CIGUATERA IN FLORIDA KEYS PATCH REEFS:

BIOGEOGRAPHIC INDICATORS OF GAMBIERDISCUS

DENSITY AND TEMPORAL ABUNDANCE (CFP:BIG DATA)

A Thesis

Presented to

The Faculty of the College of Arts and Sciences

Florida Gulf Coast University

In Partial Fulfillment

of the Requirement for the Degree of

Master of Science

By

Meghan Elizabeth Hian

2018

APPROVAL SHEET

This thesis is submitted in partial fulfillment of the

requirements for the degree of

Master of Science

Meghan Elizabeth Hian

Approved:

Dr. Michael ParsonsCommittee Chair / Advisor

Dr. Michael Savarese

Dr. S. Gregory Tolley

The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable

presentation standards of scholarly work in the above mentioned discipline.

ABSTRACT

Ciguatera fish poisoning (CFP) is a global public health concern that is associated with

Gambierdiscus, a genus of harmful algae found in coral reef environments that includes species

known to produce toxins (ciguatoxins). Outbreaks of CFP have often been linked to elevated

abundance of Gambierdiscus cells and disturbance-related degradation of coral reefs. However,

the influence of human activities on CFP risk, both directly and indirectly within the broader

context of reef health, has yet to be defined for highly exploited patch reefs in the Florida Keys.

The objectives of this study were to define spatial and temporal patterns in reef health and

Gambierdiscus abundance across the three regions (Upper, Middle, Lower), to determine

whether the drivers of those patterns were natural or anthropogenic, and to identify

biogeographic indicators of risk. To address these objectives, this study combined field sampling

with a “big data” approach to spatial analysis. Six patch reefs (two per each of three regions)

were selected as study sites from existing research stations. Datasets from long-term monitoring

of benthic cover, fish species abundance, land use, and water quality were compiled and analyzed

in ArcGIS to characterize the ecological context of each site. Analysis of samples of host

macroalgae collected from all study sites biannually revealed that Gambierdiscus cell densities

were consistently highest in the Upper Keys and lowest in the Middle Keys, regardless of season.

Conversely, reef health was lowest in the Upper Keys and improved along a gradient to the Lower

Keys. Multivariate analysis of site similarity indicated that this regional pattern was driven more

strongly by grazing than substrate availability. Additionally, there is evidence that human

activities have an indirect influence on CFP risk through reef health, as well as through

overfishing, and the destruction of inshore habitats like seagrass and mangroves. Due to a strong

positive correlation with cell densities, this study suggests that mangrove cover could be useful

as a biogeographic indicator of potential CFP risk. Whereas surgeonfish, with a strong negative

correlation with cell densities, could indicate the actual flow of toxins into higher trophic levels.

The concordance of high regional risk and high population density necessitates continued

monitoring of fish in those areas and the development of more comprehensive predictor of

potential CFP outbreaks.

Acknowledgements

This study was funded in part by NOAA CiguaHAB Award # NA11NOS4780028.

I would like to express my sincere gratitude to my advisor Dr. Parsons for his continuous

support of my work and research. It was always a dream of mine to study dinos, and that dream

came true when I joined the Parsons benthic dinoflagellate research lab. The dinos that I got to

study were just a bit smaller by an order of magnitude or so. Regardless, I was extremely

fortunate to work with such a respected researcher who also turned out to be a cool boss, to

sample down in the keys and, even when faced with immediate tire blow-outs, extreme sun,

equipment loss, and one small fire, to still do science! I would like to thank my committee

members Dr. Tolley and Dr. Savarese for their time and support, both academically and

professionally. I feel incredibly lucky to be part of CWI and have thoroughly enjoyed working

with the faculty, staff and students as both a student and a colleague.

Thanks to the lab, especially Adam Catasus, Jeff Zingre, Nick Culligan, Jesse Elmore, Alex Leynse,

Anne Smiley, Andrea James, and Katie Ribble for making our eventful trips so enjoyable, and for

all of their help with collecting data, processing samples, and creative repairs. Thanks to my

fellow counters, Sammi Blonder and Jessica Schroeder, for making good musical choices in the

microscope room. And thanks to Dr. Venture for betting on me.

I cannot thank my husband Ryan enough for the endless encouragement, patience, and support

through the entire endeavor. Thanks to Sherman, Stanley, and Schnitzel for all of their “help”

with studying and writing, to my nephews Elijah and Elliot for adding the Moana soundtrack, to

my sisters Jenn and Allie for believing in me (and putting up with my weirdness), and to my

father for inspiring me to never give up.

Table of Contents 1 INTRODUCTION ................................................................................................................................................ 3

1.1 History of CFP ........................................................................................................................................................... 4

1.2 Biogeography of Gambierdiscus ............................................................................................................................... 5

1.3 Dynamics of a Harmful Algal Bloom ......................................................................................................................... 7

1.4 Dynamics of the Reef Environment ........................................................................................................................ 11

1.5 Anthropogenic Factors in the Florida Keys ............................................................................................................. 13

1.6 Link to CFP risk—Trophic Transfer .......................................................................................................................... 15

1.7 Research Objectives................................................................................................................................................ 16

2 METHODS ....................................................................................................................................................... 19

2.1 Description of Study Sites ....................................................................................................................................... 19

2.2 Sampling ................................................................................................................................................................. 20

2.3 Sample Processing .................................................................................................................................................. 22

2.4 Measures of Reef Health ........................................................................................................................................ 23

2.5 Anthropogenic Factors ........................................................................................................................................... 24

2.6 Geodatabase and GIS Synthesis .............................................................................................................................. 25

2.7 Data Analysis .......................................................................................................................................................... 26

2.8 Relation to Gambierdiscus Density & Temporal Abundance .................................................................................. 29

2.9 CFP Risk Calculation ................................................................................................................................................ 30

3 RESULTS .......................................................................................................................................................... 32

3.1 Patterns in Land Use ............................................................................................................................................... 32

3.2 Patterns in Water Quality ....................................................................................................................................... 35

3.3 Patterns in Key Fish Species Assemblages .............................................................................................................. 37

3.4 Patterns in Benthic Cover ....................................................................................................................................... 39

3.5 Patterns in Reef Health ........................................................................................................................................... 42

3.6 Patterns in Gambierdiscus Cell Densities ................................................................................................................ 44

3.7 Biotic and Environmental Correlations ................................................................................................................... 46

3.8 Regional Risk Assessment ....................................................................................................................................... 47

4 DISCUSSION .................................................................................................................................................... 49

4.1 Population Dynamics of Gambierdiscus spp. .......................................................................................................... 49

4.2 Land Use as a driver ................................................................................................................................................ 52

4.3 Anthropogenic Influence on the reef ..................................................................................................................... 54

4.4 Limitations .............................................................................................................................................................. 56

4.5 Conclusions & Management Implications .............................................................................................................. 57

5 References ...................................................................................................................................................... 60

2

TABLES

TABLE 2.7.1 SIRHI THRESHOLD VALUES FROM MCFIELD ET AL. (2011) ..................................................................................... 27

TABLE 2.7.2 FISH BIOMASS CONVERSIONS ............................................................................................................................ 28

TABLE 3.1.1 DISCRIMINATING LAND USES BY AREA WITHIN 10 KM (SIGNIFICANT CONTRIBUTIONS IN BOLD) ........................................ 33

TABLE 3.1.2 DISCRIMINATING LAND USES BY PERCENT COVER WITHIN 10 KM OF STUDY SITES (SIGNIFICANT CONTRIBUTIONS IN BOLD) .... 35

TABLE 3.2.1 TEMPERATURE AND SALINITY AT BOTTOM (B) OF STUDY SITES FROM 2010-2015 ....................................................... 36

TABLE 3.2.2 AVERAGE WATER QUALITY WITHIN 5KM AND ANTHROPOGENIC FACTORS WITHIN 10KM OF STUDY SITES. ........................ 37

TABLE 3.3.1 DISCRIMINATING FISH SPECIES BY ABUNDANCE (SIGNIFICANT CONTRIBUTIONS IN BOLD) ................................................ 38

TABLE 3.4.1 AVERAGE ABUNDANCE OF DISCRIMINATING BENTHIC COVER GROUPS (SIGNIFICANT CONTRIBUTIONS IN BOLD) .................. 41

TABLE 3.5.1 BENTHIC COVER AND KEY FISH BIOMASS VALUES AND HEALTH SCORES BY STUDY SITE AND REGION ................................ 43

TABLE 3.7.1 BEST RESULTS FOR MULTI-CORRELATIONS WITH 5 OR FEWER VARIABLES ................................................................... 46

TABLE 3.8.1 REGIONAL ASSESSMENT OF POTENTIAL TOXIN AVAILABLE TO THE CONSUMER PER G COMMERCIAL FISH ............................. 47

FIGURES

FIGURE 2.2.1 MAP OF STUDY SITES IN EACH REGION; TP=TWO PATCHES, BF=BURR FISH, RR=RAWA REEF, DR=DUSTAN ROCKS, WL=

WONDERLAND, WW= WEST WASHERWOMEN ............................................................................................................. 21

FIGURE 3.1.1 CLUSTER ANALYSIS OF SQUARE ROOT TRANSFORMED AREAS OF LAND USE WITHIN 10 KM OF SITES. SAMPLES CONNECTED BY

RED LINES ARE NOT SIGNIFICANTLY DIFFERENTIATED BY SIMPROF. REGION 1=UPPER KEYS, 2=MIDDLE KEYS, 3=LOWER KEYS. ... 32

FIGURE 3.1.2 CLUSTER ANALYSIS OF SQUARE ROOT TRANSFORMED PERCENT COVER OF LAND USE WITHIN 10KM OF SITES. SAMPLES

CONNECTED BY RED LINES ARE NOT SIGNIFICANTLY DIFFERENTIATED BY SIMPROF. REGION 1=UPPER KEYS, 2=MIDDLE KEYS, 3=LOWER KEYS. ...................................................................................................................................................... 34

FIGURE 3.3.1 CLUSTER ANALYSIS OF SQUARE ROOT TRANSFORMED DENSITIES OF KEY FISH SPECIES WITHIN 5KM OF SITES. SAMPLES

CONNECTED BY RED LINES ARE NOT SIGNIFICANTLY DIFFERENTIATED BY SIMPROF. REGION 1=UPPER KEYS, 2=MIDDLE KEYS, 3=LOWER KEYS. ...................................................................................................................................................... 37

FIGURE 3.4.1 CLUSTER ANALYSIS OF BRAY-CURTIS SIMILARITY ON SQUARE ROOT TRANSFORMED BENTHIC COVER DATA. SAMPLES

CONNECTED BY RED LINES ARE NOT SIGNIFICANTLY DIFFERENTIATED BY SIMPROF. REGION 1=UPPER KEYS, 2=MIDDLE KEYS, 3=LOWER KEYS. ...................................................................................................................................................... 39

FIGURE 3.4.2 CLUSTER OVERLAY ON NMDS; REGION 1=UPPER KEYS, REGION 2=MIDDLE KEYS, REGION 3=LOWER KEYS................ 40

FIGURE 3.5.1 BENTHIC COVER ACROSS ALL STUDY SITES FROM 2010 TO 2015; UPPER KEYS=TWO PATCHES, BURR FISH; MIDDLE

KEYS=RAWA REEF, DUSTAN ROCKS; LOWER KEYS= WONDERLAND, WEST WASHERWOMEN. ................................................ 42

FIGURE 3.6.1 SEASONAL CELL DENSITY OF GAMBIERDISCUS SPP. ON H. GRACILIS WITH STANDARD ERROR; CROSS-HATCHED BAR REPRESENTS

MISSING DATA ......................................................................................................................................................... 44

FIGURE 3.6.2 MEAN CELL DENSITY OF GAMBIERDISCUS SPP. ON H. GRACILIS WITH STANDARD ERROR ............................................... 45

3

1 INTRODUCTION

Ciguatera Fish Poisoning (CFP), once thought to be a tropical disease confined within the latitudes

of 35°N and 35°S, is emerging as a global public health concern that may affect as many as half a

million people each year (Bravo et al., 2015; Radke et al., 2015; Roeder et al., 2010; Lehane &

Lewis, 2000). CFP is an illness caused by ingesting fish—typically reef fish—that have accumulated

naturally-occurring toxins (ciguatoxins). Unlike other forms of food-borne illness, CFP is not

caused by improper storage, handling, or preparation of the fish (Thompson et al., 2017).

Ciguatoxins (CTXs) are lipid-soluble and heat stable, meaning that a fish may remain unsafe to

eat after extended periods of freezing as well as after cooking (Friedman et al., 2017).

Additionally, CTXs are colorless, tasteless, and odorless, making them difficult to detect in a raw

fillet or a prepared meal. Once ingested, the specific set of symptoms experienced due to CTX

exposure appear to depend upon the location from which the fish was obtained (i.e., Pacific

Ocean versus Caribbean Sea versus Indian Ocean). This variability is likely attributable to the

chemical structure of CTX, which differs slightly by geographic region (Friedman et al., 2017;

Lehane & Lewis, 2000). For example, intoxication by C-CTX 1 (found in the Caribbean) is

characterized by acute gastrointestinal symptoms, such as diarrhea, nausea, and vomiting, that

occur within several hours of eating ciguatoxic fish, which are later followed by neurologic

symptoms, such as tingling sensations, itchy skin, and cold allodynia (reversal of hot-cold

sensation), and occasionally cardiac symptoms, such as hypotension (low blood pressure) and

reduced heart rate (FDA, 2011; Friedman et al., 2017). Generally, acute symptoms resolve within

several days but may be followed by chronic fatigue and recurrence of neurologic symptoms

4

(Friedman et al., 2017). The U.S. Food and Drug Administration (FDA) has established the action

level for Caribbean toxin levels at 0.1 ppb for C-CTX-1; however, a rapid test has yet to be

validated to screen fish at this low concentration (2011). Therefore, advisories for CFP and control

of potentially harmful seafood is still largely reactionary to reports of illness and anecdotal

evidence of “hotspots” to avoid.

1.1 History of CFP

The term ciguatera first appeared in a book published in Havana, Cuba in 1787 and was initially

used to describe an illness contracted after eating a certain type of sea snail (Turbo pica),

(Scheuer, 1994). Later, the definition was refined to refer specifically to an intoxication caused

by the ingestion of coral reef fishes. Although accounts of CFP date back to the 1500s in the

Americas, and records from Captain James Cook in 1774 describe a probable case in the Pacific

(Scheuer, 1994), there is evidence to suggest that it affected coastal societies much earlier.

According to Rongo et al. (2009), CFP may have even induced the great oceanic voyages of the

Polynesians from A.D. 1000 to 1450. Research on CFP in the U.S. was pioneered by a Navy

physician named Bruce Halstead who took an interest in the phenomenon during WWII while in

the Pacific theatre (Scheuer, 1994). At this point, the exact source of the toxin was still unknown,

though there was mounting evidence of a trophic connection with benthic algae (Randall, 1958;

Halstead, 1965). After several decades of investigation, microscopic algae from the genus

Gambierdiscus (Adachi and Fukuyo, 1979) were definitively linked to CFP by Yasumoto et al.

(1977; originally called Diplopsalis) and confirmed to produce the ciguatera toxins (Bagnis et al.,

1980; Lehane & Lewis, 2000). Once thought to be monospecific (G. toxicus), the genus

5

Gambierdiscus now comprises over a dozen described species of morphologically similar armored

photosynthetic dinoflagellates (Fraga & Rodriguez, 2014; Richlen et al., 2008). Dinoflagellates are

a diverse group of phytoplankton that is distinguished by their two unequal flagella, which aid in

the motility of the cells. Perhaps due to their ability to swim, dinoflagellates are able to flourish

under a diverse set of environmental conditions and have an extensive fossil record dating back

several hundred million years (Hackett et al., 2004). Unlike their naked counterparts like Karenia

brevis (responsible for red tides in Southwest Florida), armored dinoflagellate cells are covered

by a theca, made up of plates of cellulose or other polysaccharides, that are arranged in distinct

patterns within their membranes (Hackett et al., 2004). Despite the utility of this pattern as a

taxonomic identifier, the small scale of the differences within the genus Gambierdiscus makes it

difficult to identify species using light microscopy alone (Litaker et al., 2010).

1.2 Biogeography of Gambierdiscus

Of the currently described species, two (G. caribaeus and G. carpenteri) have a cosmopolitan

distribution (Litaker et al., 2010), with populations of G. caribaeus representing the most

commonly found species in both the Atlantic and Pacific (Litaker et al., 2009). Likely, the

distribution of these two species is a reflection of their broad temperature tolerance relative to

others in the genus (Tester et al., 2010). Species endemic to the Atlantic include G. belizeanus, G.

carolinianus, G. ruetzleri (now genus Fukuyoa), G. ribotype 2, G. silvae (previously G. ribotype 1),

and G. excentricus (Litaker et al., 2017; Fraga & Rodriguez, 2014; Tester et al., 2013; Litaker et al.,

2010). Aside from G. caribaeus, G. carpenteri and F. yasumotoi, populations of Gambierdiscus in

the Atlantic are phylogenetically distinct from those in the Pacific (G. australes, G. pacificus, G.

6

polynesiensis, and G. toxicus) (Litaker et al., 2010). It has been suggested that this geographic

divergence may be traced back to the period between the Miocene and the Pleistocene when

the closing of the Tethys Sea and the formation of the Isthmus of Panama disrupted the

circumtropical flow of the sea (Rodriguez et al., 2017). Vicariance during this period seems to be

supported by the high biodiversity of Gambierdiscus now found in the Atlantic, Caribbean, and

Gulf of Mexico, often with five or more different species present in the same location (Rodriguez

et al., 2017; Tester et al., 2013). Due to the lack of appropriate genetic markers, little is known

about the population structure of Gambierdiscus species, and studies of connectivity and

dispersal have only recently become possible with newly developed microsatellite methodology

(Sassenhagen & Erdner, 2017; Kuno et al., 2010). Nevertheless, the biogeographic range of

Gambierdiscus appears to be expanding into areas without a history of CFP, such as the northern

Gulf of Mexico (Tester et al., 2013; Villareal et al., 2007), East Asia (Kuno et al., 2010), and the

Canary Islands (Rodriguez et al., 2017; Bravo et al., 2015; Fraga & Rodriguez, 2014), though it is

possible that these areas harbored existing populations that had been previously overlooked or

understudied. Gambierdiscus spp. have been observed rafting on drift algae (Bomber et al.,

1988), making dispersal to new areas possible, especially in areas where artificial reefs can act as

“stepping stones” (Villareal et al., 2007). With dispersal, climate change could also play a role in

the alteration of the biogeographic range, as novel habitats that begin to fall within the

temperature tolerance of particular species could be colonized.

7

1.3 Dynamics of a Harmful Algal Bloom

Generally, Gambierdiscus spp. are found in shallow (<50 m) tropical and subtropical marine reef

habitats characterized by less than 10% of incident light (Litaker et al., 2010), stable salinities

between 29 and 34 ppt (Kibler et al., 2012; Parsons et al., 2010), annual water temperatures

between 18° and 33°C (Litaker et al., 2010; Tester et al., 2010; Chateau-Degat et al., 2005; Chinain

et al., 1999; Hales et al., 1999), and abundant natural or artificial reef substrates (Parsons et al.,

2017; Villareal et al., 2007). As motile cells, they have been observed within the water column

and swimming in the epibenthos (Parsons et al., 2011; Nakahara et al., 1996); however,

Gambierdiscus cells are predominately epiphytic and often attach to organic substrates such as

macrophytes and algal turfs (Parsons et al., 2011). Although attachment to inorganic structures

has also been noted (Parsons et al., 2017; Villareal et al., 2007), most studies have focused on

macroalgal substrates, as those are the most likely vector of CTX (via herbivory) into the food

web (Rains & Parsons, 2015). Additionally, previous studies have suggested that macroalgal hosts

exude substances that can either stimulate or inhibit Gambierdiscus growth (Parsons et al., 2011),

though the role of exudates in attachment behavior and substrate may be masked by the effects

of other environmental variables. For example, attachment behavior may also be affected by

changes in light conditions, local physical disturbance, or the presence of bacteria or other

epiphytes that drive competition and may play a role in growth inhibition (Sakami et al., 1999;

Nakahara et al., 1996; Tosteson et al., 1989). Further, differences in epiphytic behavior in relation

to a variety of macroalgal substrates could be attributed to interspecific host preferences

exhibited by the Gambierdiscus themselves (Rains & Parsons, 2015). Likely, this variability in

8

behavior and preference is a function of differential environmental tolerances among species in

the genus.

Gambierdiscus cells grow at a relatively slow rate of about one division every three days

(Lehane & Lewis, 2000). Although growth has been shown to be influenced by temperature,

salinity, and irradiance in the lab (Xu et al., 2016), the slow growth rate introduces complexity to

the relationship with bloom dynamics in the field due to the temporal scale of local

environmental variability. Typically, researchers have used cell densities, or an elevated

abundance of Gambierdiscus cells present on their macroalgal hosts, as a proxy for CFP risk

(Parsons et al., 2010). Although Gambierdiscus spp. are considered to be a type of harmful algal

bloom (HAB) (Grattan et al., 2016; Anderson et al., 2008), it has been difficult to characterize the

threshold at which a bloom occurs. The literature has suggested that a bloom occurs when the

local density exceeds 1,000 cells g-1 wet weight algae (Litaker et al., 2010). However, abundance

alone may not truly be indicative of the potential for a CFP outbreak, as there is variability in CTX

production within the genus Gambierdiscus. Although CTX production has been shown to vary

with environmental factors, such as temperature, salinity, light, and nutrients (Chinain et al.,

2010; Morton et al., 1992; Holmes et al., 1991; Bomber et al., 1988), no consistent pattern of

seasonality was observed across regions, and no correlation was found between toxicity of these

blooms and their biomass (Chinain et al., 1999). Because many of these studies were done when

Gambierdiscus was still considered to be a single species, the observed variability in toxicity in

relation to environmental factors may be confounded with other interspecific differences in

biology, physiology, and ecology (Parsons et al., 2012). Therefore, the risk of toxicity in a given

area is understood to depend more on the clonal nature of cells within the local populations than

9

seasonal or environmental factors (Chinain et al., 1999), due to the fact that the ability to produce

CTXs appears to be genetically determined (Chinain et al., 2010; Roeder et al., 2010; Richlen et

al., 2008).

Of the seven species identified in the Atlantic, all have been reported to produce some

amount of CTXs, with the slowest growing species observed to exhibit the highest toxicity per cell

(Litaker et al., 2017; Tester et al., 2013; Chinain et al., 2010). A recent study characterized

Gambierdiscus excentricus as highly toxic, Gambierdiscus silvae and Gambierdiscus ribotype 2 as

moderately toxic, Gambierdiscus belizeanus, Gambierdiscus caribaeus, Gambierdiscus carpenteri

as mildly toxic, and Gambierdiscus carolinianus to be essentially non-toxic (Litaker et al., 2017).

Although toxicity was observed to vary significantly among species, toxicity within a given species

could still vary by a factor of about 1.5 (Litaker et al., 2017). In a study comparing Atlantic with

Pacific strains, researchers found that G. polynesiensis (South Pacific) and G. excentricus (Eastern

Atlantic) could be considered the primary toxin producers in their respective regions (Pisapia et

al., 2017). Given the chance to bloom, even species with mild to moderate toxicity may

contribute significant levels of CTXs to the food web (Pisapia et al., 2017). Because of this

disparity, it will be important to map abundances of the different Gambierdiscus species in the

field to reconcile the bloom threshold with the strain-dependent differences in toxin production

and the relative contribution to the toxin flux (Litaker et al., 2017; Pisapia et al., 2017; Chinain et

al., 2010).

The potential impact of climate change has also been investigated in terms of historical

or recorded sea surface temperature (SST), reported incidence of CFP, and Gambierdiscus spp.

growth (Kibler et al., 2015; Llewellyn, 2010; Tester et al., 2010; Rongo et al., 2009; Hales et al.,

10

1999). Although there may be a complex association with El Niño Southern Oscillation (ENSO) or

other regional decadal oscillations (Rongo et al., 2009; Hales et al., 1999), no clear corresponding

global trend with increasing sea surface temperature has been observed (Radke et al., 2015). In

a Pacific study, the incidence of CFP was observed to increase when SST remained above a lower

limit of 24° C for long enough to promote the production of sufficient levels of ciguatoxin in the

ecosystem but to decrease with persistent SST above an upper limit of 29°C (Llewellyn, 2010). A

similar lower threshold (SST > 25°C) was observed in the Atlantic (Caribbean); however,

maximum growth of Gambierdiscus spp. and increased risk of CFP was expected with SST above

29°C (Tester et al., 2010). In addition to elevated SST, an increase in storminess was also linked

to an 11% increase in reports of CFP (Gingold et al., 2014; Barrett, 2014). Although the observed

relationship with climate was significant, socioeconomic factors, such as knowledge of CFP,

access to medical services, and underreporting of CFP cases, obfuscated its magnitude (Tester et

al., 2010). For example, in Florida, it has been estimated that fewer than 20% of ciguatera cases

are reported (Radke et al., 2015). Furthermore, implications for utilizing SST or storminess as

predictors of CFP risk were severely limited by uncertainty surrounding the lag (5-18 months)

between the ecological response to meteorological conditions and the actual consumption of

toxic fish. Although a probable factor in CFP risk, a more complex association may exist between

climate and the coral reef habitat in which Gambierdiscus is found.

11

1.4 Dynamics of the Reef Environment

Coral studies in the Florida Keys, which began in the 1850s to improve ship navigation (Somerfield

et al., 2008), have only recently begun to explore the dynamics of coral reef benthic assemblages

(Francini-Filho et al., 2013; Ruzicka et al., 2013; Ruzicka et al., 2010). Further, when compared

with macroalgae in reef communities, microalgal species are poorly understood (Heil et al.,

2004). Typically, elevated abundances (blooms) of Gambierdiscus spp. are linked to degradation

of the reef ecosystem (Sparrow et al., 2016; Anderson et al., 2008; Lapointe et al., 2004).

Following a disturbance to the reef, macroalgae can exploit the exposed surfaces of dead corals,

thus providing additional substrate on which Gambierdiscus populations can proliferate (Rains &

Parsons, 2015; Turquet et al., 2000; Bagnis, 1994). These disturbances include both natural

phenomena, such as hurricanes (Aronson et al., 2003), ENSO (Ruzicka et al., 2013), and climate

change (Barrett, 2014), as well as anthropogenic pressures from land use, elevated nutrient

inputs, boating or shipping traffic, and fishing (Parsons et al., 2010; Somerfield et al., 2008; Bagnis

et al., 1994; Myers & Ewel, 1990). Coral reef ecosystems that host Gambierdiscus spp. are

extremely sensitive to physical changes in their environment. Since the 1980s, reefs worldwide

have experienced the devastating effects of coral bleaching (Baker et al., 2008). Between 1996

and 1999, approximately 40% of coral cover in the Florida Keys was lost to bleaching and disease

associated with a warm-water ENSO event (Ruzicka et al., 2013). While temperatures greater

than 30° C are the leading cause of bleaching and the subsequent degradation of reef habitats

(Tester et al., 2010), cold-water events have also resulted in significant coral mortality in the

Florida Keys. Owing to the fact that corals in the Keys are near the northern limit of their

biogeographical range, they are especially susceptible to intrusions of water with temperatures

12

below their thermal survival threshold (Colella et al., 2012). Following an intrusion of cold Arctic

air that led to a multi-day cold-water event in early 2010, widespread coral mortality and

bleaching were observed in nearshore patch reefs throughout the reef tract. Because these patch

reefs had previously demonstrated resistance to thermal stressors that impacted other reef

types, the extent of the mortality following this event was significant (Colella et al., 2012). Patch

reefs, which generally have the highest stony coral cover of any reef type in the Florida Keys,

have seen little to no recovery (Ruzicka et al., 2013). Overall, coral recovery has been severely

limited (Baker et al., 2008), and vulnerability to thermal stress and disease may be amplified by

poor water quality, land-based nutrient enrichment (eutrophication), and other environmental

stressors (Yee et al., 2011; Wagner et al., 2010; Ward-Paige et al., 2005; Bruno et al., 2003; Harvell

et al., 1999; Woolfe & Lacombe, 1999). Moreover, coral resilience and recovery could be further

disrupted as calcification rates are reduced by ocean acidification (Baker et al., 2008); and in the

face of continued anthropogenic pressure, reef degradation could accelerate.

In concert with warming seas, this widespread degradation of coral may provide

Gambierdiscus spp. an opportunity to thrive by allowing for the proliferation of macroalgae and

conditions favorable for growth of Gambierdiscus spp. (Tester et al., 2010; Cruz-Rivera and

Villareal, 2006; Turquet et al., 2000). Macroalgae have been shown to proliferate temporarily in

the aftermath of major disturbances such as ENSO (1997/1998), hurricanes (2005), and winter

cold-water mortality events (Ruzicka et al., 2013). Though some researchers have reported large-

scale phase shifts from stony coral to macroalgal dominance in the Caribbean and Western

Atlantic (Norström et al., 2009; Maliao et al., 2008; Lirman & Biber, 2000; Hughes, 1994), a

prolonged shift towards sustained macroalgal dominance has yet to occur in the Florida Keys

13

(Ruzicka et al., 2010). However, if storminess and rainfall increase as predicted, the potential for

land-based nutrient-driven macroalgal blooms capable of overgrowing the corals will also

increase (Wenger et al., 2016; Somerfield et al., 2008; LaPointe et al., 2004). In addition to runoff,

rainfall is also known to cause offshore fluxes of groundwater contaminated with nutrients

(nitrogen) from septic tanks (Lapointe, 1997; Lapointe & Clark, 1992). Macroalgae, which can

rapidly uptake and store nutrients, often exhibit enhanced growth rates following such pulses

(Leichter et al., 2003). Similarly, growth of Gambierdiscus has been shown to respond

advantageously to sudden high concentrations of nutrients in the normally oligotrophic reef

environment (Leynse, 2016).

1.5 Anthropogenic Factors in the Florida Keys

Over the past few decades, development in the Florida Keys has significantly increased the

discharge of wastewater nutrients into sensitive coastal ecosystems (Lapointe & Matzie, 1996).

Although elevated nutrient levels have been reported in many coastal areas, patch reefs have

been somewhat protected from the full extent of the nutrient load from shore by nearshore algal

and seagrass communities (Szmant & Forrester, 1996). However, over 30,000 acres of seagrass

in southern Florida have been damaged by boat propellers (Crossett et al., 2008), and increasing

annual mean concentrations of dissolved inorganic nitrogen (DIN) and soluble reactive

phosphorus (SRP) have been recorded at nearby coral reefs (Lapointe et al., 2004). Coastal

eutrophication in other areas of southern Florida has been hypothesized to correspond to the

increase in human population, which would be expected to produce more sewage, more runoff

through development and altered hydrology, and more disturbance to coastal ecosystems

14

important to the sequestration of nutrients (Brand & Compton, 2007). Land use has also been

demonstrated to have an effect on the composition of benthic assemblages (Harding &

Winterbourn, 1995). A recent study coupled low natural (complex) ground cover with decreases

in coral cover due to increased water flow and energy available to dislodge potentially harmful

sediments (Roberts et al., 2017). Studies have also indicated the existence of a relationship

between coastal development and an increase in the occurrence and intensity of harmful algal

blooms (Anderson et al., 2008; Brand & Compton, 2007).

Historically, the Florida Keys had supported a low population density due to a lack of

freshwater resources (McClenachan et al., 2013). As of 1900, all of South Florida (Broward, Collier,

Dade, Hendry, Lee, Monroe, and Palm Beach counties) boasted a total population of only 24,000,

with the majority of settlements located within a mile of estuaries and beaches (Walker et al.,

1997). Much of the land cover in this area remained in its natural state until the completion of a

railway in 1912, which allowed tourists and potential residents easy access to the coast and

opened the region to intense residential and commercial development (McClenachan et al.,

2013; Walker et al., 1997). More recently, the population of Monroe County, which primarily

resides in the Florida Keys, peaked at 78,000 in 2008 (Crossett et al., 2008) and has dropped

slightly following the great recession (Monroe County, 2011). Although population growth rates

have declined, recent evidence indicates that the rates of land use and intraregional migration

continue to be high (Walker et al., 1997). In 2010, there was a net inbound migration of 3,700

with the vast majority coming from other areas within the state of Florida (Brunner, 2012). In

addition to permanent or seasonal residents, tourist visitation in the Florida Keys is estimated to

exceed four million people annually (McClenachan et al., 2013). Reef-related tourism and

15

recreational activities, which account for over 14 million person-days per year, generate an

estimated $6.2 billion in income and supported over 250,000 full and part-time jobs in the area

(Crossett et al., 2008). Although human populations derive great social and economic value from

coral reefs, anthropogenic impacts, such as physical damage from recreational boating and

fishery exploitation, often lead to the degradation of these ecosystems (Baker et al., 2008; Lidz

et al., 2006).

1.6 Link to CFP risk—Trophic Transfer

Seen as an angler’s paradise since the 1860s, the Florida Keys have been characterized by a high

reliance on coral reef fisheries (McClenachan et al., 2013; Ault et al., 2013). Research has shown

that fishing impacts in the Florida Keys are highest near human population centers such as Key

Largo, Marathon, and Key West, with intensity effectively decreasing along a gradient from

northeast to southwest (Ault et al., 2005). Since the late 1970s, reef fisheries in the Keys have

heavily targeted the Lutjanidae and Serranidae, the snapper-grouper complex (Ault et al., 2005).

Although these families of predatory fishes are known vectors of CTX in many parts of the world

(Yang et al., 2016), and the grouper-snapper complex species are considered “high-risk” in the

Florida Keys (Radke et al., 2015; deSylva, 1994), overfishing continues to be a problem (Ault et

al., 2013). Due to a lack of ecological studies conducted on fish contaminated with CTXs (O’Toole

et al., 2012), the risk to human health associated with exploitation of these fisheries remains

unclear. Generally, the primary means through which CTX is thought to move into higher trophic

levels is the predation of herbivorous fish that had fed on Gambierdiscus-associated macroalgae

(Cruz-Rivera & Villareal, 2006). However, the precise pathways and mechanisms of

16

bioaccumulation and biomagnification are complex and may be region-specific (Yang et al., 2016;

Parsons et al., 2010). Regardless, herbivorous fishes, especially those from the families Scaridae

(parrotfish) and Acanthuridae (surgeonfish), play an important role in the reef ecosystem by

providing top-down control of macroalgal encroachment of corals (Jackson & Johnson, 2014;

Kopp et al., 2010). Through grazing, coral reef herbivores can remove over 90% of the palatable

macroalgal biomass on a daily basis (Cruz-Rivera & Villareal, 2006). For this reason, factors that

negatively affect reef fish populations, such as land-based pollution (Jackson & Johnson, 2014),

overfishing (Ault et al., 2013; Ault et al., 2005), and destruction of seagrass and mangroves that

provide vital habitat to juvenile fishes (Serafy et al., 2015), are serious threats to the health of

the reef ecosystem. Furthermore, grazers such as parrotfish and surgeonfish likely play an

important role in moderating the trophic transfer of CTX, as limited grazing has been associated

with a significant increase in the abundance of Gambierdiscus cells (Loeffler et al., 2015). If the

cells are being grazed upon directly or indirectly, the subsequent flow of CTXs into commercially

important predatory fish puts human health at risk.

1.7 Research Objectives

Over the last few decades, the prevalence of HABs has been on the rise (Anderson et al., 2008).

While this increase is partly due to a growing appreciation for their impact on the economy and

public health, as well as better detection, monitoring, and communication by scientists, it is also

stimulated by factors associated with the growing human population (Grattan et al., 2016;

Anderson et al., 2008). Currently, most cases of CFP in the mainland U.S. are reported from

Florida (Villareal et al., 2007), with higher incidences predicted for the Gulf of Mexico and south

17

Atlantic coast (Kibler et al., 2015; Gingold et al., 2014). However, as the human population

becomes more interconnected through trade and travel, the risk for CFP may also increase in

non-endemic areas (Yang et al., 2016; Bravo et al., 2015). For example, a case of CFP was linked

to farm-raised salmon (a cold-water fish) that had likely been fed a diet that contained

contaminated reef fish (DiNubile & Hokama, 1995). Because there are neither routine clinical

tests nor cures for HAB-related syndromes like CFP, prediction and prevention are key to

managing public health risks (Grattan et al., 2016). In spite of the fact that CFP is the most

common non-bacterial seafood poisoning worldwide, outbreaks remain difficult to predict due

to their sporadic nature and large degree of spatial and temporal variability (Rains & Parsons,

2015; Richlen et al., 2008). As mentioned previously, associations between disturbance-related

degradation of coral reefs, increased macroalgal cover, and outbreaks of ciguatera have often

been described (Anderson et al., 2008; Lapointe et al., 2004; Turquet et al., 2000; Bagnis, 1994);

however, empirical evidence for such a link has been lacking (Parsons et al., 2010). Even though

Gambierdiscus has been studied in the Florida Keys since the 1980s (Babinchak et al., 1986;

Bomber et al., 1988), little is known about the biogeography of Gambierdiscus spp. within the

patch reefs of the Florida Keys reef tract. Further, the influence of human activities on

Gambierdiscus abundance, both directly and indirectly within the broader context of reef health,

remains unclear. Thus, the potential risk of CFP from these heavily exploited reefs has yet to be

defined and contextual indicators of risk are not currently available to aid in prediction.

The objectives of this study were to define spatial and temporal patterns in reef health

and Gambierdiscus abundance across patch reefs in the three regions of the Keys (Upper, Middle,

Lower), to determine whether the drivers of those patterns were natural or anthropogenic, and

18

to identify biogeographic indicators of risk. To address these objectives, this study combined field

sampling with a “big data” approach to spatial analysis. The term “big data” describes the various

types of monitored and measured data from repositories or virtual databases that are used to

examine possible relationships through statistical analyses, specifically correlations with

potential drivers of a response (Peters et al., 2014). As these datasets rapidly expand with citizen

science initiatives and advancing GIS, monitoring, and genomic sequencing technologies,

paradigm shifts that depend on “big data” have been suggested as the future of ecology and the

environmental sciences (Peters et al., 2014). Biogeography studies, in particular, stand to benefit

from a “big data” approach, as they require data collected over relatively large spatial and

temporal scales (Devictor et al., 2010), and the integration of historic “big data” with new data

provides an efficient way to yield powerful insights with limited resources (Peters et al., 2014).

Datasets were obtained from a variety of existing monitoring and planning efforts such as the

Florida Fish and Wildlife Research Institute’s (FWRI) Coral Reef Evaluation and Monitoring Project

(CREMP), The Reef Environmental Education Foundation’s (REEF) Volunteer Fish Survey Project,

the Southeast Environmental Research Center’s (SERC) Water Quality Monitoring Network, and

the Monroe County Planning and Environmental Resources Department. These datasets were

synthesized with sample data collected from the study sites to characterize the role spatial

patterns in the reef and terrestrial environments play in the CTX pathway.

19

2 METHODS

2.1 Description of Study Sites

The Florida Keys are a chain of islands of the southern tip of the Florida (U.S.) peninsula that

stretch from the south of Biscayne Bay toward Key West and the Dry Tortugas in a gradual

westward arc. The islands currently lie on top of a fossil coral reef that flourished about 125,000

years ago during a sea level high stand in the late Pleistocene (Halley et al., 1997). Geologically,

the island chain can be divided into two distinct sections (Upper and Lower) at Pigeon Key. The

Upper Keys are narrow islands of coralline limestone that are aligned parallel to the island arc,

and the Lower Keys are wider, composed of lithified Pleistocene oolitic shoals with a

perpendicular orientation (Zhang et al., 2011). The present-day reef tract that lies offshore of the

Florida Keys islands is the only living coral reef in North America (Lapointe & Matzie 1996).

Generally, the reefs and their accompanying islands are partitioned into three regions, Upper,

Middle, and Lower, as defined by geographic and environmental criteria (Ginsburg and Shinn

1994). The Lower Keys reefs are located offshore from Key West to Big Pine Key; the Middle Keys

reefs extend east-northeast from Pigeon Key to Upper Matecumbe (Zhang et al., 2011); and the

Upper Keys reefs comprise the area from Key Largo to Elliot Key (Ginsburg & Shinn, 1994; Monroe

County, 2010).

Within each region, reefs can be divided into two habitat types: bank reefs, which are

located along the edge of the Florida shelf; and patch reefs, which lie inshore of the bank reefs

in Hawk Channel (Colella et al., 2012; Lirman & Fong, 2007). Because inherent environmental and

20

ecological differences exist between the habitat types (Lirman & Fong, 2007), this study focused

exclusively on patch reefs. Patch reefs of the Florida Keys are assemblages of massive, long-lived,

framework-building corals that exhibit a complex vertical structure that may be several meters

high (Colella et al., 2012). Due to the influence the geological history of the islands had on reef

formation, the condition of patch reefs in one region may not be generalizable to the tract as a

whole (Ginsburg & Shinn, 1994). Therefore, it was essential to include patch reefs from all three

regions (Upper, Middle, Lower) in this study. From the list of sites that had been monitored by

the Coral Reef Evaluation and Monitoring Project (CREMP), two study sites in each region were

selected based on habitat type and accessibility. These sites included: Burr Fish and Two Patches

in the Upper Keys; Rawa Reef and Dustan Rocks in the Middle Keys; and Wonderland and West

Washerwomen in the Lower Keys (Fig. 2.1.1). All study sites are situated between the latitudes

of 24.9992 and 24.5475 degrees N and -80.4669 and -81.5866 degrees W. Distance offshore

ranged from 37 km, and site depth varied between 2 and 7 meters.

2.2 Sampling

Samples of macroalgae were collected in triplicate from all sites once per season

(Summer/Winter) over two consecutive years in accordance with procedures described by

Parsons et al. (2017). Several species of macroalgae known to host Gambierdiscus populations in

most areas, including Halimeda spp., Dictyota spp., Laurencia sp., and Thalassia sp., were

selected as potential targets for collection; however, only Halimeda gracilis (Harvey ex J.Agardh

1887) was found to be present at all sites during the summer and was collected exclusively. To

collect macroalgae samples, a 50-ml tube was placed over the Halimeda, a careful cut was made

21

at the bottom of its thallus, and the cap was quickly closed to prevent the loss of any epiphytes

that may have been dislodged by disturbance. After collection, the labeled sample tubes were

kept in a cooler on top of ice to prevent exposure to extreme heat or cold.

Figure 2.2.1 Map of Study Sites in each region; TP=Two Patches, BF=Burr Fish, RR=Rawa Reef, DR=Dustan Rocks, WL= Wonderland, WW= West Washerwomen

22

2.3 Sample Processing

Within a few hours of collection, the liquid content of the sample tubes was filtered through a

series of PVC sieves (a 200-µm sieve placed on top of a 20-µm sieve) to separate the epiphytes

from the macroalgal sample per Parsons et al. (2017). The cap of a sample tube was removed,

and up to one half of the liquid was poured into the top (200-µm) sieve. Once the liquid had

filtered completely through the set, the cap was replaced and the sample tube was shaken

vigorously to dislodge epiphytes. The remaining liquid content was then immediately poured

onto the sieve and allowed to filter through the series. Stored seawater of a similar salinity to the

collection site that had been filtered using the 20-µm PVC sieve was used to refill the sample tube

about two thirds of the way. The contents of the tube were then shaken, poured into the sieve,

and refilled with filtered seawater as previously described for a total of five filtration cycles. Once

the final cycle had drained through the sieves completely, the top 200-µm PVC sieve was

removed and a squirt bottle filled with Keller’s media was used to rinse the epiphyte sample

collected on the bottom 20-µm PVC sieve into a labeled 15-mL tube, bringing the volume to

exactly 14 mL. To preserve the epiphyte samples, 1 mL of 1% glutaraldehyde (by volume) was

added to each 15-mL tube. Prior to filtering a new sample, the set of sieves was rinsed thoroughly

with freshwater and the 50-mL tube containing the original macroalgae sample was filled with

filtered seawater to prevent desiccation. All tubes were stored on ice in a cooler, transported

back to the lab, and then transferred to a 4° C refrigerator pending further analysis.

In the lab, algae were removed from 50-mL tubes, blotted dry, and weighed on a Mettler

Toledo AL204 balance to establish g wet weight of each sample. The abundance of Gambierdiscus

cells was determined by transferring 3 mL of the epiphyte sample stained with Uvitex

23

(Polysciences, Ltd., cat. #19517-10) into each of three wells in a six well flat-bottomed culture

plate and performing cell counts on an Olympus IX71 inverted microscope using a DAPI filter

(Parsons et al., 2017). Species identification was not possible at this level of microscopy. Sample

cell densities were determined by summing the cell counts from the three wells and extrapolating

based on the subsample proportion factor (Parsons et al., 2017). That is, the sum of the counts

was divided by the total volume counted (9 mL) and then multiplied by the total sample volume

(15 mL). This abundance value was then divided by the macrophyte wet weight to provide a

density value for each sample expressed as Gambierdiscus cells g-1 ww.

2.4 Measures of Reef Health

The benthic cover and fish population datasets were selected as indicators of reef health based

on the measurable ranking criteria established The Healthy Reefs for Healthy People Initiative

per McField et al. (2011). The Simplified Integrated Reef Health Index (SIRHI) is composed of two

measures of benthic cover and two measures of fish abundance. Benthic cover measures

represent the proportion of reef surface covered by either stony coral or fleshy macroalgae. Fish

abundance measures describe the biomass (total weight of fish per unit area) of herbivorous fish

represented by surgeonfish and parrotfish and the commercial fish biomass represented by

snappers and groupers. To be able to assess each reef site using the SIRHI, datasets on each

measure were obtained. Fish population data reported by the Reef Environmental Education

Foundation (REEF) Volunteer Survey Project were used to calculate the biomass for the

aforementioned herbivorous and commercial fish species. REEF is a citizen science program with

a database of over 172,000 order-of-magnitude surveys completed by recreational divers using

24

the Roving Diver Technique (RDT). Because biological populations tend to fluctuate exponentially

and have broad confidence intervals, order-of-magnitude counting is an efficient low-cost

method of surveying that can be converted to expected arithmetic mean populations with a

reasonable standard error (Wolfe & Pattengill-Semmens, 2013; Schmitt & Sullivan, 1996). Data

regarding benthic cover measure for stony coral and macroalgae were acquired from the Coral

Reef Evaluation and Monitoring Project (CREMP). CREMP began as an endeavor to evaluate the

response of benthic communities based on the five most spatially abundant benthic taxa

(macroalgae, octocorals, sponges, stony corals, and zoanthids) in the Florida Keys following the

1997/1998 El Niño-Southern Oscillation event (Ruzicka et al.,2013).

2.5 Anthropogenic Factors

Land cover, human population, onsite sewage, and water quality data were selected as measures

of anthropogenic pressure or influence. Contrasting land cover and use has often been correlated

with the alteration of benthic assemblages and the structure of aquatic communities (Roberts et

al., 2017; Serafy et al., 2015; Francini-Filho et al., 2013; Harding & Winterbourn, 1995). To

investigate this possible relationship, land cover data was obtained from a Land Cover-Habitat

Planning & Environmental Resources shapefile prepared by the Monroe County Geographic

Information System Department from the most recently available imagery (20062010). Land

cover classifications (Developed Land, Undeveloped Land, Impervious Surface, Hammock,

Pineland, Exotic, Scrub Mangrove, Freshwater Wetland, Salt marsh, Buttonwood, Mangrove,

Beach Berm, and Water) applied during photo-interpretation were used for analysis of land use

by area (Serafy et al., 2015) and % land cover (Roberts et al., 2017). Population density (people

25

km-2) as described by Serafy et al. (2015) and Yee et al. (2011) for Monroe County at the start of

the study period was derived from the U.S. Census Bureau’s 2010 census of population and

housing for Florida counties dataset downloaded from the Florida Geographic Data Library

(FGDL). Records of septic system inspections published by the Florida Department of Health

(2012) were also downloaded from the FGDL as a comparative measure of potential effluent-

based eutrophication by onsite sewage treatment (Ward-Paige et al., 2005). As a measure of

actual water quality, data collected by the Southeast Environmental Research Program (SERC) at

Florida International University for the Water Quality Protection Plan (WQPP) mandated by

NOAA, the EPA, and the State of Florida (1995) was utilized (Briceño & Boyer, 2014).

2.6 Geodatabase and GIS Synthesis

A Microsoft Access Database was created to consolidate all datasets that were recorded in

multiple tables to link geospatial data with ecological data and query for years included in this

study. In order to capture the latest U.S. census, the year 2010 was established as the start date

for all records. Based on the most recent data available, water quality data was obtained through

2013, benthic cover through 2015, and fish population data through 2017. Query results were

exported to Excel, latitude and longitude were converted to decimal degrees when necessary,

and the resultant datasets were imported as a feature class or table into ArcCatalog 10.2.2 to

create a comprehensive geodatabase. A map was created from XY data for each Geodatabase

Feature Class in ArcMap 10.2.2 using the GCS_NAD_1983_2011 Geographic Coordinate

Reference and Albers Conical Equal Area as the Projected Coordinate System. Study sites were

selected from the CREMP benthic cover dataset and added as a separate layer with buffers of 5

26

km and 10 km around each site. The 10-km buffer was chosen based on the site distances to

shore to ensure the incorporation of data from land-based measures. For water-based measures,

the buffer was implemented based on the estimated home range areas of commercial fishes such

as grouper and snapper of 1.47.6km2 (Ault et al., 2013) and the mean distance traveled of

approximately 5 km for barracuda (O’Toole et al., 2012). Thus, the 5-km buffers were intersected

with the data layers from the REEF fish surveys and SERC Water Quality monitoring sites to

produce data values for water-based variables for each study site. The intersection was repeated

with the 10-km buffer for the Monroe County Land Cover, Onsite Sewage Systems, and Human

Population data layers. The Table to Excel ArcToolbox Conversion Tool was used to export the

site-specific data for further multivariate statistical analysis.

2.7 Data Analysis

For each study site, all measures were assigned a score based on the ranked SIRHI threshold

values matrix (Table 2.8.1). Component Scores were then averaged to produce Site Scores. To

determine percent cover for stony corals and macroalgae, mean cover from 20102015 was

calculated from the CREMP data for each group by site. The expected arithmetic mean

populations derived from the REEF fish surveys using the Model 3 developed by Wolfe &

Pattengill-Semmens (2013) were then converted to biomass using length-weight relationships

constructed by the Atlantic and Gulf Reef Rapid Assessment (AGRRA) and common lengths for

the study area established in the literature as displayed in Table 2.8.2. Biomass (g 100 m-2) was

calculated using the function: W = aLb, where W is weight (g), L is length (cm), and a and b are

27

parameters estimated by linear regression of logarithmically transformed length-weight data

reported by Marks & Klomp (2003).

Table 2.7.1 SIRHI Threshold Values from McField et al. (2011)

Measures 1-Critical 2- Poor 3- Fair 4-Good 5- Very Good

Stony Coral Cover (%) < 5 5.0-9.9 10.0-19.9 20.0-39.9 ≥40

Macroalgae Cover (%) > 25.0 12.1-25 5.1-12.0 1.0-5.0 0-0.9

Key Herbivorous Fish (g 100m-2) < 960 960-1919 1920-2879 2880-3479 ≥3480

Key Commercial Fish (g 100m-2) < 420 420-839 840-1259 1260-1679 ≥1680

The total area (km2) and percent cover of each classification of land (Monroe County,

2010) was calculated in ArcGIS using the 10-km buffer for each site. Population density (persons

per km2) was calculated from the total population (U.S. Census Bureau, 2010) divided by the total

land area within 10 km of each site. Water quality data was evaluated in relation to the strategic

targets for chlorophyll a (≤0.2 micrograms L-1), light attenuation (≤0.13m-1), dissolved inorganic

nitrogen (≤0.75 micromolar), and total phosphorus (≤0.2 micromolar). Using IBM SPSS Statistics

(v23), the mean, maximum, and minimum descriptive statistics were calculated by parameter for

each site, and one-sample T-tests were run to identify any strategic targets that were exceeded.

Water quality was compared between sites and regions using independent samples Mann-

Whitney tests for EPA parameters as well as for temperature and salinity.

28

Table 2.7.2 Fish Biomass Conversions

Species L (cm) a B W (g) Source of Common Length

Gray Snapper 30.9 0.0232 2.8809 454.9 Ault et al., 2005

Lane Snapper 25.8 0.0295 2.8146 277.3 Ault et al., 2005

Mutton Snapper 49.3 0.0162 3.0112 2027.8 Ault et al., 2005

Schoolmaster 31.5 0.0194 2.9779 561.9 Ault et al., 2005

Yellowtail Snapper 29.7 0.0405 2.7180 407.8 Ault et al., 2005

Coney 20.0 0.0175 3.0000 140.0 Froese, R. and D. Pauly. Editors. 2018

Graysby 23.3 0.0135 3.0439 196.1 Ault et al., 2005

Nassau Grouper 63.5 0.0065 3.2292 4309.5 Ault et al., 2005

Red Grouper 59.2 0.0123 3.0350 2943.8 Ault et al., 2005

Blue Tang 10.0 0.0415 2.8346 28.4 Nagelkerken et al., 2000

Doctorfish 17.0 0.0040 3.5328 88.9 Cocheret de la Morinière et al., 2002

Ocean Surgeonfish 12.9 0.0237 2.9752 47.8 Cocheret de la Morinière et al., 2002

Bucktooth Parrotfish 15.0 0.0121 3.0275 44.0 Froese, R. and D. Pauly. Editors. 2018

Greenblotch Parrotfish 5.5 0.0121 3.0275 2.1 Froese, R. and D. Pauly. Editors. 2018

Midnight Parrotfish 50.0 0.0153 3.0618 2435.6 Froese, R. and D. Pauly. Editors. 2018

Queen Parrotfish 15.0 0.0250 2.9214 68.2 Nagelkerken et al., 2000

Rainbow Parrotfish 70.0 0.0155 3.0626 6936.3 Froese, R. and D. Pauly. Editors. 2018

Redband Parrotfish 20.0 0.0046 3.4291 133.1 Froese, R. and D. Pauly. Editors. 2018

Redfin Parrotfish 25.0 0.0156 3.0641 299.6 Froese, R. and D. Pauly. Editors. 2018.

Stoplight Parrotfish 15.0 0.2500 2.9214 682.0 Nagelkerken et al., 2000

Striped Parrotfish 11.9 0.0147 3.0548 28.4 Cocheret de la Morinière et al., 2002

29

To characterize the environmental conditions of each site, all anthropogenic,

environmental, and reef health variables were recorded in a matrix for multivariate statistical

analysis using PRIMER (v7).To measure the similarity of samples, a lower triangular matrix was

produced with the “Resemblance” analysis in PRIMER (v7) with region as a factor. For data

expressed as percent cover, a square root transform was applied (per Clarke & Warwick, 2001)

to standardize for biological community analysis with the zero-adjusted Bray-Curtis similarity

coefficient. For the fish population data, a shade plot was utilized to identify super dominant

species, and rarely observed species with low abundances were removed before the

“Standardise” pre-treatment was applied. Water quality parameters were prepared for similarity

analysis based on Euclidean distance by using the “Normalise” pre-treatment. From the

Resemblance matrix created for each data type, a non-metric MDS ordination plot was created

and CLUSTER analysis was performed using the similarity profile (SIMPROF) test to identify

statistically significant structural differences in the samples (sites). To identify discriminating

factors between sites, the contribution of each variable to the average dissimilarity between all

sites was evaluated using the similarity percentages (SIMPER) analysis run on data matrix. Factors

that typified a site were identified based on contributions to the average similarity from SIMPER

analysis.

2.8 Relation to Gambierdiscus Density & Temporal Abundance

Gambierdiscus cell density data were normalized using a square root transformation and

evaluated for statistically significant patterns using the IBM SPSS Statistics (v23) Linear Mixed

Model Analysis with pairwise comparisons by region, site, and season. To test the extent to which

30

the environmental data explain the biotic patterns in reef health, the BEST test was run with

Spearman rank for Bray-Curtis vs. Euclidean distance comparisons and Pearson rank for Bray-

Curtis vs. Bray-Curtis comparisons. BEST randomly permuted one set of samples in relation to the

other and then generated the best match rho using the BIOENV algorithm. This procedure was

run for 999 permutations per test. The generated values produced a histogram to represent the

null hypothesis, against which the real rho was compared (Clarke & Gorley, 2015). Null

hypotheses were rejected if the p-value was less than or equal to the significance level α (𝑝 <

0.05). Water quality data was evaluated in relation to the strategic targets for chlorophyll a (≤0.2

micrograms L-1), light attenuation (≤0.13 m-1), dissolved inorganic nitrogen (≤0.75 micromolar),

and total phosphorus (≤0.2 micromolar). Using IBM SPSS Statistics (v23), the mean, maximum,

and minimum descriptive statistics were calculated by parameter for each site. Water quality was

compared by site and region with independent sample Mann-Whitney tests and against EPA

strategic targets with one-sample T-tests. Finally, the relationship between patterns of

Gambierdiscus abundance and site conditions were explored in SPSS through correlations with

environmental variables identified by the aforementioned SIMPER and BEST analyses. The

significance level α was set at 0.05 for all statistical tests.

2.9 CFP Risk Calculation

Under the assumption that macroalgal densities correlate across species (Parsons et al., 2017),

cell densities (no. g-1 ww macroalgae) were extrapolated to number of cells per reef (300 m2).

These figures were based on percentages of benthic cover and conversions of macroalgal cover

to biomass using linear regressions developed by Parsons et al. (2017). From reef-scale cell

31

enumeration, an estimate of total micrograms of toxin per reef was also calculated using a

generic coefficient to represent toxin content per cell. Although the precise mechanisms of

trophic transfer require further study, this model operated under the assumption that all toxin

was evenly consumed by the herbivorous fish. Finally, a ratio of trophic transfer (herbivorous

biomass/commercial biomass) was established to characterize the potential amount that could

reach the consumer. Although the pathways are likely more complex, the generic coefficients

used in concert with the data in this study is sufficient to assess and compare the risk among

regions.

32

3 RESULTS

3.1 Patterns in Land Use

SIMPER analysis indicated that the average similarity between sites was 98.09% in the Upper

Keys, 86.87% in the Middle Keys and 80.33% in the Lower Keys, with significant structural

differences apparent between regions. CLUSTER analysis of the square root transformed areas

from the Monroe County Land Use and Cover dataset revealed a pattern of transition from Lower

to Upper Keys (Fig 3.1.1).

Figure 3.1.1 CLUSTER analysis of square root transformed areas of land use within 10 km of sites. Samples connected by red lines are not significantly differentiated by SIMPROF. Region 1=Upper Keys, 2=Middle Keys, 3=Lower Keys.

33

Average dissimilarity in land use area between regions was highest between the Lower and

Middle Keys at 27.28%, lower still between the Lower and Upper Keys at 24.00%, and lowest

between the Middle and Upper Keys at only 12.31%. Average abundances of Scrub Mangroves

and Developed Land within 10 km (Table 3.1.1) were defined as discriminating factors for the

Lower Keys relative to the other regions. Additionally, Salt Marsh was identified area as a key

contributor to the dissimilarity between Upper and Lower Keys, with greater cover associated

with the Lower Keys.

Table 3.1.1 Discriminating land uses by area within 10 km (significant contributions in bold)

Species

Upper Keys Middle Keys Lower Keys U-M U-L M-L

Av.Abund Av.Abund Av.Abund Contrib% Contrib% Contrib%

Scrub Mangrove 0.49 0.38 2.44 2.42 19.51 22.83

Developed Land 2.49 2.30 1.09 7.84 14.76 13.93

Salt Marsh 0.21 0.65 1.27 9.38 10.68 6.76

CLUSTER analysis was also performed on square-root-transformed percent cover of each land

use category. Between-region differences were greater than within-region differences, as each

pair of sites showed no significant structural differentiation (Fig. 3.1.2). SIMPER analysis

reinforced that the Upper Keys again had the highest resemblance and an average similarity

between sites of 98.63%, while the Middle Keys with an average similarity of 92.74% and the

Lower Keys with an average similarity of 87.44% followed a trend of decreasing resemblance.

34

Figure 3.1.2 CLUSTER analysis of square root transformed percent cover of land use within 10km of sites. Samples connected by red lines are not significantly differentiated by SIMPROF. Region 1=Upper Keys, 2=Middle Keys, 3=Lower Keys.

Average dissimilarity in the percent cover of different land uses was highest at 28.99% between

the Lower and Middle Keys, and was similarly high at 27.28% between the Lower and Upper Keys.

Setting the Lower Keys apart, an average dissimilarity of only 11.18% was seen between the

Middle and Upper Keys. Low salt marsh density in the Upper Keys contributed to 19.30% of the

dissimilarity with the Middle Keys and to 14.83% of the dissimilarity with the Lower Keys (Table

3.1.2). Low cover of exotics and high hammock cover in the Upper Keys also contributed to its

differentiation from the Middle Keys. Scrub Mangrove and Developed Land again distinguished

the Lower Keys from the rest of the study area. The high scrub mangrove cover in the Lower Keys

accounted for 26.79% of the dissimilarity with Upper Keys and 28.26% of the dissimilarity with

the Middle Keys. Furthermore, the lower percentage of developed land near the study sites in

the Lower Keys contributed to between-region dissimilarities.

35

Table 3.1.2 Discriminating land uses by percent cover within 10 km of study sites (significant contributions in bold)

Species

Upper Keys Middle Keys Lower Keys U-M U-L M-L

Av.Abund Av.Abund Av.Abund Contrib% Contrib% Contrib%

Scrub Mangrove 0.11 0.09 0.53 <1.0 26.79 28.26

Developed Land 0.57 0.58 0.24 <1.0 21.43 21.78

Salt Marsh 0.05 0.17 0.28 19.30 14.83 7.42

Exotic 0.06 0.15 0.06 15.38 <1.0 5.99

Hammock 0.40 0.33 0.26 12.17 9.07 5.19

3.2 Patterns in Water Quality

All sites fell well within the range of temperatures (18°33°C) that supports year-round

populations of Gambierdiscus spp. (per Litaker et al., 2010). Average water temperature showed

minimal variation among sites with ranges between 25.5° and 26.5°C (Table 3.2.1). Maximum

temperatures consistently reached higher than 31° but less than 32°C and fell to a minimum of

between 18° and 21°C, with the Lower Keys sites at the low-end of the range and the Middle Keys

at the high-end. Mean salinity was also consistently just over 36 ppt across all sites, with a

maximum close to 37 and a minimum between 33.8 and 35.5, which are considered higher than

optimal (2934 ppt) for Gambierdiscus spp. growth (Kibler et al., 2012; Parsons et al., 2010).

Independent samples Mann-Whitney tests did not reveal any statistically significant differences

in the distributions of either parameter across sites or regions.

36

Table 3.2.1 Temperature and salinity at bottom (B) of study sites from 2010-2015

Site

Temperature (B) °C Salinity (B) ppt

Mean Max Min Mean Max Min

Two Patches 26.30 31.04 19.22 36.08 36.90 34.72

Burr Fish 26.19 31.04 19.22 36.13 36.95 34.69

Rawa Reef 26.31 31.15 19.41 36.17 36.77 35.51

Dustan Rocks 26.35 31.77 21.95 36.06 37.15 33.82

Wonderland 26.16 31.05 18.09 36.16 36.80 34.72

W.Washerwomen 25.52 30.86 17.97 36.26 37.34 35.31

No significant structural differences were found in a CLUSTER analysis of the normalized annual

average water quality (Table 3.2.2). However, Mann-Whitney pairwise comparisons indicated

few statistically significant differences in individual water quality parameters between regions.

The distribution of phytoplankton (CHLA) at the surface was significantly different in the Upper

than the Middle Keys (p = 0.02) and the Lower Keys (p = 0.005). A significant difference in the

distribution of total phosphorus at the bottom (TP_B) was also found between the Upper and

Lower Keys (p = 0.01). Although significant differences in water quality were not found between

the Middle and Lower Keys, one sample Wilcoxon signed rank tests determined that several

parameters exceeded water quality targets (per Briceño & Boyer, 2014). Mean CHLA exceeded

the standard at all sites in the Middle and Lower Keys (p < 0.03), mean Kd exceeded the standard

at one site within each region (p < 0.03), and mean DIN exceeded the target at one site in the

Lower Keys (p = 0.007). In the Upper Keys, mean Kd did not meet the water quality target at both

sites (p = 0.001; p = 0.01).

37

Table 3.2.2 Average Water Quality within 5km and anthropogenic factors within 10km of study sites.

Site DIN_B TP_B CHLA Kd Depth (m) Distance to shore (km)

Population Density (per km2)

Onsite Sewage

Two Patches 0.53 0.14 0.64 0.32 2.4 5.3 620 1391

Burr Fish 0.50 0.15 0.22 0.62 4.8 4.0 554 966

Rawa Reef 0.42 0.16 0.30 0.10 6.6 4.7 503 483

Dustan Rocks 0.81 0.17 0.34 0.24 5.1 3.3 436 1015

Wonderland 0.75 0.16 0.38 0.15 5.7 6.9 390 32

W.Washerwomen 0.57 0.18 0.51 0.18 7.1 4.7 171 1050

3.3 Patterns in Key Fish Species Assemblages

Figure 3.3.1 CLUSTER analysis of square root transformed densities of key fish species within 5km of sites. Samples connected by red lines are not significantly differentiated by SIMPROF. Region 1=Upper Keys, 2=Middle Keys, 3=Lower Keys.

Cluster analysis of the square-root-transformed estimated mean population calculated from the

REEF order of magnitude surveys 20102017 illustrated that key fish species assemblages also

38

follow a generally regional pattern (Fig. 3.3.1). Assemblages from the pairs of sites in the Upper

and Lower Keys do not show any significant structural within-region differences, though their

particular degree of similarity varied between the two regions. There were also significant

differences in the Lower Keys fish populations in relation to those of the Middle and Upper Keys.

SIMPER analysis suggested that key fish species assemblages at the sites in the Upper Keys are

nearly identical with an average between-site similarity of 98.23%. The Lower Keys are shown to

have more within-region variability in their assemblages with an average between-site similarity

of only 74.14%. Within-region variability was even greater in the Middle Keys, with two distinct

structural groupings in the cluster and the lowest average between-site similarity of 69.23%.

Between-region differences in key fish assemblages seemed to follow a gradient from West to

East, as an average dissimilarity of 41.63% characterized the Lower and Middle Keys, an average

dissimilarity of 34.94% was estimated between the Lower and Upper Keys, and an average

dissimilarity of 28.44% defined the Middle and Upper Keys pairing.

Table 3.3.1 Discriminating fish species by abundance (significant contributions in bold)

Species

Upper Keys Middle Keys Lower Keys U-M U-L M-L

Av.Abund Av.Abund Av.Abund Contrib% Contrib% Contrib%

Striped Parrotfish (Scarus iseri) 2.67 2.68 15.01 2.04 21.43 18.36

Schoolmaster (Lutjanus apodus) 9.15 2.48 10.07 18.19 2.70 11.32

Mahogany Snapper (L. mahogoni) 5.31 0.74 0.00 12.43 9.22 <1.0

Yellowtail Snapper (Ocyurus chrysurus) 7.98 8.95 11.94 10.88 6.87 6.02

39

The differential abundance (Table 3.3.1) of the Striped Parrotfish (Scarus iseri) had the highest

contribution (21.43%) to the dissimilarity between the Lower and Upper Keys. Along with the

Schoolmaster (Lutjanus apodus), the Striped Parrotfish also contributed highly to the dissimilarity

between the Lower and Middle Keys. The dissimilarity between the Middle and Upper Keys was

also marked by the contrasting abundance of the Schoolmaster (L. apodus). The high abundance

of the Mahogany Snapper (Lutjanus mahogoni) and the low abundance Yellowtail Snapper

(Ocyurus chrysurus) in the Upper Keys further defined the regional dissimilarity.

3.4 Patterns in Benthic Cover

Figure 3.4.1 CLUSTER analysis of Bray-Curtis similarity on square root transformed benthic cover data. Samples connected by red lines are not significantly differentiated by SIMPROF. Region 1=Upper Keys, 2=Middle Keys, 3=Lower Keys.

CLUSTER analysis in PRIMER7 of square-root-transformed averages of the CREMP coral point

counts indicated a high level of similarity among benthic assemblages at the regional level (Fig.

40

3.4.1). Aside from one outlier at Burr Fish reef in 2010 and one at Rawa Reef in 2014, benthic

cover was also highly similar over time, with very few significant differences within sites and

within regions.

Figure 3.4.2 CLUSTER overlay on nMDS; Region 1=Upper Keys, Region 2=Middle Keys, Region 3=Lower Keys

A non-metric MDS with an overlay of the previous CLUSTER (Fig. 3.4.2) and SIMPER analyses

confirmed this pattern in benthic cover with an acceptable 2-dimensional stress of 0.12. Based

on the nMDS, average benthic cover was at least 75% similar across all sites and all years between

2010 and 2015. Within regions, a SIMPER analysis revealed that the benthic cover in the Lower

Keys had an average similarity of 86.59% among its sites, which was the highest similarity

observed. Similarity in benthic cover among sites in the Middle and Upper Keys was comparable,

though slightly less than that of the Lower Keys, with average similarities of 84.97% and 84.51%,

41

respectively. Between regions, the highest average dissimilarity (30.99%) was found between the

Upper Keys and Lower Keys, and lowest (19.65%) was found between the Upper and Middle Keys.

Table 3.4.1 Average abundance of discriminating benthic cover groups (significant contributions in bold)

Benthic Cover Group

Upper Keys Middle Keys Lower Keys U-M U-L M-L

Av.Abund Av.Abund Av.Abund Contrib% Contrib% Contrib%

Stony Coral 0.24 0.35 0.57 12.87 25.25 20.76

Macroalgae 0.45 0.35 0.13 17.82 25.16 20.88

Substrate 0.78 0.75 0.63 8.13 11.38 11.52

Zoanthid 0.00 0.15 0.01 18.42 <1.0 13.73

The average dissimilarity between the Middle and Lower Keys (24.38%) was nearly half that of

the other two pairings. SIMPER analysis also distinguished the groups of species that contributed

most to the average dissimilarity between regions. These discriminating species groups were

defined as those that contributed >10% to the average dissimilarity and had a Diss/SD ratio >2.0

(Wakefield et al., 2013).

Analysis of between-region dissimilarity (Table 3.4.1) suggested that the percentages of Stony

Coral and substrate cover contributed most to the differentiation of the Middle Keys from the

Upper Keys and Lower Keys from both other regions. Macroalgae cover also contributed heavily

(>25%) to the distinction of the Lower Keys from the Upper Keys. In addition to the

aforementioned discriminating species groups, the percent cover of Zoanthids uniquely

distinguished the Middle Keys from other regions.

42

3.5 Patterns in Reef Health

Figure 3.5.1 Benthic Cover across all study sites from 2010 to 2015; Upper Keys=Two Patches, Burr Fish; Middle Keys=Rawa Reef, Dustan Rocks; Lower Keys= Wonderland, West Washerwomen.

Stony coral cover followed a gradient from “good” in the Lower Keys, with decreasing cover

moving East-Northeast, to “critical” in the Upper Keys (Table 3.5.1). Inter-annual variability was

relatively low across all sites, though a steeper decline in cover was apparent in the Lower Keys

over the most recent years in the dataset (Fig. 3.5.1). A one-way ANOVA on the log-transformed

percentage of stony coral cover revealed significant differences between each of the three

regions (p < 0.001), as well as between sites in the Lower Keys (p = 0.003). Macroalgal cover

followed a similar trend and was rated as “good” in the Lower Keys; however, the rating jumped

to “poor” in the Middle and peaked at “critical” in the Upper Keys (Table 3.5.1). A relatively high

level of within-region concordance in macroalgal cover was evident in the Middle and Lower

Keys, with little inter-annual variability in the Lower Keys (Fig. 3.5.1). Although macroalgal cover

in the Upper Keys appeared to be more volatile, a one-way ANOVA on the square-root-

0%

15%

30%

45%

2010 2011 2012 2013 2014 2015

Year

Stony Coral CoverTwo Patches

Burr Fish

Rawa Reef

Dustan Rocks

Wonderland

WestWasherwomen

0%

15%

30%

45%

2010 2011 2012 2013 2014 2015

Year

Macroalgal Cover

43

transformed percentage of cover revealed that any within-region differences in cover were not

statistically significant. ANOVA also confirmed that differences in macroalgal cover between the

Lower Keys and both the Middle and Upper Keys were statistically significant (cover was lower at

the Lower Keys sites; p < 0.001), as were those between the Middle and Upper Keys (cover was

higher at the Upper Keys sites; p = 0.027).

Table 3.5.1 Benthic Cover and Key Fish Biomass values and Health Scores by study site and region

Region

Site

Stony Coral

Macroalgae

Herbivorous Fish Biomass

Commercial Fish Biomass

Assessed

Score Cover Score Cover Score g 100m-2 Score g 100m-2 Score

Upper Keys

Two Patches 4.9% 1 16.9% 2 86.33 1 324.22 1 1

Burr Fish 7.9% 2 26.9% 1 81.61 1 312.61 1 1

Middle Keys

Rawa Reef 11.9% 3 12.7% 2 233.72 1 37.53 1 2

Dustan Rocks 13.0% 3 12.4% 2 224.73 1 87.30 1 2

Lower Keys

Wonderland 37.9% 4 1.7% 4 95.52 1 440.50 2 3

W. Washerwomen 26.8% 4 2.2% 4 215.12 1 386.27 1 3

The densities of key fishes were consistently low throughout the entire Florida Keys Reef

Tract (Table 3.5.1), with key herbivorous fish biomass rated at “critical” across all sites. The

biomass of key commercial species was also rated at “critical” at all sites except for Wonderland

in the Lower Keys. Independent samples Kruskal-Wallis tests across regions confirmed that the

differences in the distributions of commercial and herbivorous fish biomass across regions were

statistically significant (p = 0.005; p = 0.027), with the Middle Keys sites containing the highest

and lowest densities of herbivorous and commercial fish species, respectively. Overall, the

Assessed Health Score was consistent within regions and ranged between regions from “critical”

(1) in the Upper Keys to “fair” (3) in the Lower Keys.

44

3.6 Patterns in Gambierdiscus Cell Densities

The temporal abundance of Gambierdiscus cells did not indicate a clear pattern of seasonality at

the study sites (Fig. 3.6.1). In the Upper and Middle Keys, samples collected in winter (February

2017) hosted cell densities that were similar to or slightly higher than those recorded from the

previous summer (August 2016). Cell densities observed in samples from the following summer

(August 2017) followed a similar pattern, but generally had higher densities than those from the

previous two seasons. In the Lower Keys, Halimeda spp. samples could not be collected from

either site during the winter due to the near absence of macrophytes at both sites. This apparent

seasonal low in macroalgal abundance was confirmed by another attempt to sample during

winter at both Lower Keys sites in December 2018. Regardless, statistically significant (p = 0.002)

interannual variability was apparent across all regions through an increase in cell densities

between August 2016 and 2017.

Figure 3.6.1 Seasonal cell density of Gambierdiscus spp. on H. gracilis with standard error; Cross-hatched bar represents missing data

0

10

20

30

40

50

60

70

80

Upper Keys Middle Keys Lower Keys

Cel

ls g

-1 w

w

Seasonality

Aug-16

Feb-17

Aug-17

45

Across all study sites, the highest average cell density of Gambierdiscus spp. was recorded at Burr

Fish reef in the Upper Keys and the lowest average at Rawa Reef in the Middle Keys (Fig. 3.6.2).

Linear mixed model analysis in IBM SPSS Statistics 23 revealed that the square root transformed

mean cell density at Burr Fish reef was significantly higher than that at Two Patches (p = 0.01) in

the Upper Keys, both Rawa Reef (p < 0.001) and Dustan Rocks (p < 0.001) in the Middle Keys, and

West Washerwomen (p < 0.001) in the Lower Keys. The analysis also indicated the significance of

the lower mean cell density at Rawa Reef in the Middle Keys as compared to both Burr Fish (p <

0.001) and Two Patches (p = 0.005) in the Upper Keys, and Wonderland (p = 0.008) in the Lower

Keys. Statistically significant within-region differences in mean cell density were only found in the

Upper Keys.

Figure 3.6.2 Mean cell density of Gambierdiscus spp. on H. gracilis with standard error

On a regional scale, cell densities observed in the Upper Keys were significantly higher in relation

to both the Middle Keys (p < 0.001) and the Lower Keys (p = 0.02). Differences in cell densities

B C ABA BC BC0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Upper Keys Middle Keys Lower Keys

SQR

T C

ells

g-1

ww

Site Average Cell Density

46

between the Lower Keys and the Middle Keys were less pronounced. Although the Lower Keys

generally had higher cell densities than the Middle Keys, the difference was marginal (p = 0.05).

3.7 Biotic and Environmental Correlations

The BEST analysis in PRIMER7 was utilized to describe how well patterns in land use area and

density described patterns in benthic cover and fish species assemblages (Table 3.7.1).

Table 3.7.1 BEST results for multi-correlations with 5 or fewer variables

Dataset

Benthic Cover Key Fish Species Assemblages

rho Best Variables Sig. rho Best Variables Sig.

Land Use Area

0.932 Water, Freshwater Wetland, Buttonwood

0.032 0.944 Impervious Surface, Water, Mangrove, Exotic, Hammock

0.005

Land Use Density

0.928 Mangrove, Scrub Mangrove, Salt Marsh, Freshwater Wetland, Beach Berm

0.024 0.882 Impervious Surface, Mangrove, Exotic, Buttonwood ,Hammock

0.017

A Spearman rank correlation analysis in SPSS (n = 6) uncovered significant strong negative

relationships between Gambierdiscus spp. cell densities and several biotic factors, including

Zoanthid cover (⍴ = -0.928, p = 0.008), key herbivorous fish biomass (⍴ = -0.943, p = 0.005), and

surgeonfish abundance (⍴ = -0.886, p = 0.019). Correlation analysis also exposed several

relationships with land uses. These included a strong negative correlation with freshwater

wetland area (⍴ = -0.812, p = 0.05), and strong positive correlations with the percent cover of

mangroves (⍴ = 0.926, p = 0.008) and the total hammock area (⍴ = 0.829, p = 0.042). Although a

moderate negative correlation between Gambierdiscus spp. cell densities and assessed reef

health was found (⍴ = -0.598), the relationship was not statistically significant.

47

Human population density was found to have several strong relationships with biotic and

environmental variables through Spearman rank correlation analysis in SPSS (n = 6). Significant

positive relationships included macroalgal cover (⍴ = 0.829, p = 0.042), Mahogany Snapper

abundance (⍴ = 0.820, p = 0.046), hammock area (⍴ = 0.829, p = 0.042), and hammock density (⍴

= 0.928, p = 0.008). Strong negative correlations were found between assessed reef health (⍴ = -

0.956, p = 0.003), stony coral cover (⍴ = -0.943, p = 0.005), substrate cover (⍴ = -0.943, p = 0.005),

salt marsh area (⍴ = -0.886, p = 0.019), freshwater wetland area (⍴ = -0.812, p = 0.05), and TP_B

(⍴ = -0.883, p = 0.020). Despite the potential insignificance of some correlations with Bonferroni

corrections, the identified relationships were found to make ecological sense and merit further

study.

3.8 Regional Risk Assessment

Table 3.8.1 Regional assessment of potential toxin available to the consumer per g commercial fish

Model Parameter Lower Keys Middle Keys Upper Keys

Macroalgal Biomass per Reef (g/300 m2) 300 1,800 3,000

Gambierdiscus Cells per Reef (cells/300 m2) 12,000 36,000.00 180,000

Total Toxin 6,000 18,000 90,000

Herbivorous Fish per Reef (g/300 m2) 450 690 252

Commercial Fish per Reef (g/300 m2) 1,200 195 960

Potential Toxin Available to Consumer (ug/g) 5 92 94

Based on the very rough Regional Risk Assessment model (Table 3.8.1), the estimated number of

cells per reef and total toxin were, by far, highest in the Upper Keys. However, due to the

influence of the ratio of herbivorous to commercial fish biomass, there was only a marginal

48

difference in potential toxin available to the consumer when compared to the Middle Keys.

Despite the disparity in cell densities between the regions, CFP risk in the Middle Keys is much

higher than that of the Lower Keys and is nearly identical to that of the Upper Keys. This suggests

that cell densities alone are not sufficient indicators of CFP risk, and emphasizes the importance

of the broader ecological context in which cells are found to future risk assessment.

49

4 DISCUSSION

Analysis of samples of host macroalgae collected from all study sites biannually revealed that

Gambierdiscus cell densities were consistently highest in the Upper Keys and lowest in the Middle

Keys, regardless of season. Conversely, reef health was lowest in the Upper Keys and improved

along a gradient to the Lower Keys. Multivariate analysis of site similarity indicated that this

regional pattern was driven more strongly by grazing than substrate availability. Additionally,

there is evidence that human activities have an indirect influence on CFP risk through reef health,

as well as through overfishing, and the destruction of inshore habitats like seagrass and

mangroves. Due to a strong positive correlation with cell densities, this study suggests that

mangrove cover could be useful as a biogeographic indicator of potential CFP risk. Whereas

surgeonfish, with a strong negative correlation with cell densities, could indicate the actual flow

of toxins into higher trophic levels. The concordance of high regional risk and high population

density necessitates continued monitoring of fish in those areas and the development of more

comprehensive predictor of potential CFP outbreaks.

4.1 Population Dynamics of Gambierdiscus spp.

Based on variations in cell density between winter and summer samplings, Gambierdiscus spp.

do not seem to exhibit a clear seasonality in the Florida Keys. This lack of seasonal pattern may

reflect interspecific differences in growth response as conditions change throughout the year.

Other substrates, which are known also to host Gambierdiscus spp., may have also played a role

in seasonal dynamics, especially that of the Lower Keys. Similar to the mechanism described by

50

Cruz-Rivera & Villareal (2006), substrates such as sediments and algal turfs may have provided a

reservoir for Gambierdiscus cells during the winter, which could have then repopulated the fleshy

macroalgae once its abundance rebounded. The significant interannual variability observed in

this study suggests that longer-term ecosystem-level factors may be a stronger influence on the

patterns of cell densities and that smaller scale variability is likely overridden by regional

differences. A regional pattern is consistent with other research in the Keys on benthic

communities, geomorphology, circulation, and water quality (Briceño & Boyer, 2014; Ginsburg &

Shinn, 1994).

Within all study sites, temperatures remained within the range that supports year-round

populations of Gambierdiscus spp. (per Litaker et al., 2010); however, overall growth may have

been limited by salinity that was consistently at or above the optimal maximum of 34 ppt (Kibler

et al., 2012; Parsons et al., 2010). Beyond this potential limitation on growth, few significant

differences were found in mean or maximum water quality parameters across regions. This

suggests a complex role for water quality, as bottom-up control did not clearly define the

patterns of cell density seen in this study. Although DIN definitively exceeded the target in the

Lower Keys, variability was substantial enough to obscure any larger spatial patterns. Conversely,

TP did not exceed the EPA target; however, a gradient was found from the Lower to Upper Keys.

These subtle patterns, especially in the southwestern part of the tract, were in accordance with

other research that found minor offshore TP enrichment from the back bay via southerly currents

(Briceño & Boyer, 2014). The patterns in DIN and the absence of a direct correlation with on-site

sewage, land use, or population density were also consistent with the dilution of onshore sources

of nitrogen over the distance to reef sites in Hawk Channel (Briceño & Boyer, 2014; Lapointe et

51

al., 1994; Lapointe et al., 1992). Although nearshore water quality was not included this study,

degradation of inshore habitats due to eutrophication could plausibly affect reef assemblages

due to their importance to many reef inhabitants (Ault et al., 2005a). The only direct relationship

found between cell density and the physical patch reef environment was an inverse correlation

with site depth. This relationship provides some explanation for the highest cell densities being

located at relatively shallower sites in the Upper Keys that were also characterized by a higher

attenuation of light. Although the suite of physical factors and the interplay of depth, light,

temperature, and dissolved oxygen most likely played a role in interannual and spatial variability,

any direct influence on the pattern of cell densities was confounded by regional differences in

other factors.

Aside from their epiphytic connection with the collected macroalgal substrate, cell

densities were only found to have one significant relationship with the benthic community. The

negative correlation with zoanthids, which are a type of colonial cnidarian that obtains its

nutrition from endosymbiotic photosynthetic dinoflagellates of the genus Symbiodinium, could

be a product of their relative rarity. Zoanthids were not found at all in the Upper Keys and had a

very low percentage of cover in the Lower Keys. However, free-living Symbiodinium spp. have

been known to colonize adjacent beds of Halimeda (Porto et al., 2008) and could therefore

present a source of competition for other dinoflagellates like Gambierdiscus. Additionally,

stoplight parrotfish (Sparisoma viride), which were also most abundant in the Middle Keys, have

been associated with the dispersal of Symbiodinium in the Caribbean and could have readily

played a role in this dynamic. In light of the paradox of the plankton (Hutchinson, 1961),

competition with phytoplankton is also a plausible contributor to the patterns of Gambierdiscus

52

cell densities. The influence of some level of competition is supported by the excess CHLA at all

sites in Middle and Lower Keys where cell densities were lower. Moreover, silicon enrichment

from Florida Bay, as described by Briceño & Boyer (2014), builds a case for potential limitation

by diatoms in the Middle and Lower regions. In the absence of excess CHLA and other benthic

competitors, the high levels of light extinction (Kd) seen in the Upper Keys also played a role in

this pattern. The Kd values seen in the Upper Keys had been associated by other researchers with

an influx of colored dissolved organic matter (CDOM) from nearby mangrove forests (Briceño &

Boyer, 2014). This contribution to the attenuation of light, which could otherwise be too intense

for Gambierdiscus spp. growth in the more shallow waters of the Upper Keys sites (Kibler et al.,

2012; Villareal & Morton, 2002), also elucidates the observed significant relationship between

mangrove cover and the density of Gambierdiscus cells.

4.2 Land Use as a driver

Benthic cover of study sites grouped in a manner consistent with subsets of land use variables

(per Clarke & Warwick, 2001). The significantly ordinated combinations of variables included

mangrove, scrub mangrove, salt marsh, freshwater wetland, buttonwood, water, and beach

berm. Scrub mangrove was defined in the metadata of the land use dataset as characteristic of

the Lower Keys and was identified, along with salt marsh, as regional discriminating factors by

the SIMPER analysis. Although they were not found to be discriminatory in this study,

buttonwood and freshwater wetlands are representative of larger scale patterns described by

other researchers. For example, Ross et al. (1992) related the gradients in land cover from the

coast inshore and from Lower to Upper Keys to soil depth, hydrology, and the width of the

53

islands. The width and positioning of the islands is also a factor in area of water found in each

region. Overall, the connection with these particular patterns in land cover suggests that the

patterns in benthic cover are primarily driven by inherent regional differences in geomorphology

and is consistent with Ginsburg & Shinn (1994).

Similar to benthic cover, fish assemblages were best matched with a subset of land use

variables that was mostly comprised of regionally characteristic vegetation. Although this

suggests that inherent regional differences also play a major role in the composition of fish

assemblages, as in the latitudinal limitation of the striped parrotfish (Scarus iserti) to the Lower

Keys (Serafy et al. (2015), anthropogenic influences are also indicated. A pattern of transition

was apparent in the snapper community, whereas Yellowtail Snapper (Ocyurus chrysurus)

abundance followed a gradient from Lower to Upper Keys with the inverse observed for

Mahogany Snapper (L. mahogoni). Conversely, Schoolmaster Snapper (Lutjanus apodus) was

abundant in both the Upper and Lower Keys, but relatively rare in the Middle Keys. This departure

from the transitional pattern suggests potential anthropogenic influence. Mangroves, which had

characteristically low cover in the Middle Keys, were found by Nagelkerken et al. (2000) to be the

most important biotope for juvenile Schoolmaster (L. apodus). Therefore, recovery of

populations that had been historically subject to overfishing could be limited by a lack of the

mangrove biotope. Further evidence of habitat limitation was seen in the other two

discriminating lutjanid species. For juvenile Yellowtail Snapper, with a distinctive low abundance

in the Upper Keys, seagrass beds were found to be most important (Nagelkerken et al., 2000).

Although seagrass meadows outside the patch reef sites were not evaluated, inshore seagrass

communities tend to increase in abundance from northeast to southwest (Zieman et al., 1989).

54

The dense areas of seagrass in the “Sluiceway”, just north of the area between the Lower and

Middle Keys (Briceño & Boyer, 2014; Fourqurean et al., 2002), support the patterns observed in

this study. Further, the high abundance of Mahogany Snapper (L. mahogoni) in the more shallow

reefs of the Upper Keys is consistent with a study by Nagelkerken et al. (2000) that found reefs

with a depth of 3 m or less were the most important habitat for juveniles. For that reason, the

correlation with human population density, which gradually decreases from Upper to Lower

Keys, is likely a function of Mahogany Snapper abundance having been limited by the increasing

depth of sites along that gradient. Depth is also important to surgeonfish (Acanthurids) (Cocheret

de la Morinière et al., 2002; Nagelkerken et al., 2000); however, the study sites all fell within the

range of preferred habitat. Therefore, depth cannot fully account for the disparity in the

abundance Blue Tang (A. coeruleus) or herbivorous biomass. Because the amount of impervious

surface is linked with the more highly populated regions of the Keys, its inclusion in the subset of

best matching variables suggests that anthropogenic pressure may be a driver of these patterns.

4.3 Anthropogenic Influence on the reef

Although human development has altered the landscape of the Keys, the correlations between

population density and land cover variables do not provide direct evidence of this alteration.

According to Ross et al. (1992), salt marsh and wetland area was naturally greater in the Lower

Keys prior to the period of this study. Although hammock area was greater in the more densely

populated Upper Keys, its definition as undisturbed land cover suggests that these patterns were

also linked to underlying regional differences in geomorphology rather than anthropogenic

influence. Moreover, higher density development tends to allow for the preservation of larger

55

areas of natural lands (Berke et al., 2006). With that in mind, population density may not be an

effective measure of anthropogenic pressure. Regardless, the impacts of anthropogenic

influence on water quality have been well documented near the study sites (Briceño & Boyer,

2014; Lapointe et al., 1994; Lapointe et al., 1992), though the relationship is complex. Although

the correlation between population density and total phosphorus (TP) is inverse, with higher TP

associated with lower population densities, major contributions come from areas outside the

scope of this study. Altered hydrology in southern Florida and the Everglades, the result of human

development, plays a role in the influx of water and nutrients into the Middle and Lower Keys

(Briceño & Boyer, 2014). Therefore, population of areas in the greater watershed, including the

Everglades and Southwest Florida, should be considered in order to appropriately define

anthropogenic influence on water quality in those regions.

The inverse correlation between population density and reef health also suggests

anthropogenic influence, though the specific pressures seem to be unclear and may vary by

locality. For example, the data support the existence of a relationship between population

density and stony coral cover; however, that pattern is in accordance with aforementioned

regional trends in distribution (Ginsburg & Shinn, 1994). Conversely, macroalgae is not

distributed in relation to higher nutrient (TP, DIN) concentrations in the Lower Keys as might be

expected, but is more abundant at sites in proximity to higher population density. If grazing

pressure were considered, macroalgae would presumably be the lowest in the Middle Keys;

however, that is not supported by the data in this study. Accordingly, the composition of the

herbivorous assemblages, namely the balance between scarids, which are able to consume

calcified algae, and acanthurids that cannot (Kopp et al., 2010), may be an important factor in

56

the management of macroalgal cover. When reef health as a whole is taken into account, the

reef with the highest stony coral cover and lowest macroalgal cover (Wonderland) also supports

the highest commercial biomass. This suggests that top-down control mechanisms play a role in

overall reef health by regulating the composition and/or biomass of herbivorous assemblages. In

spite of being subject to similar anthropogenic pressure in the form of human population density,

the differences in key fish species biomass between the Upper and Middle Keys may be evidence

of differential fishing pressure. This lack of correlation between population density and

commercial biomass may be explained by the presence of marine zones that restrict harvesting.

Study sites in the Upper Keys are surrounded by five Sanctuary Preserve areas in which fishing by

any means is prohibited (NOAA, 2018). The movement of exploited fishes between these marine

protected areas and adjacent study sites could explain the higher commercial biomass in the

region as compared to the Middle Keys (Ault et al., 2013). Although the entirety of the Keys reef

tract has been historically overfished (Ault et al., 2005), the lower abundance of mangroves and

diverse inshore habitats in the Middle Keys may further limit recovery of commercial species in

that area (Lidz et al., 2006; Robertson et al., 2005). Overall, the data suggest that anthropogenic

influence does affect the amount of macroalgal substrate available to host Gambierdiscus spp.

(Lirman & Fong, 2007).

4.4 Limitations

The weaknesses of this study include a lack of coincidence in the long-term datasets with sample

collections, a limited number of samplings, and only rough estimates of biomass. Additionally,

due to land use data only being available for the beginning of the study period, trends in human

57

development could not be assessed and important variance may have been lost as other data

was reduced to site means for comparison. Although not necessarily detrimental to the

conclusions drawn at the regional level, the high level of similarity in fish assemblages in the

Upper Keys was likely due to geographical proximity (Schmitt & Sullivan, 1996). Distances

between some sites was smaller than the radius of commercial fish ranges (5 km) and should be

avoided in future studies to avoid any potentially confounding effects of migration or survey

overlap on site-specific differences.

4.5 Conclusions & Management Implications

In response to the objectives of this study, spatial and temporal patterns in Gambierdiscus

abundance in patch reefs were defined as being consistently highest in the Upper Keys and

Lowest in the Middle Keys, regardless of season. Based on a lack of correlation between mean

percentage cover of macroalgae and Gambierdiscus cell density, this study does not support the

theory that cell abundance is a function of substrate availability. Although the Upper Keys

exhibited the highest of both, a similar relationship was not evident in the other regions. The

availability of macroalgal substrate was significantly higher in the Middle Keys in relation to the

Lower Keys; however, cell densities did not follow this pattern. As has been proposed in other

studies (Parsons et al., 2017; Loeffler et al., 2015; Cruz-Rivera & Villareal, 2006), the discordance

could be explained by the effects of grazing. The significant negative relationship between

surgeonfish biomass and Gambierdiscus cell density supports this theory. Even though

surgeonfish do not consume Halimeda due to their small bite size and avoidance of calcified

algae, they do tend to target epiphytic microalgae on larger macroalgal hosts (Cruz-Rivera &

58

Villareal, 2006). Therefore, a reduction in cell densities could be expected without any influence

on the abundance Halimeda. Additionally, these fishes would be considered indicative of overall

grazing pressure due to their relatively high abundance and small size (Kopp et al., 2010).

Although more work needs to be done to determine whether surgeonfish in the Florida Keys are

actively ingesting Gambierdiscus spp. cells, this study suggests that under favorable growth

conditions, observed cell densities are a product of grazing rather than substrate availability.

Additionally, cell densities alone may not be able to appropriately describe or predict CFP risk

because they are representative of only what was not, or has not yet, been consumed. The

disparity between CFP risk derived from the Regional Risk Assessment (Table 3.10.1) and cell

densities illustrates the important role that reef health plays in moderating the flow of toxins

from dinoflagellate to consumer.

The concordance of high regional risk and high population density necessitates continued

monitoring of fish in those areas and the development of a more comprehensive predictor of

potential CFP outbreaks. In addition to favorable growth conditions, this study suggest that

mangrove cover and surgeonfish could be useful as biogeographic indicators of potential CFP risk.

Mangrove cover could indicate favorable conditions in shallow (35m) reef habitats due to their

potential reduction of the amount of light reaching the benthos, and may also point toward

populations of commercial fish species that could ultimately pass CTX along to human consumers.

If confirmed to consume Gambierdiscus cells, the presence of surgeonfish may indicate areas

with an active influx of CTX into the system. In conjunction with these potential indicators, long-

term monitoring efforts are needed to elucidate the flow of CTX from dinoflagellate to human.

Future research should include gut content analysis of key fish species and environmental DNA

59

(eDNA) analysis, as well as more refined spatial analysis of nutrient loading. Ideally, this data

would then be utilized to develop and calibrate a reef-specific risk assessment model capable of

relating cell growth and trophic transfer of CTX to watershed-scale influences.

Although this study did not find a direct linear relationship between anthropogenic

factors and cell densities, there is evidence that human activities have an indirect influence on

CFP risk through reef health, as well as through overfishing, and the destruction of inshore

habitats like seagrass and mangroves. At the watershed level, the interconnectedness of the

aquatic systems and the influence of Southwest Florida (SWFL) and the Everglades on the waters

of the Middle and Lower Keys (Briceño & Boyer, 2014) subject the area to myriad sources of

anthropogenic stress. By the year 2030, population in the Keys is expected to increase by 3.7%

based on projections from the update to Monroe County’s Comprehensive Plan (2011).

Furthermore, SWFL is currently one of the fastest growing areas in the nation and has been

ranked in the top 20 metropolitan areas for population change from 2010 to 2017 (U.S. Census

Bureau, 2018). This projected increase in anthropogenic influence in the watershed may lead to

further habitat degradation, which would jeopardize both reef and human health.

60

5 References

Anderson, D. M., Burkholder, J. M., Cochlan, W. P., Glibert, P. M., Gobler, C. J., Heil, C. A., . . . Vargo, G.

A. (2008). Harmful algal blooms and eutrophication: Examining linkages from selected coastal

regions of the United States. Harmful Algae, 8(1), 39-53.

doi:http://dx.doi.org/10.1016/j.hal.2008.08.017

Ault, J. S., Bohnsack, J. A., Smith, S. G., & Luo, J. (2005). Towards sustainable multispecies fisheries in the

Florida, USA, coral reef ecosystem. Bulletin of Marine Science, 76(2), 595-622.

Ault, J. S., Smith, S. G., & Bohnsack, J. A. (2005a). Evaluation of average length as an estimator of

exploitation status for the Florida coral-reef fish community. ICES Journal of Marine Science, 62(3),

417-423.

Ault, J. S., Smith, S. G., Bohnsack, J. A., Luo, J., Zurcher, N., McClellan, D. B., . . . Feeley, M. W. (2013).

Assessing coral reef fish population and community changes in response to marine reserves in the

Dry Tortugas, Florida, USA. Fisheries Research, 144, 28-37.

Babinchak, J. A., Jollow, D. J., Voegtline, M. S., & Higerd, T. B. (1986). Toxin production by Gambierdiscus

toxicus Isolated from the Florida Keys. Marine Fisheries Review, 48(4), 53-56.

Bagnis, R., Chanteau, S., Chungue, E., Hurtel, J. M., Yasumoto, T., & Inoue, A. (1980). Origins of ciguatera

fish poisoning: A new dinoflagellate, Gambierdiscus toxicus Adachi and Fukuyo, definitively involved

as a causal agent. Toxicon, 18(2), 199-208. doi:10.1016/0041-0101(80)90074-4

Bagnis, R. (1994). Natural versus anthropogenic disturbances to coral reefs- comparison in

epidemiological patterns of ciguatera. Memoirs of the Queensland Museum. Brisbane, 34(3), 455-

460.

61

Baker, A. C., Glynn, P. W., & Riegl, B. (2008). Climate change and coral reef bleaching: An ecological

assessment of long-term impacts, recovery trends and future outlook. Estuarine, Coastal and Shelf

Science, 80(4), 435-471.

Barrett, J. R. (2014). Under the weather with ciguatera fish poisoning: Climate variables associated with

increases in suspected cases. Environmental Health Perspectives, 122(6), A167-A167.

doi:10.1289/ehp.122-A167 [doi]

Berke, P. R., Godschalk, D. R., Kaiser, E. J., & Rodriguez, D. A. (2006). Urban land use planning (5th ed.).

Urbana, IL: University of Illinois Press.

Bomber, J. W., Guillard, R. L., & Nelson, W. G. (1988). Roles of temperature, salinity, and light in

seasonality, growth, and toxicity of ciguatera-causing Gambierdiscus toxicus Adachi et Fukuyo

(Dinophyceae). Journal of Experimental Marine Biology and Ecology, 115(1), 53-65.

doi:10.1016/0022-0981(88)90189-X

Brand, L. E., & Compton, A. (2007). Long-term increase in Karenia brevis abundance along the southwest

Florida coast. Harmful Algae, 6(2), 232-252.

Bravo, J., Suárez, F., Ramírez, A., & Acosta, F. (2015). Ciguatera, an emerging human poisoning in

Europe. Journal of Aquaculture and Marine Biology, 3, 00053.

Briceño, H. O., & Boyer, J. N. (2014). 2104 annual report of the water quality monitoring project for the

water quality protection program of the Florida Keys national marine sanctuary. (No. Technical

Report # T-762). OE-148, Florida International University: Southeast Environmental Research

Center. Retrieved from http://serc.fiu.edu/wqmnetwork/Reports/FKNMS/2014FKNMS.pdf

Brunner, J. (2012). American migration interactive map Forbes. Retrieved from:

https://www.forbes.com/special-report/2011/migration.html.

Bruno, J. F., Petes, L. E., Drew Harvell, C., & Hettinger, A. (2003). Nutrient enrichment can increase the

severity of coral diseases. Ecology Letters, 6(12), 1056-1061.

62

Chateau-Degat, M., Chinain, M., Cerf, N., Gingras, S., Hubert, B., & Dewailly, É. (2005). Seawater

temperature, Gambierdiscus spp. variability and incidence of ciguatera poisoning in French

Polynesia. Harmful Algae, 4(6), 1053-1062.

Chinain, M., Germain, M., Deparis, X., Pauillac, S., & Legrand, A. M. (1999). Seasonal abundance and

toxicity of the dinoflagellate Gambierdiscus spp. (dinophyceae), the causative agent of ciguatera in

Tahiti, French Polynesia. Marine Biology, 135(2), 259-267. doi:10.1007/s002270050623

Chinain, M., Darius, H. T., Ung, A., Cruchet, P., Wang, Z., Ponton, D., . . . Pauillac, S. (2010). Growth and

toxin production in the ciguatera-causing dinoflagellate Gambierdiscus polynesiensis (Dinophyceae)

in culture. Toxicon, 56(5), 739-750.

Clarke, K., & Gorley, R. (2015). Getting started with PRIMER v7. PRIMER-E: Plymouth, Plymouth Marine

Laboratory.

Clarke, K. R., & Warwick, R. (2001). Change in marine communities. An Approach to Statistical Analysis

and Interpretation. PRIMER-E Limited.

Cocheret de la Moriniere, E, Pollux, B., Nagelkerken, I., & Van der Velde, G. (2002). Post-settlement life

cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove

habitats as nurseries. Estuarine, Coastal and Shelf Science, 55(2), 309-321.

Colella, M., Ruzicka, R., Kidney, J., Morrison, J., & Brinkhuis, V. (2012). Cold-water event of January 2010

results in catastrophic benthic mortality on patch reefs in the Florida Keys. Coral Reefs, 31(2), 621-

632.

Cooper, M. (1964). Ciguatera and other marine poisoning in the Gilbert Islands. Pacific Science, 18, 411-

440.

Crossett, K. M., Clement, C. G., & Rohmann, S. O. (2008). Demographic baseline report of US territories

and counties adjacent to coral reef habitats. Silver Spring, MD: NOAA, National Ocean Service,

Special Projects. 65 pp.

63

Cruz-Rivera, E., & Villareal, T. A. (2006). Macroalgal palatability and the flux of ciguatera toxins through

marine food webs. Harmful Algae, 5(5), 497-525.

de Sylva, D. P. (1994). Distribution and ecology of ciguatera fish poisoning in Florida, with emphasis on

the Florida Keys. Bulletin of Marine Science, 54(3), 944-954.

Devictor, V., Whittaker, R. J., & Beltrame, C. (2010). Beyond scarcity: Citizen science programmes as

useful tools for conservation biogeography. Diversity and Distributions, 16(3), 354-362.

DiNubile, M. J., & Hokama, Y. (1995). The ciguatera poisoning syndrome from farm-raised salmon.

Annals of Internal Medicine, 122(2), 113-114.

Food and Drug Administration. (2011). Fish and Fishery Products Hazards and Controls Guidance, 4th

ed., U.S. Department of Health and Human Services, Washington, D.C., 470 pp.

Fourqurean, J. W., & Zieman, J. C. (2002). Nutrient content of the seagrass Thalassia testudinum reveals

regional patterns of relative availability of nitrogen and phosphorus in the Florida Keys USA.

Biogeochemistry, 61(3), 229-245.

Fraga, S., & Rodríguez, F. (2014). Genus Gambierdiscus in the Canary Islands (NE Atlantic Ocean) with

description of Gambierdiscus silvae sp. nov., a new potentially toxic epiphytic benthic

dinoflagellate. Protist, 165(6), 839-853.

Francini-Filho, R. B., Coni, E. O., Meirelles, P. M., Amado-Filho, G. M., Thompson, F. L., Pereira-Filho, G.

H., . . . Gibran, F. Z. (2013). Dynamics of coral reef benthic assemblages of the Abrolhos Bank,

eastern Brazil: Inferences on natural and anthropogenic drivers. PloS One, 8(1), e54260.

Friedman, M. A., Fernandez, M., Backer, L. C., Dickey, R. W., Bernstein, J., Schrank, K., . . . Bienfang, P.

(2017). An updated review of ciguatera fish poisoning: Clinical, epidemiological, environmental, and

public health management. Marine Drugs, 15(3), 72.

64

Gingold, D. B., Strickland, M. J., & Hess, J. J. (2014). Ciguatera fish poisoning and climate change: Analysis

of national poison center data in the United States, 2001-2011. Environmental Health Perspectives,

122(6), 580-586. doi:10.1289/ehp.1307196 [doi]

Ginsburg, R., & Shinn, E. (1995). Preferential distribution of reefs in the Florida reef tract: The past is the

key to the present. Oceanographic Literature Review, 8(42), 674.

Grattan, L. M., Holobaugh, S., & Morris Jr., J. G. (2016). Harmful algal blooms and public health. Harmful

Algae, 57, Part B, 2-8. doi:https://doi.org/10.1016/j.hal.2016.05.003

Hackett, J. D., Anderson, D. M., Erdner, D. L., & Bhattacharya, D. (2004). Dinoflagellates: A remarkable

evolutionary experiment. American Journal of Botany, 91(10), 1523-1534.

Hales, S., Weinstein, P., & Woodward, A. (1999). Ciguatera (fish poisoning), El Niño, and Pacific sea

surface temperatures. Ecosystem Health, 5(1), 20-25. doi:10.1046/j.1526-0992.1999.09903.x

Halley, R. B., Vacher, H., & Shinn, E. A. (1997). Geology and hydrogeology of the Florida Keys. Geology

and hydrogeology of carbonate islands (pp. 217-248) Elsevier Amsterdam.

Halstead, B. W. (1965). Poisonous and Venomous Marine Animals of the World, Vol. I Invertebrates. U.S.

Government Printing Office, Washington, D.C., 994 pp.

Harding, J. S., & Winterbourn, M. J. (1995). Effects of contrasting land use on physico‐chemical

conditions and benthic assemblages of streams in a Canterbury (South Island, New Zealand) river

system. New Zealand Journal of Marine and Freshwater Research, 29(4), 479-492.

Harvell, C. D., Kim, K., Burkholder, J. M., Colwell, R. R., Epstein, P. R., Grimes, D. J., . . . Vasta, G. R. (1999).

Emerging marine diseases--climate links and anthropogenic factors. Science (New York, N.Y.),

285(5433), 1505-1510.

Holmes, M. J., Lewis, R. J., Poli, M. A., & Gillespie, N. C. (1991). Strain dependent production of

ciguatoxin precursors (gambiertoxins) by Gambierdiscus toxicus (Dinophyceae) in culture. Toxicon,

29(6), 761-775.

65

Hughes, T. P. (1994). Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef.

Science-AAAS-Weekly Paper Edition, 265(5178), 1547-1551.

Hutchinson, G. E. (1961). The paradox of the plankton. The American Naturalist, 95(882), 137-145.

Jackson, J., & Johnson, A. E. (2014, September 18). We can save the Caribbean’s coral reefs. The New

York Times Retrieved from http://www.nytimes.com/2014/09/18/opinion/we-can-save-coral-

reefs.html?_r=1

Kibler, S. R., Litaker, R. W., Holland, W. C., Vandersea, M. W., & Tester, P. A. (2012). Growth of eight

Gambierdiscus (Dinophyceae) species: Effects of temperature, salinity and irradiance. Harmful

Algae, 19, 1-14.

Kibler, S. R., Tester, P. A., Kunkel, K. E., Moore, S. K., & Litaker, R. W. (2015). Effects of ocean warming on

growth and distribution of dinoflagellates associated with ciguatera fish poisoning in the Caribbean.

Ecological Modelling, 316, 194-210.

Kopp, D., Bouchon-Navaro, Y., Cordonnier, S., Haouisée, A., Louis, M., & Bouchon, C. (2010). Evaluation

of algal regulation by herbivorous fishes on Caribbean coral reefs. Helgoland Marine Research,

64(3), 181.

Kuno, S., Kamikawa, R., Yoshimatsu, S., Sagara, T., Nishio, S., & Sako, Y. (2010). Genetic diversity of

Gambierdiscus spp.(Gonyaulacales, Dinophyceae) in Japanese coastal areas. Phycological Research,

58(1), 44-52.

Lapointe, B. E. (1997). Nutrient thresholds for bottom-up control of macroalgal blooms and coral reefs.

Limnology and Oceanography, 44, 1586-1592.

Lapointe, B. E., Barile, P. J., & Matzie, W. R. (2004). 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(1), 23-58.

66

Lapointe, B. E., & Clark, M. W. (1992). Nutrient inputs from the watershed and coastal eutrophication in

the Florida Keys. Estuaries, 15(4), 465-476.

Lapointe, B. E., & Matzie, W. R. (1996). Effects of stormwater nutrient discharges on eutrophication

processes in nearshore waters of the Florida Keys. Estuaries, 19(2), 422-435.

Lapointe, B. E., Tomasko, D. A., & Matzie, W. R. (1994). Eutrophication and trophic state classification of

seagrass communities in the Florida Keys. Bulletin of Marine Science, 54(3), 696-717.

Lehane, L., & Lewis, R. (2000). Ciguatera: Recent advances but the risk remains. International Journal of

Food Microbiology, 61(2-3), 91-125.

Leichter, J. J., Stewart, H. L., & Miller, S. L. (2003). Episodic nutrient transport to Florida coral reefs.

Limnology and Oceanography, 48(4), 1394-1407.

Leynse, A. K. (2016). Nutritional and Photophysiological Approaches to Identifying the Niche of

Gambierdiscus: Insight into the Ecology of Ciguatera Fish Poisoning, (M.S. Thesis; Florida Gulf Coast

University).

Lidz, B. H., Reich, C. D., Peterson, R. L., & Shinn, E. A. (2006). New maps, new information: Coral reefs of

the Florida Keys. Journal of Coastal Research, 22(2) , 260-282.

Lirman, D., & Biber, P. (2000). Seasonal dynamics of macroalgal communities of the northern Florida

reef tract. Botanica Marina, 43(4), 305-314.

Lirman, D., & Fong, P. (2007). Is proximity to land-based sources of coral stressors an appropriate

measure of risk to coral reefs? An example from the Florida reef tract. Marine Pollution Bulletin,

54(6), 779-791.

Litaker, R. W., Holland, W. C., Hardison, D. R., Pisapia, F., Hess, P., Kibler, S. R., & Tester, P. A. (2017).

Ciguatoxicity of Gambierdiscus and Fukuyoa species from the Caribbean and Gulf of Mexico. PloS

One, 12(10), e0185776.

67

Litaker, R. W., Vandersea, M. W., Faust, M. A., Kibler, S. R., Chinain, M., Holmes, M. J., . . . Tester, P. A.

(2009). Taxonomy of Gambierdiscus including four new species, Gambierdiscus caribaeus,

Gambierdiscus carolinianus, Gambierdiscus carpenteri and Gambierdiscus ruetzleri (Gonyaulacales,

Dinophyceae). Phycologia, 48(5), 344-390. doi:http://dx.doi.org/10.2216/07-15.1

Litaker, R. W., Vandersea, M. W., Faust, M. A., Kibler, S. R., Nau, A. W., Holland, W. C., . . . Tester, P. A.

(2010). Global distribution of ciguatera causing dinoflagellates in the genus Gambierdiscus. Toxicon,

56(5), 711–730.

Llewellyn, L. E. (2010). Revisiting the association between sea surface temperature and the

epidemiology of fish poisoning in the south Pacific: Reassessing the link between ciguatera and

climate change. Toxicon, 56(5), 691-697.

Loeffler, C. R., Richlen, M. L., Brandt, M. E., & Smith, T. B. (2015). Effects of grazing, nutrients, and depth

on the ciguatera-causing dinoflagellate Gambierdiscus in the US Virgin Islands. Marine Ecology

Progress Series, 531, 91-104.

Maliao, R. J., Turingan, R. G., & Lin, J. (2008). Phase-shift in coral reef communities in the Florida Keys

National Marine Sanctuary (FKNMS), USA. Marine Biology, 154(5), 841-853.

Marks, K. W., & Klomp, K. D. (2003). Fish biomass conversion equations. Atoll Research Bulletin 496,

625-630

McClenachan, L., & Kittinger, J. N. (2013). Multicentury trends and the sustainability of coral reef

fisheries in Hawai‘i and Florida. Fish and Fisheries, 14(3), 239-255.

McField, M., Drysdale, I., Rueda, M., Marks, K. & Thompson, A. (2011). Current condition for the

Mesoamerican reef and the local, national and regional management efforts to improve it.

Retrieved from HealthyReefs.org

68

Monroe County Florida. (2011). Monroe county comprehensive plan update. (). Retrieved from

https://www.monroecounty-fl.gov/DocumentCenter/View/1252/Monroe-County---

Unincorporated-Population-Project?bidId=.

Morton, S. L., Norris, D. R., & Bomber, J. W. (1992). Effect of temperature, salinity and light intensity on

the growth and seasonality of toxic dinoflagellates associated with ciguatera. Journal of

Experimental Marine Biology and Ecology, 157(1), 79-90.

Myers, R. L., & Ewel, J. J. (1990). Ecosystems of Florida. Orlando, FL: The University of Central Florida

Press.

Nagelkerken, I., Van der Velde, G., Gorissen, M., Meijer, G., Van't Hof, T., & Den Hartog, C. (2000).

Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral

reef fishes, using a visual census technique. Estuarine, Coastal and Shelf Science, 51(1), 31-44.

Nakahara, H., Sakami, T., Chinain, M., & Ishida, Y. (1996). The role of macroalgae in epiphytism of the

toxic dinoflagellate Gambierdiscus toxicus (Dinophyceae). Phycological Research, 44, 113-117.

doi:doi: 10.1111/j.1440-1835.1996.tb00385.x

Nishimura, T., Hariganeya, N., Tawong, W., Sakanari, H., Yamaguchi, H., & Adachi, M. (2016).

Quantitative PCR assay for detection and enumeration of ciguatera-causing dinoflagellate

Gambierdiscus spp. (Gonyaulacales) in coastal areas of Japan. Harmful Algae, 52, 11-22.

doi:http://dx.doi.org/10.1016/j.hal.2015.11.018.

NOAA. (2018). Florida Keys National Marine Sanctuary: Sanctuary preservation areas. Retrieved from

https://floridakeys.noaa.gov/zones/spas/welcome.html

Norström, A. V., Nyström, M., Lokrantz, J., & Folke, C. (2009). Alternative states on coral reefs: Beyond

coral-macroalgal phase shifts. Marine Ecology Progress Series, 376, 295-306.

O'Toole, A. C., Dechraoui Bottein, M., Danylchuk, A. J., Ramsdell, J. S., & Cooke, S. J. (2012). Linking

ciguatera poisoning to spatial ecology of fish: A novel approach to examining the distribution of

69

biotoxin levels in the great barracuda by combining non-lethal blood sampling and biotelemetry.

Science of the Total Environment, 427-428, 98-105

doi:https://doi.org/10.1016/j.scitotenv.2011.11.053

Parsons, M. L., Aligizaki, K., Dechraoui Bottein, M. Y., Fraga, S., Morton, S. L., Penna, A., & Rhodes, L.

(2012). Gambierdiscus and Ostreopsis: Reassessment of the state of knowledge of their taxonomy,

geography, ecophysiology, and toxicology. Harmful Algae, 14, 107–129.

Parsons, M. L., Settlemier, C. J., & Bienfang, P. K. (2010). A simple model capable of simulating the

population dynamics of Gambierdiscus, the benthic dinoflagellate responsible for ciguatera fish

poisoning. Harmful Algae, 10, 71-80.

Parsons, M. L., Brandt, A. L., Ellsworth, A., Leynse, A. K., Rains, L. K., & Anderson, D. M. (2017). Assessing

the use of artificial substrates to monitor Gambierdiscus populations in the Florida Keys. Harmful

Algae, 68, 52-66.

Parsons, M. L., Settlemier, C. J., & Ballauer, J. M. (2011). An examination of the epiphytic nature of

Gambierdiscus toxicus, a dinoflagellate involved in ciguatera fish poisoning. Harmful Algae, 10(6),

598-605. doi:http://dx.doi.org/10.1016/j.hal.2011.04.011

Peters, D. P., Havstad, K. M., Cushing, J., Tweedie, C., Fuentes, O., & Villanueva-Rosales, N. (2014).

Harnessing the power of big data: Infusing the scientific method with machine learning to

transform ecology. Ecosphere, 5(6), 1-15.

Pisapia, F., Holland, W. C., Hardison, D. R., Litaker, R. W., Fraga, S., Nishimura, T., . . . Amzil, Z. (2017).

Toxicity screening of 13 Gambierdiscus strains using neuro-2a and erythrocyte lysis bioassays.

Harmful Algae, 63, 173-183.

Radke, E. G., Reich, A., & Morris, J. G., Jr. (2015). Epidemiology of ciguatera in Florida. The American

Journal of Tropical Medicine and Hygiene, 93(2), 425-432. doi:10.4269/ajtmh.14-0400 [doi]

70

Rains, L. K., & Parsons, M. L. (2015). Gambierdiscus species exhibit different epiphytic behaviors toward

a variety of macroalgal hosts. Harmful Algae, 49, 29-39. doi:10.1016/j.hal.2015.08.005

Randall, J. E. (1958). A review of ciguatera, tropical fish poisoning, with a tentative explanation of its

cause. Bulletin of Marine Science, 8(3), 236-267.

Richlen, M. L., Morton, S. L., Barber, P. H., & Lobel, P. S. (2008). Phylogeography, morphological variation

and taxonomy of the toxic dinoflagellate Gambierdiscus toxicus (Dinophyceae). Harmful Algae, 7,

614-629.

Roberts, M., Hanley, N., Williams, S., & Cresswell, W. (2017). Terrestrial degradation impacts on coral

reef health: Evidence from the Caribbean. Ocean & Coastal Management, 149, 52-68.

Robertson, D. R., Choat, J. H., Posada, J. M., Pitt, J., & Ackerman, J. (2005). Ocean surgeonfish

Acanthurus bahianus. II. Fishing effects on longevity, size and abundance? Marine Ecology Progress

Series, 295, 245-256.

Rodríguez, F., Fraga, S., Ramilo, I., Rial, P., Figueroa, R. I., Riobó, P., & Bravo, I. (2017). Canary Islands (NE

atlantic) as a biodiversity ‘hotspot’ of Gambierdiscus: Implications for future trends of ciguatera in

the area. Harmful Algae, 67, 131-143.

Roeder, K., Erler, K., Kibler, S., Tester, P., Van The, H., Nguyen-Ngoc, L., . . . Luckas, B. (2010).

Characteristic profiles of ciguatera toxins in different strains of Gambierdiscus spp. Toxicon, 56(5),

731-738.

Rongo, T., Bush, M., & Van Woesik, R. (2009). Did ciguatera prompt the late Holocene Polynesian

voyages of discovery? Journal of Biogeography, 36(8), 1423-1432.

Ross, M. S., O'Brien, J. J., & Flynn, L. J. (1992). Ecological site classification of Florida Keys terrestrial

habitats. Biotropica, 488-502.

Ruzicka, R., Colella, M., Semon, K., Brinkhuis, V., Morrison, J., Kidney, J., . . . Meyers, M. (2010). Coral

Reef Evaluation and Monitoring Program 2009 Final Report. Fish & Wildlife Research

71

Institute/Florida Fish & Wildlife Conservation Commission. Saint Petersburg, FL. 110 pp.

Ruzicka, R., Colella, M., Porter, J., Morrison, J., Kidney, J., Brinkhuis, V., . . . Meyers, M. (2013). Temporal

changes in benthic assemblages on Florida Keys reefs 11 years after the 1997/1998 El Niño. Marine

Ecology Progress Series, 489, 125-141.

Sakami, T., Nakahara, H., Chinain, M., & Ishida, Y. (1999). Effects of epiphytic bacteria on the growth of

the toxic dinoflagellate Gambierdiscus toxicus (Dinophyceae). Journal of Experimental Marine

Biology and Ecology, 233, 231-246.

Sassenhagen, I., & Erdner, D. L. (2017). Microsatellite markers for the dinoflagellate Gambierdiscus

caribaeus from high-throughput sequencing data. Journal of Applied Phycology, 29(4), 1927-1932.

Scheuer, P. J. (1994). Tetrahedron perspective number 2: Ciguatera and its off-shoots—chance

encounters en route to a molecular structures. Tetrahedron, 50(1), 3-18.

Schmitt, E., & Sullivan, K. (1996). Analysis of a volunteer method for collecting fish presence and

abundance data in the Florida Keys. Bulletin of Marine Science, 59(2), 404-416.

Serafy, J. E., Shideler, G. S., Araújo, R. J., & Nagelkerken, I. (2015). Mangroves enhance reef fish

abundance at the Caribbean regional scale. PloS One, 10(11), e0142022.

Somerfield, P., Jaap, W., Clarke, K., Callahan, M., Hackett, K., Porter, J., . . . Yanev, G. (2008). Changes in

coral reef communities among the Florida Keys, 1996–2003. Coral Reefs, 27(4), 951-965.

Sparrow, L., & Heimann, K. (2016). Key environmental factors in the management of ciguatera. Journal

of Coastal Research, 75(sp1), 1007-1011.

Szmant, A., & Forrester, A. (1996). Water column and sediment nitrogen and phosphorus distribution

patterns in the Florida Keys, USA. Coral Reefs, 15(1), 21-41.

Tester, P. A., Feldman, R. L., Nau, A. W., Kibler, S. R., & Litaker, R. W. (2010). Ciguatera fish poisoning and

sea surface temperatures in the Caribbean Sea and the West Indies. Toxicon, 56(5), 698-710.

72

Tester, P. A., Vandersea, M. W., Buckel, C. A., Kibler, S. R., Holland, W. C., Davenport, E. D., . . . Litaker, R.

W. (2013). Gambierdiscus (Dinophyceae) species diversity in the Flower Garden Banks National

Marine Sanctuary, northern Gulf of Mexico, USA. Harmful Algae, 29, 1-9.

doi:http://dx.doi.org/10.1016/j.hal.2013.07.001

Thompson, C. A., Jazuli, F., Taggart, L. R., & Boggild, A. K. (2017). Ciguatera fish poisoning after Caribbean

travel. CMAJ : Canadian Medical Association Journal = Journal De l'Association Medicale

Canadienne, 189(1), E19-E21. doi:10.1503/cmaj.151207 [doi]

Tosteson, T. R., Ballantine, D. L., Tosteson, C. G., Hensley, V., & Bardales, A. T. (1989). Associated

bacterial flora, growth, and toxicity of cultured benthic dinoflagellates Ostreopsis lenticularis and

Gambierdiscus toxicus. Applied and Environmental Microbiology, 55(1), 137-141.

Turquet, J., Quad, J. P., Ten-Hage, L., Dahalani, Y., & Wendling, B. (2000). (2000). Example of a

Gambierdiscus toxicus flare-up following the 1998 coral bleaching event in Mayotte Island

(Comoros, south-west Indian Ocean). Paper presented at the HAB 2000, 50-53. Retrieved from

http://www.researchgate.net/publication/259382393

Vandersea, M. W., Kibler, S. R., Holland, W. C., Tester, P. A., Schultz, T. F., Faust, M. A., . . . Litaker, R. W.

(2012). Development of semi-quantitative PCR assays for the detection and enumeration of

Gambierdiscus species (Gonyaulacales, Dinophyceae). Journal of Phycology, 48, 902-915.

doi:10.1111/j.1529-8817.2012.01146.x

Villareal, T., Hanson, S., Qualia, S., Jester, E., Granade, H., & Dickey, R. (2007). Petroleum production

platforms as sites for the expansion of ciguatera in the northwestern Gulf of Mexico. Harmful

Algae, 6(2), 253-259.

Wagner, D. E., Kramer, P., & Van Woesik, R. (2010). Species composition, habitat, and water quality

influence coral bleaching in southern Florida. Marine Ecology Progress Series, 408, 65-78.

73

Wakefield, C. B., Lewis, P. D., Coutts, T. B., Fairclough, D. V., & Langlois, T. J. (2013). Fish assemblages

associated with natural and anthropogenically-modified habitats in a marine embayment:

Comparison of baited videos and opera-house traps. PloS One, 8(3), e59959.

Walker, R. T., Solecki, W. D., & Harwell, C. (1997). Land use dynamics and ecological transition: The case

of south Florida. Urban Ecosystems, 1(1), 37-47.

Ward-Paige, C. A., Risk, M. J., Sherwood, O. A., & Jaap, W. C. (2005). Clionid sponge surveys on the

Florida reef tract suggest land-based nutrient inputs. Marine Pollution Bulletin, 51(5), 570-579.

Wenger, A. S., Williamson, D. H., da Silva, E. T., Ceccarelli, D. M., Browne, N. K., Petus, C., & Devlin, M. J.

(2016). Effects of reduced water quality on coral reefs in and out of no-take marine reserves.

Conservation Biology, 30(1), 142-153. doi:10.1111/cobi.12576

Woolfe, K. J., & Larcombe, P. (1999). Terrigenous sedimentation and coral reef growth: A conceptual

framework. Marine Geology, 155(3), 331-345.

Wolfe, J. R., & Pattengill-Semmens, C. V. (2013). Estimating fish populations from REEF citizen science

volunteer diver order-of-magnitude surveys. California Cooperative Oceanic Fisheries Investigations

Reports, 54, 127-140.

Xu, Y., Richlen, M. L., Liefer, J. D., Robertson, A., Kulis, D., Smith, T. B., . . . Anderson, D. M. (2016).

Influence of environmental variables on Gambierdiscus spp. (Dinophyceae) growth and

distribution. PloS One, 11(4), e0153197.

Yang, Z., Luo, Q., Liang, Y., & Mazumder, A. (2016). Processes and pathways of ciguatoxin in aquatic

foodwebs and fish poisoning of seafood consumers. Environmental Reviews, 24(2), 144-150.

Yasumoto, T., Nakajima, I., Bagnis, R., Adachi, R. (1977). Finding of a Dinoflagellate as a Likely

Culprit of Ciguatera, Nippon Suisan Gakkaishi, 43(8), 1021-1026.

Yee, S. H., Santavy, D. L., & Barron, M. G. (2011). Assessing the effects of disease and bleaching on

Florida Keys corals by fitting population models to data. Ecological Modelling, 222(7), 1323-1332.

74

Zhang, K., Dittmar, J., Ross, M., & Bergh, C. (2011). Assessment of sea level rise impacts on human

population and real property in the Florida Keys. Climatic Change, 107(1), 129-146.

Zieman, J., Fourqurean, J. W., & Iverson, R. L. (1989). Distribution, abundance and productivity of

seagrasses and macroalgae in Florida Bay. Bulletin of Marine Science, 44(1), 292-311.