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 Parsons Committee 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
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
2 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
3 (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
4 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.
5 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.
6 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
7 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
8 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., 9 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.
10 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
11 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
12 (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
13 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 14 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
15 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
16 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
17 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.
18 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
19 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
20 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
21 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 22 (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
23 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
24 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
25 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
26 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.
27
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
28 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
29 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 (푝 <
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
30 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.
31 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.
32 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)
Upper Keys Middle Keys Lower Keys U-M U-L M-L
Species 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.
33
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.
34 Table 3.1.2 Discriminating land uses by percent cover within 10 km of study sites (significant contributions in bold)
Upper Keys Middle Keys Lower Keys U-M U-L M-L Species 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.
35 Table 3.2.1 Temperature and salinity at bottom (B) of study sites from 2010-2015
Temperature (B) °C Salinity (B) ppt Site 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).
36 Table 3.2.2 Average Water Quality within 5km and anthropogenic factors within 10km of study sites.
Distance to Population Density Onsite Site DIN_B TP_B CHLA Kd Depth (m) shore (km) (per km2) 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
37 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)
Upper Keys Middle Keys Lower Keys U-M U-L M-L
Species 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
38 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.
39 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%,
40 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)
Upper Keys Middle Keys Lower Keys U-M U-L M-L Benthic Cover Group 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.
41 3.5 Patterns in Reef Health
Stony Coral Cover Macroalgal Cover Two Patches 45% 45% Burr Fish
30% 30% Rawa Reef
Dustan Rocks 15% 15%
Wonderland 0% 0% 2010 2011 2012 2013 2014 2015 West 2010 2011 2012 2013 2014 2015 Washerwomen Year Year
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- 42 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
Herbivorous Commercial Fish Biomass Fish Biomass Region Site Stony Coral Macroalgae Assessed
Cover Score Cover Score g 100m-2 Score g 100m-2 Score Score
Upper Two Patches 4.9% 1 16.9% 2 86.33 1 324.22 1 1 Keys Burr Fish 7.9% 2 26.9% 1 81.61 1 312.61 1 1 Middle Rawa Reef 11.9% 3 12.7% 2 233.72 1 37.53 1 2 Keys Dustan Rocks 13.0% 3 12.4% 2 224.73 1 87.30 1 2 Lower Wonderland 37.9% 4 1.7% 4 95.52 1 440.50 2 3 Keys 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.
43 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.
Seasonality
80 70 Aug-16 60 Feb-17 50 Aug-17
ww 40
1 1 - 30
20 Cellsg 10 0 Upper Keys Middle Keys Lower Keys
Figure 3.6.1 Seasonal cell density of Gambierdiscus spp. on H. gracilis with standard error; Cross-hatched bar represents missing data
44 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.
Site Average Cell Density
8.00 7.00 6.00
ww 5.00 1 1 - 4.00 3.00 2.00 1.00 SQRT Cells SQRT Cells g B A C BC AB BC 0.00 Upper Keys Middle Keys Lower 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
45 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
Benthic Cover Key Fish Species Assemblages Dataset rho Best Variables Sig. rho Best Variables Sig.
Land Use 0.932 Water, Freshwater Wetland, 0.032 0.944 Impervious Surface, Water, 0.005 Area Buttonwood Mangrove, Exotic, Hammock
Land Use 0.928 Mangrove, Scrub Mangrove, Salt Marsh, 0.024 0.882 Impervious Surface, Mangrove, 0.017 Density Freshwater Wetland, Beach Berm Exotic, Buttonwood ,Hammock
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.
46 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
47 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.
48 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
49 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 50 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 51 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
52 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). 53 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
54 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 55 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
56 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 &
57 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 58 (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.
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