Final Report: Southeast Florida Coral Biomarker Local Action Study

John E. Fauth 

Department of Biology, University of Central Florida, Orlando, FL 32816, USA 

E-mail: jfauth@mail.ucf.edu 

Phillip Dustan and Eric Pante 

Department of Biology, College of Charleston, Charleston, SC 29424, USA 

Kenneth Banks 

Broward County Department of Environmental Protection, Ft. Lauderdale, FL 33301, 

USA 

Bernardo Vargas-Angel 

National Coral Reef Institute, NOVA Southeastern University Oceanographic Center, 

Dania Beach, FL 33004, USA 

Craig A. Downs 

Haereticus Environmental Laboratory, Amherst, VA 24521, USA 

19 January 2006 
2

Abstract We tested the feasibility of using cellular diagnostics to link land-based sources 

of pollution with coral reef degradation. We collected tissue samples of mustard hill 

coral (Porites astreiodes) from four paired inshore and offshore stations off Broward 

Country, FL: 1) control sites (FTL1 and FTL 3) monitored by Broward County 

Department of Environmental Protection, 2) a sewage outfall (Hollywood Outfall: 

HWO2 and HWO3), 3) an inlet mouth (Port Everglades: PE2 and PE3), and 4) a sewage 

outfall adjacent to an inlet mouth (Hillsborough Inlet: HI2 and HI3). Coral cover was 

<4% at all sites and cluster analysis grouped all inshore sites (except HI) together based 

on having the least bare substrate and the most Lyngbya. Rates of sampling lesion 

regeneration were significantly greater than zero only at three inshore sites and the 

offshore biomonitoring control site. Cellular diagnostics revealed that corals at all sites 

were stressed compared to colonies from a more pristine site in the Bahamas. Offshore 

corals consistently had higher accumulations of stress markers than inshore colonies. 

Stress responses of corals from HWO and the Florida Keys National Marine Sanctuary 

were consistent with sewage exposure, while responses of offshore colonies were 

consistent with xenobiotic detoxification. Accumulations of biomarkers of xenobiotic 

response and decreased protein turnover were associated with decreased rates of coral 

regeneration. We compared these results to experimental corals exposed to water 

collected from Port Everglades. Differences between coral clones within a depth zone 

remained apparent even after two months’ acclimation to lab conditions, suggesting a 

genetic component to coral responses. Exposure to Port Everglades water decreased 

accumulation of multidrug resistance protein compared to colonies in the artificial sea 

water control; the levels were similar to the response observed in the field. This project 
3

is a first step in identifying the chain of causality between land-based pollutants, 

responses of individual reef-building corals, and health of coral reef communities in the 

South Florida watershed. 

Keywords: biomarkers, coral, Porites astreoides, reefs, regeneration, stressor 
4

Introduction 

Coral reefs are declining world-wide due to deteriorating environmental conditions 

(Dustan and Halas 1987; Bryant and Burke 1998; Dustan 1999; Hoegh-Guldberg 1999; 

Wilkinson 1999; Gardner et al. 2003). Surveys indicate that 58–70% of coral reefs 

worldwide are threatened by anthropogenic activities (Bryant and Burke 1998; Wilkinson 

1999; Hoegh-Guldberg 1999; Goreau et al. 2000; Gardner et al. 2003). Coral colonies 

that experience persistent environmental disturbance suffer mass mortality, reduced 

growth and reproductive rates, and increased prevalence and severity of disease 

(Richmond 1993; Hoegh-Guldberg 1999; Nystrom et al. 2000; Knowlton 2001; Porter 

and Tougas 2001; Patterson et al. 2002). Unfortunately, most coral monitoring and 

mapping programs cannot identify the causes of coral declines because these programs 

were designed to quantify baseline conditions and determine trends, not identify stressors 

(Risk 1999; Pennisi 2002; Vasseur and Cossu-Leguille 2003). 

 In 2003, the Southeast Florida Coral Reef Initiative Team (SEFCRI Team) 

identified land-based sources of pollution as a potentially critical factor influencing the 

long-term fate of coral reefs off southeastern Florida. Here, we report results of a project 

that integrates traditional coral monitoring techniques with a novel biotechnology – 

cellular diagnostics – to address how land-based sources of pollution identified by the 

SEFCRI Team affect coral reefs. Cellular diagnostics is a new approach to 

environmental health assessment. It is based on concepts and methodologies widely used 

in medical diagnostics and epidemiology, and combines biomarkers of exposure, 

physiological effect, and potential risk to identify likely environmental stressors and 

forecast their biological effects (Downs 2005). Cellular diagnostics was designed to 
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assess the cellular-physiological condition of reef-building corals and identify 

mechanisms of coral pathologies (Downs et al. 2000, 2002; Brown et al. 2002; Woodley 

et al. 2002; Fauth et al. 2003; Downs 2005). Parameters evaluated include cell 

membrane integrity and composition, anti-oxidant redox status, protein metabolic 

condition, xenobiotic detoxification pathways, intra-cellular metal regulation, and 

genomic integrity. Cellular diagnostics works because environmental stressors affect 

organisms by overwhelming defenses at lower levels of the biological hierarchy: 

molecular, cellular, and organismal-level homeostatic processes. Stressors then reduce 

individual fitness, which cascades into altering demographic parameters, and diminishes 

the structure, function, and resilience of ecological communities. By evaluating coral 

responses at the molecular, cellular, organismal, and community levels, scientists can 

provide resource managers with critical information needed to identify and ameliorate 

stressors before an ecosystem-scale environmental crisis occurs (Fauth et al. 2003; 

Downs et al. in press). 

 Most studies of environmental stress and response are limited to using a weightof-evidence or ecoepidemiological approach to infer causality (Adams 2003). To 

strengthen inferences of our field study, we conducted a laboratory experiment that 

exposed coral colonies to water collected from a putative source of stress. This 

mechanistic approach permits rigorous application of the scientific method, including the 

potential to falsify hypotheses derived from field sampling. By explicitly testing the 

chain of causality, we provide the evidence that resource managers, citizens, and 

legislators need to evaluate when making critical decisions about the effects of sewage 
6

discharges, shipping channels, and other land-based sources of pollution on southeast 

Florida coral reefs. 

Materials and methods 

Study sites and species

The high-latitude coral reefs of southeastern Florida off Broward County are highly 

variable communities comprised of typical Caribbean taxa. These reef communities 

occupy four parallel submarine ridges. The two middle ridges (approx. 10 and 20 m 

depth) are remnants of earlier reefs that were drowned as sea level rose after the last 

glaciation (Moyer et al. 2003). Benthic cover is dominated by sponges, macroalgae and 

soft corals, and hermatypic or stony coral coverage comprise just 1-6% of projected cover 

(Moyer et al. 2003, this study). Biogeographic patterns on southeastern Florida reefs do 

not mirror those of reefs further south in the Florida Keys or the Caribbean basin. 

Species richness of southeastern Florida reefs tends to increase from north to south while 

diversity and evenness do not. This has been attributed to the harsher climate associated 

with higher latitude, including reduced winter water temperatures, higher frequency of 

major storms, and disturbances associated with close proximity to the Miami-Ft. 

Lauderdale-West Palm Beach metropolitan region. 

 Our sampling scheme was designed for analysis of variance; we sampled coral 

reefs near a sewage outfall, an inlet mouth, a sewage outfall located within an inlet 

mouth, and a biomonitoring control site off Broward Country, Florida, USA (Table 1, 

Fig. 1). Our focal species was the mustard hill coral (Porites astreoides: Fig. 2), which is 

a common reef-building coral throughout the western Atlantic, Gulf of Mexico, and the 
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Table 1. Paired sampling stations and nearby potential sources of land-based pollution off 

the southeastern Florida coastline, Broward County, Florida, USA. 

Station/ 

depth 

Ridge 

position 

Latitude/Longitude Location Potential source of landbased pollution 

FTL1 

9-9.5 m 

1st 26.095968 

-80.052802 

Fort Lauderdale Biomonitoring control 

site 

FTL3 

17.5 m 

3rd 26.095183 

-80.046402 

Fort Lauderdale Biomonitoring control 

site 

HWO2a 

9-9.5 m 

2nd 26.011414 

 -80.059973 

 

Hollywood Sewage outfall 

HWO3 

15.5-

16.5 m 

3rd 26.011554 

 -80.052497 

Hollywood Sewage outfall 

PE2 

8 m 

2nd 26.054585 

 -80.057387 

Port Everglades Shipping channel 

PE3 

14-15.5 

m 

3rd 26.059902 

-80.050184 

Port Everglades Shipping channel 

HI2 

9-9.5 m 

2nd 26.142301 

-80.047515 

Hillsboro Inlet Sewage outfall + 

Shipping channel 

HI3 

15.5-

16.5 m 

3rd 26.151344 

 -80.039077 

 

Hillsboro Inlet Sewage outfall + 

Shipping channel 
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Figure 1. Chart of paired sampling locations off Broward County, Florida. Sites labeled 

C2 and C3 correspond to sites FTL1 and FTL3, respectively, in the text of this report.
9

Figure 2 A colony of mustard hill coral (Porites astreoides) with a sampling lesion filled 

with nontoxic modeling clay (gray oval). Note the recent tissue loss (whitish spots) on 

the opposite side of this colony. Ruler markings are in cm. 
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Caribbean Sea. Porites astreoides is a hardy species that already was verified as 

acceptable for cellular-diagnostic analyses. 

 We used AGRRA (Atlantic and Gulf Reef Rapid Assessment) protocols to assess 

coral condition (Kramer et al. 2005). We selected five colonies of Porites astreoides at 

each site and measured their length, width, height, distance and direction from the U-post 

that marked our sites. We calculated colony surface area (SA) using the equation for a 

cone: 

SA = Π R √(R2

/2 + h2) 

where R = (colony length + width)/2 and h = colony height. We also estimated percent 

old and recent tissue loss, recorded signs of bleaching, disease and overgrowth, and 

recorded colony depth (m). 

Ecosystem assessment

To assess coral reef community structure, we examined digital video imagery collected 

along a 15-25 m transect at each of the paired stations in January, 2005. Video was 

converted to single-frame images through frame grabbing and subsequent digital 

processing. Point counting (15 pts/frame) was used to estimate projected coverage 

(percent cover) of stony corals and other species (Dustan et al. 1999). We also estimated 

percent cover of functional groups including gorgonians, zooanthids, porifera, 

macroalgae, and members of the cyanobacteria genus Lyngbya. 

 To quantify environmental variation among sites, we deployed in submersible 

cases two HOBO H08 temperature loggers (Onset Corporation, Bourne, MA) that 

recorded water temperature hourly (accuracy: ± 0.7° at 21° C). We collected sediment by 
11

attaching a 5.1 cm diameter x 60 cm long trap to a steel U-post painted with epoxy. One 

sediment trap was deployed at each site in January and retrieved in August. We 

monitored salinity of surface water using using a VeeGee Model A366ATC 

refractometer, turbidity with an Orbeco-Hellige Model 966 turbidimeter (Orbeco 

Analytical Systems, Farmingdale, NY) and water temperature, dissolved oxygen, and 

conductivity using a Yellow Springs Instruments Model 556 Multiprobe System (Yellow 

Springs Instruments, Inc., Yellow Springs, OH) . We also collected ~250 mL bottom 

sediment sample and 4 L of surface water in U.S. Environmental Protection Agencycertified glass containers. These samples remain stored at -80° C at the University of 

Central Florida (UCF). 

 

Organismal assessment: lesion regeneration

We used a new method to assess the health of coral colonies – their ability to heal 

sampling lesions (Fisher et al. in preparation). Briefly, when we removed coral tissue 

and its underlying skeletal structure for cellular-diagnostic analyses, a standardized, 

circular lesion was created, which was approximately 1-2 cm2 in area, 3 mm deep, and 

surrounded by live tissue. We filled the hole with nontoxic modeling clay (Fig. 2) to 

prevent colonization by fouling and bioeroding organisms, then measured its major and 

minor chords using a ruler. We then photographed each lesion using a digital 

videocamera or digital camera. We included a ruler or scale bar to calibrate 

measurements. We re-measured and photographed each lesion in August, 2005 to 

observe lesion regeneration over time (Fisher et al. in preparation). 
12

We used in situ measurements to calculate the area (AL) and perimeter (PL) of all 

lesions, which remained completely surrounded by live tissue (Type I lesions; Meesters 

et al. 1997). We calculated lesion area using the equation for an ellipse, AL = πab, where a 

and b are one-half of the length and width, respectively, of a lesion. We expressed 

regeneration rate as mm2

 d-1 for statistical analyses. 

Cellular diagnostics

Reagents

Chemicals for buffered solutions were obtained from Sigma Chemicals Co. (St. Louis, 

Missouri, USA). PVDF membrane was obtained from Millipore Corp. (Bedford, 

Massachusetts, USA). Antibodies against all cellular parameters and their calibrant 

standards were obtained as gifts from Robert Richmond, University of Hawai‘i, which in 

turn were gifts from EnVirtue Biotechnologies, Inc. (Winchester, Virginia, USA). 

Antibodies were raised against an 8–12 residue polypeptide conjugated to ova albumin. 

Antigens were designed based on extremely conserved and unique domains found within 

the target protein. Rabbits were immunized with the antigen with a Ribi-adjuvant carrier. 

All antibodies used in this study were immuno-purified with a Pierce SulfoLink Kit (cat.# 

44895) using the original unconjugated peptide as the affinity binding agent. Anti-rabbit 

conjugated horseradish peroxidase antibodies were obtained from Jackson 

Immunoresearch (West Grove, PA, USA). 
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Sampling coral colonies 

We collected samples (1.5 cm diameter) from five colonies at each site in January, 2005. 

Samples were biopsied using a leather punch and placed in labeled, opaque film 

canisters. On deck, under shaded conditions, water was quickly removed from the film 

canister, blotted dry on paper towels, placed in labeled containers, and transferred to dry 

ice. Samples were stored at -80° C until analysis. 

Sample preparation, ELISA validation, and ELISA 

Frozen coral samples were ground to a powder using a liquid nitrogen-chilled ceramic 

pestle and mortar. Samples (~10 mg) of frozen tissue were placed in 1.8 ml 

microcentrifuge tubes with 1400 μl of a denaturing buffer consisting of 2% SDS, 50 mM 

Tris–HCl (pH 6.8), 15 mM dithiothreitol, 10 mM EDTA, 0.001 mM sorbitol, 7% 

polyvinylpolypyrrolidone (wt/vol), 0.1% polyvinylpyrrolidone (wt/vol), 0.01 mM alphatocopherol, 0.005 mM salicylic acid, 2 mM benzamidine, 0.04 mM Bestatin, 0.001 E-64, 

2 mM phenylmethylsulfonyl fluoride, 0.01 mM apoprotin, 5 :M 

a-amino-caproic acid, and 1 :g/100 ul pepstatin A. Samples were heated to 92° C for 3 

min, vortexed for 20 s, incubated at 92° C for another 3 min, and then incubated 

at 25° C for 5 min. Samples were centrifuged at 10,000 · g for 10 min. Supernatant free 

of the lipid/glycoprotein mucilage matrix was transferred to a new tube, centrifuged at 

10,000 · g for 5 min and free supernatant again was transferred to a new tube and 

subjected to a protein concentration assay (Ghosh et al. 1988). 
14

 To ensure equal sample loading, 20 μg of total soluble protein (TSP) of samples 

were loaded onto a 12.5% SDS-PAGE gel (16 cm), the gel was run until the 

bromophenol blue dye front was near the bottom, stained with a Coomassie blue 

solution (BB R-250) overnight, and then destained for 4 h with multiple washes of 

destaining solution. Equal loading was determined by visualization and optical density 

using a Canonscan scanner and analysis performed on a Macintosh computer using the 

public domain NIH Image program (developed at the US National Institutes of Health 

and available on the Internet at http://rsb.info.nih.gov/nih-image/). This protocol visually 

validated the protein concentration assay and ensured that sample artifact did not occur 

between the time of sample homogenization and sample analysis. 

 One-dimensional SDS-PAGE and western blotting validated the legitimacy of an 

ELISA (enzyme linked immunosorbent assay) on this species of coral using a specific 

antibody (Downs 2005). Five to 15 :g TSP of coral supernatant was loaded 

onto a 20-, 16-, or 8-cm SDS-polyacrylamide gel with various concentrations of 

bis/acrylamide. A Tri(2-carboxyethyl) phosphine (TCEP) concentration of 1 mM 

was added to gels loaded with samples to be assayed with antibody to the chloroplast 

sHsp and the invertebrate sHsp. Tri(2-carboxyethyl)phosphine is a reductant 

that can be used in acrylamide gels without interfering with acrylamide polymerization. 

Proteins can often spontaneously form disulfide bonds in the loading buffer, in the 

stacker gel, and in the separating gel, causing homomeric or heterometic dimerization. 

Proteins (e.g., small heat-shock proteins) that dimerize under adverse environmental 

conditions readily aggregate under standard SDS-PAGE procedures. Gels were blotted 

onto PVDF membrane using a wet transfer system. Membrane was blocked in 7% non-
15

fat dry milk, and incubated with the primary antibody for 1 h. Blots were washed in trisbuffered saline (TBS)-0.05% Tween (v/v) four times, and incubated in a horseradish 

peroxidase-conjugated secondary antibody solution for 1 h. Blots were washed four times 

in TBS and developed using a chemiluminescent reporter system. 

 Once validated, antibodies and samples were optimized for ELISA using an 8 · 6 · 

4 factorial design (Crowther 1999). Every ELISA assay must be optimized for proper 

concentration of protein loading, antibody titer, sample-to-standard calibration, and 

handling procedures as a measure of quality control and quality assurance (Downs 2005). 

A Bio-Tek 404 plate washer was used in conducting the ELISAs using a 96-well microtiter plate format. Antibodies were developed using a luminol-based chemiluminenscent 

solution and documented using a Bio-Tek fluorescent/luminescent microplate reader. 

 Samples were assayed with Haereticus Environmental Laboratory’s antibodies 

against heat shock protein 60 (Hsp 60), cytochrome P450-2 class (Cyp 2), cytochrome 

P450-6 class (Cyp 6), Glucose-regulated protein 75 (Grp 75), total small heat shock 

proteins (sHsp), ubiquitin, copper-zinc superoxide dismutase (Cu/Zn SOD), cnidarian 

glutathione-S-transferase (GST), ferrochelatase (FC) and multi-drug resistance protein 

(MDR). The biological significance of each cellular-diagnostic parameter (biomarker) is 

summarized in Table 2. Samples were assayed in triplicate with intra-specific variation < 

6% for the whole plate. An eight-point calibrant curve using a calibrant relevant to each 

antibody was plated in triplicate for each plate. 
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Table 2 Cellular-diagnostic parameters and their biological significance 

Parameter Interpretation 

Copper-Zinc 

superoxide 

dismutase 

(Cu/Zn SOD) 

Superoxide dismutases are enzymes that catalyze superoxide radicals to molecular 

oxygen and hydrogen peroxide, and comprise a main antioxidant defense pathway 

(Wu et al. 1999). Increased SOD levels have been linked to increased longevity and 

tolerance to ischemic/reperfusion events and oxidative stress (Fridovich 1995). 

Heat shock protein 

60 (Hsp 60) 

Heat shock proteins are molecular chaperones universal to all eukaryotic cells. 

During stress, Hsp's protect cells from elevated temperature and are important in 

repairing cellular damage (Near et al. 1990; Welch 1993). Hsp 60 accumulates in 

response to stress, specifically increased protein synthesis and denaturation. 

Total small heat 

shock proteins 

(sHsp) 

Cnidarians have up to 5–6 major sHsp isoforms, including αB-crystallin, Hsp22, 

Hsp23, Hsp26, and Hsp28 (Downs et al. 1999). In general, sHsp’s are absent 

under optimal growing conditions and are only elicited by stress. For example, αBcrystallin is only found in the cytosol of animals, where it protects cytoskeletal 

elements during stress (Derham and Harding 1999). Thus, the presence and 

concentration of different small heat-shock proteins reflects the physiological status 

of several cellular metabolic and structural pathways. 

Glucose-regulated 

protein (Grp) 

Glucose regulated protein is a mitochondrial matrix protein related to the Hsp 70 

family and is induced under conditions of low glucose and other environmental 

stresses. It also is involved in various chaperoning functions and possibly in antigen 

recognition, cell proliferation and senescence (Pockley 2001). Concentrations of Grp 

often are used as indicators of nutritional stress. 

Glutathione-Stransferase (Gst) Glutathione-S-transferase detoxifies genotoxic and cytotoxic xenobiotic electrophiles 

by conjugating them to glutathione (Ketterer et al. 1988). It also can repair DNA by 

detoxifying DNA hydroperoxides (Tan et al. 1988) and is a main defense for 

detoxifying 4-hydroxynonenal, an extremely reactive product of lipid peroxidation 

that cross-links proteins and forms adducts with DNA (de Zwart et al. 1999). 
17

Ubiquitin Ubiquitin is a 76-residue protein found in most phyla and marks proteins for rapid 

degradation. Ubiquinated proteins are degraded by proteolytic enzymes known as 

proteosomes. Accumulations of ubiquitin indicate increased protein degradation and 

turnover. Thus, ubiquitin levels are an index of the structural integrity of the protein 

component of the cell superstructure. 

Ferrochelatase 

(FC) 

Ferrochelatase is an indicator of changes in metabolic state, usually in response to 

stress. Ferrochelatase inserts ferrous iron into protoporphyrin IX to form heme, 

which is essential for cellular metabolism and detoxification (Ferreira 1999). For 

example, cytochrome c requires a form of heme to become an active electron carrier 

and cytochrome P450 requires heme to function. In addition, breakdown of heme 

leads to formation of billirubin and biliverdin (Smith et al. 1994); the former is a very 

effective anti-oxidant, rivaling Vitamin E as a scavenger of lipid hydroperoxyls. 

Multidrug 

resistance protein 

(MDR) 

Multidrug resistance protein protects against toxicity of 4-hydroxynonenal, a major 

product of lipid peroxidation that can inhibit DNA, RNA, and protein synthesis, stop 

the cell cycle, disrupt mitochondrial functions, and produce pathological disorders 

(Renes et al. 2000). Elevated MDR accumulations indicate defenses were mobilized 

against cellular stress. 

Cytochrome 

P450-2 class 

(CYP 2) 

Cytochrome P450-2 class mainly detoxifies electrophilic carcinogens, drugs, and 

environmental pollutants. 

 Cytochrome 

P450-6 class 

(CYP 6) 

Overexpression of CYP 6 has been implicated in the evolution of pesticide resistance 

– including to DDT – in arthropods and other invertebrates (Walters et al. 1992). 
18

Laboratory stressor-challenge experiment

Coral collection and maintenance

Four colonies of Porites astreoides were obtained from reefs off Fort Lauderdale, Florida 

and transported to Nova Southeastern University Oceanographic Center (NSUOC). Two 

colonies were collected from a depth of 7 m and two colonies from 15 m. In the 

laboratory, colonies were placed into a 150 L holding tank linked to a ~520 L closed 

system equipped with a 3590 Lh−1 recirculation pump and an 80 L protein skimmer. 

Aquarium water consisted of a mixture (~1:1 ratio) of filtered natural seawater and 

reverse-osmosis/deionized water mixed with commercial sea salt. Additional water 

circulation within the holding tank was provided by submersible power heads. Tanks 

were illuminated by four 175 W, 10000 K metal halide bulbs on a 12 h photoperiod. 

Water temperature in the holding tank ranged from 23.8 - 28.3° C (25.8 ± 0.9; mean ± 

SD) and salinity between 35 - 36‰ (35.4 ± 0.4). Colony fragments acclimated in this 

tank for one month. Subsequently, twelve 13 mm diameter cores were removed from 

each colony using a diamond core drill. Cores were glued in an upright position to epoxy 

resin plugs using cyanoacrylate gel and allowed to recover in the holding tank for 

approximately 1 month. During this recovery period, coral tissue grew and extended 

down the side of each core. 

Experimental manipulations 

After the two-month acclimation period, coral cores were transferred to 24 separate, 9.5 L 

closed-system aquaria. Coral cores (two per tank) and experimental treatments were 

randomly assigned to each tank. Treatments consisted of a control exposed to filtered 
19

natural seawater and reverse-osmosis/deionized water mixed with commercial sea salt as 

described previously, and a stressor challenge, which consisted of water collected daily at 

high tide from Port Everglades, filtered to 50 microns, and stored briefly in glass aquaria 

before use. Both the control and stressor treatments were replicated twelve times. 

During the experiment, water circulation and filtration were provided by Aquaclear 

MiniTM power filters and 50% water changes were performed every 12 h. Light 

conditions were similar to acclimation conditions. Temperature, salinity and pH of all 

tanks was tested every 24 hrs. Temperature was measured with Cole-Parmer Instrument 

Co. NIST-traceable mercury thermometer (accuracy 0.1°C) and averaged 25.7 ± 1.0°C. 

Salinity was determined with Atago ATC-S/Mill-E Refractometer and averaged 35.8 

±0.6 ppt. An Orion Research Model SA230 pH/mV/°C meter (electrode calibrated with 

NBS Standard buffers of pH 2, 7 & 9) pH(NBS) was used to calculate pH from millivolt 

measurements, which averaged 7.97 ± 0.73. Coral cores were maintained under 

experimental conditions for 4 days (96 h). 

 Ammonia, nitrite, nitrate, alkalinity and phosphate were measured in the water 

used for the water changes with LaMotte Scientific Test Kits. In the control tanks, 

ammonia averaged 0.13 ± 0.25 ppm; nitrite and nitrate remained below detection limits. 

Mean water alkalinity was 186 ± 3 ppm and mean phosphate concentration was 0.06 ± 

0.03 ppm. In the treatment tanks with Port Everglades water, mean ammonia 

concentration was 0.16 ± 0.23 ppm; nitrite and nitrate remained below detection limits. 

Mean alkalinity was 192.0 ± 4.0 ppm and phosphate averaged 0.14 ± 0.08 ppm. 

 After each daily, 50% water change, ammonia, nitrite, nitrate, alkalinity, and 

phosphate were measured in two tanks (randomly selected control and treatment tank) 
20

with LaMotte Scientific Test Kits. In both the control and treatment tanks, ammonia 

averaged 0.09 ±0.14 ppm; nitrite and nitrate remained below detection limits. In the 

control tanks, alkalinity averaged 189.0 ± 6.0 ppm and phosphate averaged 0.03 ± 0.03 

ppm. In the treatment tanks, alkalinity averaged 194.0 ± 3.0 ppm and phosphate 

averaged 0.05 ± 0.0 ppm. One-way analysis of variance indicated no significant 

differences in temperature or salinity between acclimation, control, and treatment 

conditions (p > 0.05). In addition, differences in pH, alkalinity, ammonia, and phosphate 

between control and treatment tanks were not statistically significant (p > 0.05). 

 At the end of the experiment, we recorded the position of all tanks relative to the 

lab door and aisle. These data were used as covariates to account for potential twodimensional gradients in the laboratory. All corals were removed from tanks, gently 

blotted with paper towel to remove excess mucous and sacrificed. Each core was divided 

in two pieces; one half was fixed in liquid nitrogen and stored at −80°C for cellular 

diagnostic analysis, and the remaining half was fixed in either buffered, zinc-formalin (ZFix) for histological analysis, or in glutaraldehyde for electron microscopy analyses. One 

frozen sample from each tank, selected at random, was stored at the UCF and shipped to 

Haereticus Environmental Laboratory for analysis. The other sample was frozen at 

−80°C at the National Coral Research Institute (NCRI) at NSUOC. Unfortunately, the 

freezer lost power for several days after Hurricane Wilma so these samples are no longer 

useable. 

Statistical analyses

We used multivariate of variance to test the general linear model 
21

{Y1ijkl, Y2ijkl, . . . Yzijkl} = μ + αi + βj + γk + Iij + . . . + εijkl 

where {Y1ijkl, Y2ijkl, . . . Yzijkl} was the response vector of CDS parameters from fieldcollected samples, μ was the grand mean of the response, αi was the deviation due to 

depth, βj was the deviation due to sewage outfall, γk was the deviation due to shipping 

inlet, Iij, etc. were deviations due to interactions between the fixed effects of depth, 

sewage outfall, and shipping inlet, and the εijkl were the residual errors. 

We used nested analysis of covariance (ANCOVA) to examine coral responses in 

the laboratory experiment, using the general linear model 

Yijkl = μ + B1X1 + B2 X1 + αi + βj + Iij + Dk(j) + εijkl 

where Yijkl was the accumulation of a particular CDS parameter in lab-reared corals, μ 

was the grand mean of the response, B1 and B2 were regressors of the covariates X1 (aisle 

gradient) and X2 (door gradient), αi was the deviation due to colony depth, βj was the 

deviation due to water source, Iij was the colony depth x water source interaction, Dk(j)

was the random effect of coral colony nested within depth, and the εijkl were the residual 

errors. In this analysis, the main effect of colony depth was tested using a residual that 

contained the nested coral colony[depth] term. All other effects were tested using the 

residual error in the denominator. Separate nested ANCOVA’s were performed for each 

cellular-diagnostic parameter. 

We adhered to model assumptions of multivariate random, normally-distributed 

and independent residuals by transforming cellular-diagnostic responses as log10(x + 1). 

We used separate univariate tests to interpret significant MANOVA results and Tukey's 

Honestly Significant Difference method to separate univariate means. This procedure 

limited the probability of committing a Type I error even when responses were 
22

correlated. We used hierarchical clustering to examine patterns in coral diversity, bottom 

cover and cellular-diagnostic responses. All statistical analyses were performed using 

JMP V. 4.0.4 (SAS Institute, Inc., Cary, NC, USA), with α = 0.05 for all hypothesis tests. 

Results 

Percent cover of corals and associated reef organisms

Bottom substrate or Lyngbya was the dominant cover at all sites (Fig. 3) and these two 

cover types were significantly negatively correlated (Pearson’s r = -0.91, P < 0.002). At 

each site, sponges comprised 2.4-7.1% cover, and sponge cover was positively correlated 

with substrate cover (Pearson’s r = 0.78, P < 0.03) and negatively correlated with 

coverage by Lyngbya (Pearson’s r = -0.74, P < 0.04). All sites had <4% coral cover (Fig. 

4) and soft corals (gorgonians and zooanthids) varied from 1-11% cover (Appendix I). 

Differences in reef depth and the presence of sewage outfalls and shipping inlets did not 

explain significant variation in bottom composition, regardless of whether the analysis 

used MANOVA on original variates, on data summarized as principal components, or 

ANOVA on each category separately. 
23

Fig. 3 Percentage bottom cover at the four paired inshore and offshore sites off Broward 

County, Florida. 
Fig. 4. Typical bottom cover at non-biomonitoring control sites off Broward County, Florida, which were domin

and the invasive red alga Lyngbya. The floating white chains were used to temporarily mark locations of colonieastreoides. Photo by Ken Banks.
Sites clustered into two distinct groups based on bottom composition (Fig. 5). One group 

was comprised of three inshore sites (FTL1, PE2 and HWO2), which had the lowest 

percentages of bare substrate, the highest cover by Lyngbya, and the least cover by 

sponges, stony corals, and soft corals (Fig. 3). The other group comprised all four 

offshore sites plus the inshore HI2 site (Fig. 5). These five sites were characterized by 

the highest percentages of bare substrate, the lowest cover by Lyngbya, and the most 

cover by sponges, stony corals, and soft corals (Fig. 3). 

Coral condition 

Coral surface area varied significantly with depth (ANOVA on log10-transformed data: 

F1,32 = 4.60, P < 0.04) during the initial sampling period, January 26-27, 2005. On 

average, colonies at inshore sites had 43% less estimated surface area than colonies at 

offshore sites (368 ± 36.3 versus 642.3 ± 108.6 cm2, mean ± SE). Significant differences 

in length (ANOVA on log10-transformed data: F1,38 = 6.33, P < 0.02) and height 

(ANOVA on log10-transformed data: F1,38 = 4.47, P < 0.04), but not width, contributed to 

this pattern. 

 Percentages of old and recent tissue loss varied significantly with the exact 

combination of sewage outfall and shipping channel at each pair of sites (significant 

interaction terms, Table 3) in January, 2005. Old loss of live coral tissue was 

significantly lower at the biomonitoring control sites than at the paired sites with just a 

sewage outfall (Fig. 6). The percentages of old tissue loss at the other two sites (PE and 

HI) were indistinguishable statistically from these two extremes. New tissue loss had a 

similar pattern (Fig. 6); it was significantly higher at paired sites having just a sewage 
26

Fig. 5 Sites clustered by bottom composition, estimated as percentage cover of substrate, 

Lyngbya, other algae species beside Lyngbya, Porifera, Scleractinia, and Octocoralia. 

Shading identifies inshore sites and black boxes denote biomonitoring control sites 

established by the Broward County Department of Environmental Protection. 
27

outfall (HWO) than at the other three paired sites. Most new tissue loss (Fig. 2) appeared 

to be caused by parrotfish or other grazers. 

We observed little recent tissue loss when we resampled colonies on August 15-

16, 2005. However, the percentage of total tissue loss, which includes mortality due to 

non-regenerated sampling lesions, varied significantly with depth, the sewage outfall x 

shipping channel interaction, and the three-way interaction between them (Table 4). On 

average, total tissue loss was low at all inshore sites and at the offshore biomonitoring 

control site, highest at the offshore site near a sewage outfall, and also high at the 

offshore sites that had shipping channels (Fig. 7). The estimate of total tissue loss at the 

Port Everglades offshore site (PE3) is conservative because we only re-located one 

colony there (Appendix II). The missing colonies must have been overturned, buried by 

sediment, moved off the site or destroyed. One colony at another offshore/shipping 

channel site (HI3) was knocked loose from the bottom and moved 30 cm from its original 

position near a sponge. We identified this colony from the photograph of its sampling 

lesion. 

Lesion regeneration

Regeneration rates of biopsy lesions varied significantly with their initial area 

(F1,34 = 7.53, P < 0.01, R2

 = 0.18); larger lesions regenerated faster than smaller lesions 

(regression equation: regeneration rate [mm2

/d] = -0.96 + 6.65 x 10-3 x initial lesion size 

[mm2]). Lesion regeneration rate also varied significantly with depth and the sewage 

outfall x shipping channel interaction (Table 5). Coral colonies at all inshore sites except 

PE had lesion regeneration rates that were significantly greater than zero (Fig. 8). In 
28

Table 3 Results of analysis of variance on log10-transformed estimates of percentage old 

and recent tissue loss in mustard hill corals (Porites astreoides). Five colonies were 

sampled on each of four paired, inshore-offshore sites off Broward County, Florida, in 

January, 2005. Statistically significant terms (α = 0.05) are in bold. 

Old tissue loss 

Source df SS F P 

Depth (D) 1 0.4238 3.352 0.077 

Sewage outfall (O) 1 0.3887 3.074 0.089 

Shipping channel (C) 1 0.0120 0.095 0.760 

D x O 1 0.0252 0.200 0.658 

D x C 1 0.1473 1.165 0.289 

O x C 1 0.5467 4.324 0.046

D x O x C 1 0.0351 0.278 0.602 

Error 32 4.0462 

Total 39 5.6252 

Recent tissue loss 

Source df SS F P 

Depth (D) 1 0.2554 2.929 0.097 

Sewage outfall (O) 1 0.7080 8.121 0.008 

Shipping channel (C) 1 0.8048 9.232 0.005 

D x O 1 0.0832 0.955 0.336 

D x C 1 0.2859 3.279 0.080 

O x C 1 1.4372 16.487 0.0003 

D x O x C 1 0.0047 0.054 0.817 

Error 32 2.7895 

Total 39 6.3687 
29

Fig. 6 Estimated percentages of old and recent tissue loss on mustard hill corals (Porites 

astreoides) at four locations off Broward County, Florida. Data are untransformed means 

± 1 SE; N = 10 colonies at each location, sampled January 26-27, 2005. Levels of old or 

recent tissue loss that are not connected by the same letter differed significantly in 

ANOVA on log10(x+1)-transformed variates. 
30

Table 4 Results of analysis of variance on log10-transformed estimates of percentage total 

tissue loss on mustard hill corals (Porites astreoides). Up to five colonies were resampled on each of four paired, inshore-offshore sites off Broward County, Florida, in 

August, 2005. Statistically significant terms (α = 0.05) are in bold. Model R2

 = 0.59. 

Source df SS F P 

Depth (D) 1 0.3554 20.485 0.0001

Sewage outfall (O) 1 0.0001 0.003 0.954 

Shipping channel (C) 1 0.0263 1.520 0.228 

D x O 1 0.0166 0.960 0.336 

D x C 1 0.0039 0.225 0.639 

O x C 1 0.1802 10.386 0.003 

D x O x C 1 0.1313 7.565 0.010 

Error 28 0.4858 

Total 35 1.1958 
31

Fig. 7 Estimated percentages of total tissue loss on mustard hill corals (Porites 

astreoides) at four paired sites off Broward County, Florida. Data are untransformed 

means ± 1 SE; N = 1-5 colonies at each location, sampled August 14-15, 2005. Levels 

that are not connected by the same letter differed significantly in ANOVA on square-root 

transformed variates. 
32

Table 5 Results of analysis of covariance on regeneration rates of mustard hill corals 

(Porites astreoides). Up to five colonies were re-sampled at each of four paired, inshoreoffshore sites off Broward County, Florida, in August, 2005. Statistically significant 

terms (α = 0.05) are in bold. Model R2

 = 0.60. 

Source df SS F P 

Initial lesion size 1 3.4202 11.40 0.002

Depth (D) 1 4.4854 14.95 0.001 

Sewage outfall (O) 1 0.0041 0.014 0.908 

Shipping channel (C) 1 0.2244 0.813 0.375 

D x O 1 0.4213 1.404 0.246 

D x C 1 0.0021 0.007 0.934 

O x C 1 1.3334 4.444 0.044 

D x O x C 1 0.6497 2.165 0.153 

Error 27 8.1031 

Total 35 20.0190 
33

 

Fig. 8 Regeneration rates (mm2

/d) of mustard hill corals (Porites astreoides) at four 

paired sites off Broward County, Florida. Data are least squares means (± 1 SE) from 

analysis of covariance, with initial lesion size as the covariate. Asterisks signify 

regeneration rates that differed significantly from zero. N = 1-5 colonies at each location. 
34

contrast, among the offshore sites only colonies at the biomonitoring control site were 

significantly greater than zero (Fig 9) . Regeneration rates at the three other offshore 

sites were indistinguishable statistically from zero; mean regeneration rates at the 

offshore sewage outfall (HWO3) and shipping channel (PE3) were negative (Fig. 8). 

Cellular-diagnostic responses of biopsied corals

Multivariate and univariate analyses 

The main effects of depth (MANOVA: F10,20 = 5.48, P < 0.001) and sewage outfall 

(MANOVA: F10,20 = 4.38, P < 0.003), and the depth x shipping channel (MANOVA: 

F10,20 = 2.47, P < 0.04) and sewage outfall x shipping channel (MANOVA: F10,20 = 3.35, P 

< 0.02) interactions all explained significant variation in the vector of ten cellulardiagnostic responses. Eight parameters contributed most to the significant multivariate 

response: Hsp 60, Grp 75, ubiquitin, CYP 2, CYP 6, FC, MDR and GST. Accumulations 

of sHsp and Cu/Zn SOD did not vary significantly with depth or proximity to potential 

land-based sources of pollution (Appendix III). 

 Heat shock protein 60 and ubiquitin comprised assays of protein metabolic 

condition. Averaged across all sites, offshore corals had significantly more Hsp 60 than 

did inshore corals (ANOVA F1,29 = 9.00, P < 0.006) and colonies from HI had less Hsp 

60 than expected for a site near both a sewage outfall and shipping channel (significant 

interaction term: ANOVA F1,29 = 10.57, P < 0.003; Fig. 10A). Ubiquitin levels varied 

significantly with the depth x shipping channel interaction (ANOVA F1,29 = 4.51, P < 

0.043). Corals at inshore sites without a nearby shipping channel had twice as much 
35

Fig. 9. Sampling lesion on colony 16 from PE2: A) initial, and B) expanding, thereby 

increasing the total area of tissue loss. Sampling lesion of colony 33 from FTL3: A) 

initial, B) almost totally regenerated. 
36

Figure 10 Mean (± 1 SE) accumulations of parameters indicative of protein metabolic 

condition in Porites astreoides sampled off Broward County, Florida, and two other 

regions. A) Heat shock protein 60, B) ubiquitin. Abbreviations are as in the text. 
37

ubiquitin as colonies at inshore sites with a shipping channel nearby (Fig. 10B). In 

contrast, corals at offshore sites had intermediate ubiquitin levels. 

 Total sHsp’s and Grp 75 assayed of overall metabolic condition. Total sHsp 

levels did not vary among the Broward County sites (Fig. 11A). Levels of Grp 75 varied 

significantly with the sewage outfall x shipping channel interaction (ANOVA F1,29 = 

15.79, P = 0.0004). Averaged across both depths, corals at HWO had more than three 

times as much Grp 75 as colonies at the biomonitoring and HI sites (difference 

significant: Tukey’s HSD test; Fig. 11B). 

 Copper/zinc superoxide dismutase and FC comprised assays of oxidative stress 

and response. Levels of Cu/Zn SOD did not vary significantly among the Broward 

County sites (Fig. 12A). In contrast, FC levels varied significantly with the depth x 

sewage outfall interaction (ANOVA F1,29 = 5.86, P < 0.03). Corals at inshore sites with a 

sewage outfall nearby had significantly less FC than colonies at inshore sites without a 

sewage outfall and offshore sites with a sewage outfall nearby. Offshore colonies at sites 

without a sewage outfall were indistinguishable statistically from these two groups (Fig. 

12B). 

 Cytochrome P450-2 class, CYP 6, GST and MDR assayed coral responses to 

xenobiotics. Responses of CYP 2 and GST were very similar (Fig. 13A,B); both 

parameters were significantly higher at offshore than at inshore sites (ANOVA’s: F1,29 > 

6.17, P < 0.02). Mean CYP 6 levels were significantly higher at sites without shipping 

channels compared to sites near them (ANOVA: F1,29 = 4.39, P = 0.045). On average, 

sites without shipping channels had CYP 6 levels 26% higher than sites with shipping 

channels (Fig. 13C). Mean levels of MDR varied significantly with depth, sewage 
38

Figure 11 Mean (± 1 SE) accumulations of parameters indicative of cellular metabolic 

condition in Porites astreoides sampled off Broward County, Florida, and two other 

regions. A) Total small heat shock proteins, B) glucose-regulated protein. Abbreviations 

are as in the text. 
39

Figure 12 Mean (± 1 SE) accumulations of parameters indicative of oxidative stress 

response in Porites astreoides sampled off Broward County, Florida, and two other 

regions. A) Cu/Zn superoxide dismutase, B) ferrochelatase. Abbreviations are as in the 

text. 
40

Figure 13 Mean (± 1 SE) accumulations of parameters indicative of xenobiotic response 

in Porites astreoides sampled off Broward County, Florida, and two other regions. A) 

Cytochrome P450-2 class, B) glutathione-S-transferase, C) Cytochrome P450-6 class, 

and D) multidrug resistance protein. Abbreviations are as in the text. 
41

outfall, and the sewage outfall x shipping channel interaction (ANOVA: all F1,29 > 4.97, P 

< 0.034). On average, mean MDR levels were 26% lower on inshore sites compared to 

offshore sites, and 30% lower at sites near sewage outfalls compared to those without 

them (Fig. 13D). In addition, mean MDR levels were significantly higher at the FTL 

biomonitoring sites than at the two HWO sites (0.117 ± 0.00820 versus 0.061 ± 0.00531 

fmol/pg TSP, respectively). Mean MDR levels at the paired PE and HI sites were 

statistically indistinguishable from both of these groups (Tukey’s HSD test). 

Comparisons with other sites

Mean accumulations of every cellular-diagnostic parameter at the eight Broward County 

sites were indistinguishable statistically from those of P. astreoides sampled in Biscayne 

National Park and the Florida Keys (one-way ANOVA followed by Tukey’s HSD test). 

However, mean accumulations of all biomarkers at every U.S. site were significantly 

higher than those of P. astreoides from the Bahamas (Figs. 10-13). The only exception 

was Hsp 60, which was undetectable in Bahamian corals and did not differ significantly 

from zero in inshore HI colonies. 

 Cluster analysis confirmed that corals from the Bahamas had very different 

cellular-diagnostic responses than corals sampled in BNP, FKNMS, and off Broward 

County (Fig. 14). The two Hollywood sewage outfall sites clustered with FKNMS 

corals; the remaining inshore and offshore sites formed natural groups (Fig. 14). 
42

Fig. 14 Sites clustered by mean cellular-diagnostic responses into four groups. Corals 

from the Bahamas had low levels of all parameters. The two stations near the City of 

Hollywood’s sewage outfall (HWO2 and HWO3) group with colonies sampled in Florida 

Keys National Marine Sanctuary (Molasses Reef, Looe Key, and Marquesas Atoll). The 

remaining three inshore (blue) and offshore sites (green) cluster together. The former 

group also includes corals sampled at Biscayne National Park (BNP). 
43

Cellular-diagnostic responses of corals in the laboratory experiment

After adjusting for the two-dimensional gradient in the laboratory, mean MDR 

accumulations were significantly higher in coral explants exposed to artificial sea water 

compared to PE water (least-squares means and SE’s of log10(MDR + 1): for artificial sea 

water, 0.034 ± 0.00226; for PE water, 0.026 ± 0.00225; F1,16 = 5.55, P < 0.032). Mean 

accumulations of every other CDS parameter did not differ significantly between 

treatments or depths (Appendix IV). Instead, mean accumulations of all CDS parameters 

except sHsp differed significantly between coral colonies nested within depth (Table 6). 

There was substantial genetic variation (up to 90% of total phenotypic variation) between 

coral colonies collected within inshore and offshore sites, particularly in mean 

accumulations of GST, Cu/Zn SOD, ubiquitin, Hsp 60, MDR and CYP 6. Genetic 

variation between colonies nested within depths was moderate for GRP 75 and FC (56 

and 49%, respectively), and extremely low (1%) for sHsp. When the colony nested 

within depth term was removed from statistical models, all except sHsp showed 

significant differences in mean CDS levels between depths, which was consistent with 

the pattern observed in field samples. 

Correlated responses

Developing prognostic indicators of coral condition requires linking parameters of 

molecular and cellular function with the fitness of individuals and ecosystem structure 

and function (Depledge et al. 1993, Moore 2001, Fauth et al. 2003). In Porites astreoides

sampled off Broward County, percent tissue loss regressed positively on GRP 75 

(mortalin) accumulation (Fig. 15), which is essential for cell proliferation. Backward 
44

Table 6 Results of analysis of covariance on cellular-diagnostic parameters of mustard 

hill corals (Porites astreoides) from inshore and offshore biomonitoring control sites 

exposed to artificial sea water or water from Port Everglades, Florida. 

Source of variation GST sHsp Cu/Zn SOD Ubiquitin Grp 75 

 

Door gradient 3.76 0.08 5.43* 2.41 10.00 

Aisle gradient 0.48 2.00 1.45 3.12 2.52 

Water 0.02 0.89 0.63 0.86 0.02 

Colony depth 2.55 2.89 2.61 0.33 4.32 

Colony[Colony depth] 13.4**** 0.86 18.1  28.8** ** 4.44* 

Water*Colony depth 1.39 1.98 0.100 0.17 0.23 

Model R2

 0.903 0.380 0.902 0.878 0.817 

% variation genetic 87.4 1.04 87.2 87.1 56.2 

*P < 0.05; ****P < 0.0001 
45

Table 6 continued. 

Source of variation Hsp 60 MDR FC CYP 6 

Door gradient 4.33 10.6** 5.99* 0.9 

Aisle gradient 2.12 5.01* 2.79 1.27 

Water 1.06 5.63* 1.08 0.47 

Colony depth 4.03 2.01 1.58 0 

Colony[Colony depth] 18.6**** 34.4**** 5.81* 8.84** 

Water*Colony depth 0.02 0.29 0.45 1.66 

Model R2

 0.935 0.896 0.658 0.640 

% variation genetic 90.6 87.0 49.1 61.1 

*P < 0.05; **P < 0.01; ****P < 0.0001 
Fig. 15 Log-linear regression of coral % mortality as a function of the concentration of 

glucose regulated protein 75 (Grp 75), which also is known as mortalin. Regression 

equation: Log10(% mortality + 1) = 0.48 + 0.042 Grp 75 (femtomoles/ng total soluble 

protein). F1,32 = 8.10, P < 0.008, R2 = 0.20. 
47

Table 7 Regression model relating log10(percentage mortality + 1) of Porites astreoides

as a function of cellular-diagnostic parameters. Statistically significant regressors are in 

bold. Whole model F4,29 = 4.78, P < 0.005, R2 = 0.40. 

Source Estimate Std. error t-ratio P 

Intercept 1.32 0.5521 2.39 0.0237

Log10(Grp + 1) 0.419 0.2108 1.99 0.0565 

Log10(ubiquitin + 1) -0.845 0.2564 -3.30 0.0026

Log10(CYP 2 + 1) 0.636 0.3241 1.96 0.0593 

Log10(CYP 6 + 1) 2.150 0.7130 3.01 0.0053
48

Table 8 Log-linear regression model of the regeneration rate (mm/d) of Porites 

astreoides as a function of cellular-diagnostic parameters. Statistically significant 

regressors are in bold. Whole model F3,33 = 3.00, P < 0.046, R2 = 0.24. 

Source Estimate Std. error t-ratio P 

Intercept -0.0907 1.015 -0.09 0.9294 

Log10(ubiquitin + 1) 1.03 0.471 2.20 0.0359 

Log10(CYP 2 + 1) -1.32 0.596 -2.22 0.0342

Log10(CYP 6 + 1) -3.14 1.284 -2.44 0.0207
49

stepwise selection identified a log-log model with Grp 75, ubiquitin, Cyp 2 and CYP 6 as 

predictors of percent tissue loss (Table 7). Coral colonies with low ubiquitin levels and 

high levels of Grp 75, CYP 2, and CYP 6 lost the most tissue. Similarly, levels of 

ubiquitin, Cyp 2 and CYP 6 were significant predictors of lesion regeneration rate (Table 

8). Coral colonies with high ubiquitin levels and low levels of CYP 2 and CYP 6 had the 

highest regeneration rates. 

Discussion 

The objective of this pilot project was to test the feasibility of using cellular diagnostics 

to link land-based sources of pollution with coral reef degradation. Our research detected 

elevated biomarker levels in tissue samples of mustard hill coral (Porites astreiodes) 

collected at four paired inshore and offshore stations off Broward County, FL, which 

included biomonitoring control sites and sites near a sewage outfall, an inlet mouth, and a 

sewage outfall adjacent to an inlet mouth. We used standard ecological methods to 

assess coral health, including monitoring regeneration of lesions that we created while 

sampling colonies. The results show that coral colonies at different sites varied in tissue 

loss and regeneration rate, which in turn were associated with specific changes in 

cellular-diagnostic parameters. 

 Typical coral monitoring programs use temporal changes in the percentage of 

substrate occupied by live coral tissue as an indicator of reef health (Gardner et al. 2003). 

This method requires increasingly greater sampling effort to detect significant changes as 

coral cover approaches zero. At our four paired sites off Broward County, mean coral 
50

cover was <4%, making this method imprecise and impractical. Instead we used cluster 

analysis to determine that three inshore stations (FTL 1, PE2 and HWO2) had the lowest 

percentages of bare substrate and cover by corals and sponges. In contrast, the four 

offshore stations and the inshore Hillsborough Inlet station (HI2) grouped together and 

had the highest percentages of bare substrate, corals and sponges, and the least area with 

the cyanobateria Lyngbya. The area near the mouth of Hillsborough Inlet may present a 

challenging environment for P. astreoides; the species was absent from the first area that 

we dove and we had to explore further south. The dichotomy between inshore and 

offshore stations is a repeated theme in the data, and illustrates the insight provided by 

multivariate analyses of coral reef community structure. 

 Colonies at offshore stations were significantly longer and higher than those at 

inshore stations and also had higher percentages of total tissue loss and lower 

regeneration rates. The exception was the offshore FTL3 biomonitoring control site, 

where coral colonies had little tissue loss and high regeneration rates. Total tissue loss 

was greatest near the City of Hollywood’s sewage outfall and moderately high at the 

offshore Port Everglades and Hillsborough Inlet stations. These three stations also had 

lesion regeneration rates that were indistinguishable from zero, and in two cases (HWO3 

and PE3) tended to be negative; lesions grew larger instead of healing. The inability to 

regenerate small lesions indicates that conditions were poor for coral growth and 

reproduction (e.g., Lester and Bak 1985; Meesters et al. 1997; Mascarelli and BunkleyWilliams 1999; Fine et al. 2002) when we sampled colonies in January, 2005. 

 Cellular-diagnostic parameters were elevated at all four paired stations and at 

several other sites that we sampled the following week in Biscayne National Park and 
51

Florida Keys National Marine Sanctuary, compared to two coral colonies sampled in the 

remote Exhumas, Bahamas. This pattern was clear and consistent and illustrates how 

much easier it is to identify patterns when at least one site is distant from major sources 

of anthropogenic stress. Cluster analysis organized sites into four main groups: Bahamas, 

Hollywood outfall sites + FKNMS, the remaining offshore sites, and the three remaining 

inshore sites + Biscayne National Park. Porites astreoides from the Bahamas had very 

low accumulations of all cellular-diagnostic parameters, and total sHsp was below 

detection limits there. This enzyme localizes to the mitochondria in cnidarians and other 

invertebrates, where it protect aspects of oxidative phosphorylation (Morrow et al., 

2000). High levels of cnidarian sHsp at all eight Broward County stations indicate they 

were responding to an oxidative stress (Downs et al. 2006). 

 The two stations near the City of Hollywood’s sewage outfall and sites in Florida 

Keys National Marine Sanctuary were characterized by high levels of GRP 75, also 

known as mortalin. This enzyme is induced by glucose deprivation and is involved in 

pathways to cellular senescence and transformation. In our study, elevated GRP 75 

levels were associated with increased amounts of coral tissue loss. Colonies at these sites 

also had high levels of ubiquitin, which tags damaged proteins for degradation. 

Combined, these results suggest that coral nutrition was altered by sewage discharge and 

resulted in higher than normal rates of protein turnover. Nutrient enrichment and 

sediment loads from sewage discharge are known to directly and indirectly inhibit growth 

and recruitment of corals and decrease coral cover, including in the Florida Keys (e.g., 

Pastorak and Bilyard 1985, LaPointe et al. 2004). 
52

 Three offshore sites (FTL3, PE3 and HI3) were characterized by high levels of 

Hsp 60, CYP 2 and MDR. Elevated levels of these three parameters are consistent with 

oxidative damage caused by exposure to xenobiotics. Cytochrome P450-2 class is 

induced by electrophilic carcinogens, drugs, and other environmental pollutants, which it 

oxidizes in a cellular suicide reaction. Glutathione-S-transferase, which also was 

elevated at these sites, conjugates the oxidized xenobiotic to glutathione, which is 

pumped from the cell by MDR. Multidrug resistance protein is an ABC cassette protein 

that lowers the intracellular concentration of toxic compounds below their level of 

toxicity (Bard 2000). Increases in MDR usually occur only in response to an organic 

xenobiotic (Bard, 2000; Sauna et al. 2001). The antibody used in this study binds both the 

cnidarian and dinoflagellate isoforms, hence results are a composite of MDR expression 

in both. Together, these results are consistent with offshore corals at the FTL3 

biomonitoring site and off Port Everglades and Hillsborough Inlet reacting to exposure to 

anthropogenic contaminants. Regeneration of sampling lesions was negatively correlated 

with elevated levels of CYP 2, which suggests that mounting xenobiotic defenses had a 

metabolic cost: impaired ability to repair tissue damage. We plan to analyze frozen 

sediment and water samples to identify likely stressors at these sites. 

 Coral colonies at three inshore stations (FTL1, PE2 and HI2) and Biscayne 

National Park were characterized by cellular-diagnostic responses that tended to be lower 

than at the other Broward County sites. Corals at these three inshore stations also had 

moderate to high regeneration rates and little tissue loss, which is consistent with the 

defense trade-off hypothesis. While seemingly contrary to conventional wisdom, 

nearshore patch reefs in the Florida Keys have lost less live coral cover than more 
53

offshore reef communities (Porter et al. 2001). On such inshore reefs, coral colonies 

appear to be in better condition than conspecifics on offshore reefs, in part because 

inshore colonies accumulate defensive compounds more rapidly and return to 

homeostasis quickly once stressors recede (e.g., Downs et al. 1999; Fauth 2004; Downs 

et al. in press). Much of the difference between inshore and offshore corals may have an 

ecotypic basis, as differences between coral clones were strongly expressed in the 

laboratory experiment even after two months of acclimation. 

 The laboratory experiment tested whether Port Everglades water induced cellulardiagnostic responses similar to those of corals samples offshore. We found no evidence 

that water collected at high tide from Port Everglades induced coral stress responses 

compared to the artificial seawater control. This suggests that contaminants (if present) 

must be bioaccumulated, combined with other stressors, or act indirectly (e.g., by 

increasing the competitive effects of algae and invertebrates) to impair corals. 

Alternatively, contaminant release may be episodic, tidal, or seasonal; the single 

sampling event that we conducted cannot detect temporal variation in coral responses. 

Reciprocal transplants or common garden experiments using coral colonies from the 

more “pristine” and impacted sites would be useful for evaluating longer-term coral 

responses to environmental stress. Combined with water quality and contaminant 

analyses conducted at the same time and place, these experiments can strengthen the 

chain of causality between land-based sources of pollution and coral reef degradation. 

Porites astreoides is considered a hardy, r-selected or weedy coral species that is 

capable of persisting under conditions that kill less-tolerant species. Consequently, 

cellular-diagnostic parameters are generally induced less under environmental stress in P. 
54

astreoides compared to members of the Montastraea annularis species complex (Fauth 

and Downs, unpublished data from Flower Garden Banks National Marine Sanctuary and 

the Florida reef tract). Greatly elevated levels of cellular-diagnostic parameters and low 

coral cover at all sites we sampled off Broward County are cause for concern, especially 

when regeneration rates were indistinguishable from zero at 4 of 8 stations. Observations 

made during sampling suggest that these reef habitats were more luxuriant in the past 

twenty years than at present. Our preliminary study begins establishing a line of 

evidence suggesting that land-based sources of pollution are affecting the status and 

trends of these communities. While this line of inquiry is in its infancy, it suggests that 

the reefs supporting vital elements of Southeast Florida’s tourist and recreation 

economies (Leeworthy and Bowker 1997) and should receive greater protection from 

potential damage due to land-based sources of pollution. 
55

Acknowledgments We greatly appreciated sampling help provided by Les Paul and her 

postdoctoral students. We thank Erin Hodel and Abby Renegar for their dedicated 

assistance with the laboratory experiment. This study was permitted by the Florida Fish 

and Wildlife Conservation Commission (Special Activity License #04SRP-846 and 

04SR-830), Biscayne National Park (Permit #BISC-2004-SCI-0031), and National Ocean 

Service Permit FKNMS-2004-022. This project and the preparation of this report was 

funded in part by a Coastal Zone Management Administration grant from the United 

States Department of Commerce through an agreement/contract with the Office of 

Coastal and Aquatic Managed Areas, of the Florida Department of Environmental 

Protection. The total cost of the project was $52,746, of which $52,746 or 100% was 

provided by the United States Department of Commerce. 
56

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63

Appendix I Estimated percent cover of substrate, corals and other benthic taxa at four 

paired stations off Broward County, Florida. 

Species / group HWO2A HWO3 PE2 PE3 

 mean se mean se mean se mean se 

Substrate 33.34 3.27 81.11 1.14 38.65 1.31 69.82 1.48 

 

Porites asteroides 0.16 0.10 0.06 0.06 0.06 0.06 0.00 0.00 

Favia fragum 0.00 0.00 0.00 0.00 0.06 0.06 0.00 0.00 

Porites porites 0.00 0.00 0.06 0.06 0.00 0.00 0.00 0.00 

Siderastrea siderea 0.06 0.06 0.00 0.00 0.12 0.07 0.11 0.06 

Montastraea cavernosa 0.62 0.62 0.15 0.09 0.00 0.00 0.29 0.07 

Meandrina meandrites 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Acropora cervicornis 0.36 0.29 0.00 0.00 0.00 0.00 0.00 0.00 

Dichocoenia stokesii 0.22 0.22 0.00 0.00 0.00 0.00 0.00 0.00 

Stephanocoenia michelinii 0.00 0.00 0.06 0.06 0.00 0.00 0.00 0.00 

Diploria labyrinthiformis 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Diploria strigosa 0.68 0.55 0.45 0.27 0.00 0.00 0.00 0.00 

Siderastrea radians 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Scleractinia 0.00 0.00 0.06 0.06 0.00 0.00 0.04 0.04 

Colpophyllia natans 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Solenastrea bournoni 0.00 0.00 0.00 0.00 0.12 0.12 0.00 0.00 

 

Porifera 2.93 0.87 6.73 1.59 2.54 0.44 7.13 0.84 

zoanthidae (Palythoa) 13.42 2.76 0.00 0.00 0.36 0.29 2.26 1.76 

Octocoral 1.87 0.63 4.27 0.91 0.72 0.36 0.83 0.38 

Dictyota 0.12 0.07 0.00 0.00 0.00 0.00 0.00 0.00 

Halimeda 0.00 0.00 0.06 0.06 0.00 0.00 2.30 1.35 

Lobophora 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 

Lyngbya 36.84 5.80 6.40 0.96 56.00 2.38 14.67 0.55 

Padina 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.00 

Macroalgae 0.67 0.32 0.13 0.07 1.30 0.65 0.36 0.25 

Algal mat community 7.97 3.79 0.30 0.11 0.06 0.06 2.16 0.55 

Crustose coralline algae 0.17 0.10 0.00 0.00 0.00 0.00 0.00 0.00 

 

TOTAL 100.00 0.00 99.88 0.00 100.00 0.00 100.00 0.00 
64

Species / group HI2 HI3 FTL1 FTL3 

 mean se mean se mean se mean se 

Substrate 66.53 1.75 62.64 5.88 48.49 3.14 64.27 2.95 

Porites asteroides 0.37 0.22 0.31 0.19 0.24 0.16 0.13 0.08 

Favia fragum 0.06 0.06 0.07 0.07 0.08 0.08 0.00 0.00 

Porites porites 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.07 

Siderastrea siderea 0.27 0.01 0.00 0.00 0.00 0.00 0.27 0.19 

Montastraea cavernosa 1.13 0.38 0.31 0.19 0.86 0.57 0.26 0.26 

Meandrina meandrites 0.37 0.37 0.05 0.05 0.07 0.07 0.00 0.00 

Acropora cervicornis 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Dichocoenia stokesii 0.06 0.06 0.00 0.00 0.14 0.14 0.00 0.00 

Stephanocoenia michelinii 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Diploria labyrinthiformis 0.28 0.28 0.00 0.00 0.00 0.00 0.00 0.00 

Diploria strigosa 0.14 0.14 0.00 0.00 0.00 0.00 0.00 0.00 

Siderastrea radians 0.00 0.00 0.05 0.05 0.00 0.00 0.35 0.35 

Scleractinia 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.07 

Colpophyllia natans 0.90 0.45 0.11 0.11 0.00 0.00 0.00 0.00 

Solenastrea bournoni 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 

Porifera 5.34 0.79 4.97 1.35 3.01 0.85 2.38 0.64 

zoanthidae (Palythoa) 0.14 0.08 0.00 0.00 4.75 0.83 0.00 0.00 

Octocoral 8.58 0.78 6.88 2.03 1.34 0.56 11.25 2.41 

Dictyota 0.00 0.00 0.00 0.00 0.77 0.33 0.00 0.00 

Halimeda 0.27 0.27 0.00 0.00 0.15 0.09 0.00 0.00 

Lobophora 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Lyngbya 11.25 1.35 21.04 2.77 36.75 2.17 17.88 1.24 

Padina 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Macroalgae 2.42 0.25 1.46 0.45 1.20 0.38 1.31 0.91 

Algal mat community 1.56 0.25 2.11 0.32 1.35 1.35 1.77 0.88 

Crustose coralline algae 0.33 0.15 0.00 0.00 0.00 0.00 0.00 0.00 

 

TOTAL 100.00 0.00 100.00 0.00 100.00 0.00 100.00 0.00 
65

Appendix II Ecological aspects of Porites colonies. 

Site Coral # depth (m) initial lesion regen rate total tissue loss 

 size mm2 (mm2

/d) (%) 

HWO3 1 14.5 176.8 -0.6059701 67 

HWO3 2 14.5 176.8 -0.1208955 15 

HWO3 3 14.5 153.9 -1.2124378 20 

HWO3 4 15 300 -0.1497512 55 

HWO3 5 15 254.5 -0.219403 3 

HWO2 6 7 330 1.64179104 0 

HWO2 7 7 213.8 1.06368159 2 

HWO2 8 7 201.1 0.95263184 1 

HWO2 9 7 201.1 0.92139303 2 

HWO2 10 7 143.1 0.39547264 4 

PE3 11 14 283.5 0.07313433 30 

PE3 12 14.5 176.8 . . 

PE3 13 14.5 165.1 . . 

PE3 14 15.5 176.8 . . 

PE3 15 15.5 176.8 . . 

PE2 16 8 194.8 -0.4412935 15 

PE2 17 8 176.8 0.16766169 2 

PE2 18 8 213.8 0.50099502 2 

PE2 19 8 201.1 0.68402985 2 

PE2 20 8 254.5 1.26616915 4 

HI3 21 15.5 254.5 0.83537313 4 

HI3 22 16.5 188.7 0.00004975 5 

HI3 23 16.5 176.8 -2.1838308 36 

HI3 24 16 188.7 0.89094527 8 

HI3 25 16 213.8 0.87223881 3 

HI2 26 9.5 363.1 1.55636816 2 

HI2 27 9.5 227 1.08148756 2 

HI2 28 9.5 188.7 0.71900498 3 

HI2 29 9.5 176.8 0.85517413 1 

HI2 30 9 176.8 0.659801 1 

FTL3 31 17.5 213.8 0.45313433 3 

FTL3 32 17.5 240.5 0.43069652 2 

FTL3 33 17.5 201.1 0.95263184 1 

FTL3 34 17.5 201.1 0.28835821 5 

FTL3 35 17.5 176.8 0.40681592 2 

FTL2 36 9.5 254.5 0.98383085 3 

FTL2 37 9 254.5 1.26577841 1 

FTL2 38 9.5 213.8 0.74716418 2 

FTL2 39 9 176.8 0.76139303 2 

FTL2 40 9 165.1 0.30462687 2 
66

Appendix III Accumulations of cellular-diagnostic parameters in Porites astreoides

sampled off Broward County, Florida; Biscayne National Park and Florida Keys National 

Marine Sanctuary, Florida; and Little Exuma Island, Bahamas. 

Site Coral # Hsp 60 Ubiquitin sHsp GRP75 Cu/Zn SOD 

HWO3 1 8.29 920 2.58 15 2.69 

HWO3 2 7.07 459 1.56 6.14 2.07 

HWO3 3 2.57 218 0.61 7.39 1.22 

HWO3 4 3.64 199 0.88 20.7 1.34 

HWO3 5 8.69 975 1.67 8.14 2.25 

HWO2 6 4.68 1208 1.72 4.62 2.28 

HWO2 7 3.2 550 1.23 8.55 1.79 

HWO2 8 3.28 1228 0.91 5.3 1.58 

HWO2 9 4.63 1190 2.57 10.3 2.19 

HWO2 10 4.06 1688 2.45 3.55 2.5 

PE3 11 5.76 219 0.32 8.42 0.88 

PE3 12 3.95 833 1.63 0.63 1.95 

PE3 13 10.3 847 1.34 8.98 2.42 

PE3 14 4.16 442 1.2 1.42 2.01 

PE3 15 6.01 1391 3.76 5.73 3.23 

PE2 16 4.25 377 1.2 2.07 1.51 

PE2 17 5.79 466 0.4 12.3 1.35 

PE2 18 5.12 547 2.33 2.79 3.01 

PE2 19 . . . . . 

PE2 20 4.74 298 0.61 7.72 1.62 

HI3 21 3.75 585 1.89 2.92 2.84 

HI3 22 6.16 619 2.19 2.65 2.29 

HI3 23 1.89 302 0.94 1.11 1.7 

HI3 24 6.92 1077 4.47 4.23 3.1 

HI3 25 1.61 527 0.82 0.91 1.75 

HI2 26 0.95 185 0.89 0.89 1.52 

HI2 27 -2.2 68.5 0.34 0.47 0.65 

HI2 28 5.02 1418 2.1 4.89 2.36 

HI2 29 0.78 136 0.45 10.3 0.72 

HI2 30 1.6 222 1.51 0.45 1.85 

FTL3 31 3.66 556 1.94 2.27 2.09 

FTL3 32 3.81 599 1.51 1.45 1.85 

FTL3 33 8.46 1297 2.85 5.23 2.6 

FTL3 34 8.12 522 4.75 3.45 2.17 

FTL3 35 3.04 471 1.12 1.06 1.52 

FTL2 36 2.76 244 1.27 1.72 1.05 

FTL2 37 4.32 526 2.46 2.58 2.31 

FTL2 38 1.19 1261 0.77 0.98 1.66 

FTL2 39 2.93 876 1.82 1.99 2.78 

FTL2 40 4.81 540 1.54 2.42 1.4 

BNP Al 2.75 321 0.96 3.51 1.51 

BNP Al 4.36 1205 1.44 2.27 3.27 

FKNMS Lo 3.01 2113 2.11 1.05 2.29 

FKNMS Lo 6.81 319 1.67 6.6 1.73 

FKNMS Mo 8.71 587 0.52 22.7 1.53 

FKNMS Mo 0.95 85.4 0.72 10.9 0.99 

FKNMS Ma 7.78 172 1.26 12.3 1.53 

Bahamas Ba -2.6 6.84 0.34 0.54 0.13 

Bahamas Ba -2.6 28.8 0.34 0.53 0.17 
67

Site Coral # FC CYP 2 CYP 6 cnidarian GST MDR 

HWO3 1 6.76 2.22 3.15 3.86 0.07 

HWO3 2 7.42 2.8 3.07 3.37 0.07 

HWO3 3 4.02 5.08 1.38 4.45 0.1 

HWO3 4 5.65 0.41 3.11 1.63 0.04 

HWO3 5 6.73 6.88 1.71 4.73 0.1 

HWO2 6 5.02 0.99 3.01 2.66 0.06 

HWO2 7 4.43 1.01 3.17 2.58 0.05 

HWO2 8 3.35 1.99 1.41 3.77 0.05 

HWO2 9 4.5 1.5 3.16 2.4 0.05 

HWO2 10 4.53 3.81 3.02 4.37 0.05 

PE3 11 4.88 0.71 2.97 2.36 0.04 

PE3 12 3.78 4.31 1.71 5.29 0.05 

PE3 13 6.52 3.58 1.7 5.9 0.08 

PE3 14 3.72 1.05 1.94 1.64 0.12 

PE3 15 10 5.76 2.2 4.25 0.2 

PE2 16 6.19 1.75 2.05 1.99 0.1 

PE2 17 7.84 3 1.56 3.28 0.06 

PE2 18 6.9 2.67 2.21 3.48 0.13 

PE2 19 . . . . . 

PE2 20 6.68 1.29 1.81 2.97 0.06 

HI3 21 5.11 4.41 2.2 5.51 0.15 

HI3 22 5.42 3.81 2.17 3.69 0.13 

HI3 23 3.56 3.18 3.02 2.99 0.07 

HI3 24 7.82 5.92 1.91 3.9 0.17 

HI3 25 4.37 6.03 2.51 4.7 0.13 

HI2 26 2.89 1.12 1.51 1.1 0.08 

HI2 27 0.87 0.29 0.96 0.46 0.03 

HI2 28 5.7 1.78 3.17 2.41 0.05 

HI2 29 2.29 0.42 1.32 1.08 0.02 

HI2 30 3.44 2.05 1.28 3.94 0.03 

FTL3 31 5.65 1.41 1.9 1.97 0.12 

FTL3 32 4.96 0.62 3.68 2.98 0.09 

FTL3 33 9.51 7.24 3.36 4.68 0.15 

FTL3 34 5.3 4.18 1.48 3.03 0.14 

FTL3 35 3.94 0.46 3.36 2.31 0.09 

FTL2 36 4.6 0.91 1.35 1.84 0.08 

FTL2 37 6.17 1.48 1.96 2.55 0.13 

FTL2 38 2.91 1 1.91 1.73 0.09 

FTL2 39 5.47 1.32 2.71 2.84 0.15 

FTL2 40 6.93 0.99 1.7 2.59 0.12 

BNP Al 2.99 0.57 1.88 2.63 0.07 

BNP Al 6.42 3 1.95 3.07 0.15 

FKNMS Lo 4.09 1.32 2.82 4.18 0.07 

FKNMS Lo 5.58 5.7 1.88 4.62 0.06 

FKNMS Mo 5.26 2.56 2.67 3.53 0.05 

FKNMS Mo 1.78 0.93 1.35 2.09 0.03 

FKNMS Ma 3.53 0.81 1.4 2.49 0.06 

Bahamas Ba 0.09 0.32 0 0.21 0 

Bahamas Ba 0.35 0.48 0.1 0.33 0 
68

Appendix IV Accumulations of cellular-diagnostic parameters in Porites astreoides

during the laboratory experiment. 

Sample Water Depth Colony Hsp 60 Ubiquitin sHsp GRP 75 

10J-51 PE Shallow J 1.88 1959 0.5 13.1 

11J-56 ASW Shallow J 2.24 1938 0.26 10.2 

12C-21 PE Deep C 4.32 2767 0.21 13.9 

13C-16 ASW Deep C 3.12 2925 0.45 7.98 

14E-27 ASW Shallow E 1.76 403 0.28 4.53 

14E-31 ASW Shallow E 2.52 645 0.23 6.68 

15A-5 PE Deep A 6.77 1431 0.24 14.5 

16J-52 ASW Shallow J 0.83 1884 0.26 4.66 

17E-26 PE Shallow E 2.05 539 0.19 6.72 

18C-18 PE Deep C 3.3 2096 0.28 13.8 

19E-34 ASW Shallow E 2.25 271 0.2 5.87 

20E-25 PE Shallow E 3.1 441 0.18 7.27 

20E-35 PE Shallow E 1.19 115 0.35 3.99 

21A-2 ASW Deep A 7.08 1144 0.35 17 

23J-59 PE Shallow J 2.17 3235 0.39 17.9 

24J-50 PE Shallow J 1.81 2956 0.25 15.7 

2C-24 ASW Deep C 3.09 2872 0.18 8.64 

3A-4 PE Deep A 5.5 1186 0.19 13 

4E-32 ASW Shallow E 2.75 879 0.21 9.61 

5C-20 ASW Deep C 3.32 2912 0.18 12.1 

6A-7 PE Deep A 7.7 1482 0.2 21.8 

7C-19 PE Deep C 2.25 1485 0.42 9.01 

8J-49 ASW Shallow J 2.01 2088 0.18 9.55 

9A-1 ASW Deep A 6.53 988 1.49 16.3 
69

Sample Cu/Zn SOD FC CYP P450 6 class cnid GST MDR 

10J-51 2.61 5.81 4.2 5.46 0.04 

11J-56 2.31 5.48 1.58 4.32 0.06 

12C-21 4.19 4.48 1.83 6.81 0.08 

13C-16 3.39 4.12 0.9 6.13 0.05 

14E-27 1.66 3.37 0.93 2.94 0.03 

14E-31 2.5 4.59 2.81 4.19 0.05 

15A-5 6.17 5.61 3.41 10.5 0.13 

16J-52 1.37 3.21 0.98 2.47 0.02 

17E-26 2.25 3.83 1.19 3.92 0.04 

18C-18 2.75 5.55 1.06 5.68 0.07 

19E-34 2.34 4.07 1.36 3.35 0.04 

20E-25 2.36 5.12 4.62 5.81 0.06 

20E-35 1.79 2.62 0.92 2.79 0.02 

21A-2 6.09 7.34 5.3 10.3 0.2 

23J-59 3.00 6.57 5.79 5.1 0.05 

24J-50 2.88 4.92 5.07 6.31 0.03 

2C-24 2.38 3.78 0.85 4.72 0.05 

3A-4 5.23 4.82 3.15 10.1 0.1 

4E-32 3.27 4.76 2.79 5.47 0.06 

5C-20 3.53 5.48 1.17 5.92 0.08 

6A-7 7.37 7.95 5.69 11.8 0.18 

7C-19 2.12 4.45 0.81 3.49 0.04 

8J-49 2.76 4.54 3.57 4.7 0.04 

9A-1 6.07 7.21 3.95 11.4 0.14