Final Report: Southeast Florida Co ral 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 Environm ental Protection, Ft. Lauderdale, FL 33301,

USA

Bernardo Vargas-Angel National Coral Reef Institute, NOVA South eastern University Oceanographic Center, Dania Beach, FL 33004, USA

Craig A. Downs

Haereticus Environmental La boratory, Amherst, VA 24521, USA

19 January 20062 Abstract We tested the feasibility of using cellu lar diagnostics to link land-based sources of pollution with cora l reef degradation. We collected tissue samples of mustard hill coral ( Porites astreiodes ) from four paired inshore a nd offshore stations off Broward Country, FL: 1) control sites (FTL1 a nd 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 (Hillsbo rough Inlet: HI2 and HI3) . Coral cover was

<4% at all sites and cluster an alysis grouped all inshore site s (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 dia gnostics 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 Nati onal Marine Sanctuary were consistent with sewage exposure, while responses of offshore colonies were

consistent with xenobiotic deto xification. 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 afte r two months’ acclimation to lab conditions, suggesting a genetic component to coral responses. Expos ure to Port Everglad es water decreased accumulation of multidrug resistance protein comp ared to colonies in the artificial sea water control; the levels were similar to the response observed in the field. This project3 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, stressor4 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). Surv eys indicate that 58–70% of coral reefs worldwide are threatened by anthropogenic act ivities (Bryant and Burke 1998; Wilkinson 1999; Hoegh-Guldberg 1999; Goreau et al. 2000; Gardner et al. 2003). Co ral colonies that experience persistent environmental disturbance suffer mass mortality, reduced growth and reproductive rates, and increa sed prevalence and severity of disease (Richmond 1993; Hoegh-Guldberg 1999; Nystro m et al. 2000; Knowlton 2001; Porter and Tougas 2001; Patterson et al. 2002). Un fortunately, most co ral 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; Vasse ur and Cossu-Leguille 2003). In 2003, the Southeast Florida Coral Reef Initiative Te am (SEFCRI Team)

identified land-based sources of pollution as a potentially crit ical factor influencing the long-term fate of coral reefs of f 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-ba sed sources of pollution identified by the

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

environmental health assessment. It is ba sed on concepts and methodologies widely used in medical diagnostics and epidemiology, and combines biomarkers of exposure, physiological effect, and potential risk to identify likely environm ental stressors and forecast their biological effects (Downs 2005) . Cellular diagnosti cs was designed to5 assess the cellular-physiological condition of reef-building corals and identify mechanisms of coral pathol ogies (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 detoxifi cation pathways, in tra-cellular metal regulation, and genomic integrity. Cellular diagnostics wo rks because environmental stressors affect organisms by overwhelming defenses at lowe r levels of the biological hierarchy: molecular, cellular, and organismal-level homeo static processes. St ressors then reduce individual fitness, which cascades into alte ring demographic parameters, and diminishes the structure, function, and resilience of ecological commun ities. By evaluating coral responses at the molecular, cellular, organi smal, and community levels, scientists can provide resource managers with critical inform ation needed to identify and ameliorate stressors before an ecosystem-scale environm ental 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 laborator y experiment that exposed coral colonies to wa ter collected from a putative source of stress. This mechanistic approach permits rigorous applic ation of the scientif ic method, including the potential to falsify hypotheses derived from field sampling. By explicitly testing the chain of causality, we provi de the evidence that resource managers, citizens, and legislators need to evaluate when making critical decisions about the effects of sewage6 discharges, shipping channels, and other la nd-based sources of pollution on southeast

Florida coral reefs.

Materials and methods Study sites and species The high-latitude coral reefs of southeaste rn 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 we re drowned as sea leve l rose after the last glaciation (Moyer et al. 2003). Benthic cover is dominated by sponges, macroalgae and soft corals, and hermatypic or stony coral covera ge 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 a ttributed to the harsher climate associated with higher latitude, including reduced winter water temperat ures, 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 an alysis 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 th roughout the western Atlantic, Gulf of Mexico, and the7 Table 1. Paired sampling stations and nearby potential sources of la nd-based pollution off

the southeastern Florida coastlin e, 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 channel8 Figure 1. Chart of paired sampling locations off Broward County, Florid a. Sites labeled C2 and C3 correspond to sites FTL1 and FTL 3, 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). No te the recent tissue loss (whitish spots) on the opposite side of this colony. Ruler markings are in cm.10 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, he ight, distance and direction from the U-post

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

cone:

SA = Π R √ (R 2 /2 + h 2 ) where R = (colony length + width)/2 and h = co lony 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 pair ed 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 spec ies (Dustan et al. 1999). We also estimated percent cover of functiona l groups including gorgonian s, zooanthids, porifera, macroalgae, and members of the cyanobacteria genus Lyngbya

. To quantify environmental variation am ong sites, we deployed in submersible cases two HOBO H08 temperat ure loggers (Onset Corpor ation, Bourne, MA) that recorded water temperature hour ly (accuracy: ± 0.7° at 21° C) . We collected sediment by11 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 us ing using a VeeGee Model A366ATC refractometer, turbidity with an Orbeco -Hellige Model 966 turbidimeter (Orbeco Analytical Systems, Farmingdale, NY) a nd water temperature, dissolved oxygen, and conductivity using a Yellow Springs Instrume nts 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 rema in 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 preparatio n). Briefly, when we removed coral tissue and its underlying skeletal structure for cellular-diagnostic analyses, a standardized, circular lesion was created, wh ich was approximately 1-2 cm 2 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 incl uded a ruler or scal e bar to calibrate measurements. We re-measured and photographed each lesion in August, 2005 to observe lesion regeneration over time (F isher et al. in preparation).12 We used in situ measurements to calculate the area (A L ) and perimeter (P L ) of all lesions, which remained completely surrounde d by live tissue (Type I lesions; Meesters et al. 1997). We calculate d lesion area using the e quation for an ellipse, A L = π ab, where a and b are one-half of the length and width, re spectively, of a lesi on. We expressed regeneration rate as mm 2 d -1 for statistical analyses.

Cellular diagnostics Reagents

Chemicals for buffered solutions were obtaine d from Sigma Chemicals Co. (St. Louis, Missouri, USA). PVDF membrane was obtained from Millipore Corp. (Bedford, Massachusetts, USA). Antibodies against a ll cellular parameters and their calibrant standards were obtained as gi fts from Robert Richmond, Univer sity of Hawai‘i, which in turn were gifts from EnVirtue Biotechnol ogies, Inc. (Winchester, Virginia, USA). Antibodies were raised against an 8–12 resi due polypeptide conjugated to ova albumin. Antigens were designed based on extremely c onserved and unique domains found within the target protein. Rabbits were immunized with the antigen with a Ribi-adjuvant carrier.

All antibodies used in this st udy were immuno-purified with a Pierce SulfoLink Kit (cat.# 44895) using the original unconjug ated peptide as the affinity binding agent. Anti-rabbit conjugated horseradish peroxidase an tibodies were obtained from Jackson Immunoresearch (West Grove, PA, USA).13 Sampling coral colonies We collected samples (1.5 cm diameter) from fi ve 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 dena turing buffer consisting of 2% SDS, 50 mM Tris–HCl (pH 6.8), 15 mM dithiothreito l, 10 mM EDTA, 0.001 mM sorbitol, 7% polyvinylpolypyrrolidone (wt/vol ), 0.1% polyvinylpyrrolidon e (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 fl uoride, 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 centrifuge d 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 agai n was transferred to a new tube and subjected to a protein concentr ation 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 determ ined by visualization and optical density using a Canonscan scanner and analysis perf ormed 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.inf o.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 bl otting validated the legitimacy of an ELISA (enzyme linked immunosorbe nt 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-polyacrylamid e gel with various concentrations of bis/acrylamide. A Tri(2-carboxyethyl) phosphi ne (TCEP) concentration of 1 mM was added to gels loaded with samples to be assayed with anti body to the chloroplast sHsp and the invertebrate sHsp. Tr i(2-carboxyethyl)phosphine is a reductant that can be used in acrylamide gels without interfering with acryl amide polymerization. Proteins can often spontaneously form disu lfide bonds in the load ing buffer, in the stacker gel, and in the separating gel, cau sing homomeric or heterometic dimerization. Proteins (e.g., small heat-shock proteins) that dimerize under adverse environmental conditions readily aggregate under standard SD S-PAGE procedures. Gels were blotted onto PVDF membrane using a wet transfer sy stem. 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 solutio n 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 ELI SA assay must be optimized for proper concentration of protein loading, antibody ti ter, 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 devel oped using a luminol-based chemiluminenscent solution and documented using a Bio-Tek fl uorescent/luminescent microplate reader. Samples were assayed with Haereticus Environmental Laboratory’s antibodies against heat shock protein 60 (Hsp 60), cyto chrome 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 supe roxide dismutase (Cu/Zn SOD), cnidarian glutathione-S-transferase (GST), ferrochelatase (FC) and multi-drug resistance protein (MDR). The biological signi ficance of each cellular-diagnostic parameter (biomarker) is summarized in Table 2. Samples were assayed in triplicate with intr a-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.16 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 mol ecular chaperones universal to all eukaryotic cells. During stress, Hsp's protect cells from el evated 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 α B-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, α Bcrystallin 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 produ ct of lipid peroxidation that cross-links proteins and forms adduct s with DNA (de Zwart et al. 1999).17 Ubiquitin Ubiquitin is a 76-residue protein foun d 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 accumul ations 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 of f Fort Lauderdale, Florida and transported to Nova Southeastern Univ ersity Oceanographic Ce nter (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 pr ovided by submersible power heads. Tanks were illuminated by four 175 W, 10000 K meta l 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 frag ments 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 upr ight position to epoxy resin plugs using cyanoacrylat e gel and allowed to recover in the holding tank for approximately 1 month. During this recove ry period, coral tissu e grew and extended

down the side of each core.

Experimental manipulations After the two-month acclimation period, coral core s were transferred to 24 separate, 9.5 L closed-system aquaria. Coral cores (two per tank) and experime ntal treatments were randomly assigned to each tank. Treatments c onsisted of a control exposed to filtered19 natural seawater and reverse-osmosis/deionize d 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

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

conditions were similar to acclimation cond itions. Temperature, sa linity 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 Refractomet er and averaged 35.8 ±0.6 ppt. An Orion Research Model SA230 pH/m V/°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 n itrate remained below detection limits. Mean water alkalinity was 186 ± 3 ppm a nd mean phosphate concentration was 0.06 ± 0.03 ppm. In the treatment tanks with Po rt 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, amm onia, nitrite, nitrate, alkalinity, and phosphate were measured in two tanks (rando mly 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 18 9.0 ± 6.0 ppm and phosphate averaged 0.03 ± 0.03 ppm. In the treatment tanks, alkalin ity 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 be tween 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 si gnificant (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. A ll corals were removed from tanks, gently blotted with paper towel to remove excess muco us 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 wa s fixed in either buffered, zinc-formalin (ZFix) for histological analysis, or in glutaral dehyde for electron microscopy analyses. One frozen sample from each tank, selected at ra ndom, was stored at the UCF and shipped to

Haereticus Environmental Labo ratory for analysis. The ot her sample was frozen at

− 80°C at the National Coral Research Instit ute (NCRI) at NSUOC. Unfortunately, the freezer lost power for several days after Hu rricane Wilma so these samples are no longer

useable.

Statistical analyses We used multivariate of variance to test the general linear model21 {Y1 ijkl, Y2 ijkl, . . . Yz ijkl } = μ + α i + β j + γ k + I ij + . . . + ε ijkl where {Y1 ijkl, Y2 ijkl, . . . Yz ijkl } was the response vector ofCDS parameters from fieldcollected samples, μ was the grand mean of the response, α i was the deviation due to depth, β j was the deviation due to sewage outfall, γ k was the deviation due to shipping inlet, I ij , etc. were deviations due to interactio ns between the fixed effects of depth, sewage outfall, and shipping inlet, and the ε ijkl were the residual errors. We used nested analysis of covariance (ANCOVA) to examine coral responses in the laboratory experiment, us ing the general linear model

Y ijkl = μ + B 1 X 1 + B 2 X 1 + α i + β j + I ij + D k(j) + ε ijkl where Y ijkl was the accumulation of a particular CDS parameter in lab-reared corals, μ was the grand mean of the response, B 1 and B 2 were regressors of the covariates X 1 (aisle gradient) and X 2 (door gradient), α i was the deviation due to colony depth, β j was the deviation due to water source, I ij was the colony depth x wa ter source interaction, D k(j) was the random effect of coral colony nested within depth, and the ε ijkl were the residual errors. In this analysis, the main effect of colony depth was tested using a residual that

contained the nested coral col ony[depth] term. All other ef fects were tested using the residual error in the denominator. Separa te 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 cellu lar-diagnostic responses as log10(x + 1). We used separate univariate tests to interpret significant MANOVA results and Tukey's Honestly Significant Difference method to sepa rate univariate means. This procedure limited the probability of committing a Type I error even when responses were22 correlated. We used hierarchical clustering to examine patterns in coral diversity, bottom cover and cellular-diagnostic responses. Al l 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 co rrelated (Pearson’s r = -0.91, P < 0.002). At each site, sponges comprised 2.4-7.1% cover, an d 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 (gorgoni ans and zooanthids) varied fr om 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 su mmarized as principal components, or ANOVA on each category separately.23 Fig. 3 Percentage bottom cover at the four pair ed inshore and offshore sites off Broward County, Florida.Fig. 4. Typical bottom cover at non-biomonitoring control sites off Broward County, Florida, which were dominated by bare substrateand the invasive red alga Lyngbya. The floating white chains were used to temporarily mark locations of colonies ofPoritesastreoides. Photo by Ken Banks.Sites clustered into two dis tinct groups based on bottom co mposition (Fig. 5). One group was comprised of three inshore sites (FTL 1, 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 ot her 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, a nd soft corals (Fig. 3).

Coral condition Coral surface area varied signifi cantly with depth (ANOVA on log 10 -transformed data:

F 1,32 = 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 cm 2 , mean ± SE). Significant differences in length (ANOVA on log 10 -transformed data: F 1,38 = 6.33, P < 0.02) and height (ANOVA on log 10 -transformed data: F 1,38 = 4.47, P < 0.04), but not width, contributed to this pattern. Percentages of old and recent tissue lo ss varied significan tly with the exact combination of sewage outfall and shipping ch annel at each pair of sites (significant interaction terms, Table 3) in Januar y, 2005. Old loss of live coral tissue was significantly lower at the biomon itoring control sites than at the paired sites with just a sewage outfall (Fig. 6). The pe rcentages 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 hi gher at paired sites ha ving just a sewage26 Fig. 5 Sites clustered by bottom composition, estimat ed as percentage c over of substrate, Lyngbya , other algae species beside Lyngbya , Porifera, Scleractinia, and Octocoralia. Shading identifies inshore sites and black boxes denote biomon itoring control sites established by the Broward County Depa rtment of Environmental Protection.27 outfall (HWO) than at the other three paired site s. Most new tissue loss (Fig. 2) appeared to be caused by parrotfish or other grazers.

We observed little recent tissue loss wh en we resampled colonies on August 15- 16, 2005. However, the percentage of total ti ssue loss, which includes mortality due to non-regenerated sampling lesions, varied sign ificantly with depth, the sewage outfall x shipping channel interaction, a nd the three-way interaction between them (Table 4). On average, total tissue loss was low at all in shore sites and at the offshore biomonitoring control site, highest at the offshore site n ear 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 c onservative because we only re-located one colony there (Appendix II). The missing coloni es must have been overturned, buried by sediment, moved off the site or destroyed. One colony at anothe r offshore/shipping channel site (HI3) was knocked loose from th e 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 (F 1,34 = 7.53, P < 0.01, R 2 = 0.18); larger lesions regenerate d faster than smaller lesions (regression equation: regeneration rate [mm 2 /d] = -0.96 + 6.65 x 10 -3 x initial lesion size [mm 2 ]). Lesion regeneration rate also varied significantly with depth and the sewage outfall x shipping channel interact ion (Table 5). Coral colonies at all inshore sites except PE had lesion regeneration rates that were si gnificantly greater than zero (Fig. 8). In28 Table 3 Results of analysis of variance on log 10 -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-offs hore sites off Broward County, Florida, in January, 2005. Statistica lly 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 6.368729 Fig. 6 Estimated percentages of old and recen t tissue loss on mustard hill corals ( Porites

astreoides ) at four locations off Broward County, Fl orida. 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 log 10 (x+1)-transformed variates.30 Table 4 Results of analysis of variance on log 10 -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-offs hore sites off Broward County, Florida, in August, 2005. Statistically significant terms ( α = 0.05) are in bold. Model R 2 = 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 1.195831 Fig. 7 Estimated percentages of total tissue loss on mustard hill corals ( Porites

astreoides ) at four paired sites off Broward C ounty, 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 re generation 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. Stat istically significant terms ( α = 0.05) are in bold. Model R 2 = 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 20.019033 Fig. 8 Regeneration rates (mm 2 /d) of mustard hill corals ( Porites astreoides ) at four paired sites off Broward Count y, Florida. Data are least squares means (± 1 SE) from analysis of covariance, with initial lesi on 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) . Re generation rates at the three other offshore sites were indistinguishable statistically from zero; mean regeneration rates at the offshore sewage outfall (HWO3) and shippi ng channel (PE3) were negative (Fig. 8).

Cellular-diagnostic respons es of biopsied corals Multivariate and univariate analyses The main effects of depth (MANOVA: F 10,20 = 5.48, P < 0.001) and sewage outfall (MANOVA: F 10,20 = 4.38, P < 0.003), and the depth x shipping channel (MANOVA:

F 10,20 = 2.47, P < 0.04) and sewage outfall x shipping channel (MANOVA: F 10,20 = 3.35, P < 0.02) interactions all explained significant variation in the vector of ten cellulardiagnostic responses. Eight parameters contri buted 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 signifi cantly with depth or proximity to potential land-based sources of pollution (Appendix III). Heat shock protein 60 and ubiquitin co mprised assays of protein metabolic condition. Averaged across all sites, offshor e corals had significantly more Hsp 60 than did inshore corals (ANOVA F 1,29 = 9.00, P < 0.006) and colonies from HI had less Hsp 60 than expected for a site near both a se wage outfall and shipping channel (significant

interaction term: ANOVA F 1,29 = 10.57, P < 0.003; Fig. 10A). Ubiquitin levels varied significantly with the depth x sh ipping channel in teraction (ANOVA F 1,29 = 4.51, P < 0.043). Corals at inshore sites without a nearby shipping channel had twice as much35 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 Count y, Florida, and two other regions. A) Heat shock protei n 60, B) ubiquitin. Abbreviati ons are as in the text.37 ubiquitin as colonies at inshore sites with a shipping channel near by (Fig. 10B). In contrast, corals at offshore site s 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 si tes (Fig. 11A). Levels of Grp 75 varied significantly with the sewage outfall x sh ipping channel interaction (ANOVA F 1,29 = 15.79, P = 0.0004). Averaged across both dept hs, 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 le vels varied significantly with the depth x sewage outfall interaction (ANOVA F 1,29 = 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. Offshor e colonies at sites without a sewage outfall were indistinguishab le 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 GS T were very simila r (Fig. 13A,B); both parameters were significantly higher at offshore than at inshore sites (ANOVA’s: F 1,29 > 6.17, P < 0.02). Mean CYP 6 levels were signi ficantly higher at s ites without shipping channels compared to sites near them (ANOVA: F 1,29 = 4.39, P = 0.045). On average, sites without shipping channels had CYP 6 le vels 26% higher than sites with shipping channels (Fig. 13C). Mean levels of MD R varied significantly with depth, sewage38 Figure 11 Mean (± 1 SE) accumulations of parame ters indicative of cellular metabolic

condition in Porites astreoides sampled off Broward Count y, Florida, and two other regions. A) Total small heat shock proteins, B) glucose-regulated pr otein. Abbreviations

are as in the text.39 Figure 12 Mean (± 1 SE) accumulations of para meters 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 parame ters indicative of xenobiotic response in Porites astreoides sampled off Broward County, Florid a, and two other regions. A) Cytochrome P450-2 class, B) glutathione-S-t ransferase, C) Cytochrome P450-6 class, and D) multidrug resistance protein. A bbreviations are as in the text.41 outfall, and the sewage outfall x ship ping channel interaction (ANOVA: all F 1,29 > 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 le vels 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 bo th 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-w ay 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 sewa ge outfall sites clustered with FKNMS corals; the remaining inshore and offshore sites formed natural groups (Fig. 14).42 Fig. 14 Sites clustered by mean cel lular-diagnostic responses in to four groups. Corals from the Bahamas had low levels of all parame ters. The two stations near the City of Hollywood’s sewage outfall (H WO2 and HWO3) group with co lonies sampled in Florida Keys National Marine Sanctuary (Molasses R eef, Looe Key, and Marquesas Atoll). The remaining three inshore (blue) and offshore s ites (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 cora l explants exposed to artificial sea water compared to PE water (least-squares means and SE’s of log 10 (MDR + 1): for artificial sea water, 0.034 ± 0.00226; for PE water, 0.026 ± 0.00225; F 1,16 = 5.55, P < 0.032). Mean accumulations of every other CDS parame ter did not differ significantly between treatments or depths (Appendix IV). Instea d, 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 an d offshore sites, particularly in mean accumulations of GST, Cu/Zn SOD, ubiqu itin, 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 stat istical 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 fitn ess 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 tissu e loss regressed positively on GRP 75 (mortalin) accumulation (Fig. 15), which is esse ntial for cell proliferation. Backward44 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 wa ter 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 R 2 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 < 0.000145 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 R 2 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 < 0.0001Fig. 15 Log-linear regression of coral % mortality as a function of the concentration of glucose regulated prot ein 75 (Grp 75), which also is kno wn as mortalin. Regression equation: Log10(% mortality + 1) = 0.48 + 0.042 Grp 75 (femtomoles/ng total soluble protein). F 1,32 = 8.10, P < 0.008, R 2 = 0.20.47 Table 7 Regression model relating log 10 (percentage mortality + 1) of Porites astreoides as a function of cellular-diagnostic parameters. Statistically significan t regressors are in

bold. Whole model F 4,29 = 4.78, P < 0.005, R 2 = 0.40.

Source Estimate Std. error t-ratio P Intercept 1.32 0.5521 2.39 0.0237 Log 10 (Grp + 1) 0.419 0.2108 1.99 0.0565 Log 10 (ubiquitin + 1) -0.845 0.2564 -3.30 0.0026 Log 10 (CYP 2 + 1) 0.636 0.3241 1.96 0.0593 Log 10 (CYP 6 + 1) 2.150 0.7130 3.01 0.005348 Table 8 Log-linear regression model of th e regeneration rate (mm/d) of Porites

astreoides as a function of cellular-diagnostic para meters. Statistically significant regressors are in bold. Whole model F 3,33 = 3.00, P < 0.046, R 2 = 0.24.

Source Estimate Std. error t-ratio P Intercept -0.0907 1.015 -0.09 0.9294 Log 10 (ubiquitin + 1) 1.03 0.471 2.20 0.0359 Log 10 (CYP 2 + 1) -1.32 0.596 -2.22 0.0342 Log 10 (CYP 6 + 1) -3.14 1.284 -2.44 0.020749 stepwise selection identified a log-log model with Grp 75, ub iquitin, 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 pred ictors of lesion regene ration 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 proj ect was to test the feasibility of using cellular diagnostics

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

elevated biomarker levels in tissu e samples of mustard hill coral ( Porites astreiodes ) collected at four paired inshore and offs hore stations off Brow ard County, FL, which included biomonitoring control si tes 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 regene ration 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 te mporal changes in the percentage of substrate occupied by live coral tissue as an indicator of reef health (G ardner et al. 2003). This method requires increasingly greater sampli ng effort to detect significant changes as coral cover approaches zero. At our four paired sites off Broward County, mean coral50 cover was <4%, making this method imprecise and impractical. Instead we used cluster analysis to determine that three inshore st ations (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 ins hore Hillsborough Inlet station (HI2) grouped together and had the highest percentages of bare substrate, corals and sp onges, 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 s outh. The dichotomy between inshore and offshore stations is a repeated theme in th e data, and illustrates the insight provided by multivariate analyses of coral reef community structure.

Colonies at offshore stat ions were significantly longe r and higher than those at inshore stations and also had higher pe rcentages of total tissue loss and lower regeneration rates. The exception was th e 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 indisti nguishable 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; Meeste rs et al. 1997; Mascarelli and BunkleyWilliams 1999; Fine et al. 2002) when we sampled colonies in January, 2005. Cellular-diagnostic para meters were elevated at all four paired stations and at several other sites that we sampled the following week in Biscayne National Park and51 Florida Keys National Marine Sanctuary, compared to two coral colonies sampled in the remote Exhumas, Bahamas. This pattern wa s 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 orga nized sites into four main groups: Bahamas, Hollywood outfall sites + FKNMS, the remainin g 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 ei ght Broward County sta tions indicate they were responding to an oxidativ e stress (Downs et al. 2006). The two stations near the City of Holly wood’s sewage outfall and sites in Florida Keys National Marine Sanctuary were char acterized by high levels of GRP 75, also known as mortalin. This enzyme is induced by glucose deprivati on and is involved in pathways to cellular senescen ce and transformation. In our study, elevated GRP 75

levels were associated with increased amounts of coral tissue loss. Co lonies at these sites also had high levels of ubiquitin, which ta gs damaged proteins for degradation. Combined, these results suggest that coral nutrition was altered by sewage discharge and resulted in higher than normal rates of pr otein turnover. Nutrient enrichment and sediment loads from sewage discharge are known to directly and indire ctly 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 xe nobiotics. Cytochrome P450-2 class is induced by electrophilic carcinogens, drugs, and other environmental pollutants, which it

oxidizes in a cellular suicide reaction. Gl utathione-S-transfera se, which also was elevated at these sites, conjugates the oxi dized xenobiotic to glutathione, which is pumped from the cell by MDR. Multidrug resistance protein is an ABC cassette protein

that lowers the intracellular concentrati on of toxic compounds below their level of toxicity (Bard 2000). Increases in MDR usua lly 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 resu lts 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 sugge sts 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 sta tions (FTL1, PE2 and HI2) and Biscayne National Park were characterized by cellular-d iagnostic responses that tended to be lower

than at the other Broward Count y sites. Corals at these three inshore stations also had moderate to high regeneration ra tes and little tissue loss, which is consistent with the defense trade-off hypothesis. While seem ingly contrary to conventional wisdom, nearshore patch reefs in the Florida Keys ha ve lost less live coral cover than more53 offshore reef communities (Porter et al. 2001). On such inshore reefs, coral colonies appear to be in better condi tion than conspecifics on offs hore reefs, in part because

inshore colonies accumulate defensive compounds more rapidly and return to homeostasis quickly once stressors reced e (e.g., Downs et al. 1999; Fauth 2004; Downs et al. in press). Much of the difference be tween inshore and offshore corals may have an ecotypic basis, as differences between co ral clones were strongl y expressed in the laboratory experiment even afte r two months of acclimation. The laboratory experiment tested whether Port Evergl ades 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 seawat er control. This suggests th at contaminants (if present) must be bioaccumulated, combined with othe r 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 ex periments using coral colonies from the more “pristine” and impacted sites would be useful for evaluating longer-term coral responses to environmental stress. Comb ined 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 cora l 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 Ga rden Banks National Marine Sanctuary and the Florida reef tract) . Greatly elevated levels of ce llular-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 st ations. Observations made during sampling suggest that these reef habitats were more luxuriant in the past twenty years than at present. Our prel iminary study begins establishing a line of evidence suggesting that land-based sources of pollution are affecting the status and

trends of these communities. Wh ile this line of inquiry is in its infancy, it suggests that the reefs supporting v ital elements of Southeast Fl orida’s tourist and recreation economies (Leeworthy and Bowker 1997) and should receive greater protection from potential damage due to la nd-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 (S pecial Activity License #04SRP-846 and 04SR-830), Biscayne National Park (Perm it #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 Manageme nt Administration grant from the United States Department of Comm erce through an agreement/contract with the Office of Coastal and Aquatic Managed Areas, of th e Florida Department of Environmental

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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 0.0064 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 0.0065 Appendix II Ecological aspects of Porites colonies.

Site Coral # depth (m) initial lesion regen rate total tissue loss size mm 2 (mm 2 /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 266 Appendix III Accumulations of cellular-diagnostic parameters in Porites astreoides sampled off Broward County, Flor ida; Biscayne National Park and Florida Keys National Marine Sanctuary, Florida; and Li ttle 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 0.1767 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 0.33 068 Appendix IV Accumulations of cellula r-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 16.369 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