

Retreat 30.8 30.7 ±23 ±9 >200 >200

East and West Combined

Growth 51.3 36.6 ±13 ±5 170 >200

Retreat 34.5 32.3 ±15 ±6 >200 >200

East compared with West

Growth 55.9/51.1 36.8/38.6 ±13 ±5 90 110

Retreat 33.7/30.8 35.2/30.8 ±16 ±6 140 170

Increasing sampling frequencies to biannual samples slightly improved the ability to detect 

change in lateral growth variables (Tables 3.32 and 3.33). With 30 samples per bank after the 

second year of sampling (four sampling events), 50% net declines in growth were detectable 

with the desired power when data were combined for both banks and at EFGB. Contrary to the 

other sampling plans 50% net declines in growth at WFGB were not detectable with the desired 

power after two years of sampling. These declines were detectable after five years of sampling 

(10 sampling events). After 10 years of sampling 50% declines in retreat were detected with the 

desired power in all cases. This is an improvement over the other sampling strategies, under 

which 50% net changes in retreat at the individual banks was not detected with adequate power 

after 10 or 11 years. Adequate power was also achieved to detect 50% net increases in retreat at 

the banks when data were combined and at EFGB after 10 years of sampling. Minimum 

detectable annual changes were lower across the board for the biannual sampling strategy 

compared to the other sampling strategies.

Lateral Growth Summary

Power to detect change in coral growth and retreat through the lateral growth method was low 

across the board. Whether the realistic sample size of seven per bank or the ideal sample size of 

30 per banks were used in power simulations, power to detect 20% changes after five years was

below the desired level of 0.8. Of the sampling strategies evaluated, the biannual sampling 

strategy resulted in the best power to detect change. However, this strategy was still inadequate 

to detect a 20% net change in lateral growth or retreat after five years or even 10 years of 

sampling. 
147

Table 3.32 Lateral Growth Biannual Sampling Design Power Results (Ideal 

Sample Size)

Power analysis results for lateral growth data with the alternative design of biannual sampling and 30 

stations per bank per year.

Parameter Pilot Data Power for Percent Net Changes

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Growth 55.9 36.8 0.803 0.128 0.067 0.063 0.110 0.581

Retreat 33.7 35.2 0.455 0.070 0.065 0.056 0.068 0.328

West

Growth 51.1 38.6 0.653 0.107 0.071 0.044 0.103 0.478

Retreat 30.8 30.7 0.460 0.086 0.062 0.050 0.089 0.316

East and West Combined

Growth 51.3 36.6 0.973 0.210 0.072 0.081 0.196 0.895

Retreat 34.5 32.3 0.876 0.133 0.074 0.069 0.124 0.721

East compared with West

Growth 55.9/51.1 36.8/38.6 0.964 0.217 0.084 0.077 0.196 0.887

Retreat 33.7/30.8 35.2/30.8 0.856 0.153 0.065 0.067 0.129 0.669

After Five Years

East

Growth 55.9 36.8 0.955 0.232 0.058 0.052 0.133 0.622

Retreat 33.7 35.2 0.740 0.098 0.046 0.046 0.069 0.298

West

Growth 51.1 38.6 0.931 0.162 0.068 0.061 0.085 0.528

Retreat 30.8 30.7 0.753 0.077 0.051 0.056 0.074 0.342

East and West Combined

Growth 51.3 36.6 1.000 0.272 0.080 0.055 0.191 0.926

Retreat 34.5 32.3 0.995 0.165 0.089 0.058 0.114 0.773

East compared with West

Growth 55.9/51.1 36.8/38.6 1.000 0.273 0.078 0.077 0.249 0.926

Retreat 33.7/30.8 35.2/30.8 0.992 0.203 0.060 0.057 0.118 0.711

After Ten Years

East

Growth 55.9 36.8 0.999 0.213 0.059 0.059 0.137 0.806

Retreat 33.7 35.2 0.938 0.100 0.046 0.047 0.084 0.460

West

Growth 51.1 38.6 0.999 0.143 0.060 0.069 0.120 0.672

Retreat 30.8 30.7 0.976 0.107 0.056 0.054 0.073 0.471

East and West Combined

Growth 51.3 36.6 1.000 0.415 0.079 0.068 0.268 0.992

Retreat 34.5 32.3 1.000 0.249 0.087 0.067 0.190 0.925

East compared with West

Growth 55.9/51.1 36.8/38.6 1.000 0.436 0.063 0.070 0.288 0.995

Retreat 33.7/30.8 35.2/30.8 1.000 0.242 0.081 0.058 0.160 0.884

Values in red are above the desired power level of 0.80
148

Table 3.33 Lateral Growth Biannual Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (Ideal Sample Size)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for lateral growth data with the alternative design 

of biannual sampling and 30 stations per bank per year.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Growth 55.9 36.8 ±38 ±11 ±4 140 170

Retreat 33.7 35.2 ±56 ±15 ±6 >200 >200

West

Growth 51.1 38.6 ±41 ±13 ±5 170 200

Retreat 30.8 30.7 ±50 ±15 ±6 >200 >200

East and West Combined

Growth 51.3 36.6 ±28 ±8 ±3 150 200

Retreat 34.5 32.3 ±34 ±10 ±4 >200 >200

East compared with West

Growth 55.9/51.1 36.8/38.6 ±27 ±8 ±3 80 110

Retreat 33.7/30.8 35.2/30.8 ±36 ±11 ±4 130 180

Power to detect change would be improved by increasing sample size, but even with the biannual 

sampling strategy predicted sample size requirements to meet the power objectives are 140 to 

more than 200 samples per bank. Achieving such a sample size would require an unrealistic 

amount of time and labor. It is likely with increased sample size, sample variance may decrease 

for this dataset resulting in greater power to detect change than predicted using the pilot data. 

Increasing sample size any may require substantial effort as site integrity issues have greatly 

reduced the useable sample size year after year. Considering this, and recommendations made in 

previous chapters, we recommend that the lateral growth method be discontinued and replaced 

with a method that can more reliably measure coral growth and that will provide adequate power 

to detect changes.

3.2.2.2 Sclerochronology

Over the history of the FGBLTM program, the sampling strategy for the sclerochronology 

method has varied, including changes to the number of cores collected and the length of those 

cores. Initially, two cores were collected per bank; the length of these cores spanned 79 years of 

growth (Gittings et al. 1993). Later, the lengths of the cores extracted were reduced to include at 

least 10 years of growth (Dokken et al. 1999), and then to include at least 50 millimeters

(approximately eight years of growth) (Johnston et al. 2013). The number of cores collected 

varied from one core per bank to four cores per bank over the LTM program history (Dokken et 

al. 2003; Johnston et al. 2013). 

The current sampling strategy as specified by the IA is that four cores of at least 70 mm in length 

(approximately 11 years of growth) are extracted per bank once during the two year contract 

period. Each core yields annual accretionary growth rate measurements that are compared across 

the years measured by ANOVA and between banks by a t-test (BOEM 2014).
149

Current Sampling Strategy Power Evaluation

Sclerochronology data were determined to be sufficiently normal, using the Andersen-Darling 

test (Appendix A). No data transformations were applied to these data. The current sampling 

strategy for sclerochronology was found to be inadequate to detect 20% net changes in coral 

growth with cores exhibiting five to 10 years of growth (Table 3.34). Power simulations showed 

that with five years of growth data, 50% net declines in growth rate could be detected with the 

desired power of 0.80 or greater when data were combined for both banks and at EFGB when 

banks were considered individually. With ten years of growth data, a 50% net decline in growth 

at WFGB could be detected with the desired power. A 50% increase in growth could also be 

detected with the desired power with ten years of data when data were combined for both banks. 

Even with 10 years of growth data, 10 and 20% increases and decreases in growth could not be 

detected with adequate power. Minimum detectable annual changes ranged from 11% to 17% 

with five years of data and 4% to 6% with 10 years of data (Table 3.35).

When EFGB and WFGB were compared by two-way ANOVA, power simulations predicted that 

50% differences in coral growth could be detected with adequate power with five years of core 

data (Table 3.34). The minimum detectable annual differences were 11% for five years of data 

and 4% for ten years of data (Table 3.35).

Alternative Sampling Strategy Power Evaluation

Coral growth is currently sampled by sclerochronology once during every two-year contract 

period. Because each core provides multiple years of continuous data, increasing or decreasing 

sampling frequency has no bearing on the ability to analyze data from all years. It is unnecessary 

to evaluate increasing and reducing sampling frequency. Core length on the other hand, does 

control how many years of consecutive growth data are available per collection period. Here we 

evaluate how increasing and decreasing core lengths will affect the power to detect changes in 

coral growth rate with the current sample size.

Increasing core lengths to include 15 of 20 years of growth data provided greater power to detect 

small annual trend values (Table 3.35). Minimum detectable annual changes ranged from 3% to 

4% with 15 years of data, and 2% to 3% with 20 years of data. Although the results are not 

shown, these minimum detectable changes continued to decrease with longer datasets. Annual 

trend values of 2% to 3% for 20 years of data are equal to net declines of 33 to 46% or net 

increases of 49 to 80%. When EFGB and WFGB were compared by two-way ANOVA, 

increasing core length to 15 or 20 years of growth also provided advantages. The minimum 

detectable annual differences were 2% for 15 or 20 years of data and continued to decrease as

dataset length increased.
150

Table 3.34 Sclerochronology Current Sampling Design Power Results (ANOVA)

Power analysis results for sclerochronology data with the current design of four cores per bank analyzed 

by one- and two-way ANOVAs.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

For Five Year Cores

East

Growth 5.6 1.1 0.812 0.140 0.068 0.054 0.113 0.517

West

Growth 6.7 1.5 0.734 0.093 0.066 0.059 0.087 0.358

East and West Combined

Growth 6.1 1.4 0.990 0.179 0.060 0.079 0.150 0.779

East compared with West

Growth 5.6/6.7 1.1/1.5 0.996 0.222 0.088 0.069 0.180 0.854

For Ten Year Cores

East

Growth 5.6 1.1 0.951 0.137 0.057 0.057 0.098 0.609

West

Growth 6.7 1.5 0.925 0.116 0.066 0.044 0.105 0.477

East and West Combined

Growth 6.1 1.4 1.000 0.284 0.078 0.072 0.178 0.896

East compared with West

Growth 5.6/6.7 1.1/1.5 1.000 0.265 0.101 0.078 0.172 0.945

Values in red are above the desired power level of 0.80

Table 3.35 Sclerochronology Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (ANOVA)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for sclerochronology data with the current of four 

cores per bank analyzed by one- and two-way ANOVAs.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 5 yrs 10 yrs 15 yrs 20 yrs -20% 20%

East

Growth 5.6 1.1 ±15 ±6 ±3 ±2 22 28

West

Growth 6.7 1.5 ±17 ±6 ±4 ±3 28 36

East and West Combined

Growth 6.1 1.4 ±11 ±4 ±3 ±2 30 38

East compared with West

Growth 5.6/6.7 1.1/1.5 ±11 ±4 ±2 ±2 14 16

Another alternative that was explored to increase power was changing the statistical analysis 

used to evaluate the results, as ANOVA may not be the most appropriate analysis for detecting 

trends in coral growth as measured by sclerochronology. The power simulations were repeated 

for a simple regression type analysis. Simple linear regression has a different set of assumptions 

from ANOVA which should be tested on the datasets being analyzed. These include normality of 

residuals, constant variance of residuals, and independence of residuals. These assumptions 

could not be tested efficiently for the power simulations, but should be tested on data from 

sampling events before using the results of a regression analysis to draw conclusions. 
151

Simple regression was more powerful than ANOVA for detecting changes in coral growth, but 

still did not provide adequate power to detect 10 and 20% net changes in growth in five and 10 

year datasets (Table 3.36). With the simple regression setup, power simulations were able to 

detect both 50% decrease and increases in coral growth with 10 years of data. Minimum 

detectable annual trends were also lower with the simple regression approach, and with 15 or 20 

years of data 20% net changes in growth could be detected with adequate power when data were 

combined for both banks (Table 3.37).

Sclerochronology Summary

The current sampling strategy involves collecting 5 to 10 years of data in each core. With this 

core length power to detect change in coral growth is inadequate to detect 20% net changes in 

growth over the period of time represented by the core. Increasing core length provides more 

power to detect change, and analyzing data by simple regression rather than ANOVA provided 

greater power to detect change. We recommend that longer cores including at least 15 to 20 

years of coral growth be collected. Sampling frequency can be reduced to compensate for any 

increased time and labor costs associated with collecting longer cores because the frequency of

core collections has no bearing on the power to detect change.

Table 3.36 Sclerochronology Current Sampling Design Power Results (Simple 

Regression)

Power analysis results for sclerochronology data with the current design of four cores per bank analyzed 

by simple regression.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

For Five Year Cores

East

Growth 5.6 1.1 0.993 0.325 0.134 0.117 0.290 0.859

West

Growth 6.7 1.5 0.965 0.276 0.112 0.100 0.235 0.775

East and West Combined

Growth 6.1 1.4 1.000 0.484 0.116 0.115 0.360 0.964

For Ten Year Cores

East

Growth 5.6 1.1 1.000 0.511 0.190 0.195 0.388 0.980

West

Growth 6.7 1.5 1.000 0.541 0.173 0.099 0.335 0.933

East and West Combined

Growth 6.1 1.4 1.000 0.748 0.295 0.175 0.684 0.994

Values in red are above the desired power level of 0.80
152

Table 3.37 Sclerochronology Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes (Simple Regression)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for sclerochronology data with the current of four 

cores per bank analyzed by simple regression.

Parameter

Pilot Data Min Annual Trend Min Sample Size for

20% Net Change after 5 yrs

Mean SD 5 yrs 10 yrs 15 yrs 20 Yrs -20% 20%

East

Growth 5.6 1.1 ±10 ±3 ±2 ±1 14 18

West

Growth 6.7 1.5 ±11 ±4 ±2 ±1 16 22

East and West Combined

Growth 6.1 1.4 ±8 ±3 ±2 ±1 18 24

3.2.2.3 Coral Growth Conclusions

For both lateral growth and sclerochronology methodology the current sampling strategy was 

found to be inadequate to detect 20% change in coral growth with the desire power. Achieving 

adequate power for the lateral growth method will likely require increasing the sample size 

beyond a feasible amount of samples. For this reason and those stated in previous chapters, 

discontinuation of this method is recommended. The sclerochronology method shows potential 

to achieve adequate power to detect change in coral growth if the core length is increased. This 

method adjustment should be relatively inexpensive and is therefore recommended as the best 

way to increase the LTM program’s ability to detect change in coral growth.

3.2.3 Coral Health

Coral size and condition surveys were introduced into the FGBLTM program in 2013. The 

current sampling strategy as discussed in the IA (BOEM 2014) is to identify and measure coral 

colonies greater than 4 cm in diameter along eight of the 16 random transects per bank that are 

otherwise used for benthic cover assessment. Presence of bleaching, disease, and mortality are 

also noted with estimated percentages. This provides a sample size of eight per bank and values

for average coral diameter, colony count, and qualitative information about the presence of 

bleaching and disease. The intended statistical analyses for coral size data include comparison 

across years by ANOVA and comparison between banks by t-test. Statistical assessment of coral 

condition data are not addressed in the IA and therefore will not be included in this evaluation of 

statistical power.

Due to the relative newness of the coral size and condition assessments at FGB, pilot study data 

was limited to the one year of data that was available, 2013. It is likely that total variation in the 

2013 pilot data may be inflated by the small sample size (total of 12 transects). We recommend

that these power analyses be repeated after a few more years of data are available for this 

method.
153

Current Sampling Strategy Power Evaluation

Inter-specific differences in growth rate and colony formation type lead to highly variable and 

skewed datasets when size datasets for multiple coral species are lumped (Vermeij and Bak 

2003). Here the coral size data for the four most dominant species in the coral size transects are 

analyzed individually. Those species are Psuedodiploria strigosa (previously called Diploria 

strigosa), Porites astreoides, Orbicella cavernosa (previously called Montastrea cavernosa), and 

Orbicella franksi (previously called Montastrea franksi). We recommend that coral size data be 

log transformed to attain normality and facilitate comparison between species (Vermeij and Bak 

2003). The Anderson-Darling test for normality was used to evaluate whether the untransformed 

or log-transformed data were appropriately normal and a histogram of each dataset was visually

assessed. Log-transformed data sets were determined to be adequately normal for P. strigosa and 

O. franksi. Both transformed and untransformed data for P. astreoides was determined to be nonnormal; however visual assessment of the histograms revealed that the log-transformed dataset 

more closely matched the target bell-shaped distribution. Log transformed data was used to 

calculate total variation for these three species. Untransformed O. cavernosa data was found to 

be adequately normal by the Anderson-Darling test and transforming the data weakened the 

relationship to the normal distribution. Untransformed data was used to calculate total variation 

for this species. Data were analyzed by individual banks using a one-way ANOVA type analysis 

and between banks using a two-way ANOVA type analysis.

Power simulations for the one-way ANOVA setup showed low power to detect 10 and 20% 

changes in colony size for all four species after two, five, and ten years of sampling at both 

EFGB and WFGB (Table 3.38). After two years of sampling, a 50% decline in mean colony size 

for O. cavernosa at EFGB and WFGB and a 50% increase in mean colony size in all four species 

at EFGB and all species except P. strigosa at WFGB could be detected with the desired power of 

0.80 or greater. After five years of sampling, a 50% decline in mean colony size could also be 

detected with the desired power for P. strigosa at EFGB and O. franksi at both banks. After ten 

years of sampling a 50% decrease in mean colony size of P. strigosa could be detected at both 

banks. Minimum detectable annual trends ranged from 31% to 58% after two years, from 9% to 

23% after five years, and from 3% to 11% after ten years (Table 3.39). The greatest power and 

smallest minimum detectable annual trends were associated with P. strigosa at EFGB and O. 

cavernosa at WFGB.

Power simulations for the two-way ANOVA setup showed similarly low power (Table 3.38).

After two years of sampling a 50% decrease in mean colony size of P. strigosa and O. cavernosa

or a 50% increase in mean colony size for P. strigosa, P. astreoides, and O. franksi could be 

detected with the desired power. After five years of sampling a 50% net decrease in mean colony 

size for O. cavernosa could also be detected with the desired power. Minimum detectable annual 

changes ranged from 26% to 51% after two years, from 7% to 20% after five years, and from 3% 

to 8% after ten years (Table 3.39).

High variation in the pilot study data led to the low power estimations discussed above. Predicted 

minimum sample sizes to achieve a power of 0.8 for 20% changes in mean colony size after five 

years ranged from 16 to 70, depending on the species and bank (Table 3.39). A sample size of 70 
154

is unrealistically high, but increase sample sizes to 16 or 20 may be achievable. This alternative 

sampling plan is discussed further below.

Table 3.38 Coral Size Current Sampling Design Power Results

Power analysis results for coral size data with the current design of annual sampling for the four most 

dominant coral species.

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

P. strigosa* 1.9 0.3 0.459 0.240 0.094 0.063 0.254 0.965

P. astreoides* 1.3 0.2 0.093 0.093 0.195 0.203 0.350 0.898

O. franksi* 1.9 0.3 0.782 0.274 0.202 0.168 0.244 0.968

O. cavernosa 57.0 39.4 0.807 0.242 0.202 0.214 0.227 0.860

West

P. strigosa* 1.7 0.4 0.743 0.217 0.181 0.198 0.239 0.748

P. astreoides* 1.2 0.2 0.023 0.031 0.099 0.224 0.314 0.877

O. franksi* 1.9 0.3 0.642 0.283 0.172 0.131 0.240 0.966

O. cavernosa 72.7 13.5 0.991 0.439 0.136 0.094 0.331 0.991

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.417 0.218 0.184 0.436 0.999

P. astreoides* 1.3/1.2 0.2/0.2 0.233 0.191 0.205 0.222 0.489 0.998

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.504 0.200 0.200 0.579 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.773 0.157 0.088 0.087 0.187 0.789

After Five Years

East

P. strigosa* 1.9 0.3 0.999 0.267 0.031 0.042 0.096 0.988

P. astreoides* 1.3 0.2 0.220 0.038 0.038 0.087 0.218 0.820

O. franksi* 1.9 0.3 0.985 0.223 0.042 0.038 0.138 0.885

O. cavernosa 57.0 39.4 0.813 0.166 0.070 0.069 0.104 0.620

West

P. strigosa* 1.7 0.4 0.785 0.168 0.061 0.071 0.101 0.510

P. astreoides* 1.2 0.2 0.086 0.044 0.019 0.056 0.195 0.752

O. franksi* 1.9 0.3 0.997 0.264 0.031 0.040 0.153 0.933

O. cavernosa 72.7 13.5 1.000 0.330 0.104 0.094 0.234 0.951

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.471 0.084 0.071 0.161 0.991

P. astreoides* 1.3/1.2 0.2/0.2 0.507 0.227 0.078 0.115 0.371 0.984

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.445 0.100 0.088 0.379 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.908 0.185 0.072 0.073 0.115 0.548

After Ten Years

East

P. strigosa* 1.9 0.3 1.000 0.388 0.066 0.046 0.242 1.000

P. astreoides* 1.3 0.2 0.295 0.165 0.073 0.060 0.199 0.892

O. franksi* 1.9 0.3 1.000 0.200 0.066 0.051 0.166 0.964

O. cavernosa 57.0 39.4 0.954 0.163 0.055 0.057 0.111 0.757

West

P. strigosa* 1.7 0.4 0.924 0.184 0.071 0.054 0.099 0.665

P. astreoides* 1.2 0.2 0.182 0.095 0.046 0.049 0.151 0.824

O. franksi* 1.9 0.3 1.000 0.367 0.057 0.060 0.172 0.986

O. cavernosa 72.7 13.5 1.000 0.556 0.143 0.098 0.271 0.986

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.411 0.094 0.081 0.186 1.000
155

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

P. astreoides* 1.3/1.2 0.2/0.2 0.648 0.254 0.118 0.096 0.420 1.000

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.529 0.108 0.089 0.399 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.986 0.201 0.082 0.060 0.090 0.684

*log transformed for normality

Values in red are above the desired power level of 0.80

Table 3.39 Coral Size Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral size data with the current design of 

annual sampling for the four most dominant coral species.

Species Pilot Data Min Annual Trend (%) Min Sample Size for 20% 

Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

P. strigosa* 1.9 0.3 -35/+31 ±9 ±3 30 30

P. astreoides* 1.3 0.2 +41 -23/+11 -11/+5 40 34

O. franksi* 1.9 0.3 -40/+37 ±10 ±4 32 36

O. cavernosa 57.0 39.4 -50/+47 -15/+13 ±5 38 52

West

P. strigosa* 1.7 0.4 -58/+54 -17/+14 -6/+5 40 66

P. astreoides* 1.2 0.2 +46 -23/+11 -10/+5 38 70

O. franksi* 1.9 0.3 +34 ±9 ±4 30 32

O. cavernosa 72.7 13.5 -32/+33 ±9 ±4 18 24

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 -30/+28 -8/+7 ±3 20 24

P. astreoides* 1.3/1.2 0.2/0.2 +27 -20/+7 -8/+3 18 26

O. franksi* 1.9/1.9 0.3/0.3 ±26 ±7 ±3 16 18

O. cavernosa 57.0/72.7 39.4/13.5 -52/+50 ±14 ±5 46 48

* log transformed for normality
156

Alternative Sampling Strategy Power Evaluation

Four alternative sampling strategies were evaluated for power to detect change in mean colony 

size: 

1. reducing the sampling frequency to biennial sampling;

2. reducing the sampling frequency to triennial sampling;

3. increasing the sampling frequency to biannual sampling; and 

4. decreasing the sampling frequency to biennial while simultaneously increasing the 

sample size to 16 transects per bank.

Reducing the sampling frequency to every other year or every third year had very minor effects 

on the power simulation results (Tables 3.40 through 3.45). Minimum detectable annual changes 

after five years for the annual and biennial sampling strategies varied only 2% or less from one 

another, the annual sampling strategy detecting only slightly smaller trends. Minimum detectable 

annual changes after 10 years in the annual and triennial sampling strategy and after 11 years for 

the every other year strategy were also all within 2% of each other, the annual sampling strategy 

in some cases and the biennial strategy in other cases detecting just slightly smaller trends than 

the other two strategies. 

Increasing the sampling frequency to biannual sampling also had only very minor effects on the 

power simulation results. Under this strategy the probability of detecting a 50% decline in P. 

strigosa at EFGB after two years was increased to above the desired power level (from 0.459 to 

0.994). Other minor increases in power values did not result in additional detectable changes of 

the desired power level or greater. Minimum detectable annual trends were decreased by 10% to 

more in some cases after two years by sampling biannually. After 10 years, minimum detectable 

annual trends were no more than 2% less for biannual sampling compared to annual sampling.

Because power to detect change in mean colony size was only minutely affected by reducing 

sampling frequency to every other or every third year and increasing sample size appeared to be 

the best way to increase power in the dataset, one recommended alternative sampling strategy is 

to reduce the sampling frequency to every other year and double the sample size. Although this 

does not provide adequate power to detect 20% net changes after five years for some of the cases 

according to the current power simulations, this strategy did provide about double the power of 

the current sampling strategy in the power simulations (Tables 3.46 and 3.47). Under this 

strategy, 20% declines in mean colony size were detectable with the desired power after three 

years (two sampling events) for O. cavernosa at WFGB and O. franksi and P. strigosa in a twoway ANOVA comparison between the banks. 20% increases in mean colony size were also 

detectable after three years for P. astreiodes and O. franski for a two-way ANOVA comparison 

between the banks. Minimum detectable annual changes ranged from 8% to 40% after three 

years, from 5% to 20% after five years, and from 2% to 8% after to ten years.

3.2.3.2 Coral Health Conclusions

Power to detect change in mean colony size at individual banks and FGB in general under the 

current sampling strategy was well below the desired levels to detect 20% changes with 0.80 

power after five years. 50% changes in mean colony size could be detected in most, but not all 
157

cases and power to detect changes in P. astreiodes colonies were particularly low. Sample sizes 

required to achieve the desired power levels in the current power simulations were unachievably 

high numbers in some cases, but in other cases increasing sample size to 16 or 20 would provide 

adequate power. The fact that minimal data were available for use as pilot data may have led to 

inflated sample variance. Actual population variance is likely lower than the sample variance 

reported, and actual power to detect change would increase with smaller variances.

Increasing and decreasing the sampling frequency had minimal effects on power estimations, and 

combining decreased sampling frequency with increased sample size may be the best option for 

increasing the power of the coral size method. Sampling every other year and doubling the 

sample size from eight transects per bank to 16 transects per bank will provide about twice the 

power of the current sampling strategy, but will require about the same level of effort. Under this 

strategy 20% changes in mean colony size were also detectable in some cases. We recommend

that FGBLTM adopt this strategy in order to make the coral size method more efficient.

Table 3.40 Coral Size Biennial Sampling Design Power Results. 

Power analysis results for coral size data with the alternative design of biennial sampling for the four most 

dominant coral species.

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

P. strigosa* 1.9 0.3 0.378 0.293 0.152 0.074 0.198 0.879

P. astreoides* 1.3 0.2 0.079 0.079 0.165 0.253 0.330 0.885

O. franksi* 1.9 0.3 0.745 0.285 0.188 0.182 0.248 0.936

O. cavernosa 57.0 39.4 0.855 0.284 0.197 0.200 0.225 0.777

West

P. strigosa* 1.7 0.4 0.827 0.255 0.204 0.199 0.226 0.669

P. astreoides* 1.2 0.2 0.028 0.028 0.103 0.214 0.295 0.795

O. franksi* 1.9 0.3 0.603 0.367 0.189 0.118 0.251 0.937

O. cavernosa 72.7 13.5 1.000 0.476 0.133 0.090 0.374 0.982

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.489 0.215 0.176 0.355 0.998

P. astreoides* 1.3/1.2 0.2/0.2 0.276 0.151 0.183 0.233 0.555 0.998

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.485 0.212 0.202 0.551 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.911 0.204 0.084 0.082 0.185 0.686

After Fifth Year

East

P. strigosa* 1.9 0.3 0.962 0.178 0.038 0.040 0.133 0.966

P. astreoides* 1.3 0.2 0.076 0.052 0.070 0.105 0.204 0.756

O. franksi* 1.9 0.3 0.968 0.199 0.071 0.060 0.132 0.862

O. cavernosa 57.0 39.4 0.789 0.166 0.094 0.068 0.124 0.662

West

P. strigosa* 1.7 0.4 0.722 0.177 0.085 0.072 0.090 0.543

P. astreoides* 1.2 0.2 0.058 0.014 0.023 0.074 0.179 0.709

O. franksi* 1.9 0.3 0.966 0.203 0.088 0.066 0.105 0.935

O. cavernosa 72.7 13.5 0.999 0.471 0.106 0.094 0.237 0.957

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.484 0.079 0.106 0.178 0.990

P. astreoides* 1.3/1.2 0.2/0.2 0.438 0.226 0.119 0.135 0.387 0.987

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.477 0.122 0.124 0.391 1.000
158

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

O. cavernosa 57.0/72.7 39.4/13.5 0.903 0.139 0.067 0.071 0.130 0.525

After Eleventh Year

East

P. strigosa* 1.9 0.3 1.000 0.280 0.026 0.014 0.245 0.987

P. astreoides* 1.3 0.2 0.203 0.093 0.061 0.085 0.266 0.745

O. franksi* 1.9 0.3 0.999 0.217 0.066 0.047 0.174 0.923

O. cavernosa 57.0 39.4 0.884 0.168 0.074 0.064 0.119 0.550

West

P. strigosa* 1.7 0.4 0.834 0.151 0.064 0.055 0.091 0.489

P. astreoides* 1.2 0.2 0.120 0.030 0.023 0.091 0.257 0.705

O. franksi* 1.9 0.3 1.000 0.257 0.055 0.044 0.181 0.908

O. cavernosa 72.7 13.5 1.000 0.419 0.076 0.090 0.302 0.985

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.311 0.068 0.100 0.321 0.997

P. astreoides* 1.3/1.2 0.2/0.2 0.553 0.236 0.068 0.103 0.439 0.990

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.545 0.137 0.091 0.463 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.966 0.112 0.077 0.074 0.116 0.603

* log transformed for normality

Values in red are above the desired power level of 0.80

Table 3.41 Coral Size Biennial Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral size data with the alternative design of 

biennial sampling for the four most dominant coral species.

Species Pilot Data Min Annual Trend (%) Min Sample Size for 20% 

Net Change after 5 yrs

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

East

P. strigosa* 1.9 0.3 -17/+15 -9/+8 -4/+3 26 28

P. astreoides* 1.3 0.2 +22 -25/+11 -10/+4 46 34

O. franksi* 1.9 0.3 -21/+19 ±11 ±4 34 36

O. cavernosa 57.0 39.4 -24/+24 -18/+13 -6/+5 40 58

West

P. strigosa* 1.7 0.4 -28/+26 -20/+14 -6/+5 50 66

P. astreoides* 1.2 0.2 -47/+23 -24/+12 -9/+4 38 48

O. franksi* 1.9 0.3 +17 ±10 ±4 28 34

O. cavernosa 72.7 13.5 -16/+17 ±9 ±4 20 24

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 ±15 ±8 ±3 20 24

P. astreoides* 1.3/1.2 0.2/0.2 -40/+14 -20/+7 -8/+3 18 26

O. franksi* 1.9/1.9 0.3/0.3 -14/+13 ±7 ±3 16 18

O. cavernosa 57.0/72.7 39.4/13.5 -26/+25 -14/+15 -5/+6 46 60

* log transformed for normality
159

Table 3.42 Coral Size Triennial Sampling Design Power Results

Power analysis results for coral size data with the alternative design of triennial sampling for the four most 

dominant coral species.

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Fourth Year

East

P. strigosa* 1.9 0.3 0.370 0.321 0.133 0.071 0.372 0.898

P. astreoides* 1.3 0.2 0.083 0.097 0.211 0.233 0.293 0.826

O. franksi* 1.9 0.3 0.715 0.338 0.202 0.184 0.204 0.926

O. cavernosa 57.0 39.4 0.825 0.295 0.194 0.196 0.238 0.747

West

P. strigosa* 1.7 0.4 0.837 0.254 0.185 0.198 0.223 0.653

P. astreoides* 1.2 0.2 0.060 0.022 0.117 0.211 0.305 0.788

O. franksi* 1.9 0.3 0.574 0.344 0.169 0.109 0.221 0.928

O. cavernosa 72.7 13.5 1.000 0.449 0.149 0.127 0.364 0.971

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.483 0.203 0.174 0.403 0.995

P. astreoides* 1.3/1.2 0.2/0.2 0.344 0.194 0.190 0.240 0.485 0.993

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.589 0.201 0.188 0.551 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.936 0.223 0.063 0.094 0.172 0.682

After Tenth Year

East

P. strigosa* 1.9 0.3 0.993 0.361 0.044 0.038 0.191 0.971

P. astreoides* 1.3 0.2 0.238 0.130 0.082 0.087 0.199 0.763

O. franksi* 1.9 0.3 0.993 0.193 0.087 0.052 0.136 0.861

O. cavernosa 57.0 39.4 0.854 0.159 0.071 0.055 0.100 0.575

West

P. strigosa* 1.7 0.4 0.812 0.149 0.048 0.055 0.080 0.521

P. astreoides* 1.2 0.2 0.131 0.050 0.036 0.094 0.150 0.726

O. franksi* 1.9 0.3 0.997 0.222 0.086 0.041 0.120 0.929

O. cavernosa 72.7 13.5 0.998 0.319 0.108 0.061 0.228 0.922

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.472 0.088 0.073 0.253 0.979

P. astreoides* 1.3/1.2 0.2/0.2 0.538 0.180 0.096 0.070 0.333 0.985

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.584 0.096 0.117 0.347 0.998

O. cavernosa 57.0/72.7 39.4/13.5 0.920 0.166 0.071 0.066 0.110 0.508

* log transformed for normality

Values in red are above the desired power level of 0.80
160

Table 3.43 Coral Size Triennial Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral size data with the alternative design of 

triennial sampling for the four most dominant coral species.

Species Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 4 yrs

Mean SD 4 yrs 10 yrs -20% 20%

East

P. strigosa* 1.9 0.3 -11/+10 ±4 24 24

P. astreoides* 1.3 0.2 +14 -11/+5 36 30

O. franksi* 1.9 0.3 -15/+12 ±5 30 32

O. cavernosa 57.0 39.4 -16/+26 -7/+6 32 50

West

P. strigosa* 1.7 0.4 -19/+17 -7/+6 42 52

P. astreoides* 1.2 0.2 +15 -10/+5 64 34

O. franksi* 1.9 0.3 +11 ±4 24 28

O. cavernosa 72.7 13.5 ±11 ±4 16 20

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 ±10 -4/+3 16 20

P. astreoides* 1.3/1.2 0.2/0.2 -27/+9 -9/+3 16 24

O. franksi* 1.9/1.9 0.3/0.3 ±9 ±3 14 16

O. cavernosa 57.0/72.7 39.4/13.5 -17/+18 ±6 38 48

* log transformed for normality

Table 3.44 Coral Size Biannual Sampling Design Power Results

Power analysis results for coral size data with the alternative design of biannual sampling for the four 

most dominant coral species.

Species Pilot Data Power for Percent Net Changes

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

P. strigosa* 1.9 0.3 0.994 0.217 0.049 0.048 0.224 0.995

P. astreoides* 1.3 0.2 0.226 0.100 0.071 0.082 0.220 0.835

O. franksi* 1.9 0.3 0.967 0.189 0.059 0.050 0.156 0.927

O. cavernosa 57.0 39.4 0.796 0.153 0.065 0.054 0.116 0.680

West

P. strigosa* 1.7 0.4 0.724 0.121 0.039 0.054 0.090 0.649

P. astreoides* 1.2 0.2 0.078 0.049 0.035 0.078 0.176 0.795

O. franksi* 1.9 0.3 0.989 0.220 0.081 0.049 0.174 0.961

O. cavernosa 72.7 13.5 0.994 0.298 0.093 0.117 0.261 0.965

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.345 0.079 0.065 0.077 0.999

P. astreoides* 1.3/1.2 0.2/0.2 0.374 0.186 0.093 0.085 0.348 0.998

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.421 0.085 0.061 0.403 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.808 0.161 0.068 0.074 0.130 0.607

After Five Years

East

P. strigosa* 1.9 0.3 1.000 0.268 0.040 0.073 0.215 0.998

P. astreoides* 1.3 0.2 0.287 0.165 0.076 0.084 0.184 0.880

O. franksi* 1.9 0.3 0.999 0.195 0.072 0.044 0.143 0.968

O. cavernosa 57.0 39.4 0.994 0.218 0.057 0.062 0.120 0.758
161

Species Pilot Data Power for Percent Net Changes

Mean SD -50% -20% -10% 10% 20% 50%

West

P. strigosa* 1.7 0.4 0.917 0.168 0.075 0.057 0.103 0.675

P. astreoides* 1.2 0.2 0.161 0.090 0.037 0.060 0.249 0.857

O. franksi* 1.9 0.3 1.000 0.223 0.049 0.056 0.167 0.990

O. cavernosa 72.7 13.5 1.000 0.392 0.115 0.085 0.257 0.995

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.382 0.088 0.083 0.311 1.000

P. astreoides* 1.3/1.2 0.2/0.2 0.608 0.232 0.127 0.108 0.586 0.999

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.528 0.129 0.077 0.443 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.977 0.221 0.077 0.058 0.124 0.656

After Ten Years

East

P. strigosa* 1.9 0.3 1.000 0.669 0.048 0.053 0.423 1.000

P. astreoides* 1.3 0.2 0.449 0.282 0.140 0.079 0.319 0.976

O. franksi* 1.9 0.3 1.000 0.328 0.065 0.076 0.273 0.999

O. cavernosa 57.0 39.4 0.998 0.256 0.057 0.065 0.141 0.912

West

P. strigosa* 1.7 0.4 0.996 0.180 0.053 0.057 0.134 0.854

P. astreoides* 1.2 0.2 0.311 0.105 0.062 0.080 0.242 0.962

O. franksi* 1.9 0.3 1.000 0.380 0.053 0.077 0.309 1.000

O. cavernosa 72.7 13.5 1.000 0.651 0.097 0.101 0.450 1.000

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.638 0.078 0.068 0.505 1.000

P. astreoides* 1.3/1.2 0.2/0.2 0.840 0.399 0.143 0.106 0.654 1.000

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.785 0.178 0.118 0.689 1.000

O. cavernosa 57.0/72.7 39.4/13.5 1.000 0.228 0.070 0.062 0.172 0.837

* log transformed for normality 

Values in red are above the desired power level of 0.80
162

Table 3.45 Coral Size Biannual Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral size data with the alternative design of 

biannual sampling for the four most dominant coral species.

Species Pilot Data Min Annual Trend (%)

Min Sample Size for 

20% Net Change after 

5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

P. strigosa* 1.9 0.3 -23/+22 ±7 ±3 22 22

P. astreoides* 1.3 0.2 +30 -21/+9 -9/+3 32 28

O. franksi* 1.9 0.3 -30/+24 ±8 ±3 24 26

O. cavernosa 57.0 39.4 -38/+34 ±10 ±4 30 46

West

P. strigosa* 1.7 0.4 -42/+36 -12/+11 ±5 38 50

P. astreoides* 1.2 0.2 +32 -20/+8 -9/+4 56 32

O. franksi* 1.9 0.3 ±25 -8/+7 ±3 22 28

O. cavernosa 72.7 13.5 ±23 ±7 ±3 14 20

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 -21/+20 -7/+6 ±3 16 18

P. astreoides* 1.3/1.2 0.2/0.2 -53/+20 -16/+6 -6/+2 16 20

O. franksi* 1.9/1.9 0.3/0.3 -20/+18 -6/+5 +3/-2 12 14

O. cavernosa 57.0/72.7 39.4/13.5 -37/+38 ±11 ±4 36 48

* log transformed for normality 
163

Table 3.46 Coral Size Biennial Sampling Design Power Results for Double the 

Sample Size

Power analysis results for coral size data with the alternative design of biennial sampling with double the 

sample size (16 per bank per year) for the four most dominant coral species.

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

P. strigosa* 1.9 0.3 0.634 0.606 0.160 0.130 0.670 0.996

P. astreoides* 1.3 0.2 0.365 0.225 0.223 0.217 0.587 0.994

O. franksi* 1.9 0.3 0.929 0.561 0.217 0.187 0.562 1.000

O. cavernosa 57.0 39.4 0.997 0.488 0.189 0.187 0.302 0.982

West

P. strigosa* 1.7 0.4 0.993 0.408 0.175 0.460 0.310 0.962

P. astreoides* 1.2 0.2 0.237 0.053 0.118 0.215 0.538 0.996

O. franksi* 1.9 0.3 0.842 0.642 0.192 0.162 0.604 0.998

O. cavernosa 72.7 13.5 1.000 0.828 0.307 0.245 0.725 1.000

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.810 0.179 0.228 0.770 1.000

P. astreoides* 1.3/1.2 0.2/0.2 0.821 0.596 0.255 0.314 0.829 1.000

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.952 0.360 0.213 0.900 1.000

O. cavernosa 57.0/72.7 39.4/13.5 1.000 0.423 0.132 0.118 0.332 0.967

After Fifth Year

East

P. strigosa* 1.9 0.3 1.000 0.539 0.089 0.087 0.535 1.000

P. astreoides* 1.3 0.2 0.511 0.309 0.121 0.112 0.414 0.975

O. franksi* 1.9 0.3 1.000 0.419 0.107 0.091 0.358 0.999

O. cavernosa 57.0 39.4 0.993 0.356 0.100 0.072 0.207 0.934

West

P. strigosa* 1.7 0.4 0.993 0.324 0.100 0.084 0.180 0.888

P. astreoides* 1.2 0.2 0.315 0.097 0.085 0.136 0.269 0.982

O. franksi* 1.9 0.3 1.000 0.759 0.123 0.094 0.403 1.000

O. cavernosa 72.7 13.5 1.000 0.820 0.209 0.193 0.578 1.000

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.834 0.143 0.157 0.607 1.000

P. astreoides* 1.3/1.2 0.2/0.2 0.941 0.541 0.181 0.258 0.762 1.000

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.977 0.297 0.113 0.758 1.000

O. cavernosa 57.0/72.7 39.4/13.5 0.999 0.299 0.083 0.106 0.219 0.862

After Eleventh Year

East

P. strigosa* 1.9 0.3 1.000 0.414 0.077 0.080 0.622 1.000

P. astreoides* 1.3 0.2 0.654 0.303 0.089 0.116 0.498 0.991

O. franksi* 1.9 0.3 1.000 0.355 0.086 0.097 0.452 0.997

O. cavernosa 57.0 39.4 1.000 0.315 0.072 0.087 0.277 0.982

West

P. strigosa* 1.7 0.4 0.999 0.336 0.100 0.063 0.224 0.944

P. astreoides* 1.2 0.2 0.542 0.128 0.078 0.077 0.411 0.985

O. franksi* 1.9 0.3 1.000 0.628 0.073 0.086 0.311 1.000

O. cavernosa 72.7 13.5 1.000 0.819 0.145 0.163 0.661 1.000

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 1.000 0.633 0.122 0.128 0.737 1.000

P. astreoides* 1.3/1.2 0.2/0.2 0.966 0.570 0.258 0.230 0.825 1.000

O. franksi* 1.9/1.9 0.3/0.3 1.000 0.913 0.152 0.162 0.881 1.000
164

Species Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

O. cavernosa 57.0/72.7 39.4/13.5 1.000 0.374 0.119 0.070 0.247 0.830

* log transformed for normality 

Values in red are above the desired power level of 0.80

Table 3.47 Coral Size Biennial Sample Design Minimum Detectable Trends for 

Double the Sample Size

Minimum detectable annual trend values for coral size data with the alternative design of biennial 

sampling with double the sample size (16 per bank per year) for the four most dominant coral species.

Species Pilot Data Min Annual Trend (%)

Mean SD 3 yrs 5 yrs 11 yrs

East

P. strigosa* 1.9 0.3 -13/+11 ±6 -3/+2

P. astreoides* 1.3 0.2 -40/+13 -20/+7 -8/+2

O. franksi* 1.9 0.3 -14/+13 ±7 ±3

O. cavernosa 57.0 39.4 ±16 ±9 ±3

West

P. strigosa* 1.7 0.4 ±18 ±10 ±4

P. astreoides* 1.2 0.2 -39/+14 -19/+8 -8/+3

O. franksi* 1.9 0.3 ±13 ±7 ±3

O. cavernosa 72.7 13.5 ±11 ±6 ±2

East compared with West

P. strigosa* 1.9/1.7 0.3/0.4 ±12 ±6 ±2

P. astreoides* 1.3/1.2 0.2/0.2 -28/+9 ±6 -5/+2

O. franksi* 1.9/1.9 0.3/0.3 -10/+9 ±5 ±2

O. cavernosa 57.0/72.7 39.4/13.5 -18/+17 ±10 ±4

* log transformed for normality

3.2.4 Coral Recruitment

Coral recruitment surveys were introduced into the FGBLTM program in 2013. The current 

sampling strategy as discussed in the IA (BOEM 2014) is to measure and count coral recruits (< 

4 cm in diameter) along eight of the 16 random transects per bank that are otherwise used for 

benthic cover assessment. This provides a sample size of eight per bank and with a single coral 

recruit count per transect. The intended statistical analyses for coral recruitment are not outlined 

in the IA, but are assumed to be similar to those discussed for coral size and condition, because

the coral size and condition assessment is preformed simultaneously with the coral recruit 

surveys and produces a similar dataset. Statistical analysis is therefore assumed to include 

comparison across years by ANOVA and comparison between banks by t-test.

Due to the relative newness of the coral recruit method at FGB, pilot study data was limited to 

the one year of data that was available, 2013. Coral recruits were also enumerated previously 

during an AGRRA survey at FGB; however, raw data were not available and no measure of 

variation was reported in the resulting technical report (Pattengill-Semmens et al. 2000). It is 

likely that total variation in the 2013 pilot data may be inflated by the small sample size (total of 

12 transects). We recommend that these power analyses be repeated after a few more years of 

data are available for this method.
165

Current Sampling Strategy Power Evaluation

Anderson-Darling test results failed to detect non-normality in the dataset, but the small sample 

size of the pilot data likely prevented this statistical assessment from being adequately powerful. 

A histogram distribution was generated and visually assessed for the appearance of normality. 

The dataset appeared to be adequately close to the target bell shaped distribution given that the 

sample size was small. Log and square root transformations did not improve the histogram 

shape, so untransformed data were used to calculate total variation for the power simulations.

Power simulations for the number of coral recruits per transect showed that the current sampling 

strategy may be inadequate to detect changes with the desired power (Table 3.48). After two 

years of sampling, there was not sufficient power to detect 10, 20 or 50% changes in the number 

of recruits whether data were pooled for the banks or each was treated separately. After five 

years of sampling, a 50% decline in the number of recruits could be detected when data were 

combined for both banks. Minimum detectable annual trends ranged from 58% to greater than 

100% for two years of sampling, from 15% to 24% for five years of sampling, and from 6% to 

9% for ten years of sampling (Table 3.49).

Power was similarly low when data were analyzed by two-way ANOVA to detect differences 

between the two banks (Table 3.48). After two years of sampling the power of the analyses was 

insufficient to detect 10, 20, and 50% changes in coral recruit counts between EFGB and WFGB. 

After five years and ten years of sampling adequate power was achieved to detect a 50% net 

change in coral recruit counts between the banks. Minimum detectable annual trends in coral 

recruit counts were 62% after two years, 16% after five years, and 6% after ten years (Table 

3.49).

Low power under the current sampling strategy resulted from high variation in the data relative 

to the mean values. Standard deviations were near 50% the magnitudes of the sample means in 

the pilot data. As a result of the apparent high variation predicted minimum sample sizes to 

detect 20% net changes in the number of recruits after five years of sampling ranged from 110 to 

170 samples per bank, and minimum sample sizes to detect a 20% difference in means after five 

years for the two-way ANOVA type analysis was 60 to 80 samples per bank (Table 3.49). These 

are unrealistically high numbers of replicates given that the current budget allows for only eight 

samples per bank per year. Other strategies for increasing sample power will be discussed further 

below.

Alternative Sampling Strategy Power Evaluation

Four alternative sampling strategies were evaluated for power to detect change in coral recruit 

counts: 

1. reducing the sampling frequency to biennial sampling, 

2. reducing the sampling frequency to triennial sampling, 

3. increasing the sampling frequency to biannual samples, and 

4. decreasing the sampling frequency to biennial sampling while simultaneously increasing 

the sample size to 16 transects per bank. Each sampling strategy was evaluated using the 

current sample size of eight transects per bank. 
166

Reducing the sampling frequency to biennial sampling did not have a great effect on power to 

detect change (Tables 3.50 and 3.51). After five years of sampling (three sampling events), a 

50% decline in the number of recruits could be detected when data were combined for both 

banks. Minimum detectable annual trends ranged from 15% to 24% for five years of sampling 

and from 6% to 10% for eleven years of sampling. With the two-way ANOVA type design, after 

five years and eleven years of sampling adequate power was achieved to detect a 50% net change 

in coral recruit counts between the banks. Minimum detectable annual trends in coral recruit 

counts were 16% after five years, and 6% after eleven years.

Table 3.48 Coral Recruits Current Sampling Design Power Results

Power analysis results for coral recruit data with the current design of annual sampling.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

#/transect 3.7 1.9 0.306 0.099 0.092 0.090 0.083 0.306

West

#/transect 3.8 1.8 0.340 0.105 0.080 0.073 0.126 0.339

East and West Combined

#/transect 3.8 1.8 0.675 0.149 0.087 0.086 0.139 0.694

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.652 0.138 0.065 0.070 0.137 0.670

After Five Years

East

#/transect 3.7 1.9 0.380 0.060 0.055 0.047 0.075 0.168

West

#/transect 3.8 1.8 0.444 0.063 0.064 0.064 0.075 0.181

East and West Combined

#/transect 3.8 1.8 0.852 0.131 0.062 0.064 0.107 0.426

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.806 0.107 0.067 0.047 0.081 0.444

After Ten Years

East

#/transect 3.7 1.9 0.526 0.069 0.054 0.057 0.033 0.199

West

#/transect 3.8 1.8 0.575 0.085 0.054 0.044 0.068 0.226

East and West Combined

#/transect 3.8 1.8 0.965 0.153 0.063 0.048 0.110 0.562

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.937 0.125 0.064 0.062 0.112 0.494

Values in red are above the desired power level of 0.80
167

Table 3.49 Coral Recruits Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size for detecting 20% net changes after 

five years at desired power level of 0.8 for coral recruit data with the current design of annual sampling.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

#/transect 3.7 1.9 >100 ±25 ±9 120 170

West

#/transect 3.8 1.8 ±98 ±24 ±9 110 150

East and West Combined

#/transect 3.8 1.8 ±58 ±15 ±6 110 150

East compared with West

#/transect 3.7/3.8 1.9/1.8 ±62 ±16 ±6 60 80

Table 3.50 Coral Recruits Biennial Sampling Design Power Results

Power analysis results for coral recruit data with the alternative design of biennial sampling.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

#/transect 3.7 1.9 0.421 0.122 0.088 0.092 0.085 0.279

West

#/transect 3.8 1.8 0.438 0.124 0.097 0.075 0.108 0.311

East and West Combined

#/transect 3.8 1.8 0.813 0.138 0.093 0.086 0.163 0.566

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.768 0.186 0.090 0.085 0.110 0.577

After Fifth Year

East

#/transect 3.7 1.9 0.396 0.062 0.059 0.049 0.066 0.200

West

#/transect 3.8 1.8 0.404 0.068 0.051 0.061 0.065 0.200

East and West Combined

#/transect 3.8 1.8 0.805 0.119 0.058 0.059 0.103 0.474

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.794 0.118 0.076 0.062 0.110 0.420

After Eleventh Year

East

#/transect 3.7 1.9 0.427 0.073 0.060 0.048 0.057 0.191

West

#/transect 3.8 1.8 0.484 0.065 0.055 0.056 0.072 0.172

East and West Combined

#/transect 3.8 1.8 0.904 0.125 0.072 0.067 0.099 0.405

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.866 0.114 0.062 0.049 0.084 0.552

Values in red are above the desired power level of 0.80
168

Table 3.51 Coral Recruits Biennial Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral recruit data with the alternative design 

of biennial sampling.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

East

#/transect 3.7 1.9 ±53 ±27 ±10 130 170

West

#/transect 3.8 1.8 ±51 ±25 ±9 120 150

East and West Combined

#/transect 3.8 1.8 ±28 ±15 ±6 110 150

East compared with West

#/transect 3.7/3.8 1.9/1.8 ±20 ±16 ±6 60 80

Reducing the sampling frequency to triennial sampling also did not have a great effect on power 

to detect change (Tables 3.52 and 3.53). After four years of sampling (two sampling events), a 

50% decline in the number of recruits could be detected when data were combined for both 

banks. Minimum detectable annual trends ranged from 19% to 33% for four years of sampling 

and from 7% to 12% for ten years of sampling. With the two-way ANOVA type design, after 

four years and eleven years of sampling adequate power was achieved to detect a 50% net 

change in coral recruit counts between the banks. Minimum detectable annual trends in coral 

recruit counts were 21% after four years, and 7% after ten years.

Finally, increasing the sampling frequency to biannual sampling did not have a great effect on 

power to detect change (Tables 3.54 and 3.55). After five years of sampling (six sampling 

events), a 50% decline in the number of recruits could be detected when data were combined for 

both banks. Minimum detectable annual trends were smaller than those detected with annual 

sampling. Minimum detectable annual trends ranged from 41% to 74% for two years of 

sampling, from 12% to 19% for five years of sampling, and from 5% to 7% for ten years of 

sampling. With the two-way ANOVA type design, after five years and ten years of sampling 

adequate power was achieved to detect a 50% net change in coral recruit counts between the 

banks. Minimum detectable annual trends in coral recruit counts were 12% after five years, and 

5% after eleven years, which were again smaller than predicted with annual sampling.

Because power to detect change in coral recruit counts were only minutely affected by reducing 

sampling frequency to every other or every third year and increasing sample frequency appeared 

to be the best way to increase power in the dataset, one recommended alternative sampling 

strategy is to reduce the sampling frequency to every other year and double the sample size. 

Although this does not provide adequate power to detect 20% net changes after five years of 

sampling according to the current power simulations, this strategy did provide about double the 

power of the current sampling strategy in the power simulations (Table 3.56). Under this strategy 

50% changes in coral recruit counts could be detected with the desired power after three years 

(two sampling events) when data were combined for both banks and in a two-way ANOVA 

comparison between the two banks. Minimum detectable annual changes ranged from 20% to 
169

31% after three years, from 11% to 17% after five years, and from 4% to 6% after ten years

(Table 3.57).

Table 3.52 Coral Recruits Triennial Sampling Design Power Results

Power analysis results for coral recruit data with the alternative design of triennial sampling.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Fourth Year

East

#/transect 3.7 1.9 0.423 0.099 0.097 0.093 0.106 0.218

West

#/transect 3.8 1.8 0.495 0.137 0.088 0.096 0.091 0.29

East and West Combined

#/transect 3.8 1.8 0.832 0.167 0.084 0.071 0.149 0.589

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.816 0.169 0.066 0.078 0.13 0.525

After Tenth Year

East

#/transect 3.7 1.9 0.396 0.069 50 0.054 0.064 0.158

West

#/transect 3.8 1.8 0.419 0.061 0.054 0.049 0.053 0.167

East and West Combined

#/transect 3.8 1.8 0.846 0.106 0.048 0.069 0.075 0.446

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.79 0.102 0.066 0.065 0.093 0.437

Values in red are above the desired power level of 0.80

Table 3.53 Coral Recruits Triennial Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral recruit data with the alternative design 

of triennial sampling.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 4 yrs

Mean SD 4 yrs 10 yrs -20% 20%

East

#/transect 3.7 1.9 ±33 ±12 110 140

West

#/transect 3.8 1.8 ±33 ±11 100 120

East and West Combined

#/transect 3.8 1.8 ±19 ±7 100 130

East compared with West

#/transect 3.7/3.8 1.9/1.8 ±21 ±7 60 70
170

Table 3.54 Coral Recruits Biannual Sampling Design Power Results

Power analysis results for coral recruit data with the alternative design of biannual sampling.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Second Year

East

#/transect 3.7 1.9 0.289 0.070 0.057 0.048 0.065 0.189

West

#/transect 3.8 1.8 0.303 0.064 0.04 0.048 0.058 0.222

East and West Combined

#/transect 3.8 1.8 0.697 0.103 0.071 0.067 0.091 0.570

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.635 0.105 0.049 0.063 0.099 0.494

After Fifth Year

East

#/transect 3.7 1.9 0.465 0.064 0.055 0.057 0.060 0.201

West

#/transect 3.8 1.8 0.570 0.067 0.062 0.055 0.076 0.222

East and West Combined

#/transect 3.8 1.8 0.942 0.145 0.065 0.053 0.107 0.531

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.896 0.107 0.058 0.057 0.100 0.500

After Tenth Year

East

#/transect 3.7 1.9 0.738 0.075 0.049 0.048 0.076 0.258

West

#/transect 3.8 1.8 0.791 0.086 0.063 0.059 0.066 0.276

East and West Combined

#/transect 3.8 1.8 0.996 0.183 0.064 0.063 0.109 0.725

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.998 0.129 0.054 0.056 0.107 0.726

Values in red are above the desired power level of 0.80
171

Table 3.55 Coral Recruits Biannual Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for coral recruit data with the alternative design 

of biannual sampling.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for 20% Net 

Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

#/transect 3.7 1.9 ±74 ±19 ±7 100 140

West

#/transect 3.8 1.8 ±70 ±18 ±7 90 120

East and West Combined

#/transect 3.8 1.8 ±41 ±12 ±5 90 120

East compared with West

#/transect 3.7/3.8 1.9/1.8 ±44 ±13 ±5 50 70
172

Table 3.56 Coral Recruits Biennial Sampling Design Power Results for Double 

the Sample Size

Power analysis results for coral recruit data with the alternative design of biennial sampling with double 

the sample size (16 per bank per year).

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Second Year

East

#/transect 3.7 1.9 0.757 0.152 0.082 0.070 0.151 0.578

West

#/transect 3.8 1.8 0.770 0.184 0.082 0.077 0.137 0.555

East and West Combined

#/transect 3.8 1.8 0.988 0.344 0.103 0.093 0.283 0.912

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.986 0.305 0.122 0.082 0.265 0.895

After Fifth Year

East

#/transect 3.7 1.9 0.757 0.120 0.084 0.067 0.087 0.396

West

#/transect 3.8 1.8 0.758 0.123 0.058 0.081 0.103 0.449

East and West Combined

#/transect 3.8 1.8 0.994 0.239 0.080 0.067 0.198 0.814

East compared with West

#/transect 3.7/3.8 1.9/1.8 0.989 0.300 0.075 0.081 0.185 0.782

After Tenth Year

East

#/transect 3.7 1.9 0.855 0.117 0.057 0.066 0.081 0.361

West

#/transect 3.8 1.8 0.894 0.125 0.055 0.056 0.105 0.391

East and West Combined

#/transect 3.8 1.8 1.000 0.299 0.067 0.067 0.229 0.886

East compared with West

#/transect 3.7/3.8 1.9/1.8 1.000 0.269 0.069 0.058 0.213 0.782

Values in red are above the desired power level of 0.80

Table 3.57 Coral Recruits Biennial Sample Design Minimum Detectable Trends 

for Double the Sample Size

Minimum detectable annual trend values for coral recruit data with the alternative design of biennial 

sampling with double the sample size (16 per bank per year).

Parameter Pilot Data Min Annual Trend (%)

Mean SD 2 yrs 5 yrs 10 yrs

East

#/transect 3.7 1.9 ±31 ±17 ±6

West

#/transect 3.8 1.8 ±29 ±16 ±6

East and West Combined

#/transect 3.8 1.8 ±20 ±11 ±4

East compared with West

#/transect 3.7/3.8 1.9/1.8 ±20 ±11 ±4
173

3.2.4.2 Coral Recruitment Conclusions

Power to detect change in coral recruit number at individual banks and FGB in general under the 

current sampling strategy was well below the desired levels to detect 20% changes with 0.80 

power after five years. 50% changes in coral recruitment could be detected across FGB, but not 

at individual banks. Sample sizes required to achieve the desired power levels in the current 

power simulations were unachievably high numbers. The fact that minimal data was available for 

use as pilot data may have led to inflated sample variance. Actual population variance is likely 

lower than the sample variance reported, and actual power to detect change would increase with 

smaller variances.

Increasing and decreasing the sampling frequency had minimal effects on power estimations, and 

combining decreased sampling frequency with increased sample size may be the best option for 

increasing the power of the coral recruitment method. Sampling every other year and doubling 

the sample size from eight transects per bank to 16 transects per bank will provide about twice 

the power of the current sampling strategy, but will require a similar level of effort. We 

recommend that FGBLTM adopt this strategy in order to make the coral recruitment method

more efficient.

3.2.5 Sea Urchins, Lobsters, and Lionfish

Diadema antillarum (long-spined sea urchin) occurrence at EFGB for sampling years 2009–2012 

was very low and typically no statistical analyses are run on these survey data by FGBLTM. 

Power analyses were run for EFGB using a significance level of α = 0.1 and presence and 

absence data with mean abundance. WFGB had higher abundance of D. antillarum during these 

sampling years, so analyses were run with the standard significance level of a = 0.05, using mean 

density (urchins/100 m

2

) and standard deviation. Data were provided by FGBLTM as a single 

value pooled from both transects for each sampling year, but the surveys/plot/interval value in 

program Monitor (Gibbs and Ene 2010) was entered as three because the current IA has 

increased sample size to six perimeters per bank starting in the 2013 sampling period (BOEM 

2014). Because EFGB data were necessarily presence or absence and WFGB were mean 

abundance, between-bank comparisons were not conducted. Sample size was too small to 

evaluate data normality distributions through the Anderson-Darling test or frequency histograms. 

Due to the very low power of most results, minimum annual trends were not determined, and 

minimum sample sizes were estimated to detect changes of 50% after 10 years at desired power 

levels. Almost no lobsters of either species (Panulirus argus and Panulirus guttatus) were 

observed from 2009-2012 at either bank (Johnston et al. 2013, 2015), so analyses could not be 

run. Lionfish data were not available from FGBLTM at the time of analysis. 

Current and Alternative Sampling Design Power Evaluation

The current design of annually sampling six transects per bank along the study site perimeters, a 

total area of 1200 m

2

(BOEM 2014), yielded low power for both banks (Tables 3.58 and 3.59). 

No analysis in the current design reached the desired power level of 0.8. Similar results were 

found for every other year, every third year, and biannual sampling designs (Tables 3.60 and 

3.61). The biannual design yielded the lowest power results of any design at EFGB, particularly 
174

in the shorter timeframes of two and five years. Power of the biannual design at EFGB improved 

after 10 years, and was more similar to the every third year design. At WFGB, the biannual 

design yielded similar power results to the other designs. Analyses showed that sample size 

would need to exceed 100 transects (an unrealistic number) at EFGB for all designs for power to 

reach 0.8 for changes of 50% after 10 years of sampling. At WFGB the smallest sample sizes 

were found in the biannual design, with seven transects to detect a decrease of 50% after 10 

years at the desired power level, and 14 to detect a 50% increase in the same timeframe. To 

detect 20% changes after 10 years at the desired power level, sample sizes would need to 

increase significantly. 

Table 3.58 Sea Urchin Current Sample Design Power Results

Power analysis results for D. antillarum transect data with the current design of annual sampling.

Location/Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Presence/Absence* 0.3 N/A 0.302 0.291 0.284 0.285 0.319 0.297

West

Mean Density 9.7 4.1 0.291 0.103 0.072 0.049 0.090 0.311

After Five Years

East

Presence/Absence* 0.3 N/A 0.078 0.076 0.080 0.079 0.091 0.095

West

Mean Density 9.7 4.1 0.415 0.081 0.048 0.049 0.082 0.180

After Ten Years

East

Presence/Absence* 0.3 N/A 0.061 0.039 0.074 0.066 0.067 0.091

West

Mean Density 9.7 4.1 0.574 0.054 0.057 0.045 0.077 0.199

*alpha = 0.1

Table 3.59 Sea Urchin Current Sample Design Minimum Detectable Trends and 

Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 50% net changes 

after 10 years of sampling at desired power level of 0.8 for sea urchin data with the current design of 

annual sampling.

Location/Parameter Pilot Data Min Sample Size for 50% Net 

Change after 10 yrs 

Mean SD -50% 50%

East

Presence/Absence* 0.3 N/A 175 283

West

Mean Density 9.7 4.1 9 20

*alpha = 0.1
175

Table 3.60 Sea Urchin Alternative Sample Design Power Results

Power analysis results for D. antillarum transect data with the alternative designs of biennial, triennial, 

and biannual sampling.

Location/Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Biennial Sampling – After Third Year

East

Presence/Absence* 0.3 N/A 0.250 0.292 0.293 0.295 0.300 0.298

West

Mean Density 9.7 4.1 0.401 0.098 0.054 0.064 0.077 0.247

Biennial Sampling – After Fifth Year

East

Presence/Absence* 0.3 N/A 0.121 0.138 0.131 0.105 0.150 0.124

West

Mean Density 9.7 4.1 0.353 0.058 0.049 0.056 0.057 0.146

Biennial Sampling – After Eleventh Year

East

Presence/Absence* 0.3 N/A 0.071 0.085 0.085 0.092 0.086 0.089

West

Mean Density 9.7 4.1 0.571 0.052 0.034 0.050 0.046 0.211

Triennial Sampling – After Fourth Year

East

Presence/Absence* 0.3 N/A 0.288 0.313 0.299 0.290 0.288 0.294

West

Mean Density 9.7 4.1 0.431 0.090 0.068 0.060 0.074 0.224

Triennial Sampling – After Tenth Year

East

Presence/Absence* 0.3 N/A 0.073 0.050 0.049 0.053 0.078 0.085

West

Mean Density 9.7 4.1 0.409 0.074 0.037 0.039 0.054 0.165

Biannual Sampling – After Two Years

East

Presence/Absence* 0.3 N/A 0.123 0.106 0.143 0.118 0.133 0.147

West

Mean Density 9.7 4.1 0.289 0.085 0.049 0.049 0.081 0.200

Biannual Sampling – After Five Years

East

Presence/Absence* 0.3 N/A 0.055 0.063 0.063 0.073 0.087 0.073

West

Mean Density 9.7 4.1 0.520 0.057 0.050 0.048 0.052 0.0197

Biannual Sampling – After Ten Years

East

Presence/Absence* 0.3 N/A 0.077 0.060 0.052 0.066 0.066 0.104

West

Mean Density 9.7 4.1 0.738 0.074 0.038 0.060 0.061 0.270

*alpha = 0.1
176

Table 3.61 Sea Urchin Alternative Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 50% net changes 

after 10 years of sampling at desired power level of 0.8 for sea urchin data with the alternative designs of 

biennial, triennial, and biannual sampling.

Location/Parameter Pilot Data

Min Sample Size for 50% Net Change after 10 yrs 

Biennial 

Sampling

Triennial 

Sampling

Biannual 

Sampling

Mean SD -50% 50% -50% 50% -50% 50%

East

Presence/Absence* 0.3 N/A 180 300 225 360 125 220

West

Mean Density 9.7 4.1 9 20 11 25 7 14

*alpha = 0.1

3.2.5.2 Urchin Conclusions

In the comprehensive surveys conducted by NOAA at FGBNMS, urchins were surveyed in 

conjunction with benthic cover surveys (Buckel et al. 2014b). Densities reported in that study 

from similar total areas surveyed (456 m2at EFGB and 344 m2at WFGB) were lower than those 

found by FGBLTM. Buckel et al. (2014b) state that their surveys likely under-sampled urchin 

densities because surveys were conducted in the daytime, but FGBLTM surveys are conducted at 

night when the nocturnal urchins are foraging. 

The first comprehensive survey of FGBNMS (Caldow 2009) found even lower urchin densities 

than either the NOAA study or the FGBLTM. Buckel et al. (2014b) suggest that there is 

evidence of a steady increase in D. antillarum density, and the limited spatial extent of the 

FGBLTM should be expanded to improve sampling for this important species. However, based 

on the power results presented here, the number of transects would need to increase by orders of 

magnitude at EFGB with the current D. antillarum densities to detect changes in their

populations with power approaching desired levels. At WFGB the number of transects would 

need to increase to by a much smaller margin, but incorporating up to 25 100 m by 2 m transects 

into the sampling program would likely require expansion outside the current study site. Based 

on the power analyses presented here, we recommend that FGBLTM managers consider 

changing the sample design to an every other or every third year sampling interval and to expend 

resources on increasing sample size. This will likely include areas beyond the current 100 m by 

100 m study sites, as is also recommended by Buckel et al. (2014b). 

3.2.6 Fish Surveys

Fish data have been collected in some form since the beginning of the FGBLTM, with large, 

conspicuous species surveyed from the perimeter video transects since 1989 (Gittings et al.

1993). In 2002 dedicated fish surveys began using stationary point counts (Precht et al. 2006) 

using the methods of Bohnsack and Bannerot (1986). The sampling design for fish surveys has 

largely remained the same throughout the history of the LTM, with only the number of point 

counts changing with time. The design used for power analyses was that outlined in the current 

IA of 24 point counts per bank (BOEM 2014). The current IA has also added 16 belt transects to 

the fish survey methodology, but no LTM data are currently available for power analyses. To 
177

approximate the means and variances necessary for power analysis of the belt transect 

methodology, mean total fish density and biomass estimates from the comprehensive FGBNMS 

survey reported by Buckel et al. (2014a) were used. The FGBLTM study sites are encompassed 

within the shallow, high coral cover stratum as delineated in Buckel et al. (2014a), so only these 

data were considered for power analyses.

Within the LTM point count data, the majority of fish species collected from 2009-2012 were not 

consistently distributed throughout the sampling points, creating a large number of zero data 

entries with high SD. To alleviate this and increase the interpretability of results, species data 

were pooled by family and trophic guild before power analyses. Four dominant species were 

identified in the 2009–2010 technical report: Chromis multilineata (Brown Chromis), Clepticus 

parrae (Creole Wrasse), Menidia menidia (Silversides), and Emmelichthyops atlanticus 

(Bonnetmouth) (Johnston et al. 2013); two did not occur in any sampling points during the 2011-

2012 sampling period (Johnston et al. 2015), illustrating the benefit in pooling data to Family for 

analysis. 

Power analysis was conducted on the five families with the highest abundance. These were 

Acanthuridae, Labridae, Pomacentridae, Scaridae and Serranidae. Density (per 100 m

2

) estimates 

of each family were used for analysis. The four trophic guilds designations assigned in the LTM 

technical reports and data (Johnston et al. 2013, 2015) were also analyzed using density 

estimates. These guilds were Herbivore, Piscivore, Invertivore, and Planktivore. 

Biomass estimates of the top five fish families were also pooled and analyzed as a single 

parameter for each sampling design. All fish data were log transformed prior to analysis, as 

required by the current IA (BOEM 2014). Power analyses across years within the same bank 

were calculated for one-way ANOVAs. Comparisons between banks and years were calculated 

for two-way ANOVAs with replication. Normality, an assumption of parametric ANOVA tests, 

was evaluated using the Anderson-Darling test and frequency histograms. In most cases this 

assumption was violated (Appendix A). However, log transformation did improve the data 

towards a more normal distribution. ANOVA tests are robust against error due to normality 

violations (Schmider et al. 2010), so power analyses presented here should be considered valid. 

In the presentation of power analysis results below, all 12 fish categories are first regarded 

individually. Then some results within each sampling design are averaged by bank and 

presented in text and as summary tables at the end of each data collection method. It is 

recognized that density and biomass measurements for different fish categories (i.e., trophic 

guild, family, etc.) would not be compared during statistical analyses, but because the same 12 

parameters are analyzed for every sampling design, this method is intended only for the purpose 

of facilitating comparisons among the different designs being analyzed. Independent results can 

be observed in the tables associated with each sample design discussion. 

3.2.6.1 Stationary Point Counts

Current Sampling Design Power Evaluation

The results for both EFGB and WFGB were similar with the current sampling strategy of 24 

point counts per bank, sampled once a year. Most of the analyses (density and biomass) met or 
178

exceeded the desired power level of 0.8 for a net decline of 50% after two, five and 10 years 

(Table 3.62). The majority of tests also exceeded the 0.8 threshold for detecting a net increase of 

50% after two years, but fewer exceeded this threshold after five and 10 years. For a summary of 

the annual sampling design and comparison to alternate designs, see Tables 3.70 and 3.71. 

At EFGB the Piscivore trophic guild was the only one that did not exceed the power threshold 

after two years for a 50% net increase or decrease from initial means. After five years the 

Piscivore guild remained below the 0.8 limit for both a 50% increase and decrease. 

Acanthuridae, Serranidae, and Invertivores dropped below the 0.8 limit for detecting a net 

increase of 50% after five years, but remained above the threshold for a net decrease of 50%. 

After 10 years all analyses remained above a 0.8 probability for detecting a net decrease of 50%, 

and only Serranidae, Piscivores, and Invertivores remained below the 0.8 power level for a 

positive net change of 50%. Of all the analyses at EFGB, no analyses exceeded the 0.8 threshold 

for detection of 20% or 10% change. These results were the same for two years, five years and 

10 years, and for both net increase and decrease in density. The average number of sampling 

points required to detect a net decrease of 20% for each analysis after five years was 88 points, 

and to detect an increase of 20% required an average of 117 points.

On average, the minimum detectable annual trend at EFGB after two years was 39%, with the 

smallest detectable trend for Pomacentridae at 25% and the largest for Piscivores at 58% (Table 

3.63). After five years of sampling, the average detectable trend was 11% per year, with the 

smallest trend for Herbivores at 8%, and the largest for Piscivores at 13%. After 10 years of 

sampling, the average detectable trend was roughly 5% per year, with 3% for both 

Pomacentridae and Herbivores and 7% for Piscivores.

Table 3.62 Fish Point Count Current Sample Design Power Results

Power analysis results for stationary point count data with the current design of annual sampling.

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Acanthuridae 0.5 0.2 0.936 0.248 0.093 0.092 0.243 0.946

Labridae 1.3 0.4 0.998 0.392 0.130 0.112 0.379 0.996

Pomacentridae 1.7 0.5 1.000 0.639 0.189 0.150 0.633 1.000

Scaridae 0.7 0.3 0.947 0.233 0.093 0.086 0.250 0.943

Serranidae 1.0 0.5 0.831 0.170 0.077 0.070 0.182 0.853

Herbivores 1.3 0.4 0.998 0.488 0.153 0.147 0.491 1.000

Piscivores 1.3 0.8 0.656 0.130 0.071 0.077 0.139 0.649

Invertivores 1.2 0.6 0.852 0.192 0.085 0.071 0.192 0.862

Planktivores 1.3 0.5 0.988 0.342 0.106 0.108 0.326 0.987

Biomass Top 5 Families 2.5 0.9 0.996 0.374 0.100 0.107 0.357 0.991

West

Acanthuridae 0.5 0.2 0.849 0.188 0.087 0.077 0.198 0.828

Labridae 1.3 0.5 0.995 0.401 0.123 0.125 0.369 0.999

Pomacentridae 1.7 0.5 0.999 0.502 0.142 0.144 0.500 1.000

Scaridae 0.7 0.3 0.838 0.192 0.085 0.080 0.158 0.821

Serranidae 0.9 0.5 0.766 0.173 0.068 0.078 0.160 0.781

Herbivores 1.2 0.4 0.999 0.503 0.151 0.129 0.470 0.999

Piscivores 1.2 0.8 0.575 0.113 0.064 0.065 0.131 0.589

Invertivores 1.2 0.6 0.834 0.175 0.082 0.057 0.154 0.823
179

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Planktivores 1.4 0.5 0.986 0.349 0.113 0.090 0.353 0.990

Biomass Top 5 Families 2.4 0.9 0.995 0.374 0.099 0.109 0.326 0.992

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.769 0.241 0.229 0.752 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.959 0.376 0.381 0.957 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 0.996 0.541 0.589 0.998 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.747 0.204 0.207 0.733 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.665 0.166 0.171 0.652 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 0.989 0.503 0.497 0.991 1.000

Piscivores 1.3/1.2 0.8/0.8 0.998 0.478 0.123 0.130 0.446 0.998

Invertivores 1.2/1.2 0.6/0.6 0.991 0.682 0.167 0.211 0.666 0.988

Planktivores 1.3/1.4 0.5/0.5 1.000 0.917 0.354 0.359 0.915 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.993 0.370 0.396 0.951 1.000

After Five Years

East

Acanthuridae 0.5 0.2 0.995 0.194 0.062 0.056 0.161 0.788

Labridae 1.3 0.4 1.000 0.326 0.093 0.076 0.243 0.954

Pomacentridae 1.7 0.5 1.000 0.635 0.107 0.129 0.494 1.000

Scaridae 0.7 0.3 0.994 0.197 0.079 0.065 0.155 0.813

Serranidae 1.0 0.5 0.967 0.171 0.060 0.067 0.115 0.623

Herbivores 1.3 0.4 1.000 0.473 0.126 0.098 0.363 0.989

Piscivores 1.3 0.8 0.836 0.115 0.059 0.045 0.080 0.420

Invertivores 1.2 0.6 0.961 0.134 0.058 0.052 0.129 0.657

Planktivores 1.3 0.5 1.000 0.302 0.073 0.072 0.189 0.920

Biomass Top 5 Families 2.5 0.9 1.000 0.327 0.077 0.072 0.226 0.941

West

Acanthuridae 0.5 0.2 0.933 0.125 0.058 0.055 0.119 0.583

Labridae 1.3 0.5 1.000 0.302 0.078 0.095 0.210 0.953

Pomacentridae 1.7 0.5 1.000 0.421 0.102 0.100 0.337 0.988

Scaridae 0.7 0.3 0.955 0.137 0.060 0.061 0.094 0.588

Serranidae 0.9 0.5 0.927 0.119 0.064 0.059 0.088 0.534

Herbivores 1.2 0.4 1.000 0.415 0.091 0.105 0.350 0.990

Piscivores 1.2 0.8 0.707 0.103 0.062 0.045 0.066 0.329

Invertivores 1.2 0.6 0.939 0.122 0.066 0.057 0.111 0.542

Planktivores 1.4 0.5 0.999 0.276 0.064 0.064 0.199 0.906

Biomass Top 5 Families 2.4 0.9 1.000 0.295 0.076 0.096 0.205 0.929

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.217 0.074 0.071 0.180 0.965

Labridae 1.3/1.3 0.4/0.5 1.000 0.983 0.399 0.404 0.946 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 1.000 0.631 0.584 0.998 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.788 0.223 0.192 0.705 0.959

Serranidae 1.0/0.9 0.5/0.5 1.000 0.702 0.171 0.164 0.567 0.922

Herbivores 1.3/1.2 0.4/0.4 1.000 0.997 0.525 0.511 0.950 1.000

Piscivores 1.3/1.2 0.8/0.8 0.986 0.486 0.133 0.119 0.374 0.989

Invertivores 1.2/1.2 0.6/0.6 1.000 0.715 0.170 0.167 0.591 0.999

Planktivores 1.3/1.4 0.5/0.5 1.000 0.929 0.348 0.350 0.859 0.998

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.978 0.350 0.355 0.845 1.000

After Ten Years

East

Acanthuridae 0.5 0.2 1.000 0.294 0.073 0.074 0.164 0.886

Labridae 1.3 0.4 1.000 0.446 0.083 0.088 0.253 0.989
180

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Pomacentridae 1.7 0.5 1.000 0.761 0.129 0.110 0.489 1.000

Scaridae 0.7 0.3 1.000 0.243 0.059 0.049 0.136 0.888

Serranidae 1.0 0.5 0.997 0.170 0.058 0.054 0.119 0.732

Herbivores 1.3 0.4 1.000 0.595 0.102 0.102 0.404 0.997

Piscivores 1.3 0.8 0.954 0.107 0.049 0.050 0.082 0.477

Invertivores 1.2 0.6 0.998 0.148 0.065 0.054 0.107 0.748

Planktivores 1.3 0.5 1.000 0.349 0.080 0.090 0.225 0.964

Biomass Top 5 Families 2.5 0.9 1.000 0.399 0.094 0.093 0.221 0.983

West

Acanthuridae 0.5 0.2 0.998 0.161 0.072 0.068 0.112 0.718

Labridae 1.3 0.5 1.000 0.446 0.091 0.105 0.231 0.988

Pomacentridae 1.7 0.5 1.000 0.582 0.120 0.129 0.325 1.000

Scaridae 0.7 0.3 0.995 0.191 0.048 0.053 0.106 0.720

Serranidae 0.9 0.5 0.984 0.160 0.080 0.055 0.129 0.629

Herbivores 1.2 0.4 1.000 0.605 0.114 0.134 0.495 1.000

Piscivores 1.2 0.8 0.907 0.087 0.032 0.054 0.058 0.409

Invertivores 1.2 0.6 0.992 0.155 0.056 0.061 0.118 0.658

Planktivores 1.4 0.5 1.000 0.396 0.104 0.060 0.200 0.969

Biomass Top 5 Families 2.4 0.9 1.000 0.386 0.088 0.083 0.259 0.973

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.857 0.176 0.210 0.631 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.998 0.300 0.266 0.955 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 1.000 0.628 0.437 1.000 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.846 0.194 0.128 0.642 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.745 0.146 0.185 0.494 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 0.997 0.571 0.554 0.978 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.496 0.103 0.092 0.330 0.998

Invertivores 1.2/1.2 0.6/0.6 1.000 0.844 0.103 0.111 0.645 1.000

Planktivores 1.3/1.4 0.5/0.5 1.000 0.977 0.332 0.209 0.875 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.978 0.363 0.387 0.916 1.000

* All analyses based on mean density (/100m2) (except biomass) and log transformed for normality as 

specified in IA

Values in red are above the desired power level of 0.80
181

Table 3.63 Fish Point Count Current Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for fish point count data with the current design 

of annual sampling.

Location or Category Pilot Data* Min Annual Trend (%)

Min Sample Size 

for 20% Net 

Change after 5 yrs 

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Acanthuridae 0.5 0.2 ±39 ±12 ±5 92 120

Labridae 1.3 0.4 ±32 ±9 ±4 58 76

Pomacentridae 1.7 0.5 ±25 ±7 ±3 34 46

Scaridae 0.7 0.3 ±40 ±11 ±5 90 122

Serranidae 1.0 0.5 ±47 ±13 ±6 124 164

Herbivores 1.3 0.4 ±28 ±8 ±3 46 60

Piscivores 1.3 0.8 ±58 ±16 ±7 182 234

Invertivores 1.2 0.6 ±47 ±13 ±6 124 168

Planktivores 1.3 0.5 ±36 ±10 ±4 70 94

Biomass Top 5 Families 2.5 0.9 ±34 ±9 ±4 64 86

West

Acanthuridae 0.5 0.2 ±49 ±14 ±6 130 178

Labridae 1.3 0.5 ±32 ±9 ±4 58 76

Pomacentridae 1.7 0.5 ±29 ±8 ±4 46 60

Scaridae 0.7 0.3 ±49 ±14 ±6 132 180

Serranidae 0.9 0.5 ±52 ±14 ±6 148 194

Herbivores 1.2 0.4 ±28 ±8 ±4 44 60

Piscivores 1.2 0.8 ±65 ±18 ±7 226 296

Invertivores 1.2 0.6 ±50 ±14 ±6 132 179

Planktivores 1.4 0.5 ±35 ±10 ±4 70 93

Biomass Top 5 Families 2.4 0.9 ±35 ±10 ±4 64 87

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 ±22 ±8 ±3 54 70

Labridae 1.3/1.3 0.4/0.5 ±16 ±5 ±2 30 36

Pomacentridae 1.7/1.7 0.5/0.5 ±13 ±4 ±2 20 24

Scaridae 0.7/0.7 0.3/0.3 ±22 ±6 ±3 54 68

Serranidae 1.0/0.9 0.5/0.5 ±24 ±7 ±3 68 84

Herbivores 1.3/1.2 0.4/0.4 ±14 ±4 ±2 22 28

Piscivores 1.3/1.2 0.8/0.8 ±29 ±8 ±4 104 128

Invertivores 1.2/1.2 0.6/0.6 ±24 ±7 ±3 64 82

Planktivores 1.3/1.4 0.5/0.5 ±17 ±5 ±2 34 44

Biomass Top 5 Families 2.5/2.4 0.9/0.9 ±17 ±5 ±2 30 42

At WFGB both Serranidae and Piscivores were below the desired power level for 50% increase 

and decrease after two years. After five years, only Piscivores remained below the desired power 

level for a net decline of 50%, but Acanthuridae, Scaridae, Serranidae, Piscivores and 

Invertivores were below the desired power level for a net increase of 50%. After 10 years, all 

analyses exceeded the desired power level for a net decrease of 50%, but Acanthuridae, Scaridae, 

Serranidae, Piscivores and Invertivores all remained below the detection threshold for a net 

increase of 50%. As at EFGB, no analyses exceeded desired power levels for any percent change 

below 50%. The average number of sampling points required to detect a net decrease of 20% for 
182

each analysis after five years was 105 points, and to detect an increase of 20% required an 

average of 140 points.

On average, the minimum detectable annual trend at WFGB after two years was 42%, with the 

smallest detectable trend for Herbivores at 28% and the largest for Piscivores at 65% (Table 

3.63). After five years of sampling, the average detectable trend was 12% per year, with the 

smallest trend for Pomacentridae at 8%, and the largest for Piscivores at 18%. After 10 years of 

sampling, the average detectable trend was roughly 5% per year, with 4% for five out of the 10 

analyses and 7% for Piscivores.

Power analyses for comparisons between EFGB and WFGB (two-way ANOVA) for the current 

sampling strategy had more results above the 0.8 detection threshold than either bank 

individually. All comparison analyses exceeded the detection threshold for a net change of 50% 

after two, five and 10 years. A majority also met desired power levels for a net change of 20% 

for all time frames analyzed. After two years, Acanthuridae, Scaridae, Serranidae, Piscivores and 

Invertivores did not meet desired power levels for a net 20% increase or decrease, while all 

others did. The same five analyses did not surpass the desired power level for either net increase 

or decrease of 20% after five years. These five analyses also did not meet the detection threshold 

for a net increase of 20% after 10 years, but only Serranidae and Piscivores remained under the 

desired power level for a net decrease of 20% in the same timeframe. With the current sampling 

strategy, Acanthuridae, Scaridae, Serranidae, Piscivores and Invertivores would require an 

increase in the number of sampling points to be able to detect a net 20% change between banks 

after five years of sampling. The average number of sampling points required to detect a net 

decrease of 20% was found to be 48, and the average number to detect a net increase of 20% was 

found to be 61 sampling points across both banks combined. 

On average, the minimum detectable annual trend between the two banks after two years was 

20%, with the smallest detectable trend for Pomacentridae at 13%, and the largest for Piscivores 

at 15%. After five years of sampling, the average detectable trend was 6% per year, with the 

smallest trend for Herbivores and Pomacentridae at 4%, and the largest for Piscivores at 9%. 

After 10 years of sampling, the average detectable trend was roughly 3% per year, with 2% for 

five of the 10 analyses, and 4% for Piscivores.

Alternative Sampling Design Power Evaluation

Sampling Every Other Year

Changing to a biennial sampling frequency decreased the power for some analyses across all 

timeframes and had little effect on others. The impact of decreasing sampling frequency was 

similar for both banks. Most of the analyses met or exceeded the desired power level of 0.8 for a 

net decline of 50% after three, five and 11 years, similar to sampling every year (Table 3.64). 

The majority of tests also exceeded the 0.8 threshold for detecting a net increase of 50% after 

three years, but fewer exceeded this threshold after five and 11 years, and the impact of sampling 

less frequently was more evident in these analyses. For a summary of this sampling design and 

comparison to alternate designs, see Tables 3.70 and 3.71 below. 
183

At EFGB Serranidae, Piscivores and Invertivores did not exceed the power threshold after three 

years for a 50% net increase from initial mean density, while all analyses met the threshold for a 

decrease of 50% after three years. After five years the Piscivore guild dropped below the 0.8 

limit for a net 50% decrease, while Acanthuridae, Scaridae, Serranidae, Piscivores, and 

Invertivores were below the 0.8 limit for detecting a net increase of 50% after five years. After 

11 years all analyses were above a 0.8 probability for detecting a net decrease of 50%, and 

Scaridae, Serranidae, Piscivores and Invertivores remained below the 0.8 desired power level for 

a positive net change of 50%. The average number of sampling points required to detect a net 

decrease of 20% for each analysis after five years was 89 points, and to detect an increase of 

20% required an average of 119 points. 

The average minimum detectable annual trend at EFGB after three years with a biennial 

sampling strategy was 20%, with the smallest detectable trend for Pomacentridae at 13% and the 

largest for Piscivores at 29% (Table 3.65). After five years of sampling the average detectable 

trend was 11% per year, with the smallest trend for Pomacentridae at 7%, and the largest for 

Piscivores at 16%. After 11 years of sampling the average detectable trend was roughly 5% per 

year, with 3% for Pomacentridae and 7% for Piscivores.

At WFGB Piscivores were below the desired power level for 50% increase and decrease after 

three years, and Acanthuridae, Scaridae, Serranidae, and Invertivores were also under the desired 

power level for an increase of 50% in three years. After five years, the same analyses remained 

below the desired power level for a net decrease or increase of 50%. After 11 years, all analyses 

exceeded the desired power level for a net decrease of 50%, but Acanthuridae, Scaridae, 

Serranidae, Piscivores and Invertivores all remained below the detection threshold for a net 

increase of 50%. The average number of sampling points required to detect a net decrease of 

20% after five years was 107 points, and to detect an increase of 20% required an average of 143

points.

Table 3.64 Fish Point Count Biennial Sample Design Power Results

Power analysis results for stationary point count data with the alternative design of biennial sampling.

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Third Year

East

Acanthuridae 0.5 0.2 0.986 0.282 0.093 0.101 0.248 0.877

Labridae 1.3 0.4 1.000 0.444 0.128 0.143 0.382 0.982

Pomacentridae 1.7 0.5 1.000 0.692 0.198 0.208 0.596 1.000

Scaridae 0.7 0.3 0.988 0.259 0.098 0.099 0.223 0.883

Serranidae 1.0 0.5 0.945 0.211 0.079 0.070 0.188 0.751

Herbivores 1.3 0.4 1.000 0.543 0.166 0.166 0.471 0.995

Piscivores 1.3 0.8 0.828 0.145 0.076 0.067 0.119 0.544

Invertivores 1.2 0.6 0.946 0.210 0.086 0.075 0.174 0.746

Planktivores 1.3 0.5 0.999 0.375 0.094 0.095 0.287 0.956

Biomass Top 5 Families 2.5 0.9 0.999 0.406 0.117 0.122 0.333 0.963

West

Acanthuridae 0.5 0.2 0.938 0.197 0.062 0.068 0.153 0.745

Labridae 1.3 0.5 1.000 0.434 0.125 0.129 0.371 0.974

Pomacentridae 1.7 0.5 1.000 0.545 0.163 0.133 0.464 0.996

Scaridae 0.7 0.3 0.953 0.198 0.086 0.064 0.180 0.722

Serranidae 0.9 0.5 0.918 0.166 0.085 0.079 0.152 0.672
184

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Herbivores 1.2 0.4 1.000 0.580 0.160 0.190 0.431 0.996

Piscivores 1.2 0.8 0.733 0.133 0.060 0.070 0.115 0.477

Invertivores 1.2 0.6 0.929 0.197 0.078 0.062 0.148 0.716

Planktivores 1.4 0.5 0.997 0.359 0.121 0.121 0.317 0.958

Biomass Top 5 Families 2.4 0.9 1.000 0.386 0.123 0.123 0.331 0.964

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.794 0.239 0.262 0.718 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.983 0.468 0.469 0.948 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 0.999 0.643 0.614 0.995 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.789 0.254 0.269 0.720 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.721 0.218 0.213 0.632 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 0.993 0.600 0.601 0.991 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.501 0.143 0.151 0.447 0.991

Invertivores 1.2/1.2 0.6/0.6 1.000 0.711 0.191 0.210 0.655 1.000

Planktivores 1.3/1.4 0.5/0.5 1.000 0.949 0.392 0.391 0.899 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.951 0.398 0.415 0.930 1.000

After Fifth Year

East

Acanthuridae 0.5 0.2 0.993 0.235 0.072 0.066 0.175 0.768

Labridae 1.3 0.4 1.000 0.369 0.080 0.090 0.276 0.969

Pomacentridae 1.7 0.5 1.000 0.636 0.162 0.129 0.479 0.998

Scaridae 0.7 0.3 0.993 0.248 0.076 0.074 0.172 0.777

Serranidae 1.0 0.5 0.963 0.157 0.065 0.069 0.130 0.647

Herbivores 1.3 0.4 1.000 0.497 0.113 0.097 0.355 0.993

Piscivores 1.3 0.8 0.790 0.127 0.057 0.071 0.099 0.447

Invertivores 1.2 0.6 0.943 0.159 0.068 0.070 0.118 0.620

Planktivores 1.3 0.5 1.000 0.316 0.080 0.068 0.231 0.900

Biomass Top 5 Families 2.5 0.9 0.999 0.336 0.081 0.091 0.256 0.920

West

Acanthuridae 0.5 0.2 0.929 0.166 0.072 0.075 0.125 0.592

Labridae 1.3 0.5 1.000 0.364 0.109 0.077 0.253 0.938

Pomacentridae 1.7 0.5 1.000 0.506 0.139 0.097 0.354 0.985

Scaridae 0.7 0.3 0.937 0.146 0.061 0.066 0.135 0.596

Serranidae 0.9 0.5 0.908 0.136 0.068 0.067 0.130 0.546

Herbivores 1.2 0.4 1.000 0.480 0.131 0.112 0.356 0.983

Piscivores 1.2 0.8 0.718 0.086 0.045 0.053 0.068 0.332

Invertivores 1.2 0.6 0.913 0.168 0.076 0.065 0.118 0.577

Planktivores 1.4 0.5 0.998 0.301 0.087 0.079 0.208 0.906

Biomass Top 5 Families 2.4 0.9 0.999 0.359 0.095 0.088 0.244 0.932

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.755 0.203 0.155 0.598 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.976 0.375 0.286 0.907 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 0.997 0.419 0.442 0.975 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.754 0.213 0.196 0.592 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.642 0.159 0.132 0.581 0.998

Herbivores 1.3/1.2 0.4/0.4 1.000 0.985 0.499 0.381 0.963 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.467 0.121 0.084 0.306 0.981

Invertivores 1.2/1.2 0.6/0.6 1.000 0.649 0.182 0.143 0.459 1.000

Planktivores 1.3/1.4 0.5/0.5 1.000 0.937 0.323 0.244 0.834 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.945 0.358 0.253 0.861 1.000
185

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Eleventh Year

East

Acanthuridae 0.5 0.2 0.998 0.219 0.069 0.054 0.162 0.809

Labridae 1.3 0.4 1.000 0.399 0.099 0.087 0.242 0.955

Pomacentridae 1.7 0.5 1.000 0.647 0.156 0.105 0.429 0.999

Scaridae 0.7 0.3 0.998 0.191 0.063 0.068 0.118 0.766

Serranidae 1.0 0.5 0.989 0.161 0.062 0.065 0.108 0.598

Herbivores 1.3 0.4 1.000 0.501 0.109 0.084 0.293 0.992

Piscivores 1.3 0.8 0.899 0.116 0.046 0.074 0.090 0.393

Invertivores 1.2 0.6 0.980 0.152 0.085 0.057 0.106 0.616

Planktivores 1.3 0.5 1.000 0.310 0.095 0.064 0.187 0.904

Biomass Top 5 Families 2.5 0.9 1.000 0.331 0.087 0.085 0.222 0.936

West

Acanthuridae 0.5 0.2 0.981 0.141 0.061 0.065 0.109 0.574

Labridae 1.3 0.5 1.000 0.355 0.068 0.068 0.222 0.955

Pomacentridae 1.7 0.5 1.000 0.483 0.092 0.088 0.315 0.979

Scaridae 0.7 0.3 0.977 0.143 0.076 0.059 0.114 0.555

Serranidae 0.9 0.5 0.950 0.130 0.047 0.060 0.086 0.497

Herbivores 1.2 0.4 1.000 0.527 0.098 0.112 0.305 0.988

Piscivores 1.2 0.8 0.805 0.094 0.052 0.045 0.070 0.302

Invertivores 1.2 0.6 0.974 0.128 0.061 0.066 0.101 0.563

Planktivores 1.4 0.5 1.000 0.305 0.073 0.089 0.203 0.892

Biomass Top 5 Families 2.4 0.9 1.000 0.331 0.088 0.088 0.200 0.924

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.820 0.197 0.139 0.545 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.969 0.428 0.270 0.885 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 1.000 0.593 0.420 0.979 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.792 0.178 0.135 0.559 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.662 0.124 0.117 0.441 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 0.999 0.415 0.365 0.966 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.390 0.115 0.086 0.300 0.984

Invertivores 1.2/1.2 0.6/0.6 1.000 0.691 0.145 0.130 0.465 0.999

Planktivores 1.3/1.4 0.5/0.5 1.000 0.956 0.245 0.234 0.812 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.979 0.278 0.245 0.840 1.000

* All analyses based on mean density (/100m2) (except biomass) and log transformed for normality as 

specified in IA

Values in red are above the desired power level of 0.80
186

Table 3.65 Fish Point Count Biennial Sample Design Minimum Detectable Trends 

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for fish point count data with the alternative 

design of biennial sampling.

Location or Category Pilot Data* Min Annual Trend (%)

Min Sample Size 

for 20% Net 

Change after 5 yrs

Mean SD 3 yrs 5 yrs 11 yrs -20% 20%

East

Acanthuridae 0.5 0.2 ±20 ±12 ±5 92 124

Labridae 1.3 0.4 ±16 ±9 ±4 58 78

Pomacentridae 1.7 0.5 ±13 ±7 ±3 34 46

Scaridae 0.7 0.3 ±20 ±12 ±5 93 125

Serranidae 1.0 0.5 ±23 ±13 ±6 124 164

Herbivores 1.3 0.4 ±15 ±8 ±4 46 61

Piscivores 1.3 0.8 ±29 ±16 ±7 185 242

Invertivores 1.2 0.6 ±24 ±14 ±6 125 168

Planktivores 1.3 0.5 ±19 ±10 ±4 70 96

Biomass Top 5 Families 2.5 0.9 ±17 ±10 ±4 63 86

West

Acanthuridae 0.5 0.2 ±25 ±14 ±6 131 175

Labridae 1.3 0.5 ±16 ±9 ±4 56 80

Pomacentridae 1.7 0.5 ±16 ±8 ±4 46 62

Scaridae 0.7 0.3 ±24 ±14 ±6 135 179

Serranidae 0.9 0.5 ±26 ±15 ±6 149 195

Herbivores 1.2 0.4 ±15 ±8 ±4 46 61

Piscivores 1.2 0.8 ±32 ±18 ±7 233 311

Invertivores 1.2 0.6 ±27 ±14 ±6 139 188

Planktivores 1.4 0.5 ±18 ±10 ±4 71 94

Biomass Top 5 Families 2.4 0.9 ±17 ±10 ±4 64 88

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 ±11 ±7 ±3 58 76

Labridae 1.3/1.3 0.4/0.5 ±8 ±5 ±2 30 40

Pomacentridae 1.7/1.7 0.5/0.5 ±7 ±4 ±2 20 28

Scaridae 0.7/0.7 0.3/0.3 ±12 ±6 ±3 58 76

Serranidae 1.0/0.9 0.5/0.5 ±13 ±7 ±3 68 92

Herbivores 1.3/1.2 0.4/0.4 ±8 ±4 ±2 22 30

Piscivores 1.3/1.2 0.8/0.8 ±15 ±9 ±4 108 140

Invertivores 1.2/1.2 0.6/0.6 ±12 ±7 ±3 66 90

Planktivores 1.3/1.4 0.5/0.5 ±9 ±5 ±2 36 46

Biomass Top 5 Families 2.5/2.4 0.9/0.9 ±9 ±5 ±2 34 46
187

The average minimum detectable annual trend at WFGB after three years was 22%, with the 

smallest detectable trend for Herbivores at 15% and the largest for Piscivores at 32% (Table 

3.65). After five years of sampling, the average detectable trend was 12% per year, with the 

smallest trend for Herbivores and Pomacentridae at 8%, and the largest for Piscivores at 18%. 

After 11 years, the average detectable trend was roughly 5% per year, with 4% for five of the 10 

analyses and 7% for Piscivores.

Power analyses for comparisons between EFGB and WFGB for a biennial sampling strategy had 

more results above the 0.8 detection threshold than either bank individually. All comparison 

analyses exceeded the detection threshold for a net change of 50% after three, five and 10 years. 

A majority also met desired power levels for a net change of 20% for all time frames analyzed. 

After three years, Acanthuridae, Scaridae, Serranidae, Piscivores, and Invertivores did not meet 

desired power levels for a net increase or decrease of 20%, while all others did. The same five 

analyses did not surpass the desired power level for either net increase or decrease of 20% after 

five years. These five analyses also did not meet the detection threshold for a net increase of 20% 

after 11 years, and only Acanthuridae surpassed desired power levels for a net decrease of 20%, 

while Scaridae, Serranidae, Piscivores, and Invertivores remained under the desired power level 

for the same parameters. With the biennial sampling strategy, Acanthuridae, Scaridae, 

Serranidae, Piscivores, and Invertivores would require an increase in the number of sampling 

points to detect a net 20% change between banks after five years of sampling. The average 

number of sampling points required to detect a net decrease of 20% was found to be 50, and the 

average number to detect a net increase of 20% was found to be 66 sampling points across both 

banks combined. 

On average, the minimum detectable annual trend between the two banks after three years was 

10%, with the smallest detectable trend for Pomacentridae at 7% and the largest for Piscivores at 

15%. After five years of sampling, the average detectable trend was 6% per year, with the 

smallest trend for Herbivores and Pomacentridae at 4%, and the largest for Piscivores at 9%. 

After 11 years, the average detectable trend was 3% per year, with 2% for five of the 10 analyses

and 4% for Piscivores.

Sampling Every Third Year

Changing to an every third year sampling frequency decreased the power for some analyses and 

had little effect on others. The impact of decreasing sampling frequency was similar for both 

banks. Most of the analyses met or exceeded the desired power level of 0.8 for a net decline of 

50% after 4 and 10 years, similar to sampling every year and every other year (Table 3.66). The 

tests that exceeded the 0.8 threshold for detecting a net increase of 50% after four years followed 

the same pattern seen for sampling biennially, but fewer exceeded this threshold after 10 years, 

and the impact of sampling less frequently was more evident in this longer-term analysis, as well 

as in the smaller 20% and 10% net changes. For a summary of this sampling design and 

comparison to alternate designs, see Tables 3.70 and 3.71. 
188

Table 3.66 Fish Point Count Triennial Sample Design Power Results

Power analysis results for stationary point count data with the alternative design of triennial sampling.

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Four Years

East

Acanthuridae 0.5 0.2 0.902 0.237 0.154 0.166 0.214 0.733

Labridae 1.3 0.4 0.970 0.251 0.164 0.162 0.283 0.925

Pomacentridae 1.7 0.5 1.000 0.552 0.186 0.191 0.453 0.994

Scaridae 0.7 0.3 0.961 0.230 0.163 0.163 0.162 0.684

Serranidae 1.0 0.5 0.934 0.196 0.133 0.134 0.152 0.607

Herbivores 1.3 0.4 0.968 0.247 0.178 0.177 0.331 0.973

Piscivores 1.3 0.8 0.833 0.152 0.097 0.094 0.097 0.496

Invertivores 1.2 0.6 0.908 0.180 0.128 0.113 0.152 0.628

Planktivores 1.3 0.5 0.961 0.268 0.136 0.151 0.241 0.889

Biomass Top 5 Families 2.5 0.9 0.999 0.375 0.148 0.130 0.295 0.944

West

Acanthuridae 0.5 0.2 0.715 0.205 0.158 0.155 0.178 0.608

Labridae 1.3 0.5 0.957 0.289 0.156 0.167 0.299 0.943

Pomacentridae 1.7 0.5 0.999 0.469 0.145 0.141 0.360 0.988

Scaridae 0.7 0.3 0.866 0.227 0.126 0.133 0.180 0.576

Serranidae 0.9 0.5 0.882 0.196 0.146 0.113 0.167 0.552

Herbivores 1.2 0.4 0.967 0.272 0.166 0.177 0.294 0.963

Piscivores 1.2 0.8 0.736 0.177 0.099 0.089 0.119 0.424

Invertivores 1.2 0.6 0.897 0.184 0.117 0.110 0.165 0.612

Planktivores 1.4 0.5 0.961 0.278 0.157 0.130 0.262 0.891

Biomass Top 5 Families 2.4 0.9 0.996 0.363 0.145 0.117 0.258 0.943

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.636 0.186 0.150 0.509 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.832 0.338 0.284 0.819 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 0.995 0.602 0.463 0.970 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.689 0.170 0.178 0.516 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.605 0.177 0.167 0.465 0.995

Herbivores 1.3/1.2 0.4/0.4 1.000 0.821 0.283 0.320 0.884 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.480 0.139 0.131 0.365 0.979

Invertivores 1.2/1.2 0.6/0.6 1.000 0.618 0.175 0.169 0.464 0.998

Planktivores 1.3/1.4 0.5/0.5 1.000 0.801 0.225 0.274 0.742 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.947 0.353 0.369 0.845 1.000

After Ten Years

East

Acanthuridae 0.5 0.2 0.857 0.145 0.063 0.068 0.119 0.561

Labridae 1.3 0.4 0.976 0.147 0.089 0.073 0.155 0.838

Pomacentridae 1.7 0.5 1.000 0.441 0.100 0.109 0.328 0.987

Scaridae 0.7 0.3 0.966 0.176 0.060 0.071 0.101 0.519

Serranidae 1.0 0.5 0.912 0.097 0.052 0.063 0.086 0.482

Herbivores 1.3 0.4 0.971 0.162 0.064 0.080 0.199 0.919

Piscivores 1.3 0.8 0.832 0.103 0.043 0.047 0.081 0.343

Invertivores 1.2 0.6 0.931 0.091 0.051 0.047 0.105 0.499

Planktivores 1.3 0.5 0.972 0.154 0.064 0.056 0.139 0.788

Biomass Top 5 Families 2.5 0.9 0.999 0.273 0.080 0.075 0.191 0.878

West

Acanthuridae 0.5 0.2 0.643 0.108 0.068 0.070 0.098 0.432

Labridae 1.3 0.5 0.960 0.164 0.061 0.064 0.155 0.847
189

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Pomacentridae 1.7 0.5 0.998 0.353 0.078 0.075 0.231 0.949

Scaridae 0.7 0.3 0.856 0.133 0.043 0.049 0.086 0.421

Serranidae 0.9 0.5 0.868 0.109 0.062 0.065 0.088 0.403

Herbivores 1.2 0.4 0.986 0.132 0.060 0.066 0.157 0.915

Piscivores 1.2 0.8 0.710 0.082 0.060 0.059 0.065 0.288

Invertivores 1.2 0.6 0.908 0.112 0.050 0.056 0.077 0.433

Planktivores 1.4 0.5 0.963 0.155 0.069 0.065 0.147 0.806

Biomass Top 5 Families 2.4 0.9 0.999 0.271 0.074 0.073 0.187 0.876

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.514 0.133 0.098 0.362 0.998

Labridae 1.3/1.3 0.4/0.5 1.000 0.747 0.179 0.186 0.682 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 0.977 0.349 0.336 0.901 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.578 0.118 0.128 0.426 0.999

Serranidae 1.0/0.9 0.5/0.5 1.000 0.495 0.092 0.101 0.317 0.991

Herbivores 1.3/1.2 0.4/0.4 1.000 0.752 0.165 0.231 0.758 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.364 0.081 0.087 0.245 0.945

Invertivores 1.2/1.2 0.6/0.6 1.000 0.492 0.089 0.099 0.359 0.998

Planktivores 1.3/1.4 0.5/0.5 1.000 0.712 0.153 0.171 0.626 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.916 0.215 0.212 0.768 1.000

* All analyses based on mean density (/100m2) (except biomass) and log transformed for normality as 

specified in IA

Values in red are above the desired power level of 0.80

At EFGB Acanthuridae, Scaridae, Serranidae, Piscivores, and Invertivores did not exceed the 

power threshold after four years for a 50% net increase from initial mean density, but all 

analyses met the threshold for a decrease of 50% after four years. After 10 years, all analyses 

were above a 0.8 probability for detecting a net decrease of 50%, and Acanthuridae, Scaridae, 

Serranidae, Piscivores, Invertivores, and Planktivores were below the 0.8 desired power level for

a positive net change of 50%. The average number of sampling points required to detect a net 

decrease of 20% for each analysis after four years was 106 points, and to detect an increase of 

20% required an average of 126 points. 

On average, the minimum detectable yearly trend at EFGB after four years with a sampling 

strategy of every third year was 15%, with the smallest detectable trend for Pomacentridae at 

10% and the largest for Piscivores at 21% (Table 3.67). After 10 years of sampling the average 

detectable trend was roughly 6% per year, with 4% for Pomacentridae and 8% for Piscivores.

At WFGB Acanthuridae and Piscivores were below the desired power level for 50% increase and 

decrease after four years, and Scaridae, Serranidae, and Invertivores were also under the desired 

power level for an increase of 50% after four years. After 10 years, no changes occurred in 

which analyses remained below the desired power level for a net decrease or increase of 50%. 

The average number of sampling points required to detect a net decrease of 20% after four years 

was 122 points, and to detect an increase of 20% required an average of 144 points.

The average minimum detectable yearly trend at WFGB after four years was 16%, with the 

smallest detectable trend for Pomacentridae at 4% and the largest for Piscivores at 23% (Table 

3.67). After 10 years of sampling, the average detectable trend was roughly 6% per year, with 

4% for Pomacentridae and 9% for Piscivores.
190

Table 3.67 Fish Point Count Triennial Sample Design Minimum Detectable Trends

and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for fish point count data with the alternative 

design of triennial sampling.

Location or Category

Pilot Data* Min Annual Trend (%)

Min Sample Size for 

20% Net Change 

after 5 yrs

Mean SD 4 yrs 10 yrs -20% 20%

East

Acanthuridae 0.5 0.2 ±18 ±6 116 142

Labridae 1.3 0.4 ±13 ±5 94 92

Pomacentridae 1.7 0.5 +/10 ±4 40 50

Scaridae 0.7 0.3 ±17 ±6 110 146

Serranidae 1.0 0.5 ±18 ±7 136 170

Herbivores 1.3 0.4 ±12 ±5 84 76

Piscivores 1.3 0.8 ±21 ±8 180 226

Invertivores 1.2 0.6 ±17 ±7 138 174

Planktivores 1.3 0.5 ±14 ±5 96 102

Biomass Top 5 Families 2.5 0.9 ±13 ±5 62 78

West

Acanthuridae 0.5 0.2 ±19 ±7 168 200

Labridae 1.3 0.5 ±13 ±5 90 86

Pomacentridae 1.7 0.5 ±11 ±4 52 60

Scaridae 0.7 0.3 ±19 ±7 148 182

Serranidae 0.9 0.5 ±19 ±7 154 196

Herbivores 1.2 0.4 ±11 ±5 94 82

Piscivores 1.2 0.8 ±23 ±9 206 268

Invertivores 1.2 0.6 ±18 ±7 149 184

Planktivores 1.4 0.5 ±14 ±5 94 100

Biomass Top 5 Families 2.4 0.9 ±13 ±5 64 79

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 ±9 ±4 70 82

Labridae 1.3/1.3 0.4/0.5 ±7 ±3 46 46

Pomacentridae 1.7/1.7 0.5/0.5 ±5 ±2 24 28

Scaridae 0.7/0.7 0.3/0.3 ±9 ±4 62 82

Serranidae 1.0/0.9 0.5/0.5 ±10 ±4 74 94

Herbivores 1.3/1.2 0.4/0.4 ±7 ±3 44 40

Piscivores 1.3/1.2 0.8/0.8 ±10 ±4 94 128

Invertivores 1.2/1.2 0.6/0.6 ±10 ±4 68 90

Planktivores 1.3/1.4 0.5/0.5 ±7 ±3 46 50

Biomass Top 5 Families 2.5/2.4 0.9/0.9 ±7 ±3 34 46

Power analyses for comparisons between EFGB and WFGB for a sampling strategy of every 

third year had more results above the 0.8 detection threshold than either bank individually, as 

seen in other sampling strategies. All comparison analyses exceeded the detection threshold for 

a net change of 50% after 4 and 10 years. Approximately half of the analyses also met desired 

power levels for a net change of 20% for all time frames analyzed. After four years, 

Acanthuridae, Scaridae, Serranidae, Piscivores, and Invertivores did not meet desired power 

levels for a net increase or decrease of 20%, but all others did. Planktivores were also under the 

desired power level for a net increase of 20% after 4 years. After 10 years, the majority of 

analyses did not meet the desired power level for 20% net change. Pomacentridae exceeded the 
191

limit for both a net increase and decrease of 20%. Biomass exceeded the desired power level for 

only a net decrease of 20% only after 10 years. With the every third year sampling strategy, 

Acanthuridae, Scaridae, Serranidae, Piscivores, and Invertivores would require an increase in the 

number of sampling points to detect a net 20% change between banks after four years of 

sampling. The average number of sampling points required to detect a net decrease of 20% was 

found to be 56, and the average number to detect a net increase of 20% was found to be 69

sampling points across both banks combined. 

On average, the minimum detectable annual trend between the two banks after four years was 

8%. The smallest detectable trend was for Pomacentridae at 5%, and the largest was for 

Serranidae, Piscivores, and Invertivores at 10%. After 10 years of sampling the average 

detectable trend was 3% per year, with 2% for Pomacentridae, and 4% for five of the 10 

analyses.

Sampling Twice a Year

Changing to a biannual sampling frequency had little impact on most analyses surpassing the 

detection threshold, with negative impact on detection of 50% decreasing trends for the two year 

timeframe. The impact of increasing sampling frequency did decrease the total number of points 

required to detect 20% change after five years, and the minimum detectable annual trend. Similar 

patterns were observed for both banks. Most or all of the analyses met or exceeded the desired 

power level of 0.8 for a net decrease of 50% after 2, 5 and 10 years, similar to sampling annually 

(Table 3.68). The majority of tests also exceeded the 0.8 threshold for detecting a net increase of 

50% for all sampling timeframes. For a summary of this sampling design in comparison to 

alternate designs, see Tables 3.70 and 3.71. 

Table 3.68 Fish Point Count Biannual Sample Design Power Results

Power analysis results for stationary point count data with the alternative design of biannual sampling.

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Acanthuridae 0.5 0.2 0.910 0.174 0.063 0.060 0.172 0.884

Labridae 1.3 0.4 0.987 0.276 0.084 0.090 0.268 0.994

Pomacentridae 1.7 0.5 1.000 0.535 0.133 0.139 0.502 1.000

Scaridae 0.7 0.3 0.896 0.178 0.072 0.076 0.188 0.916

Serranidae 1.0 0.5 0.789 0.131 0.055 0.056 0.142 0.791

Herbivores 1.3 0.4 0.996 0.379 0.131 0.103 0.358 0.999

Piscivores 1.3 0.8 0.548 0.088 0.064 0.056 0.115 0.563

Invertivores 1.2 0.6 0.782 0.140 0.050 0.071 0.170 0.771

Planktivores 1.3 0.5 0.970 0.257 0.086 0.074 0.232 0.974

Biomass Top 5 Families 2.5 0.9 0.972 0.258 0.091 0.093 0.309 0.977

West

Acanthuridae 0.5 0.2 0.723 0.111 0.063 0.063 0.155 0.751

Labridae 1.3 0.5 0.988 0.286 0.073 0.098 0.256 0.986

Pomacentridae 1.7 0.5 0.998 0.372 0.098 0.115 0.401 0.998

Scaridae 0.7 0.3 0.753 0.146 0.052 0.070 0.137 0.737

Serranidae 0.9 0.5 0.670 0.107 0.070 0.064 0.126 0.668

Herbivores 1.2 0.4 0.997 0.362 0.092 0.108 0.390 0.999

Piscivores 1.2 0.8 0.491 0.102 0.054 0.054 0.098 0.489
192

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Invertivores 1.2 0.6 0.741 0.135 0.054 0.054 0.113 0.686

Planktivores 1.4 0.5 0.969 0.230 0.082 0.091 0.212 0.966

Biomass Top 5 Families 2.4 0.9 0.988 0.235 0.075 0.079 0.259 0.978

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.648 0.118 0.156 0.648 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.934 0.304 0.323 0.920 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 0.985 0.457 0.446 0.990 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.614 0.178 0.155 0.650 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.543 0.143 0.130 0.516 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 0.973 0.415 0.410 0.976 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.571 0.141 0.142 0.566 1.000

Invertivores 1.2/1.2 0.6/0.6 1.000 0.509 0.142 0.162 0.541 1.000

Planktivores 1.3/1.4 0.5/0.5 1.000 0.845 0.249 0.246 0.866 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.883 0.271 0.279 0.890 1.000

After Five Years

East

Acanthuridae 0.5 0.2 1.000 0.248 0.078 0.070 0.180 0.888

Labridae 1.3 0.4 1.000 0.442 0.088 0.090 0.345 0.987

Pomacentridae 1.7 0.5 1.000 0.640 0.101 0.133 0.628 1.000

Scaridae 0.7 0.3 0.999 0.244 0.078 0.055 0.212 0.900

Serranidae 1.0 0.5 0.996 0.179 0.064 0.055 0.138 0.744

Herbivores 1.3 0.4 1.000 0.566 0.099 0.093 0.459 1.000

Piscivores 1.3 0.8 0.933 0.108 0.048 0.055 0.078 0.508

Invertivores 1.2 0.6 0.993 0.170 0.068 0.061 0.134 0.748

Planktivores 1.3 0.5 1.000 0.351 0.075 0.070 0.242 0.973

Biomass Top 5 Families 2.5 0.9 1.000 0.394 0.076 0.081 0.312 0.980

West

Acanthuridae 0.5 0.2 0.984 0.174 0.060 0.059 0.157 0.722

Labridae 1.3 0.5 1.000 0.447 0.080 0.082 0.370 0.989

Pomacentridae 1.7 0.5 1.000 0.600 0.100 0.091 0.473 1.000

Scaridae 0.7 0.3 0.994 0.168 0.063 0.053 0.129 0.702

Serranidae 0.9 0.5 0.985 0.158 0.061 0.057 0.115 0.629

Herbivores 1.2 0.4 1.000 0.599 0.114 0.100 0.441 1.000

Piscivores 1.2 0.8 0.851 0.075 0.052 0.050 0.067 0.411

Invertivores 1.2 0.6 0.983 0.160 0.049 0.046 0.109 0.679

Planktivores 1.4 0.5 1.000 0.383 0.121 0.070 0.261 0.978

Biomass Top 5 Families 2.4 0.9 1.000 0.410 0.070 0.078 0.278 0.980

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.878 0.157 0.167 0.789 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 0.997 0.366 0.376 0.984 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 1.000 0.560 0.551 0.996 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.866 0.141 0.168 0.777 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.780 0.112 0.125 0.628 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 0.999 0.493 0.469 0.996 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.420 0.086 0.091 0.411 0.993

Invertivores 1.2/1.2 0.6/0.6 1.000 0.799 0.138 0.132 0.649 1.000

Planktivores 1.3/1.4 0.5/0.5 1.000 0.993 0.305 0.484 0.956 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 0.998 0.148 0.284 0.975 1.000

After Ten Years

East

Acanthuridae 0.5 0.2 1.000 0.348 0.085 0.068 0.237 0.973
193

Location or Category Pilot Data* Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Labridae 1.3 0.4 1.000 0.630 0.116 0.093 0.408 1.000

Pomacentridae 1.7 0.5 1.000 0.917 0.227 0.155 0.785 1.000

Scaridae 0.7 0.3 1.000 0.346 0.074 0.064 0.235 0.967

Serranidae 1.0 0.5 1.000 0.200 0.056 0.067 0.156 0.877

Herbivores 1.3 0.4 1.000 0.773 0.157 0.105 0.569 1.000

Piscivores 1.3 0.8 0.997 0.159 0.059 0.045 0.106 0.681

Invertivores 1.2 0.6 1.000 0.222 0.063 0.086 0.140 0.870

Planktivores 1.3 0.5 1.000 0.490 0.093 0.079 0.349 0.998

Biomass Top 5 Families 2.5 0.9 1.000 0.541 0.104 0.105 0.397 1.000

West

Acanthuridae 0.5 0.2 1.000 0.229 0.074 0.075 0.154 0.857

Labridae 1.3 0.5 1.000 0.612 0.121 0.094 0.440 1.000

Pomacentridae 1.7 0.5 1.000 0.778 0.135 0.112 0.569 1.000

Scaridae 0.7 0.3 1.000 0.234 0.052 0.059 0.146 0.858

Serranidae 0.9 0.5 0.999 0.193 0.066 0.056 0.120 0.800

Herbivores 1.2 0.4 1.000 0.789 0.149 0.124 0.596 1.000

Piscivores 1.2 0.8 0.982 0.119 0.050 0.059 0.092 0.544

Invertivores 1.2 0.6 1.000 0.205 0.057 0.062 0.139 0.829

Planktivores 1.4 0.5 1.000 0.507 0.110 0.102 0.344 0.998

Biomass Top 5 Families 2.4 0.9 1.000 0.567 0.109 0.106 0.374 0.999

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 1.000 0.985 0.202 0.196 0.893 1.000

Labridae 1.3/1.3 0.4/0.5 1.000 1.000 0.680 0.422 0.998 1.000

Pomacentridae 1.7/1.7 0.5/0.5 1.000 1.000 0.704 0.688 1.000 1.000

Scaridae 0.7/0.7 0.3/0.3 1.000 0.981 0.185 0.174 0.866 1.000

Serranidae 1.0/0.9 0.5/0.5 1.000 0.935 0.233 0.146 0.792 1.000

Herbivores 1.3/1.2 0.4/0.4 1.000 1.000 0.604 0.585 1.000 1.000

Piscivores 1.3/1.2 0.8/0.8 1.000 0.755 0.104 0.095 0.534 1.000

Invertivores 1.2/1.2 0.6/0.6 1.000 0.956 0.114 0.156 0.784 1.000

Planktivores 1.3/1.4 0.5/0.5 1.000 1.000 0.347 0.365 0.996 1.000

Biomass Top 5 Families 2.5/2.4 0.9/0.9 1.000 1.000 0.387 0.377 0.995 1.000

* All analyses based on mean density (/100m2) (except biomass) and log transformed for normality as 

specified in IA

Values in red are above the desired power level of 0.80

At EFGB Serranidae, Piscivores, and Invertivores did not exceed the power threshold after two 

years for a 50% net increase or decrease from initial mean density, but all others did. After five 

years, all analyses exceeded the threshold to detect a 50% decrease, but Serranidae, Piscivores, 

and Invertivores remained below the 0.8 limit for detecting a net increase of 50% in this 

timeframe. After 10 years Herbivores and Biomass were the only analyses below a 0.8 

probability for detecting a net decrease of 50%, and Herbivores, Piscivores, and biomass were 

below the 0.8 desired power level for a positive net change of 50%. The average number of 

sampling points required to detect a net decrease of 20% for each analysis after five years was 70

points, and to detect an increase of 20% required an average of 95 points. 

On average, the minimum detectable annual trend at EFGB after two years with a biannual 

sampling strategy was 43%, with the smallest detectable trend for Pomacentridae at 26% and the 

largest for Piscivores at 64% (Table 3.69). After five years of sampling the average detectable 

trend was 9% per year, with the smallest trend for Pomacentridae at 6%, and the largest for 
194

Piscivores at 13%. After 10 years of sampling the average detectable trend was roughly 4% per 

year, with 3% for five of the 10 analyses and 5% for Piscivores.

At WFGB Acanthuridae, Scaridae, Serranidae, Piscivores, and Invertivores did not exceed the 

power threshold after two years for a 50% net increase or decrease from initial mean density, but 

all others did. After five years, all analyses exceeded the threshold to detect a 50% decrease, 

while Acanthuridae, Scaridae, Serranidae, Piscivores, and Invertivores still remained below the 

0.8 limit for detecting a net increase of 50% in this timeframe. After 10 years, all analyses 

remained above the threshold to detect a 50% decrease, but Piscivores still remained below the 

0.8 limit for detecting a net increase of 50% in this timeframe. No analyses exceeded the 

threshold for 10% change in any timeframe. The average number of sampling points required to 

detect a net decrease of 20% for each analysis after five years was 84 points, and to detect an 

increase of 20% required an average of 114 points. 

On average, the minimum detectable annual trend at WFGB after two years with a biannual 

sampling strategy was 46%, with the smallest detectable trend for Herbivores and Pomacentridae 

at 31% and the largest for Piscivores at 69% (Table 3.69). After five years of sampling, the 

average detectable trend was 10% per year, with the smallest trend for Herbivores, Labridae and 

Pomacentridae at 7%, and the largest for Piscivores at 14%. After 10 years of sampling, the 

average detectable trend was roughly 4% per year, with 3% for five of the 10 analyses and 6% 

for Piscivores.

Power analyses for comparisons between EFGB and WFGB for a biannual sampling strategy had 

more results above the 0.8 detection threshold than either bank individually, as in all other 

sampling strategies. All comparison analyses exceeded the detection threshold for a net change 

of 50% after two, five, and 10 years. A majority also met desired power levels for a net change 

of 20% for all time frames analyzed. After two years, Acanthuridae, Scaridae, Serranidae, 

Piscivores, and Invertivores did not meet desired power levels for a net increase or decrease of 

20%, while all others did. Of these five analyses, none surpassed the desired power level for 

either net increase or decrease of 20% after five years, except Acanthuridae for a 20% decrease. 

After 10 years, only Piscivores did not exceed the detection threshold for a decrease of 20%, and 

Serranidae, Piscivores and Invertivores did not surpass desired power levels for a net increase of 

20%. With the biannual sampling strategy, Acanthuridae, Scaridae, Serranidae, Piscivores, and 

Invertivores would require an increase in the number of sampling points to detect a net 20% 

change between banks after five years of sampling. The average number of sampling points 

required to detect a net decrease of 20% was found to be 38, and the average number to detect a 

net increase of 20% was found to be 52 sampling points across both banks combined. 

On average, the minimum detectable annual trend between the two banks after two years was 

21%, with the smallest detectable trend for Herbivores at 14% and the largest for Scaridae, 

Serranidae, Piscivores, and Invertivores at 25%. After five years of sampling the average 

detectable trend was 5% per year, with the smallest trend for Pomacentridae at 3%, and the 

largest for Piscivores at 7%. After 10 years of sampling the average detectable trend was 2% per 

year, with 1% for Herbivores, and 3% for Serranidae, Piscivores, and Invertivores.
195

Table 3.69 Fish Point Count Biannual Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for fish point count data with the alternative 

design of biannual sampling.

Location or Category Pilot Data* Min Annual Trend (%)

Min Sample Size 

for 20% Net 

Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Acanthuridae 0.5 0.2 ±44 ±9 ±4 71 98

Labridae 1.3 0.4 ±35 ±7 ±3 45 62

Pomacentridae 1.7 0.5 ±26 ±6 ±3 27 36

Scaridae 0.7 0.3 ±45 ±10 ±4 72 98

Serranidae 1.0 0.5 ±52 ±10 ±4 99 133

Herbivores 1.3 0.4 ±32 ±7 ±3 36 50

Piscivores 1.3 0.8 ±64 ±13 ±5 145 196

Invertivores 1.2 0.6 ±52 ±10 ±4 98 129

Planktivores 1.3 0.5 ±39 ±8 ±3 55 74

Biomass Top 5 Families 2.5 0.9 ±37 ±8 ±3 51 69

West

Acanthuridae 0.5 0.2 ±53 ±11 ±5 103 142

Labridae 1.3 0.5 ±35 ±7 ±3 47 63

Pomacentridae 1.7 0.5 ±31 ±7 ±3 36 49

Scaridae 0.7 0.3 ±53 ±11 ±5 103 140

Serranidae 0.9 0.5 ±57 ±12 ±5 122 156

Herbivores 1.2 0.4 ±31 ±7 ±3 37 48

Piscivores 1.2 0.8 ±69 ±14 ±6 178 241

Invertivores 1.2 0.6 ±53 ±12 ±5 113 151

Planktivores 1.4 0.5 ±41 ±8 ±3 54 75

Biomass Top 5 Families 2.4 0.9 ±38 ±8 ±3 51 70

East compared with West

Acanthuridae 0.5/0.5 0.2/0.2 ±24 ±5 ±2 44 58

Labridae 1.3/1.3 0.4/0.5 ±17 ±4 ±2 24 32

Pomacentridae 1.7/1.7 0.5/0.5 ±15 ±3 ±2 16 22

Scaridae 0.7/0.7 0.3/0.3 ±25 ±5 ±2 44 60

Serranidae 1.0/0.9 0.5/0.5 ±25 ±6 ±3 54 74

Herbivores 1.3/1.2 0.4/0.4 ±14 ±4 ±1 18 26

Piscivores 1.3/1.2 0.8/0.8 ±25 ±7 ±3 80 110

Invertivores 1.2/1.2 0.6/0.6 ±25 ±5 ±3 52 74

Planktivores 1.3/1.4 0.5/0.5 ±19 ±4 ±2 28 38

Biomass Top 5 Families 2.5/2.4 0.9/0.9 ±18 ±4 ±2 24 30

Point Count Summary

As can be seen in summary Table 3.70, there is not a consistent pattern of increase or decrease in 

statistical power with the varying sampling designs. Annual and biennial sampling designs are 

nearly identical in the number of analyses that exceed the desired power threshold for all 

magnitudes of change analyzed. Sampling every third year begins to show a general decline, 

particularly in the between bank comparisons for smaller magnitudes of change (20%), but this 

pattern is not consistent through all results. Biennial sampling also has mixed results of better or 
196

equal power for some analyses, and less power for others. In Table 3.71, results for the mid 

(approximately five years for each design) and long-term (approximately 10 years) analyses, 

average minimum detectable trends at the desired power level are very similar for annual and 

biennial designs. But there is again a general decline in detectable trends with the increase of 

sampling intervals to every third year. This is also evident in the average minimum sample size 

necessary to detect a 20% change after approximately five years, which is similar for annual and 

biennial designs, but sample size increases for the every third year design. In these comparisons 

biannual sampling shows results that are consistently better than all other sampling designs with 

both smaller average detectable annual trends at the desired power level, and fewer samples 

needed to detect a 20% change in the designated timeframe. 

Table 3.70 Comparison of Power Analyses for Fish Point Counts

A comparison of how many analyses (12 total) met desired power levels for each sampling design.

Sampling Strategy

# of Analyses above the 0.8 Power After ~10 Years

Net Change

-50% 50% 20% 20% 10% 10%

East

Annual 10 7 0 0 0 0

Biennial 10 6 0 0 0 0

Triennial 10 6 0 0 0 0

Biannual 8 7 0 0 0 0

West

Annual 8 5 0 0 0 0

Biennial 10 5 0 0 0 0

Triennial 8 5 0 0 0 0

Biannual 10 9 0 0 0 0

East compared with West

Annual 10 10 8 5 0 0

Biennial 10 10 6 5 0 0

Triennial 10 10 2 1 0 0

Biannual 10 10 9 7 0 0
197

Table 3.71 Comparison of Power Analyses for Fish Point Counts

Comparison of average minimum detectable annual trend and average number of samples required to 

reach desired power level for each sampling design.

Sampling Strategy Average Min Trend (%) Average Min Sample Size

~5 Yrs ~10 Yrs -20% 20%

East

Annual 11 5 88 117

Biennial 11 5 89 119

Triennial 15 6 106 126

Biannual 9 4 70 95

West

Annual 12 5 105 140

Biennial 12 5 107 143

Triennial 16 6 122 144

Biannual 10 4 84 114

East compared with West

Annual 6 3 48 61

Biennial 6 3 50 66

Triennial 8 3 56 69

Biannual 5 2 38 52

3.2.6.2 Belt Transects

Mean total density and standard error were provided for EFGB and WFGB in Buckel et al.

(2014a) from extensive surveys throughout FGBNMS. Standard error was used to calculate 

standard deviation, necessary for power analyses in the program Monitor. The reported values in 

Buckel et al. (2014a) were stratified by habitat, and the data from the shallow (18-33.5 m), high 

relief stratum were used in the power analyses presented here. A review of maps provided in 

multiple chapters of Clark et al. (2014) show that the 100 m by 100 m study sites used for 

FGBLTM are completely encompassed by this strata. The mean densities reported in Buckel et 

al. (2014a) were based on 114 transects at EFGB and 86 transects at WFGB. The power analyses 

performed for this chapter used these mean and standard deviation values with a surveys, plot, 

and interval value of 16 transects per site per sampling period, as is proposed in the current IA 

(BOEM 2014). Belt transects were 25 m x 4 m in Buckel et al. (2014a), though the exact 

dimensions of the belt transects for FGBLTM are not defined in the current IA (BOEM 2014). 

Additionally, because only summary statistics were available, no tests for normality or data 

transformations were possible prior to performing power analyses, so it should be noted that the 

analyses discussed below are a rough approximation of what can be expected from belt transect 

data by FGBLTM.

Current Sampling Design Power Evaluation

Power was low for both banks with the current sampling strategy of 16 transects per bank per 

year (Tables 3.72 and 3.73). Neither bank approached the desired probability threshold of 0.8 

for any percent change or timeframe tested. After two years the minimum detectable annual 

trend for EFGB was 92% and >100% for WFGB. After five years the minimum trend for EFGB 

and WFGB was 25% and 29% respectively. After 10 years the minimum detectable annual 

trends were 10% for EFGB and 12% for WFGB. To detect a 20% net change over five years, 

the sample size for EFGB would need to be 284 for a negative change and 388 for a positive 
198

trend. At WFGB the sample sizes would need to be 384 to detect a negative 20% change and 

520 to detect a positive 20% change in the same timeframe. 

When the power to detect differences between the two banks across time is analyzed, power was 

higher for all timeframes, similar to the point count analyses. An increase or decrease of 50% 

could be detected above the 0.8 threshold after two years. After five and 10 years, a decrease of 

50% remained above the detection threshold, but an increase of 50% dropped below 0.8 for both 

timeframes. The minimum detectable annual trend was 49% for two years, 14% for five years 

and 6% for 10 years. The number of transects necessary to detect a net change of 20% after five 

years was determined to be 180 for a 20% increase and 240 for a 20% decrease.

Table 3.72 Fish Belt Transect Current Sample Design Power Results

Power analysis results for belt transects with the current design of annual sampling.

Location/Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Density (/100m2) 304.6 236.5 0.315 0.071 0.043 0.048 0.082 0.321

West

Density (/100m2) 414.1 375.7 0.219 0.068 0.042 0.043 0.055 0.233

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 0.846 0.199 0.079 0.073 0.187 0.836

After Five Years

East

Density (/100m2) 304.6 236.5 0.362 0.053 0.047 0.044 0.058 0.166

West

Density (/100m2) 414.1 375.7 0.276 0.047 0.04 0.034 0.047 0.125

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 0.938 0.145 0.054 0.06 0.104 0.605

After Ten Years

East

Density (/100m2) 304.6 236.5 0.497 0.051 0.048 0.034 0.047 0.147

West

Density (/100m2) 414.1 375.7 0.346 0.053 0.034 0.051 0.047 0.105

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 0.997 0.169 0.066 0.069 0.096 0.64

Values in red are above the desired power level of 0.80
199

Table 3.73 Fish Belt Transect Current Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for fish belt transect data with the current design 

of annual sampling.

Location/Parameter Pilot Data Min Annual Trend (%)

Min Sample Size 

for 20% Net 

Change after 5 

yrs 

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Density (/100m2) 304.6 236.5 ±92 ±25 ±10 284 388

West

Density (/100m2) 414.1 375.7 ±>100 ±29 ±12 384 520

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 ±49 ±14 ±6 180 240
200

Alternative Sampling Design Power Evaluation

Because the belt transect data is a rough estimate of what may be found at FGBLTM and 

because of the poor power of the current design analyses, only the sampling design most likely to 

improve power (based on stationary point count results) was analyzed. These results are 

discussed below.

Sampling Twice a Year

Changing to a biannual sampling frequency had little impact on the power analysis results for 

EFGB and WFGB individually (Tables 3.74 and 3.75). No net percent change or timeframe 

approached the power threshold. There was some marginal improvement in overall power for 

the majority of analyses at both banks, particularly in the longer timeframes of 5 and 10 years. 

After two years the minimum detectable annual trend was >100% for EFGB and WFGB. After 

five years the minimum trend for EFGB and WFGB was 23% and 27% respectively. After 10 

years the minimum detectable annual trends were 9% for EFGB and 10% for WFGB. In order to 

detect a 20% net change over five years, the sample size for EFGB would need to be 205 for a 

negative change and 314 for a positive trend. At WFGB the sample sizes would need to be 315 

to detect a negative 20% change and 428 to detect a positive 20% change in the same timeframe. 

In the between-bank comparisons, power was higher for 50% change in all timeframes compared 

to individual banks, but not clearly better than sampling annually. An increase or decrease of 

50% could be detected above the 0.8 threshold after 10 years. After five years, a decrease of 50% 

remained above the detection threshold, but an increase of 50% dropped below 0.8. After two 

years, neither positive nor negative net change could be detected at the desired limit. The 

minimum detectable annual trend was 53% for two years, 13% for five years and 5% for 10 

years. The number of transects necessary to detect a net change of 20% after five years was 

determined to be 150 for a 20% increase and 208 for a 20% decrease.

3.2.6.3 Fish Conclusions

Analyses of individual banks revealed that monitoring most categories of fish assemblage, 

regardless of how species are pooled, will lack sufficient power at the desired level to detect 

changes beyond a net of 50%, even over longer time periods. These analyses also showed that 

increasing sample size to detect more subtle trends would need to be executed on large scales of 

ten or more times the current sampling number to achieve this goal. This was particularly 

evident in the sample transect analyses, which had very low power. 

Additional power could be gained by pooling larger bodied fish species data by Family and 

Pomacentrids by genera during analysis. This method has had success for the Great Barrier Reef 

LTM (Sweatman 1997). A negative to this method of data pooling however, is complications in 

interpretation and further investigation is required to determine which species is the cause of any 

observed changes.
201

Table 3.74 Fish Belt Transect Biannual Sampling Power Results

Power analysis results for belt transects with the alternative design of biannual sampling.

Location/Parameter Pilot Data Power After Two Years

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Density (/100m2) 304.6 236.5 0.214 0.068 0.048 0.047 0.057 0.206

West

Density (/100m2) 414.1 375.7 0.156 0.061 0.041 0.026 0.058 0.144

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 0.743 0.124 0.068 0.062 0.122 0.756

After Five Years

East

Density (/100m2) 304.6 236.5 0.397 0.051 0.061 0.046 0.053 0.168

West

Density (/100m2) 414.1 375.7 0.304 0.060 0.045 0.042 0.051 0.131

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 0.988 0.199 0.071 0.059 0.109 0.690

After Ten Years

East

Density (/100m2) 304.6 236.5 0.702 0.072 0.048 0.046 0.070 0.200

West

Density (/100m2) 414.1 375.7 0.497 0.062 0.039 0.059 0.060 0.168

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 1.000 0.216 0.062 0.071 0.159 0.830

Values in red are above the desired power level of 0.80

Table 3.75 Fish Belt Transect Biannual Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for fish belt transect data with the alternative 

design of biannual sampling.

Location/Parameter Pilot Data Min Annual Trend (%)

Min Sample Size 

for 20% Net 

Change after 5 

yrs 

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Density (/100m2) 304.6 236.5 ±>100 ±23 ±9 205 314

West

Density (/100m2) 414.1 375.7 ±>100 ±27 ±10 315 428

East compared with West

Density (/100m2) 304.6/414.1 236.5/375.7 ±53 ±13 ±5 150 208

FGBLTM fish data have high variance proportionally to the corresponding means, but variability 

in fish assemblage increases with habitat heterogeneity and structural complexity (Brown et al.

2011a), so large variance is expected in areas such as the FGBLTM study sites. For example the 

raw total density data in Buckel et al. (2014a) for the shallow, high coral cover strata had a 

variance that equaled over 75% of the mean at EFGB, and 90% of the mean at WFGB. And 

when compared to other LTM power analyses, the results for FGBLTM point count data are 

better than might be expected. For example, the Pacific Islands Network of the National Park 
202

Service expects powers in the range of 0.22–0.33 to detect 50% changes in fish abundance, 

density and biomass at family and trophic levels over 10 years (Brown et al. 2011a). This is 

considerably lower than the results presented here for the FGBLTM. Brown et al. (2011a) point 

out that though their design yields low power, they anticipate better results by taking into 

account sites that are visited every year in their mixed panel design, similar to the current 

FGBLTM design of sampling the same sites annually. 

There were no clear benefits to any of the alternative sampling strategies tested, and the variance 

and power results found at FGBLTM are similar to or better than other data collected at 

FGBNMS and elsewhere. Also, there was little difference between power analysis results for 

annual and biennial sampling designs, so shifting to a biennial sampling frequency could be 

considered. Because most analyses for annual and biennial sampling showed that improvement 

in power would require increasing sample size by double or more from the current design, it is 

suggested sample size remain the same until further analysis of potential quantitative impacts 

such as spatial autocorrelation (Legendre 1993) can be determined. 

3.2.7 Water Quality

The FGBLTM water quality method has consisted of several different methods throughout the 

program history. These methods have included water quality grab samples, deployment of 

SPMDs, water profiles with in situ instrumentation, and extended deployments of data loggers 

and multi-parameter probes. The current sampling strategy for water quality at FGB includes 

quarterly grab samples that are analyzed for nutrients and deployment of SeaBird MicroCAT to 

collect continuous temperature and salinity data. These two methods are discussed separately 

below.

3.2.7.1 Nutrient Grab Samples

Nutrient grab samples were introduced into the FGBLTM program in 2002, and the current suite 

of nutrient parameters was established in 2004 (Precht et al. 2006; Zimmer et al. 2010). From 

2004 through the current IA quarterly grab samples have been collected at three depths (near 

bottom, near surface, and mid water) at each bank and are analyzed for soluble reactive 

phosphorus, Chl a, TKN, ammonia, nitrate, and nitrite (Zimmer et al. 2010; Johnston et al. 2013,

2015; BOEM 2014). Currently two samples are collected at each depth for a total of 6 samples 

per bank per quarter. Statistical analysis of these nutrient grab samples has not been reported in 

the FGBLTM reports to date, and no statistical analysis for this dataset is discussed in the IA 

(BOEM 2014). Furhter recommendations will be made about appropriate statistical analysis of 

the nutrient sample results (Chapter 4).

Nutrient water quality data presents several challenges for statistical analysis (Hirsch and Slack 

1984; Hirsch et al. 1982; Helsel 1987). Water quality data are often non-normally distributed, 

high skewed, bounded by zero, and include infrequent, high values (Helsel 1987). This causes 

major violations to the assumptions of parametric tests such as ANOVA. Nutrient water quality 

data also may contain values below detection limits. These values cannot be easily interpreted 

for parametric analyses (Hirsch et al. 1982; Helsel 1987). Because of these challenges, nonparametric statistical approaches may be most appropriate (Hirsch and Slack 1984; Hirsch et al.

1982; Helsel 1987; Helsel and Hirsch 2002).
203

The FGBLTM nutrient water quality dataset contains mostly values below the detection limit. In 

the pilot study period of 2009-2013 over 85% of the values reported were below the detection 

limit. The entire dataset for nitrite and phosphorus were non-detect values. Two common 

strategies for dealing with values below the detection limit include treating that data as missing 

data or replacing the less than value with either the detection limit or zero (Helsel 1987). Either 

of these strategies will have huge implications in interpreting the monitoring results. Treating the 

data as missing will reduce the sampling size, severely limiting the power of any statistical test,

and will skew the mean toward the detected values. Replacing the non-detect values with any 

other value will skew the data largely toward the selected replacement value, having a huge 

effect of the tests outcomes. Here replacing non-detect values with both zero and the detection 

limits is attempted to demonstrate the power that is provided by these strategies when attempting 

to fit these data to parametric test. A simple non-parametric alternative is tested for power. The 

non-parametric test was performed by converting the pilot data into ranked data and performing 

a one-way ANOVA for data from an individual bank or a two-way ANOVA between the two 

banks. For rank transformation, all values below the detection limit are ranked the same and fall 

below the detection limit value (Hirsch et al. 1982). Additional power to detect change may be 

gained by performing more complex non-parametric evaluations such as permutational ANOVA 

or multivariate ANOVA (MANOVA) and evaluating data transformed to Euclidean distances. 

Current Sampling Strategy Power Evaluation

Datasets resulting from replacing non-detect values with the detection limit and zero were nonnormally distributed as indicated by the Anderson-Darling test (Appendix A). This is because the 

majority of the values are either zero or the detection limit. Log and square-root transformations 

would do very little to improve normality with such a skewed dataset. As a result, untransformed 

data were used to calculate standard deviation values for the power simulations. ANOVA is 

frequently cited as being robust to non-normality when sample sizes are larger (greater than 30) 

(Helsel 1987). Here the sample size is 24 per bank per year so the violation may have significant 

effects of the tests’ validity.

Nitrite and phosphorous datasets could not be assessed, because no values were reported above 

the detection limit. Power simulation results for ammonia, Chl a, nitrate, and TKN are presented 

in Tables 3.76 through 3.81. 

Power simulations for one-way ANOVAs on the datasets where zero was used to replace the 

non-detect values resulted in extremely low power estimations (Tables 3.76 and 3.77). 10, 20 and 

50% net changes were not detected at the desired power levels after two, five or 10 years for any 

of the four nutrients evaluated. Power simulations for the two-way ANOVA between the two 

banks for datasets where zero was used to replace non-detect values also resulted in no trend 

detections at the desired power level. Minimum detectable annual trends ranged from 74% to 

greater than 100% after two years, from 20% to greater than 100% after five years, and from 8% 

to greater than 100% after 10 years. In all cases, greater than 200 samples per bank per year were 

required to achieve 0.8 power to detect 20% net changes in five years.

Power simulations for one-way ANOVAs on the datasets where the detection limits were used to 

replace the non-detect values resulted in slightly greater power estimations (Tables 3.78 and 
204

3.79). 10 and 20% net changes were not detected at the desired power levels after two, five or 10 

years for any of the four nutrient evaluations. A 50% net decrease in ammonia or Chl a could be 

detected at WFGB after five and 10 years. A 50% net increase in Chl a could be detected at 

WFGB after two, five, and 10 years. Power simulations for the two-way ANOVA between the 

two banks for datasets where the detection limits were used to replace non-detect values also 

resulted in no detections for 10 or 20% net changes at the desired power level. 50% net declines 

and increases in ammonia and Chl a could be detected after two, five and 10 years. Minimum 

detectable annual trends ranged from 26% to greater than 100% after two years, from 9% to 

greater than 100% after five years, and from 3% to 14% after 10 years. 60 to greater than 200 

samples per bank per year were required to achieve 0.8 power to detect 20% net changes in five 

years.

Power simulations for one-way ANOVAs performed on ranked datasets resulted in more trend 

detections above the desired power level than the parametric tests; however, power was still 

insufficient to detect changes of 10% and 20%. 50% net declines and increases in Chl a were 

detected at both banks after two, five and 10 years (Tables 3.80 and 3.81). 50% net declines in 

ammonia were detected at both banks after 10 years. Two-way ANOVAs between the banks 

provided greater power to detect change than the one-way ANOVA approach. 20% net decreases 

in Chl a were detected with desired power after two, five, and 10 years. 50% net decreases and 

increases in ammonia and Chl a were detected after two, five and 10 years. 50% net decreases in 

nitrate were detected after five and 10 years, and 50% decreases in TKN were detected after 10 

years. Minimum detectable annual trends ranged from 18% to 100% after two years, from 5% to 

26% after five years, and from 2% to 10% after 10 years. In most cases sample sizes of greater 

than 200 samples per bank per year were required to achieve 0.8 power to detect 20% net 

changes in five years; however, Chl a required only 20 to 26 samples per bank per year to detect 

20% net changes after five years through a two-way ANOVA.

Table 3.76 Nutrient Water Quality Current Sampling Power Results (ND Replaced 

with Zero)

Power analysis results for nutrient water quality samples with the current design of quarterly sampling. 

Non-detect values were replaced with zero before calculating mean and SD.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Ammonia (mg/L) 0.0 0.1 0.193 0.086 0.049 0.053 0.061 0.187

Chl a (mg/m3) 0.2 0.6 0.227 0.161 0.146 0.135 0.132 0.204

Nitrate (mg/L) 0.0 0.1 0.156 0.054 0.037 0.044 0.058 0.159

TKN (mg/L) 3.5 7.4 0.192 0.065 0.061 0.069 0.074 0.192

West

Ammonia (mg/L) 0.0 0.1 0.223 0.079 0.069 0.055 0.070 0.203

Chl a (mg/m3) 0.1 0.4 0.264 0.171 0.155 0.174 0.176 0.211

Nitrate (mg/L) 0.0 0.3 0.034 0.138 0.204 0.204 0.202 0.247

TKN (mg/L) 3.5 7.4 0.193 0.067 0.061 0.058 0.065 0.185

East compared with West

Ammonia (mg/L) 0.0/0.0 0.1/0.1 0.439 0.098 0.052 0.053 0.099 0.464

Chl a (mg/m3) 0.2/0.1 0.6/0.4 0.417 0.147 0.108 0.130 0.122 0.339

Nitrate (mg/L) 0.0/0.0 0.1/0.3 0.264 0.285 0.298 0.234 0.261 0.238

TKN (mg/L) 3.5/3.5 7.4/7.4 0.460 0.108 0.059 0.057 0.103 0.432
205

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Five Years

East

Ammonia (mg/L) 0.0 0.1 0.242 0.060 0.053 0.041 0.063 0.139

Chl a (mg/m3) 0.2 0.6 0.217 0.057 0.056 0.059 0.059 0.094

Nitrate (mg/L) 0.0 0.1 0.157 0.063 0.054 0.047 0.048 0.084

TKN (mg/L) 3.5 7.4 0.252 0.058 0.053 0.039 0.056 0.112

West

Ammonia (mg/L) 0.0 0.1 0.258 0.062 0.050 0.042 0.063 0.121

Chl a (mg/m3) 0.1 0.4 0.227 0.061 0.038 0.053 0.048 0.103

Nitrate (mg/L) 0.0 0.3 0.101 0.037 0.029 0.046 0.051 0.101

TKN (mg/L) 3.5 7.4 0.250 0.063 0.040 0.058 0.058 0.119

East compared with West

Ammonia (mg/L) 0.0/0.0 0.1/0.1 0.578 0.088 0.053 0.056 0.086 0.271

Chl a (mg/m3) 0.2/0.1 0.6/0.4 0.456 0.077 0.040 0.046 0.075 0.178

Nitrate (mg/L) 0.0/0.0 0.1/0.3 0.164 0.064 0.035 0.051 0.059 0.113

TKN (mg/L) 3.5/3.5 7.4/7.4 0.570 0.078 0.055 0.056 0.071 0.243

After Ten Years

East

Ammonia (mg/L) 0.0 0.1 0.329 0.063 0.057 0.043 0.067 0.130

Chl a (mg/m3) 0.2 0.6 0.286 0.061 0.051 0.052 0.035 0.103

Nitrate (mg/L) 0.0 0.1 0.212 0.049 0.055 0.051 0.056 0.085

TKN (mg/L) 3.5 7.4 0.413 0.053 0.057 0.059 0.068 0.135

West

Ammonia (mg/L) 0.0 0.1 0.357 0.058 0.052 0.058 0.059 0.134

Chl a (mg/m3) 0.1 0.4 0.326 0.058 0.062 0.056 0.054 0.104

Nitrate (mg/L) 0.0 0.3 0.110 0.062 0.051 0.053 0.059 0.097

TKN (mg/L) 3.5 7.4 0.338 0.075 0.052 0.049 0.044 0.117

East compared with West

Ammonia (mg/L) 0.0/0.0 0.1/0.1 0.745 0.082 0.049 0.054 0.062 0.300

Chl a (mg/m3) 0.2/0.1 0.6/0.4 0.671 0.058 0.054 0.051 0.061 0.211

Nitrate (mg/L) 0.0/0.0 0.1/0.3 0.139 0.031 0.044 0.053 0.057 0.100

TKN (mg/L) 3.5/3.5 7.4/7.4 0.717 0.088 0.055 0.051 0.063 0.271

mg = milligram, m = meter, L = liter
206

Table 3.77 Nutrient Water Quality Current Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes (ND Replaced with Zero)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for nutrient water quality samples with the 

current design of quarterly sampling. Non-detect values were replaced with zero before calculating mean 

and SD.

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Ammonia (mg/L) 0.0 0.1 ±>100 ±32 ±12 >200 >200

Chl a (mg/m3) 0.2 0.6 ±>100 -33/+36 -12/+14 >200 >200

Nitrate (mg/L) 0.0 0.1 ±>100 ±39 ±14 >200 >200

TKN (mg/L) 3.5 7.4 ±>100 ±31 ±12 >200 >200

West

Ammonia (mg/L) 0.0 0.1 ±>100 ±30 ±11 >200 >200

Chl a (mg/m3) 0.1 0.4 ±>100 -78/+36 -16/+15 >200 >200

Nitrate (mg/L) 0.0 0.3 +>100 -100/+38 +16 >200 >200

TKN (mg/L) 3.5 7.4 ±>100 -31/+30 ±12 >200 >200

East compared with West

Ammonia (mg/L) 0.0/0.0 0.1/0.1 ±76 ±20 ±8 >200 >200

Chl a (mg/m3) 0.2/0.1 0.6/0.4 -78/+88 -21/+24 -8/+9 >200 >200

Nitrate (mg/L) 0.0/0.0 0.1/0.3 ±>100 +36 -100/+13 >200 >200

TKN (mg/L) 3.5/3.5 7.4/7.4 -74/+76 -21/+20 ±8 >200 >200

mg = milligram, m = meter, L = liter

Table 3.78 Nutrient Water Quality Current Sampling Power Results (ND Replaced 

with DL)

Power analysis results for nutrient water quality samples with the current design of quarterly sampling. 

Non-detect values were replaced with the detection limit before calculating mean and SD. 

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Ammonia (mg/L) 0.1 0.1 0.638 0.123 0.063 0.069 0.146 0.612

Chl a (mg/m3) 1.0 0.4 0.579 0.216 0.178 0.152 0.262 0.939

Nitrate (mg/L) 0.2 0.0 0.022 0.131 0.200 0.206 0.180 0.369

TKN (mg/L) 3.8 7.3 0.221 0.073 0.069 0.055 0.065 0.229

West

Ammonia (mg/L) 0.1 0.1 0.689 0.132 0.065 0.083 0.163 0.668

Chl a (mg/m3) 1.0 0.3 0.576 0.160 0.149 0.173 0.306 1.000

Nitrate (mg/L) 0.2 0.3 0.160 0.211 0.201 0.188 0.202 0.361

TKN (mg/L) 3.8 7.3 0.214 0.061 0.071 0.050 0.075 0.213

East compared with West

Ammonia (mg/L) 0.1/0.1 0.1/0.1 0.941 0.241 0.092 0.093 0.261 0.950

Chl a (mg/m3) 1.0/1.0 0.4/0.3 0.889 0.285 0.150 0.160 0.519 1.000

Nitrate (mg/L) 0.2/0.2 0/0.3 0.390 0.191 0.191 0.153 0.185 0.654

TKN (mg/L) 3.8/3.8 7.3/7.3 0.470 0.086 0.054 0.048 0.085 0.467

After Five Years

East

Ammonia (mg/L) 0.1 0.1 0.774 0.100 0.053 0.069 0.076 0.381

Chl a (mg/m3) 1.0 0.4 0.776 0.126 0.066 0.053 0.153 0.811

Nitrate (mg/L) 0.2 0.0 0.102 0.043 0.037 0.043 0.058 0.162
207

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

TKN (mg/L) 3.8 7.3 0.270 0.052 0.051 0.057 0.056 0.125

West

Ammonia (mg/L) 0.1 0.1 0.830 0.108 0.063 0.042 0.084 0.401

Chl a (mg/m3) 1.0 0.3 0.866 0.044 0.024 0.056 0.216 0.984

Nitrate (mg/L) 0.2 0.3 0.259 0.065 0.045 0.052 0.071 0.144

TKN (mg/L) 3.8 7.3 0.274 0.068 0.054 0.052 0.052 0.109

East compared with West

Ammonia (mg/L) 0.1/0.1 0.1/0.1 0.998 0.204 0.063 0.076 0.146 0.800

Chl a (mg/m3) 1.0/1.0 0.4/0.3 0.998 0.189 0.077 0.102 0.331 0.997

Nitrate (mg/L) 0.2/0.2 0/0.3 0.463 0.092 0.065 0.065 0.074 0.366

TKN (mg/L) 3.8/3.8 7.3/7.3 0.596 0.077 0.043 0.063 0.080 0.266

After Ten Years

East

Ammonia (mg/L) 0.1 0.1 0.916 0.093 0.056 0.055 0.088 0.444

Chl a (mg/m3) 1.0 0.4 0.912 0.120 0.063 0.061 0.130 0.879

Nitrate (mg/L) 0.2 0.0 0.126 0.048 0.041 0.048 0.053 0.184

TKN (mg/L) 3.8 7.3 0.339 0.059 0.051 0.035 0.050 0.135

West

Ammonia (mg/L) 0.1 0.1 0.938 0.108 0.064 0.060 0.078 0.515

Chl a (mg/m3) 1.0 0.3 0.987 0.074 0.053 0.068 0.235 0.998

Nitrate (mg/L) 0.2 0.3 0.371 0.067 0.048 0.055 0.070 0.192

TKN (mg/L) 3.8 7.3 0.360 0.065 0.057 0.062 0.077 0.147

East compared with West

Ammonia (mg/L) 0.1/0.1 0.1/0.1 1.000 0.218 0.116 0.087 0.149 0.875

Chl a (mg/m3) 1.0/1.0 0.4/0.3 1.000 0.227 0.081 0.135 0.370 1.000

Nitrate (mg/L) 0.2/0.2 0/0.3 0.618 0.107 0.078 0.051 0.108 0.520

TKN (mg/L) 3.8/3.8 7.3/7.3 0.769 0.091 0.050 0.058 0.071 0.250

Values in red are above the desired power level of 0.80

mg = milligram, m = meter, L = liter
208

Table 3.79 Nutrient Water Quality Current Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes (ND Replaced with DL)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for nutrient water quality samples with the 

current design of quarterly sampling. Non-detect values were replaced with the detection limit before 

calculating mean and SD. 

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Ammonia (mg/L) 0.1 0.1 ±62 -17/+16 ±7 >200 >200

Chl a (mg/m3) 1.0 0.4 -62/+41 -16/+11 -7/+5 150 140

Nitrate (mg/L) 0.2 0.0 -100/+90 -100/+25 +10 >200 >200

TKN (mg/L) 3.8 7.3 ±>100 ±30 ±11 >200 >200

West

Ammonia (mg/L) 0.1 0.1 ±60 -15/+17 ±6 180 >200

Chl a (mg/m3) 1.0 0.3 -58/+31 -15/+9 -6/+35 >200 90

Nitrate (mg/L) 0.2 0.3 +94 +27 +11 >200 >200

TKN (mg/L) 3.8 7.3 ±>100 -29/+30 ±11 >200 >200

East compared with West

Ammonia (mg/L) 0.1/0.1 0.1/0.1 ±40 ±11 ±4 90 130

Chl a (mg/m3) 1.0/1.0 0.4/0.3 -45/+26 -12/+7 -5/+3 100 60

Nitrate (mg/L) 0.2/0.2 0/0.3 -100/+62 -55/+17 -14/+7 >200 >200

TKN (mg/L) 3.8/3.8 7.3/7.3 -74/+72 -20/+20 ±8 >200 >200

mg = milligram, m = meter, L = liter

Table 3.80 Nutrient Water Quality Current Sampling Power Results (Nonparametric Rank Data)

Power analysis results for nutrient water quality samples with the current design of quarterly sampling. 

Ranked data were used for the analyses.

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Ammonia (mg/L) 5.9 4.0 0.635 0.121 0.049 0.053 0.149 0.634

Chl a (mg/m3) 6.1 1.9 0.997 0.433 0.121 0.118 0.441 1.000

Nitrate (mg/L) 1.2 0.9 0.458 0.135 0.096 0.088 0.141 0.503

TKN (mg/L) 16.2 21.7 0.305 0.075 0.052 0.060 0.074 0.318

West

Ammonia (mg/L) 5.9 3.9 0.660 0.140 0.060 0.055 0.129 0.648

Chl a (mg/m3) 6.1 1.4 1.000 0.741 0.210 0.227 0.743 1.000

Nitrate (mg/L) 1.3 1.5 0.334 0.108 0.108 0.081 0.087 0.316

TKN (mg/L) 15.5 21.8 0.296 0.077 0.066 0.055 0.078 0.257

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 0.928 0.245 0.090 0.103 0.250 0.936

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 0.891 0.369 0.313 0.881 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 0.711 0.180 0.097 0.068 0.133 0.706

TKN (mg/L) 16.2/15.5 21.7/21.8 0.579 0.107 0.051 0.071 0.129 0.590

After Five Years

East

Ammonia (mg/L) 5.9 4.0 0.776 0.101 0.058 0.054 0.077 0.397

Chl a (mg/m3) 6.1 1.9 1.000 0.483 0.101 0.100 0.282 0.984
209

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

Nitrate (mg/L) 1.2 0.9 0.569 0.102 0.062 0.052 0.095 0.323

TKN (mg/L) 16.2 21.7 0.382 0.070 0.051 0.059 0.065 0.170

West

Ammonia (mg/L) 5.9 3.9 0.796 0.098 0.066 0.069 0.089 0.411

Chl a (mg/m3) 6.1 1.4 1.000 0.754 0.181 0.149 0.588 1.000

Nitrate (mg/L) 1.3 1.5 0.396 0.067 0.050 0.058 0.057 0.197

TKN (mg/L) 15.5 21.8 0.338 0.077 0.064 0.047 0.070 0.146

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 0.996 0.191 0.090 0.067 0.157 0.800

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 0.884 0.425 0.135 0.603 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 0.837 0.113 0.078 0.060 0.092 0.433

TKN (mg/L) 16.2/15.5 21.7/21.8 0.738 0.108 0.058 0.056 0.072 0.310

After Ten Years

East

Ammonia (mg/L) 5.9 4.0 0.932 0.134 0.060 0.053 0.079 0.484

Chl a (mg/m3) 6.1 1.9 1.000 0.464 0.123 0.084 0.326 0.997

Nitrate (mg/L) 1.2 0.9 0.745 0.108 0.052 0.054 0.073 0.387

TKN (mg/L) 16.2 21.7 0.484 0.090 0.052 0.049 0.077 0.200

West

Ammonia (mg/L) 5.9 3.9 0.959 0.127 0.067 0.064 0.094 0.500

Chl a (mg/m3) 6.1 1.4 1.000 0.819 0.255 0.132 0.826 1.000

Nitrate (mg/L) 1.3 1.5 0.562 0.059 0.055 0.054 0.065 0.210

TKN (mg/L) 15.5 21.8 0.518 0.051 0.053 0.063 0.053 0.187

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.214 0.082 0.063 0.154 0.900

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 0.959 0.379 0.196 0.648 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 0.961 0.196 0.073 0.059 0.068 0.584

TKN (mg/L) 16.2/15.5 21.7/21.8 0.889 0.122 0.058 0.055 0.094 0.411

Values in red are above the desired power level of 0.80

mg = milligram, m = meter, L = liter
210

Table 3.81 Nutrient Water Quality Current Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes (Non-parametric Ranked Data)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for nutrient water quality samples with the 

current design of quarterly sampling. Ranked data were used for the analyses. 

Parameter Pilot Data Min Annual Trend (%) Min Sample Size for

20% Net Change after 5 yrs

Mean SD 2 yrs 5 yrs 10 yrs -20% 20%

East

Ammonia (mg/L) 5.9 4.0 -62/+60 ±17 ±6 190 >200

Chl a (mg/m3) 6.1 1.9 ±30 ±8 ±3 50 68

Nitrate (mg/L) 1.2 0.9 -76/+70 -20/+19 -8/+7 >200 >200

TKN (mg/L) 16.2 21.7 -98/+94 ±25 ±10 >200 >200

West

Ammonia (mg/L) 5.9 3.9 ±60 -17/+16 ±6 180 >200

Chl a (mg/m3) 6.1 1.4 -21/+22 ±6 ±2 28 38

Nitrate (mg/L) 1.3 1.5 -92/+86 -24/+23 ±9 >200 >200

TKN (mg/L) 15.5 21.8 -98/+100 ±26 ±10 >200 >200

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 ±40 ±11 ±4 94 126

Chl a (mg/m3) 6.1/6.1 1.9/1.4 ±18 ±5 ±2 20 26

Nitrate (mg/L) 1.2/1.3 0.9/1.5 -56/+52 ±15 ±6 180 >200

TKN (mg/L) 16.2/15.5 21.7/21.8 ±64 ±17 ±7 >200 >200

mg = milligram, m = meter, L = liter

Alternative Sampling Strategy Power Evaluation

Due to the low power to detect change compared to the non-parametric assessment, parametric 

ANOVA simulations on datasets with non-detect values replaced by zero or the detection limit 

were not further evaluated in the alternative sampling strategy evaluations. Alternative sampling 

strategies presented below include ANOVA testing (non-parametric) with the ranked datasets 

only. Because power under the current quarterly sampling strategy was not sufficient to detect 

20% changes after five years and reducing sampling frequency to quarterly sampling every other 

or every third year would only reduce power, these strategies were not evaluated. 

Because sampling frequency is already quarterly, monthly sampling was tested as an appropriate 

sampling frequency increase. Power simulations for monthly sampling did not have adequate 

power to detect 10% changes in any of the nutrients measured after two, five or 10 years (Table 

3.82). 20% decreases and increases in Chl a were detected with the desired power at EFGB, 

WFGB, and when the two banks were compared after two, five, and 10 years. 50% decreases and

increases in ammonia, Chl a, and nitrate were detected at EFGB, WFGB, and when the two 

banks were compared after two, five, and 10 years. A 50% decrease in TKN was also detected 

after two, five, and 10 years, and a 50% increase in TKN was detected after two years at EFGB, 

WFGB, and when the two banks were compared. Minimum detectable annual trends ranged 

from 10% to 51% after two years, from 3% to 14% after five years, and from 1% to 6% after 10 

years (Table 3.83).
211

Table 3.82 Nutrient Water Quality Monthly Sampling Power Results (Nonparametric Rank Data)

Power analysis results for nutrient water quality samples with the alternative design of monthly sampling. 

Ranked data were used for the analyses. 

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Ammonia (mg/L) 5.9 4.0 0.992 0.373 0.125 0.112 0.365 0.985

Chl a (mg/m3) 6.1 1.9 1.000 0.926 0.371 0.384 0.933 1.000

Nitrate (mg/L) 1.2 0.9 0.949 0.253 0.104 0.104 0.270 0.974

TKN (mg/L) 16.2 21.7 0.828 0.142 0.079 0.064 0.167 0.819

West

Ammonia (mg/L) 5.9 3.9 0.994 0.374 0.137 0.120 0.385 0.990

Chl a (mg/m3) 6.1 1.4 1.000 0.997 0.614 0.560 0.999 1.000

Nitrate (mg/L) 1.3 1.5 0.852 0.181 0.086 0.075 0.196 0.860

TKN (mg/L) 15.5 21.8 0.801 0.164 0.071 0.071 0.184 0.800

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.674 0.207 0.204 0.642 1.000

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 1.000 0.777 0.800 1.000 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 0.998 0.392 0.136 0.105 0.446 0.998

TKN (mg/L) 16.2/15.5 21.7/21.8 0.984 0.336 0.092 0.088 0.326 0.986

After Five Years

East

Ammonia (mg/L) 5.9 4.0 1.000 0.311 0.084 0.097 0.318 0.937

Chl a (mg/m3) 6.1 1.9 1.000 0.892 0.364 0.373 0.890 1.000

Nitrate (mg/L) 1.2 0.9 0.989 0.206 0.070 0.077 0.109 0.865

TKN (mg/L) 16.2 21.7 0.928 0.116 0.068 0.065 0.127 0.559

West

Ammonia (mg/L) 5.9 3.9 1.000 0.348 0.088 0.067 0.223 0.957

Chl a (mg/m3) 6.1 1.4 1.000 1.000 0.706 0.320 0.996 1.000

Nitrate (mg/L) 1.3 1.5 0.959 0.159 0.071 0.075 0.126 0.635

TKN (mg/L) 15.5 21.8 0.916 0.116 0.065 0.074 0.105 0.524

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.638 0.158 0.162 0.366 1.000

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 1.000 0.749 0.783 1.000 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 1.000 0.364 0.090 0.086 0.198 0.978

TKN (mg/L) 16.2/15.5 21.7/21.8 1.000 0.258 0.090 0.076 0.149 0.904

After Ten Years

East

Ammonia (mg/L) 5.9 4.0 1.000 0.349 0.108 0.070 0.246 0.985

Chl a (mg/m3) 6.1 1.9 1.000 0.982 0.440 0.215 0.912 1.000

Nitrate (mg/L) 1.2 0.9 1.000 0.233 0.080 0.074 0.256 0.950

TKN (mg/L) 16.2 21.7 0.994 0.156 0.053 0.052 0.122 0.664

West

Ammonia (mg/L) 5.9 3.9 1.000 0.365 0.110 0.121 0.262 0.989

Chl a (mg/m3) 6.1 1.4 1.000 1.000 0.830 0.832 1.000 1.000

Nitrate (mg/L) 1.3 1.5 0.996 0.166 0.074 0.075 0.142 0.773

TKN (mg/L) 15.5 21.8 0.991 0.137 0.060 0.070 0.093 0.663

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.744 0.218 0.098 0.593 1.000

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 1.000 0.693 0.698 1.000 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 1.000 0.418 0.120 0.119 0.321 0.997
212

Parameter Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

TKN (mg/L) 16.2/15.5 21.7/21.8 1.000 0.424 0.096 0.079 0.213 0.969

Values in red are above the desired power level of 0.80

mg = milligram, m = meter, L = liter

Table 3.83 Nutrient Water Quality Monthly Sample Design Minimum Detectable 

Trends and Minimum Sample Sizes (Non-parametric Ranked Data)

Minimum detectable annual trend values and minimum sample size values for detecting 20% net changes 

after five years of sampling at desired power level of 0.8 for nutrient water quality samples with the 

alternative design of monthly sampling. Ranked data were used for the analyses. 

Parameter Pilot Data Min Annual Trend (%)

Mean SD 2 yrs 5 yrs 10 yrs

East

Ammonia (mg/L) 5.9 4.0 ±33 ±9 ±4

Chl a (mg/m3) 6.1 1.9 ±17 ±5 ±2

Nitrate (mg/L) 1.2 0.9 -40/+38 ±11 ±4

TKN (mg/L) 16.2 21.7 ±50 -13/+14 ±5

West

Ammonia (mg/L) 5.9 3.9 -33/+32 ±9 ±4

Chl a (mg/m3) 6.1 1.4 ±12 ±3 ±1

Nitrate (mg/L) 1.3 1.5 ±46 ±13 ±5

TKN (mg/L) 15.5 21.8 ±51 ±14 -5/+6

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 -23/+24 -7/+6 ±3

Chl a (mg/m3) 6.1/6.1 1.9/1.4 -11/+10 ±3 ±1

Nitrate (mg/L) 1.2/1.3 0.9/1.5 -31/+30 ±9 ±3

TKN (mg/L) 16.2/15.5 21.7/21.8 ±35 ±9 ±4

mg = milligram, m = meter, L = liter

An additional strategy that may be useful to increase statistical power is increasing the number of 

sampling sites per bank. We have recommended that the spatial replication of nutrient water 

quality samples be increased (Chapter 2). The effect of doubling the number of sampling sites 

(two sites per bank) on statistical power is presented in Tables 3.84 and 3.85. Tripling the 

number of sampling sites (three per bank) would have the same results for the current power 

evaluations as increasing the sampling frequency to monthly sampling, because all quarterly or 

monthly events are being pooled for each year in this evaluation. If seasonal trends in water 

quality are accounted for, power results would be different for the two strategies.

With double the sample size, 10% changes in could not be detected with the desired power for 

any nutrient tested after two, five, or 10 years. 20% decreases or increases in Chl a were detected 

at WFGB and when data were compared between the two banks after two, five, and 10 years. 

50% decreases and increases in ammonia and Chl a could be detected at EFGB, WFGB, and 

when data were compared between the two banks after two, five, and 10 years. 50% changes in 

nitrate and TKN were also detected in some cases after two, five and 10 years. Minimum 

detectable annual trends ranged from 13% to 64% after two years, from 4% to 17% after five 

years, and from 2% to 7% after 10 years.
213

Nutrient Grab Sample Summary

The current nutrient sampling strategy does not provide adequate power to detect 20% changes 

after five years of sampling. Both increasing sampling frequency to monthly sampling and 

increasing sample size to two or more sites per bank would provide increased power, and in a 

few cases, adequate power to detect 20% changes in nutrient concentrations (Chl a) after five 

years of sampling. Of the two options, increasing the number of sampling sites is the most cost 

effective and probably the most logistically feasible option. Increasing sampling frequency to 

monthly sampling would require triple the number of trips to the FGB, effectively tripling the 

cost of the method. Monthly sampling is also likely to be limited in feasibility due to poor

weather and sea conditions during certain months of the year, which may prevent access to the 

sites. Although tripling the number of sampling sites would triple lab analysis costs and require 

longer working hours on collection days, it would be feasible to collect these samples without the 

cost of making additional trips to the site. We recommend that FGBLTM considers increasing 

the number of sampling sites per bank to improve the power to detect trends in nutrient water 

quality.

Table 3.84 Nutrient Water Quality Quarterly Sampling Power Results for Double 

the Sample Size (Non-parametric Rank Data)

Power analysis results for nutrient water quality samples with the alternative design of quarterly sampling 

with double the sample size. Ranked data were used for the analyses. 

Parameter

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

After Two Years

East

Ammonia (mg/L) 5.9 4.0 0.939 0.249 0.085 0.110 0.257 0.926

Chl a (mg/m3) 6.1 1.9 1.000 0.776 0.237 0.229 0.794 1.000

Nitrate (mg/L) 1.2 0.9 0.812 0.177 0.083 0.075 0.217 0.853

TKN (mg/L) 16.2 21.7 0.589 0.134 0.070 0.076 0.137 0.594

West

Ammonia (mg/L) 5.9 3.9 0.946 0.246 0.091 0.087 0.267 0.953

Chl a (mg/m3) 6.1 1.4 1.000 0.971 0.441 0.451 0.973 1.000

Nitrate (mg/L) 1.3 1.5 0.687 0.143 0.079 0.080 0.143 0.693

TKN (mg/L) 15.5 21.8 0.564 0.130 0.066 0.073 0.110 0.563

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.501 0.159 0.148 0.523 1.000

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 0.998 0.583 0.597 0.996 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 0.958 0.292 0.105 0.100 0.293 0.966

TKN (mg/L) 16.2/15.5 21.7/21.8 0.900 0.249 0.085 0.085 0.204 0.927

After Five Years

East

Ammonia (mg/L) 5.9 4.0 0.991 0.188 0.071 0.096 0.108 0.774

Chl a (mg/m3) 6.1 1.9 1.000 0.753 0.193 0.204 0.628 1.000

Nitrate (mg/L) 1.2 0.9 0.923 0.137 0.057 0.062 0.125 0.538

TKN (mg/L) 16.2 21.7 0.776 0.089 0.056 0.068 0.076 0.317

West

Ammonia (mg/L) 5.9 3.9 0.995 0.190 0.106 0.085 0.163 0.806

Chl a (mg/m3) 6.1 1.4 1.000 0.976 0.366 0.388 0.934 1.000

Nitrate (mg/L) 1.3 1.5 0.820 0.113 0.054 0.061 0.104 0.433

TKN (mg/L) 15.5 21.8 0.762 0.105 0.067 0.055 0.055 0.353
214

Parameter

Pilot Data Power for Percent Net Change

Mean SD -50% -20% -10% 10% 20% 50%

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.427 0.059 0.106 0.319 0.991

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 0.998 0.513 0.557 0.981 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 0.997 0.196 0.103 0.064 0.204 0.879

TKN (mg/L) 16.2/15.5 21.7/21.8 0.988 0.176 0.055 0.077 0.099 0.709

After Ten Years

East

Ammonia (mg/L) 5.9 4.0 1.000 0.216 0.087 0.061 0.115 0.887

Chl a (mg/m3) 6.1 1.9 1.000 0.886 0.285 0.153 0.698 1.000

Nitrate (mg/L) 1.2 0.9 0.998 0.141 0.071 0.068 0.077 0.740

TKN (mg/L) 16.2 21.7 0.904 0.107 0.056 0.059 0.087 0.405

West

Ammonia (mg/L) 5.9 3.9 1.000 0.214 0.093 0.058 0.114 0.879

Chl a (mg/m3) 6.1 1.4 1.000 0.996 0.595 0.596 0.960 1.000

Nitrate (mg/L) 1.3 1.5 0.923 0.144 0.061 0.050 0.080 0.507

TKN (mg/L) 15.5 21.8 0.877 0.099 0.066 0.070 0.090 0.414

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 1.000 0.499 0.124 0.149 0.372 0.999

Chl a (mg/m3) 6.1/6.1 1.9/1.4 1.000 1.000 0.793 0.475 0.997 1.000

Nitrate (mg/L) 1.2/1.3 0.9/1.5 1.000 0.257 0.095 0.065 0.185 0.941

TKN (mg/L) 16.2/15.5 21.7/21.8 1.000 0.204 0.077 0.054 0.143 0.824

Values in red are above the desired power level of 0.80

mg = milligram, m = meter, L = liter

Table 3.85 Nutrient Water Quality Quarterly Sampling Design with Double the 

Sample Size Minimum Detectable Trends (Non-parametric Ranked Data)

Minimum detectable annual trend values for nutrient water quality samples with the alternative design of 

quarterly sampling with double the sample size. Ranked data were used for the analyses. 

Parameter Pilot Data Min Annual Trend (%)

Mean SD 2 yrs 5 yrs 10 yrs

East

Ammonia (mg/L) 5.9 4.0 ±41 ±11 -4/+5

Chl a (mg/m3) 6.1 1.9 ±21 ±6 ±2

Nitrate (mg/L) 1.2 0.9 -49/+47 ±13 ±5

TKN (mg/L) 16.2 21.7 -62/+64 ±17 ±7

West

Ammonia (mg/L) 5.9 3.9 -40/+41 ±11 ±4

Chl a (mg/m3) 6.1 1.4 ±15 ±4 ±2

Nitrate (mg/L) 1.3 1.5 -60/+58 ±15 ±6

TKN (mg/L) 15.5 21.8 ±64 ±17 ±7

East compared with West

Ammonia (mg/L) 5.9/5.9 4.0/3.9 ±28 ±8 ±3

Chl a (mg/m3) 6.1/6.1 1.9/1.4 ±13 ±4 ±2

Nitrate (mg/L) 1.2/1.3 0.9/1.5 ±38 ±11 ±4

TKN (mg/L) 16.2/15.5 21.7/21.8 ±43 ±12 ±5

mg = milligram, m = meter, L = liter
215

3.2.7.2 Temperature and Salinity Time Series

Since the beginning of the FGBLTM program, extended deployments of water quality sondes 

and loggers have been used to collect continuous data for parameters including temperature, 

salinity, pH, turbidity, dissolved oxygen, and light (Gittings et al. 1993; Dokken et al. 1999; 

Dokken et al. 2003; Johnston et al. 2013). Since 2008, Sea-bird MicroCAT conductivity and 

temperature loggers have been deployed to collect temperature and salinity data along with Hobo 

temperature loggers, which provide a backup temperature record (Zimmer et al. 2010). All other 

extended deployment methods have been discontinued. One Sea-Bird MicroCAT logger is 

deployed at each bank, collecting temperature and salinity information every 30 minutes 

(Johnston et al. 2013, 2015). Data are aggregate to weekly, monthly, and annual mean, min and 

max values. No statistical analysis have been reported for the temperature and salinity data to 

date, but the IA prescribes that “trends” should be analyzed over time. Due to the lack of a 

specific statistical approach to the water quality analysis, temperature and salinity data were 

analyzed for power both with an ANOVA setup and with a simple regression setup.

Net change values of 10, 20, and 50% are inappropriate for evaluating salinity and temperature 

data. A 10% net change in mean temperature at FGB would be greater than two degrees Celsius 

(°C), and a 10% net change in mean salinity would be nearly four parts per thousand (ppt). Sea 

surface temperature increase predicted under the IPCC’s RCP2.6 simulation is about 1°C in 100 

years for the Gulf of Mexico. Sea surface salinity is also predicted to increase by about 1 ppt in 

100 the next 100 years (Collins et al. 2013). Biologically damaging ocean warming can be as 

little as 1°C (Bruno et al. 2001), and biologically damaging salinity fluctuations can be less than 

2 ppt (Ferrier-Pages et al. 1999; Berkelmans et al. 2012). For these reasons, power to detect 

change in temperature and salinity are evaluated for 0.5%, 1% and 2% net changes, which are 

the equivalent of 0.12 to 0.50°C and 0.18 to 0.72 ppt. Also, minimum detectable annual trends 

are presented.

Because this method collects data continuously rather than once per year the alternative sampling 

frequencies assessed for other methods are irrelevant. Instead it is relevant to consider how the 

time series dataset are treated (whether data are evaluated as weekly or monthly datasets). 

Current Sampling Strategy Power Evaluation

Seasonal trends were removed from weekly and monthly temperature and salinity datasets by 

subtracting weekly or monthly mean values from each corresponding weekly or monthly value. 

Standard deviations were calculated from these de-seasonalized datasets as a measure of total 

variation. De-seasonalized data were found to be non-normal using the Anderson-Darling 

normality test due to several low outliers; however, with large sample sizes ANOVA is robust to 

non-normality (Schmider et al. 2010). Power simulations were conducted with both datasets to 

demonstrate that performing analyses on weekly averages provides greater power than monthly 

averages.

One-way ANOVA power simulations showed low power to detect 0.5% and 1% changes in 

temperature after two, five, and 10 years of sampling at both EFGB and WFGB, and when data 

were combined for both banks (Table 3.86). This was true for both weekly and monthly datasets. 

Monthly datasets did not provide adequate power to detect 2% changes in temperature after two, 
216

five or ten years. Weekly temperature datasets for EFGB and both banks combined provided 

adequate power to detect a 2% increase or decrease in temperature after two years. Weekly 

temperature at WFGB was more variable and did not provide adequate power to detect 2% net 

changes even after 10 years. Minimum detectable annual trends ranged from 1.6% to 5.6% after 

two years, from 0.4% to 1.6% after five years, and from 0.2% to 0.6% after 10 years (Table 

3.87).

One-way ANOVA power simulations with the weekly and monthly datasets showed low power 

to detect 0.5% changes in salinity at both EFGB and WFGB and when data were combined for 

both banks after two, five, and 10 years (Table 3.86). Changes of 2% in salinity were detected 

with the desired power in weekly datasets after two years for each bank and when data for both 

banks were combined. 1% changes in salinity were also detected with the desired power in the 

weekly dataset after two years for EFGB and when data were combined for both banks. Changes 

of 2% in salinity were detected with the desired power in monthly datasets after two years for 

EFGB and after 10 years when data from both banks were combined. Minimum detectable 

annual changes ranged from 0.7% to 4.0% after two years, from 0.2% to 1.1% after five years, 

and from 0.08% to 0.4% after ten years (Table 3.87).

Simple regression power simulations were less powerful than one-way ANOVA after two years 

of sampling, but were more powerful after 10 years (Tables 3.88 and 3.89). After two years only 

the weekly data for salinity at EFGB was able to detect 2% net increases or decreases in salinity 

with at least 0.80 power, and all other power values were below the desired level. After five 

years, the detection power was similar to the ANOVA simulations. 2% net changes were 

detected with the desired power for most weekly dataset for both salinity and temperature, and 

1% net changes in salinity were detectable with the desired power at EFGB. After 10 years, 0.5% 

net changes in salinity could be detected at EFGB with the weekly dataset. With the ANOVA 

simulations power to detect 0.5% net changes were below the desired level for all instances. 

Minimum detectable annual changes in temperature for the simple regression setup ranged from 

1.2% to 4.4% after two years, from 0.3% to 3.5% after five years, and from 0.1% to 1.0% after 

10 years. Minimum detectable annual changes in temperature for the simple regression setup 

ranged from 0.6% to 3.4% after two years, from 0.2% to 2.4% after five years, and from 0.06% 

to 0.7% after 10 years.

Table 3.86 Temperature and Salinity Time Series Current Sampling Power 

Results (ANOVA)

Power analysis results for temperature and salinity data with the current design of continuous sampling 

analyzed by ANOVA. 

Parameter Pilot Data Power for Percent Net Change

Mean SD -2.0% -1.0% -0.5% 0.5% 1.0% 2.0%

After Two Years

East

Weekly Temp (°C) 24.9 0.7 0.851 0.281 0.104 0.108 0.271 0.876

Monthly Temp (°C) 24.9 0.8 0.239 0.142 0.134 0.149 0.146 0.220

Weekly Sal (ppt) 36.3 0.3 1.000 0.968 0.471 0.514 0.981 1.000

Monthly Sal (ppt) 36.3 0.3 0.889 0.286 0.184 0.226 0.587 0.974

West

Weekly Temp (°C) 24.7 1.0 0.557 0.161 0.086 0.090 0.148 0.532

Monthly Temp (°C) 24.6 0.9 0.177 0.109 0.119 0.104 0.109 0.191
217

Parameter Pilot Data Power for Percent Net Change

Mean SD -2.0% -1.0% -0.5% 0.5% 1.0% 2.0%

Weekly Sal (ppt) 36.1 1.0 0.916 0.329 0.106 0.106 0.335 0.918

Monthly Sal (ppt) 36.0 1.0 0.284 0.115 0.105 0.109 0.120 0.274

East and West Combined

Weekly Temp (°C) 24.8 0.9 0.936 0.382 0.148 0.121 0.399 0.960

Monthly Temp (°C) 24.8 0.9 0.353 0.147 0.085 0.097 0.128 0.364

Weekly Sal (ppt) 36.2 0.7 1.000 0.889 0.372 0.275 0.871 1.000

Monthly Sal (ppt) 36.2 0.8 0.771 0.263 0.146 0.108 0.261 0.769

After Five Years

East

Weekly Temp (°C) 24.9 0.7 0.792 0.167 0.069 0.065 0.218 0.802

Monthly Temp (°C) 24.9 0.8 0.143 0.070 0.053 0.052 0.063 0.145

Weekly Sal (ppt) 36.3 0.3 1.000 0.960 0.386 0.390 0.987 1.000

Monthly Sal (ppt) 36.3 0.3 0.896 0.250 0.087 0.115 0.530 0.981

West

Weekly Temp (°C) 24.7 1.0 0.440 0.132 0.049 0.055 0.107 0.446

Monthly Temp (°C) 24.6 0.9 0.113 0.070 0.058 0.046 0.056 0.103

Weekly Sal (ppt) 36.1 1.0 0.889 0.258 0.091 0.091 0.257 0.891

Monthly Sal (ppt) 36.0 1.0 0.167 0.072 0.049 0.054 0.057 0.204

East and West Combined

Weekly Temp (°C) 24.8 0.9 0.929 0.329 0.092 0.091 0.294 0.956

Monthly Temp (°C) 24.8 0.9 0.269 0.077 0.054 0.062 0.074 0.310

Weekly Sal (ppt) 36.2 0.7 1.000 0.823 0.222 0.221 0.851 1.000

Monthly Sal (ppt) 36.2 0.8 0.690 0.194 0.078 0.077 0.172 0.742

After Ten Years

East

Weekly Temp (°C) 24.9 0.7 0.914 0.224 0.063 0.077 0.237 0.929

Monthly Temp (°C) 24.9 0.8 0.172 0.072 0.049 0.052 0.071 0.172

Weekly Sal (ppt) 36.3 0.3 1.000 0.997 0.191 0.409 0.993 1.000

Monthly Sal (ppt) 36.3 0.3 0.973 0.376 0.092 0.105 0.567 0.995

West

Weekly Temp (°C) 24.7 1.0 0.563 0.133 0.050 0.059 0.130 0.525

Monthly Temp (°C) 24.6 0.9 0.103 0.061 0.051 0.051 0.065 0.113

Weekly Sal (ppt) 36.1 1.0 0.961 0.253 0.076 0.071 0.282 0.953

Monthly Sal (ppt) 36.0 1.0 0.232 0.078 0.059 0.056 0.070 0.210

East and West Combined

Weekly Temp (°C) 24.8 0.9 0.998 0.342 0.104 0.086 0.329 0.981

Monthly Temp (°C) 24.8 0.9 0.303 0.093 0.057 0.060 0.079 0.301

Weekly Sal (ppt) 36.2 0.7 1.000 0.883 0.220 0.209 0.895 1.000

Monthly Sal (ppt) 36.2 0.8 0.829 0.222 0.074 0.063 0.177 0.817

Values in red are above the desired power level of 0.80

°C = degrees Celsius, ppt = parts per thousand
218

Table 3.87 Temperature and Salinity Time Series Current Sampling Design 

Minimum Detectable Trends

Minimum detectable annual trend values for temperature and salinity data with the current design of 

continuous sampling analyzed by ANOVA. 

Parameter Pilot Data Min Annual Trend (%)

Mean SD 2 yrs 5 yrs 10 yrs

East

Weekly Temp (°C) 24.9 0.7 ±2 ±0.5 ±0.2

Monthly Temp (°C) 24.9 0.8 ±4 ±1 ±0.5

Weekly Sal (ppt) 36.3 0.3 -0.8/+0.7 ±0.2 ±0.08

Monthly Sal (ppt) 36.3 0.3 -2/+1 ±0.4 -0.2/+0.1

West

Weekly Temp (°C) 24.7 1.0 ±3 ±0.7 ±0.3

Monthly Temp (°C) 24.6 0.9 -6/+5 ±2 ±0.6

Weekly Sal (ppt) 36.1 1.0 ±2 ±0.5 ±0.2

Monthly Sal (ppt) 36.0 1.0 ±4 ±1 ±0.4

East and West Combined

Weekly Temp (°C) 24.8 0.9 ±2 ±0.4 ±0.2

Monthly Temp (°C) 24.8 0.9 -4/+3 ±1 ±0.4

Weekly Sal (ppt) 36.2 0.7 ±1 ±0.3 ±0.1

Monthly Sal (ppt) 36.2 0.8 ±2 ±0.6 ±0.2

°C = degrees Celsius, ppt = parts per thousand

Table 3.88 Temperature and Salinity Time Series Current Sampling Power 

Results (Simple Regression)

Power analysis results for temperature and salinity data with the current design of continuous sampling 

analyzed by simple regression. 

Parameter

Pilot Data Power for Percent Net Change

Mean SD -2.0% -1.0% -0.5% 0.5% 1.0% 2.0%

After Two Years

East

Weekly Temp (°C) 24.9 0.7 0.501 0.172 0.084 0.068 0.194 0.539

Monthly Temp (°C) 24.9 0.8 0.151 0.073 0.061 0.055 0.088 0.145

Weekly Sal (ppt) 36.3 0.3 0.997 0.612 0.233 0.287 0.760 0.999

Monthly Sal (ppt) 36.3 0.3 0.591 0.199 0.081 0.098 0.236 0.673

West

Weekly Temp (°C) 24.7 1.0 0.291 0.108 0.059 0.061 0.129 0.310

Monthly Temp (°C) 24.6 0.9 0.102 0.055 0.053 0.056 0.063 0.095

Weekly Sal (ppt) 36.1 1.0 0.584 0.180 0.080 0.092 0.169 0.540

Monthly Sal (ppt) 36.0 1.0 0.170 0.084 0.053 0.048 0.070 0.142

East and West Combined

Weekly Temp (°C) 24.8 0.9 0.663 0.187 0.068 0.094 0.217 0.644

Monthly Temp (°C) 24.8 0.9 0.115 0.070 0.053 0.053 0.068 0.115

Weekly Sal (ppt) 36.2 0.7 0.755 0.280 0.104 0.107 0.301 0.772

Monthly Sal (ppt) 36.2 0.8 0.232 0.100 0.065 0.064 0.107 0.211

After Five Years

East

Weekly Temp (°C) 24.9 0.7 0.886 0.329 0.136 0.133 0.332 0.874

Monthly Temp (°C) 24.9 0.8 0.312 0.115 0.067 0.049 0.107 0.285

Weekly Sal (ppt) 36.3 0.3 1.000 0.986 0.527 0.606 0.982 1.000

Monthly Sal (ppt) 36.3 0.3 0.955 0.462 0.161 0.187 0.565 0.964
219

Parameter

Pilot Data Power for Percent Net Change

Mean SD -2.0% -1.0% -0.5% 0.5% 1.0% 2.0%

West

Weekly Temp (°C) 24.7 1.0 0.607 0.206 0.079 0.085 0.190 0.565

Monthly Temp (°C) 24.6 0.9 0.216 0.073 0.053 0.070 0.093 0.189

Weekly Sal (ppt) 36.1 1.0 0.939 0.352 0.002 0.155 0.366 0.916

Monthly Sal (ppt) 36.0 1.0 0.339 0.136 0.072 0.058 0.130 0.324

East and West Combined

Weekly Temp (°C) 24.8 0.9 0.971 0.442 0.120 0.240 0.511 0.945

Monthly Temp (°C) 24.8 0.9 0.265 0.101 0.068 0.071 0.109 0.224

Weekly Sal (ppt) 36.2 0.7 0.992 0.630 0.205 0.234 0.577 0.987

Monthly Sal (ppt) 36.2 0.8 0.508 0.194 0.080 0.086 0.199 0.502

After Ten Years

East

Weekly Temp (°C) 24.9 0.7 0.995 0.549 0.195 0.230 0.655 0.995

Monthly Temp (°C) 24.9 0.8 0.538 0.191 0.080 0.084 0.182 0.477

Weekly Sal (ppt) 36.3 0.3 1.000 0.996 0.846 0.876 1.000 1.000

Monthly Sal (ppt) 36.3 0.3 0.998 0.783 0.280 0.328 0.769 1.000

West

Weekly Temp (°C) 24.7 1.0 0.895 0.334 0.136 0.143 0.335 0.859

Monthly Temp (°C) 24.6 0.9 0.373 0.131 0.073 0.080 0.136 0.367

Weekly Sal (ppt) 36.1 1.0 0.999 0.734 0.255 0.183 0.649 0.998

Monthly Sal (ppt) 36.0 1.0 0.579 0.216 0.086 0.091 0.204 0.557

East and West Combined

Weekly Temp (°C) 24.8 0.9 1.000 0.737 0.290 0.293 0.724 0.998

Monthly Temp (°C) 24.8 0.9 0.412 0.166 0.075 0.085 0.212 0.401

Weekly Sal (ppt) 36.2 0.7 0.999 0.851 0.366 0.402 0.864 1.000

Monthly Sal (ppt) 36.2 0.8 0.837 0.408 0.139 0.122 0.337 0.820

Values in red are above the desired power level of 0.80

°C = degrees Celsius, ppt = parts per thousand

Table 3.89 Temperature and Salinity Time Series Current Sampling Design 

Minimum Detectable Trends

Minimum detectable annual trend values for temperature and salinity data with the current design of 

continuous sampling analyzed by simple regression. 

Parameter Pilot Data Min Annual Trend (%)

Mean SD 2 yrs 5 yrs 10 yrs

East

Weekly Temp (°C) 24.9 0.7 -2/+1 ±0.4 ±0.1

Monthly Temp (°C) 24.9 0.8 ±4 ±0.8 ±0.3

Weekly Sal (ppt) 36.3 0.3 ±0.6 ±0.2 ±0.06

Monthly Sal (ppt) 36.3 0.3 ±1 ±0.3 ±0.1

West

Weekly Temp (°C) 24.7 1.0 ±2 ±0.5 ±0.2

Monthly Temp (°C) 24.6 0.9 ±4 ±1 ±0.4

Weekly Sal (ppt) 36.1 1.0 ±1 ±0.4 ±0.1

Monthly Sal (ppt) 36.0 1.0 ±3 ±0.8 ±0.3

East and West Combined

Weekly Temp (°C) 24.8 0.9 ±1 ±0.3 ±0.1

Monthly Temp (°C) 24.8 0.9 ±4 ±1 ±0.3

Weekly Sal (ppt) 36.2 0.7 ±1 ±0.3 ±0.1

Monthly Sal (ppt) 36.2 0.8 -3/+2 ±0.6 ±0.2

°C = degrees Celsius, ppt = parts per thousand
220

Temperature and Salinity Time Series Summary

Temperature and salinity time series data collected using SeaBird MicroCAT instruments under 

the current sampling strategy could be an important record of biologically relevant climate 

change in the region. Analyzing de-seasonalized weekly averages provided more power than 

analyzing monthly averages. These monthly datasets were adequate in most cases to detect 2% 

net changes after two, five, and 10 years. Minimum detectable annual changes could be as low as 

0.1% for temperature and 0.06% for salinity. These detection levels are adequate to detect 

predicted climate change affects and potentially biologically damaging changes. We recommend

that FGBLTM continues to collect temperature and salinity time series data under the current 

sampling strategy.

3.2.7.3 Water Quality Conclusions

The current sampling strategy for the nutrient grab samples is inadequate to detect changes in 

water quality at FGB. Increasing the number of samples collected quarterly may be the best 

solution to increase the statistical power of this method. We recommend that the number of 

samples be increased both to increase statistical power and to provide more representative spatial 

coverage of the site as discussed in previous chapters. Temperature and salinity data collected at 

FGB should be sufficient to detect relevant changes. No adjustments to the temperature and 

salinity method are recommended.

3.3 Conclusions and Recommendations

We performed a sampling evaluation of the FGBLTM program, and this chapter presents a 

review of the statistical power of each monitoring technique using the current design as specified 

in the IA, and with alternative designs that adjust sampling frequency and sample size. Power 

results are then compared to those found in other LTM programs, as available, to assess if they 

are reasonable for LTM goals and objectives. 

Some techniques were found to have sufficient statistical power to detect changes of 20% or less 

at the desired threshold of 0.8, particularly after five and 10 years. Others did not have sufficient 

power to detect even 50% changes in any time frame and regardless of sampling design tested. A 

consistent cause of low power seemed to be small sample size based on power analysis results 

from alternative designs in which the sample size was manipulated. For some techniques small 

changes in sample size would yield the desired power levels. Others, however, would require 

unreasonable sample size increases. One way to easily increase power for all analyses, would be 

to adjust the significance alpha level from 0.05 to 0.1. This has been suggested by other LTM 

studies as an acceptable action for LTM programs because the risk there being a change and not 

finding it (Type II error) until it has progressed possibly to the point of not being able to correct 

it is greater than the risk of finding a change that is not there (Type I error) and having to correct 

once the mistake is discovered. This is referred to as the Precautionary Principle and is used in 

other LTM programs when abundance is low or detecting changes is considered more crucial 

than creating a false alarm (i.e., Sweatman 1997).
221

Another way to improve statistical power without adjusting the significance level, is to reduce 

variance. This can be done in a number of ways, one being to introduce a covariate (Brown et al.

2011b). For example, habitat data is already being collected concurrently with the fish point 

count data. A covariate such as rugosity, or other relevant habitat component, could be included 

in analyses to account for some of the variation in the data, therefore increasing analytical power. 

However, the relationship between the initial variable and the covariable may not remain 

consistent over time, creating potential issues with this method. Another method that is already 

strongly suggested by other comprehensive FGBNMS studies as the best way to survey FGB 

(Caldow 2009, Clark et al. 2014) is sample stratification (Brown et al. 2011b). As is outlined in 

Clark et al. mapping at FGBNMS is sufficient to stratify by both depth and habitat or structural 

complexity. However, stratification of the 100 m by 100 m study sites would need to be on a 

finer scale than has been used in other studies, because the FGBLTM sites at both banks are 

encompassed entirely within one stratum as defined by Clark et al. (2014).

For more than one technique in this review (random transect, permanent quadrats, 

sclerochronology, urchins, fish surveys), it was found that the current design of sampling 

annually had the same quantitative power as sampling at longer time intervals, particularly every 

other year. If FGBLTM managers considered reducing sampling frequency at this site, the 

additional resources could be used to increase sampling area by adding additional study sites at 

each bank and rotating sampling among them. Additional sites would not necessarily need to be 

fixed, a mixed panel design with some fixed and some random sites is recommended by 

Urquhart (2013) and is also used by the LTM programs implemented by NPS (Brown et al.

2011a, 2011b). As has been discussed previously in this report, the current 100 m by 100 m 

study site has many issues with representativeness and adequacy in ability to monitor the banks 

as a whole. Expanding the study area to additional 100 m by 100 m replicates would alleviate 

some of the issues with the current FGBLTM design. 

In summary, based on the results of this Sampling Evaluation, the following are recommended 

for improvement of the FGBLTM program:

1. Due to low power to detect change in benthic cover through random transect and 

permanent quadrat data, FGBLTM should shift these methods to biennial sampling and 

increase the sample size.

2. Achieving adequate power for the lateral growth method will likely require increasing the 

sample size beyond a feasible amount of samples. For this reason, and those stated in 

previous chapters, discontinuation of this method is recommended. 

3. The sclerochronology method shows potential to achieve adequate power to detect 

change in coral growth if the core length is increased. This method adjustment should be 

relatively inexpensive and is recommended as the best way to increase the LTM 

program’s ability to detect change in coral growth.

4. Power to detect change in mean colony size at individual banks and FGB in general 

under the current sampling strategy was well below the desired levels. We recommend

that FGBLTM switch to biennial sampling of coral size and double the sample size from 

eight transects per bank to 16 transects per bank. This will provide about twice the power 

of the current sampling strategy, but will require about the same level of effort.

5. Power to detect change in coral recruit number at individual banks and FGB in general 

under the current sampling strategy was well below the desired levels. We recommend
222

that FGBLTM switch to biennial sampling of coral recruits and double the sample size 

from eight transects per bank to 16 transects per bank. This will provide about twice the 

power of the current sampling strategy, but will require a similar level of effort.

6. Power to detect change in sea urchin densities was well below the desired levels. We 

recommend that FGBLTM managers consider changing the sample design to an everyother or every-third year sampling interval and expend resources on increasing sample 

size. This will likely include areas beyond the current 100 m by 100 m study sites, as is 

also recommended by Buckel et al. (2014b). 

7. For fish surveys, there were no clear benefits to any of the alternative sampling strategies 

tested, and the variance and power results found at FGBLTM are similar to or better than 

other data collected at FGBNMS and elsewhere. Because most analyses for annual and 

biennial sampling showed that improvement in power would require increasing sample 

size by double or more from the current design, it is suggested sample size remain the 

same until further analysis of potential quantitative impacts such as spatial 

autocorrelation (Legendre 1993) can be determined. 

8. The current sampling strategy for the nutrient grab samples is inadequate to detect 

changes in water quality at FGB. We recommend that the number of samples be 

increased both to increase statistical power and to provide more representative spatial 

coverage of the site as discussed in previous chapters. 

9. Temperature and salinity data collected at FGB should be sufficient to detect relevant 

changes. No adjustments to the temperature and salinity method are recommended.
223

3.4 References Cited

Aronson RD, Precht WF, Murdoch TJT, Robbart ML. 2005. Long-term persistence of coral 

assemblages on the Flower Garden Banks, northwestern Gulf of Mexico: implications for 

science and management. Gulf Mex Sci. 2005(1): 84-94.

Berkelmans R, Jones AM, Schaffelke B. 2012. Salinity thresholds of Acropora spp. On the Great 

Barrier Reef. Coral Reefs. 31(4): 1103-1110.

Bohnsack JA, Bannerot SP. 1986. A stationary visual census technique for quantitatively 

assessing community structure of coral reef fishes. NOAA Technical Report NMFS 41: 

1-15.

Brown E, Beets J, Brown P, Craig P, Friedlander A, et al. 2011a. Pacific Islands Network 

marine fish monitoring method (Version 1.0). Fort Collins (CO): National Park Service. 

Natural Resource Report NPS/PACN/NRR—2011/421. 282 p.

Brown E, Cox E, Jokiel P, Rodgers K, Smith W, Tissot B, Coles SL, Hultquist J. 2004. 

Development of benthic sampling methods for the Coral Reef Assessment and 

Monitoring Program (CRAMP) in Hawai’i. Pac Sci. 58(2): 145-158.

Brown E, Minton D, Daniel R, Klasner F, Basch L, et al. 2011b. Pacific Islands Network benthic 

marine community monitoring method. Fort Collins (CO): National Park Service. Natural 

Resources Report NPS/PACN/NRR—2011/339. 376 p.

Bruno JF, Siddon CE, Witman JD, Colin PL, Toscano MA. 2001. El Nino related coral 

bleaching in Palau, Western Caroline Islands. Coral Reefs. 20: 127-136.

Buckel CA, Viehman TS, Edwards K, Tester PA, Piniak GA, et al. 2014a. Benthic communities 

of the coral reef. In: Clark R, Taylor JC, Buckel CA, Kracker LM, editors. Fish and 

benthic communities of the Flower Garden Banks National Marine Sanctuary: science to 

support sanctuary management. Silver Springs (MD): National Oceanic and Atmospheric 

Administration. NOAA Technical Memorandum NOS NCCOS 179. p. 22-74.

Buckel CA, Munoz RC, Whitfield PE, Degan BP. 2014b. Fish communities of the coral reef. In: 

Clark R, Taylor JC, Buckel CA, Kracker LM, editors. Fish and benthic communities of 

the Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): National Oceanic and Atmospheric Administration. 

NOAA Technical Memorandum NOS NCCOS 179. p. 75-200.
224

[BOEM] Bureau of Ocean Energy Management. 2014a. Interagency Agreement M14PG00020 

between the Bureau of Ocean Energy Management and the National Oceanic and 

Atmospheric Administration National Ocean Service Office of National Marine 

Sanctuaries Flower Garden Banks National Marine Sanctuary for long-term coral reef 

monitoring at the Flower Garden Banks, Gulf of Mexico: 2014–2016. 28 p.

Caldow C, Clark R, Edwards K, Hile S, Menza C, et al. 2009. Biogeographic characterization of 

fish communities and associated benthic habitats within the Flower Garden Banks 

National Marine Sanctuary: sampling design and implementation of scuba surveys on the 

coral caps. NOAA Technical Memorandum NCCOS 81. 152 p.

Clark R, Taylor JC, Buckel CA, Kracker LM, editors. 2014. Fish and benthic communities of the 

Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): National Oceanic and Atmospheric Administration. 

NOAA Technical Memorandum NOS NCCOS 179. 317 p.

Collins M, Knutti R., Arblaster J, Dufresne JL, Fichefet T, et al. 2013. Long-term climate 

change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK, 

Tignor M, Allen SK, et al editors. Climate Change 2013: The Physical Science Basis. 

Contribution of Working Group I to the Fifth Assessment Report of the 

Intergovernmental Panel on Climate Change. Cambridge (UK): Cambridge University

Press.

Darnell R, Henderson B, Kroon FJ, Kuhnert P. 2012 Statistical power pf detecting trends in total 

suspended sediment loads to the Great Barrier Reef. Marine Poll Bull. 65: 203-209.

Di Stefano J. 2003. How much power is enough? Against the development of an arbitrary 

convention for statistical power calculations. Functional Ecology. 17: 707-709.

Dokken QR, MacDonald IR, Tunnell JW, Wade T, Beaver CR, et al. 2001. Long-term 

monitoring of the East and West Flower Garden Banks, 1998–1999. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 2001-101. 120 p.

Dokken QR, MacDonald IR, Tunnell JW, Wade T, Withers K, et al. 2003. Long-term monitoring 

at the East and West Flower Garden Banks, 1998–2001: final report. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 2003-031. 90 p.

Ferrier-Pages C, Gattuso JP, Jaubert J. 1999. Effect of small variations in salinity on the rates of 

photosynthesis and respiration of the zooxanthellate coral Stylophora pistillata. Marine 

Ecol Prog Ser. 181: 309-314.

Flesch AD, Steidl RJ. 2006. Population trends and implications for monitoring cactus 

ferruginous pygmy owls in northern Mexico. J Wild Manage. 70(3): 867-871.

Gerrodette T. 1987. A power analysis for detecting trends. Ecology. 68(5): 1364-1372.
225

Gibbs JP, Ene E. 2010. Program Monitor: Estimating the statistical power of ecological 

monitoring programs. Version 11.0.0. [Cited 2015 August 30]. Available from: 

www.esf.edu/efb/gibbs/monitor/

Gittings SR, Boland GS, Deslarzes KPJ, Hagman DK, Holland BS. 1993. Long-term monitoring 

at the East and West Flower Garden Banks. New Orleans (LA): U.S. Department of the 

Interior, Minerals Management Service, Gulf of Mexico OCS Regional Office. OCS 

Study MMS 92-0006. 206 p. 

Helsel DR. 1987. Advantages of nonparametric procedures for analysis of water quality data. 

Hydrolog Sci. 32(2): 179-190.

Helsel DR, Hirsch RM. 2002. Statistical methods in water resources. In: Techniques of waterresources investigations of the United States Geological Survey. Book 4, Hydrologic 

analysis and interpretation. Washington (DC): U.S. Geological Survey. 522 p.

Hirsch RM, Slack JR. 1984. A nonparametric trend for seasonal data with serial dependence. 

Water Resour Res. 20(6): 727-732.

Hirsch RM, Slack JR, Smith RA. 1982. Techniques of trend analysis for monthly water quality 

data. Water Resour Res. 18(1): 107-121.

Houk P, Van Woesik R. 2006. Coral reef benthic video surveys facilitate long-term monitoring 

in the Commonwealth of the Northern Mariana Islands: toward an optimal sampling 

strategy. Pacific Science. 60: 177-189

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2013. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2009–2010. 

Volume 1: Technical Report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2014A214. 202 p.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2015. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2011–2012. Draft 

Volume 1: Technical Report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2015-027. 

205 p.

Legendre P. 1993. Spatial autocorrelation: trouble or new paradigm? Ecol. 74: 1659-1673.

Lewis KP. 2006. Statistical power, sample sizes, and the software to calculate them easily. 

BioScience. 56(7): 607-612.

McDonald JH. 2014. Handbook of biological statistics, 3rd ed. Baltimore (MD): Sparky House 

Publishing. 303 p.
226

Menza C, Ault J, Beets J, Bohnsack J, Caldow C, et al. 2006. A guide to monitoring reef fish in 

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

Memorandum NOS NCCOS 39. 169 p. 

Meyer CFJ, Aguiar LMS, Aguirre LF, Baumgarten J, Clarke FM, et al. 2010. Long-term 

monitoring of tropical bats for anthropogenic impact assessment: gauging the statistical 

power to detect population change. Biol Cons. (2010). doi: 

10.1016/j.biocon.2010.07.029.

[MMS] Minerals Management Service. 1996. Long-term monitoring at the East and West Flower 

Garden Banks. Prepared by Continental Shelf Associates, Inc. New Orleans (LA): U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS Region. 

OCS Study MMS 96-0046. 77 p.

Otto K. 2005. Anderson-Darling normality test calculator. [Cited 2015 August 30]. Available 

from: http://www.kevinotto.com/RSS/templates/Anderson-Darling Normality Test 

Calculator.xls

Pattengill-Semmens C, Gittings SR, Shyka T. 2000. Flower Garden Banks National Marine 

Sanctuary: a rapid assessment of coral, fish and algae using the AGGRA method. Marine 

Sanctuaries Conservation Series MSD-00-3. Silver Springs (MD): U.S. Department of 

Commerce, National Oceanic and Atmospheric Administration, Marine Sanctuaries 

Division. 15 p. 

Pollock JF. 2006. Detecting population declines over large areas with presence-absence, time-toencounter, and count survey methods. Conserv Biol. 20 (3): 882-892

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Murdoch TJT, et al. 2006. Long-term 

monitoring at the East and West Flower Garden Banks, 2002–2003: final report. New 

Orleans (LA): U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region. OCS Study MMS 2006-035. 182 p.

Razali NM and Wah YB. 2011. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, 

Lilliefors and Anderson-Darling tests. J of Stat Model Analyt. 2: 21-33.

Seavy NE, Reynolds MH. 2007. Is statistical power to detect trends a good assessment of 

population monitoring? Biol Cons. 140: 187-191.

Schmider E, Zeigler M, Danay E, Beyer L, Buhner M. 2010. Is it really robust? Reinvestigating 

the robustness of ANOVA against violations of the normal distribution assumption. 

Methodol. 6:147-151.

Starcevich LAH. 2013. Trend and power analysis for National Park Service marine data from 

2006 to 2010. Natural Resource Technical Report NPS/PACN/NRTR—2013/771 86 p.

Steidl RJ, Hayes JP, Schauber E. 1997. Statistical power analysis in wildlife research. J Wildl 

Manage. 61(2): 270-279.
227

Stoddart JA, Blakeway DR, Grey KA, Stoddart SE. 2004. Rapid high-precision monitoring of 

coral communities to support reactive management of dredging in Mermaid Sound, 

Dampier, Western Australia. Corals of the Dampier Harbour: their survival and 

reproduction during the dredging programs of 2004. 17 p.

Sokal RR, Rohlf FJ. 1995. Biometry, 3rd ed. New York (NY): W.H. Freeman and Company. 887 

p. 

Sweatman H. ed. 1997. Long-term monitoring of the Great Barrier Reef. Status Report No. 2. 

Townsville, Queensland (Australia): Australian Institute of Marine Science.

Sweatman H, Cheal A, Coleman G, Emslie M, Johns K, et al. 2008. Long-term monitoring of the 

Great Barrier Reef Status Report. Townsville, Queensland (Australia): Australian 

Institute of Marine Science. 379 p.

Vermeij MJA, Bak RPM. 2003. Inferring demographic processes from population size structure 

in corals. Proc 9th Int Coral Reef Symp. 5 p.

Zimmer B, Duncan L, Aronson RB, Deslarzes KJP, Deis D, et al. 2010. Long-term monitoring at 

the East and West Flower Garden Banks, 2004–2008. Volume I: Technical Report. New 

Orleans (LA): U.S. Department of the Interior, Bureau of Ocean Energy Management, 

Regulation, and Enforcement, Gulf of Mexico OCS Region. OCS Study BOEMRE 2010-

052. 310 p.

Zorn P, Linke T, Smith M, Blazeski V. 2004. Optimizing limited monitoring capacity: applying 

power analysis as a management tool for the ecological integrity program at point Pelee 

National Park. Science and Management of Protected Areas Association 5th International 

Conference. Victoria, British Columbia, Canada. 10 p.
228

Appendix A: Anderson-Darling Normality Test Results

(Otto 2005)

Parameter N Transform AD test statistic p-value

Random Transects

East

Coral 63 remove 1 outlier 0.491 0.213

Macroalgae 63 none 1.321 0.002

Macroalgae 63 log 0.187 0.900

Substrate 63 none 0.347 0.469

P. strigosa 63 none 3.193 <0.0005

P. strigosa 63 square root 1.359 0.002

M. annularis SC 63 none 5.434 <0.0005

M. annularis SC 63 log + 1 0.856 0.026

West

Coral 64 none 0.487 0.217

Macroalgae 64 none 0.594 0.119

Substrate 64 none 1.331 0.002

Substrate 64 log 0.369 0.418

P. strigosa 64 none 2.952 <0.0005

P. strigosa 64 square root 0.686 0.070

M. annularis SC 64 none 8.959 <0.0005

M. annularis SC 64 log + 1 0.839 0.029

Quadrats

East 

Coral 145 none 13.579 <0.0005

Macroalgae 145 none 9.831 <0.0005

Macroalgae 145 square root 0.533 0.171

Substrate 145 none 1.397 0.001

Substrate 145 square root 0.292 0.601

P. strigosa 145 none 6.801 <0.0005

M. annularis SC 145 none 14.301 <0.0005

West

Coral 151 none 2.111 <0.0005

Macroalgae 151 none 2.572 <0.0005

Macroalgae 151 square root 0.420 0.322

Substrate 151 none 2.776 <0.0005

Substrate 151 square root 0.565 0.141

P. strigosa 151 none 7.8805 <0.0005

P. strigosa 151 log + 1 1.429 0.001

M. annularis SC 151 none 17.970 <0.0005

Sclerochronology

East 77 none 0.390 0.374

West 81 none 0.320 0.526

Lateral Growth

East

Growth 10 none 0.315 0.483

Retreat 10 none 0.412 0.273

West

Growth 12 none 0.441 0.240

Retreat 12 none 0.578 0.105
229

ParameterN Transform AD test statisticp-value

Coral Recruits

East

6 none 0.615 0.057

West 

6 none 0.310 0.429

Coral Size

East

P. strigosa 13 none 1.036 0.007

P. strigosa 13 log 0.324 0.481

O. cavernosa 10 none 0.263 0.616

O. cavernosa 10 log 0.325 0.457

O. franksi 57 none 1.133 0.005

O. franksi 57 log 0.707 0.061

P. asteroides 30 none 106.137 <0.0005

P. asteroides 30 log 508.886 <0.0005

West

P. strigosa 10 none 1.217 0.002

P. strigosa 10 log 0.621 0.075

O. cavernosa 11 none 0.416 0.273

O. cavernosa 11 log 0.418 0.269

O. franksi 34 none 69.034 <0.0005

O. franksi 34 log 789.749 <0.0005

P. asteroides 20 none 52.745 <0.0005

P. asteroides 20 log 281.686 <0.0005

Fish Point Counts

East

Acanthuridae 100 log 2.009 <0.0005

Labridae 100 log 1.356 0.002

Pomacentridae 100 log 4.781 <0.0005

Scaridae 100 log 1.460 0.001

Serranidae 100 log 0.847 0.028

Herbivores 100 log 3.391 <0.0005

Piscivores 100 log 4.946 <0.0005

Invertivores 100 log 2.224 <0.0005

Planktivores 100 log 1.233 0.003

Biomass Top 5 Families 500 log 16.668 <0.0005

West

Acanthuridae 102 log 3.848 <0.0005

Labridae 102 log 1.847 <0.0005

Pomacentridae 102 log 1.856 <0.0005

Scaridae 102 log 2.983 <0.0005

Serranidae 102 log 0.736 0.053

Herbivores 102 log 1.718 <0.0005

Piscivores 102 log 3.004 <0.0005

Invertivores 102 log 3.561 <0.0005

Planktivores 102 log 1.568 <0.0005

Biomass Top 5 Families 510 log 9.403 <0.0005

Temperature and Salinity

Weekly Temp 438 none 1.168 0.005

Monthly Temp 103 none 0.856 0.027

Weekly Sal 438 none 19.834 <0.0005

Monthly Sal 99 none 4.713 <0.0005

Nutrients

Ammonia (ND --> 0) 457 none 28.493 <0.0005
230

Parameter N Transform AD test statistic p-value

Chl a (ND-->0) 457 none 31.325 <0.0005

Nitrate (ND-->0) 457 none 33.772 <0.0005

TKN (ND-->0) 457 none 99.13 <0.0005

Ammonia (ND -->DL) 217 none 21.559 <0.0005

Chl a (ND-->DL) 217 none 57.427 <0.0005

Nitrate (ND-->DL) - none - -

TKN (ND-->DL) 217 none 39.241 <0.0005
231

Appendix B: Outlier Calculation for EFGB Transect Coral Data 

EFGB Coral Data in Sequential Order

4.10 Minor Outlier, removed

16.55

26.46

30.93

30.93

32.98

35.80

37.2

40.52

42.30

43.04

43.43

43.53

44.52

45.76

Q1 45.76

46.15

46.55

47.30

48.28

48.65

49.29

49.61

52.56

53.52

53.76

53.76

53.90

54.04

56.36

56.38 Interquartile Range

Inner 

Fences

Outer 

Fences

median 56.47 19.75 16.135 -13.49

56.50 95.135 124.76

57.59

58.14

58.73
232

59.83

61.20

61.46

61.48

61.74

62.36

62.37

62.50

62.89

64.30

65.38

Q3 65.51

65.54

66.09

66.19

68.02

68.66

68.75

68.99

70.12

70.98

71.76

72.42

73.69

74.95

75.11

77.95
233

CHAPTER 4.0: EVALUATION OF ANALYSES

4.1 Introduction

In this chapter, the suite of statistical techniques used in the FGBLTM program, from 2010 

through the current IA, is evaluated. Each statistical analysis is discussed in terms of its 

appropriateness for the specific data and the LTM program objectives. The conclusions reported 

in the FGBLTM technical reports of Johnston et al. (2013, 2015) are evaluated. Also discussed 

are whether the conclusions are adequately supported and defensible by the analyses used and 

the statistical power of the sampling methods.

The chapter is divided into one section for each of the monitoring variables identified as part of 

the FGBLTM program design that have been statistically evaluated in the FGBLTM monitoring 

reports since 2010:

1. benthic percent cover

2. coral recruitment

3. sea urchin, lobster and lionfish population density

4. fish populations

5. water quality

Coral health and coral recruitment methods were added to the program in 2013 and have not yet 

been discussed in a monitoring report, so these methods could not be evaluated in this chapter. 

The qualitative observations and qualitative data obtained from the perimeter video technique are 

not discussed here because data collected through these methods are not statistically evaluated as 

part of the FGBLTM program.

Hypothesis tests are frequently misused in marine ecological studies and LTM programs 

(Beninger et al. 2012; Gitzen and Millspaugh 2012; Reynolds 2012). Test assumptions are often 

ignored or overlooked, but these assumptions are vitally important to the interpretation of test 

results. Although data collection methods may be similar across LTM programs, differing 

approaches to statistical evaluation of the data may cause varied interpretation and conclusions 

(Fabricius and De’Ath 2004).

General guidance is available on statistical analyses of LTM data from Gitzen et al. (2012) and 

Helsel and Hirsch (2002). Examples of analyses specifically applied to coral reef datasets 

including fish and benthic assessments are available from the National Park Service (NPS) in 

numerous LTM program method documents and reports such as Brown et al. (2011a), Brown et 

al. (2011b), and Brown et al. (2014) among others. In this chapter, these references and those 

from other LTM programs are used to evaluate each statistical technique used in the FGBLTM 

program from 2010 through the current IA, and, where appropriate, recommendations are given 

for improvements to the FGBLTM statistical approach.

One major concern about the effectiveness of the FGBLTM program is adequate support and 

defensibility of the conclusions gleaned from the monitoring data. The major overarching

conclusion of recent FGBLTM monitoring reports is that FGB is composed of stable 

communities with high coral cover and abundant fish populations (Johnston et al. 2013, 2015). 

Little change has been detected in any of the monitoring parameters over the history of the LTM 

program. Though this conclusion is optimistic, as power analyses have indicated, the majority of 
234

the methods provide very low power to detect changes over time under the current sampling 

strategies. Furthermore, because of small sample sizes or other analytical challenges, many of the 

methods are not consistently statistically evaluated. In light of these issues, each conclusion is 

considered from the two most recent monitoring reports (Johnston et al. 2013, 2015) and the 

reliability of those conclusions is discussed. Recommendations are made on how to improve the 

defensibility of conclusions through additional or alternative statistical techniques. All 

recommended statistical analyses can be performed in R© or with the Primer software, in which 

FGB has already invested.

4.2 Analysis Evaluation

4.2.1 Benthic Cover

Benthic cover is assessed primarily using two methods for FGBLTM: random transects and 

permanent quadrats. Random transects have been the main method of collecting benthic cover 

data since the inception of the FGBLTM program. Though the methods used to collect transect 

data have changed through the history of FGBLTM, they have remained consistent since 2010. 

The current methodology uses sequential digital photographs along a 10 m transect for 16 

transects per bank. Benthic cover analysis is completed using the freeware program Coral Point 

Count with Microsoft® Excel® extensions (CPCe) (Kohler and Gill 2006). Random dot overlays 

totaling a minimum of 500 dots per transect were used to categorize benthic cover as follows: 

corals, sponges and macroalgae were identified to the lowest possible taxonomic group, crustose 

coralline algae, fine turf and bare rock were classified together as “CTB”, and “other” categories 

were live other, sand, rubble, and unknown (Johnston et al. 2013). 

Permanent quadrats have had two functions in FGBLTM. Initially they were intended to 

monitor changes in growth or retreat of individual coral tissue borders over time and to 

determine potential causes of coral decline if observed (Gittings et al. 1993). A secondary 

purpose that has become more prominent in recent years is to collect benthic percent cover data, 

similar to the random transects. In this function, the benthic categories defined within the 

permanent quadrats are the same as for the random transects, and the methods of extracting 

percent cover data are also the same (point count overlays using CPCe) (Johnston et al. 2013). In 

2003, nine deep (32–40 m) permanent quadrat stations were established at EFGB (Precht et al.

2006). Two additional stations at EFGB and 12 at WFGB were established in 2012 (Johnston et 

al. 2015). Data collection and analysis methods for the deep stations are the same as for the 

shallow permanent quadrat stations. 

4.2.1.1 Random Transects

4.2.1.2 Review and Appropriateness of Statistical Techniques

Since 2010, benthic cover transect data have been consistently analyzed in two ways. Factor 

plots were created in CPCe to compare average percent cover of substrate categories and coral 

species between banks and through time (Johnston et al. 2013, 2015). Also, two-way ANOVA 

was used to test the null hypotheses that there were no differences in response variables between 

banks, between years, and in the interaction between bank and year. 
235

Additional multivariate and non-parametric analyses were conducted beginning with the 2011–

2012 monitoring period (Johnston et al. 2015). Benthic community compositions between banks 

were tested for significant dissimilarity using the Bray Curtis similarity resemblance measure in 

an Analysis of Similarity (ANOSIM) for each year. Cluster analysis and non-metric multidimensional scaling (MDS) were also used to visualize community differences between banks. 

Species contributing most to the observed dissimilarities between banks were then identified 

using similarity percentages (SIMPER). Long-term trends within each bank were also assessed 

by converting benthic cover data to Bray-Curtis similarity matrices. Matrices were used in 

cluster analyses and between year MDS plots, with time series trajectories used to visualize 

changes over time. SIMPER was used to determine which benthic components contributed to 

observed changes.

The current IA requires random transect data to be analyzed with ANOVA to evaluate significant 

differences and variance among transects and among years at each site. Tukey’s Honest 

Significant Difference (HSD) test is used to determine which parameters and transects are 

significantly different from each other within a site. T-tests are to be used to determine any 

differences in benthic parameters between sites. Additional parameters calculated from transect 

data are dominance, total live cover, mortality, species frequency, diversity, evenness, and signs 

of coral distress, including disease, damage, bleaching, fish biting, hyperplasia and recent death. 

With the exception of diversity, these parameters are not included in statistical analyses.

ANOVA tests are widely used in ecological studies and are a powerful data analysis tool for 

hypothesis testing (Sokal and Rholf 2005). The inclusion of this test in the FGBLTM statistical 

suite can be useful when appropriately applied. As was demonstrated with pilot data in the 

power analyses, the majority of random transect benthic category data can be transformed to 

meet normality assumptions, making the use of a parametric test valid. Analyzing univariate 

summary statistics, such as diversity indices, using ANOVA is one way to assess mean 

differences in the benthic assemblage (Magurran 2004) in addition to individual benthic 

components. However, the specific components that contribute to any observed changes are not 

readily evident without further analyses. Also, analyzing each category separately as an 

independent variable is often not appropriate for multi-species matrices and can have a high 

probability of leading to false inferences (Clarke and Warwick 2001), so the addition of nonparametric multivariate tests is important.

Parametric tests are applied only to the gross benthic categories of coral, macroalgae, CTB, and 

summary statistics. When using two-way ANOVA tests, it is important to acknowledge that 

three separate null hypotheses are being tested, and if there is a significant interaction effect (i.e.,

between year and bank) then the single parameter hypotheses cannot be interpreted because the 

assumption of independence is proved false (Sokal and Rolfe 1995). Though these parametric 

tests of benthic categories have ecological relevance in terms of overall reef health, differences 

in individual species abundance patterns are generally overlooked. 

Different benthic species have different responses to environmental influences, and investigation 

into more detailed community responses may be desirable. However, percent cover at the species 

level will more likely violate parametric assumptions in a manner that could not be remedied by 

standard transformations (Clarke and Warwick 2001), in which case non-parametric tests are 

more useful. Analyzing how percent cover or abundance of multiple benthic categories or 

species change in relation to each other simultaneously can be an informative process, leading to 
236

multivariate statistics. The use of non-parametric, multivariate tests in the 2011–2012 monitoring 

period was appropriate for the data and increased the ability of the FGBLTM program to answer 

various questions about the status of the FGB study sites. However, certain points should be 

noted for the tests that were employed in 2011–2012, as will be discussed below. 

4.2.1.3 Review and Appropriateness of FGBLTM Conclusions

The conclusions reached from transect data results have remained largely the same since the 

beginning of the FGBLTM: that both banks consist of a stable coral community with high coral 

cover and moderate fluctuations in other benthic components (i.e., Dokken et al. 1999; Precht et 

al. 2006; Johnston et al. 2013). In the 2009–2010 technical report (Johnston et al. 2013),

statistical results showed no significant differences between site or year for coral and sponge, but 

a significant site effect for macroalgae, and a significant year effect for CTB. Other conclusions 

are based largely on the qualitative comparisons of yearly percent covers with standard errors. 

For example, Johnston et al. (2013) stated that 2009–2010 data showed that during that time 

period, coral cover remained above 56%, which was similar to coral percent cover found in 

previous FGBLTM studies, but contrasted with the reported decline of coral cover in other areas 

of the Caribbean in the same time period. This qualitative comparison was used to make the 

conclusions that coral communities at FGB are stable. Results of individual benthic species, 

primarily corals, were also based on qualitative comparisons of yearly means. No statistical 

analyses included datasets beyond those collected in the 2009–2010 monitoring period.

In the 2011–2012 technical report, the ANOVA test results showed no significant differences 

between banks or years for coral, sponges, macroalgae and CTB (Johnston et al. 2015). The 

addition of multivariate, non-parametric tests increased the overall statistical support upon which 

conclusions were made for random transect data. ANOSIM, cluster analysis, and MDS plots, 

based on Bray-Curtis similarity matrices, showed no significant differences between the benthic 

assemblages of EFGB compared to WFGB or between 2011 and 2012 within the same bank. 

This non-parametric comparison between banks was the first statistical evidence provided by 

FGBLTM that WFGB and EFGB have similar benthic composition, though they have been 

routinely compared since FGBLTM began (Gittings et al. 1993), with no clear reasoning 

provided as to what useful information is obtained from this continual comparison. 

Additional analyses done during the 2011–2012 monitoring period included SIMPER, cluster 

analyses, and MDS plots of mean percent cover for data from 1994–2012. These analyses all 

showed a similar picture of the benthic composition remaining consistent from 1994–1998, 

changing due to macroalgae and CTB shifts from 1999–2002, and re-stabilizing from 2003–

2012. These and other results led to conclusions supporting those found throughout the 

FGBLTM history, of a stable community with high coral cover, though further details of 

community fluctuations were available due to the analysis of data across a larger increment of 

the program timeframe than just the current monitoring period. 

The statistical tests incorporated into the FGBLTM program are appropriate and used correctly; 

however, the conclusions based on those tests are less reliable. Though there are exceptions, the 

tendency for the FGBLTM program has been to only statistically analyze data from the two 

years of the immediate monitoring period and to make mostly qualitative comparisons to inform 

long-term conclusions, as was done in Johnston et al. (2013). Making qualitative conclusions in 

the absence of the analytical rigor that statistical testing requires is potentially misleading and 
237

detrimental to the overall program goals. The danger of assuming a trend of stability based on 

two years of data is that a different picture may develop over many years (Johnson 2012). The 

2011–2012 monitoring period remedied that somewhat with time series MDS plots of 18 years of 

data (Johnston et al. 2015). However, no trend analyses have been used to model the long-term 

trajectory of the benthic communities at the FGBLTM study sites. Trend analysis is an important 

component of LTM; its absence from FGBLTM is counter to the stated program objective of 

documenting community changes over time. 

Further confounding the conclusions of the FGBLTM is the statistical power of the analyses used 

and the question of whether they can legitimately support the conclusions. Power analyses were 

run on the parametric tests used for the random transect data for the three main benthic 

categories and the two dominant coral species. As seen from the results of those analyses, in a 

two-year time span with the current sampling design, coral cover would need to change by 50% 

to detect a significant difference with a confidence 0.8. After sampling for 10 years, the 

cumulative change in coral cover would need to be 50% to detect a significant difference 

between the first and tenth year of sampling, which equals a consistent change in the same 

direction (decrease or increase in cover) of approximately 5% (for annual increase) and 7% (for 

annual decrease) every year for ten years. Macroalgae would require a cumulative change of 

20% after two and 10 years to be detected as a significant difference with a confidence 0.8, and 

analysis of sponge cover has little to no power even with a change of 100% after two or 10 years. 

Analyses of individual coral species have even less power, with only 50% decreases of D. 

strigosa after five and 10 years surpassing the confidence limit of 0.8. The FGBLTM program 

reports’ assertion that there are no significant changes in random transect benthic cover within a 

two year monitoring period has little meaning in light of the statistical power of the analyses 

used.

4.2.1.4 Random Transect Conclusions

Random transect data has been analyzed using both parametric and non-parametric tests to 

determine if changes are occurring in the percent cover of the four major functional benthic 

categories. Additional analyses were focused on particular species of interest. The parametric 

tests are being applied properly, with assumptions being addressed and data transformed as 

necessary, but due to large variance from year to year and within years, the power of these tests

is very low, beyond being able to accurately detect changes of a large magnitude. This is 

exacerbated by the tendency of FGBLTM to statistically analyze data from only the two years of 

the immediate monitoring period, a timeframe that was shown in the power analyses to require 

the greatest magnitude of annual change for a significant statistical result. In addition to the low 

power of ANOVAs assessing only two years of FGBLTM data, the questions that these 

parametric tests are answering are not congruent with the stated FGBLTM program objectives. 

The purpose of this monitoring program is to document benthic changes over time, but this 

cannot be achieved without taking into account the data collected before to the current 

monitoring period.

The inclusion of non-parametric testing in the most recent technical report has provided useful 

additional information to FGBLTM. For example, the use of multivariate techniques has 

allowed comparisons of benthic community data over longer time periods, which brought forth 

community dynamics that had not previously been understood beyond the timeframe in which 
238

they had been collected. For example, Dokken et al. (2003) note that macroalgae increased in 

1999, but they state that levels decreased again in 2000 and 2001; the multivariate analysis from 

Johnston et al. (2013) shows that the benthic community was significantly different due to 

macroalgae and CTB fluctuations from 1999–2002. Also, similarity comparisons of the benthic 

habitats at EFGB and WFGB are the first demonstration by FGBLTM that the two banks are 

legitimately similar in composition, despite comparing them statistically since program 

inception. Nevertheless, it remains unclear what is gained from this comparison, given that the 

two banks are under the same regulations and similar environmental conditions, so neither is 

considered a control. Program managers should evaluate why these two study sites are routinely 

compared and what useful information is provided. See Section 4.3 for further discussion of 

between-bank comparisons.

4.2.1.5 Permanent Quadrats

4.2.1.6 Review and Appropriateness of Statistical Techniques

In 2010, permanent quadrat percent cover and standard error data for each benthic category, and

dominant species were graphically compared between 2009 and 2010. Diversity indices were 

also calculated and compared between years, but additional statistical analyses were not 

performed on percent cover data on either shallow or deep permanent quadrat stations. For 

planimetric analysis, up to four corals were chosen within each pair of quadrats (from 2009 and 

2010) to calculate tissue changes. Due to changes in photographic methods between years, it was 

determined that results of these analyses may be biased, so no statistical analyses were applied to 

these calculations.

In 2011–2012, percent cover and standard error data were calculated in the same manner as used

previously and graphically compared (Johnston et al. 2015). In this monitoring period additional 

parametric and non-parametric statistical analyses were also performed. Each benthic category 

and diversity index was compared separately to test for differences between banks and years 

using both one- and two-way ANOVAs. At deep stations, percent cover data of major benthic 

categories were analyzed by graphical comparison of means and standard errors. ANOVA 

comparing sampling year for EFGB, and bank for 2012 data were also completed. Shallow and 

deep quadrats were also compared by depth with ANOVA. 

In addition to these parametric tests, shallow and deep benthic community compositions based 

on percent cover data were converted to Bray-Curtis similarity matrices and tested for significant 

dissimilarities with ANOSIM. Cluster analyses were also performed on similarity matrices and 

visualized with MDS analysis. SIMPER was used to analyze differences between year and bank, 

and species contributing to any observed dissimilarities were identified. Long-term trends in 

percent cover data were assessed from Bray-Curtis similarity matrices. Cluster analysis and 

MDS plots were used to determine between year differences, and a time-series trajectory showed 

differences over time. SIMPER analysis was used to identify the species contributing most to 

observed changes. 

Planimetry analysis also included parametric statistical analyses in the 2011–2012 monitoring 

period. Two-tailed t-tests were performed to test the null hypothesis that overall changes in tissue 

boundaries did not differ between banks. T-tests were also used to calculate differences in 

boundary changes of dominant corals. 
239

The current IA requires only planimetry analysis for shallow and deep quadrats (BOEM 2014). 

Photographs are to be compared between years within a monitoring period to quantify changes in 

coral tissue boundaries, and apparent causes of change are to be documented. In addition to this, 

graphical comparisons of tissue boundary changes from previous monitoring period are to be 

compared, as possible. No specific statistical requirements beyond graphical comparisons for 

either percent cover or planimetry calculations are specified.

As discussed with the random transect data, ANOVA tests are powerful hypothesis tests, and are 

appropriate for use in LTM studies as part of a larger array of statistical analyses. Data in these 

reports are treated appropriately in terms of testing assumptions prior to analysis and in the use 

of this test. However, ANOVAs have been applied to test only for differences between year and 

bank within the timeframe of a single monitoring period. This only answers very short-term 

questions that are not the ultimate objective of FGBLTM. As previously discussed, the use of 

univariate analyses can be misleading in analyses of a diverse community matrix. The addition 

of non-parametric, multivariate testing in the 2011–2012 monitoring period contributed valuable 

information to the overall results. By facilitating analysis of the benthic community as a 

wholeand analyzing community shifts over longer periods of time, non-parametric, multivariate 

tests allowed FGBLTM to better meet the program objectives of documenting long-term changes 

at these study sites.

Planimetry data were only analyzed with parametric tests, which is likely problematic. Unlike 

transect data, in which parametric statistical comparisons were limited to major benthic 

categories and normality was typically not violated or data were easily transformed, the 

permanent quadrat planimetry data were used to analyze changes in individual corals and coral 

species. One issue with these analyses, noted in Johnston et al. (2015), was that data were highly 

non-normal and could not be transformed to meet parametric assumptions. Nevertheless, 

parametric analyses were run. It may benefit program managers to consider alternative nonparametric tests that will not be affected by these non-normality issues. One example is the use 

of a rank-sum test, such as Wilcoxon/Mann-Whitney, in place of a t-test (Helsel and Hirsch 

2002) for tissue change comparisons. This type of test could be easily performed in R.

4.2.1.7 Review and Appropriateness of FGBLTM Conclusions

The 2009–2010 monitoring period used summary statistics as a basis for permanent quadrat 

conclusions (Johnston et al. 2013). Reported means with standard errors were used to 

demonstrate that few changes had occurred in the benthic community between years. Qualitative 

comparisons were also made between transect data and quadrat data from this time period to 

demonstrate that both methods were showing a similar overall outcome. The standard conclusion 

that the FGBLTM study sites are stable and consistent was reached. Planimetric analysis largely 

did not reach conclusions in this monitoring period due to problems with photo distortion and 

inability to accurately quantify tissue changes.

In 2011–2012, additional statistical tests were used to support conclusions of a stable coral 

community with minor fluctuations (Johnston et al. 2015). ANOVA tests of the shallow quadrat 

benthic categories showed no significant differences for coral or macroalgae either between 

years or banks. The results for sponge and CTB showed significant differences between banks 

but not years; CTB was significantly higher at WFGB and sponges higher at EFGB. The 

ANOSIM analysis showed no differences between years or banks when looking at the whole 
240

benthic assemblage, and cluster analysis and MDS plots agreed with the ANOSIM results. Longterm comparisons of quadrat data were possible only for all benthic categories from 2002–2012. 

It was reported that cluster analysis and MDS plots showed that the benthic community at both 

banks in this time period was 90% similar. 

Deep quadrat ANOVA results showed no significant differences between years or banks, which 

agreed with the ANOSIM, cluster analysis and MDS plots. By depth, ANOVA comparisons 

showed no difference in macroalgae or sponges, but significant differences between coral and 

CTB. Qualitative comparisons were made for EFGB deep quadrat benthic category percent 

covers from 2003–2012 and showed a generally stable community with coral and CTB 

fluctuating inversely with macroalgae. 

Planimetry analysis results were summed by species and bank. In general, it was determined that 

coral tissue increased between 2011 and 2012 at EFGB, WFGB, and EFGB deep stations. The 

five dominant coral species for the shallow stations were compared with two-tailed t-test to 

determine if there were differences between banks. Three of the five showed no significant 

differences, but Porites astreoides and Orbicella franksi tissue increased significantly more at 

EFGB than WFGB. These t-test results were not incorporated into the overall conclusions from 

the 2011–2012 technical report. 

Many of the issues found for the FGBLTM conclusions for the random transect data apply to the 

permanent quadrat conclusions, too. Most analyses are run on only two years’ worth of data, 

which may not reveal longer-term patterns and do not meet overall FGBLTM objectives. Many 

general conclusions are based on qualitative comparisons of summary statistics, which could also 

be potentially misleading. The power analyses demonstrated that parametric analyses are able to 

detect changes in coral of approximately 20% in shallow quadrats and 50% in deep quadrats 

after two, five, and 10 years with a confidence of 0.8. Changes of approximately 50% at shallow 

and deep quadrats are detectable for macroalgae in the same timeframes. Analyses of sponge 

cover have little to no power. In addition, quadrat data, particularly for individual coral species, 

severely violate parametric assumptions and transformations do not necessarily ameliorate the 

problem, as was shown in power analyses and discussed above. These issues culminate into 

analysis results that are unreliable. 

The addition of non-parametric multivariate analyses for the quadrat data provides multiple 

benefits. Managers are able to review changes in the benthic community as a whole, which may 

provide more meaningful information than analyzing one component at a time. Assumptions are 

not violated, so results are more reliable, and non-parametric tests often have much higher power 

than parametric when non-normal data are being used (Hirsch et al. 1991). 

4.2.1.8 Permanent Quadrat Conclusions

The current IA (BOEM 2014) requires statistical analysis only of the planimetry data for 

permanent quadrats, which suggests the return of this method to primarily collecting process data 

(Van Woesik 2009). These variables should help inform managers about causes of overall 

community shifts observed within the study sites, as well as within the quadrats. Understanding 

causes behind observed changes is one of the overall objectives of the FGBLTM, implied in the 

stated concern that oil and gas development in the area may eventually cause decline. 
241

Nevertheless, the treatment of quadrat data in recent years has focused more on percent benthic 

cover and comparing results to transect data. 

In review of the percent cover data analysis, the benefits of including non-parametric tests are 

made evident. ANOVA results from 2011–2012 showed significant differences for sponges and 

CTB between banks, while all non-parametric tests showed no significant differences. With the 

low power of parametric tests for quadrat data demonstrated by power analyses, particularly for 

sponges, and the highly non-normal distribution of sponge data, it is likely that these significant 

results are misleading.

Many LTM program manuals (i.e., Ayotte et al. 2011; Brown et al. 2004; Hill and Wilkinson 

2004; Lang et al. 2010; Mumby et al. 2014) extensively outline project design and data 

collection methods, but do not address statistical analyses. However, the NPS Caribbean LTM 

method (Miller et al. 2007) does suggest repeated measures Analysis of Covariance for detection 

of subtle trends in percent coral cover for their repetitive site analyses. NOAA uses repeated 

measures ANOVA in the Dry Tortugas no-take reserve assessment (Jeffrey et al. 2012). 

Repeated measures analyses would be appropriate for repetitive quadrat data, with time as the 

independent variable, and use of this technique would further partition variance and increase the 

F-statistic (Green 1993). 

4.2.1.9 Benthic Cover Conclusions

Overall, the tests applied to benthic cover data are used appropriately and can be beneficial to 

long-term data analysis. The tendency to analyze data only from the immediate monitoring 

period is problematic however, and incongruous with the FGBLTM program goals. The addition 

of non-parametric multivariate tests in 2011–2012 not only improved the analyses of that period, 

but also allowed for analyses over longer time periods. Year to year comparisons should be 

maintained within the program, as many ecological cycles occur on shorter, annual time scales 

(Urquhart 2012), but assessment of long-term changes should be included as a regular task in 

FGBLTM data analysis. The positive changes to the data analysis methodology seen in 2011–

2012 should be incorporated into the FGBLTM IA and a methods document. 

The non-parametric tests used by FGBLTM in 2011–2012 were based on Bray-Curtis similarity 

matrices. This is one of the more common and widely accepted matrices used in ecological 

studies (Clarke and Warwick 2001) due in part to its robustness (Faith et al. 1987); however, as 

for all analyses, the limitations of Bray-Curtis should be considered when using this method. 

Bray-Curtis is robust in most scenarios until ecological distance becomes very large (Faith et al.

1987) or sample size is very small (Clarke et al. 2006), such as during recolonization after a 

catastrophic event. Either similarity or dissimilarity matrixes can be calculated, and it is 

important to document which one is being used, and to employ tests that are congruous with the 

type of matrix (i.e use a similarity matrix for tests of similarity and vice versa) (Clarke and 

Warwick 2001). In the most recent technical reports (Johnston et al. 2013, 2015) it is stated that 

Bray-Curtis similarity matrices were used to test for significant dissimilarities between bank 

communities, and wording should be modified for clarity and consistency. 

Though the addition of non-parametric tests to analyses of both transect and quadrat data in 2012 

was an overall improvement to the program, the tests being used should be carefully considered. 

For example, work has shown that ANOSIM, a modified Mantel Test, does not correctly 
242

partition variation and provides incorrect Type-I Error rates when used to analyze beta-diversity 

(Legendre et al. 2005; Legendre et al. 2008; Pelissier et al. 2008; Tuomisto and Ruokolainen 

2008). np-MANOVA and Canonical Analysis of Principal Coordinates (CAP) procedure may be 

more appropriate than ANOSIM for the goals of FGBLTM (Anderson 2001; Anderson and 

Willis 2003; Legendre 2008; Anderson et al. 2011). It must be understood that cluster analysis 

and MDS plots are for data exploration only, and do not provide any hypothesis testing on which 

defensible conclusions can be made (Clarke and Warwick 2001; Quinn and Keough 2002). 

Alternatively, CAP analyses, or other canonical ordinations such as Principal Coordinates 

Analysis (PCoA), provide visualization while also retaining the high-dimensionality of the data 

and providing additional information in the form of percent variability that is explained by each 

of the canonical axes (Anderson and Willis 2003; Legendre 2008; Anderson et al. 2011). Finally, 

the most recent technical report states that cluster analysis and SIMPER were used to visualize 

and analyze community differences. There are many approaches to cluster analysis, and the one 

used should be explicitly stated, as some are more heavily biased and dependent on the discretion 

of the user than others (Quinn and Keough 2002). In place of SIMPER an Indicator Value

(IndVal) analysis could be used to identify which species are highly indicative of a factor and 

which are statistically significant using permutation testing (Clarke et al. 2008). Alternatively, if 

np-MANOVA was adopted overall in place of univariate ANOVA, CAP or PCoA analysis could 

be used for visualization as well as describing the influence of each variable on the observed 

separation of groups (Legendre 2008; Anderson et al. 2011). 

Non-parametric alternatives to the parametric tests currently used by FGBLTM should also be 

considered. For example, the rank-sum test was suggested above as an alternative to the t-test 

when data cannot be made to meet assumptions by transformation. Other tests that could be 

informative alternatives to the ANOVA are the Kruskal-Wallis test (see Clarke and Warwick 

2001) and the np-MANOVA (Anderson 2001; McArdle and Anderson 2001), which allows for 

multivariate community analysis similar to a parametric MANOVA without the rigorous 

assumptions. Another method that avoids the variability reduction of ranked data used in some 

non-parametric tests is distance-based Redundancy Analysis (Legendre and Anderson 1999; 

McArdle and Anderson 2001; Anderson et al. 2011). With these suggestions, it should be noted 

that if data can be made to conform to parametric assumptions, parametric tests include more 

information, are less conservative (i.e., less likely to result in Type II error), and are more 

powerful than alternative non-parametric tests (Horst n.d.; Quinn and Keough 2002), so they can 

be important tools in ecological data analysis. However, ecological data are frequently highly 

non-normal, or benefit from being analyzed at a multivariate, community level, avoiding the 

univariate test assumption that each factor is independent. In these cases multivariate, nonparametric tests can be powerful and useful alternatives (Clarke and Warwick 2001).

One of the primary objectives of the FGBLTM is to record community changes over time and to 

determine if the reef populations remain healthy or declining. Though the introduction of nonparametric multivariate analyses in the most recent technical report took steps to meet this 

objective by comparing data over multiple monitoring periods, further effort on trend analysis 

should be considered. Regression analysis, or non-parametric alternatives such as regression 

analysis on ranked data, the np-MANOVA, or the Mann-Kendall trend test are recommended by 

Helsel and Hirsch (2002) and could provide a model of community shifts over time. However, 

trend analysis is complex and many considerations must be taken into account, such as clearly 

defining the parameters of interest, determining the scale on which analyses will be completed 
243

(Bart and Beyer 2012) and determining the type of trend being looked for (i.e., step compared 

with monotonic) (Hirsch et al. 1991). With the program objectives of FGBLTM, we recommend

that options for more in depth trend analysis be explored. 

4.2.2 Coral Growth

From 2010 through the current IA, two methods have been used to monitor coral growth changes 

over time at EFGB and WFGB and to compare coral growth between the banks. Accretionary 

growth has been measured by sclerochronology; lateral growth has been measured at permanent 

photostations, and the results of the two methods have been evaluated separately using similar

statistical analyses.

4.2.2.1 Sclerochronology

4.2.2.2 Review and Appropriateness of Statistical Techniques

Four cores were collected from each bank in 2010, yielding coral growth rate data for the years 

1991 through 2010. Statistical analysis of the 2010 sclerochronology results included two 

student’s t-test comparisons between the two banks (Johnston et al. 2013). Growth rates were 

compared between the banks for the years 1998 to 2010, resulting in a p-value of 0.0011 (α = 

0.05). Growth rates were also compared between banks by t-test after combining data from the 

years 1991 to 2010. This resulted in a p-value of 0.0010 (α = 0.05). These two significant pvalues indicated the growth rates differed between EFGB and WFGB during the time frames 

measured. Growth rates were compared with those from other monitoring periods (cores 

collected in 2003, 2005, and 2007). The statistical test was not specified, but it is assumed that a 

one-way ANOVA was used to make the comparison, which resulted in a p-value of 0.067 (α = 

0.05). This indicated that growth rates did not differ significantly between the cores collected at 

these four monitoring events.

In 2012, four cores from each bank were again collected. These cores yielded coral growth rates 

for the years 1995 through 2012. Statistical analysis of the 2012 sclerochronology results was 

limited to a one-way ANOVA between growth rates in the EFGB and WFGB cores (Johnston et 

al. 2015). This resulted in a p-value of 0.42 (α = 0.05), indicating no significant difference 

between mean growth rates at the two banks.

The main objective of the FGBLTM program is to detect spatial and temporal changes in the 

various parameters measured at FGB. The statistical tests used in 2010 only touched on detecting 

these two types of changes, and those used in 2012 only touched on spatial changes. 

Improvements to the statistical analyses for sclerochronology data are necessary to achieve 

program objectives.

Spatial differences in coral growth between the two banks can be appropriately assessed with 

paired t-tests as is suggested for FGB in the IA (BOEM 2014) if the test assumptions are met. 

One main assumption of a t-test is that the data are normally distributed. Sclerochronology data 

from 2010 and 2012, which were assessed for normality as part of the power analyses, were 

found to be normally distributed. It is likely that other sclerochronology datasets will be similar 

and normally distributed; however, this assumption should be tested with each new dataset. T-
244

tests are relatively robust to violations of normality, but if future datasets are found to be nonnormally distributed it would be prudent to use a non-parametric test such as the signed-rank test 

(also known as Wilcoxon) (Helsel and Hirsch 2002). T-tests also assume equal variance between 

groups. This can be tested by applying a hypothesis test to the variance data. Unequal variances 

can be adjusted by modifying the t-statistic and degrees of freedom using Satterthwaite's 

approximation (Helsel and Hirsch 2002). 

In analyzing differences between banks, we recommend that the sample size and years assessed 

are identical for both sites. For example, in 2010 three cores were analyzed for EFGB and four 

cores were analyzed for WFGB (Johnston et al. 2013). The shortest core produced data for the 

years 2005 through 2010, and the longest core produced data for the years 1991 through 2010. It 

would be inappropriate to assess mean values using the full range of years from 1991 to 2010, 

because some mean values would be based on four cores per site and others only one core. 

Dodge and Lang (1983) found that using datasets that include growth rates from variable 

timeframes causes certain parts of the chronology to be weighted differently and would have 

significant effects on the results of the analysis. Similarly, given that growth rate is variable 

across each sampling site (Johnston et al. 2013; Lough and Cooper 2011), unequal sample 

numbers at the two banks could skew the datasets, especially with low sample numbers. When 

assessing differences between means using the same timeframes for each bank, a paired t-test 

would be an appropriate statistical test. 

Temporal changes in coral growth can be assessed by ANOVA as suggested in the IA; however, 

this type of change detection will not provide information about any specific temporal trends. It 

appears that this test was applied incorrectly in 2010. Mean growth rates from the cores collected 

in 2010 were compared to mean growth rates of cores collected in previous monitoring periods 

(Johnston et al. 2013). Instead, the ANOVA should have been used to test if mean growth rates 

differed between years based on data that were derived from the same cores. This could have 

been accomplished by a repeated measures ANOVA or two-way ANOVA with replication. 

A more appropriate analysis than ANOVA for assessing long time series datasets like those 

produced by the sclerochronology method is a correlation or regression analysis (Helsel and 

Hirsch 2002). Simple linear regression analyses are routinely applied to coral growth rate 

datasets derived by similar methods to the FGB sclerochronology method (Dodge and Lang 

1983; Klein and Loya 1991; Hudson et al. 1994; Bessat and Buigues 2001; Torres 2001; Cooper 

et al. 2008; Death et al. 2009; Tanzil et al. 2009; Lough and Cooper 2011 and references 

therein). Linear declines in coral growth parameters in recent years are evident in coral core 

datasets from the Great Barrier Reef (Cooper et al. 2008; Death et al. 2009). Through regression

analysis, temporal trends in coral growth parameters have been related to environmental datasets 

like sea surface temperature and sedimentation worldwide (Lough and Cooper 2011 and 

references therein).

Currently, FGB coral cores are assessed by individual collection period. For example, data from 

cores collected in 2010 were analyzed separately from those collected in 2012 even though the 

records of growth overlapped by several years. Temporal trends in coral growth could be 

assessed over a longer time frame by combining data from different collection periods to create a 

“master chronology” as was done by Dodge and Lang (1983). Dodge and Lang (1983) combined 

data from 12 coral cores collected at EFGB to create a dataset spanning 1946 to 1979. This
245

dataset was related to environmental parameters, and it was determined that sea surface 

temperature and river discharge were likely controlling factors of coral growth at EFGB. Slowley 

and Crowely (1995) further assessed this dataset and showed that a decline in coral growth rate 

detected in the late 1950s was related to a decline in ocean temperature. Assessments similar to 

these could be made for the breadth of the FGBLTM program if data were combined across 

collection periods.

Although linear regression is routinely used to assess sclerochronology growth datasets, the 

assumptions of this analysis should not be overlooked. Linear regression analysis assumes that: 

1. there is a linear relationship between the two variables (growth and time); 

2. the variance of the residuals is constant (homoscedastic); 

3. the residuals are independent; and 

4. the residuals are normally distributed (Helsel and Hirsch 2002). 

The first assumption can be visually assessed by plotting a scatterplot of the data. If the data 

appear to have a linear relationship and do not show a more complex pattern (like bi-modal) than 

the linear regression model is likely appropriate. Homoscedacity can be assessed by looking at a 

scatterplot of the residuals or putting the residuals through an ANOVA test. If the variance 

changes with time, a data transformation may be necessary to achieve homoscedacity. 

Independence and normality must be tested. Residual independence can be tested using the 

Durbin-Watson test (Helsel and Hirsch 2002). Temporal autocorrelation is not unusual in this 

type of dataset. Subsampling the dataset or creating a residual correlation model can remove 

temporal autocorrelation (Helsel and Hirsch 2002; Castillo et al. 2012). Normality of residuals 

can be assessed using tests such as Andersen-Darling (Otto 2005), and data transformations may 

correct non-normality. If the linear regression assumptions cannot be met, non-parametric 

Kendall’s tau or Spearman’s Rho correlation test should be used for evaluating the data for 

temporal trends (Helsel and Hirsch 2002).

Another appropriate option for assessing differences in coral growth rates would be Analysis of 

Covariance (ANCOVA). ANCOVA would allow FGBLTM to assess both differences between 

banks (discreet variable) and changes over time (continuous variable). The analysis would be 

similar to that discussed in Tanzil et al. (2009), who analyzed coral growth rates using Alizarin 

staining. The assumptions of ANCOVA are the same as those of regression analyses. 

One additional consideration in analyzing coral growth data is the potential for an age-effect on 

coral growth. There is some evidence that massive Porites corals exhibit slower growth rates 

during the first two decades of growth before reaching a continuous growth rate (Lough 2008; 

Ridd et al. 2013). No literature was found indicating age-effects with Orbicella or Montastrea 

colonies, but the potential should be considered. By considering the overall age of colonies from 

which cores are collected, such an effect can be accounted for. Colony size should be recorded as 

an indication of colony age. If larger colonies are used and the first couple of decades of growth 

are excluded from the core analysis, age effects are not anticipated. Ridd et al. (2013) discusses 

how age-effects can be tested using a mixed linear regression model if this becomes a concern. 
246

4.2.2.3 Review and Appropriateness of FGBLTM Conclusions

Cores collected in 2010 were discussed in Johnston et al. (2013). The range and mean of annual 

growth rates were discussed for EFGB and WFGB. Cores produced annual growth rate values 

for the years 1991 through 2010 for EFGB and 1998 through 2010 for WFGB. A t-test 

comparing the annual growth rates at the two banks indicated significant differences for 1998 

through 2010 data and 1991 through 2010 data. It is unclear if the 1998 through 2010 data were 

compared using a paired or unpaired t-test. As discussed above, this analysis may be biased 

because the years of data compared were unequally represented (Dodge and Lang 1983). 

Annual growth rates from the 2010 cores are discussed in the context of other studies in the 

region and data from past core collections at FGB. Johnston et al. (2013) states that the 2010 

results are “similar” to growth rates reported by Zimmer et al. (2010), but “differ slightly” from 

rates reported by Precht et al. (2006) and Dokken et al. (2003). It is reported that the 2010 data 

“continued to be in the middle to upper range of the FGB growth rates as recorded by Hudson 

and Robbin (1980).” No statistical analysis is reported to support any of these statements, and the 

reasoning is not discussed in full. Without justification and support, these statements cannot be 

considered reliable. 

Data were compared to the 2003, 2005, and 2007 core data, but the specific statistical analysis 

used is not reported. In future reports, FGBLTM should state exactly what statistical analyses are 

used to support all conclusions and explicitly state the results of the assessment of adherence to 

test assumptions. Mean annual growth rate for the 2010 cores were found to be significantly 

different from mean annual growth rates from the other three coring events. Johnston et al.

(2013) concluded from this result that “annual growth rate decreased in 2010” for cores from 

EFGB and WFGB. This conclusion is misleading and invalid. Core data represents multiple 

years of growth. To determine if coral growth decreased in 2010 relative to the previous year, 

growth rates for multiple cores for the year 2010 would need to be compared to growth rates for 

other years represented in the same cores. The statistical test between cores from different 

monitoring events showed that the mean growth rates for the individual cores differed in 2010. 

This could have been the result of several factors, including but not limited to: 

1. cores were of different lengths than previous monitoring periods, which may have caused 

biases; 

2. core collection and growth rate analysis were conducted in a slightly different manner in 

2010 (possibly due to a lack of quality control procedures and different observers, as has 

been discussed in previous chapters); or

3. a drastic change in growth occurred since 2007 that would cause a significant result 

despite the overlap in growth records between the cores collected in the different 

monitoring periods.

Cores collected in 2012 were discussed in Johnston et al. (2015). The range and mean of annual 

growth rates were discussed for EFGB and WFGB. Cores produced annual growth rate values 

for the years 1995 through 2012 for EFGB and 1997 through 2012 for WFGB. Data were tested 

for normality and found to be normally distributed. A one-way ANOVA was performed, 

comparing mean annual growth rates at the two banks. No significant difference was found. 

Johnston et al. (2015) is justified in concluding that no significant difference between the banks 
247

was detected; however, the sclerochronology sampling design has low power to detect changes 

in growth and differences in growth between the two banks. Differences between growth rate at 

EFGB and WFGB may have gone undetected during this monitoring period.

Annual growth rates from the 2012 cores were discussed in the context of other studies in the 

region and data from past core collections at FGB (Johnston et al. 2015). The conclusions of this 

discussion were identical to those of Johnston et al. (2013). The 2012 results were “similar” to 

growth rates reported by Zimmer et al. (2010), but “differ slightly” from rates reported by Precht 

et al. (2006) and Dokken et al. (2003). It is reported that the 2012 data “continued to be in the 

middle to upper range of the FGB growth rates as recorded by Hudson and Robbin (1980).” No 

statistical analysis was reported to support any of these statements. Johnston et al. (2015) also 

reported that the data from the last three monitoring periods “displays a decrease in range of 

values when compared to previous monitoring efforts”. No descriptive statistics or statistical test 

was presented to support this conclusion. Although these conclusions may be acceptable for 

discussing management actions in the absence of data that is powerful enough to obtain as 

statistically powerful result, rigorous statistical testing would allow the conclusions to be 

defensible.

Finally, Johnston et al. (2015) discussed the effect of the bleaching event observed in 2010 on 

growth rates within the cores. Seven of the eight coral cores collected in 2012 showed slight 

declines in extension rate “from about 2008–2009 to 2010–2011”. Johnston et al. (2015) stated 

that the information available was “insufficient to ascertain whether this decline is statistically 

significant and a consequence of bleaching and paling.” Though it is true that causality cannot be 

proven by hypothesis testing (Fabricius and De’Ath 2004), an ANOVA could be used to 

determine if the 2008–2011 growth rates were significantly different from the other years’ data 

in the cores. This would be a “step trend” analysis (Helsel and Hirsch 2002) between data before 

the bleaching event and data after the bleaching event. Power to detect changes is low for this 

sampling method, but a 50% cumulative change in growth rate can likely be detected with 

greater than 80% power.

4.2.2.4 Sclerochronology Conclusions

Limited statistical analysis was performed on the sclerochronology results from 2009 through 

2012, as documented in Johnston et al. (2013) and Johnston et al. (2015). T-tests and ANOVAs 

were used to test for differences between banks and between cores from past monitoring periods. 

There is no documentation of if the assumptions of t-tests and ANOVA were tested, and the 

results do not provide adequate support for statements made about differences in growth rates 

between monitoring periods. Additional statistical analysis is necessary to evaluate if changes in 

coral growth rate have occurred over time. Linear regression analysis is recommended to detect 

temporal trends in the sclerochronology data if the test assumptions are met. 
248

4.2.2.5 Lateral Growth

4.2.2.6 Review and Appropriateness of Statistical Techniques

In 2009 and 2010, colonies of Pseudodiploria strigosa (formerly called Diploria strigosa) were

photographed to measure lateral growth on the EFGB and WFGB (Johnston et al. 2013). The 

number of photographed stations ranged from 29 to 41 per bank per year. Of the photographs 

taken, five matching pairs were comparable from 2009 and 2010 for EFGB, and nine matching 

pairs were comparable for WFGB. For these matching pairs the proportion of the colony margin 

growing, retreating, and remaining stable were calculated. The statistical test that is prescribed in 

the IA for this method is repeated measures ANOVA, but, because of the low sample size, it was 

determined that this test would not have enough statistical power to detect differences. Data were 

used for descriptive purposes only.

In 2011 and 2012, colonies of P. strigosa were photographed at EFGB and WFGB (Johnston et 

al. 2015). The number of photographed stations ranged from 19 to 40 per bank per year. Of the 

photographs taken only four photo pairs for EFGB and three photo pairs for WFGB were 

comparable for 2010–2011, and one photo pair for EFGB and no photo pairs for WFGB were 

comparable for 2011–2012. Data were again used for descriptive purposes only.

Due to the low sample sizes achieved in the years 2009 through 2012, combined with the 

evidence of low power to detect change that was found for this method, it is appropriate that the 

lateral growth data were not statistically analyzed. There is little value in the descriptive statistics 

(mean and standard deviations) derived from these datasets due to the low sample size.

If sample sizes were greater, and stations were reliably sampled year after year, repeated 

measures ANOVA might be appropriate, but this test is intolerant to missing values (Jennrich 

and Schluchter 1986). With the great number of missing values in the lateral growth dataset, it is 

unlikely repeated measures ANOVA could ever be appropriately applied. Low sample numbers 

also prevented robust analysis to test for normality during the statistical power assessments. If 

data are not normally distributed, one of the major assumptions of ANOVA will be violated.

A more appropriate analysis for this challenging dataset is the non-parametric rank-sum test 

(Helsel and Hirsch 2002). The rank-sum test does not carry the assumptions of a parametric test 

(ANOVA, t-tests, etc.) such as normality and equal variance and is more tolerant to small 

samples sizes. With the high variance and low sample sizes generally achieved by the lateral 

growth method, it is unlikely that even the non-parametric tests would have sufficient power to 

detect change. In addition to the reasons stated in previous chapters, inability to efficiently 

analyze the data to detect meaningful trends lends further weight to the recommendation to 

discontinue this method.

4.2.2.7 Review and Appropriateness of FGBLTM Conclusions

Lateral growth data from the 2009 and 2010 monitoring period were discussed in Johnston et al.

(2013). Data for five stations at EFGB and nine stations at WFGB were used in the analysis. 

Johnston et al. (2013) reported that net lateral growth was positive, the degree of variability was 

high among the data, and an overall increase in marginal growth from 2009 to 2010 was shown 

at both banks. 
249

Lateral growth data from the 2011 and 2012 monitoring period were discussed in Johnston et al.

(2015). Data for five stations at EFGB and three at WFGB were used in the analysis. Johnston et 

al. (2015) reported that on average coral margins were advancing from 2010 to 2012. It is 

reported that EFGB stations showed an overall increase in marginal growth from 2010 to 2011 

and a decrease from 2011 to 2012. WFGB stations showed an increase from 2010 to 2011, and 

no data were available to assess the growth rate from 2011 to 2012. There is no indication of 

what data these “increases” and “decreases” were relative to and no statistical analyses reported 

to support the statements. Data from 2001 through 2012 were inspected for apparent trends. A 

4% increase in marginal growth was observed at EFGB and a 23% increase in marginal growth 

was observed at WFGB during this timeframe according to Johnston et al. (2015). No statistical 

analysis was reported to assess whether the increase was statistically significant, and due to small 

sample size it is unlikely the apparent increase is representative of the population. A two-tailed ttest between EFGB and WFGB lateral growth data for 2011–2012 revealed no significant 

differences between growth rates at the two banks. 

Given the large standard deviation of the growth and retreat values relative to the mean values 

(mean ± standard deviation for growth was 55.89 ± 36.78 at EFGB and 51.11 ± 38.58 at WFGB 

and for retreat 33.67 ± 35.17 at EFGB and 30.76 ± 30.68 at WFGB) and the small sample size, it 

is risky making any conclusions about coral growth based on these data. Certainly, in the 

absence of statistical testing the “overall increases” and “overall decreases” reported are 

indefensible conclusions. Power to detect change was found to be extremely low for this method

even with a much larger sample size (see Chapter 3), so it is unlikely that statistical testing would 

add any value to interpretation of the results. Likewise the insignificant p-values obtained from 

two-tailed t-tests performed on the 2011–2012 data (Johnston et al. 2015) are of extremely 

limited value, because the type II error rate would have been extremely high.

4.2.2.8 Lateral Growth Conclusions

Due to low sample sizes in 2009 through 2012, no statistical analyses of temporal trends were 

conducted with the lateral growth data. Over the course of the FGBLTM program, the lateral 

growth method has consistently produced low sample numbers. It is unlikely that the repeated 

measures ANOVA, the analysis prescribed in the IA, can ever be appropriately applied. Nonparametric rank-sum tests may be applied to the data, but it is unlikely that adequate power will 

ever be achieved for this method given the unreliable sample size.

As in all previous chapters, we recommend that this method be discontinued. In Johnston et al.

(2013) and Johnston et al. (2015) the many issues with implementation of the method that led to 

the small sample sizes during these and previous monitoring periods are discussed. In the 

absence of reliable sample numbers and the extremely low power to detect change given the high 

variation in the datasets, this method has little value for FGBLTM. Managers and decision 

makers cannot rely on conclusions that are based on arbitrary and apparent trends, and the 

dataset provides little information that can be affectively evaluated using statistical hypothesis 

testing.

4.2.2.9 Coral Growth Conclusions

Both sclerochronology and lateral growth data have been collected in 2009 through 2012, and 

limited statistical analyses have been applied to the data (Johnston et al. 2013, 2015). For 
250

sclerochronology data, t-tests and ANOVAs were used to test for differences between banks and 

between cores from past monitoring periods. There is no documentation if the assumptions of ttests and ANOVA were tested, and the results do not provide adequate support for qualitative 

statements made about differences in growth rates between monitoring periods. Additional 

statistical analysis is necessary to evaluate whether changes in coral growth rate have occurred 

over time. Linear regression analysis is recommended to detect temporal trends in the 

sclerochronology data if the test assumptions are met. 

For lateral growth data, due to low sample sizes from 2009 through 2012, no statistical analyses 

of temporal trends were conducted with the lateral growth data. Non-parametric rank-sum tests 

may be applied to the data, but it is unlikely that adequate power will ever be achieved for this 

method given the unreliable sample size. We recommend that this method be discontinued, and 

greater focus be placed on achieving high quality data and meaningful analysis of the 

sclerochronology growth and repetitive quadrat planimetry data.

4.2.3 Urchins, Lobsters, and Lionfish

Sea urchin surveys were incorporated into the FGBLTM method in 1996, sometime after the 

decline of the important herbivorous species Diadema antillarum Caribbean-wide in the late 

1980s (Dokken et al. 1999). Data were collected at night, and divers swam along study site 

perimeters. The number of perimeter transects used in this survey has changed through the years, 

and the current IA requires all four perimeter boundaries be used in the surveys. Lobsters 

(Panulirus argus and P. guttatus) were included in these night surveys beginning in 2002 (Precht 

et al. 2006), and the current IA requires observations of the invasive lionfish Pterois volitans to 

also be included (BOEM 2014).

Historic observations from night surveys have been few and no statistical analyses have been 

performed on resulting data, though the current IA requires ANOVA analyses for detection of 

year to year variations in both sea urchin and lobster populations. The power of such analyses 

would be extremely low. When four years of pilot data were pooled and converted to presence

and absence for Chi square analysis rather than ANOVA, power to detect changes was 

essentially zero. Expanding the surveys is unlikely to improve detection rates currently, as 

previous, comprehensive FGB surveys (Caldow et al. 2009; Buckel et al. 2014) included 

considerably more sampling sites for urchin surveys and got density results that were lower than 

FGBLTM, though Buckel et al. (2014) acknowledge that surveys for these nocturnal species 

were conducted during the day. The reasons for monitoring these species are clearly identified in 

the technical reports, and monitoring should be continued, but statistical analyses are not likely 

to provide meaningful information while density estimates remain so low. Should densities 

increase in the future, univariate ANOVA tests will provide information with the same 

limitations as discussed above for benthic habitat analyses (section 2.1). Only one sampling 

interval is compared at a time, and no additional explanatory variables are assessed. So if 

statistical analysis becomes a necessary function for urchin, lobster, and lionfish data, we 

recommend that additional analysis methods be considered, similar to those used in the 2011–

2012 technical report for benthic cover and fish surveys. 
251

4.2.4 Fish Surveys

Data on fish populations at FGBLTM study sites have been collected in some form since the 

beginning of the program. Initially, large bodied species were observed and recorded during 

analysis of the two perimeter video surveys (Gittings et al. 1993). Identification of most species 

through this method was difficult and did not provide the quantitative data that was preferred, so 

stationary point count surveys became part of the LTM method beginning in 2002 (Precht et al.

2006). The number of surveys per study site has varied, with the current IA requiring 24 per 

bank. Point counts are patterned after the methods outlined in Bohnsack and Bannerot (1986), 

with the diver recording all fish species that enter a 7.5 m radius around them for 5 minutes, with 

additional time then used to record abundance and size of those species. The current IA adds 16 

belt transects to the fish survey methods (BOEM 2014). The details of the transect methods, such 

as the width, length and census methods are not described. Transect data are not yet available for 

FGBLTM, but statistical treatment of data outlined in the IA are the same for both point counts 

and transects, so further discussion will apply to both field methods.

4.2.4.1 Review and Appropriateness of Statistical Techniques

During the 2009–2010 monitoring period, fish data were summarized in the following ways: 

density/100 m2, species richness, percent sighting frequency, size-frequency distributions, 

Shannon-Weiner diversity index, evenness, and biomass (Johnston et al. 2013). Summary 

statistics were then compared between year and bank by species, family, and trophic guild using 

t-tests and ANOVAs. Diurnal abundance patterns for species of interest were assessed using twoway ANOVA with bank and time of day as fixed factors.

In the 2011–2012 monitoring period the same summary statistics were calculated as in the 

previous technical report (Johnston et al. 2015). Dominance plots were generated for species 

abundance and biomass and W-values were calculated (difference between biomass and 

abundance) for each survey. Two-sample and student’s t-tests were used to analyze between year 

and between bank differences of summary statistics. Bray-Curtis similarity matrices were also 

calculated for use in non-parametric tests such as SIMPER to analyze community dissimilarity 

between year and bank. MDS plots were generated to look for differences between year and 

bank, and ANOSIM was used to further evaluate observed significant differences. Trend analysis 

of biomass and density data was also included, using the Mann-Kendall test.

The current IA (BOEM 2014) requires the same calculations of summary statistics as described 

above. It is specified in the IA that density and biomass data will be Log (n+1) transformed 

before analysis. Two-sample t-tests will be used to compare densities and species-richness by 

bank and by year, as well as species abundance and richness by bank. MDS plots will be used to 

look for community differences in density and biomass between years, banks and depth.

Assessing fish populations is a difficult and time consuming process, particularly in areas where 

the fish have few boundaries to their movement in and out of the sampling site (Pope et al.

2010). To adequately estimate population parameters, many variables should be considered, as 

each has its own benefits and limitations. It is appropriate that FGBLTM calculate multiple 

summary statistics to evaluate fish data collected from the study sites. However, the statistical 

analyses used by FGBLTM are somewhat limited and do not produce as much valuable 

information as is available from the data collected. As with the benthic cover data, emphasis is 
252

frequently put on univariate comparisons of single year data by bank, or two year data by bank 

and year. No justification for a bank comparison has been made in the technical reports, so it is 

unclear what beneficial information FGBLTM gains from conducting these analyses. And, as 

was discussed with benthic cover data, very little focus is put on assessing data over longer 

timeframes than the immediate monitoring period. The non-parametric multivariate tests also 

only focus on limited timeframes and by bank comparisons, leaving the analyses of less value 

than could be gained.

FGBLTM could use the summary statistics calculated from fish data and go one step further in 

analysis to gain additional information about fish population dynamics. Indices (i.e., size) are 

snapshots of the fish population at a certain moment in time, and population dynamics (i.e.,

recruitment rates) provide insight into the mechanisms behind observed change (Pope et al.

2010). In some instances, population dynamics can be estimated from the static indices that 

FGBLTM already collects. For example, monitoring how abundances within different size 

classes vary over time can give an estimate of recruitment rates and age distribution. Declines of 

these measures can be indicators of fishing pressure, an activity that was noted as a possible 

cause for concern for carnivores by Johnston et al. (2013). However, these types of analyses 

generally require data from multiple years for patterns to emerge, emphasizing the importance of 

FGBLTM using data collected over multiple monitoring periods. 

4.2.4.2 Review and Appropriateness of FGBTLM Conclusions

The 2009–2010, conclusions for fish surveys were based on many two-sample t-tests and 

ANOVAs from species, family and trophic levels of population partitioning, and qualitative 

comparisons of year to year variation in density, abundance and biomass means with standard 

errors and diversity indices. The overall conclusion was that FGB has a thriving fish population 

similar to the results of previous LTM surveys (Johnston et al. 2013). Dominant families were 

Pomacentridae, Labridae, Serranidae, and Scaridae, also found by previous surveys. 

Pomacentrids were the most species rich family. Invertivores were found to be the dominant 

guild, but herbivores were determined to be a healthy population that seemed to have increased 

since the sea urchin Diadema antillarum decline. The final conclusion was that species-richness 

of reef carnivores was high, but that their densities seemed to be depressed compared to previous 

studies.

In 2011–2012 conclusions were also based upon many univariate t-test and ANOVA 

comparisons between years and banks, in addition to non-parametric community evaluations 

between years and banks, and qualitative comparisons of mean and standard error data and 

diversity indices to previous years’ results. The general conclusion was again that the fish 

population was healthy and stable at both banks. In longer-term observations from 2009–2012 it 

was noted that two fewer families and six new species were recorded in the more recent 

monitoring period. Species richness was similar between banks, but significantly higher in 2012 

compared to 2011. The most species-rich families were Pomacentridae, Labridae and Scaridae. 

Diversity indices were similar between year and bank and to previous LTM surveys. 

Invertivores were the dominant guild, but herbivores were considered a stable population 

because less than 21% of all observed variation between banks was contributed by herbivores. In 

2011 it was reported that piscivores were the dominant guild in terms of biomass, which 

Johnston et al. (2015) stated is considered an inverted food pyramid and indicative of a pristine 
253

reef. In 2012, piscivores and herbivores were the dominant guilds for biomass and Johnston et al.

(2015) stated that this shift should be monitored. Abundance-biomass curves indicated an evenly 

distributed population, indicating a mildly disturbed population. In general piscivores observed 

in the survey were small and sexually immature, except for some large bodied individuals of a 

few species. For some groupers, no individuals above the catch-size limits were observed. 

Diurnal patterns were shown to have an impact on survey results. And long-term trends of 

biomass showed no significant trends, while density was found to have a significant increasing 

trend at WFGB.

Many of the fish conclusions found in the technical reports from 2010–2012 are based on 

univariate statistical analyses or qualitative comparisons to previous years. As discussed in the 

benthic cover review, observing changes using univariate analyses does not take into account the 

diverse population interactions, and can provide a limited and potentially misleading view 

(Clarke and Warwick 2001). Qualitative comparisons of means over time within this study and 

to other areas in the Caribbean should be treated with caution and it should be noted that these 

statements have not been validated statistically. 

However, even the parametric statistical results should be treated with caution, as the power 

analysis demonstrated that the current sampling design does not yield sufficient power (0.8) to 

make the conclusions being reported. For the five dominant families and the trophic guilds, 

changes would need to be between 25% and nearly 60% in a single sampling interval to be 

detected. If the same data were analyzed over a ten year period, annual changes would need to be 

consistently in the same direction with a magnitude of three to seven percent to be detected by 

the current FGBLTM sampling design. With the current tendency of FGBLTM to focus on 

monitoring two-year spans, even consistent gradual changes will not be noticed. This is counter

to the overall objectives of the FGBLTM program and demonstrates the importance of 

emphasizing analysis of data from longer time periods. Additionally, though the standard 

treatment of data in the technical reports and the IA was to use a Log (n+1) transformation, it 

was not found in the pilot data used for power analyses that this fully alleviated parametric 

assumption violations, such as normality. In this case, further ways to alleviate these issues is to 

look for alternative, non-parametric tests, which are shown to have significantly greater power 

than their parametric counterparts when assumptions are violated (Hirsch et al. 1991). As has 

been recommended for the benthic data analyses, tests such as np-MANOVA and Distancebased Redundancy Analysis (db-RDA) should be considered. 

4.2.4.3 Fish Survey Conclusions

The parametric tests used to analyze fish survey data did not address assumption violations, such 

as normality and independence of variables, possibly reducing the benefit to the LTM program. 

Cyclical fluctuations that occur on an annual level may be of interest to resource managers (Bart 

and Beyer 2012), making year-to-year comparisons potentially informative. However, reliance 

on these univariate, limited time-frame tests alone does not allow the program to detect 

meaningful changes over longer timeframes or meet overall program goals and stated objectives. 

In addition, the power of the parametric tests does not allow FGBLTM to detect changes at a 

level that is desired or possibly relevant, as changes of less than 50% in a sampling interval can 

be ecologically relevant. The inclusion of non-parametric multivariate tests is a useful change to 

the program and should be expanded and further incorporated into program methods. Similar 
254

suggestions as seen in the benthic cover conclusions in Section 2.1.3 apply to fish analyses, too, 

namely, the incorporation of np-MANOVA and db-RDA in place of parametric ANOVAs and 

ANOSIM, alternative community comparison and visualization techniques such as PCoA in 

place of MDS, and IndVal in place of SIMPER.

As discussed above, the analyses used for FGBLTM fish data, though useful, were limited in the 

amount of information that was provided. Further exploration of the data currently collected 

could yield interesting and relevant results. However, FGBLTM currently does not collect 

habitat data in conjunction with fish point counts beyond indicating whether the reef is high or 

low relief, and that information is not considered in the analysis. There is extensive literature 

available on the strength of reef fish-habitat associations (i.e., Clarke 1988; Sale 1988; 

McCormick 1994; Sale 1998; Chittaro 2004; Gratwicke and Speight 2005; Wilson et al. 2008) 

and how benthic changes will affect the fish community (Syms and Jones 2000; Jones et al.

2004). One study of shallow reefs found that habitat complexity accounted for as much as 71% 

of the variation in fish species richness (Gratwicke and Speight 2005). While FGBLTM study 

sites could be considered as fully encompassed by high relief, shallow coral habitat (Clark et al.

2014), Sale (1988) found that on small scales, reef fish distribution is very patchy and somewhat 

stochastic based on mortality and recruitment, while on the larger scale of a whole atoll or reef it 

is much more predictable. However, a Caribbean-based study found that fish variation was 

largely independent of spatial scale, but that habitat explained from 31–81% of the variation in 

number of individuals and species observed (Chittaro 2004). This demonstrates that by analyzing 

fish assemblages with point counts or transects without including the context of habitat, a 

potentially critical explanatory variable is missing. Bohnsack and Bannerot (1986) specifically 

designed the point count technique to limit the confounding effects of crossing multiple habitats 

in a single sample, so collecting basic habitat data for each point should be relatively simple. We 

recommend that additional habitat information, such as major benthic components and rugosity, 

be collected in concurrence with the fish point counts. Transects will inherently include a range 

of microhabitats, and we recommend that they coincide with benthic random transects, or that 

inclusion of habitat variables is considered. 

In comparison to other LTM programs, the NPS fish monitoring method recommends inclusion 

of the covariate benthic variables coral cover and rugosity to increase statistical power of fish 

analyses (Brown et al. 2011b). The Australian Institute of Marine Science LTM manual (Van 

Woesik et al. 2009) includes fish surveys within the same sampling sites from which benthic data 

were collected, as does the AGGRA method (Lang et al. 2010). The NOAA Pacific Reef 

Assessment and Monitoring method includes percent benthic cover, habitat type, and habitat 

complexity as additional variables collected at fish survey sites (Ayotte et al. 2011). These 

examples demonstrate the importance of collecting habitat variables in conjunction with fish 

data, and the inclusion of this technique to the FGBLTM fish surveys is recommended. 

4.2.5 Water Quality

From 2010 through the current IA, two methods have been used to monitor water quality 

changes over time at EFGB and WFGB and to compare water quality between the banks. 

Nutrient grab samples have been collected quarterly at each bank and Seabird MicroCAT 

instruments have collected continuous temperature and salinity data at each bank. These two 

methods have been discussed separately, but neither has been assessed statistically. Despite the 
255

lack of statistical analysis documented in the FGBLTM reports, the IA calls for assessment of 

trends over time, including all previous monitoring periods (BOEM 2014). Appropriate 

statistical analyses for detecting temporal trends in water quality are discussed here.
256

4.2.5.1 Nutrient Grab Samples

4.2.5.2 Review and Appropriateness of Statistical Techniques

Several characteristics of the nutrient grab sample dataset at FGB and those of similar datasets 

from other programs need special consideration in deciding on appropriate statistical tests. A full 

discussion of these characteristics and how to deal with them can be found in Helsel and Hirsch 

(2002), but a summary is provided here. First, the data are bound by zero (no negative values are 

possible), and high outliers are common. This causes the data to be positively skewed and nonnormally distributed. Although in some cases parametric tests may be robust to non-normality, 

these highly skewed, non-normal data cannot be assessed using parametric tests without further 

consideration of the assumption violation. Second, much of the dataset is reported as below the 

detection limit rather than as a specific value. This data, also known as “censored data”, makes 

parametric tests inappropriate in most cases because parametric tests cannot handle censored 

data. All censored data points need to be deleted, or other values (typically zero or the detection 

limit) need to be substituted for the censored data to apply parametric tests. These types of 

dataset modifications have serious impacts of the analytical results. For example, replacing all 

censored data with zero will skew the mean severely toward zero even though the majority of the 

concentrations may have been a range of values below the detection limit. Third, data are likely 

dependent on uncontrollable factors such as weather and ocean circulation dynamics and can 

follow seasonal patterns. Seasonality and other controlling factors can be accounted for to reduce 

“background noise” in the datasets that might cause high variation or false trends. Finally, data 

may be temporally autocorrelated (serial correlation) if samples are collected too closely together 

in time. This can be solved by subsampling the dataset or by grouping data and using summary 

statistics for each group (Helsel and Hirsch 2002).

One non-parametric test that is frequently used for nutrient datasets is the seasonal Kendall’s 

test. The US Geological Survey (USGS) developed this test in the 1980s for long-term water 

quality data sets where serial correlation exists (Hirsch et al. 1982; Hirsch and Slack 1984), and 

the test is still recommended and widely used for water quality datasets (Helsel et al. 2005). The 

NPS Inventory and Monitoring Program uses the seasonal Kendall’s test for quarterly freshwater 

and marine nutrient grab sample data that is very similar to the dataset collected by FGBLTM 

(Jones et al. 2011). The seasonal Kendall’s test is an adaptation of the Mann-Kendall correlation 

test. It calculates a test statistic for each season and evaluates changes among the same seasons 

from different years (Hirsch and Slack 1984). The test can be easily applied to censored data. It 

is robust against serial correlation, but has less power than the Mann-Kendall test when serial 

correlation is not present (Hirsch and Slack 1984). Detailed descriptions of this test can be found 

in Hirsch and Slack (1984) and Helsel and Hirsch (2002). A free computer program, developed 

by USGS, is available to perform the seasonal Kendall’s test at and is documented in Helsel et al.

(2005).

The FGBLTM nutrient grab sample dataset for any given year is largely composed of values 

below the detection limits of the specific analytical procedures used (85% of the data were listed 

as “ND”). Nitrite and phosphorus were not detected above the detection limit in any samples 

between 2009 and 2012. Helsel and Hirsch (2002) recommend that datasets made up of greater 

than 50% censored data, such as the FGB dataset, be analyzed by logistic regression as all other 

options require that inferences are made about the censored values. Logistic regression is 
257

essentially a presence and absence analysis. Values above the detection limit are assigned the 

value of one, and those below the detection limit are assigned the value of zero. The test then 

assesses the probability that the true population value is zero or one. A third value can be also 

added to the analysis to represent a threshold such as a regulatory limit. One major assumption of 

logistic regression is that values are independent of one another. Care must be taken in sample 

collection to ensure independence.

Table 4.1 shows sample results for logistic regression analysis of TKN data from 2009 through 

2013. All concentrations below the detection limit were assigned the value zero and all 

concentrations detected above the detection limit were assigned the value one. The analysis was 

run in StatviewTM, and results are provided for all samples considered together, as well as 

analyses for data grouped by depth (bottom, midwater, and surface) and bank (EFGB and 

WFGB). R2values were small indicating any observed relationship is weak, but all p-values 

except for the midwater value were less than 0.05 (a typical α value for this analysis), indicating 

that the relationship is significant. This result is just an example, but it demonstrates that logistic 

regression may potentially allow change in nutrient concentrations to be detected at FGB even 

with the large number of non-detect values in the datasets. 

Table 4.1 Logistic Regression Results for TKN Concentrations 2009 to 2013

TKN concentrations are compared by logistic regression. Values below the detection limit were assigned 

the value 0, and values above the detection limit were assigned the value 1.

Data Group N Log Likelihood Intercept Log Likelihood R

2 P-Value

All 109 -68.238 -75.181 0.092 0.0005

Bottom 36 -22.189 -24.898 0.109 0.0304

Midwater 36 -22.899 -24.731 0.074 0.0683

Surface 37 -23.068 -25.525 0.096 0.0377

EFGB 54 -33.691 -37.282 0.096 0.0119

WFGB 54 -34.080 -37.282 0.086 0.0168

4.2.5.3 Review and Appropriateness of FGBLTM Conclusions

In 2010, Chl a and nutrient results were discussed in terms of range of concentrations compared 

to typical concentrations in the Gulf of Mexico, referencing Nowlin et al. (1998) for typical 

concentrations. Concentrations from the 2009–2010 monitoring samples and those from 2002 

through 2008 were also compared and discussed (Johnston et al. 2013). Chlorophyll a 

concentrations were relatively high in May 2010, and Johnston et al. (2013) suggested that this 

may be indicative of an algal bloom. It is important to point out spikes in the datasets, like those 

seen for Chl a, that may be indicative of an event (algal bloom), but it is more useful if those 

spike concentrations are compared to a threshold value. Chlorophyll a data are available for the 

region through NOAA’s CoastWatch program. Considering FGB Chl a data in context of 

regional data may help in development of threshold values. Statistical tests comparing current 

values to background levels at FGB would provide support for drawing conclusions about 

potential events.

Johnston et al. (2013) stated that “no definitive trends could be determined from the nutrient 

data”, and additionally that the data “could not elucidate annual patterns of nutrient 

concentrations”, but no statistical tests were performed. Without statistical testing these 

conclusions are unsupported. As demonstrated above, logistic regression analysis may provide a 

means of detecting trends in the nutrient results.
258

In 2012, similar descriptive comparisons to 2010 were discussed for the Chl a and nutrient 

results (Johnston et al. 2015). A potential algal bloom was discussed in regard to a spike in Chl a 

concentrations in February 2011. Again these statements would have more meaning if discussed 

in context of a threshold value and statistically compared to background levels at FGB and the 

region.

Johnston et al. (2015) also stated that “one historical trend that is apparent at EFGB and WFGB 

is the increase in TKN since the first measurements were made in 2002.” Though the increase is 

qualitatively visible, no analytical test was performed to determine if a statistically significant 

trend exists in the TKN data. This leaves the conclusion unsupported and indefensible. As 

demonstrated above, logistic regression analysis may provide a means of detecting trends in the 

TKN results as well as those of other nutrients. Defensible conclusions can be drawn with the 

proper application of statistical hypothesis tests.

Johnston et al. (2015) also suggested that the TKN concentrations may correlate with macroalgae 

cover at the banks, but no attempt is reported to compare macroalgae cover measured in the 

random transect and permanent quadrat data to the nutrient results. These conclusions are again 

quantitatively unsupported. Logistic regression could also be used in this case to compare 

macroalgae cover to annual TKN concentrations and test for a correlation.

4.2.5.4 Nutrient Grab Sample Conclusions

The nutrient grab sample data presents numerous challenges for analysis including that most of 

the data are reported as below a detection limit and other characteristics that make parametric 

tests inappropriate. Non-parametric tests such as the seasonal Kendall test have been successfully 

applied to similar datasets; however, the large proportion of non-detect values in the FGB dataset 

may make this test inappropriate. Logistic regression analysis is recommended and will allow 

FGBLTM to evaluate if data are trending toward more values above the detection limits 

compared to historical datasets.

Due to the fact that no statistical analyses were reported, nearly all conclusions about nutrient 

results given in the monitoring reports are unsupported and are not defensible. Apparent 

historical “trends” need to be evaluated statistically in future monitoring reports.

4.2.5.5 Temperature and Salinity Time Series

4.2.5.6 Review and Appropriateness of Statistical Techniques

The temperature and salinity time series data also present challenges for data analysis. First, 

ocean temperature and salinity datasets are highly seasonal. Data must be de-seasonalized to 

prevent apparent trends derived from seasonally unbalanced datasets and to remove the 

background variation that seasonal effects introduce to the dataset. Undoubtedly, the highresolution datasets collected at FGB (30 minute intervals) are temporally autocorrelated. Some of 

the temporal autocorrelation is removed by grouping and averaging data into weekly, monthly, 

and annual values. Still, these data must be tested for autocorrelation. Finally, the temperature 

and salinity data collected at FGB are non-normally distributed.
259

Recommended analyses for assessing long-term trends in environmental time series include 

regression analyses and ANOVA or a non-parametric equivalent analysis (Helsel and Hirsch 

2002). Linear regression analysis is useful in assessing continuous datasets where the response 

variable is expected to change linearly with time. ANOVA is useful in assessing step trends 

when there are large gaps in time between sample collections or when an event has caused one 

time frame to be distinguishable from another time frame. Because the temperature and salinity 

datasets for FGBLTM are continuous datasets, and no specific events mark abrupt changes in 

these datasets, regression analysis is the most appropriate statistical test for these data. Linear 

trends in temperature and salinity are well-documented (L’Heureux et al. 2013, Lluch-Cota et al.

2013, Boyer et al. 2005, Delcroix et al. 2007).

Linear regression analysis assumes that: 

1. there is at linear relationship between the two variables (growth and time); 

2. the variance of the residuals is constant (homoscedastic); 

3. the residuals are independent; and 

4. the residuals are normally distributed (Helsel and Hirsch 2002). 

As discussed in the sclerochronology section (Section 2.2.1), the first assumption can be visually 

assessed by plotting a scatterplot of the data. If the data appears to have a linear relationship and 

does not show a more complex pattern (like bi-modal) than the linear regression model is likely 

appropriate. Homoscedacity can be assessed by looking at a scatterplot of the residuals or 

running an ANOVA on the residuals. If the variance is changing with time, a data transformation 

may be necessary to achieve homoscedasticity. Independence and normality must be tested. 

Residual independence can be tested using the Durbin-Watson test (Helsel and Hirsch 2002). 

Temporal autocorrelation is not unusual in this type of dataset. Subsampling the dataset or 

creating a residual correlation model can remove temporal autocorrelation (Helsel and Hirsch 

2002, Castillo et al. 2012). Normality of residuals can be assessed using tests such as AndersenDarling (Otto 2005), and data transformations may correct non-normality. If the linear regression 

assumptions cannot be met, non-parametric Kendall’s tau or Spearman’s Rho correlation test 

should be used for evaluating the data for temporal trends (Helsel and Hirsch 2002).

Temperature and salinity datasets are both influenced by oceanographic conditions, such as 

ocean circulation. They are not independent of one another. In light of this, multivariate tests 

such as non-parametric MANOVA may also provide valuable information by taking into account 

patterns that lie across both variables.

4.2.5.7 Review and Appropriateness of FGBLTM Conclusions

The range and annual mean of daily temperatures for EFGB and WFGB were presented for 2009 

and 2010 in Johnston et al. (2013). Apparent “thermal anomalies” were also discussed, including 

a low temperature event in the summer of 2009, a low temperature event in spring 2010, and 

high temperatures in summer and fall 2010. These events were observed at both banks. “Thermal 

anomalies” equated to differences that were visually obvious between the average time series 

from 1990 to 2008 and the 2009 and 2010 time series. No statistical tests were used to determine 

if these “anomalies” were statistically significant or if trends existed in the long-term datasets. 

Without statistical evaluation of the data, “anomalies” is just a subjective term and cannot be 

relied on by managers and decision makers. These temperature data are also of little value to 
260

decision makers without statistical evaluation of trends and changes overtime. We recommend

that linear regression analysis be applied to these data to determine if long-term changes in 

temperature are detectable. 

In Johnston et al. (2013), discussion was also given to events when temperature exceeded 30 °C, 

which is considered the coral bleaching threshold for the region. This is a valuable discussion, as 

it is important for managers to know if corals may have been temperature-stressed, potentially 

leading to bleaching events in a given year. Additional value may be given to this discussion if 

the changes in the occurrence of temperatures (number of days of hours per year) exceeding 30 

degrees is evaluated statistically. This could also be done using linear regression or ANOVA and 

would allow conclusions to be drawn as to if the 30 degree threshold is being reached more or 

less frequently as the climate is changing.

The range and annual mean of daily salinity for EFGB and WFGB were presented for 2009 and 

2010 in Johnston et al. (2013). Two low salinity events were discussed at WFGB in August 2009 

and September to November 2010. These low salinity events were relative to the salinity 

measurements that were recorded for the rest of the two year period, which was fairly stable with 

the exception of the two events. Discussion of low salinity events provides important insight into 

oceanographic conditions during a given year. Additional value may be given to this discussion 

if the data are placed in context of the longer-term data (that collected by FGBLTM through the 

history of the program). Potential trends in the salinity dataset can be evaluated using linear 

regression.

The range and annual mean of daily temperatures for EFGB and WFGB were presented for 2011 

and 2012 in Johnston et al. (2015). “Thermal anomalies” were again discussed, including low 

temperature events in summer 2011 and in summer 2012. These “anomalies” were visually 

observed in relation to the average time series from 1990 to 2012. Johnston et al. (2015) stated 

that the data show “warming trends of temperatures above the historical mean temperatures”. No 

statistical analysis comparing temperatures over the long-term to determine if the “trend” is 

statistically significant is reported to support this statement. As discussed for the conclusions of 

Johnston et al. (2013) above, these statements are unreliable for managers and decision makers 

and should be made in the context of statistical evaluation to improve defensibilty. Discussion 

was also given to events when temperature exceeded 30 °C. Again, this discussion is valuable, 

but would benefit from statistical evaluation for possible trends.

The range and annual mean of daily salinity for EFGB and WFGB were presented for 2011 and 

2012 in Johnston et al. (2015). Low salinity events relative to the rest of the two year period were 

discussed, including events in July 2011, May 2011, and June 2012. As mentioned above, 

discussion of low salinity events provides important insight into oceanographic°onditions during 

a given year, but statistical evaluation for long-term trends would provide additional value to the 

discussion.

4.2.5.8 Temperature and Salinity Time Series Conclusions

The temperature and salinity time series datasets present a few challenges for statistical analysis 

including serial autocorrelation and non-normality. Data should be de-seasonalized and tested for 

autocorrelation prior to analysis. Although ANOVA may appropriately assess step trends in the 

datasets, we recommend that linear regression analysis be used to evaluate continuous trends 
261

over the history of the LTM program as long as the assumptions are met. If the assumptions 

cannot be met, non-parametric Kendall’s tau or Spearman’s Rho correlation test should be used.

Apparent “thermal anomalies” were reported for the FGBLTM temperature datasets (Johnston et 

al. 2013, 2015), but no statistical evaluations were reported. Without statistical evaluation, 

“anomalies” is not a quantitatively meaningful term and cannot be relied on by managers and 

decision makers. Likewise low salinity events are discussed but not statistically evaluated. We 

recommend that statistical evaluation (linear regression) be used in this discussion to provide 

defensible results. 

4.2.5.9 Water Quality Conclusions

Neither nutrient grab sample data nor temperature and salinity data collected at FGB are 

adequately analyzed by the statistical techniques used since 2010. No statistical evaluation has 

been performed on nutrient grab sample data. We recommend that logistic regression is the most 

appropriate technique for evaluating changes in nutrient concentrations over time at FGB. 

Likewise, no statistical evaluation has been performed on temperature and salinity data. Linear 

regression analysis is recommended for assessing temporal changes in these datasets. Special 

consideration must be given to the test assumptions of linear regression.

Because no statistical analyses are reported, nearly all conclusions about water quality given in 

the monitoring reports are not adequately supported. Apparent historical “trends” need to be 

evaluated statistically in future monitoring reports to provide support for the conclusions.

4.3 Evaluation of Bank Comparisons

In every method discussed in this overall program review, comparisons are made between EFGB 

and WFGB, both quantitatively and qualitatively. This bank comparison has been made since the 

beginning of the FGBLTM program, but no clear reasoning for it has ever been provided. 

Ecologists frequently make generalized comparisons to other similar habitats within the same 

region, but statistical comparisons inherently test the null hypothesis that two means are from the 

same population (Sokal and Rolf 1995), and very little evidence of this for FGB bank 

comparisons has been provided in the FGBLTM reports. The first quantitative justification that 

the bank study sites were similar was given by Johnston et al. (2015) in the cluster and MDS 

analyses of 2011–2012 data for both banks, showing 80% similarity in benthic composition. 

Assuming EFGB and WFGB are from the same population and hypothesis testing is valid 

between the two banks, it is still unclear what benefit is gained from quantitatively comparing 

the two. Both banks are near oil drilling activities and are part of the same NOAA National 

Marine Sanctuary, so the same environmental regulations apply to both. Neither bank could be 

considered a “control”. The objectives of the FGBLTM program do not currently include goals 

of understanding community dynamics across banks. If the communities of both banks were 

changing with trends in the same direction, a statistical comparison may not yield a significant 

result. In comparison to other LTM methods, the Great Barrier Reef LTM program compares 

results across different patch reefs (Sweatman 1997), but there is clearly stated reasoning for 

such comparisons in the LTM report, and statistical analyses are tailored to the exact questions 

the program is aiming to answer. Though it is not suggested that FGBLTM should stop 

comparing EFGB to WFGB, we recommend that program managers determine why the 
262

comparison is made, if it warrants the importance currently being placed on it, what information 

is sought by the comparison, and how it should be framed with appropriate statistical analyses to 

answer specific LTM questions.

4.4 Conclusions and Recommendations

We performed an evaluation of analyses used in the FGBLTM program, and this chapter

presents a review of each analysis technique used from 2010 through the current IA, outlining 

the statistical methods used, and the conclusions reached based on analysis results. We evaluated

whether tests were applied correctly, and compared the types of analyses used by FGBLTM to 

those used in other LTM programs to assess if they are appropriate for LTM.

We noted multiple issues consistently throughout each monitoring method evaluated in this 

document. Most critical was the reliance of conclusions about the status of study sites on 

qualitative comparisons of data that are not quantitatively supported or necessarily valid. This 

practice should be modified to include application of appropriate and powerful statistical testing

before making conclusive statements about the data. Another issue common to each method was 

that of statistical power. As shown in Chapter 3, many of the tests currently used have the ability 

to detect changes only of a much larger magnitude at the desired power level than FGBLTM 

technical reports have stated are occurring, which makes these conclusions weak or invalid. 

Finally, analysis of all methods tended to focus on two year time intervals, when the objective of 

FGBLTM is to monitor change through the entirety of the program. Some exploration into data 

from multiple monitoring periods as well as trend analysis was seen in the 2011–2012 technical 

report, and this should be continued and expanded to include more comprehensive long-term 

analyses.

 

Another issue with FGBLTM conclusions that was not discussed in detail in this chapter, but has 

been addressed in previous chapters, is that FGBLTM technical reports use data collected at the 

100 m by 100 m study sites to make statements regarding the status of FGB. Little information is 

available in the technical reports on how the study sites were placed and if they include a 

representative sample of the FGB habitat within the same depth range (i.e., shallow coral cap). 

Though it is possible that these sites are representative, their limited size in proportion to the 

entirety of the coral caps at EFGB and WFGB make it unlikely. Generalized statements about the 

status of FGB based upon results from these study sites have limited reliability and could be 

misleading. This limitation should either be addressed, and the representativeness of the study 

sites quantitatively validated, or FGBLTM technical reports should refrain from making overbroad conclusions that are beyond the scope of data collected. Alternatively, FGBLTM could 

follow suggestions made in previous chapters and expand the number of study sites at each bank, 

improving representativeness of data and power of analyses. 

 

In summary, based on this Evaluation of Analyses, the following are recommended for 

improvement of the FGBLTM program:

1. Random transect percent cover data could be more consistently evaluated over longer 

timeframes than a two year monitoring period, with inclusion of more extensive trend 

analysis. 
263

2. Repeated measures ANOVA should be considered in place of simple ANOVA for 

repeated quadrat percent cover and planimetry data.

3. Repetitive quadrat analysis should include longer timeframes and trend analysis.

4. When benthic cover data violate parametric assumptions, non-parametric tests should be 

substituted to improve analysis power or to incorporate multivariate analyses. 

Recommended alternative tests include np-MANOVA and db-RDA.

5. For all benthic data, alternative visualization and comparison techniques should be 

considered in place of MDS and SIMPER. These alternatives include CAP, PCoA and 

IndVal. 

6. Linear regression analysis is recommended to detect temporal trends in the 

sclerochronology data if the test assumptions are met. 

7. We recommend the lateral growth method be discontinued and greater focus be placed 

on achieving high quality data and meaningful analysis of the sclerochronology growth 

and repetitive quadrat planimetry data.

8. Urchin, lobster, and lionfish data should continue to be collected, but analysis of these 

datasets will not currently produce useful results.

9. The fish survey transect method needs to be described in the IA so that methods are 

carried out consistently and power analysis can be conducted on the exact methodology.

10. Fish survey analyses should be expanded to further explore additional parameters such 

as indications of recruitment and fishing pressure.

11. Fish survey data should also be evaluated over longer time periods and using more 

extensive trend analysis to meet FGBLTM monitoring objectives.

12. As with benthic data, alternative analyses should be considered, including npMANOVA, db-RDA in place of parametric ANOVA and ANOSIM, CAP or PCoA in 

place of MDS, and IndVal in place of SIMPER for fish data.

13. Benthic characterization within fish survey sites should be included in both stationary 

point count and transect surveys.

14. Logisitic regression is recommended for evaluating changes in nutrient concentrations 

over time at FGB. 

15. Linear regression analysis is recommended for assessing temporal changes in 

temperature and salinity time series data. 

16. FGBLTM technical reports should use quantitatively rigorous statistical analyses for the 

basis of conclusions rather than qualitative comparisons and apparent trends.

17. FGBLTM technical reports should limit conclusions to the study sites until further 

analysis of representativeness in relation to FGB can be evaluated. 
264

4.5 References Cited

Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral 

Ecol. 26: 32-46.

Anderson MJ, Crist TO, Chase JM, Vellend M, Inouye BD, et al. 2011. Navigating the multiple 

meanings of β diversity: a roadmap for the practicing ecologist. Ecol Lett. 14: 19-28.

Anderson MJ, Willis TJ. 2003. Canonical analysis of principal coordinates: a useful method of 

constrained ordination for ecology. Ecology. 84: 511-525.

Ayotte P, McCoy K, Williams I, Zamzow J. 2011. Coral Reef Ecosystem Division standard 

operating procedures: data collection for Rapid Ecological Assessment fish surveys. 

Honolulu (HI): Pacific Islands Fisheries Science Center, National Marine Fisheries 

Service, NOAA. Pacific Islands Fisheries Science Center Administrative Report H-11-08. 

24 p.

Bart J, Beyer HL. 2012. Analysis options for estimating status and trends in long-term 

monitoring. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and 

analysis of long-term ecological monitoring studies. New York (NY): Cambridge 

University Press. p. 253-278.

Beninger PG, Boldina I, Katsanevakis S. 2012. Strengthening statistical usage in marine ecology. 

J Exp Mar Biol Ecol. 426-427(2012): 97-108.

Bessat F, Buigues D. 2001. Two centuries of variation in coral growth in a massive Porites 

colony from Moorea (French Polynesia): a response of ocean-atmosphere variability from 

south central Pacific. Palaeogeogr Palaeocl. 175: 381-392.

Boyer TP, Levitus S, Antonov JI, Locarnini RA, Garcia HE. 2005. Linear trends in salinity for 

the World Ocean, 1955–1998. Geophys Res Lett. 32: L01604

Brown E, Beets J, Brown P, Craig P, Friedlander A, Jones T, Kozar K, Capone M, Kramer L, 

Basch L. 2011a. Pacific Islands Network marine fish monitoring method (Version 1.0). 

Fort Collins (CO): National Park Service. Natural Resource Report NPS/PACN/NRR—

2011/421. 282 p.

Brown E, Minton D, Daniel R, Klasner F, Basch L, Snyder A, Criag P, Dicus G, DeVerse K, 

Jones T. 2011b. Pacific Islands Network benthic marine community monitoring method. 

Fort Collins (CO): National Park Service. Natural Resources Report NPS/PACN/NRR—

2011/339. 376 p.

Brown E, Lee SJ, Tice K, McKenna SA. 2014. Kalaupapa National Historical Park benthic 

marine community monitoring trend report for 2006–2010: Pacific Island Network. Fort 

Collins (CO): National Park Service. Natural Resource Technical Report. 

NPS/KALA/NRTR—2014/913. 64 p. 
265

Buckel CA, Viehman TS, Edwards K, Tester PA, Piniak GA, et al. 2014a. Benthic communities 

of the coral reef. In: Clark R, Taylor JC, Buckel CA, Kracker LM, editors. Fish and 

benthic communities of the Flower Garden Banks National Marine Sanctuary: science to 

support sanctuary management. Silver Springs (MD): National Oceanic and Atmospheric 

Administration. NOAA Technical Memorandum NOS NCCOS 179. p. 22-74.

[BOEM] Bureau of Ocean Energy Management. 2014. Interagency Agreement M14PG00020 

between the Bureau of Ocean Energy Management and the National Oceanic and 

Atmospheric Administration National Ocean Service Office of National Marine 

Sanctuaries Flower Garden Banks National Marine Sanctuary for long-term coral reef 

monitoring at the Flower Garden Banks, Gulf of Mexico: 2014–2016. 28 p.

Caldow C, Clark R, Edwards K, Hile S, Menza C, et al. 2009. Biogeographic characterization of 

fish communities and associated benthic habitats within the Flower Garden Banks 

National Marine Sanctuary: sampling design and implementation of scuba surveys on the 

coral caps. NOAA Technical Memorandum NCCOS 81. 152 p.

Castillo KD, Ries JB, Weiss JM, Lima FP. 2012. Decline of forereef corals in response to recent 

warming linked to history of thermal exposure. N Climate DOIL 

10.1038/NCLIMATE1577. 5 p.

Chittaro PM. 2004. Fish-habitat associations across multiple spatial scales. Coral Reefs. 23: 235-

244.

Clarke KR, Somerfield PJ, Chapman MG. 2006. On resemblance measures for ecological 

studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient 

for denuded assemblages. J Exp Mar Biol Ecol. 330: 55-80.

Clarke KR, Warwick RM. 2001. Change in marine communities: an approach to statistical 

analysis and interpretation, 2nd ed. Primer-E: Plymouth.

Clarke RD. 1988. Chance and order in determining fish-species composition on small coral 

patches. J Exp Mar Biol Ecol. 115: 197-212.

Clark R, Taylor JC, Buckel CA, Kracker LM, editors. 2014. Fish and benthic communities of the 

Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): National Oceanic and Atmospheric Administration. 

NOAA Technical Memorandum NOS NCCOS 179. 317 p.

Cooper TF, De’Ath G, Fabricius KE, Lough JM. 2008. Declining coral calcification in massive 

Porites in two nearshore regions of the northern Great Barrier Reef. Glob Change Biol. 

14: 529-538.

De’Ath G, Lough JM, Fabricius KE. 2009. Declining coral calcification on the Great Barrier 

Reef. Science. 323: 116-119.

Delcroix T, Cravatte, McPhaden MJ. 2007. Decadal variations and trends in tropical Pacific sea 

surface salinity since 1970. J Geophys Res. 112: C03012. 15 p.
266

Dodge RE, Lang JC. 1983. Environmental correlates of hermatypic coral (Montastrea annularis) 

growth on the East Flower Gardens Bank, Northwest Gulf of Mexico. Limnol Oceonag. 

28: 228-240.

Dokken QR, MacDonald IR, Tunnell JW, Beaver CR, Boland GS, et al. 1999. Long-term 

monitoring of the East and West Flower Garden Banks, 1996–1997. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 99-0005. 101 p.

Faith DP, Minchin PR, Belbin L. 1987. Compositional dissimilarity as a robust measure of 

ecological distance. Vegetatio. 69: 57-68.

Fabricius KE, De’Ath G. 2004. Identifying ecological change and its causes: a case study on 

coral reefs. Ecol Appl. 14(5): 1448-1465.

Gitzen RA, Millspaugh J. 2012. Ecological Monitoring: The heart of the matter. In: Gitzen RA, 

JJ Millspaugh, AB Cooper, DS Licht, editors. Design and Analysis of Long-term 

Ecological Monitoring Studies. New York (NY): Cambridge University Press. p. 2-24.

Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. 2012. Design and analysis of longterm ecological monitoring studies. New York (NY): Cambridge University Press. 590 p.

Gittings SR, Boland GS, Deslarzes KPJ, Hagman DK, Holland BS. 1993a. Long-term 

monitoring at the East and West Flower Garden Banks. New Orleans (LA): U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Regional Office. OCS Study MMS 92-0006. 206 p. 

Gratwicke B, Speight MR. 2005. The relationship between fish species richness, abundance and 

habitat complexity in a range of shallow tropical marine habitats. J Fish Biol. 66: 650-

667.

Green SR. 1993. Application of repeated measures designs in environmental impact and 

monitoring studies. Austral Ecol. 18: 81-98.

Helsel DR, Hirsch RM. 2002. Statistical methods in water resources. In: Techniques of waterresources investigations of the United States Geological Survey. Book 4, Hydrologic 

analysis and interpretation. Washington (DC): U.S. Geological Survey. 522 p.

Helsel DR, Mueller DK, Slack JR. 2006. Computer program for the Kendall family of trend 

tests. Reston (VA): U.S. Geological Survey. Scientific Investigations Report 2005-5275 4 

p.

Hirsch RM, Alexander RB, Smith RA. 1991. Selection of methods for the detection and 

estimation of trends in water quality. Water Resour Res. 27: 803-813.

Hirsch RM, Slack JR. 1984. A nonparametric trend test for seasonal data with serial dependence. 

Wat Resour Res. 20(6): 727-732.
267

Hirsch RM, Slack JR, Smith RA. 1982. Techniques of trend analysis for monthly water quality 

data. Water Resour Res. 18(1): 107-121.

Horst, A. n.d. Parametric vs. non-parametric data. Bren School of Environmental Science and 

Management. [Cited 2015 September 1]. Available from: 

http://statsthewayilikeit.com/about/parametric-v-non-parametric-data/.

Hudson JH, Robbin DM. 1980. Effects of drilling mud on the growth rate of the reef- building 

coral Montastraea annularis. In: Geyer, R.A., ed. Marine environmental pollution. 

Amsterdam (Netherlands): Elsevier Oceanography. p. 455-470.

Hudson JH, Hanson KJ, Halley RB, Kindinger JL. 1994. Environmental implications of growth 

rate changes in Montastrea annularis: Biscayne National Park, Florida. Bull Mar Sci. 

54(3): 647-669.

Jennrich RI, Schluchter MD. 1986. Unblanaced repeated-measures models with structured 

covariance matrices. Biometrics. 42: 805-820.

Johnson DH. 2012. Monitoring that matters. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht 

DS, editors. Design and analysis of long-term ecological monitoring studies. New York 

(NY): Cambridge University Press. p. 54-73.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2013. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2009–2010. 

Volume 1: technical report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2014A214. 202 p.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2015. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2011–2012. Draft 

Volume 1: technical report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2015-027. 

205 p.

Jones GP, McCormick MI, Srinivasan M, Eagle JV. 2004. Coral decline threatens fish 

biodiversity in marine reserves. P Natl Acad Sci USA. 101: 8251-8253.

Jones T, DeVerse K, Dicus G, McKay D, Farahi A, et al. 2011. Water quality vital signs 

monitoring method for the Pacific Island Network: Volume 1; Version 1.0. Natural 

Resource Report NPS/PACN/NRR—2011/418. Fort Collins (CO): National Park Service.

112 p.

Lang JC, Marks KW, Kramer PA, Kramer PW, Ginsburg RN. 2010. AGRRA Protocols Version 

5.4. Atlantic and Guld Rapid Reef Assessment, Miami, FL. 31 p.

Legendre P. 2008. Studying beta diversity: ecological variation partitioning by multiple 

regression and canonical analysis. J Plant Ecol. 1: 3-8.
268

Legendre P, Anderson MJ. 1999. Distance-based redundancy analysis: testing multispecies 

responses in multifactorial ecological experiments. Ecol Monogr. 69: 1-24.

Legendre P, Borcard D, Peres-Netro PR. 2005. Analyzing beta diversity: partitioning the spatial 

variation of community composition data. Ecol Monogr. 75:435-450.

Legendre P, Borcard D, Peres-Netro PR. 2008. Analyzing or explaining beta diversity? 

comment. Ecology. 89: 3238-3244

L’Heureux ML, Collins D, Hu Z, Kumar A. 2013. Linear trends in the sea surface temperature of 

the Tropical Pacific Ocean and implications for the El Nino-Southern Oscillation. Clim 

Dynam. 40: 1331-1335.

Lluch-Cota SE, Tripp-Valdez M, Lluch-Cota DB, Lluch-Belda D, Verbesselt J, HerreraCervantes H, Bautista-Romero JJ. 2013. Recent trends in sea surface temperature off 

Mexico. Atmosfera. 26(4): 537-546.

Lough JM. 2008. Coral calcification from skeletal records revisited. Mar Ecol Prog Ser. 373: 

257-264.

Lough JM, Cooper TF. 2011. New insights from coral growth band studies in an era of rapid 

environmental change. Earth-Sci Rev. 108: 170-184.

Klein R, Loya Y. 1991. Skeletal growth and density patterns of two Porites coral from the Gulf 

of Eilat, Red Sea. Mar Ecol Prog Ser. 77: 253-259.

Magurran AE. 2004. Measuring biological diversity. New York (NY): Wiley-Blackwell. 256 p.

McArdle BH, Anderson MJ. 2001. Fitting multivariate models to community data: a comment on 

distance-based redundancy analysis. Ecol. 82: 290-297.

McCormick MI. 1994. Comparison of field methods for measuring surface topography and their 

associations with a tropical reef fish assemblage. Mar Ecol Prog Ser. 112: 87-96.

Miller WJ, Rogers C, Atkinson A, Muller A, Davis A, et al. 2007. Coral Reef Monitoring 

Method. Miami (FL): National Park Service. Natural Resource Report 

NPS/SER/SFCN/NRTR -2007/003.

[NOAA] National Oceanographic and Atmospheric Administration. 2015. NOAA CoastWatch 

[Cited 2015 September 1]. Available from: http://coastwatch.noaa.gov/. 

Nowlin WD, Jochens AE, Reid RO, DiMarco SF. 1998. Texas-Louisiana shelf circulation and 

transport processes study: synthesis report. Volume II: appendices. New Orleans (LA): 

U.S. Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region. OCS Study MMS 98-0036. 288 p.

Otto K. 2005. Anderson-Darling normality test calculator. [Cited 2015 September 1]. Available 

from: http://www.kevinotto.com/RSS/templates/Anderson-Darling Normality Test 

Calculator.xls.
269

Pélissier R, Couteron P, Dray S. 2008. Analyzing or explaining beta diversity? comment. 

Ecology. 89: 3227-3232.

Pope KL, Lochman SE, Young MK. 2010. Methods for assessing fish populations. Nebraska 

Fish & Wildlife Research Unit - Staff Publications. Paper 73.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Murdoch TJT, et al. 2006. Long-term 

monitoring at the East and West Flower Garden Banks, 2002–2003: final report. New 

Orleans (LA): U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region. OCS Study MMS 2006-035. 182 p.

Quinn GP, Keough MJ. 2002. Experimental design and data analysis for biologists. Cambridge 

(UK): Cambridge University Press. 557 p.

Reynolds JH. 2012. An overview of statistical considerations in long term monitoring. In: Gitzen 

RA, JJ Millspaugh, AB Cooper, DS Licht, editors. Design and Analysis of Long-term 

Ecological Monitoring Studies. New York (NY): Cambridge University Press. p. 23-53.

Ridd PV, Stieglitz T, Wust R, Halide H, Read W, et al. 2013. Have coral calcification rates 

slowed in the past 20 yrs? Mar Geo. 346: 392-399.

Sale PF. 1988. Perception, pattern, chance and the structure of reef fish communities. Environ 

Biol Fish. 21: 3-15.

Sale PF. 1998. Appropriate spatial scales for studies of reef-fish ecology. Austral Ecol. 23: 202-

208.

SAS Institute Inc. 1998. Statview for Windows. Version 5.0.1

Slowey N, Crowley T. 1995. Interdecadal variability of Northern Hemisphere circulation 

recorded by Gulf of Mexico corals. Geophys Res Lett. 22:2345-2348.

Sokal RR, Rohlf FJ. 1995. Biometry, 3rd ed. New York (NY): W.H. Freeman and Company. 887 

p. 

Sweatman H. ed. 1997. Long-term monitoring of the Great Barrier Reef. Status Report No. 2. 

Townsville, Queensland (Australia): Australian Institute of Marine Science.

Syms C, Jones GP. 2000. Disturbance, habitat structure, and the dynamics of a coral-reef fish 

community. Ecology. 81: 2714-2729.

Tuomisto H and Ruokolainen K. 2008. Analyzing or explaining beta diversity? reply. Ecology. 

89: 3244-3256. 
270

Urquhart NS. 2012. The role of monitoring design in detecting trend in long-term ecological 

monitoring studies. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design 

and analysis of long-term ecological monitoring studies. New York (NY): Cambridge 

University Press. p. 151-173.

Tanzil JTI, Brown BE, Tudhope AW. 2009. Decline in skeletal growth of the coral Porites lutea

from the Andaman Sea, South Thailand between 1984 and 2005. Coral Reefs DOI 

10.1007/s00338-008-0457-5. 10 p.

Torres JL. 2001. Impacts of sedimentation on the growth rates of Montastraea annularis in 

southwest Puerto Rico. Bull Mar Sci. 69(2): 631-637.

Van Woesik R, Gilner J, Hooten AJ. 2009. Standard operating procedures for repeated measures 

of process and state variables of coral reef environments. Melbourne (Australia): Currie 

Commun. 36 p.

Wilson SK, Burgess SC, Cheal AJ, Emslie M, Fisher R, et al. 2008. Habitat utilization by coral 

reef fish: implications for specialists vs. generalists in a changing environment. J Anim 

Ecol. 77: 220-228.

Zimmer B, Duncan L, Aronson RB, Deslarzes KJP, Deis D, et al. 2010. Long-term monitoring at 

the East and West Flower Garden Banks, 2004–2008. Volume I: technical report. New 

Orleans (LA): U.S. Department of the Interior, Bureau of Ocean Energy Management, 

Regulation, and Enforcement, Gulf of Mexico OCS Region. OCS Study BOEMRE 2010-

052. 310 p.
271

CHAPTER 5.0: RECOMMENDATIONS

5.1 Introduction

In this chapter, we summarize the recommendations made in the first four chapters. The pros and 

cons associated with each recommendation are discussed with respect to costs of the project and 

quality of the scientific evaluation. Some recommendations will result in cost savings because 

they increase the efficiency of the LTM program; others may result in additional costs, but 

significantly improve the scientific integrity of the program and its ability to satisfy overall 

program objectives.

The chapter is divided into three sections. First are recommendations associated with the LTM 

program design. Next are recommendations associated with methodology and sampling 

frequency changes. Finally, recommendations associated with the statistical analysis of results 

are discussed. Within the methods section, recommendations for each of the general monitoring 

variables identified as part of the FGBLTM program design are discussed. These monitoring 

variables are:

1. benthic cover;

2. coral growth;

3. coral health;

4. coral recruitment;

5. sea urchin, lobster and lionfish population density;

6. fish populations;

7. water quality; and

8. qualitative observations.

5.2 Program Design

LTM at FGB has been conducted since the late 1980s, and many scientific surveys before the 

establishment of an official monitoring program (Gittings et al. 1993). In Chapter 1 of this 

program review we evaluated the overall program design and made recommendations for 

program improvement. The general recommendations to improve FGBLTM program design 

discussed in this section include:

1. development of clear program goals and objectives;

2. development of SOP documents for each survey method;

3. development of a central database where all project data are stored, including hard copy 

data that need to be appropriately digitized;

4. determination if the current study site is representative of the target population;

5. expansion of study area and sample size through increased number of sampling sites; and

6. modification of the sampling schedule to incorporate the additional study sites.

One of critical finding was the lack of a clear program mission statement and objectives. Every 

aspect of a successful LTM program depends upon explicit objectives and overall goals that are 

understood at all levels of the program. The NPS I&M guidelines for developing a successful 
272

LTM program define goals as concise, general statements about the overall purpose of the 

program (NPS 2012). Objectives are detailed, specific statements that add focus to the individual 

goals of the program. They can be divided into three categories: management; monitoring; and 

sampling. These objectives will determine which variables are collected, how the data are 

analyzed, the appropriate power level for analyses, and the benchmarks that determine if the 

program is successfully meeting long-term program goals. With goals and objectives playing a 

fundamental role in program design and measurement of success, it is imperative that managers 

develop clear and concise documentation of these directives for the FGBLTM program. These 

decisions should include input from resource managers, stakeholders and quantitative experts 

(Gitzen and Millespaugh 2012) and should follow the s.m.a.r.t. goals of being specific, 

measurable, achievable, results-oriented, and set within a specific timeframe (Williams et al.

2009). 

Development and documentation of s.m.a.r.t objectives for the FGBLTM program could result in 

major changes in program design in the future. For example, if it is deemed a high priority for 

FGBLTM data to be usable in ecological risk assessments and/or natural resource damage 

assessments associated with spills (potential or after-the-fact) of oil, dispersants or drilling fluids, 

the program would need to be adjusted accordingly. A comprehensive conceptual model would 

have to be developed to identify and characterize key chemical stressors, sensitive receptor 

species and key ecological relationships, and measurable acute and chronic endpoints in 

accordance with accepted risk assessment methods (Yender and Michel 2010). This conceptual 

model would then inform the selection of sample sites; physical, biological, and chemical data 

needs; and quantitative data analysis tools and database requirements.

FGBLTM also needs detailed SOPs for each method method. The current IA refers to more 

recent technical reports for details on methods for each method, but those documents lack the 

level of detail necessary for consistent execution. Clear standards on photograph size, equipment 

set-up, distance from substrate, and other data collection processes are not available, the results 

of which can be observed in the inability to directly compare data across some monitoring 

periods due to methods shifts (discussed in Chapter 2). New methods that are being implemented 

with the current IA (BOEM 2014), such as fish transect surveys, do not specify details, such as 

length or width of transect or process for recording various fish species. Having clear SOPs for 

each method ensures that data will be collected in the same manner and with the same integrity 

regardless of who is doing the collecting. This is imperative for a LTM program where data will 

be compared across many years with turnover in field personnel very likely (NPS 2012). Without 

detailed SOPs, it is difficult to determine if variance observed in data represents naturally 

occurring and legitimate ecosystem changes, or artifacts due to differences in collection 

procedures. FGB is currently working on creating SOP documents. These SOPs should be 

finalized before the next monitoring period begins.

Data management is another administrative aspect of LTM that is necessary to program success. 

FGBLTM data management needs to be standardized and improved. Data are currently stored in 

various formats, including hard copy documents, making data management and preservation an 

issue. We recommend that all data is digitized and stored in a central database with uniform 

formatting and including clear metadata for each sampling period. This will improve ease of 

access and future analysis of the long-term dataset. Dedicated annual funding for data 

management, perhaps in the form of a part-time data manager position is recommended to 

accomplish creating the database and maintaining it during future monitoring periods.
273

The spatial design of the FGBLTM program is limited to one sample site per bank that covers a 

very small proportion of either coral cap. As has been discussed in multiple sections of this 

program review, it is very unlikely that these sample sites are representative of the entire shallow 

coral community, and inferences to this larger area are not statistically valid with the current 

spatial design. Significant differences found within an un-replicated site can only be used to 

discuss differences between the two samples being tested. Any inference to differences between 

years would be pseudoreplication, and statistically invalid (Hurlburt 1984). Power analyses of 

the current FGBLTM design also demonstrated that sample sizes are insufficient to detect subtle, 

long-term changes with the desired power. We recommend that FGBLTM conduct a quantitative 

comparison of program data to other, more comprehensive FGB surveys (such as Caldow et al.

2009 and Clark et al. 2014) to assess the validity of historical LTM data, and that additional 

study sites be installed at each bank for future monitoring. 

A number of design options would meet the needs of the FGBLTM, and careful discussion of the 

best design should include input from all interested parties, and quantitative experts. However, 

certain aspects should be included in any scientifically sound LTM design. For example, study 

site locations should be probabilistic, i.e., the entire area where FGBLTM would like to make 

inferences should have equal likelihood of being selected as a study site location (Fancy 2000; 

Schreuder et al. 2004; McDonald 2012). This can be done by overlaying a sampling grid to the 

entire areas of interest and randomly choosing study site locations. If specific grid cells are of 

more interest than others, sampling intensity can be increased in those areas to create an uneven 

sampling design, but this must be taken into account for appropriate analyses (McDonald 2012). 

If certain cells are excluded as potential survey locations for any reason, those areas must not be 

included in inferences made from the data collected (NPS 2012). One problem with simple 

random site selection is that sites may be clumped, with large areas that are not sampled 

(Haysmith et al. 2005). A recommended method to alleviate site clumping is the generalized 

random tessellation stratified design (GRTS) (Olsen et al. 2012). This method creates a spatially 

well-balanced design that is adequately representative of the area(s) of interest and also provides 

known probabilities for each sample site. 

The number of additional sites that are possible will likely depend on budget constraints, as any 

increase in study sites will incur additional cost in set-up and site maintenance. The power 

analyses demonstrated that with the current single site and sample size, sampling biennially 

would have little effect on overall statistical power, which could easily be modified into a two 

panel, biennial design. However, the added power from increasing sample size with more than 

one additional panel, and the additional spatial coverage and overall representativeness of the 

samples may outweigh the power loss from less frequent sampling. We recommend that the 

optimum design be explored with further power analyses to develop a design that balances 

maximizing spatial coverage and sample size, while maintaining a reasonable sampling 

frequency and budget. 

Once the appropriate number of sites has been determined and locations have been chosen using 

a scientific method, the sampling schedule will need to be determined. There are multiple options 

for this, and the logistics of sampling multiple sites will need to be discussed by all stakeholders. 

The additional quantitative considerations incurred should also be planned out prior to beginning 

sampling. It is suggested that some form of rotating panel design be adopted, where a subset of 

the sites are sampled each year (Urquhart 2012). By sampling only a subset of sites, the total area 

sampled can be maximized without proportionally increasing the yearly cost. Some example 
274

rotating panel designs outlined by Urquhart (2012) are demonstrated in Table 5.1 using an 

arbitrary three sites per bank. 

Table 5.1 Rotating Panel Sample Designs

Examples of potential panel designs per bank in which a subset of sites is sampled each year. 

Sample Years 1 2 3 4

Design 1

Site 1 x x

Site 2 x

Site 3 x

Design 2

Site 1 x x x x

Site 2 x x

Site 3 x x

Design 3

Site 1 x x x

Site 2 x x x

Site 3 x x

 

Design 1 samples one site each year and rotates among sites (Table 5.1). This design was 

effectively tested as part of the power analyses, where the study site was sampled every third 

year. Power results showed that little effect would be seen in this design compared to the current 

annual method. However, in the scenario seen above, total sample size would be increased, 

increasing power, also demonstrated by power analyses. Design 2 would maintain the annual 

sampling regime at the currently established site, while increasing sample size by surveying 

additional sites on a rotating basis. Design 3 rotates sampling among all sites, but allows for 

consecutive years of sampling so that two years of data are available per site for each sampling 

round. 

Design 1 would incur the least cost, because once sites were established, the cost of sampling 

one site a year would be similar to the current design. Other designs will include sampling 

additional sites per year, increasing time in the field and for analyses. This will increase overall 

project costs beyond initial expenditures for site establishment; however, program power should 

improve from more frequent sampling per site, increasing the ability to detect long-term changes 

at a smaller scale than is currently possible.

The inclusion of these program modifications would incur additional cost. However, we have 

recommended that some methods currently included in the FGBLTM program be removed, 

which will alleviate some of the additional costs. The greatest costs will be from adding 

additional study sites to each bank; however, the current FGBLTM design cannot be considered 

a truly scientific design (i.e., random), nor has it been demonstrated that the current sample sites 

are adequate to be considered representative of the entire shallow coral caps, which are the area 

FGBLTM is designed to monitor. The power analyses have shown that the current sample sizes 

are mostly inadequate to detect the levels of change desired by the FGBLTM program. 

Furthermore, when compared to other LTM program designs, all other examples reviewed 

include multiple sites (Sweatman 1997; Hill and Wilkinson 2004; Miller et al. 2007; VanWoesik 

et al. 2009; Brown et al. 2011). It is imperative that FGBLTM increase sample size and improve 

the representativeness of study sites to the area proposed for monitoring. It is our 
275

recommendation, based on the results of this program analysis, that this be achieved with an 

increased number of sample sites and a modified sampling schedule. We recommend that this be 

achieved in one of two ways: either sample size is increased within the current study site and 

sampling frequency is reduced, or sample size is increased with additional study sites per bank 

and sampling schedule is rotated among them. If only one study site is visited per year per bank, 

sample size coverage will still be increased while keeping the sampling costs per year 

comparable to the current design of visiting the same site every year. 

5.3 Methodology and Techniques

A technique evaluation of the FGBLTM program (as documented in Chapter 2) and a sampling 

evaluation (as documented in Chapter 3) were performed. We recommended numerous 

methodology changes related to standardizing methods and increasing scientific merit. 

Additional methodology changes were recommended related to improving the statistical power 

of the methods and making the program more efficient. The methodology recommendations 

made throughout these and other chapters include the following:

1. Ensuring that random transects are truly random,

2. Standardizing the random transect method for photograph size and area coverage,

3. Returning the focus of repetitive quadrats to primarily collecting process variable data in 

the form of coral tissue growth and retreat and the potential causes for these changes,

4. Establishing additional quadrat stations in the study site in a stratified design to increase 

the robustness of the method,

5. Decreasing sampling frequency for benthic cover methods and increasing the sample 

size,

6. Discontinuing the lateral growth method,

7. Reducing the sampling frequency for the sclerochronology method to once every five 

years and increasing the required length of the cores to include more years per sample,

8. Changing the coral health method from line intercept to belt transects to improve 

comparability to other LTM programs in the Gulf region,

9. Reducing the sampling frequency for both coral health and size and coral recruitment 

methods while doubling the sample size from eight transects per bank to 16 transects per 

bank,

10. Changing the sample design for sea urchins, lobsters, and lionfish to an every other or 

every third year sampling interval while expanding the surveys to random transects, fish 

belt transects, or areas beyond the current 100 m by 100 m study sites to increase the total 

area surveyed,

11. Reducing the qualitative video transect method to opportunistic surveys that are not 

analyzed post hoc,

12. Evaluating the value of maintaining both point count and transect data for fish after the 

first belt transect data are available and considering choosing one or the other method,

13. Including benthic data within fish surveys,

14. Expanding water quality sampling to include PAR, hydrocarbons, and metals associated 

with oil or natural gas drilling, 

15. Collecting water samples at multiple locations across each bank to increase statistical 

power and provide more representative spatial coverage of the site. 
276

Here the pros and cons of each of these recommendations are discussed in reference to the 

financial cost to the project and its effect on the scientific integrity of the program. An effort is 

made to determine alternatives that optimize these two aspects of the program. 

5.3.1 Benthic Cover

5.3.1.1 Random Transects

Three methodology changes are recommended for the random transect surveys. First, slightly 

redesign the random transect sampling design to ensure that the sample is truly random. Second, 

standardize the transect photography equipment and technique to ensure area coverage of the 

photograph remains consistent from year to year. Finally, reduce the sampling frequency in favor 

of a rotating panel design with more spatial coverage, as discussed in Section 5.1, and that the 

sample number be increased to increase the statistical power of the method.

The current method used to place the random transects is more haphazard than random (see 

Chapter 2). Divers navigate to a random distance and direction from a random point on the 

perimeter line to conduct the first transect. Then, they navigate to the next location using the first 

transect as a starting location for navigating to a new random distance and direction, and to the 

third using the second as a starting location, etc. Because the second transect location is 

dependent on the first and the third on the second, etc., this design is partially haphazard, and the 

sample data may be bias (Lewis 2004).

For the sampling plan to be truly random, each transect starting location must be independent of 

other transects. This could be accomplished by using random GPS locations within the study site 

and navigating between them underwater. Large areas of sand or other undesirable locations 

could be excluded from the pool of potential sampling locations, and randomized locations could 

be generated within each quadrat to ensure even distribution throughout the site. It may be 

possible to temporarily mark random sites by dropping small marker floats from the surface to 

aide with locating random sites underwater, but this method might damage corals especially 

fragile branching or plating corals. Otherwise, it will be necessary to estimate kick-cycle 

distances to judge the location of the next random transect. Such a sampling design may require 

a little more time under water than the haphazard pattern. It is anticipated that altering the 

navigation strategy for locating transects will add no more than a few minutes per transect. In 

light of the improvement to reliability of the benthic cover data that will result from true 

randomization of the sample, these few minutes per transect are a small cost consideration, but 

the safety of divers should be considered becuase FGB is a deep reef and depth may severely 

limit safe bottom times.

We have found that the total area sampled by the photographic transects has varied over the 

years due mainly to changes in photography equipment. The largest total area sampled was 4.5 

times greater than the smallest total area sampled. As discussed, this could affect the power of 

the sample data to accurately represent the total population, as larger area coverage should result 

in a more representative sample and reduced sample variance. The FGBLTM program needs to 

standardize the area covered by the photograph so that when new camera types are used as 

technology changes, the results remain comparable. To standardize the method it will be 

necessary to determine the optimum area coverage for the photographs. This will be a balance 

among photo quality, area coverage, and benthic cover variation. Brown et al. (2004) and Jokiel 
277

et al. (2015) provide examples of how to make this determination. Standardizing the method, 

may require a few weeks of labor hours, including a few dive days to try out different photo sizes 

and camera configurations, but this will be a one-time cost that will greatly improve the integrity 

of the FGBLTM data throughout the future duration of the LTM program.

Power evaluations revealed that the random transect method has very low power to detect 

changes in benthic cover under the current sampling strategy. This is due mainly to the large 

variance in the sample data. An obvious solution to reducing sample variance and increasing 

statistical power is increasing the sample size. However, doubling the sample size will easily 

double the cost of the method. According to the power simulations, biennial sampling provided 

similar statistical power as annual sampling. By reducing the sampling frequency to biennial and 

doubling the sample size with additional study sites, FGBLTM could keep the cost of the random 

transect method about that same as current while improving its statistical power.

5.3.1.2 Repetitive Quadrats

Three methodology changes are recommended for the repetitive quadrat method. First, return the 

focus of repetitive quadrats to primarily collecting process variable data in the form of coral 

tissue growth and retreat and the potential causes for these changes. This was the original intent 

of the method (Gittings et al. 1993). Second, establish additional quadrat stations at each study 

site, following a stratified design to increase the robustness of the method. Finally, reduce the 

sampling frequency be reduced and increase the sample number to increase the statistical power 

of the method.

Permanent stations, such as the repetitive quadrat stations, are more suitable for identifying and 

evaluating process variables like coral growth or recruitment, rather than measuring state 

variables like benthic cover (see Chapter 2). This is because permanent stations provide limited 

spatial coverage compared to random stations, but at the same time provide less year to year 

variance (Van Woesik et al. 2009). Coral growth is an important process variable that FGBLTM 

has attempted to monitor through the lateral growth and sclerochronology methods, but issues 

with the lateral growth method have limited its effectiveness at detecting potential changes. In 

the discussion below, we recommend that the lateral growth method be discontinued. Although 

the focus of the data analysis has shifted to mainly identifying changes in benthic cover, the 

repetitive quadrat method is already collecting information on lateral coral growth and retreat. 

With renewed focus on the coral growth aspect of the photo analysis, the repetitive quadrat 

method could provide a valuable coral growth dataset and fill the role of lateral growth method. 

FGB may also wish to consider replacing repetitive quadrat surveys with coral calcification 

surveys such as performed by USGS (2016), but this will not be comparable to the historical data 

that has already been collected.

Random dot analysis is a time consuming process, and it is unnecessary for the repetitive 

quadrats if the focus is returned to evaluating changes in growth and retreat of specific colonies 

within the quadrats. This methodology recommendation will save time and labor costs, while 

making the method more efficient at providing valuable information for FGB. Changes in 

benthic cover are evaluated appropriately in the random transect method, so no variables are 

ignored by refocusing the repetitive quadrat method.
278

We discussed the representativeness of the repetitive quadrats in previous chapters (1 and 2). The 

quadrats have been specifically installed in areas of “premium” coral cover using professional 

judgement (Johnston et al. 2013). This was done to maximize observations of changes in 

individual coral colonies when the focus of the analysis was on process variables. For this 

purpose, the spatial layout does not pose a huge problem, but if the method continues to be used 

to measure changes in benthic cover, it will be necessary to change the spatial sampling plan. 

Because the samples are “premium” and not representative of the study area or the larger bank, 

no inferences can be made about the larger population using these data (McDonald 2012). 

Additional quadrats could be stratified by habitat type, as is the recommended by Buckel et al.

(2014). The current stations could represent high coral cover areas, and additional quadrats 

could be installed in areas of lower cover. Again, this spatial restructuring is mainly necessary 

only if the photographs are used to assess changes in benthic cover.

Similar to the random transect method, the power evaluations reported revealed that the 

repetitive quadrat method has very low power to detect changes in benthic cover under the 

current sampling strategy. This is due mainly to the large variance in the sample data. By 

reducing the sampling frequency and increasing the sample size, FGBLTM could keep the cost 

of the repetitive quadrat method comparable to current while improving its statistical power. The 

power to detect changes in coral growth using the repetitive quadrats was not assessed because 

coral growth was not evaluated in the quadrats for recent monitoring reports. However, it is 

expected that high variation would also be a problem for coral growth under the current sampling 

strategy. The less frequent sampling strategy with increased sample numbers is expected to be a 

beneficial strategy even if the method is refocused to coral growth. Establishing more permanent 

stations may be costly and time consuming, but once established, no additional cost is expected 

during subsequent sampling years.

5.3.2 Coral Growth

5.3.2.1 Lateral Growth

Throughout the chapters of this report, we have recommended that the lateral growth method be 

discontinued. Issues with site integrity and comparability of photographs have made the method

ineffective at collecting adequate data to evaluate changes in coral growth. Huge efforts have 

been spent installing, reinstalling, and maintaining stations; photographing stations; and 

matching and analyzing photographs, but year after year, the number of useable samples is too 

small to provide adequate statistical power. Johnston et al. (2013) expresses a need for even 

more time to be allotted to the method to deal with site integrity issues and creating more reliable 

maps of the stations. This is unlikely to be worthwhile.

The repetitive quadrat photographs have the potential to provide a more efficient evaluation of 

lateral coral growth. Initially the repetitive quadrats were installed to measure changes such as 

growth and retreat in colonies and determine the causes of any mortality (Gittings et al. 1993). 

Issues with photograph quality and quadrat size early in the FGBLTM made determining the 

cause of coral tissue loss difficult, but current digital technology should allow for the more 

detailed analyses required. Some of the main issues reported for the lateral growth methods are 

difficulty locating photo stations, loss of one or more corner pins that allow the camera to be 

aligned properly, the subject coral overgrowing the photo frame, and inconsistent photograph 
279

angle (Johnston et al. 2013). Without the burden of maintaining 60 lateral growth stations, 

additional energy can be spent maintaining the repetitive quadrat stations and creating more 

reliable maps for those stations. The larger photo frame size of the repetitive quadrat stations 

should greatly reduce the problem of corals overgrowing the frame, and the larger frames should 

be easier to photograph at an adequately consistent angle due to the wider area of focus. The 

wider area of focus will result in less image resolution than is possible for the smaller lateral 

growth frames, but current digital camera technology should provide sufficient image resolution 

to determine growth and retreat of the coral margins.

5.3.2.2 Sclerochronology

For the sclerochronology method we recommend that the sampling frequency be reduced to once 

every five years and the required length of the cores be increased to include more years per 

sample. Current core collection requirements are to collect cores of 70 millimeters minimum 

length every other year. This equates to about 10–12 years of growth. This will only allow 

repeated measures statistical analyses to be applied for that 10–12 year period (see Chapter 2). If 

FGBLTM wishes to compare data for a longer time period, core data would have to be combined 

across cores collected in different years. This would add biases when unequal numbers of cores 

were collected and would introduce more variation. A more efficient strategy would be to collect 

longer cores (perhaps 50 to 70 centimeters in length) to allow repeated measures analyses on 50 

years of data or more. Power simulations revealed that 10-year long cores have inadequate power 

to detect 10 and 20% net changes in coral growth rate (see Chapter 3). Statistical power was 

improved by increasing the core lengths to include 15 or 20 years of data and is expected to

continue to improve with increasing core length.

The cost of collecting longer cores and having them analyzed will be greater than the current 

cost, but given the period of overlap in growth archives between cores will be greater, cores 

could be collected less frequently. We recommend that cores are collected every five years rather 

than every other year. This will offset the cost of collecting longer cores while preventing 

missing any significant short term changes in coral growth that might be overlooked or detected 

too late if cores were collected with even less frequency. 

5.3.3 Coral Health

Two methodology changes are recommended for the coral health and size method. First, switch

the method from line intercept to belt transects to improve comparability to other LTM programs 

in the Gulf region. Second, reduce the sampling frequency of the method to biennial sampling 

and double the sample size be 16 transects per bank.

The coral size and health method was added to the FGBLTM program in 2013. Surveys entail 

measuring corals that fall under eight randomly placed transects per bank and identifying any 

bleaching, disease, or mortality for each coral (BOEM 2014). Line intercept surveys such as 

these have the disadvantage of not providing a comparable assessment of disease area if coral 

sizes vary among sites (Raymundo et al. 2008). Gittings et al. (1993) suggested that coral sizes 

vary between EFGB and WFGB, so line intercept surveys are likely inappropriate for comparing 

coral disease prevalence between the two banks. The AGRRA is long-standing regional LTM 

program that includes similar coral size and health surveys. Current AGRRA methods use belt 
280

transects rather than the line intercept methods that FGBLTM is using (Lang et al. 2010). Belt 

transects are also commonly used for coral size and health assessments worldwide (Hill and 

Wilkinson 2004; Willis et al. 2004; Page and Willis 2006; Williams et al. 2010). Belt transects 

provide similar information to the line intercept method, but do assess consistent areas, 

increasing comparability across sites (Hill and Wilkinson 2004; Raymundo et al. 2008). We 

recommend that FGBLTM adopt the methodology used by AGRRA to assess coral size and 

health. Because the method has only recently been added to FGBLTM, now is a good time to 

change methods before the change will result is any larger period of incomparable data. Line 

intercept and belt transect data will not be directly comparable, but the new belt transect data will 

be comparable between banks and with data from AGRRA around the region.

Belt transect methods involve measuring the size of all colonies within a certain width belt along 

a transect line and assessing the percent mortality, bleaching, and disease. AGRRA uses a 1 m 

wide belt transect (Lang et al. 2010). One major disadvantage to belt transects is that they can be 

time intensive, sometimes requiring more than one dive per transect (Raymundo et al. 2008). 

This method change would require additional time in the field. FGB considered this method 

when the coral size and health surveys were added to the LTM program, but the time intensive 

nature of the belt transects lent to line intercept transects being chosen (M. Johnston, FGBNMS, 

personal communication November 11, 2015). We recommend that this decision be reconsidered 

based on scientific merit and added comparability, but it is recognized that this may not be 

feasible given time and cost constraints.

The power evaluations revealed that the coral size and health method has low power to detect 

changes under the current sampling strategy. This is due to the large variance in the sample data 

with the line intercept data. By increasing the sample size, sample variance can be reduced, 

increasing statistical power. Because the coral size and health surveys are conducted on eight of 

the 16 random transects, an obvious way to increase the sample size would be to survey on all of 

the random transects. However, doubling the sample size will easily double the cost of the 

method. According to the power simulations, biennial sampling provided similar statistical 

power as annual sampling while maintaining the current single site design. By reducing the 

sampling frequency and doubling the sample size, FGBLTM could keep the cost of the coral size 

and health method about that same as current while improving its statistical power. 

If FGBLTM decides to take the recommendation to switch to belt transect surveys, it may not be 

necessary to double the sample size to achieve adequate power, but this recommendation should 

still be considered. Belt transects cover a larger area than line intercept transects, so it is likely 

that the sample variance will be smaller with belt transects. Pilot data should be collected using 

the new methodology and power evaluations should be repeated before a decision is made 

regarding sample size requirements for belt transects.

5.3.4 Coral Recruitment

We recommend that the sampling frequency for the coral recruitment method be reduced and the 

sample size be doubled to 16 transects per bank. This strategy was discussed to increase 

statistical power of the method to detect changes in coral recruitment over time (Chapter 3). 

Power simulations showed that the pilot study data had very low power to detect changes. By 

reducing the sampling frequency and increasing the sample size, power to detect changes was 

about double that of the current sampling strategy. Such a change would allow the method to be 
281

more efficient at meeting the monitoring objectives while incurring no additional cost. Coral 

recruitment is currently measured along half of the random transects (eight of 16 transects). To 

increase the sampling frequency, coral recruitment surveys should be conducted on all of the 

random transects.

The coral recruitment method was added to the LTM program in 2013 and few details about the 

methodology are provided in the IA. Additional methodology changes may be necessary after the 

method has been implemented for a few years and the data can be assessed. Examples of peerreviewed and field-tested coral recruitment methods are available in regional programs such as 

AGRRA (Lang et al. 2010) and can be used as guidelines for the FGB coral recruitment work.

5.3.5 Sea Urchins, Lobsters, Lionfish

We recommend that the sample design for sea urchin, lobster, and lionfish surveys be changed to 

a biennial or triennial sampling interval while expanding the surveys to random transects, fish 

belt transects, or areas beyond the current 100 m by 100 m study sites to increase the total area 

surveyed. Power simulations revealed that the current sampling design has very low statistical 

power (see Chapter 3). Recorded sea urchin and lobster densities have been consistently too low 

to conduct statistical analyses (Johnston et al. 2013, 2015), and lionfish surveys have only 

recently been added, so no attempts to analyze the data have yet been reported. In the current 

spatial design, surveys are conducted along the perimeter lines of the 100 m by 100 m study site 

(BOEM 2014). This is a convenience sample, and as such, little statistically useful information 

could be gathered even if encounter rates were higher, because the surveys are not likely 

representative of the target communities (McDonald 2012). Expanding the surveys to the random 

transects or fish belt transects would allow a more representative sample to be collected and 

increase the sample size which will likely increase the power of the method to detect changes.

Adding more transects would increase the time required in the field for this method and the total 

cost of the method. As with the majority of the methods, the power simulations showed that 

decreasing sampling frequency to biennial or triennial sampling had little effect on the power to 

detect changes in sea urchin populations (see Chapter 3). A substantial increase in the number of 

transects could be balanced with a decrease in sampling frequency to biennial or triennial 

surveys. This could maintain the current budget for the method while greatly increasing the 

statistical power and the ability of the method to document changes in sea urchin, lobster, and 

lionfish populations at FGB.

5.3.6 Qualitative Observations

We recommend that the qualitative video transect method effort be reduced to opportunistic 

surveys that are not analyzed post hoc. Currently, FGBLTM spends a few man hours collecting 

video along the perimeter transects at each bank and analyzing the videos for presence of coral 

bleaching and disease and signs of human impacts (BOEM 2014). The datasets are considered 

qualitative and are not statistically analyzed. The perimeter video surveys are a convenience 

sample and cannot be considered representative of the study site. The data gathered is of little 

use for drawing conclusions about the banks even if it were being statistically analyzed. This 

method should be either eliminated or reduced to allow more time and money for other methods 

that are collecting information that can be statistically analyzed and used to inform managers. 
282

There is value in having a long-term visual record of the area, but the time and effort are not 

justified when no useable results are produced. FGBLTM could continue to collect the visual 

record for archival records only and not perform routine analysis. This would save time and 

allow videos to be revisited if need be later on.

5.3.7 Fish

No specific immediate methodology changes have been recommended for the fish method. 

However, we recommend that FGBLTM evaluate the value of maintaining both point count and 

transect data for fish after the first belt transect data are available and consider choosing one or 

the other method. Stationary point count and belt transects are generally considered equivalent 

surveys each with their own strengths and weaknesses, so there are few advantages to including 

both types of surveys (see Chapter 2). No reviewed study comparing these visual census 

techniques suggested the use of both point counts and transects when conducting surveys (i.e.,

Bortone et al. 1989, Watson and Quinn 1997, Samoileys and Carlos 2000, Willis 2001, 

Colvocoresses and Acosta 2007). Once data have been collected for each survey type, it should 

be determined which strategy best fits the needs of the LTM program. 

Reducing the fish survey method back to one survey method will save considerable fieldwork 

and analysis time. These time and cost savings could be put to use to increase the sample size of 

the remaining fish survey method. Power simulations based on pilot study data showed that the 

current sampling strategy lacks adequate power to detect changes in fish density, and it is 

suggested that it may be necessary to increase the sample size (see Chapter 3).

FGBLTM currently does not collect habitat data in conjunction with fish point counts (see 

Chapters 2 and 4). Extensive literature is available on the strength of reef fish-habitat 

associations (i.e., Clarke 1988, Sale 1988, McCormick 1994, Sale 1998, Chittaro 2004, 

Gratwicke and Speight 2005, Wilson et al. 2008) and how benthic changes will affect the fish 

community (Syms and Jones 2000, Jones et al. 2004). By analyzing fish assemblages with point 

counts or transects without including the context of habitat, a potentially critical explanatory 

variable is missing. We recommend that additional habitat information, such as major benthic 

components and rugosity, be collected in concurrence with the fish surveys. Given the small 

benthic area covered by stationary point counts (7m radius), collecting this additional 

information should not significantly increase the amount of time needed to complete a survey. 

Likewise, if it were possible to combine fish transects with random benthic transects, habitat 

information would already be collected, incurring no additional cost. Otherwise, collection of 

benthic data along separate fish transects would likely cause a noticeable increase in time needed 

to complete a single transect, incurring additional program costs in time and consumables. 

Alternatively, if only one method were maintained, additional costs would be absorbed. 

5.3.8 Water Quality

5.3.8.1 Nutrient Grab Samples

Two methodology changes are recommended for the nutrient grab sample method. First, at 

minimum, the analytes measured should be expanded to include hydrocarbons and metals 
283

associated with oil or natural gas drilling. Second, samples should be collected at multiple 

locations across each bank.

The current suite of analytes measured from the nutrient grab samples (soluble reactive 

phosphorus, chlorophyll a, total Kjeldahl nitrogen, ammonia, nitrate, and nitrite) does not include 

any parameters that could be directly linked to oil and gas production in the Gulf, but are good 

indicators of land-based pollution (White et al. 2006; Carpenter et al. 1998). To discern among 

drivers of observed changes in biological parameters, it is important that FGBLTM include water 

quality parameters indicating land-based pollution sources and oil and gas production pollution 

sources. Produced waters can contain elevated levels of total dissolved solids, radium, petroleum 

hydrocarbons, and heavy metals such as iron, lead, barium, and cadmium, and often elevated 

levels of lead and barium are found in sediment near drilling areas (Trefry et al. 1995). Including 

some of these parameters in the analysis of grab water samples will provide a basis from which 

to determine whether nearby oil and gas production is affecting water quality. This will 

undoubtedly increase the cost of the method substantially, but without this addition FGBLTM is 

missing an important piece of the puzzle in determining potential drivers of change, and not 

collecting data that will achieve some stated program objectives.

The current sampling strategy for the nutrient grab sample method includes collecting three 

replicate samples at three depths at one location per bank quarterly. This strategy does not take 

into account spatial variation in water quality. To obtain a more representative sample of water 

quality at the banks, samples should be collected at multiple locations per bank. It was 

determined that the current number of samples does not provide adequate power to detect 

change. Increasing the sample size is recommended to improve statistical power. Although 

collecting more samples will require additional time in the field and will multiply the analytical 

lab costs, doing so will increase the statistical power and the representativeness of the sample, 

and these are necessary improvements if FGBLTM wishes to use the water quality data to assess 

drivers of change in the biological parameters.

5.3.8.2 Temperature and Salinity Time Series

One methodology change is recommended for temperature and salinity method. We recommend

that PAR light be added back to the continuous datasets. FGBLTM monitored PAR until 2001, 

when extended deployments of YSI multi-parameter sondes were introduced (Precht et al. 2006). 

The sondes measured turbidity, which would give an indirect indication of light availability. 

However, the YSI sondes were replaced with SeaBird MicroCATs, which do not record light or 

turbidity, in 2009. Most scleractinian coral species are dependent on single-celled photosynthetic 

organisms called zooxanthellae that live within the corals tissues, and as a result, an appropriate 

amount of PAR light is a requirement of coral survival. PAR is a critical parameter to monitor 

when the health of a coral community is being evaluated, and FGBLTM should consider adding 

the parameter back to the continuous monitoring method. Costs associated with this addition will 

include purchasing PAR loggers, and labor costs for maintaining the sensors, uploading data, and 

analyzing the results.
284

5.4 Analyses

Chapter 4 of this program review was an evaluation of statistical methods used to analyze 

FGBLTM data since 2012. In addition to findings specific to each data collection methodsome

issues were common to the entire program. These recommendations include:

1. No longer basing program conclusions on qualitative comparisons and apparent trends or 

other apparent evidence that is not quantitatively tested with appropriate tests,

2. Acknowledging that current program design does not have the statistical power to make 

the conclusions that are currently being asserted,

3. Improving program power by modifying the types of statistical tests used to more 

appropriate alternatives (detailed by method below),

4. Increasing the number of years analyzed and further exploring trend analyses to achieve a 

better long-term view of data interpretation and overall reef status.

Of the issues discussed, the most problematic for the overall success of the FGBLTM program 

were conclusions based on qualitative comparisons and apparent, but untested, trends. This was 

seen in almost every method that included data analyses. For conclusions to be defensible and 

consistent, appropriate statistical tests must be applied. Making general comparisons to other 

datasets to demonstrate a point or to augment a particular theory is acceptable and potentially 

informative. However, using those comparisons to form definitive conclusions, as is seen in the 

FGBLTM technical reports, without the support of quantitative statistical analyses is not 

appropriate for a scientifically rigorous program. It is imperative that FGBLTM use only 

appropriately applied and accurate quantitative analyses to form conclusions about the overall 

health and trends of the target communities at FGB.

Another issue discussed that was applicable to most methods, was that of statistical power. The 

power analyses demonstrated that most methods lacked the sample size to meet the detection 

power goals of FGBLTM. These findings were based on power analyses of the statistical tests 

currently used most frequently, namely univariate, parametric tests such as ANOVA. The low 

statistical power of this program will be alleviated by several recommendations found in this 

review for both program design and methodology (i.e., increased sample area and size, detailed 

method SOPs) and the use of different statistical methods. For each method reviewed, we

determined that FGBLTM would benefit from incorporating multivariate and non-parametric 

tests either in addition to, or in place of the univariate, parametric tests currently used. 

Multivariate analyses allow for analyzing community variables as they change in relation to each 

other, which is often a more ecologically accurate way to evaluate community dynamics (Clarke 

and Warwick 2001). Also, the assumption of univariate analyses is that each variable is 

independent, a largely inaccurate assumption for ecosystem community variables, especially in 

time-series data as seen in LTM monitoring (Quinn and Keough 2002). Likewise, nonparametric tests often have greater power when parametric assumptions are violated (Quinn and 

Keough 2002), common in ecological datasets (Clarke and Warwick 2001). We recommend that 

FGBLTM shift statistical analyses to more appropriate multivariate, non-parametric tests, which 

will improve statistical power and provide more accurate and defensible results upon which to 

base overall program conclusions. Specific analytical recommendations will be further discussed 

in the individual method reviews below. 
285

The final topic we discussed in Chapter 4 that was found in all methods was the lack of longterm evaluations or trend analyses, potentially leading to incorrect conclusions about the overall 

status of FGB reefs. Analyses were consistently restricted to the two years within a monitoring 

period, and only qualitative comparisons were made to data collected in prior sampling periods. 

This began to change with the most recent technical report by Johnston et al. (2015), in which 

some long-term trend analyses were performed. The goals and objectives of the FGBLTM 

program are such that analysis of longer-term datasets is required. This is especially true given 

that the power of the statistical analyses currently used would only detect a change of ±50% for 

most community variables in the timeframe of a single monitoring period (as demonstrated in 

Chapter 3) with the confidence desired by FGBLTM. Over longer time periods, the annual 

change needed to be within the desired detection threshold reduces considerably, highlighting the 

importance of evaluating data over several years. We recommend that FGBLTM continue to 

analyze data from a single monitoring period, as meaningful information can be gained from 

these analyses, but also expand on the long-term analyses seen in the latest technical report, and 

further explore options in long-term trend analyses.

Individual methods within the FGBLTM program have unique datasets, and therefore the 

specific recommendations for each differed somewhat. Benthic cover data analyses consisted 

primarily of parametric one and two-way ANOVAs, with the recent inclusion non-parametric 

methods such as ANOSIM, cluster analysis, MDS and SIMPER. It was determined in the that 

these data would provide more informative results if alternative parametric and non-parametric, 

multivariate tests were used (see Chapter 4). Specifically, it was recommended that repeated 

measures ANOVAs replace standard ANOVAs for repetitive quadrat data. In transect analyses it 

was recommended that ANOVAs be replaced with np-MANOVA (Anderson 2001) or db-RDA 

(Legendre and Anderson 1999) combined with CAP (Anderson and Willis 2003) for data 

visualization and further interpretation. Though the inclusion of ANOSIM, MDS and SIMPER 

were a step in the right direction for the FGBLTM program, it was recommended that these nonparametric tests be replaced with a canonical evaluation, such as CAP, or Principal Coordinates 

Analysis (PCoA). These tests are better alternatives because they maintain the data 

dimensionality that MDS reduces, and also provides additional information about which 

community variables contribute to the observed axes (Quinn and Keough 2002).

Coral growth surveys consist of sclerochronology samples and lateral growth stations. As has 

been recommended in every chapter of this review, it was found that the lateral growth station 

method should be discontinued due to a continual lack of consistent, quality data. 

Sclerochronology data have historically been analyzed with ANOVAs and t-tests. It was 

determined that t-tests were appropriate for comparing coral growth between banks, as long as 

parametric assumptions continue to be met with future datasets. ANOVA is also an appropriate 

test for examining differences in between year growth; however, it was determined that the 

ANOVAs were inappropriately applied to growth rates between cores rather than between years 

within the same core, and the appropriate ANOVA would be a repeated measures or two-way 

with replication rather than a one-way ANOVA. Also, ANOVA cannot be used to evaluate 

continuous long-term trends in coral growth that are of interest to FGBLTM. We recommend

that longer timeframes be assessed using linear regression analysis on combined data from 

multiple collections of core samples, as long as the assumptions of linear regression can be met. 

If assumptions are violated, we recommend that a non-parametric alternative be used, such as 

Kendall’s tau or Spearman’s Rho correlation test (Helsel and Hirsch 2002).
286

The nighttime surveys used to collect data on sea urchins, lobsters and lionfish have yielded few 

observations for each monitoring period since the introduction of the method. Though the current 

IA (BOEM 2014) requires ANOVA analyses to detect year to year variation, the power analyses

demonstrated that ANOVA would be an inappropriate test for such scarce data, and even 

changing to a Chi square test on presence and absence data yielded little power. While 

abundances of these species remain low, statistical testing will be of little value. However, 

should abundances increase to levels that do warrant quantitative analysis, ANOVA tests will 

yield the same limited results as discussed for both benthic cover and sclerochronology data. The 

addition of tests for trend analysis and detection of long-term changes, as recommended for other 

methods, is advised. 

Fish survey data have been analyzed in a manner similar to benthic cover data. For example, data 

are pooled into major functional categories (family, trophic guild, etc.) and then analyzed using 

the same parametric ANOVAs for between year and between bank comparisons, with the 

inclusion of non-parametric ANOSIM, SIMPER, cluster analysis and MDS introduced in the 

2011–2012 monitoring period. Many of the recommendations for improvements in data analysis 

techniques are the same. ANOVA assumptions are violated, even with the required Log (+1) 

transformation (as determined in Chapter 3), likewise the univariate assumption of independence 

is also violated, making alternative tests such as np-MANOVA or db-RDA more appropriate. 

Similarly, we recommend that MDS and SIMPER analyses be changed to canonical methods 

such as CAP or PCoA for the same reasons as discussed for benthic data. 

In addition to changing the statistical tests used for fish data analysis, it was determined that 

further modifications to data collection and analysis would improve the method overall (see 

Chapter 4). First, we recommend that additional analyses be conducted on existing data, such as 

size classes, to calculate indices of population dynamics, such as recruitment rates. This would 

provide valuable information to the FGBLTM program, such as indications of fishing pressure, 

or other impacts on fish population, that would not be as evident from the variables currently 

collected and analyzed. Second, as discussed in Section 3.7, we recommend that habitat variables 

such as rugosity and major benthic components also be collected with each fish survey, as this 

information has been shown to explain a significant amount of observed variation in fish 

population data (i.e., Chittaro 2004; Gratwicke and Speight 2005), which would increase 

quantitative analysis power and improve interpretability of results. The inclusion of these data 

has the potential for additional program costs, particularly for transects, but alternative solutions 

such as only keeping one fish survey method or combining fish transects with benthic random 

transects have been proposed to minimize this effect. 

Water quality data is collected with two methods: nutrient grab samples and temperature and 

salinity time series. Nutrient grab sample data pose a challenge for quantitative analysis because 

many values are below detection limits. This, along with being zero bound (no negative values), 

positively skewed, and typically highly non-normal, make parametric tests inappropriate for 

analysis (Helsel and Hirsch 2002). Based on these challenges, we recommend that FGBLTM use 

logistic regression to analyze grab sample data when the test assumptions are met. Time series 

water quality data also present unique challenges for analysis including the seasonal nature of the 

data, serial autocorrelation and non-normality. It was recommended by this review that time 

series data be “de-seasonalized” to remove background variation introduced by seasonal 

fluctuations, tested for autocorrelation prior to analysis, and either transformed to address 

normality and other parametric assumptions, or analyzed with a non-parametric alternative. 
287

Linear regression is appropriate for continuous datasets, and ANOVA is more appropriate for 

step trends (large gaps in the data due to sampling times or natural events). Based on the 

continuous nature of the collection methods for FGBLTM time series data, linear regression is 

the most appropriate analysis. If parametric assumptions are violated, this review recommends 

Kendall’s tau or Spearman’s Rho correlation test in place of linear regression.

In this review of FGBLTM program analyses and conclusions, most recommendations can be 

summarized to suggest more careful interpretation of data or alternative statistical tests to the 

ones currently in use. These recommendations should not incur additional program costs, unless 

in the form of additional statistical software to conduct alternative analyses, though free software 

that can perform most, if not all suggested alternatives is available (i.e., R core team 2013). With 

minimal change to overall project cost, the benefits of incorporating these recommendations are 

increased power, more reliable results, and defensible conclusions that can withstand scientific 

scrutiny. 

5.5 Conclusions and Final Recommendations

This section presents a brief summary of the recommendations resulting from this program 

analysis for the program design, methodology, and analyses. Implementation of some of these 

recommendations will result in cost savings as they increase the efficiency of the LTM program, 

while implementation of others may result in additional costs, but significantly improve the 

scientific integrity of the program.

First, the overall FGBLTM program should be evaluated using a s.m.a.r.t assessment in order to 

refine and update goals and objectives. Current goals and objectives for the program are not 

clearly stated and do not contain enough detail to steer the program toward quantitative results

(see Chapter 4). After program objectives have been developed, the costs and benefits of the 

other recommendations discussed herein can be evaluated. This assessment would also allow for 

identifying and prioritizing which potential drivers of change (e.g. offshore oil pollution, 

physical reef damage, global warming, ocean acidification, etc.) are of most concern to involved 

scientists and stakeholders. 

Several of the recommendations made in this review are one-time cost investments that will 

significantly improve the scientific integrity of the FGBLTM program. Included with these costs 

are writing SOP documents for each survey method, and improving the data management 

procedures for the program. Although these items will require a large time investment, the 

FGBLTM program cannot be effective at producing defensible results without addressing these 

issues. We recommend that they be prioritized. 

A major program design change that is recommended is increasing the number of study sites 

from one 100 m x 100 m site to multiple sites per bank. If this is implemented, installation of 

new study sites will be a huge one-time investment, requiring surveys to determine locations for 

new sites and time, personnel, and equipment for installing the sites. This work could be 

attempted in one study season or staggered, installing new sites when they came up in the 

rotation for survey. After the sites have initially been installed, no additional costs are expected if 

FGBLTM chooses to survey one site per bank per year. Alternative designs that will require 

significant cost increases are also described in Section 1.
288

During the development of the SOP documents, all other recommendations made in this review 

for methodology and analysis changes should be considered. Most other changes will require 

little additional cost. Costs associated with increasing sample sizes, as recommended for 

numerous methods, can be balanced by decreasing sampling frequencies. Slight changes to 

survey methods, such as switching from haphazard survey patterns to random samples, are not 

expected to incur much cost, and adjusting statistical analyses to the recommended methods 

should be feasible using free software and little additional time investments.

Potentially expensive methodology changes that were recommended include expanding water 

quality grab sample analyses to include more parameters indicative of oil and gas pollution and 

increasing the spatial replication of the method. Also, switching from line intercept transects for 

coral health and size to belt transects as recommended will increase the time and cost investment. 

Some of the costs associated with adjusting methodology can be recovered by eliminating or 

reducing the time invested in less valuable methods. For example, the lateral growth method is 

recommended for discontinuation and we recommend that the time investment in the qualitative 

video transects be reduced.

The final recommendations for the FGBLTM program are outlined below. In considering the 

costs and benefits of each of the recommendations made in this review, FGBLTM must consider 

the program’s budget and the desired outcome of the program. It is important that the program 

produce quantitative results that can be used by managers and stakeholders to inform decisions. 

At the same time, the program’s budget is limited, and attempting to implement too many 

method adjustments could result in reduced work quality across all methods. It is important that 

the FGBLTM program be sustainable in terms of budget limitations and that it satisfies 

objectives (Johnson 2012; Possingham et al. 2012).

The following items are recommended for improvement of the FGBLTM program design:

1. Immediately develop clear program goals and objectives,

2. Create SOP documents for each survey method,

3. Create a central database where all project data are stored, including hard copy data that 

need to be appropriately digitized, 

4. Determine whether the current study site is representative of the target population,

5. Increase the study area and sample size through increased number of sampling sites,

6. Modify the sampling schedule to incorporate the additional study sites.

The following are recommended to improve the FGBLTM program techniques:

1. Ensure that random transects are truly random,

2. Standardize the random transect method for photograph size and area coverage,

3. Return the focus of repetitive quadrats to primarily collecting process variable data in the 

form of coral tissue growth and retreat and the potential causes for these changes,

4. Establish additional quadrat stations to the study site in a stratified design to increase the 

robustness of the method,

5. Decrease sampling frequency for both benthic cover methods to biennial sampling and 

increase the sample size,
289

6. Discontinue the lateral growth method,

7. Reduce the sampling frequency for the sclerochronology method to once every five years 

and increase the required length of the cores to include more years per sample,

8. Change the coral health method from line intercept to belt transects to improve 

comparability to other LTM programs in the Gulf region,

9. Reduce the sampling frequency for both coral health and size and coral recruitment 

methods to biennial sampling, while doubling the sample size from eight to 16 transects 

per bank,

10. Change the sample design for sea urchins, lobsters, and lionfish to an every other, or 

every third year sampling interval, while expanding the surveys to random transects, fish 

belt transects, or areas beyond the current 100 m by 100 m study sites to increase the total 

area surveyed,

11. Reduce the qualitative video transect method to opportunistic surveys that are not 

analyzed post hoc,

12. Evaluate the value of maintaining point count and transect data for fish after the first belt 

transect data are available and consider choosing one method or the other.

13. Include benthic data within fish surveys,

14. Expand water quality sampling to include PAR, hydrocarbons, and metals associated 

with oil or natural gas drilling,

15. Collect water samples at multiple locations across each bank, to increase statistical 

power and provide more representative spatial coverage of the site. 

The following are recommended to improve the FGBLTM program analyses and apply to all 

methods:

1. Develop program conclusions based only on quantitative and statistically supported 

results, not on qualitative comparisons and apparent trends,

2. Consider that current program design does not have the statistical power to make the 

conclusions that are currently being asserted, 

3. Improve program power by modifying the types of statistical tests used to more 

appropriate alternatives,

4. Increase the number of years analyzing and further exploring trend analyses to achieve a 

better long-term view of data interpretation and overall reef status.

The following recommendations could improve the analyses for specific methods at FGBLTM:

1. Use repeated measures ANOVA in place of simple ANOVA for repeated quadrat percent 

cover and planimetry data,

2. Use non-parametric tests when benthic cover data violate parametric assumptions. 

Recommended alternative tests include np-MANOVA and db-RDA,

3. Use alternative visualization and comparison techniques for benthic in place of MDS and 

SIMPER. There alternatives include CAP, PCoA, and IndVal, 

4. Use linear regression analysis to detect temporal trends in the sclerochronology data if 

the test assumptions are met,

5. Continue to collect urchin, lobster, and lionfish data, but do not perform analyses unless 

number increase considerably,
290

6. As with benthic data, use alternative analyses including np-MANOVA; db-RDA in place 

of parametric ANOVA and ANOSIM; CAP or PCoA in place of MDS; and IndVal in 

place of SIMPER for fish data,

7. Use logistic regression for evaluating changes in nutrient concentrations over time at 

FGB,

8. Use linear regression analysis for assessing temporal changes in temperature and salinity 

time series data. 
291

5.6 References Cited

Anderson MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral 

Ecol. 26: 32-46.

Anderson MJ, Willis TJ. 2003. Canonical analysis of principal coordinates: a useful method of 

constrained ordination for ecology. Ecology. 84: 511-525.

Bortone SA, Kimmel JJ, Bundrick CM. 1989. A comparison of three methods for visually 

assessing reef fish communities: time and area compensated. Northeast Gulf S. 10(2): 85-

96.

Brown E, Cox E, Jokiel P, Rodgers K, Smith W, et al. 2004. Development of benthic sampling 

methods for the Coral Reef Assessment and Monitoring Program (CRAMP) in Hawai’i. 

Pac Sci. 58(2): 145-158.

Brown E, Minton D, Daniel R, Klasner F, Basch L, et al. 2011b. Pacific Islands Network benthic 

marine community monitoring method. Fort Collins (CO): National Park Service. Natural 

Resources Report NPS/PACN/NRR—2011/339. 376 p.

Buckel CA, Viehman TS, Edwards K, Tester PA, Piniak GA, et al. 2014. Benthic communities 

of the coral reef. In: Clark R, Taylor JC, Buckel CA, Kracker LM, editors. Fish and 

Benthic Communities of the Flower Garden Banks National Marine Sanctuary: Science 

to Support Sanctuary Management. Silver Springs (MD): National Oceanic and 

Atmospheric Administration. NOAA Technical Memorandum NOS NCCOS 179. p. 22-

74.

[BOEM] Bureau of Ocean Energy Management. 2014. Interagency Agreement M14PG00020 

between the Bureau of Ocean Energy Management and the National Oceanic and 

Atmospheric Administration National Ocean Service Office of National Marine 

Sanctuaries Flower Garden Banks National Marine Sanctuary for long-term coral reef 

monitoring at the Flower Garden Banks, Gulf of Mexico: 2014–2016. 28 p.

Caldow C, Clark R, Edwards K, Hile S, Menza C, et al. 2009. Biogeographic characterization of 

fish communities and associated benthic habitats within the Flower Garden Banks 

National Marine Sanctuary: Sampling Design and Implementation of Scuba Surveys on 

the Coral Caps. NOAA Technical Memorandum NCCOS 81. 152 p.

Carpenter SR. Caraco NF, Correll DL, Howarth RW, Sharpley AN, et al. 1998. Nonpoint 

pollution of surface waters with phosphorus and nitrogen. Ecol Appl. 8(3): 559-568.

Chittaro PM. 2004. Fish-habitat associations across multiple spatial scales. Coral Reefs. 23: 235-

244.
292

Clark R, Taylor JC, Buckel CA, Kracker LM, editors. 2014. Fish and benthic communities of the 

Flower Garden Banks National Marine Sanctuary: science to support sanctuary 

management. Silver Springs (MD): National Oceanic and Atmospheric Administration. 

NOAA Technical Memorandum NOS NCCOS 179. 317 p.

Clarke KR, Warwick RM. 2001. Change in marine communities: an approach to statistical 

analysis and interpretation, 2nd ed. Primer-E: Plymouth.

Clarke RD. 1988. Chance and order in determining fish-species composition on small coral 

patches. J Exp Mar Biol Ecol. 115: 197-212.

Colvocoresses J, Acosta A. 2007. A large-scale field comparison of strip transect and stationary 

point count methods for conducting length-based underwater visual surveys of reef fish 

populations. Fish Res. 85: 130-141.

Fancy SG. 2000. Guidance for the design of sampling schemes for Inventory and Monitoring in 

National Parks. [Cited 2015 September 15]. Available from: 

http://science.nature.nps.gov/im/monitor/docs/nps_sg.doc.

Gittings SR, Boland GS, Deslarzes KPJ, Hagman DK, Holland BS. 1993. Long-term monitoring 

at the East and West Flower Garden Banks. New Orleans (LA): U.S. Department of the 

Interior, Minerals Management Service, Gulf of Mexico OCS Regional Office. OCS 

Study MMS 92-0006. 206 p. 

Gitzen RA, Millspaugh J. 2012. Ecological monitoring. In: Gitzen RA, Millspaugh JJ, Cooper 

AB, Licht DS, editors. Design and analysis of long-term ecological monitoring studies. 

New York (NY): Cambridge University Press. p. 3-22.

Gratwicke B, Speight MR. 2005. The relationship between fish species richness, abundance and 

habitat complexity in a range of shallow tropical marine habitats. J Fish Biol. 66: 650-

667.

HaySmith L, Klasner FL, Stephens SH, Dicus GH. 2005. Pacific Island Network vital signs 

monitoring plan. Fort Collins (CO): National Park Service. Natural Resource Report 

NPS/PACN/NRR-2006/003. 178 p.

Helsel DR, Hirsch RM. 2002. Statistical methods in water resources. In: Techniques of waterresources investigations of the United States Geological Survey. Book 4, Hydrologic 

analysis and interpretation. Washington (DC): U.S. Geological Survey. 522 p.

Hill J, Wilkinson C. 2004. Methods for ecological monitoring on coral reefs. Townsville (MC): 

Australian Institute of Marine Science. 123 p.

Hurlburt SH. 1984. Pseudoreplication and the design of ecological field experiments. Ecol 

Monogr. 54: 187-211.

Johnson DH. 2012. Monitoring that matters. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht 

DS, editors. Design and analysis of long-term ecological monitoring studies. New York 

(NY): Cambridge University Press. p. 54-73.
293

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2013. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2009–2010. 

Volume 1: technical report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2014A214. 202 p.

Johnston MA, Nuttall MF, Eckert RJ, Embesi JA, Slowey NC, et al. 2015. Long-term monitoring 

at the East and West Flower Garden Banks National Marine Sanctuary, 2011–2012. Draft 

Volume 1: technical report. New Orleans (LA): U.S. Department of Interior, Bureau of 

Ocean Energy Management, Gulf of Mexico OCS Region. OCS Study BOEM 2015-027. 

205 p.

Jokiel PL, Rodgers KS, Brown EK, Kenyon JC, Aeby G, et al. 2015. Comparison of methods 

used to estimate coral cover in the Hawaiian Islands. PeerJ [Internet]. [August 27, 2015]; 

3:e954. Available from: 

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4435506/pdf/peerj-03-954.pdf DOI 

10.7717/peerj.954

Jones GP, McCormick MI, Srinivasan M, Eagle JV. 2004. Coral decline threatens fish 

biodiversity in marine reserves. P Natl Acad Sci USA. 101: 8251-8253.

Lang JC, Marks KW, Kramer PA, Kramer PW, Ginsburg RN. 2010. AGRRA Protocols Version 

5.4. Atlantic and Guld Rapid Reef Assessment, Miami, FL. 31 p.

Legendre P, Anderson MJ. 1999. Distance-based redundancy analysis: testing multispecies 

responses in multifactorial ecological experiments. Ecol Monogr. 69: 1-24.

Lewis JB. 2004. Has random sampling been neglected in coral reef faunal surveys? Coral Reefs. 

23: 192-194.

McCormick MI. 1994. Comparison of field methods for measuring surface topography and their 

associations with a tropical reef fish assemblage. Mar Ecol Prog Ser. 112: 87-96.

McDonald T. 2012. Spatial sampling designs for long-term ecological monitoring. In: Gitzen 

RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of long-term 

ecological monitoring studies. New York (NY): Cambridge University Press. p. 101-125.

Miller WJ, Rogers C, Atkinson A, Muller A, Davis A, et al. 2007. Coral Reef Monitoring 

Method. Miami (FL): National Park Service. Natural Resource Report 

NPS/SER/SFCN/NRTR-2007/003. 

[NPS] National Park Service. 2012. Guidance for designing an integrated monitoring program. 

Fort Collins (CO): NPS Inventory and Monitoring Division. National Resource Report 

NPS/NRSS/NRR—2012/545. 58 p.

Olsen AR, Kincaid TM, Payton Q. 2012. Spatially balanced survey designs for natural resources. 

In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design and analysis of 

long-term ecological monitoring studies. New York (NY): Cambridge University Press. 

p. 126-150.
294

Page C, Willis B. 2006. Distribution, host range and large-scale spoatial variability in black band 

disease prevalence on the Great Barrier Reef, Australia. Dis. Aquat. Org. 69: 41-51.

Penin L, Michonneau F, Carroll A, Adjeroud M. 2011. Effects of predators and grazers exclusion 

on early post-settlement coral mortality. Hydrobiologia. 663: 259-264.

Possingham HP, Fuller RA, Joseph LN. 2012. Choosing among long-term ecological programs 

and knowing when to stop. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. 

Design and analysis of long-term ecological monitoring studies. New York (NY): 

Cambridge University Press. p. 498-508.

Precht WF, Aronson RB, Deslarzes KJP, Robbart ML, Murdoch TJT, et al. 2006. Long-term 

monitoring at the East and West Flower Garden Banks, 2002–2003: final report. New 

Orleans (LA): U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region. OCS Study MMS 2006-035. 182 p.

Quinn GP, Keough MJ. 2002. Experimental design and data analysis for biologists. Cambridge

(UK): Cambridge University Press. 557 p.

Raymundo LJ, Couch CS, Harvell CD. 2008. Coral disease handbook: guidelines for assessment, 

monitoring, and management. Melbourne (Australia): Currie Communications. 124 p.

R Core Team. 2013. The R Project for Statistical Computing. [Cited 2015 September 15]. 

Available from: http://www.R-project.org/.

Sale PF. 1988. Perception, pattern, chance and the structure of reef fish communities. Environ 

Biol Fish. 21: 3-15.

Sale PF. 1998. Appropriate spatial scales for studies of reef-fish ecology. Austral Ecol. 23: 202-

208.

Samoileys MA, Carlos G. 2000. Determining methods of underwater visual census for 

estimating the abundance of coral reef fishes. Environ Bio Fishes. 57: 289-304.

Schreuder HT, Ernst R, Ramirez-Maldonado H. 2004. Statistical techniques for sampling and 

monitoring natural resources. Fort Collins (CO): Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS-GTR-126. 111 p.

Sweatman H. ed. 1997. Long-term monitoring of the Great Barrier Reef. Status Report No. 2. 

Townsville, Queensland (Australia): Australian Institute of Marine Science.

Syms C, GP Jones. 2000. Disturbance, habitat structure, and the dynamics of a coral-reef fish

community. Ecology. 81: 2714-2729. 

Trefry JH, Naito KL, Trocine RP, Metz S. 1995. Distribution and bioaccumulation of heavy 

metals from produced water discharges to the Gulf of Mexico. Wat. Sci. Tech. 32(2): 31-

36.
295

Urquhart NS. 2012. The role of monitoring design in detecting trend in long-term ecological 

monitoring studies. In: Gitzen RA, Millspaugh JJ, Cooper AB, Licht DS, editors. Design 

and analysis of long-term ecological monitoring studies. New York (NY): Cambridge 

University Press. p. 151-173.

[USGS] US Geological Survey. 2016. Coral Reef Ecosystem Studies (CREST). [Internet]. 

[February 18, 2016]. Available from: http://coastal.er.usgs.gov/crest/researchthemes/calcification.html. 

Watson RA, Quinn II TJ. 1997. Performance of transect and point count underwater visual 

census methods. Ecol Model. 104: 103-112.

Weil E. 2004. Coral reef diseases in the wider Caribbean. In: Rosenberg E and Loya Y, editors. 

Coral Health and Disease. Berlin (Germany): Springer-Verlag. p. 35-68 

White JA, Giggleman CM, Connor PJ. 2006. Recommended water quality for federally listed 

species in Texas. United States Fish and Wildlife Technical Report. 117 p.

Williams BK, Szaro RC, Shapiro CD. 2009. Adaptive management: the U.S. Department of 

Interior technical guide. Washington (DC): Adaptive Management Working Group. 84 p.

Williams GJ, Aeby GS, Cowie ROM, Davy SK. 2010. Predictive modeling of coral disease 

distribution within a reef system. Plos One. 5(2): e9264-9274.

Willis BL, Page CA, Dinsdale EA. 2004. 3. Coral disease on the Great Barrier Reef. In: 

Rosenberg E, Loya Y, editors. Coral health and disease. Berlin (Germany): SpringerVerlag. p. 69-104.

Willis TJ. 2001. Visual census methods underestimate density and diversity of cryptic reef 

fishes. J Fish Bio. 59: 1408-1411.

Wilson SK, SC Burgess, AJ Cheal, M Emslie, R Fisher, et al. 2008. Habitat utilization by coral 

reef fish: implications for specialists vs. generalists in a changing environment. J Anim 

Ecol. 77: 220-228.

Van Woesik R, Gilner J, Hooten AJ. 2009. Standard operating procedures for repeated measures 

of process and state variables of coral reef environments. Melbourne (Australia): Currie 

Communications. 36 p.

Yender RA and Michel J, editors. 2010. Oil spills in coral reefs: Planning and response 

considerations. 2nd ed. NOAA. Office of Response and Restoration. 84 p.
The Department of the Interior Mission

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

responsibility for most of our nationally owned public lands and natural

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

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

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

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

Department assesses our energy and mineral resources and works to ensure

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

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

major responsibility for American Indian reservation communities and for people

who live in island communities.

The Bureau of Ocean Energy Management Mission

The Bureau of Ocean Energy Management (BOEM) works to manage the

exploration and development of the nation's offshore resources in a way that

appropriately balances economic development, energy independence, and

environmental protection through oil and gas leases, renewable energy

development and environmental reviews and studies.