The Impacts of Harmful Algal Blooms on a Florida Reef Fish Community

by

Rex E. Baumberger, jr.

A Thesis Submitted to the Faculty of

The Charles E. Schmidt College of Science

in Partial Fulfillment of the Requirements for the Degree of

Master of Science

Florida Atlantic University

Boca Raton, Florida

December 2008

ACKNOWLEDGEMENTS

I thank B. Lapointe for his guidance and support for this work. I thank T. Sutton and

J. Baldwin for valuable insight and expertise in preparation of the work. The Florida Fish

and Wildlife Research Institute and the State of Florida for funding HAB research which

allowed this project to happen. Thanks to S. Hurley, B. Bedford and C. Miller for field assistance; and as my scientific dive buddies. I thank N. Beaman and E. Reese for software assistance and database development. I gratefully acknowledge E. Proffitt and J.

Voss for statistical advice and assistance. For constructive comments on my manuscript, I thank P. Winder and E. Guzman.

iii ABSTRACT

Author: Rex E. Baumberger, jr.

Title: The Impacts of Harmful Algal Blooms on a

Florida Reef Fish Community

Institution: Florida Atlantic University

Thesis Advisor: Dr. Brian E. Lapointe

Degree: Master of Science

Year: 2008

Coral reefs worldwide are threatened by many environmental disturbances including

harmful algal blooms (HABs) which have been increasing on Florida coasts over the past

decade. Research has mainly focused on HAB identification, percent cover and other

effects on the benthos but the relationship of HABs with upper trophic levels has received

less attention. To study this relationship, a two-year investigation on a 10-m deep reef off of Hallandale Beach, was conducted. Stationary fish census coupled with benthic transect videos were conducted quarterly between April 2005-July 2007. A significant correlation between Lyngbya sp. blooms and alterations in fish assemblages was observed. Lyngbya had a negative interaction with fish species and abundance; additionally, HAB sample periods were significantly different from low algal abundance periods. Blooms of Dictyota sp. had no measurable relationship with the fish assemblage indicating HABs may have variable impacts on fishes depending on family, species and chemistry of the algae. iv For my Great Aunt and Uncle McMillan, in Torchwood, Little Torch Key, who inspired my love of the sea. TABLE OF CONTENTS

List of Tables ...... vi

List of Figures ...... vii

List of Tables ...... v

List of Figures...... v

INTRODUCTION ...... 1

MATERIALS AND METHODS...... 8

I. Study Site...... 8

II. Fish Census ...... 10

III. Benthic Composition ...... 12

IV. Statistical Methods...... 13

RESULTS ...... 15

I. Fish Assemblage...... 15

II. Benthic Analysis ...... 19

III. Integrative statistics ...... 21

DISCUSSION...... 25

SUMMARY...... 34

LITERATURE CITED ...... 44

v LIST OF TABLES

Table 1. Sampling Dates, n counts, abundance and total number of species recorded for

each of the eight monitoring events...... 35

Table 2. Percent benthic cover of eight major benthic groups...... 35

Table 3. Rank and log abundance of all species recorded 2005-2007...... 36

vi LIST OF FIGURES

Figure 1. LADS bathymetry of study site BR 16, off Hallandale Beach, Florida...... 37

Figure 2. Permanent station establishment: a. transect end floats, b. transect center float, c. float and label, d. stainless pin, e. EVA float...... 37

Figure 3. Average number of fishes recorded per count (+/- SE) in each sample period, April 22, 2005-June 14, 2007...... 38

Figure 4. Trophic distribution of species of fish, overall 2005-2007...... 38

Figure 5. Trophic distribution by number of individuals, per sample period, April 2005-June 2007...... 39

Figure 6. Trophic distribution by number of individuals, summary 2005-2007...... 39

Figure 7. Percent relative abundance of six main trophic groups, April 2005- June 2007...... 40

Figure 8. Percent relative abundance of each life history stage of fishes, April 2005-June 2007...... 40

Figure 9. Shannon’s diversity index, H1, and equitability constant, Eh, April 2005-June 2007...... 41

Figure 10. Fish species richness vs. average % cover of Lyngbya sp, by sample period...... 41

Figure 11. Bray-Curtis similarity dendrogram: Square root transformed data, by sampling period...... 42

Figure 12. Two-dimensional MDS ordination plot, by sample date...... 42

Figure 13. Rainfall and Canal flows 2004-2005, Port Everglades area, Hollywood/Ft. Lauderdale, Florida, SFWMD DBHYDRO data. a - b: Lyngbya sp. bloom, c: Dictyota sp. bloom………………………43

vii

INTRODUCTION

Coral reef communities worldwide are increasingly threatened by perturbations of

their environment. The threats include bleaching and mortality from high temperatures

(Oliver 1985, Goreau and Hayes 1994, Hoegh-Guldberg 1999), eutrophication (Johannes

1975, Lapointe 1997, NRC 2000, Kuntz et al 2005), overfishing (Grigg 1994, Hughes

1994, Jackson et. al 2001), sedimentation from coastal development and natural

disturbances (Woodley et al. 1981, Bythell et al. 1993, Rogers 1990, Fabricius et al.

2007) and outbreaks of the crown-of-thorn starfish, Acanthaster planci (Barnes 1966,

Chesher 1969, Birkland 1982, Brodie et al. 2005).). Studies on the contribution of land- based sources of nutrients to global coral reef decline are also relevant because eutrophication from point and non-point sources is linked to increases in macroalgae, coral disease and loss of diversity on coral reefs (Windom 1992, Lapointe 1997,

GESAMP 2001, Pew Oceans Commission 2003, MEA 2005, Kaczmarsky et al. 2005).

Research focusing on anthropogenic sources of nutrients, such as sewage outfalls, terrestrial runoff and submarine groundwater discharges, indicates these land-based sources influence coral reef communities. Dissolved inorganic nitrogen (DIN) and soluble reactive phosphorous (SRP) are important nutrient constituents in algal communities, (Hatcher and Larkum 1983, Lapointe et al. 2004a) and are linked to enhancement of coral disease when found in excess (Voss and Richardson 2006). The 1 elimination of sewage outfalls and other point sources can lead to a substantial decrease

in macroalgal abundance and an increase in live coral cover (Maragos et al. 1985, Goreau

and Thacker 1994). Alternately, increased sewage impacts and land-based pollution

(Banner 1974, Smith et al. 1981, Edinger et al. 1998, Lapointe and Thacker 2002) have

contributed to increases in macroalgal biomass in coral reef environments and loss of live

coral which also suggest an inverse relationship between live coral cover and algae cover.

ECOHAB (The Ecology and Oceanography of Harmful Algal Blooms, 1995) revised

the definition of harmful algal blooms (HABs) to include macroalgal blooms when

macroalgal biomass and or percent cover increased to well above background levels. The

ECOHAB group also note that macroalgal HABs have impacts such as habitat

destruction and oxygen depletion. HABs contribute to declines in available benthic

habitat, losses in live coral cover, (Lewis 1997) and, in the case of phytoplankton HABs,

fish kills leading to health impacts on humans (Burkholder et al. 1992, Anderson et al.

2002). While the connection between eutrophication and HABs has been investigated

(Lapointe et al.1997), the coupling between macroalgal HABs and upper trophic levels has not received as much attention. Near the Great Barrier Reef, Fabricius et al. (2005) reported fishes and live coral decline over an increasing nutrient gradient while macroalgae and algal turfs increase. The eutrophication of reefs in South Florida

(Lapointe and Clark 1992, Lapointe et al 2005a and b, Lapointe and Bedford 2007, Barile

2004) and the greater Caribbean (Kaczmarsky et al. 2005, Lapointe and Thacker 2002,

Lapointe et al. 1997) may have cascading effects on coral reef food webs and fish assemblages, since numerous organisms depend on those reefs for habitat and sustenance.

2 Coral reef fishes are susceptible to alterations of their environment (Feary et al. 2007,

Syms and Jones 2000, McClanahan et al. 2002). Decreases in live coral cover have a negative effect on reef fish assemblages (Sano et al. 1984, Lirman 1999) worldwide.

Fishes on reefs with live and dead coral are more diverse and more abundant then on reefs containing large macroalgal communities on the Great Barrier Reef (Feary et al.

2007). Live coral areas maintain the highest diversity and abundance of fishes.

Unfortunately, the loss of live coral is increasing on Florida’s reefs. Current estimates indicate loss of up to 90% of live coral in the Florida Keys (Porter et al. 2002,

Miller et al. 2002). One recent study of the Hollywood, FL area showed coral cover averaged 4.25% in 2002 (Moyer et al. 2003). Southeast Florida Coral Reef Evaluation and Monitoring Project, SECREMP, also recently surveyed the Broward benthos

(SECREMP 2006) and found only two sampling sites in Broward have coral cover above the mean of 5.9%. Both sites are on shallow nearshore ridges, an Acropora cervicornis thicket off Ft. Lauderdale Beach, and another nearshore patch reef in close proximity to the A. cervicornis site. The remainder of the study reports low coral cover at eight other sites in southern Florida, from 0.90 to 1.18% (SECREMP 2006), and increases in macroalgal cover from 2003-2005. Thus fishes in South Florida may currently be affected by many of the same synergistic factors that have caused losses of coral elsewhere in the Caribbean (Gardner et al. 2003) and worldwide.

Many coral reef researchers have linked HABs to declines in herbivores due to overfishing and pathogens. Randall (1961, 1965) describes the importance of teleost grazers in determining algal and seagrass distribution when herbivores “overgraze” seagrasses around coral reefs. Losses of dominant top-down forces of herbivory can

3 have strong effects on marine plant growth. Reef decline and macroalgal dominance linked to overfishing has been observed in areas where fish traps capture herbivorous fishes (Hughes 1994, Jackson et al. 2001) and intensive fishing efforts target ever lower trophic groups (Pauly et al. 1998). Also, losses of other grazers, such as Diadema antillarum, which declined by over 90% in the Caribbean from a pathogen in 1983-84,

(Lessios 1988) contributed to declines in herbivory on many reefs (Hay 1984).

Overfishing of herbivores, the loss of D. antillarum due to a pathogen, coastal eutrophication and coral bleaching each played a role in the decline of reefs in Jamaica,

(Carpenter 1988, Goreau 1992, Hughes 1994, Lapointe 1997, Aronson and Precht 2000,

Jackson et al. 2001). Historical reef stress in Jamaica also correlated with development and sedimentation when the coral bleaching phenomenon began (Goreau 1992). South

Florida and the Florida Keys have reef habitats in similar decline (Porter and Meier 1992,

SECREMP 2006) which would allow researchers to further test many of these factors.

During a study in 2002 at Looe Key National Marine Sanctuary in the Florida Keys we found shifts in benthic community structure accompanied by alteration of the trophic distribution of fishes. Data were compared with fish assemblage data collected from 1978 to 1986, and indicated substantial trophic shifts have taken place in this marine protected area since its designation (Baumberger 2002). Importantly, herbivorous fishes increased in relative abundance in the assemblage at Looe Key, while predators and other commercially important fishes declined, despite being protected from fishing within the

MPA. Macroalgal grazers on the South Florida coral reef system are not overfished and families such as Scaridae (parrotfishes) and Acanthuridae (surgeonfishes) are common throughout the area. Fish traps and gill nets are not permitted in Florida coastal waters,

4 eliminating the potential for mortality of herbivores from the two methods used for their

capture elsewhere in the Caribbean. This may allow comparisons between degraded reefs

and fish assemblages which are not overfished.

Project Rationale

Past studies have investigated algal selectivity of herbivores by comparing the

nutritive dividends and chemical composition of macroalgae (Mattson 1980, Hay et al.

1987, Pennings et al. 1997, Thacker et al. 2001, Lapointe et al. 2004b). Results suggest

a relationship between the amount of enrichment algae receive and preferential selection by herbivores (Lapointe et al. 2004b, Boyer et al. 2004). Herbivores consumed more nutritious offerings first, and once those were depleted algae of lesser value were eaten.

Additionally, chemically-defended algae were avoided when undefended algae were available. However, parrotfishes and surgeonfishes have developed the ability to eat some chemically defended algae, such as Dictyota spp. (Randall 1967, Cronin et al. 1997,

Paul and Hay 1986) but prefer undefended species with higher nitrogen content.

Selective avoidance of chemically defended algae and cyanobacteria, such as

Lyngbya sp., may be a factor in trophic restructuring of the fishes. Herbivorous fishes tested against cyanobacterial compounds such as ypaoamide found in Lyngbya sp., avoid cyanobacterial tissue when offered other choices (Nagle and Paul, 1998). Cyanobacterial toxins also cause lower survival rates in fishes which consumed them. All macroalgal phyla contain members which produce secondary metabolites, many of which have been tested in feeding assays. Dictyota and other Phaeophytes were tested for anti-herbivory in laboratory and field assays (Cronin et al 1997). Dictyota acutiloba produces three

5 secondary metabolites, acutilol A acetate, acutilol A, and acutilol B which deters some temperate grazers but not tropical herbivores. Cyanobacterial toxins promote tumors in reptilian herbivores (Arthur et al. 2008) and may cause similar symptoms in fishes.

Cyanobacteria are selectively consumed by sacogalossians and gastropods in south

Florida (Capper and Paul 2008), but not by fishes in Guam (Nagle and Paul 1998).

The increases worldwide in cyanobacterial blooms (Ritson-Williams and Paul 2005,

Arthur et al 2006) have been linked to eutrophication and coastal development (Ahearn et al 2007, Kuffner and Paul 2004), and impact human and turtle populations in Australia

(Hamilton et al. 2007 Arthur et al. 2008). Blooms of Lyngbya cf. confervoides in Florida were first observed in 2002 by Paul et al. (2005) off the coast of Broward County .

Harbor Branch Oceanographic Institute, HBOI, conducted research off south Florida and identified many areas with chronic blooms of Lyngbya sp. from Dade County north to

Palm Beach County during 2004-2007. Indian River Lagoon researchers from HBOI also observed high biomass blooms smothering seagrasses and covering the benthos during

2006-2008 seagrass monitoring (D. Hanisak, pers. comm.).

Coastal eutrophication is an important factor affecting Florida’s reefs; however, any linkage to alterations of reef fish assemblages remains untested. Relationships between

HABs and fishes have been limited mainly to studies of fish kills from red tides

(Steidinger 1983), but the effects of macroalgal and cyanobacterial HABs on the epibenthos and fishes have received less attention (Kuffner et al. 2006). Fish demographics in relation to HABs and water column nutrient availability in South Florida have recently been studied by Harbor Branch Oceanographic Institute through a grant from the Florida Fish and Wildlife Research Institute (FWRI). The FWRI-HAB project

6 encompassed three years of research to determine the status and spatial variability in overall health of the reefs from St. Lucie County to Miami-Dade County, including water quality, benthic cover, macroalgal nutrients and fish assemblages. Within that project, one study site, BR 16, displayed trends in fish assemblage structure suggesting an inverse relationship between the distribution of fishes on the reefs and the presence of HABs.

These trends were cause for further investigation.

Hypotheses

Fish census and benthic assessments were used to test the following hypotheses: [1]

Harmful algal blooms have a negative impact on both the abundance of fishes and fish species richness [2] there are differences in response of fish trophic level and size class of fishes and [3] the taxon of algae forming the harmful algal bloom influences the abundance and richness of the fishes.

7

MATERIALS AND METHODS

I. Study Site

The study site, Broward BR 16 was established in 2004 during FWRI HAB funded

monitoring (Figure 1). Sites were randomly selected by FWRI from LIDAR surveys, and

ground-truthed by divers. The BR 16 site was located in southern Broward County, off

of Hallandale Beach. The average depth recorded was 8 m with a maximum relief of 1.5

m, on the first reef.

The southeastern Florida reef tract consists of three main reef lines paralleling the

coast, each of different geologic age and construction. The outer reef is made up of

Holocene deposited coral which extends from Biscayne Bay north to Palm Beach

County. The middle reef was described as previously existing shoreline from lower sea level stand approximately 3,700 BP (before present), calibrated from 14C core data

(Banks et al. 2007). The inner reef is made up of relict Acropora palmata which grew on shoreline deposits approximately 6,000 BP.

The third reef ranges from 28 m depth with a crest near 23 m, and runs from Biscayne

Bay northward to Riviera Beach, FL. The middle reef depth crests at 15 m, and runs from Biscayne Bay to Boca Raton Inlet in southern Palm Beach County. The first reef begins south of Biscayne Bay and terminates near Hillsboro Inlet in northern Broward

8 County. The first reef crest is at approximately 8 m depth with the seaward foot of the

slope occurring at about 12 m depth. The structural complexity and feasible working

depth on the first reef were important factors in the choice of the study site, working

depth allowing rigorous in-situ work, as well as ease in relocation of the site during

monitoring trips.

Moyer et al. (2003) surveyed the benthos of four areas on the Broward County reef

tract from shore to East of the outer, deep reef. Flora, Fauna and bare substratum were

recorded from scuba transects, and identified to the lowest possible taxonomic level in

situ. The findings indicated scleractinian corals averaged below 6% live cover overall,

soft corals made up the most important faunal group and large patches of macroalgae

were common. The southernmost of the four areas sampled on the inner reef off of

Hollywood, FL was closest to the study site BR 16. Benthic cover during 2003 consisted

of 51% uncolonized substratum, 16% macroalgae, 13% zooanthids, tunicates and hydroids, 12% alcyonaceans, 5% sponges, and 3% Scleractinian and Hydrocorals.

Study protocols on the site were the same used at all FWRI HAB sites, and involved quarterly censuses of fishes, benthic belt transect videos, algal and cyanobacterial tissue collection, and water sampling. The benthic transect videos of site

BR 16 filmed during the FWRI HAB project were collected in conjunction with fish assemblage data. Other available data such as algal tissue nutrient concentration in the

HABs and water nutrient analysis to determine water column availability was also included in FWRI reports (FWRI HAB 2007). Benthic video was used to estimate percent cover of HAB species, algae, corals, octocorals, sponges, sand and rubble which provided the general composition of the reef. Quarterly assessment of BR 16 began in

9 April, 2005. Sampling by quarter allowed researchers to separate more natural, seasonal fluctuations in the biota from other alterations of reef biota.

The station was marked from a surface vessel with sub-meter accurate Trimble

DGPS, and delineated in situ with permanent markers in the substrate. Pins were installed by hand, using 4lb sledge and a star drill to place 30cm 3/8-16 gauge stainless steel eyes in the bottom (Figure 2a). The eyes were secured with Ultrabond® two-part fast cure epoxy, (Figure 2d.), applied through a constant mixing tip with calking guns. The eyes were marked with 3/8 inch yellow polypropylene line and EVA white foam floats, which were oval and measured five inches long by 3.5 inches wide, (Aquatic Eco-Systems,

Apopka, FL; P/N NF4, Figure 2d). Floats were marked: ‘HBOI research: DO NOT

REMOVE’ with yellow tags affixed with cable ties, (Figure 2c.) Two stainless steel eyes were placed one meter apart at each end of the 25 m transect, (Figure 2a). Transect was placed in a north to south orientation, along the reef. A two-meter long line and float were also tied to a rock outcrop at the approximate center of the transect (Figure 2b).

This center float provided a visual reference to assist navigation from one transect end to the other while divers deployed transect lines. Transect lines served to delineate a one- meter strip for benthic recordings. Two types of transect lines were utilized; weighted line deployed from plastic reels (Figure 2a, b) or fiberglass-bladed 30.8 m Keson® tape measures.

II. Fish Census Visual census of fishes has become a preferred method for monitoring of fish assemblages (Bohnsak and Bannerot 1986, Schmitt et al. 1998, ) since the harmful

10 consequences of Rotenone and cyanide collection were acknowledged in the late 1970’s

(Antonius et. al 1978). Stationary and roving methods have each yielded acceptable

results in species richness data and abundance of fishes on coral reefs (Bohnsack and

Bannerot 1986, Samoilys and Carlos 2000). Ten minute stationary counts allow the

researcher to survey the water column for five minutes, followed by five minutes locating

cryptic species. The stationary method produces superior abundance data than other

methods, and eight replicates per sampling interval provide statistical resolution.

Eight stationary counts, each from the center of a 7.5 m radius circle were conducted quarterly at BR 16, except the initial sampling in April 2005 when three counts were done. The diver began counts oriented north from the center of the 7.5 m radius circle, scanning 90° until all fishes in the water column were recorded. The diver turned, continuing at 90° intervals until a 360°, five minute long scan was made. The remaining five minutes were spent locating benthic and cryptic fishes, scanning the bottom one quarter of the sampling area at a time by 90° interval. Fish species and abundance were recorded in situ on dive slates with Ticonderoga™ Sensematic® mechanical pencils.

Each species of fish, the life history stage and total abundance of each stage were enumerated on dive slates. Life history stage and/or size monitoring provides researchers rigorous data on age and size distribution of the fishes for in-depth trophic analysis. The life history stages recorded were Recruit, Juvenile, Sub-adult, and Adult, which were species specific and also provided an estimate of size. Recruits were post-settlement fishes, juveniles represented fishes in the first stage following establishment in an area, sub-adults were the transitional phase before adult, adults represented the final color phase, and/or upper size class of fishes, depending on species.

11 Fish raw data was entered into a Microsoft Access database and exported to

Microsoft Excel for analysis. A species list was made for each sampling and overall,

(Table 3), per-count averages were also calculated for species richness and abundance.

Shannon-Weiner index, H1 for richness, and Shannon’s Equitability Constant, Eh for evenness, were calculated for each sampling.

III. Benthic Composition

Habitat analysis at BR 16 was performed by FWRI HAB technicians using protocols

similar to the US EPA Florida Keys Coral Reef Monitoring Project (Dustan et al. 1998).

The method consisted of filming quarterly video transects that were subsequently

analyzed in the laboratory using a random point count method. A 25 m fiberglass tape

was placed on the reef top and two belt transects, one on either side of the tape measure,

were recorded. Video imagery was collected with a Sony MiniDV Handicam DCR-

TRV900, housed in Amphibico Navigator 900 housing with Amphibico wide angle lens

attachment and Underwater Kinetics/Niterider HID lights. A diver held the camera 0.5 m

off bottom and directed the camera straight down. This provided a flat image of the

bottom, which is used in post dive analysis.

Individual video frames were captured at 15 non-overlapping frames per transect,

providing a “video quadrat” which was analyzed on computer for % cover. Twenty

points were randomly arrayed on the screen for each quadrat where scorers identified

each point and entered data via mouse or keyboard stroke. The scorer utilized Coral Point

Count software, CPCe v3.3, developed by Kevin E. Kohler at the National Coral Reef

12 Institute/Nova Southeastern University Oceanographic Center (Kohler and Gill 2006).

Functional groups identified were stony corals, soft corals, sponges, macroalgae, turf

algae, hydrozoan, bryozoan, rock, and sand-rubble substratum. Data collected from BR

16 for FWRI HAB research was utilized for comparisons with fish census data. A grand total of 240 quadrats were analyzed by FWRI HAB personnel.

IV. Statistical Methods

To compare trends over time, the collected raw data for community representation

was plotted on graphs and charts. Descriptive statistics (mean, standard error, standard

deviation) were calculated for data to provide a summary. Discrimination of changes in

the fish assemblage between sampling intervals were quantified through univariate

statistics and General Linear Model, GLM. SAS® Proc Univariate procedure was used,

which included Shapiro-Wilk test for normality, and when data passed; GLM tests were

run to determine significance. If data were not normally distributed, data was transformed

using log10 and square root, followed by GLM. If normality was still not met, Kruskal-

Wallis test was utilized.

Synthesis of the data employed correlation analysis using both Pearson correlation

coefficient and Spearman ranked coefficient, Rs, as well as regression analysis. Finally, multi-factorial analyses (MF; Kruskal and Wish 1978) were conducted using Premier-E software, (Clarke and Warwick, 2001). A matrix was assembled in an Excel spreadsheet, based on species and abundance for each sampling. The abundance data were square-root transformed to normalize highs and lows in abundant species. Dendrograms of Bray-

13 Curtis similarity were generated using cluster analysis to compare groupings, complete

linkage (furthest neighbor) was used to generate distance between groups. Ordination of

the data in two dimensional plots to elucidate the degree of dissimilarity of observed

changes was then done. ANOSIM (Analysis of similarities) was run to test statistically

whether there was a significant difference between the eight sample periods, the multi-

factorial equivalent of an ANOVA This generated an R value which was scaled between

-1 and +1, with a value of zero representing the null hypothesis (no difference among the set of samples). The ANOSIM procedure also calculates p-values for the MDS run,

which were interpreted as significant at p < 0.05. The R statistic is based on the

difference of mean ranks between groups and within groups, and when combined with

p<0.05, a value of R >0.75 is significant. The results were interpreted as well separated

(R>0.75), overlapping but different (R>0.5), and barely separable (R<0.25), in

accordance with the PRIMER-E manual (Clarke and Gorley 2001). These values

corresponded to the two-dimensional MDS plots of the data by date and presence/absence

of Lyngbya. The closer the points in 2D space, the more similar the assemblages were

during those samplings. Synthesis of these previous steps involved review of

increases/decreases in diversity, change in % relative abundance of trophic groups, and

changes in fish life history distribution (demographics) related to the groupings observed

in MDS/Cluster analyses.

14

RESULTS

I. Fish Assemblage Eight quarterly assessments were made of the fish assemblage at BR 16 (Table 1).

The following variables were assessed for each sample period: fish total abundance, life

history stage, and total species richness. Shannon’s diversity index, evenness, % relative

abundance (trophic distribution of the fish assemblage), and relative % life history stage

were subsequently calculated.

Abundance

Total abundance of fishes recorded through eight quarterly samplings was 5,249 individuals, representing 99 species of fish (Table 3). Quarterly abundance ranged from

166 individuals in three counts in April 2005 to 972 individuals, n=8, in July 2006.

Average per-count abundance was calculated in the laboratory for each quarter (Figure

3). Average per-count abundance was lowest during February 2006 (38.50 +/- 16.84 SE)

and highest during July 2006 (121.50 +/- 45.09 SE).

Statistical analysis compared the abundance of fishes with sample period using the

GLM procedure. However, the residuals were not normally distributed, (Shapiro-Wilk

p<0.0001), so a Log10 transformation was done. The residuals then approached

normality, but due to the strong heteroscedasticity of field data, transformed data still

15 failed Sharpiro-Wilk test, (p=0.0379, W=0.957467). However, this represented an

improvement of two orders of magnitude, so the GLM process was conducted on the

transformed data (P=0.0006, F=4.49, df 7), suggesting rejection of the null hypothesis

that there were no differences in total abundance by sampling period.

The most abundant of the fishes recorded was Haemulon aurolineatum, Tomtate,

(1190, n=8) which were present in schools during each sampling. Appendix A contains the ranked abundance and log abundance of the fishes. Second in abundance was a planktivore, Thalassoma bifasciatum, Bluehead Wrasse, which was recorded 899 times during the eight sampling periods. Haemulon flavolineatum, French Grunt, was third in ranked abundance at 577 (n=8). The most abundant herbivore observed was Stegastes partitus, Bicolor Damselfish which was observed 219 times. Other abundant herbivores were Sparisoma aurofrenatum, Redband Parrotfish (199, n=8), Stegastes variabilis,

Cocoa Damselfish (133), and Acanthurus bahianus, Ocean Surgeonfish (129). Least

abundant fishes had representatives in each trophic group, eighteen species were recorded

only once during the two years of monitoring (Table 3).

Species

The number of species recorded during each sampling was tallied in the Access

database developed for the project (Table 1). The lowest number of species recorded was

34 (n=3) in April 2005. The remaining seven sample periods each received eight

stationary counts, of those, the least species (42) were recorded in February 2006. For the

period of July 2006 to June 2007, the number of species ranged from 53-55 species, with

a mode of 54.

16 ANOVA testing of species data indicated significant variation in the assemblage by sample period. The residuals were normally distributed (Shapiro-Wilk p=0.225) so the

GLM procedure was run on the raw data. GLM results (p<.0001, F 5.56, df 7) indicated the number of species per sampling was significantly different over the eight sample periods. Least squared means indicated April 2005 and February 2006 were the source of variation in sample periods.

Trophic Structure

The trophic structure of species making up the fish assemblage was determined overall for the two-year sampling and individually for each sample period. Fishes were classified into six major trophic groups based on their primary diet: Browsers, Piscivores,

Herbivores, Microinvertivores, Macroinvertivores, and Planktivores. The overall structure of the assemblage by number of species in each trophic group, (Figure 4) indicated two main groups, Macroinvertivores (35%) and Herbivores (22%) accounted for 57% of all fishes recorded.

Trophic distribution by relative abundance of each group during each sampling period (Figure 5) indicated quarterly fluctuations in trophic groups. One large school of

33 Bar Jacks, Caranx ruber, observed during the November 2005 sampling, increased piscivore relative abundance to its highest quarterly value. ANOVA was run by date with the dependent variable being abundance of each trophic group. The results indicated significant differences between samplings in the composition of the assemblage by trophic group, (P<0.001, F=14.55).

17 Distribution of individual fishes was similar to species trophic distribution, (Figure

6). Herbivores made up 22% of all fishes surveyed, just as in the species distribution.

The contribution of browsers and piscivores decreased significantly when the entire

assemblage was summed both decreased to ~ 1% of the total. These decreases were

reflected mainly in the increase of importance of planktivores in the total abundance of

fishes at BR 16. The number of macroinvertivore species also increased, and maintained

the upper abundance numbers as seen in the trophic distribution of species.

Life History Stage

The fish census method classified each individual as adult, sub-adult, juvenile or

recruit in situ, and grouped them according to life history stage in the database. The

relative proportions of each stage were calculated for each sample period (Figure 7) from

the raw abundance dataset. Adults were the numerical dominant in all sample periods

and their relative contribution to the total assemblage varied from 37.85% to 62.05%.

Highs in adult class fishes were accompanied by lows in sub-adult fishes. Juvenile and

recruit class fishes were not as proportionally related, but showed some indications of

seasonal fluctuations. Lows in juveniles and recruits were observed during the winter

months followed by increases in recruits during each spring. In the subsequent summer

quarters, juveniles increased, although not significantly.

Diversity Indices

Shannon’s diversity index, H1 was calculated to evaluate relative richness of the fish

assemblage using the species richness recorded per sampling and their relative abundance

18 (Figure 8). Shannon’s equitability constant, Eh was calculated from the diversity index, to provide a measure of evenness. Greatest Shannon diversity, (H1 = 2.967) occurred in spring 2005 and coincided with lowest abundance and species richness. This coincidence was also observed with evenness, (Eh = 0.84) during the same sampling. The contribution to abundance of a few dominant species was lowest during the spring 2005 sampling. This indicates the contribution of less abundant species to the whole assemblage became more important.

The lowest of each measure also occurred in same sampling period, spring 2006,

(H1= 2.55, Eh= 0.66). Abundance during this period was high, (745 individuals), however 63% of them were split between three abundant species, altering the evenness of fishes substantially. Due to the assemblage being less speciose in spring 2006 (48) compared to fall 2006 (54), the higher abundance of the three species was not balanced by greater number of species. Fall 2006 richness returned to H1= 2.96, and evenness

increased to Eh=0.74, with total abundance of 841 fishes, illustrating the importance of

the six additional species to both measures.

II. Benthic Analysis

The percent cover of each benthic functional group was determined from 240 video

quadrats, 30 per sampling. The dominant benthic cover was sediment-covered hard

bottom and sand, (mean 68.01% +/- 5.10 SE). Gorgonians were the highest in live cover,

ranging from 1% to 14%. Live coral cover was below 5% throughout monitoring at the

site. Macroalgal groups identified during the assessments were from four phyla, 19 Phaeophyta, Rhodophyta, Chlorophyta and Cyanophyta. Cyanobacteria (primarily

Lyngbya sp.) were the dominant cover organisms during two of the sampling periods,

April 2005 (36.79%) and February 2006 (10%). One other algal bloom was recorded in

May 2006 when Dictyota sp. reached 41.08% cover of the benthos. The remaining five

sample periods each had <5% algal cover of all four phyla (Table 2)

Statistical comparisons of benthic cover between seasons were made difficult by the

predominance of zeros in percent cover. The two dominant algal types, Dictyota Sp.

(Phaeophyta) and Lyngbya sp. (Cyanophyta) were compared with student’s t test, with

treatment: date and groups: % Lyngbya cover and % Dictyota. These data failed normality (p<0.001), so a Wilcoxon Signed Rank test was performed (p=0.935) which indicated no relationship between the two response variables, changes occurring with the treatment were not significantly different than by chance.

Despite the lack of normality in the raw data, comparison of individual algal type by date did indicate significant differences in algal cover. Lyngbya % cover was compared by date with ANOVA, however, normality testing failed, so Kruskal-Wallis test was applied to ranks: (H=184.675, df 7, P<0.001). Significant differences found in graphical representation of Lyngbya % cover were supported. Dunn’s Method pairwise multiple comparison procedures indicated spring 2005 and winter 2006 were not significantly different from one another, but significantly different from all other seasons with p<0.05.

Regression analysis was performed to further test the response variable Dictyota % cover on independent variable Lyngbya % cover and was not significant, (p=0.294). I therefore concluded there was no relationship between the different types of algae forming blooms in this study.

20 III. Integrative statistics

Correlation analysis utilized Pearson Correlation Coefficients and indicated

abundance and species richness of fishes were positively related with R2=0.623, p<.0001.

Spearman correlation was more significant, the model described 12% more of the relationship, (R2=0.742, p<.0001). As suggested in the results, Shannon’s diversity index

1 H and equitability Eh of the assemblage were also positively correlated, (p=0.008,

R2=0.669, F=15.17).

The lack of Chlorophytes and Rhodophytes in reported % cover at Broward 16

indicated Lyngbya and Dictyota were the dominant algal groups during this project, and

comparison with fishes was conducted only with these two dominant types. As

previously discussed, the large number of zeros in benthic cover data made relationships difficult to define statistically. To provide additional resolution of the data, Spearman rank correlation test was used to compare the factors abundance of fishes, average fish per count, species richness, and % cover of Lyngbya sp. Abundance (Rs= -0.375), fish species richness (Rs= -0.393) and average number of fishes/count (Rs= -0.352) each indicated inverse relationships with percent Lyngbya sp. cover. These tests support the inverse relationship observed in graphs of the raw data. The level of significance for these values was 75% due to the low n which is recognized as an issue with ranked correlations. The zero % cover estimates are assigned an averaged rank, and more than two paired rankings decrease the power of this test. 95% confidence interval for n=8

required a coefficient of Rs=0.643

21 Analysis of variance was conducted with the General Linear model. To overcome the

lack of normality, fish abundance data were square-root transformed and tested. To eliminate the problem of zeros, Lyngbya was made a categorical variable (present/absent) and used to compare with ranked abundance data. When season was used as a covariate with presence or absence of Lyngbya and compared with abundance of fishes, results were significant for Lyngbya (p=0.028, F=5.083, R2=0.212) but not season (p=0.236, F.

1.458). The same data were then run against the ranked fish abundance which passed

Shapiro-Wilk test (p=0.502) for normality and showed a significant improvement over non-ranked data. (p=0.008, F=7.683, R2=0.265).

Analysis of summary Lyngbya % cover as the dependent variable and summary species diversity as the independent variable was also conducted. Regression analysis was significant (p=0.002, F=24.901, R2=0.773). The model Lyngbya % cover showed a

negative correlation with species richness (Figure 9), with the equation of the line:

Lyngbya % cover = 76.45314 - (1.42117* species fish) which had a negative slope. The

inverse relationship between Lyngbya sp. and fishes was also significant when comparing

total abundance to % Lyngbya sp. cover, (P=0.005, F=18.014. R2=0.709). The equation:

Lyngbya % cover = 32.1139 –(0.0401*abundance of fish). However, the comparison

between average number of fishes per count with Lyngbya % cover was not significant

by linear regression, (p=0.0698), although graphic representation of the data implied an

inverse relationship.

To provide further inferences from these data, Multi-Dimensional Scaling, Cluster

and analysis of similarity, ANOSIM, were run with Primer® software. To normalize the

data for the model, spring 2005 data (n=3) were converted by multiplying abundances of

22 each fish by a factor of 2.67, to standardize with the other seven samplings which were n=8. Abundance data were square-root transformed prior to the analyses. Factors entered into the model for each sample period were [1] % Algal Cover, [2] presence or absence of Lyngbya, [3] season, [4] n counts, [5] Shannon’s H1, [6] Shannon’s equitability

constant, and [7] average fish per count. To test relationships between the samples by

date, a Resemblance S17 Bray-Curtis dendrogram was created (Figure 11).

The April 2005 sampling and February 2006 samplings grouped together, and were

45 % dissimilar from the remaining six. The November 2005 and April 2006 samplings

also grouped together, but were more similar (65% similar) to the remaining four sample

periods. The final four samplings grouped closely in similarity. Following the raw data

grouping, a square root transform was applied to the abundance data, and the Bray-Curtis

similarity was re-run. These data grouped similarly to the raw data, but were closer

relative to one another in the dendrogram. April 2005 and February 2006 were closely

grouped, and November 2005 and April 2006 grouped but not as closely as in the raw

data. The cluster of samples from July 2006 to June 2007 was still significant, indicating

high relatedness between sample periods in the last year of sampling.

A two-dimensional (2D) ordination plot was generated from the Bray-Curtis

resemblance matrix (Figure 12). The groupings observed in the dendrogram were

significant (stress =0.01) in 2D ordination by sample date, the four samplings from July

2006 to June 2007 were not significantly different from one another, but significantly

different from the other four samplings. November 2005 and April 2006 grouped

together, but less closely; while April 2005 and February 2006 were dissimilar from the

remaining six samplings. ANOSIM results (R=0.885, p=0.036) were significant,

23 suggesting rejection of the null hypothesis. This indicated the difference of mean ranks between groups was more significant than any differences within the sampling groups, and that the clusters observed were significantly different from each other.

24

DISCUSSION

The significant negative correlation found between cyanobacterial HABs of the genus

Lyngbya and the fish assemblages on the shallow reef supported graphical data trends .

The results of two years of monitoring supported the hypothesis that harmful algal

blooms alter fish assemblages. Both abundance and species richness of fishes declined

significantly in response to increased Lyngbya cover. Trophic level impacts were

primarily on the Macroinvertivores, three species of Haemulon, Grunts, declined substantially during Lyngbya blooms, but made up a large % of the assemblage otherwise. The assemblages responded negatively to blooms of Cyanobacteria but showed no response to higher percent cover blooms of Phaeophyta. This supported the selective avoidance behavior tested as hypothesis three. Fish abundance and species diversity were positively correlated and showed similar negative responses to the HAB periods when Lyngbya sp. formed the algal blooms.

Alteration of the fish assemblage correlated with the two bloom periods (Fig. 9) in both correlation analyses and ANOSIM. As abundance and species richness increased from a per-count low in February 2006 and approached similarity, the sampling periods became statistically indistinguishable. Multi-Dimensional Scaling (MDS) analysis tightly grouped four samplings, July 2006 through June 2007, all of which occurred in the absence of HABs, indicating an order-based system on this particular reef. . 25 The trophic structure of the fish assemblages was comparable to other tropical fish

assemblages around the world, as well as locally, (Hobson 1991, Bohnsack 1987, Baron

et al. 2004) when fishes are grouped into similar trophic arrangements. The percent

relative abundance of macroinvertivores showed decreases during HAB periods, as the

numerical dominant in non-bloom conditions, they became equal or decreased to

significantly less of the assemblage. Microinvertivore species ranged from 17% to 23%

of the total assemblage, but were highest (20-23%) during winter and spring, while their

contribution decreased in summer (17-19%) and fall, (17%). Planktivore relative

abundance suggested seasonality with peaks in spring, and also increased in relative

abundance during algal bloom periods. Planktivore relative abundance did not match

species richness, except for winter 2006 when species richness of planktivores decreased

by 56% compared to winter 2007 data. For the other seven seasons, Spring 05 through

June 07, planktivore richness did not vary by more than 3% of the total assemblage.

Comparison of trophic level richness and relative abundance over time indicated species

of all trophic groups were affected similarly by the HABs as opposed to one group

exhibiting significant variability in conjunction with the HAB.

Fishes abundance was dramatically altered in the presence of Lyngbya HABs.

Comparison of percent relative abundance of trophic levels of the fishes during February

2006 period (Lyngbya sp. bloom of 10% benthic cover) with the distribution of fishes during the February 2007 period, (<1% benthic cover of Lyngbya sp.) showed alteration of three of the major trophic groups. Macroinvertivores were the most dramatically effected, representing only 15% of the total abundance, while in 2007, their contribution was over 50%. This was offset by a relative increase in herbivore contribution to the

26 whole, which was 37% during the HAB, and only 19% during the 2007 non-bloom period in the same month. This may indicate herbivores are less displaced by blooms, as opposed to increasing in abundance. Microinvertivores also displayed an increase (17% of total) during February 2006, compared to the February 2007 sampling (8% of total), and over the previous sample period, November 2005, when the contribution was 10% of total abundance. This observation could also be related to release from predation pressure and/or fluctuation in small invertebrate prey which utilize macroalgae for cover and grazing. However, smaller fluctuations in relative abundance over the final four sample periods were observed for the Microinvertivores, Planktivores, and

Macroinvertivores during non-HAB periods.

When comparing the overall trophic distribution of summary data, February 2006 represented the largest departure from the norm, followed by February 2007. Spring

2005 and 2006 were similar, as were fall 2005-2006. July 2006 and June 2007 were different, when Planktivores and Macroinvertivores were inversely proportional, an increase of 9% overall in Planktivores was accompanied by a decrease of 11% of the total in Macroinvertivores. This pair of samplings suggested other factors were shaping the community, which may have been piscivores exhibiting forcing on macroinvertivores, as they were seen in some abundance during summer 2007, but <1% in 2006. Since some macroinvertivores also consume other fishes, this may have equated to lower predation pressure on planktivores. It should also be noted that while species distribution and percent relative abundance displayed similar overall trends, they did not significantly correlate.

27 HAB effects on life history stages were difficult to elucidate (Fig. 7). Seasonality of

juvenile and recruit classes outweighed fluctuations during HAB periods. During the July

2006 to June 2007 sample periods, recruit stage fishes increased in each subsequent quarter, which may indicate some recovery following periods of disturbance in 2005.

The increase of recruits in the spring was followed by summer increase in juvenile phase fishes during the 2006 sampling. The potential retention of recruits at the reef implies niche space was available following the HABs and other disturbances during 2005. This was not the case during following year, possibly due to the lack of large-scale disturbance

and no algal blooms. The 2005-2006 HAB impact was strongest on adult fishes, as those

absent during Lyngbya blooms were adults. Adult and subadult groups were inversely

proportional, although Adult fishes were always the numerical dominant, losses of Adults

were met with similar increases in subadult classes. This phenomenon suggests

competition for resources between the upper two life history stages occurs on the reef.

Another interesting trend investigated as hypothesis #3 was fish selective avoidance

of the reef depending on the type of HAB making up benthic cover. The Lyngbya bloom

observed in spring 2005 and again in winter 2005 had significant negative impacts on the

fish assemblage, while the Dictyota spp. bloom observed in spring 2006 had no

measurable effect. The fish assemblage did not display similar graphical trends in

response to each HAB. Multi-factorial analysis allowed comparison of these trends by

groups and in two dimensional space.

ANOSIM results indicated the highest dissimilarity in Haemulon aurolineatum

which made up 26% of the assemblage during non-HAB periods, but only 7.7% in the

presence of Lyngbya blooms. Conversely, Thalassoma bifasciatum increased from 19%

28 of the assemblage to over 37% in the presence of HABs. Other species of note were

Sparisoma aurofrenatum, which remained similar in abundance during presence and

absence of HABs. The complete absence of Haemulon plumieri during HABs was the

highest abundance difference, but four additional species were also absent in the presence

of Lyngbya HABs. Coryphopterus dicrus was not found during Lyngbya blooms, and

since it is a benthic dweller, their disappearance supports the habitat loss aspect of the algal bloom periods. Two species of Chromis, scotti and multilineata, were also absent during the Lyngbya HAB periods. Their plankton diet may have been affected by the bloom, or increased competition with Thalassoma bifasciatum, which has a broad diet

that overlaps with Chromis sp. Atlantic spadefish, Chaetodipterus faber, were only

found during the Lyngbya bloom period in April 2005. However, large schools of this

species are considered hunting megafauna which constantly pursue prey (Hayse 1990) so

it is likely they were recorded by chance rather than preferring the HAB period.

The two periods of Lyngbya sp. blooms, April 2005 and February 2006, were 40%

dissimilar from the summer 2006 through spring 2007, and grouped closely to one

another in the dendrograms created from MDS factorial analysis. Although April 2005

and February were significantly different from the remaining six sampling periods, they

were not the same when considered in the 2D MDS plot (Figure 13). The unequal n

between April 2005 (n=3) and February 2006 (n=8) may have been a factor influencing

the difference but were normalized for testing. The difference in percent cover of the two

blooms offers the most parsimonious explanation. The Lyngbya sp. bloom of 10% benthic cover in February 2006 was less severe than in April 2005 (37%) but had similar results, depressing the fish assemblage.

29 The other HAB that occurred in November 2005, comprised of Dictyota sp. (41% cover) was also different from the four July 2006-June 2007 samplings, but similar to the

May 2006 sampling. Cluster analysis grouped these together, confirming this relationship; both were one sampling interval after Lyngbya sp. blooms. This may suggest a fish assemblage recovery phase following the decreases observed during

Lyngbya sp. bloom periods. The fact that these grouped together while richness data were not similar, indicated abundance was a better linker than richness in this case. This suggests a lesser effect on total number of individuals, the macroalgae covering 41% of the benthic habitat decreased available habitat for specific species. A loss of 41% available benthic space due to macroalgae should have had an effect similar to that of the

37% Lyngbya spp. bloom. However, this was not the case as the Dictyota sp. bloom period grouped tightly with a zero % cover period with high relatedness in 2D MDS plot and cluster analysis(Figures 11- 13). These data support the hypothesis that the Dictyota sp. bloom had no measurable effect on the fish assemblage, while Lyngbya sp. blooms did. Therefore, habitat loss due to algal coverage was less of a factor than the algal taxa making up the bloom.

These results may be indicative of the difference in toxicity of the two HAB species, suggesting Lyngbya sp. may have more negative effects on reef dwellers due to its chemistry. The potent cytotoxicity has a more negative effect on herbivorous fishes than the relatively less toxic Phaeophytes (Nagle and Paul 1998, Steinberg et al. 1991, Pereira et al. 2000). Lyngbya majuscula produces the natural product compound ypaoamide, which deters feeding of parrotfishes and sea urchins and led to mortality of rabbitfishes in

Guam in 1994 (Nagle and Paul 1998). HBOI department of Biomedical Marine Research

30 identified Microcolens A in samples of the BR 16 Lyngbya bloom, a tumor promoter and

cytotoxic compound (Koehn et al 1992, Armstrong et al. 1991). The diterpenoids found

in Dictyota species are not as cytotoxic as Lyngbya spp. toxins, and some herbivorous

fishes are able to consume them with no negative physiological effects (Cronin et al.

1997). Further, tumor promoting compounds found in Lyngbya majuscula (Koehn et al

1992) correlate with sea turtle Fibropapillomatosis (Arthur et al. 2008) and could lead to

avoidance by fishes. Therefore, the effects of phyla of algae forming the HAB may be

important in determining the degree of impact on the fish assemblage. The recent

appearance of Lyngbya sp. as a HAB former in Florida (Paul et al 2005) compared to the

relatively long-term existence of Dictyota sp. on Florida reefs, (Littler et al 1986), may

indicate fishes are better adapted to more typical reef algae than new invaders. Fisheries

and marine protected areas may become increasingly threatened by the relative increases

of toxic cyanobacteria, as they are in Moreton Bay (Arthur et al. 2006, Elmetri and Bell

2004, Ahern et al. 2006) and elsewhere based on the avoidance observed in this study.

Prior to beginning the monitoring in April 2005, South Florida was inundated with

rain during the fall of 2004 when two hurricanes made landfall on the east coast. The

dominance of Lyngbya in spring 2005 followed increased terrigenous water input after

the September – October 2004 hurricanes. (Figure 14, DBHYDRO, website address:

http://www.sfwmd.gov/org/ema/dbhydro). The Lyngbya bloom was then physically

removed during 2005 hurricane season, as were similar blooms in Palm Beach County

(Lapointe et al. 2006). Lyngbya majuscula growth is enhanced by iron, phosphorous and nitrogen, and responds strongest to addition of organically chelated iron (Ahern et al

2007). It is possible that land based nutrients and elements in the runoff contributed to

31 bloom formation through excess water column availability as they do in Queensland,

Australia (Albert et al. 2005).

Additional data collection from FWRI HAB research at BR 16 was conducted by

HBOI personnel. Water column nutrients were measured from samples collected during

each sample period. Data consisted of bottom water sample analysis of dissolved

nutrients and Chl-a and algal tissue nutrient analysis of C:N, C:P, N:P ratios and δ15N for nitrogen source profiling. Data indicated the reef is consistently above accepted thresholds for eutrophication (Bell 1992, Lapointe 1997) with respect to water column nutrients. Tissue nutrient analysis suggested that algae were not nutrient limited during

2005 and winter 2006, but by spring 2006, tissue N:P increased to a high of 75:1, indicating P limitation. This could represent the uptake of phosphate during higher availability in 2005 which corresponds to findings indicating P is more important than N in Lyngbya bloom initiation (Ahearn et al. 2007). δ15N analysis ranged from 6-7.75 ppt

during the bloom periods fall 2005 to spring 2006, and then decreased to lowest in the

study during summer 2006. The linkage to inlet discharge and potential runoff is best

illustrated by this decrease, as the spring 2006 drought resulted in significant decrease in

δ15N by that summer. As rainfall and discharges increased following the summer

monitoring in 2006, δ15N increased to similar rainy season levels of 6 ppt, during the

subsequent fall and winter samplings. The further investigation of water and tissue

nutrients should be conducted to determine if the link between HAB formation and

nutrients observed elsewhere exists in south Florida as well. Higher frequency

monitoring of both reef water quality as well as Lyngbya bloom formation would be required to provide a statistical basis for comparisons.

32 Studies of herbivory in relation to the HABs could also be conducted, to determine if avoidance behavior is another factor causing alteration of the assemblage during bloom periods. Exclusion cages could be utilized to simulate habitat loss, to differentiate between toxicity effects versus HAB mediated habitat loss. This has already been suggested by regression analysis herin, with algal type exhibiting more influence on the assemblage than % cover which would represent habitat loss.

33

SUMMARY

A significant negative correlation was found between harmful algal blooms, HABs,

of cyanobacteria of the genus Lyngbya and the fish assemblage at the shallow reef site,

BR16, off Hallandale Beach, Fl. Abundance and richness of the fishes at BR16 exhibited a strong positive correlation, and factors acting on one influenced the other. Both measures decreased significantly during Lyngbya sp. blooms versus non-bloom periods in regression analysis. These data were further supported via cluster analysis, where periods

without HABs were very closely (>90%) related, and dissimilar (<40% similarity) from

periods with HABs. Multi-dimensional scaling and analysis of similarity metrics

confirmed the significant differences in sample periods, separating Lyngbya blooms from

all other periods with p=0.036.

Shifts in trophic structure were observed, and comparison of HABs of Lyngbya sp.

and Dictyota sp. suggested the two algae had significantly different effects on the fish

assemblage. The Dictyota sp. bloom grouped closely with a period of no algal bloom

through cluster analysis, implying there was no deleterious effect on the fishes. The

differences in toxicity of the two bloom-forming algal groups may play a key role in their

effects on fish assemblages.

34

Table 1. Sampling Dates, n counts, abundance and total number of species recorded for each of the eight monitoring events.

Sample Date # Counts Total Abundance Species Recorded April 22, 2005 3 166 34 November 16, 2005 8 705 58 February 8, 2006 8 308 42 May 2, 2006 8 745 48 July 6, 2006 8 972 54 October 10, 2006 8 841 54 February 2, 2007 8 758 55 June 14, 2007 8 754 53

Table 2. Percent benthic cover of eight major benthic groups.

Percent Benthic Cover by Sample Period Benthic Cover Spring Fall Winter Spring Summer Fall Winter Spring Type 2005 2005 2006 2006 2006 2006 2007 2007 Sand, Pavement,Rubble 43.81 80.13 79.50 53.54 56.76 80.80 73.70 75.80 Cyanobacteria 36.79 0.00 10.00 0.00 0.00 0.00 0.34 0.00 Phaeophyta 5.35 0.00 0.00 41.08 3.84 0.50 0.00 1.18 Zoanthids 2.34 2.50 2.83 0.00 4.67 5.01 3.18 3.70 Live Coral 1.67 0.67 1.83 1.18 0.17 1.00 1.68 3.53 Gorgonians 1.67 14.36 3.33 0.67 3.67 4.84 9.72 4.71 Porifera 1.34 2.34 2.33 2.86 2.67 5.51 6.70 2.86 Unidentified live cover 0.00 0.00 0.17 0.67 28.21 2.17 4.69 8.24 Unscorable 7.02 0.17 0.00 1.00 0.17 0.17 0.50 0.83

35 Table 3. Rank and log abundance of all species recorded 2005-2007.

Rank Genus Species Log Rank Genus Species Log 1 Haemulon aurolineatum 3.08 51 Gnatholepis thompsoni 0.85 2 Thalassoma bifasciatum 2.95 52 Holacanthus bermudensis 0.85 3 Haemulon flavolineatum 2.76 53 Hypoplectrus puella 0.85 4 Stegastes partitus 2.34 54 Pomacanthus arcuatus 0.85 5 Coryphopterus hyalinus 2.32 55 Balistes capriscus 0.78 6 Sparisoma aurofrenatum 2.30 56 Holacanthus tricolor 0.78 7 Stegastes variabilis 2.12 57 Cryptotomus roseus 0.70 8 Acanthurus bahianus 2.11 58 Holacanthus ciliaris 0.70 9 Halichoeres garnoti 2.10 59 Cephalopholis cruentata 0.60 10 Halichoeres bivittatus 2.10 60 Diodon holocanthus 0.60 11 Halichoeres maculipinna 2.05 61 Haemulon macrostomum 0.60 12 Haemulon plumieri 2.03 62 Hypoplectrus sp. (Tan) 0.60 13 Scarus iseri 1.93 63 Lutjanus jocu 0.60 14 Scarus taeniopterus 1.86 64 Acanthemblemaria aspera 0.48 15 Kyphosus sectatrix/incisor 1.84 65 Apogon pseudomaculatus 0.48 16 Malacoctenus triangulatus 1.82 66 Chromis insolata 0.48 17 Abudefduf saxatilis 1.80 67 Epinephelus morio 0.48 18 Gobiosoma oceanops 1.76 68 Lutjanus griseus 0.48 19 Caranx ruber 1.68 69 Pomacanthus paru 0.48 20 Coryphopterus glaucofraenum 1.63 70 Scarus guacamaia 0.48 21 Parablennius marmoreus 1.57 71 Scomberomorus regalis 0.48 22 Anisotremus virginicus 1.53 72 Scorpaena plumieri 0.48 23 Canthigaster rostrata 1.52 73 Aluterus scriptus 0.30 24 Acanthurus coeruleus 1.51 74 Cantherhines pullus 0.30 25 Anisotremus surinamensis 1.51 75 Epinephelus guttatus 0.30 26 Bodianus rufus 1.49 76 Gymnothorax miliaris 0.30 27 Stegastes planifrons 1.49 77 Haemulon sciurus 0.30 28 Chaetodon sedentarius 1.45 78 Halichoeres radiatus 0.30 29 Hypoplectrus unicolor 1.41 79 Hypoplectrus indigo 0.30 30 Coryphopterus dicrus 1.40 80 Scarus coelestinus 0.30 31 Sparisoma viride 1.38 81 Serranus baldwini 0.30 32 Chromis scotti 1.36 82 Acanthurus chirurgus 0.10 33 Chromis multilineata 1.32 83 Echidna catenata 0.10 34 Stegastes fuscus 1.32 84 Enchelycore carychroa 0.10 35 Serranus tigrinus 1.30 85 Epinephelus fulvus 0.10 36 Stegastes leucostictus 1.28 86 Gymnothorax funebris 0.10 37 Haemulon carbonarium 1.23 87 Haemulon album 0.10 38 Pseudupeneus maculatus 1.23 88 Holocentrus adscensionis 0.10 39 Sparisoma atomarium 1.18 89 Holocentrus rufus 0.10 40 Chaetodipterus faber 1.08 90 Hypoplectrus gummigutta 0.10 41 Hypoplectrus gemma 1.08 91 Hypoplectrus guttavarius 0.10 42 Apogon maculatus 1.00 92 Lachnolaimus maximus 0.10 43 Chaetodon capistratus 1.00 93 Lutjanus apodus 0.10 44 Chaetodon ocellatus 1.00 94 Lutjanus synagris 0.10 45 Malacoctenus macropus 0.95 95 Ocyurus chrysurus 0.10 46 Lactophrys triqueter 0.90 96 Scarus coeruleus 0.10 47 Apogon binotatus 0.85 97 Serranus tabacarius 0.10 48 Apogon townsendi 0.85 98 Sparisoma rubripinne 0.10 49 Chromis cyanea 0.85 99 Synodus foentens 0.10 50 Epinephelus cruentatus 0.85

36 Figure 1. LADS bathymetry of study site BR 16, off Hallandale Beach, Florida.

N

Figure 2. Permanent station establishment: a. transect end floats, b. transect center float, c. float and label, d. stainless pin at transect end, e. EVA replacement float.

37 Figure 3. Average number of fishes recorded per count (+/- SE) in each sample period, April 22, 2005-June 14, 2007.

Average # fish per count 180

160

140

120

100

80 60 Avg. Fish/Count Avg. 40

20 0 Spring Fall 05 Winter Spring Summer Fall 06 Winter Spring Summer 05 06 06 06 07 07 08

Figure 4. Trophic distribution of species of fish, overall 2005-2007.

BR16 Species Trophic Dist 12% 7% 9% Browser Piscivore Herbivore 22% 35% Microinvertivore Macroinvertivore

15% Planktivore

38 Figure 5. Trophic distribution by number of individuals, per sample period, April 2005- June 2007.

Browser Piscivore Herbivore Microinvertivore Macroinvertivore Planktivore

480

440

400

360

320

280

240

Total Abund Total 200

160

120

80

40

0 Apr05 Nov05 Feb06 Apr06 Jul06 Oct06 Feb07 Jun07

Figure 6. Trophic distribution by number of individuals, summary 2005-2007.

Br16 Percent Rel Abundance 1% Browser 1% 21% 22% Piscivore Herbivore

Microinvertivore 15% Macroinvertivore

40% Planktivore

39 Figure 7. Percent relative abundance of six main trophic groups, April 2005- June 2007.

Browser Piscivore Herbivore Microinvertivore Macroinvertivore Planktivore 60%

50%

40%

30%

% Relative Abundance Relative % 20%

10%

0% Apr05 Nov05 Feb06 Apr06 Jul06 Oct06 Feb07 Jun07

Figure 8. Percent relative abundance of each life history stage of fishes, April 2005-June 2007.

Adult SubAdult Juvenile Recruit 70%

60% e 50%

40%

30%

20% % Total Abundanc Total % 10%

0% Spring Fall 05 Winter Spring Summer Fall 06 Winter Spring 05 06 06 06 07 07

40 Figure 9. Shannon’s diversity index, H1, and equitability constant, Eh, April 2005-June 2007.

1 p = 0.008 Shannons Diversity Index (H ) and Equitibility (Eh) F = 15.17 H' Eh r2 =0.669 3.50

3.00

2.50

2.00

H' and Eh H' 1.50

1.00

0.50

0.00 Spring 05 Fall 05 Winter 06 Spring 06 Summer 06 Fall 06 Winter 07 Spring 07

Figure 10. Fish species richness vs. average % cover of Lyngbya sp, by sample period.

41 Figure 11. Bray-Curtis similarity dendrogram: Square root transformed data, by sampling period.

Bray-Curtis Similarity 50

60

70

80

90

100 *** *** * * * * ** ** Jul 06 Apr 05 Oct 06 Jun 07 Feb 06 Feb 07 Feb Nov 05 May 06

Figure 12. Two-dimensional MDS ordination plot, by sample date.

May 06 Stress: 0.01 Jul 06 Nov 05 Oct 06 R=0.885 Feb 07 Jun 07 P=0.036

Feb 06 Apr 05

42

Figure 13. Rainfall and Canal flows 2004-2005, Port Everglades area, Hollywood/Ft. Lauderdale, Florida, SFWMD DBHYDRO data. a - b: Lyngbya sp. bloom, c: Dictyota sp. bloom.

43

LITERATURE CITED

Ahern, K., J. Udy and S. Pointon. 2006. Investigating the potential for groundwater from different vegetation, soil and land uses to stimulate blooms of the cyanobacterium, Lyngbya majuscula, in coastal waters. Marine and Freshwater Research 57: 177-186.

Ahern, K., C. Ahern and J. Udy. 2007. Nutrient additions generate prolific growth of Lyngbya majuscula (cyanobacteria) in field and bioassay experiments. Harmful Algae 6, No.1: 134-151.

Albert, S., J. M. O’Neil, J. W. Udy, K. S. Ahern, C. M. O’Sullivan, and W. C. Dennison. 2005. Blooms of the cyanobacterium Lyngbya majuscula in coastal Queensland, Australia: disparate sites, common factors. Marine Pollution Bulletin 51: 428-437.

Anderson, D. M., P. M. Gilbert, and J. M. Burkholder. 2002. Harmful algal blooms and eutrophication: nutrient sources, composition, and consequences. Estuaries 25 No. 4B: 704-726.

Antonius, A., A. H. Weiner, J. C. Halas and E. Davidson. 1978. Looe Key Reef resource inventory. Report to NOAA Florida Reef Foundation. Homestead, Fl. 63 pg.

Armstrong, J. E., K. E. Janda, B. Alvarado and A. E. Wright. 1991. Cytotoxin production by a marine Lyngbya strain (cyanobacterium) in a large-scale laboratory bioreactor. Journal of Applied Phycology 3, No. 3: 277-282.

Aronson, R. B. and W. F. Precht. 2000. Herbivory and algal dynamics on the coral reef at Discovery Bay, Jamaica. Limnology and Oceanography 45, No. 1: 251–255.

Arthur, K., G. Shaw, C. Limpus and J. Udy. 2006. A review of the potential role of tumour-promoting compounds produced by Lyngbya majuscula in marine turtle fibropapillomatosis. African Journal of Marine Science 28, No. 2: 441-446.

Arthur, K., C. Limpus, G. Balazs, A. Capper, J. Udy, G. Shawf, U. Keuper-Bennett and P. Bennett. 2008. The exposure of green turtles (Chelonia mydas) to tumour promoting compounds produced by the cyanobacterium Lyngbya majuscula and their potential role in the aetiology of fibropapillomatosis. Harmful Algae 7: 114-125.

44 Banks, K. W., B. M. Riegl, E. A. Shinn,W. E. Piller and R. E. Dodge. 2007. Geomorphology of the Southeast Florida continental reef tract (Miami-Dade, Broward, and Palm Beach Counties, USA). Coral Reefs 26: 617-633.

Banner, A. H. 1974. Kaneohe Bay, Hawaii: Urban pollution and a coral reef ecosystem. Proceedings of the. 2nd International Coral Reef Symposium 2: 685.

Barile, P. J. 2004. Evidence of anthropogenic nitrogen enrichment of the littoral waters of east central Florida. Journal of Coastal Research 20, No. 4: 1237-1245.

Barnes, J. H. 1966. The crown of thorns starfish as a destroyer of coral. Australian Natural History 15: 257-261.

Baron, R. M., L. K. B. Jordan and R. E. Spieler. 2004. Characterization of the marine fish assemblage associated with the nearshore hardbottom of Broward County, Floirda, USA. Estuarine Coastal and Shelf Science 60: 431-443.

Baumberger, R. E. 2002. Bottom-up control of the top-down trophic assemblages at Looe Key, Fl. Link Foundation summer internship report. Harbor Branch Oceanographic Institution, Ft. Pierce, Fl. 33 pg.

Bell, P. R. F. 1992. Eutrophication and coral reefs: some examples in the Great Barrier Reef Lagoon. Water Research 26: 553-568.

Birkeland, C. 1982. Terrestrial runoff as a cause of outbreaks of Acanthaster planci (Echinodermata: Asteroidea). Marine Biology 69:175–185.

Bohnsack, J. A. and S. P. Bannerot. 1986. A stationary visual census technique for quantitatively assessing community structure of coral reef fishes. NOAA Technical Report NMFS 41. U.S. Department of Commerce. Washington, D.C.

Bohnsack, J. A., D. E. Harper, D. B. McClellan, D. L. Sutherland, and M. W. White. 1987. Resource Survey of Fishes within Looe Key National Marine Sanctuary. NOAA Technical Memoranda Series NOS/MEMD 1. U.S. Department. of Commerce. Washington, D.C.

Boyer, K. E., P. Fong, A. R. Armitage and R. A. Cohen. 2004. Elevated nutrient content of tropical macroalgae increases rates of herbivory in coral, seagrass, and mangrove habitats. Coral Reefs 23: 530–538.

Brodie, J., K. Fabricius, G. De’ath and K. Okaji. 2005. Are increased nutrient inputs responsible for more outbreaks of crown-of-thorns starfish? An appraisal of the evidence. Marine Pollution Bulletin 51: 266-278.

45 Burkholder, J. M., E. J. Noga, C. H. Hobbs, and H. B. Glasgow. 1992. New 'phantom' dinoflagellate is the causative agent of major estuarine fish kills. Nature 358: 407-410.

Bythell, J. C., M. Bythell and E. H. Gladfelter. 1993. Initial results of a long-term coral reef monitoring program: Impact of Hurricane Hugo at Buck Island Reef National Monument, St. Croix, U.S. Virgin Islands. Journal of Experimental Marine Biology and Ecology 172, No.1-2: 171-183.

Capper, A. and V. J. Paul. 2008. Grazer interactions with four species of Lyngbya in southeast Florida. Harmful Algae 7: 717-728.

Carpenter, R.C. 1988. Mass-mortality of a Caribbean sea urchin: immediate effects on community metabolism and other herbivores. Proceedings of the National Academy of Science 85: 511-514.

Chesher, R.H. 1969. Destruction of Pacific corals by the sea star Acanthaster planci. Science 165: 280-283.

Clarke, K. R. and R. M. Warwick. 2001. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation. 2nd edition. Plymouth Marine Laboratory, Plymouth, UK.

Clarke, K.R. and R. N. Gorley. 2006. PRIMER v6: User Manual/Tutorial, PRIMER-E. Plymouth, UK.

Cronin, G., V.J. Paul, M.E. Hay and W. Fenical. 1997. Are tropical herbivores more resistant than temperate herbivores to seaweed chemical defenses? Diterpenoid metabolites from Dictyota acutiloba as feeding deterrents for tropical versus temperate fishes and urchins. Journal of Chemical Ecology 23, No. 2: 289-302.

DBHYDRO Browser, South Florida Water Management District, Accessed August 2008. http://my.sfwmd.gov/dbhydroplsql/show_dbkey_info.main_menu.

Dustan, P., W. Jaap, J. Porter, J. Wheaton and O. Meier. 1998. Florida Keys National Marine Sanctuary water quality protection plan. Coral Reef Monitoring Project 1997 Annual Report. Florida Fish and Wildlife Research Institute, Tallahassee, FL.

ECOHAB. 1995. The Ecology and Oceanography of Harmful Algae Blooms. A National Research Agenda. Woods Hole, MA, WHOI.

Edinger, E. N., J. Jompa, G. V. Limmon, W. Widjatmoko and M. J. Risk. 1998. Reef degradation and coral biodiversity in Indonesia: effects of land-based pollution, destructive fishing practices and changes over time. Marine Pollution Bulletin 36, No. 8: 617-630.

46 Elmetri, I. and P. R. F. Bell. 2004. Effects of phosphorus on the growth and nitrogen fixation rates of Lyngbya majuscula: implications for management in Moreton Bay, Queensland. Marine Ecology Progress Series 201: 27-35.

Fabricius, K.E., G. De’ath, L. McCook, E. Turak and D. McB. Williams. 2005. Changes in algal, coral and fish assemblages along water quality gradients on the inshore Great Barrier Reef. Marine Pollution Bulletin 51: 384-398.

Fabricius, K. E., Y. Golbuu and S. Victor. 2007. Selective mortality in coastal reef organisms from an acute sedimentation event. Coral Reefs 26: 69.

Feary, D.A., G.R. Almany, M.I. McCormick and G.P. Jones. 2007. Habitat choice, recruitment and the response of coral reef fishes to coral degradation. Oecologia 153: 727-737.

FWRI HAB. 2007. Comparative Ecology of Harmful Macroalgal Blooms in South Florida’s Coastal Waters (CEHAB). Final Report Submitted to Florida Fish and Wildlife Commission. Tallahassee, FL, May 31, 2007.

Gardner, T. A., I. M. Côté, J. A. Gill, A. Grant and A. R. Watkinson. 2003. Long-term region-wide declines in Caribbean corals. Science 301: 958-960.

GESAMP (Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection). 2001. Protecting the oceans from land-based activities - land-based sources and activities affecting the quality and uses of the marine, coastal and associated freshwater environment. Reports and Studies 71.

Goreau, T. J. 1992. Bleaching and reef community change in Jamaica: 1951-1991. American Zoologist 32: 683-695.

Goreau, T. J. and K. Thacker, 1994, Coral Reefs, sewage, and water quality standards. Proceedings 3rd Caribbean Water and Wastewater Association conference, Water and wastewater needs for the Caribbean: 21st centry. October 3-7, Kingston, Jamaica: 98-116.

Goreau, T. J. and R. Hayes. 1994. Coral Bleaching and ocean "hotspots". Ambio 23: 176-180.

Grigg, R. W. 1994. Effects of sewage discharge, fishing pressure and habitat complexity on coral ecosystems and reef fishes in Hawaii. Marine Ecology Progress Series 103: 25-34.

47 Hamilton, G. S., F. Fielding, A. W. Chiffings, B. T. Hart, R. W. Johnstone and K. Mengersen. 2007. Investigating the Use of a Bayesian Network to Model the Risk of Lyngbya majuscula Bloom Initiation in Deception Bay, Queensland, Australia. Human and Ecological Risk Assessment 13: 1271-1287.

Hatcher, B. G. and A.W. D. Larkum. 1983. An experimental analysis of factors controlling the standing crop of the epilithic algal community on a coral reef. Journal of Experimental Marine Biology and Ecology 113: 39-59.

Hay, M. E. 1984. Patterns of fish and urchin grazing on Caribbean coral reefs: Are previous results typical? Ecology 65: 446-454.

Hay, M. E., J. E. Duffy, C. A. Pfister and W. Fenical. 1987. Chemical defense against different marine herbivores: are amphipods insect equivalents? Ecology 68, No.6: 1567-1580.

Hobson, E. S. 1991. Trophic relationships of fishes specialized to feed on zooplankters above coral reefs. In The Ecology of Fishes on Coral Reefs. P. F. Sale, Ed. Academic Press, San Diego, CA. Chapter 4: 69-95.

Hayse, J.W. 1990. Feeding habits, age, growth, and reproduction of Atlantic spadefish Chaetodipterus faber (Pisces: Ephippidae) in South Carolina. Fishery Bulletin 88, No.1: 67-8.

Hoegh-Guldberg, O. 1999. Climate change, coral bleaching and the future of the world’s coral reefs. Marine and Freshwater Research 50: 839-866.

Hughes, T. P. 1994. Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science 265, No. 5178: 1547-1551.

Jackson, J.B.C., M. X. Kirby, W. H. Berger, K. A. Bjorndal, L. W. Botsford, B. J. Bourque, R. H. Bradbury, R. Cooke, J. Erlandson, J. A. Estes, T. P. Hughes, S. Kidwell, C. B. Lange, H. S. Lenihan, J. M. Pandolfi, C. H. Peterson, R. S. Steneck, M. J. Tegner and R. R. Warner. 2001. Historical over fishing and the recent collapse of coastal ecosystems. Science 293: 629-638.

Johannes, R.E. 1975. Pollution and degradation of coral reef communities. In Tropical Marine Pollution. E.J. Ferguson Wood and R.E. Johannes, Eds. Elsevier, Amsterdam, Netherlands. Ch. 2:13-50.

Kaczmarsky, L. T., M. Draud and E. H. Williams. 2005. Is there a relationship between proximity to sewage effluent and the prevalence of coral disease? Caribbean Journal of Science 41, No. 1: 124-137.

48 Koehn, F. E., R. E. Longley and J. K. Reed. 1992. Microcolins A and B, new immunosuppressive peptides from the blue-green alga Lyngbya majuscula. Journal of Natural Products 55, No. 5: 613-619.

Kohler, K.E. and S.M. Gill. 2006. Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Computers and Geosciences 32, No. 9: 1259-1269.

Kruskal, J. B. and M. Wish. 1978. Multidimensional Scaling. Quantitative applications in the social sciences. E. M. Uslaner, ed. Sage Publications, Beverly Hills.

Kuffner, I. B. and V. J. Paul. 2004. Effects of the benthic cyanobacterium Lyngbya majuscula on larval recruitment of the reef corals Acropora surculosa and Pocillopora damicornis. Coral Reefs 23: 455-458.

Kuffner, I. B., L. J. Walters, M. A. Becerro, V. J. Paul, R. Ritson-Williams and K. S. Beach. 2006. Inhibition of coral recruitment by macroalgae and cyanobacteria. Marine Ecology Progress Series 323: 107-117.

Kuntz, N. M., D. I. Kline, S. A. Sandin and F. Rohwer. 2005. Pathologies and mortality rates caused by organic carbon and nutrient stressors in three Caribbean coral species. Marine Ecology Progress Series 294: 173-180.

Lapointe, B. E. and M. W. Clark (1992). Nutrient Inputs from the watershed and coastal eutrophication in the Florida Keys. Estuaries 15, No. 4: 465-476.

Lapointe, B. E. 1997. Nutrient thresholds for bottom-up control of macroalgal blooms on coral reefs in Jamaica and southeast Florida. Limnology and Oceanography 42, No. 5, Part 2: 1119-1131.

Lapointe, B. E., M. M. Littler and D. S. Littler. 1997. Macroalgal overgrowth of fringing coral reefs at Discovery Bay, Jamaica: bottom-up versus top-down control. Proceedings of the 8th International Coral Reef Symposium 1: 927-932.

Lapointe, B. E. and K. Thacker. 2002. Community-based water quality and coral reef monitoring in the Negril Marine Park, Jamaica: Land-based nutrient inputs and their ecological consequences. In: The Everglades, Florida Bay, and Coral Reefs of the Florida Keys: An ecosystem sourcebook. J.W. Porter and K.G. Porter, Eds. CRC press, Boca Raton, FL. 939-963.

Lapointe, B. E., P. J. Barile and W. R. Matzie. 2004a. Anthropogenic nutrient enrichment of seagrass and coral reef communities in the Lower Florida Keys: discrimination of local versus regional nitrogen sources. Journal of Experimental Marine Biology and Ecology 308: 23-58.

49 Lapointe, B. E., P. J. Barile, C. S. Yentsch, M. M. Littler, D. S. Littler and B. Kakuk. 2004b. The relative importance of nutrient enrichment and herbivory on macroalgal communities near Norman’s Pond Cay, Exumas Cays, Bahamas: a ‘natural’ enrichment experiment. Journal of Experimental Marine Biology and Ecology 298 No. 2: 275-301.

Lapointe, B. E., P. J. Barile, M. M. Littler, D. S. Littler, B. J. Bedford and C. Gasque. 2005a. Macroalgal blooms on southeast Florida coral reefs: I. Nutrient stoichiometry of the invasive green alga Codium isthmocladum in the wider Caribbean indicates nutrient enrichment. Harmful Algae 4, No. 6: 1092-1105.

Lapointe, B. E., P. J. Barile, M. M. Littler and D. S. Littler. 2005b. Macroalgal blooms on southeast Florida coral reefs. II. Cross-shelf discrimination of nitrogen sources indicates widespread assimilation of sewage nitrogen. Harmful Algae 4, No. 6: 1106-1122.

Lapointe, B. E., B. J. Bedford and R. Baumberger. 2006. Hurricanes Frances and Jeanne remove blooms of the invasive green alga Caulerpa brachypus forma parvifolia (Harvey) Cribb from coral reefs off northern Palm Beach County, Florida, USA. Estuaries and Coasts 29, No. 6A: 966-971.

Lapointe, B. E. and B. J. Bedford. 2007. Drift rhodophyte blooms emerge in Lee County, Florida, USA: Evidence of escalating coastal eutrophication. Harmful Algae 6: 421-437.

Lessios, H. A. 1988. Mass mortality of Diadema antillarum in the Caribbean: What have we learned? Annual Review of Ecology and Systematics 19: 371-393.

Lewis, A. R. 1997. Effects of experimental coral disturbance on the structure of fish communities on large patch reefs. Marine Ecology Progress Series 161: 37-50.

Lirman, D. 1999. Reef fish communities associated with Acropora palmata: relationships to benthic attributes. Bulletin of Marine Science 65, No.1: 235-252.

Littler, M. M., D. S. Littler and B. E. Lapointe. 1986. Baseline studies of herbivory and eutrophication on dominant reef communities of Looe Key National Marine Sanctuary. NOAA Tech Memo NOS MEMD 1.

Maragos, J. E., C. Evans and P. Holthus. 1985. Reef corals in Kaneohe Bay six years before and after termination of sewage discharges (Oahu, Hawaiian Archipelago). Proc. 5th Intl. Coral Reef Congress, Tahiti. 27 May-1 June 1985. Vol. 4: 189-194.

Mattson, W. J. 1980. Herbivory in relation to plant nitrogen content. Annual Review of Ecology and Systematics 11: 119-161.

50 McClanahan, T. R., J. N. Uku and H. Machano. 2002. Effect of macroalgal reduction on coral-reef fish in the Watamu Marine National Park, Kenya. Marine and Freshwater Research 53: 223-230.

MEA, Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being: wetlands and water synthesis. World Resources Institute, Washington, DC.

Miller, M. W., A. S. Bourque and J. A. Bohnsack. 2002. An analysis of the loss of Acroporid corals at Looe Key, Florida, USA: 1983-2000. Coral Reefs 21: 179-182.

Moyer, R. P., B. Riegl, K. Banks and R. E. Dodge. 2003. Spatial patterns and ecology of benthic communities on a high-latitude South Florida (Broward County, USA) reef system. Coral Reefs 22: 447-464.

Nagle, D.G. and V. J. Paul. 1998. Chemical defense of a marine cyanobacterial bloom. Journal of Experimental Marine Biology and Ecology 225: 29-38.

National Research Council. 2000. Clean coastal waters: understanding and reducing the effects of nutrient pollution. Ocean Studies Board Water - Science and Technology Board. Washington, D.C.

Oliver, J. 1985. Recurrent seasonal bleaching and mortality of corals on the Great Barrier Reef. Proc. 5th Intl. Coral Reef Congress, Tahiti. 27 May-1 June 1985. Vol. 4: 201-206.

Paul, V. J. and M. E. Hay. 1986. Seaweed susceptibility to herbivory: chemical and morphological correlates. Marine Ecology Progress Series 33: 255-264.

Paul, V. J., R. W. Thacker, K. Banks and S. Golubic. 2005. Benthic cyanobacterial bloom impacts the reefs of South Florida (Broward County, USA). Coral Reefs 24: 693-697.

Pauly, D., V. Christensen, J. Dalsgaard, R. Froese and F. Torres Jr. 1998. Fishing down marine food webs. Science 279: 860-863.

Pennings, S. C., S. R. Pablo and V. J. Paul. 1997. Chemical defenses of the tropical, benthic marine cyanobacterium Hormothamnion enteromorphoides: Diverse consumers and synergisms. Limnology and Oceanography 42, No. 5: 911-917.

Pereira, R. C., D. N. Cavalcanti and V. L. Teixeira. 2000. Effects of secondary metabolites from the tropical Brazilian brown alga Dictyota menstrualis on the amphipod Parhyale hawaiensis. Marine Ecology Progress Series 205: 95-100.

Pew Oceans Commission. 2003. America’s Living Oceans: Charting a Course for Sea Change. A Report to the Nation. May 2003. Pew Oceans Commission, Arlington, Virginia. 51 Porter, J.W. and O. W. Meier. 1992. Quantification of loss and change in Floridian reef coral populations. American Zoologist 32: 625-640.

Porter, J. W., V. Kosmynin, K. L. Patterson, K. G. Porter, W. C. Jaap, J. L. Wheaton, K. Hackett, M. Lybolt, C. P. Tsokos, G. Yanev, D. M. Marcinek, J. Dotten, D. Eaken, M. Patterson, O. W. Meier, M. Brill and P. Dustan. 2002. Detection of coral reef change by the Florida Keys Coral Reef Monitoring Project. pp. 749-769. In The Everglades, Florida Bay, and Coral Reefs of the Florida Keys: An ecosystem sourcebook. J. W. Porter and K. G. Porter, Eds. CRC press, Boca Raton, FL.

Randall, J. E. 1961. Overgrazing of algae by herbivorous marine fishes. Ecology 42: 812.

Randall, J. E. 1965. Grazing effect on sea grasses by herbivorous reef fishes in the West Indies. Ecology 46, No. 3: 255-260.

Randall, J. E. 1967. Food habits of reef fishes of the West Indies. Studies in Tropical Oceanography 5: 665-847.

Ritson-Williams, R. and V. J. Paul. 2005. Marine benthic cyanobacteria overgrow coral reef organisms. Coral Reefs 24: 629.

Rogers, C.S. 1990. Responses of coral reefs and reef organisms to sedimentation. Marine Ecology Progress Series 62: 185-202.

Samoilys, M.A. and G. Carlos. 2000. Determining methods of underwater visual census for estimating the abundance of coral reef fishes. Environmental Biology of Fishes 57: 89-304.

Sano, M., M. Shimizu and Y. Nose. 1984. Changes in structure of coral reef fish communities by destruction of hermatypic corals: observational and experimental views. Pacific Science 38, No. 1: 51-79.

Schmitt, E. F., D. F. Wells and K. M. Sullivan-Sealey. 1998. Surveying coral reef fishes. Coral Gables, FL: The Nature Conservancy, Marine Conservation Center.

SECREMP. 2006. 2005 Year 3 Final Report. FWCC/FWRI/NCRI report to Florida DEP Office of Coastal and Aquatic Managed Areas. Miami, FL.

Smith, S. V., W. J. Kimmerer, E. A. Laws, R. W. Brock and T. W. Walsh. 1981. Kaneohe Bay sewage diversion experiment: Perspectives on ecosystem responses to nutritional perturbation. Pacific Science 35, No. 4: 279-396.

Steidinger, K. A. 1983. A re-evaluation of toxic dinoflagellate biology and ecology. Progress in Phycological Research 2: 147-188.

52 Steinberg, P. D., K. Edyvane, R. de Nys, R. Birdsey and I. A. van Altena. 1991. Lack of avoidance of phenolic-rich brown algae by tropical herbivorous fishes. Marine Biology 109: 335-343.

Syms, C. and G. P. Jones. 2000. Disturbance, habitat structure, and the dynamics of a coral-reef fish community. Ecology 81: 2714–2729.

Thacker, R. W., D. W.Ginsburg and V. J. Paul. 2001. Effects of herbivore exclusion and nutrient enrichment on coral reef macroalgae and cyanobacteria. Coral Reefs 19: 318–329.

Voss, J. D. and L. I. Richardson. 2006. Nutrient enrichment enhances black band disease progression in corals. Coral Reefs 25, No. 4: 569-576.

Windom, H.L. 1992. Contamination of the marine environment from land-based sources. Marine Pollution Bulletin 25, No.1: 32-36.

Woodley, J. D., E. A. Chornesky, P. A. Clifford, J. B. C. Jackson, L. S. Kaufman, N. Knowlton, J. C. Lang, M. P. Pearson, J. W. Porter, M. C. Rooney, K. W. Rylaarsdam, V. J. Tunnicliffe, C. M. Wahle, J. L. Wulff, A. S. G. Curtis, M. D. Dallmeyer, B. P. Jupp, M. A. R. Koehl, J. Neigel and E. M. Sides. 1981. Hurricane Allen’s Impact on Jamaican Coral Reefs. Science 214, No. 4522: 749-755.

53