University of Florida Thesis Or Dissertation Formatting

THE ROLE OF HERBIVORES IN SHAPING CORAL REEF MACROALGAL COMMUNITIES: DIFFERING EFFECTS OF HERBIVOROUS FISH AND THE LONG- SPINED SEA URCHIN Diadema antillarum

LINDSAY J. SPIERS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2020

To Mom and Dad

ACKNOWLEDGMENTS

I would like to thank Dr. Tom Frazer for all his assistance in helping me focus my many disparate ideas into a comprehensive project that I am proud of. Without all the support and advice, I would still be conducting the numerous experiments I kept designing. Thank you for giving me the freedom and encouragement I needed to grow as a scientist.

I would also like to extend gratitude to the rest of my committee; Dr. Chuck Jacoby, Dr.

Don Behringer, Dr. Michal Kowalewski, and Dr. Ed Phlips for all their assistance and encouragement as I took my research data and translated it into the comprehensive work I present here. I appreciate all your help with experimental design, statistical analysis, and graphing.

I also must thank the members of my lab group, especially Dr. Anya Brown and Dr. Jana

Hilsenroth, for all their assistance and support and their willingness to act as a sounding board for new ideas. In addition, I must thank them for all the dinners and lunches we enjoyed together that helped me remain sane as I was trying to finish graduate school. In addition, I am eternally grateful for the numerous people who assisted me with all my field work. From the scientists and interns at Carrie Bow Cay to everyone at CCMI, especially Joe Kuehl, I could have not done any of this without you.

Finally, I must thank my parents for fostering my love of science and the ocean as well as continuing to support me through all my highs and lows of graduate school.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

ABSTRACT ...... 11

CHAPTER

1 REVIEW OF THE CURRENT STATE OF CARIBBEAN CORAL REEFS ...... 13

Coral Reef Stressors ...... 14 Regional Stressors: Atlantic and Caribbean ...... 21 Algae on Coral Reefs ...... 24 Herbivores on Caribbean Coral Reefs ...... 27

2 ANALYSIS OF THE BENTHIC COMMUNITY STRUCTURE ON SHALLOW PATCH REEFS NEAR CARRIE BOW CAY, BELIZE ...... 37

Introduction ...... 37 Methods ...... 40 Results...... 43 Discussion ...... 46

3 THE EFFECT OF HERBIVOROUS FISHES AND Diadema antillarum ON THE ESTABLISHMENT OF A MACROALGAL COMMUNITY ON A SHALLOW BELIZEAN CORAL REEF ...... 59

Introduction ...... 59 Methods ...... 62 Caging Experiment ...... 63 In Cage Feeding Experiment ...... 66 Herbivore Feeding Assays ...... 67 Results...... 70 In Cage Feeding Experiment ...... 74 Feeding Assays ...... 75 Discussion ...... 76

4 HOW HERBIVORES RESHAPE A MACROALGAL COMMUNITY ON A LITTLE CAYMAN CORAL REEF: THE ROLE OF HERBIVORE TYPE AND DENSITY ...... 96

Introduction ...... 96 Methods ...... 100

Results...... 105 Discussion ...... 113

5 ANALYSIS OF FEEDING CHOICE OF HERBIVOROUS FISH AND Diadema antillarum IN LITTLE CAYMAN ...... 131

Introduction ...... 131 Methods ...... 135 Fish Feeding Assays ...... 136 Diadema antillarum Feeding Assay ...... 138 No-choice feeding assays ...... 139 Choice feeding assays ...... 140 Results...... 141 Fish Feeding Assays ...... 141 Diadema antillarum Feeding Assay-No-choice ...... 144 Diadema antillarum Feeding Assay-Choice ...... 145 Discussion ...... 146

6 CONCLUSION: FINAL THOUGHTS ON THE ROLE OF HERBIVORES ON CARIBBEAN CORAL REEFS ...... 165

LIST OF REFERENCES ...... 170

BIOGRAPHICAL SKETCH ...... 183

LIST OF TABLES

Table page

2-1 Coordinates for patch reefs that were surveyed...... 50

2-2 Density of Diadema antillarum found on each patch reef...... 50

2-3 Depth and percent cover of three major categories of benthic organisms ± standard error (SE) on each surveyed patch reef...... 51

3-1 Algal assemblage by treatment and month sorted by percent cover. Palatability: C- chemically rich, S- structurally defended, P- palatable, U-unknown...... 81

3-2 Average density of fish species per m2 ± standard error (SE) on the experimental reef. * herbivorous species...... 85

3-3 Average density of fish species per m2 ± standard error (SE) on Golden Reef where feeding assays were conducted. * represent herbivorous species...... 85

4-1 Mean numbers of fish ± standard errors (SE) for each species found along transects. *indicates roaming herbivores ...... 118

4-2 Percent cover of macroalgae by genera for each month. Superscripts represent defenses: 1. Cyanobacteria, 2. Chemically rich, 3. Chemically rich and structurally defended, 4. Structurally defended, 5. Unknown...... 120

5-1 List of macrophytes used in feeding assays along with their known defenses and known responses of herbivores to congeners...... 151

5-2 Average number per m2 (± standard error, SE) of fish of each species found during 4 transects run at shallow site...... 152

5-3 Average number per m2 (± standard error, SE) of fish of each species found during 3 transects run at deep site...... 152

5-4 Number of bites per hour by each herbivorous fish species on each macrophyte species across all trials at the shallow site...... 153

5-5 Number of bites per hour by each herbivorous fish species on each macrophyte species across all trials at the deep site...... 154

LIST OF FIGURES

Figure page

2-1 Benthic composition of patch reefs with colors representing categories of benthos; algae, anthozoans other than coral, bare substrate, crustose coralline algae (CCA), corals, sponges, and turf algae...... 52

2-2 Benthic composition on patch reefs surveyed grouped according to density of Diadema antillarum on the reef...... 52

2-3 Percent cover of different types of algae divided into chemical and structurally defended, chemically rich, palatable, structurally defended, and turf on each reef...... 53

2-4 Percent cover of different types of macroalgae divided into chemical and structurally defended, chemically rich, palatable, and structurally defended on each reef...... 53

2-5 Percent cover of different types of macroalgae on each reef divided by defense exhibited and grouped according to density of Diadema antillarum...... 54

2-6 Percent cover of different types of corals divided into weedy, competitive, or stress tolerant on each reef. Patch reefs sorted according to Diadema antillarum density; increasing from left to right...... 54

2-7 Percent cover of corals divided according to coral trait and grouped according to density of Diadema antillarum...... 55

2-8 Percent cover of sponges divided according to palatability into defended, palatable, variable, or unknown. Patch reefs sorted according to Diadema antillarum density; increasing from left to right...... 55

2-9 Percent cover of sponges divided according to palatability and grouped according to density of Diadema antillarum...... 56

2-10 Ordination based on the full benthic community on all patch reefs. Colors represent different reefs, and larger bubbles represent higher densities of Diadema antillarum...... 56

2-11 Ordination based on the macroalgal community on each patch reef. Colors represent different reefs, and larger bubbles represent higher densities of Diadema antillarum...... 57

2-12 Ordination based on the full benthic community grouped according to density classes for Diadema antillarum...... 57

2-13 Ordination based on macroalgal communities and grouped according to density classes for Diadema antillarum...... 58

3-1 Photographs of different experimental treatments. Images show full herbivore exclusion cage (A), lidless fence cages (B), partial cages with two sides and a top (C), and open plots (D)...... 86

3-2 Stacked bar plot dividing percent cover of living portion of each treatment by month. n= indicates the number of replicates of that treatment...... 87

3-3 Stacked bar plots showing percent cover of algae employing different defense strategies found in each treatment type by month...... 88

3-4 Percent cover of macroalgae excluding CCA and turf for each sampling event. Error bars represent ± 1 standard error...... 89

3-5 Plots of results of non-metric multidimensional scaling indicating the community structure in different cage types at 7 weeks (A) and 11 weeks (B). Points that are closer together are more similar...... 90

3-6 Plots of results of non-metric multidimensional scaling indicating the community structure in different cage types at 20 weeks without (A) and with (B) unmanipulated reefs. Points that are closer together are more similar...... 91

3-7 Comparison of canopy heights between four cage treatment types. Error bars represent ± 1 standard error. Letters represent significant differences...... 92

3-8 Comparison of percent cover of macroalgae eaten in cage sections open to herbivorous fishes and with sections containing Diadema antillarum. Error bars represent ± 1 standard error...... 92

3-9 Percent cover of macroalgae excluding CCA and turf during the exclusion and in- cage feeding experiments. Error bars represent ± 1 standard error...... 93

3-10 Plots of results of non-metric multidimensional scaling comparing algal assemblages in fences, partial cages, open plots, fish cages, urchin cages, and unmanipulated plots...... 93

3-11 Percent macroalgae eaten by D. antillarum in no-choice feeding assays in July (A) and December (B) 2016. Number of replicates represented by numbers at the bottom of the bar. Error bars represent ± 1 standard error...... 94

3-12 Proportion of algae eaten by herbivorous fish during feeding assays. Letters indicate significant differences...... 95

4-1 Total percent cover (A) and change in percent cover (B) of macroalgae in every month of the 5-month caging experiment. Colors represent different experimental treatments. Error bars represent ± standard error...... 123

4-2 Percent cover of different types of algae divided into CCA, chemically and structurally defended, chemically defended, structurally defended, and cyanobacteria in each month in each experimental treatment...... 124

4-3 Percent cover of CCA (A), Lobophora spp. (B), and Dictyota spp. (C), over time in each experimental treatment. Error bars represent ± standard error...... 126

4-5 Height of algae found in each treatment type at the conclusion of the experiment. Letters indicate statistically significant differences. Error bars represent ± standard error...... 129

4-6 Ratio of Diadema antillarum mass to test diameter at the conclusion of the experiment. Urchin came from the 4 individuals/m2 treatments, 1 individual/m2 treatments, and “wild” urchins from a nearby reef...... 130

5-1 Photographs of macrophytes used in feeding assays. Photographs show Dictyota sp. (A), Laurencia sp. 1 (B), Laurencia sp. 2 (C), Lobophora sp. (D), Turbinaria sp. (E), Galaxaura sp. (F), Halimeda tuna (G), Red Algae 1 (H), Padina sp. (I) ...... 155

5-2 Proportion of each macrophyte eaten by fish at the shallow sites over 24 h. n = number lines per assay. Results analyzed using a G-test followed by Fisher’s exact tests, and letters indicate significant differences...... 156

5-3 Percent of bites by each herbivorous fish on each macrophyte across all shallow sites. For parrotfishes, (I) indicates intermediate stage fish and (T) indicates terminal phase...... 158

5-4 Proportion of each macrophyte eaten by fish at the deep feeding sites over 24 h. n = number lines per assay. Results analyzed using a G-test followed by Fisher’s exact tests, and letters indicate significant differences...... 159

5-5 Percent of bites by each herbivorous fish on each macrophyte across all deep sites. For parrotfishes, (I) indicates intermediate stage fish and (T) indicates terminal phase...... 161

5-6 Amount of each macrophyte eaten by Diadema antillarum in no-choice feeding assays. Different letters indicate significant differences according to an ANOVA followed by Tukey tests...... 161

5-7 Amount in grams of each macrophyte eaten by Diadema antillarum in choice feeding assays in December 2017. Different letters indicate significant differences according to Friedman’s test followed by Student-Newman-Keuls tests...... 162

5-8 Amount of each algal species eaten by Diadema antillarum in choice feeding assays in May 2018. Different letters indicate significant differences according to Friedman’s test followed by Student-Newman-Keuls tests...... 164

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE ROLE OF HERBIVORES IN SHAPING CORAL REEF MACROALGAL COMMUNITIES: DIFFERING EFFECTS OF HERBIVOROUS FISH AND THE LONG- SPINED SEA URCHIN Diadema antillarum

Lindsay J. Spiers

August 2020

Chair: Thomas K. Frazer Major: Fisheries and Aquatic Sciences

Historically, herbivorous fishes and sea urchins, particularly Diadema antillarum, played key roles in the control and regulation of macroalgae on Caribbean coral reefs. The loss of these important herbivores throughout the broader Caribbean region has likely contributed to the proliferation of macroalgae on reefs and associated decline in coral cover. As awareness of the importance of herbivores grows and some herbivore populations increase due to protections or natural recovery, it is important to more fully understand the combined effects of both D. antillarum and herbivorous fishes on the ecology of Caribbean coral reefs. Toward that end, a series of studies were designed and carried out to examine the role of herbivores in shaping coral reef ecosystems. These studies comprised benthic surveys, feeding assays, and caging experiments. Studies were conducted in two areas of the Caribbean; Carrie Bow Cay, Belize and

Little Cayman, British West Indies. Caging experiments indicated that D. antillarum, particularly at high densities, had the capacity to control macroalgae on coral reefs. In addition, herbivorous fishes were able to restrict the growth of macroalgae on a newly cleared benthic substrate throughout a 4-month long study. Furthermore, results of the caging experiments indicate the capacity of herbivorous fishes and D. antillarum to increase the amount of substrate clear of

macroalgae, a condition that favors the settlement of corals. The differing feeding preferences of herbivorous fishes and D. antillarum are likely to play a key role in determining the percent cover and community structure of algae on coral reefs. However, benthic surveys of reefs in

Belize indicated that the density of D. antillarum and/or herbivorous fishes was insufficient to maintain reefs in a state of low macroalgal cover suggesting that other factors are playing key roles in the ecology of algae. Collectively, findings from these studies provide important insights into algal/herbivore interactions on Caribbean coral reefs that should be of interest to a broad suite of marine conservation scientists and reef resource managers who seek to aid the recovery of degraded reefs throughout the region.

CHAPTER 1 REVIEW OF THE CURRENT STATE OF CARIBBEAN CORAL REEFS

Coral reefs can be found in tropical locations worldwide and are often colloquially referred to as the rainforest of the sea due to their biodiversity and ecological importance (Burke et al. 2011). Despite occupying less than 0.1% of the world’s oceans (~250,000 km2), they are home to approximately 30% of all known marine species, which includes 70,000 identified species, as well as an estimated 760,000 species yet to be identified (Burke et al. 2011; Fisher et al. 2015). Of the species identified, there are around 800 species of reef building corals and

10,000 coral reef fish (Burke et al. 2011; Victor 2015). Globally, coral reefs serve a variety of ecological roles and deliver a plethora of ecosystem services. Coral reefs are an important habitat for spawning and feeding for adults,as well as a nursery habitat for larvae and juveniles (Barbier et al. 2011). Some of the organisms using coral reefs as a nursery remain as adults, while others go on to live in other habitats (Moberg and Folke 1999). Coral reefs and their associated organisms are also important for many biogeochemical services including the routine transfer of energy and nutrients due to both daily and ontogenetic migrations by reef associated organisms, nitrogen fixation by marine cyanobacteria, and the binding of calcium by corals, foraminifera and calcifying algae (Moberg and Folke 1999; Allemand et al. 2011).

Many ecosystem services provided by coral reefs are linked to their structure and their function as vital habitat. Their size and three-dimensional structure protect shorelines from routine wave action and severe storms by dissipating wave energy, reducing erosion, lessening inundation, and decreasing wave damage (Barbier et al. 2011). Globally, it has been calculated that coral reefs protect approximately 150,000 km of shoreline in more than 100 countries and territories, along with the settlements and infrastructure of the nearly 300 million people living adjacent to these reefs (Barbier et al. 2011; Burke et al. 2011). In protecting shorelines, coral

reefs also foster the existence of many other ecologically vital ecosystems, such as seagrass beds and mangroves, which in turn provide indispensable ecosystem services (Barbier et al. 2011).

Beyond protecting people and their assests, coral reefs are vital to the livelihood of a large proportion of those living near them both through use of living resources and through their aesthetic appeal that attracts tourists (Burke et al. 2011). In terms of coral reef associated fisheries, there are multiple categories including reef associated pelagic fishes, such as sharks and mahi mahi, reef fishes, such as groupers and snappers, and large reef invertebrates, primarily lobster and conch (Barbier et al. 2011). It has been calculated that reef associated fishes account for approximately 25% of the total fish catch on average in developing countries (Burke et al.

2011). In addition to being a source of food, coral reefs are also vital for the aquarium trade, a multimillion dollar industry, and for the export and sale of shells, corals, and mother of pearl for jewelry (Moberg and Folke 1999; Barbier et al. 2011). Some other ecosystem services provided by reefs include pharmaceutical products and construction material, as well as having cultural and social importance (Moberg and Folke 1999; Barbier et al. 2011). At least 94 countries and territories benefit from tourism due to coral reefs, accounting for over 15% of the annual GDP in

23 of those locations (Burke et al. 2011). Not only do reefs bring in many tourists who want to either snorkel or dive, but also the reefs themselves are responsible for the white sandy beaches that attract tourists (Barbier et al. 2011). Unfortunately, as the global population continues to grow and the direct and indirect uses of coral reefs increase, coral reefs are facing more and more threats that continue to contribute to their decline.

Coral Reef Stressors

Worldwide, coral reefs have declined an estimated 30% through loss or degradation, and in the Caribbean, estimates of decline range from 50% to 80% (Barbier et al. 2011; de Bakker et al. 2017; Cramer et al. 2020). This decline is measured primarily through the loss of living coral 14

cover, increases in macroalgal cover, reductions in species diversity, and lowered fish abundance and biomass (Burke et al. 2011). Coral reef decline has been attributed to a variety of factors, both local and global, including anthropogenic stressors such as a nutrient pollution, sedimentation, overfishing, ocean acidification and warming ocean temperatures (Zaneveld et al.

2016; Putnam et al. 2017; Cramer et al. 2020). In fact, more than 60% of reefs are under continued threat due to local stressors, and this number rises to 75% when global threats are considered (Burke et al. 2011). As human population growth continues, there is an increased need for coastal development, agriculture, and shipping, all of which contribute to the anthropogenic stressors previously mentioned (Burke et al. 2011). These stressors have contributed to continuing direct loss of live coral cover and reduced recruitment of new coral due to factors such as coral bleaching, disease, and competitive exclusion by macroalgae (Zaneveld et al. 2016). Furthermore, none of these stressors work in isolation. It is possible that if coral reefs were to experience only one stressor, they would be able to recover once the stressor was removed, but when stressors are experienced in combination, recovery becomes more difficult

(Ban et al. 2014). When looking into stressors that affect coral reefs, nutrient pollution, sedimentation, overfishing, and climate change have the capacity to be particularly detrimental.

Nutrient pollution is one of the most widespread and well-researched local stressors on coral reefs. Influxes of nutrients can come from a number of different sources including agriculture, livestock, and sewage (VanderZwaag and Powers 2008; Burke et al. 2011). Globally, agriculture applies more than 135 million tons of fertilizer and pesticides to crops annually and a large percentage of these additives end up in waterways and eventually in the ocean (Food and

Agriculture Organization of the United Nations 2004). In addition to inputs from fertilizers, waste products are another large source of added nutrients to coastal ecosystems both from

livestock and humans (VanderZwaag and Powers 2008). It was found that between 80% and

90% of wastewater is released untreated in the Caribbean, Southeast Asia and the Pacific

(VanderZwaag and Powers 2008; Burke et al. 2011). The effects of increased nutrients on coral reefs have been widely reported, and these reports have been mixed, with both direct and indirect effects that have proven to be both positive and negative. An influx of nutrients, specifically nitrogen and phosphorus, has been shown to reduce reproductive success, calcification rates, skeletal density or linear extension of corals, as well as, increasing the likelihood of bleaching in the absence of other essential nutrients (D’Angelo and Wiedenmann 2014). In addition, nutrients were found to increase growth and virulence of pathogens that lead to many coral diseases (Ban et al. 2014; Cramer et al. 2020). Conversely, nutrients have been shown to increase growth or reduce susceptibility of corals to summer bleaching by increasing the concentration of zooxanthellae (D’Angelo and Wiedenmann 2014). Rather than the direct effects, it is the indirect effects on corals that may be considered more important. An influx of nutrients into a system will often lead to phytoplankton blooms that can have both positive and negative effects.

Phytoplankton have the capacity to act as sun protection and help corals avoid stress from ultraviolet light (UV) but, as the concentration of phytoplankton increases, the cells begin to block out light and prevent zooxanthellae from photosynthesizing and, in severe cases, phytoplankton blooms can lead to hypoxia and death of corals (Burke et al. 2011; D’Angelo and

Wiedenmann 2014). The most commonly researched effect of nutrient influx on the coral reef ecosystem is on the growth of macroalgae. Macrophytes have the capacity to react quickly to the influx of nutrients and outcompete corals. (Burke et al. 2011; Fong and Paul 2011).

Along with nutrient pollution, another common consequence of human development is increased sedimentation. Sediments can have direct influences on coral by smothering and

killing both juvenile and adult corals (Rogers 1990; Jones et al. 2016). Far more expansive, however, are the indirect effects of sedimentation that can be divided into chemical effects and physical effects (Jones et al. 2016). The chemical effects include depletion of oxygen, changes in pH, influx of nutrients and foreign substances, and both acute and chronic cellular, metabolic, and genotoxic effects (Jones et al. 2016). Physical effects can be divided into reductions in light availability, which inhibits photosynthesis by zooxanthellae, and settling of sediments on the corals that can prevent feeding by coral polyps, inhibit photosynthesis, cause corals to spend energy on cleaning, and increase both bacterial growth and the possibility of hypoxia (Box and

Mumby 2007; Jones et al. 2016). Sedimentation has been shown to inhibit the growth, fitness, fecundity, and recruitment of corals, as well as, cause changes in community composition and decreases in coral abundance and biodiversity (Rogers 1990; Jones et al. 2016; Robinson et al.

2019). Beyond the effects of sedimentation itself, it also interacts with other common stressors.

Sedimentation has been shown to both correlate with and reinforce the detrimental effects of nutrients, disease and pollution, but it does mitigate the effects of irradiance and UV exposure

(Ban et al. 2014).

Overfishing has been classified as the most pervasive immediate local threat to coral reefs globally, with 55% of reefs affected and more than half of the expected fish biomass missing from fished reefs worldwide (Burke et al. 2011; Holbrook et al. 2016). The loss of large herbivorous fishes significantly increased the percent cover of macroalgae as shown in both manipulative and observational studies (Edwards et al. 2014). Furthermore, overfishing has been implicated as the primary cause of reduced ecosystem function on coral reefs worldwide

(Holbrook et al. 2016; Shantz et al. 2019). The effects of overfishing have proven to be more complex than just loss of numbers. Instead, targeted removal of larger fish, particularly

parrotfish, seems to be particularly harmful to coral reef health (Holbrook et al. 2016; Shantz et al. 2019). Larger fishes have the capacity to remove greater amounts of macroalgae and expose more substrate for settlement by benthic organisms like coral while also removing sediments and contributing to bioerosion (Edwards et al. 2014; Adam et al. 2018). Unfortunately, the biomass of fish in areas open to fishing can be less than 50% of the biomass in areas either inaccessible or closed to fishing (20.5 g/m2 vs 56.4 g/m2) (Edwards et al. 2014). In combination with nutrients, overfishing of key herbivorous fishes can be devastating for reefs and significantly inhibits the ability of reefs to recover (Robinson et al. 2019).

Although overfishing has historically been one of the largest stressors on coral reefs, that is likely to shift as global oceans change with projected climate change. For the ocean, climate change has two primary components; increases in sea surface temperatures and acidification. As greenhouse gases have been released into the atmosphere the world’s oceans have acted as a heat sink, absorbing more than 93% of the heat generated since 1971 (Reid 2016). These increases in temperature have been shown to have both direct and indirect effects on corals. The most common direct impact is that increases in temperature lead to coral bleaching when the zooxanthellae leave the coral polyps (Hughes et al. 2017b). Severe bleaching can kill corals outright, but at any level, it can weaken corals and make them more susceptible to opportunistic bacteria and diseases, as well as reduce their reproductive efficiency, growth, and calcification rates (Burke et al. 2011; Zaneveld et al. 2016). Coral bleaching is not a new phenomenon, but its frequency is on the rise. Between 1980 and 1997, there were 370 reported observations of bleaching worldwide while between 1998-2010, there were 3,700 reports (Burke et al. 2011).

Isolated reports of coral bleaching are troublesome, but mass bleaching events that extended throughout the tropics are far more concerning (Hughes et al. 2017a). Three mass bleaching

events have occurred since the 1980s; 1998, 2010 and 2014-2017 (Hughes et al. 2017a). The most recent mass bleaching event, between 2014 and 2017, also has proven to be the most destructive and widespread with nearly continuous bleaching events affecting most of the world’s coral reefs (Eakin et al. 2019). This mass bleaching event corresponded with two El

Niño events, 2014-2015 and 2015-2016, followed by the warmest non-El Niño event year ever recorded (Eakin et al. 2019). The area that drew the most headlines during this mass bleaching event was Australia where over 60% of corals on the Great Barrier Reef bleached and only 8.9% of the reefs surveyed showed no bleaching (Hughes et al. 2017b).

Corals are not the only organisms affected by global warming. Increases in temperature may adversely affect tropical marine fishes, primarily because many species of tropical fishes are already living at their upper thermal tolerance (Cheung and Pauly 2016). Specifically, increases in seawater temperature have been found to have long-term effects on fish metabolism, growth and reproductive output (Guinotte and Fabry 2008; Cheung and Pauly 2016). Furthermore, any larvae that are produced have decreased survival, and their larval duration and dispersal are altered (O’Connor et al. 2007). The difference between corals and fishes is that fishes are mobile and will move poleward where conditions are more favorable, which may increase stress on corals by reducing control of macroalgae (Cheung and Pauly 2016). While many macrophytes will be affected negatively by ocean warming, other groups, specifically benthic cyanobacteria, may react positively (Wernberg and Straub 2016). Cyanobacteria are predicted to fare better due to physiological adaptations to higher temperatures and decreased competition with macroalgae

(Fu et al. 2007; Paerl and Paul 2012). Altogether, rising sea surface temperatures are likely to change the community structure on coral reefs significantly.

Along with changes to seawater temperatures, ocean ecosystems are expected to be affected by ocean acidification. Approximately 30% of the CO2 emitted into the atmosphere from human activities is absorbed by the ocean, which leads to a buildup of carbonic acid and an overall acidifying effect (Herr et al. 2014; IUCN 2017). The average pH throughout the ocean has decreased 0.1 units in the past 200 years, and according to model predictions, global ocean pH may decrease as much as another 0.3 to 0.4 units if atmospheric CO2 concentrations increase to 800 p.p.m. (Hughes et al. 2017a). The primary consequence of ocean acidification is a decrease in useable carbonate, specifically aragonite and calcite, which are vital for the formation of calcium carbonate skeletons and shells (Herr et al. 2014; Hughes et al. 2017a;

IUCN 2017). A plethora of organisms rely on either aragonite or calcite, including corals, mollusks, crustaceans, and echinoderms as well as some types of calcifying algae (Dupont et al.

2010; Campbell et al. 2014; Putnam et al. 2017). Ocean acidification reduces the growth of corals and, with increased acidity, corals may even lose their ability to maintain their 3-D structure, which is vital for the overall health of coral reef ecosystems (Hughes et al. 2017a;

Putnam et al. 2017). Furthermore, acidification increases susceptibility to disease by both stressing colonies and enhancing the growth of pathogens, specifically white plague (Ban et al.

2014).

In regard to echinoderms, specifically sea urchins, increasing ocean acidification has the capacity to affect reproductive efficiency adversely and slow the growth of both larvae and juveniles (Dupont et al. 2010). For fishes, ocean acidification has been shown to have detrimental effects on the development of larvae and, in particular, in their ability to sense predators, find suitable habitat, and make decisions to avoid predation (Munday et al. 2009;

2010). For macroalgae, the effects of increased CO2 levels and ocean acidification are somewhat

mixed. For many macroalgae, an increase in CO2 will be beneficial due to increased photosynthetic rates and growth (Koch et al. 2013). The exception to this are calcifying algae that require calcium carbonate, and when its availability decreases, they will be unable to grow their “skeletons” (Reis et al. 2009). Of particular concern are the crustose coralline algae that promote settlement of many benthic organisms, particularly corals (Guinotte and Fabry 2008;

Ritson-Williams et al. 2016). Similar to ocean warming, ocean acidification is likely to change the structure of coral reef ecosystems.

Despite the knowledge that anthropogenic actions are the cause of many stressors on both the local and global scale, the anthropogenic activities that have led to these stressors are unlikely to stop. It is estimated that due to the continued demand for food, fertilizer use will increase by 1% each year and reach nearly 200 million tons by 2030 (Food and Agriculture

Organization of the United Nations 2004). Along with the demand for more food, the appeal of living on the coast continues to attract more and more people. Between 2000 and 2005, the population within 10 km of the coast grew 30% faster than the global average (Burke et al.

2011). With increased population, comes increased inputs of nutrient and sediments, as well as many other stressors that contribute to decline of coral reefs (Brown et al. 2017; Cramer et al.

2020). Additionally, there is no indication that sea surface temperatures will stop rising, so the prevalence of coral bleaching is likely to increase. Between 1880 and 2015 the average sea surface temperature rose 0.57°C and is predicted to rise another 0.32°C-0.48°C by 2039 (Hughes et al. 2017a). In fact, it has been predicted that by 2030 approximately 50% of global reefs will experience annual thermal stress that causes severe bleaching (Burke et al. 2011).

Regional Stressors: Atlantic and Caribbean

The stressors discussed above contribute to degradation of reefs worldwide, however, the impact of each of these stressors differs regionally and coral reefs in these regions exhibit 21

variable resistance to and ability to recover from a disturbance. The region of interest for this paper is the western Atlantic or Caribbean. The Caribbean is home to 10% of the world’s coral reefs and 43 million people who live within 30 km of these reefs (Burke et al. 2011). Although the Caribbean has a relatively low diversity as compared to the Pacific, 90% of the fish, coral, crustaceans and other organisms on these reefs have been shown to be endemic to the region

(Burke et al. 2011). When accounting for both local and global stressors, 90% of coral reefs in the Caribbean are considered threatened with 55% considered highly or very highly threatened

(Burke et al. 2011).

The combination of stressors experienced by these Caribbean reefs have resulted in an shift from coral to macroalgal dominance on many reefs (Mumby et al. 2007; de Bakker et al.

2017). This switch has been attributed to increased loads of nutrients and sediments, reductions and disruptions in herbivore communities, and changes in competition between corals and macroalgae (Moberg and Folke 1999; Jackson et al. 2014; Cramer et al. 2020). The shift in competitive advantage has persisted due to inhibition of coral growth and a rapid increase in available substrate resulting in algal populations that exceed the grazing pressure that can be exerted by resident herbivores (Moberg and Folke 1999; Shantz et al. 2020). Of the many local stressors on these reefs, overfishing is considered the most extensive with 75% of reefs affected

(Burke et al. 2011). In comparison to overfishing, nutrient pollution, sedimentation, and coastal development affect ~25% of reefs (Burke et al. 2011). In addition to the losses of herbivorous fishes due to overfishing, the Caribbean experienced a mass die-off of another major herbivore, the long-spined sea urchin Diadema antillarum (Jackson et al. 2014; Lessios 2016). The reduction in numbers of both types of herbivores has caused further stress on Caribbean reefs

(Jackson et al. 2014). Since the 1970s, percent cover of coral throughout the Caribbean has

declined by more than 80% with mean cover currently around 14% (Jackson et al. 2014). At the same time macroalgal cover has increased from ~6% to ~24% between 1970 and now (Jackson et al. 2014).

To exacerbate the problem, Atlantic and Caribbean reefs have been shown to be less resilient than Indo-Pacific reefs (Holbrook et al. 2016). While many Indo-Pacific locations have shifted back from macroalgal dominance after a disturbance, Atlantic and Caribbean reefs have not shown this same resilience (Roff and Mumby 2012; Holbrook et al. 2016). A number of hypotheses have been proposed for these differences. In regard to the macroalgae itself, when herbivory is prevented, Caribbean macroalgae have been shown to bloom and grow significantly faster than macroalgae on Indo-Pacific reefs (Roff and Mumby 2012). This difference in macroalgal response has been attributed to higher rates of algal recruitment and/or reduced limitation of growth by vital trace metals, like iron (Roff and Mumby 2012). It also is possible that the loss of both species of fast growing Caribbean Acropora species, beginning in the 1950s and exacerbated by disease in the 1980s allowed for macroalgae to gain a competitive advantage

(Jackson et al. 2014; Roff and Mumby 2012; Cramer et al. 2020). Along with the loss of

Acroporid corals, the Caribbean also has seen a significant decrease in herbivorous fishes and D. antillarum (Jackson et al. 2014). The three-fold greater biomass of herbivores in the Indo-Pacific and the higher diversity of Indo-Pacific herbivorous fishes may have maintained grazing at sufficient levels to prevent macroalgal dominance and sustain more coral cover than in the

Caribbean (Roff and Mumby 2012). Models suggest that before the mass die off of D. antillarum, Caribbean coral reefs could recover from disturbance and avert algal dominance, but since the 1980s this resilience has been lost (Mumby et al. 2007).

Algae on Coral Reefs

Macroalgae on coral reefs come in a great variety of forms ranging from single cells to complex multicellular structures (Fong and Paul 2011). The term macroalgae refers to a functional rather than phylogenetic group, and it encompasses species in two kingdoms and at least four phyla (Fong and Paul 2011). Prior to changes in the structure of reefs caused by anthropogenic and natural stressors, tropical reefs were dominated by encrusting and turf forming macroalgae (Jackson et al. 2014; Putnam et al. 2017). Modern reefs, however, are dominated by fleshy macroalgae from genera such as Dictyota, Padina, Stypopodium, and

Lobophora along with calcified Halimeda and Galaxaura (Fong and Paul 2011). Although algae often are thought of as detrimental components of coral reef systems due to their overabundance, they do play many important ecological roles, including stabalizing reef structure, producing tropical sands, retaining and recycling nutrients, and acting as the foundation of many trophic relationships due to their role as primary producers (Fong and Paul 2011; Fulton et al. 2019).

For macroalgae on reefs, efficiency of growth and accumulation of biomass are controlled by a variety of factors including availability of suitable substrate and nutrients, quality and quantity of light, intensity of herbivory, and both interspecific and intraspecific competition

(Fong and Paul 2011; Ferrari et al. 2012). In regard to suitable substrate, most species prefer to attach to hard substrate with minimal disturbance from waves, although there are many genera adapted to live in areas battered by waves or containing only rubble or soft sediment (Fong and

Paul 2011). The availability of nutrients, particularly nitrogen and phosphorus, can drastically affect the growth and structure of macroalgal communities on coral reefs (Thacker et al. 2001;

Fong and Paul 2011). Although macroalgae can acquire nutrients from in situ sources, such as nitrogen fixing cyanobacteria and recycling from other organisms, it is the influx of nutrients from outside the system that is most important in regard to changes in coral reefs (Fong and Paul 24

2011). These outside sources include runoff from terrestrial agriculture, land development, and sewage (Burke et al. 2011; D’Angelo and Wiedenmann 2014). Algae are known to respond quickly to an influx of nutrients, especially if they are nutrient limited, although the rate of uptake is affected by algal morphology as well as attributes of the water column, such as water flow (Fong et al. 2003; Fong and Paul 2011). Another abiotic factor that greatly affects algae is the amount and quality of available light because algae photosynthesize (Fong and Paul 2011).

Light availability can vary greatly with depth and with turbidity caused by suspended sediment, as well as varying seasonally (Fong and Paul 2011; Ferrari et al. 2012). Algae also have the capacity to adjust to changes in light availability, and their accessory photosynthetic pigments vary (Runcie et al. 2008; Fong and Paul 2011). These accessory pigments, such as phycoerythrin, allow red algae to live as deep as 260 m while green algae are confined to much shallower depths

(Runcie et al. 2008; Fong and Paul 2011).

A key biotic influence on algae is competition, both interspecific and intraspecific. Algae and other sessile benthic organisms, like sponges and corals, compete for substrate (Chadwick and Morrow 2011; Bonaldo and Hay 2014). Of particular interest in the Caribbean is the competition between algae and corals. Coral-algal competition has been shown to be a major factor affecting benthic community structure, food web dynamics, topographic complexity, biodiversity, and ecosystem functions on coral reefs (Bonaldo and Hay 2014). The exact mechanism of competition can vary but possibilities include appropriation of space, shading, allelopathy, abrasion, and collection of sediments within turf algae (Chadwick and Morrow

2011; Fong and Paul 2011). Shading by algae has been shown to increase mortality of juvenile corals as well as decrease growth rates (Box and Mumby 2007; Fong and Paul 2011). Similarly, abrasion has been shown to cause a ~30% reduction in growth and/or retraction of coral polyps

(McCook et al. 2001; Box and Mumby 2007). Furthermore, collection of sediments in algae, particularly turf algae, can lead to reduced growth, fecundity and recruitment of corals (Box and

Mumby 2007; Jones et al. 2016).

Although physical factors are known to affect competition on coral reefs, it is the allelopathic interactions and other chemically mediated interactions that have been shown to have wider reaching implications. The effects of contact with algae can include bleaching, lowered photosynthetic efficiency, and even death of coral tissue at the point of contact (Rasher and Hay 2010). For interactions between adult corals and macroalgae, the extent of damage appears to be dependent on both the species of algae and species of coral (Bonaldo and Hay

2014). Some macroalgae did not affect corals at all, other species harmed corals at the point of contact, and others created damage that extended beyond that point and even continued after contact ceased (Bonaldo and Hay 2014). Most importantly, different algal groups and specific taxa are known to have different effects on the settlement of benthic organisms, especially corals

(Fong and Paul 2011). Experiments have shown that specific types of algae can affect the location and success of recruitment by coral species and also diminish post-settlement survival

(Kuffner et al. 2006). The strength of these interactions varied among different species of macroalgae, with some species deterring overall settlement, some affecting the location of settlement, and some affecting both settlement and post-settlement survival when in contact with coral recruits (Kuffner et al. 2006). Species from the genera Lyngbya, Dictyota, and Lobophora along with turfs all have been shown to be detrimental to coral larval settlement and survival

(Kuffner et al. 2006; Fong and Paul 2011). Conversely, it has been shown that crustose coralline algae (CCA) is a highly preferred settlement site for a variety of coral species (Ritson-Williams et al. 2010; 2016). It is important to note that climate change is predicted to increase the

competitive advantage macroalgae may have over other benthic organisms by increasing the concentration of secondary metabolites within the algae and therefore enhancing their ability to cause allelopathic damage (Diaz-Pulido et al. 2011).

The final biotic interaction of note in relation to algal abundance is herbivory. Numerous studies have shown that rates of herbivory can limit the amount of algae growing on a coral reef

(Fong and Paul 2011; Holbrook et al. 2016; Shantz et al. 2020). To combat herbivory, algae have developed a number of defenses to protect themselves which can be divided into chemical defenses, structural defenses, and a combination of chemical and structural defenses (Hay et al.

1994; Fong and Paul 2011). As a chemical defense, many algae have the capacity to produce secondary metabolites that can greatly deter feeding by herbivores (Erickson et al. 2006). The type and concentration of these secondary metabolites can vary greatly depending on the type of algae, with certain genera being particularly chemically rich (Erickson et al. 2006). Along with chemical defenses many algae employ structural defenses to prevent herbivory. These structural defenses can consist of calcification with calcium carbonate, general toughness, or a leathery structure (Lewis 1986; Littler et al. 1983). A large majority of calcified species also contain secondary metabolites and this combination is particularly effective in deterring herbivores

(Littler et al. 1983; Lewis 1986; Hay et al. 1994). Dominance of chemically defended algae such as Halimeda, Dictyota, and Lobophora, on coral reefs has been attributed to these differing defenses (Fong and Paul 2011). On undisturbed Caribbean coral reefs, D. antillarum and herbivorous fishes both adapted to overcome the defenses of algae. After a mass die-off of D. antillarum, control of macroalgae on coral reefs was left largely to herbivorous fishes alone.

Herbivores on Caribbean Coral Reefs

In the Caribbean, there are primarily two types of herbivorous fishes; surgeonfish, family

Acanthuridae, and parrotfish, Scaridae (Dromard et al. 2015). The parrotfish can be further 27

divided into those in the genus Sparisoma and those in the genus Scarus (Adam et al. 2018).

There are significant differences in morphology and ecology between these species, and among the different size classes of parrotfish, which likely result in different foraging behaviors, diets, and impacts to the benthos and coral reef ecosystem (Adam et al. 2018). A number of studies have examined the feeding preferences and feeding modes of Caribbean herbivores.

Although parrotfish will be included under the category of “herbivorous” fishes, many species are, in fact, omnivores grazing on corals or sponges as well as macroalgae, sea grasses, and endolithic algae living within calcareous substrate (Clements and Choat 2018). There is, however, a diversity of parrotfishes living on Caribbean reefs and with this diversity comes a variety of feeding strategies. These feeding strategies have been divided into the general categories grazers, which consists of scrapers and excavators, and browsers (Bonaldo et al.

2014).

Scrapers and excavators are identified by their strategy of exerting downward force as they bite, thus removing the entirety of the algae they are feeding on and some of the substrate leaving a visible grazing scar (Bonaldo et al. 2014; Adam et al. 2018). In general, the visible difference between scrapers and excavators is that scrapers take more rapid, but less powerful bites (Adam et al. 2018). Many grazers target a conglomeration of filamentous turf algae, macroalgal propagules, microalgae, sediment, detritus, and other associated fauna collectively called the epilithic algal matrix (EAM) (Bonaldo et al. 2014). Scrapers and grazers remove the entirety of the algae which frees up bare substrate for settlement by other benthic organisms

(Bonaldo et al. 2014).

In comparison, browsers target fleshy macroalgae and usually tear the algae away from the substrate (Bonaldo et al. 2014; Adam et al. 2018). When tearing, the fish will rotate as they

accelerate away from the substrate after closing their jaws over the target seaweed (Adam et al.

2018). This feeding strategy makes no noise and rarely leaves a mark on the substrate (Adam et al. 2018). Observational and manipulative studies have found that both calcified and fleshy algae are components of browser’s diets, but they appear to avoid algae rich with secondary metabolites (Littler et al. 1983; Bonaldo et al. 2014; Dromard et al. 2015). In general, this group is made up of Sparisoma parrotfish, although large Scarus species have also been known to consume macroalgae (Bonaldo et al. 2014). Most other Scarus species are categorized as grazers

(Adam et al. 2015).

The last category of herbivorous fishes in the Caribbean are the acanthurids or surgeonfishes. Acanthurids are known to employ a feeding strategy referred to as cropping in which fishes remove the top portion of macroalgae but, unlike parrotfish, they make no contact with the substrate (Bonaldo et al. 2014; Adam et al. 2018). Thus, the act of cropping leaves a part of the algae that can regrow (Bonaldo et al. 2014). Similar to grazing parrotfish, surgeonfish play a vital role removing EAM biomass, and in particular, they remove a significant portion of macroalgal recruits within the EAM (Marshell and Mumby 2015). They also eat a significant amount of fleshy and calcified macroalgae, invertebrates, and detritus (Dromard et al. 2015).

Historically, parrotfish, surgeonfish, and D. antillarum all exerted different influences on coral reefs due to these differing feeding preferences (Burkepile and Hay 2008; Bonaldo et al. 2014).

Prior to their die-off the long-spined sea urchin D. antillarum, was considered the most important herbivore on Caribbean coral reefs (Jackson et al. 2014). At one time, D. antillarum lived in a variety of habitats including mangroves, seagrass beds, and coral reefs from Florida south to the northern coast of South America, west to the Gulf of Mexico and across the Atlantic to the Azores, the Madeira Islands, the Canary Islands, the Cape Verde Islands, and the Gulf of

Guinea (Lessios et al. 1984; Ogden and Carpenter 1987). Usually, D. antillarum hide from predators under overhangs and in crevices during the day and venture out at night to feed (Tuya et al. 2004). Although primarily herbivorous, D. antillarum are considered omnivores due to the fact that, either purposefully or accidentally, they are known to consume benthic invertebrates during the act of scraping macroalgae off the substrate (Lewis 1964; Rodríguez-Barreras et al.

2015a). Historically, the grazing of D. antillarum was known to create large “halos” of completely consumed sea grass around patch reefs which could be up to 10 m wide and visible in satellite photos (Ogden et al. 1973). In 1983, D. antillarum populations were hit with a presumed pathogen that originated in Panama and quickly spread throughout the Caribbean (Lessios 1988).

Within two weeks of the pathogen reaching a D. antillarum population, the majority of urchins were dead (Lessios 2016). By the time the die-off ended in 1984, thirteen months after it began, there were no unaffected populations of D. antillarum, with 93-99% mortality observed throughout the Caribbean(Lessios 1988; Levitan 1988). On average, D. antillarum density decreased from 6 per m2 to 0.16 per m2 (Lessios 2016).

The massive reduction in D. antillarum populations resulted in immediate changes in macroalgal populations on coral reefs (Lessios 2016). In St. Croix, there was a 27% increase in algal biomass five days after die-off and three years later surveys showed a 300%-400% increase in macroalgal biomass (Carpenter 1988; Lessios 1988). Populations of D. antillarum were reduced to almost zero in Jamaica, which led to more than a doubling in percent cover of fleshy macroalgae at most survey sites within a month of the initial mortality event, and a further increase in macroalgal cover a year after the initial event (Liddell and Ohlhorst 1986). Along with increased macroalgal cover, there were declines in coral cover and crustose coralline algae

(CCA) cover (Liddell and Ohlhorst 1986). In St John, there was a 3,000% increase in algal biomass six months after the mass mortality event (Levitan 1988).

More than 30 years after the initial mortality event, many D. antillarum populations still have not recovered with modern populations on average 8.5 times less dense (Lessios 2016).

Historically, D. antillarum densities ranged from 0.76 to 14.38 individuals/m2 compared to modern ranges of 0.01 to 3.93 individuals/m2 (Lessios 2016). The reason that D. antillarum populations have yet to recover is still unknown, but there are a number of hypotheses.

Hypotheses with experimental support are that algal turfs are preventing settlement of D. antillarum larvae and that predators are exerting more pressure and preventing the establishment of a robust adult community (Levitan et al. 2014). It has been found that increased densities of bluehead wrasse, Thallasoma bifasciatum, and slippery dick, Halichoeres bivittatus, resulted in a decrease in the density of medium sized D. antillarum (test diameter 40.1 to 60.0 mm)

(Rodríguez-Barreras et al. 2015b). This loss been attributed to the fact that medium-sized sea urchins are too big to completely hide in crevices and too small to have spines that effectively prevent predation (Rodríguez-Barreras et al. 2015b). It is likely that a combination of factors is responsible for the overall failure of D. antillarum populations to return to premortality densities

(Lessios 2016).

There are a few locations where D. antillarum populations have recovered somewhat and, in these locations, there have been significant changes to coral reef ecosystems. On

Mesoamerican reefs it was found that reefs with sea urchin densities greater than 1.0

Diadema/m2 have less than 5% macroalgae, reefs with 0.5-1.0 Diadema/m2 have ~10% fleshy macroalgae, and reefs with reefs with <0.5 Diadema/m2 or no Diadema had ~25% macroalgal cover (Kramer et al. 2015). In Jamaica, areas where D. antillarum had increased to ~5

individuals/m2 showed 10x lower percent macroalgal cover and 11x more coral recruits as compared to nearby sites where urchin densities remained ~0 individuals/m2 (Edmunds and

Carpenter 2001). In another survey of Jamaican reefs, it was found that urchin zones with 4.1

Diadema/m2 contained 10.6% hard corals, 6.2% macroalgae, and 73.5% CCA, turf algae, and bare space (CTB) while algal zones with 0.02 Diadema/m2 contained 4.2% hard corals, 67.6% macroalgae and 16.4% CTB (Idjadi et al. 2010). In one year, experimental addition of ~0.75 juvenile D. antillarum per m2 to patch reefs in the Florida Keys resulted in significant increases in CCA, little change in green algae (both foliose and calcareous), and a significant decrease in brown foliose algae, primarily Dictyota spp. (Chiappone et al. 2003). In addition to changes in algal community structure, there was an increase in average coral cover from 9.75% to 15.25%

(Chiappone et al. 2003).

The recovery of D. antillarum has the capacity to affect algal communities in unique ways due to their feeding preferences. Based on feeding assays, D. antillarum appear to be both more capable and more willing to eat algae that herbivorous fishes avoid due to toxic secondary metabolites (Hay et al. 1987). This tendency is attributed to the smaller home range of urchins as compared to fish and the resulting evolutionary pressure to handle secondary metabolites (Littler et al. 1983). Historically, D. antillarum were known to consume all types of fleshy macroalgae

(red, green, and brown) as well as coralline algae and even the substrate itself indicating that they have the capacity to consume any algae if necessary (Lewis 1964). However, D. antillarum do appear to show preferences towards certain types of algae. Littler et al. (1983) conducted experiments looking at feeding preferences of D. antillarum based on morphological groups and found that algae in the groups “sheets” and “coarsely-branched”, both of which were characterized as soft or fleshy, were consumed significantly more than “thick leathery” algae and

these, in turn, were consumed significantly more than “jointed-calcareous” algae and “crustose” algae. Specifically, in a number of studies D. antillarum ate a large percentage of algae from the genera Dictyota and Laurencia, both of which are known to be chemically defended, while avoiding Cystoseira, Turbinaria, Halimeda, and Amphiroa all of which employ structural defenses, primarily toughness and calcification (Littler et al. 1983; Tuya et al. 2001). In particular, it appears that the presence of calcium carbonate significantly deters feeding by D. antillarum (Hay et al. 1994). This hypothesis was further supported by experiments done with

Halimeda containing differing amounts of calcium carbonate, which led to a significant decrease in feeding with increasing concentrations of calcium carbonate (Campbell et al. 2014). These experiments support the conclusion that D. antillarum appear to be deterred by structural defenses, especially when they are combined with chemical defenses (Hay et al. 1994).

A secondary result of D. antillarum mortality was an increase in the rate of fish grazing and a shift in the type and density of herbivorous fishes. Not only did the intensity of grazing by fish triple, but Sparisoma parrotfishes and surgeonfish increased in abundance and in their relative impact on macroalgal cover, whereas Scarus parrotfishes had been dominant (Morrison

1988). As densities of D. antillarum increase on Caribbean reefs, there could be a shift in fish abundance once again. Prior to die-off, D. antillarum were removed from experimental reefs, which led to an immediate and significant increase in numbers of both parrotfish (+450%) and surgeonfish (+150%) (Hay and Taylor 1985). These densities soon returned to pre-removal levels as D. antillarum recolonized reefs, leading to the conclusion that D. antillarum densities above 7 per m2 may have the capacity to suppress herbivorous fish densities on coral reefs (Hay and Taylor 1985). On modern reefs, it appears that the negative relationship between herbivorous fishes and D. antillarum persists. A series of surveys in St. Croix found that D. antillarum

appeared to deter feeding by both parrotfish and acanthurids but did not affect territorial damselfish (Onufryk et al. 2018). This result was attributed to the fact that areas with D. antillarum may be less optimal for foraging or that the presence of high densities of territorial damselfish may have deterred roaming herbivorous fishes (Onufryk et al. 2018). This competitive, or in some other way antagonistic relationship, between herbivorous fishes and D. antillarum may be disadvantageous for coral reefs in the Caribbean where it is essential that substrate is cleared of macroalgae to allow for the possibility of new coral settlement.

Furthermore, herbivorous fishes and D. antillarum appear to create different macroalgal communities likely due to their differing feeding preferences (Adam et al. 2015; 2018).

Historically, D. antillarum were vital in suppressing filamentous turf and macroalgae that herbivorous fishes considered unpalatable (Adam et al. 2015; Lessios 2016). Even among herbivorous fishes there are distinct differences in feeding modes and preferences (Bonaldo et al.

2014; Adam et al. 2018). Long-term caging studies have found that Scarus taeniopterus and

Acanthurus bahianus maintained filamentous turf and CCA while preventing the establishment of macroalgae on recently disturbed reefs, and Sparisoma aurofrenatum allowed for the growth and establishment of tall turfs and late successional stage macroalgae (Burkepile and Hay 2010).

In contrast, on established reefs, Scarus taeniopterus and Acanthurus bahianus allowed late successional stage macroalgae to flourish while Sparisoma aurofrenatum reduced the cover of upright macroalgae (Burkepile and Hay 2008). When examining areas in which both fish and D. antillarum were present, it was found that fish alone resulted in the establishment and proliferation of erect algae in shallow areas, but this trend was not present in deeper areas

(Morrison 1988). Furthermore, due to difference in feeding preferences, herbivorous fishes created a community dominated by chemically rich algae (Morrison 1988).

In the Caribbean, a secondary concern arising from the overfishing of herbivorous fishes and the loss of D. antillarum from disease is the loss of functional diversity or redundancy on these tropical coral reefs (Bonaldo et al. 2014). Ecosystems often are assumed to be buffered against degradation and declines in functional diversity by the fact that there are many species fulfilling the same ecological role (Mouillot et al. 2014). This functional redundancy has been proposed as one of the reasons that the Indo-Pacific has not seen serious declines in coral reef health, but such redundancy is not apparent in the Caribbean (Roff and Mumby 2012). While the

Indo-Pacific is home to 3,689 reef fish species, the Caribbean only has 891 species total, which leads to the Info-Pacific having double the number of species for each functional role as compared to the Caribbean (Mouillot et al. 2014). This disparity continues into the herbivorous fishes where the Caribbean has 28 species of parrotfishes while the Pacific has 57 species

(Kulbicki et al. 2018). In addition to differences in herbivorous fish richness, the loss of D. antillarum which historically were vital for maintenance of coral reef resilience, combined with overfishing of herbivorous fishes, has been devastating for Caribbean coral reefs (Mumby et al.

2007; Adam et al. 2015).

Thus far, the majority of studies have looked at only herbivorous fishes or only at D. antillarum, and those studies that have examined both herbivores in combination were predominately done prior to the D. antillarum die-off. Even fewer studies have examined how the composition of the algal assemblage is affected by the presence of different herbivores.

Although historical evidence and modern modeling have indicated that D. antillarum can control macroalgae on reefs that experience stresses caused by frequent hurricanes, overfishing of herbivorous fishes, and increased nutrification, there is considerable doubt that D. antillarum will return to pre-mortality densities (Mumby et al. 2006; Levitan et al. 2014). In comparison,

parrotfish alone were sufficient grazers under conditions of high nutrification only if coral cover was not reduced by other factors, such as frequent hurricanes (more than one every 2 decades) or disease (Mumby et al. 2006). The fate of herbivorous fish populations remains in doubt with protections being enacted in some areas and overfishing continuing in many others (Jackson et al. 2014). Due to the ever-changing condition of herbivores throughout the Caribbean and the extensive reliance on historical conditions to predict the future, it is important to understand more fully the combined effects of both D. antillarum and herbivorous fishes at a range of densities, both current and predicted, rather than just at their predicted densities. Furthermore, it is vital to not only understand how the percent cover of macroalgae may change but to also examine how the composition of macroalgal communities may change. Both the composition and abundance of macroalgal communities have a direct impact on the structure of coral reef ecosystems and specifically on the ability of coral to recover and proliferate following disturbance.

CHAPTER 2 ANALYSIS OF THE BENTHIC COMMUNITY STRUCTURE ON SHALLOW PATCH REEFS NEAR CARRIE BOW CAY, BELIZE

Introduction

Throughout the world, the observed declines in coral cover and increases in macroalgal cover have led many to conclude that coral reefs are in decline (Hughes et al. 2003; Jackson et al.

2014). Benthic communities on Caribbean coral reefs are affected by a variety of biotic and abiotic factors including herbivory, sedimentation, and availability of nutrients and light (Burke et al. 2011; Jackson et al. 2014; Shantz et al. 2020). In the Caribbean, a particularly important factor is the density and identity of herbivores. The two primary categories of Caribbean herbivores are herbivorous fishes and the sea urchin D. antillarum. Historically, these herbivores, in conjunction, maintained areas of low macroalgal biomass (Carpenter 1986; Lessios 2016;

Shantz et al. 2020). Populations of both types of herbivores have declined significantly due to overfishing of herbivorous fishes and the mass die-off of D. antillarum in the 1980s (Jackson et al. 2014; Lessios 2016). Overfishing of herbivorous fishes has been shown to disproportionately affect large-bodied fishes, especially parrotfish, which subsequently has been shown to be particularly devastating because these fishes remove large amounts of macroalgae (Edwards et al. 2014; Adam et al. 2015; Holbrook et al. 2016).

While herbivorous fishes have declined steadily due to overfishing, the majority of D. antillarum died over a 13-month period from 1983 to 1984 due to a presumptive pathogen that swept throughout the Caribbean and killed between 93-99% of all D. antillarum (Levitan 1988;

Lessios 1988; 2016). This die-off resulted in expeditious increases in macroalgal biomass on many reefs, long-term increases in macroalgal percent cover, and declines in coral cover and cover of crustose coralline algae (CCA) (Liddell and Ohlhorst 1986; Carpenter 1988; Lessios

1988). The loss of D. antillarum put additional pressure on already depleted herbivorous fish

populations to serve as the sole macro-herbivore, a role that they were unable to fill adequately

(Hay and Taylor 1985; Mumby et al. 2007; Jackson et al. 2014). The inability of herbivorous fishes to fill this role led some scientist to conclude that D. antillarum was the more effective herbivore (Mumby et al. 2007; Jackson et al. 2014).

Over 25 years after the putative pathogen first devastated D. antillarum, populations remain on average ~9 times less dense as compared to pre-mortality densities but have shown limited recovery in some locations (Lessios 2016). In the areas with increased D. antillarum densities there have been pronounced decreases in macroalgal abundance and increases in cover of coral and bare substrate (Lessios 2016). Jamaican reefs exhibited 10 times less macroalgae and 11 times higher coral recruitment on reefs with ~5 D. antillarum/m2 as compared to nearby reefs with no D. antillarum (Edmunds and Carpenter 2001). Furthermore, surveys of

Mesoamerican reefs found that reefs with <0.5 individuals/m2 or no D. antillarum had ~25% macroalgal cover which was significantly higher than the ~10% cover of fleshy macroalgae on reefs with D. antillarum densities of 0.5-1.0 individuals/m2 and the < 5% macroalgal cover on reefs with D. antillarum densities greater than 1.0 individual/m2 (Kramer et al. 2015).

Observational studies such as these have led to the hypothesis that D. antillarum may be the key to a phase shift back from a macroalgal dominated state to a coral dominated state in the

Caribbean (Edmunds and Carpenter 2001).

Coral reefs serve a vital role throughout the tropics by providing a plethora of ecosystem services, including the provision of habitat for a variety of mobile and sessile organisms, promotion of biogeochemical cycling, and protection of shorelines amongst many other services

(Moberg and Folke 1999; Barbier et al. 2011). Worldwide, stressors, such as sedimentation, inputs of nutrients, ocean acidification, and increasing temperatures have led to declines in coral

cover (Barbier et al. 2011; Zaneveld et al. 2016; Putnam et al. 2017; Cramer et al. 2020). Corals themselves vary greatly in their response to these stressors and their role as vital habitat and ecosystem engineer vary with species, life-history traits, and the diversity of the coral reef ecosystem (Alvarez-Filip et al. 2011; Darling et al. 2012; Pinzón C. et al. 2014; Clements and

Hay 2019). These relationships have contributed to a shift in many coral reef ecosystems from structurally complex species to flatter and smaller species that are less capable of supporting reef fish communities and serving other important roles (Gardner et al. 2003; Alvarez-Filip et al.

2011).

Although coral and algae have been the focus of the majority of surveys of benthic habitats, research has indicated that the role of sponges should not be ignored. Sponges are known to have both beneficial, e.g., cycling of carbon and nutrients, and detrimental, e.g., coral overgrowth and erosion, effects on Caribbean coral reefs (Loh et al. 2015; Pawlik and McMurray

2019). In relation to the role of sponges on coral reefs, they have been shown to play an important role in both the carbon and nitrogen cycles which has, in turn, been proposed to affect the resilience of coral reefs (Pawlik et al. 2016; Pawlik and McMurray 2019). It also has been proposed that higher abundances of sponges enhances the growth of macroalgae which, in turn, enhance the growth of sponges and creates a feedback loop that is detrimental to the establishment, growth, and survival of corals due to a competitive advantage in relation to settlement and overgrowth and the creation of a hostile environment for corals by increasing detritus in the water column (Mumby and Steneck 2018; Pawlik and McMurray 2019). This situation has been referred to as the vicious cycle that may be responsible for lower resilience of

Caribbean reefs as compared to Indo-Pacific reefs (Pawlik et al. 2016).

To better understand the existing benthic community in Belize, a series of surveys were conducted on patch reefs near Carrie Bow Cay, Belize that spanned different depths and different natural abundances of Diadema antillarum and herbivorous fish. These surveys provided insights into both the generality of results from previous studies and how differing herbivore populations affect percent cover and community structure of benthic communities.

Methods

Caribbean herbivores, particularly D. antillarum have been shown to significantly affect the percent cover of algae and coral recruitment on reefs. A subject less explored has been how

D. antillarum may affect the entirety of the benthos or structure of communities via changes in diversity of different benthic components. Furthermore, due to the unequal recovery of D. antillarum populations, it is important to examine how effects vary with density of sea urchins.

To better understand how herbivore density may affect the benthic composition of shallow patch reefs, a series of benthic surveys were conducted. These surveys were conducted on patch reefs found near Carrie Bow Cay, Belize (16°48'09.1"N 88°04'55.0"W).

At each site, the length and width of the patch was measured, and transects were run along the length of the reef or for 50 m if reefs exceeded this length. Two or three transects were laid out along the reef depending on its width, with wider reefs having more transects. In addition, one reef had a single transect because it was long but not wide. Transects were placed approximately equidistant from each other and the edge of the reef so that they spanned the width of the patch reef. A 1 m2 quadrat was placed every 5 meters along each transect. The percent cover of each benthic organism or bare substrate was recorded for each quadrat. Corals were identified to the species level while most macroalgae and sponges were identified to the genus level with some identified to species level. All species of sea urchins were identified and counted along 1-m bands on either side of each transect. 40

The benthos was categorized as macroalgae, bare substrate, crustose coralline algae

(CCA), corals, other anthozoans, sponges, and turf algae. Percent covers of each category in each quadrat were averaged for each reef. Pearson’s correlation tests in RStudio (version 3.2.2) examined the relationships between these average percent covers and the average D. antillarum densities (R Core Team 2015). In addition to examining the relationship between D. antillarum density and percent cover of the benthos, the correlation between D. antillarum density and depth was examined via Pearson’s correlation test. Furthermore, correlations among the three major categories of benthos, sponges, macroalgae, and corals, were examined via Pearson’s correlation tests.

In addition, percent covers of macroalgae were grouped and averaged according to known defenses against herbivory reported in the literature. These categories were chemically rich macroalgae, structurally defended macroalgae (characterized by calcification or general toughness), macroalgae with both chemical and structural components, palatable algae, and turf algae. Sponges were also divided according to defenses against predation into defended, palatable, variable defended, or unknown based on literature (Pawlik et al. 1995; Loh and Pawlik

2014).

All corals found in these benthic surveys were categorized according to growth traits, which were weedy, competitive, stress tolerant, or generalist species according to the coral traits database (Darling et al. 2012; Madin et al. 2016; “Coral Trait Database | Home” 2020). Weedy corals were defined as those species known to opportunistically settle in newly disturbed locales

(Darling et al. 2012). Competitive species are particularly efficient at using resources and dominating communities in favorable environments, but struggle to survive in suboptimal conditions (Darling et al. 2012). Stress tolerant species are adapted to live in areas that frequently

experience stressors such as aerial exposure or temperature fluctuations (Darling et al. 2012).

Generalist species share many traits with weedy, stress tolerant and competitive species, which allows them to persist in habitats where competition may be limited by repeated low-level stress and disturbance (Darling et al. 2012).

The relationships between average percent covers of categories of algae, corals, and sponges and the average densities of D. antillarum were evaluated with Pearson’s correlation tests (R Core Team 2015). The percent cover of all different categories of algae, sponges, and corals was also analyzed in relation to depth using Pearson’s correlation tests.

Along with examining relationships between D. antillarum density and percent cover of different benthic components, the relationship between D. antillarum density and benthic diversity was examined. Shannon diversity was calculated for each reef using the diversity function in the R program vegan (Oksanen et al. 2017). Diversity was calculated for each reef using data on the entire benthic community, macroalgae, corals, and sponges. Relationships between these diversities and average D. antillarum densities were evaluated using Pearson’s correlation tests (R Core Team 2015). The diversities also were analyzed to evaluate relationships with depth using Pearson’s correlation tests.

Community structure of both the whole benthic community and the macroalgal community along transects on patch reefs were compared using multivariate permutation analyses of variance applied to distance matrices. To do this, the function adonis from the vegan package in R was used (Oksanen et al. 2017). This PERMANOVAs were followed by post-hoc pairwise adonis tests (Martinez Arbizu 2017). Differences in community structure and D. antillarum densities were visualized using nonmetric multi-dimensional scaling (nMDS) based on averages for patch reefs. In addition, densities were grouped into categories for ease of

analysis and comparison to other findings reported in the literature. The categories were 0 D. antillarum/m2, over 0 to 0.1 D. antillarum/m2, and greater than 0.1 D. antillarum/m2, with a maximum of 1.0 D. antillarum/m2. Again, differences in benthic communities were examined using permutation analyses of variance and visualized using nonmetric multi-dimensional scaling

(nMDS). Euclidean distances were used for nMDS, adonis tests, and pairwise adonis tests.

Results

In total, 17 reefs were surveyed. Locations for each site can be found in Table 2-1. These patch reefs were found at depths that ranged from 1 to 10 m, and they had different natural abundances of Diadema antillarum (Table 2-2). The benthic composition of each reef was divided into major categories according to reef (Table 2-3) and visualized using stacked bar plots

(Figure 2-1). Stacked bar plots were graphed so that patch reefs were organized by increasing D. antillarum cover to show trends further elucidated in correlation and PERMANOVA analysis.

Additionally, data were grouped according to classes of D. antillarum density and graphed

(Figure 2-2).

Analyses yielded significant correlations between D. antillarum densities and cover of macroalgae (r (15) = 0.83, p-value < 0.001) and sponges (r (15) = -0.58, p-value = 0.015). The percent cover of macroalgae increased as D. antillarum density increased. The opposite was true for percent cover of sponges, with decreasing sponge cover related to increasing D. antillarum density. There were no other significant correlations with D. antillarum densities. When examining the relationship among cover of corals, macroalgae, and sponges, there were no significant correlations between cover of sponges and cover of macroalgae or cover of sponges and cover of corals. There was a significant correlation between cover of macroalgae and cover of corals (r (15) = -0.55, p-value = 0.02), with coral cover and macroalgal cover inversely related. Analysis of the relationship between Shannon diversity for the benthic community and 43

D. antillarum densities did not yield a significant correlation (Pearson’s correlation, r (15) =

0.19, p-value = 0.46). Depth and diversity also were not significantly correlated (r (15) = -0.45, p-value = 0.07).

Algal cover on each reef that was categorized according to type of defense against herbivory were graphed both with turf algae (Figure 2-3) and without turf algae (Figure 2-4).

Data for macroalgae excluding turf also were graphed according to D. antillarum density (Figure

2-5). When looking at the correlation between D. antillarum densities and macroalgae exhibiting different defenses, there was a significant correlation for cover of chemically rich algae (e.g.,

Dictyota sp., Laurencia sp.) (r (15) = 0.64, p-value = 0.005) and structurally defended algae

(e.g., Amphiroa sp., Turbinaira sp.) (r (15) = 0.62, p-value = 0.008), with cover of both types of algae increasing with increasing D. antillarum density. Analysis of the relationship between

Shannon diversity for macroalgae and D. antillarum density did not yield a significant correlation (Pearson’s correlation, r (15) = 0.41, p-value = 0.10). There also was no significant correlation between depth and algal diversity (r (15) = -0.44, p-value = 0.08).

Corals were divided into weedy, competitive, or stress tolerant, and the percent cover of each of these categories was graphed both as individual reefs (Figure 2-6) and grouped according to densities of D. antillarum (Figure 2-7). Both the stress tolerant (e.g., Orbicella spp.,

Pseudodiploria spp., Siderastrea siderea) and competitive (e.g., Acropora spp.) corals were significantly correlated with D. antillarum densities. In regard to the stress tolerant corals, their percent cover decreased with increasing D. antillarum density (r (15) = -0.60, p-value = 0.011).

In contrast percent cover of competitive coral increased with increasing D. antillarum density (r

(15) = 0.60, p-value = 0.011). Analysis of the relationship between Shannon diversity for corals and D. antillarum densities found no significant correlation (Pearson’s correlation, r (15) = 0.29,

p-value = 0.27). Coral community diversity and depth also were not correlated (r (15) = -0.30, p- value = 0.24).

Sponges were divided into palatable, defended, variably defended, or unknown and percent covers were graphed for all reefs (Figure 2-8) and for classes of D. antillarum density

(Figure 2-9). The only sponge type significantly correlated to D. antillarum density was the defended sponges (e.g., Aplysina sp., Ircinia sp.) (r (15) = -0.54, p-value = 0.026). As D. antillarum density increased, the percent cover of defended sponges decreased. Analysis of the relationship between D. antillarum density and Shannon diversity for sponges found no significant correlation (Pearson’s correlation, r (15) = -0.28, p-value = 0.27). There also was no significant correlation between diversity and depth (r (15) = -0.11, p-value = 0.68).

Correlations pointed to depth as an influence on presence of D. antillarum and cover of various organisms. Density of D. antillarum was correlated inversely with depth (r (15) = -0.58, p-value = 0.015). In relation to the correlation between depth and benthic components, there were significant relationships in regard to cover of macroalgae (r (15) = -0.64, p-value = 0.005),

CCA (r (15) = -0.53, p-value = 0.03), and corals (r (15) = 0.63, p-value = 0.007). Both macroalgal cover and CCA percent cover decreased with increasing depth while coral cover increased with depth. Specifically, within macroalgae, the cover of chemically rich macroalgae decreased with depth (r (15) = -0.65, p-value = 0.005). Within coral cover, the percent cover of stress tolerant corals increased with depth (r (15) = 0.58, p-value = 0.015).

Data on the structure of both the entire benthic community (Figure 2-10) and the algal community (Figure 2-11) were graphed and analyzed. Permutational analysis of variance using distance matrices based on transect data found significant differences in the community structure of the entire benthos among reefs (df = 16, p-value = 0.003) and D. antillarum densities (df = 10,

p-value = 0.012). When looking at the macroalgal community structure, there were significant differences both between reefs (df = 16, p-value = 0.001) and D. antillarum densities (df = 10, p- value = 0.001). To clarify the differences, data for both the entire benthic community (Figure 2-

12) and the algal community (Figure 2-13) were grouped according to D. antillarum density then analyzed and graphed. When grouped into D. antillarum density classes, there were significant differences between densities in both the entire benthic community (PERMANOVA, df = 2, p- value = 0.001) and for the algal community (df = 2, p-value = 0.001). At the community scale, the no D. antillarum reefs were significantly different from the 0.1 individuals/m2-1.0 individuals/m2 reefs (p-value = 0.003), but not the 0-0.1 individuals/m2 reefs (p-value = 0.24).

The 0-0.1 individuals/m2 reefs and 0.1 individuals/m2-1.0 individuals/m2 reefs did not significantly differ (p-value = 0.22) When looking at the algal communities specifically, they differed among all D. antillarum density classes (p-value < 0.02).

Discussion

This study presented a snapshot of relationships with D. antillarum densities for biotic factors, cover of other benthic organisms, and an abiotic, depth, factor. The observation that D. antillarum density decreased with depth is consistent with patterns seen before the mass die-off

(Morrison 1988). This result also suggests the pattern that will be observed as certain D. antillarum populations begin to recover in the Caribbean.

Overall, the results of these benthic surveys were contrary to what was expected based on previous observational and modeling studies. Specifically, these surveys were inconsistent with surveys conducted on other reefs within the Mesoamerican reef system. Ongoing studies of

Mesoamerican reefs have found that reefs with D. antillarum densities of 0.5-1.0 individuals/m2 have ~10% fleshy macroalgae with percent cover decreasing as D. antillarum density increased but the reefs surveyed here had ~35% fleshy macroalgae at those densities of D. antillarum, 46

which equaled the highest cover of macroalgae found on any reef surveyed (Kramer et al. 2015).

However, the percent cover found in our surveys was still lower than what was found on many reefs without D. antillarum. Surveys of Jamaican reefs without urchins had ~70% cover of macroalgae which was double the highest percent found on our surveyed reefs (Edmunds and

Carpenter 2001; Idjadi et al. 2010). These results may indicate that herbivorous D. antillarum are playing a role in reducing algal cover, but are unable to get the percent cover down to the less than 10% found in Jamaica (Edmunds and Carpenter 2001; Idjadi et al. 2010). This result is likely due to the lower maximum density of D. antillarum found on the Belizean reefs surveyed here (< 1 individuals/m2) as compared to the Jamaican reefs (> 4 individuals/m2) (Edmunds and

Carpenter 2001; Idjadi et al. 2010).

Furthermore, the identity of the dominant macroalgae found on Belizean reefs with higher D. antillarum density, Dictyota spp., Halimeda spp., and other chemically defended alga, was more similar to communities found in areas with low D. antillarum densities areas in previous studies (Blanco et al. 2011). Furthermore, this result was puzzling due to the documented capacity of D. antillarum to consume Dictyota spp. and other chemically defended alga effectively in feeding assays as well as remove it from reefs (Littler et al. 1983; Chiappone et al. 2003; Maciá et al. 2007). This discrepancy may be due to the rate of growth and establishment of these algae being greater than the rate at which relatively few D. antillarum can graze.

A second factor that likely contributed to the observed difference in macroalgal cover is depth. With greater depth comes decreasing light intensity, which affects the growth rate of macroalgae and the ability to compensate for mechanical loss due to factors such as herbivory

(Mathieson and Dawes 1986; Markager and Sand-Jensen 1992). At shallow depths, some algae

can grow faster, which likely played a role in the patterns seen in this study. In addition, the higher cover of corals at depth is consistent with the inability of many coral species to adapt to the combination of high temperatures and irradiance associated with the very shallow water found at some reefs in this study (Fitt et al. 2001; Jimenez et al. 2008). Unexpectedly, it was the stress tolerant species that were significantly associated with depth, with the lowest cover at the shallowest sites. This finding may be explained by the shape of the corals in this category. The stress tolerant group consists primarily of large hemispherical or boulder corals, in the genera

Montastraea, Orbicella, Pseudodiploria and Siderastrea, which are known to retain heat and suffer more detrimental effects from exposure to solar radiation and thermal stress at shallow sites than branching corals (Jimenez et al. 2008).

In addition to examining the community of macroalgae on these reefs, it was important to look at relationships between the sponge community and D. antillarum densities in order to gain a fuller understanding of the influence of these sea urchins on the benthos. The surveys conducted here indicate an inverse relationship between D. antillarum density and percent cover of sponges. In addition to differences in overall sponge cover with differing densities of D. antillarum, there were observed differences in percent cover of sponges that employ defenses against predation. The higher abundance of defended sponges on reefs without D. antillarum may indicate the higher abundance of spongivorous fishes, angelfish and parrotfish, on those reefs (Pawlik et al. 1995; 2013). In general, the cover of sponges decreased as density of D. antillarum increased, which could be due to a number of different factors. Due to the fact that sponges, corals, and algae compete for available substrate, the higher cover of algae on these reefs may have prevented establishment of sponges. Secondarily it is possible that the D. antillarum themselves directly prevented the establishment of sponges through grazing.

Although D. antillarum do not usually target sponges, sponge tissues have been found in their gut contents (Rodríguez-Barreras et al. 2015a). There was, however, no evidence of the ‘vicious cycle’ previously proposed for Caribbean reefs, which postulates a positive relationship between algae and sponges and a negative relationship between corals and sponges and algae (Mumby and Steneck 2018; Pawlik and McMurray 2019). The coral cover was consistently around 18% on all reefs despite significantly different cover of sponges.

The higher cover of corals and sponges on reefs without D. antillarum may indicate that the herbivores and spongivores on those reefs, presumably surgeonfish, parrotfish and angelfish, cleared more benthic substrate and that the sponges and corals were able to more successfully settle and colonize those areas as compared to reefs with higher densities of D. antillarum. The results of these benthic surveys indicate that factors other than top-down herbivory by D. antillarum help shape these benthic communities. Possible controlling mechanisms include light availability and thermal stress due to depth and water clarity, nutrient inputs, and behavioral differences in herbivores due to presence of predators. Of these factors, depth was shown to be impactful, but it is likely that other factors also shape these coral reef ecosystems. Further studies that isolate these controlling factors and their interactions are necessary.

Table 2-1. Coordinates for patch reefs that were surveyed. Reef Name GPS Coordinates Golden Reef A 16°48.575’ -88°05.138’ Golden Reef B 16°48.575’ -88°05.138’ LS1 16°48.536’ -88°05.145’ Spiers 1 16°45.197’ -88°05.882’ Spiers 2 16°45.318’ -88°05.893’ South CBC 16°48.120’ -88°04.935’ Patch 1 16°46.470’ -88°05.113’ Patch 2 16°48.030’ -88°05.253’ Patch 3.1 16°45.287’ -88°05.278’ Patch 3.2 small patch next to 3.1 Patch 4.1 16°45.182’ -88°05.257’ Patch 4.2 middle patch next to 4.1 Patch 5 16°45.108’ -88°05.923’ Patch 6 16°45.705’ -88°05.768’ Patch 7 16°48.33’ -88°05.002’ Patch 8 16°45.075’ -88°05.875’ Patch 9 16°45.098’ -88°05.808’

Table 2-2. Density of Diadema antillarum found on each patch reef. Reef Name Depth (m) Diadema antillarum density (per m2) Golden Reef A 9.0 0 Golden Reef B 9.1 0 LS1 7.6 0 Spiers 1 1.5 0.609 Spiers 2 2.5 0.276 South CBC 3.4 0.017 Patch 1 2.5 0.390 Patch 2 7.3 0 Patch 3.1 5.8 0 Patch 3.2 5.5 0 Patch 4.1 5.2 0.021 Patch 4.2 4.9 0 Patch 5 6.1 0.034 Patch 6 4.6 0.067 Patch 7 7.9 0.280 Patch 8 1.5 0.034 Patch 9 1.2 0.355

Table 2-3. Depth and percent cover of three major categories of benthic organisms ± standard error (SE) on each surveyed patch reef. Percent Cover Percent Cover Reef Name Percent Cover Coral Macroalgae Sponges Golden Reef A 4.08 ± 0.89 24.23 ± 4.10 10.23 ± 2.72 Golden Reef B 8.20 ± 1.80 24.70 ± 4.52 16.00 ± 3.34 LS1 10.64 ± 1.64 24.27 ± 2.59 10.09 ± 1.30 Spiers 1 35.29 ± 4.36 16.00 ± 4.73 1.76 ± 0.78 Spiers 2 29.77 ± 3.05 16.50 ± 3.39 7.55 ± 1.61 South CBC 11.81 ± 1.73 20.62 ± 3.32 7.86 ± 1.98 Patch 1 36.73 ± 4.44 10.27 ± 1.87 8.09 ± 2.10 Patch 2 11.33 ± 1.15 30.11 ± 4.09 9.78 ± 2.28 Patch 3.1 12.89 ± 3.26 17.22 ± 4.39 16.33 ± 3.30 Patch 3.2 14.71 ± 2.45 16.00 ± 5.67 8.29 ± 2.19 Patch 4.1 19.17 ± 2.64 26.08 ± 3.14 8.25 ± 2.05 Patch 4.2 9.50 ± 1.25 16.50 ± 3.69 17.50 ± 3.74 Patch 5 27.85 ± 3.38 8.23 ± 1.09 19.00 ± 2.91 Patch 6 13.17 ± 2.77 22.42 ± 2.71 23.42 ± 3.37 Patch 7 24.09 ± 3.86 24.00 ± 4.37 0.64 ± 0.43 Patch 8 18.71 ± 2.05 6.07 ± 1.15 24.93 ± 3.15 Patch 9 25.91 ± 4.66 16.73 ± 2.45 6.18 ± 1.95

Figure 2-1. Benthic composition of patch reefs with colors representing categories of benthos; algae, anthozoans other than coral, bare substrate, crustose coralline algae (CCA), corals, sponges, and turf algae. Patch reefs sorted according to Diadema antillarum density; increasing from left to right.

Figure 2-2. Benthic composition on patch reefs surveyed grouped according to density of Diadema antillarum on the reef.

Figure 2-3. Percent cover of different types of algae divided into chemical and structurally defended, chemically rich, palatable, structurally defended, and turf on each reef. Patch reefs sorted according to Diadema antillarum density; increasing from left to right.

Figure 2-4. Percent cover of different types of macroalgae divided into chemical and structurally defended, chemically rich, palatable, and structurally defended on each reef. Patch reefs sorted according to Diadema antillarum density; increasing from left to right.

Figure 2-5. Percent cover of different types of macroalgae on each reef divided by defense exhibited and grouped according to density of Diadema antillarum.

Figure 2-6. Percent cover of different types of corals divided into weedy, competitive, or stress tolerant on each reef. Patch reefs sorted according to Diadema antillarum density; increasing from left to right.

Figure 2-7. Percent cover of corals divided according to coral trait and grouped according to density of Diadema antillarum.

Figure 2-8. Percent cover of sponges divided according to palatability into defended, palatable, variable, or unknown. Patch reefs sorted according to Diadema antillarum density; increasing from left to right.

Figure 2-9. Percent cover of sponges divided according to palatability and grouped according to density of Diadema antillarum.

Figure 2-10. Ordination based on the full benthic community on all patch reefs. Colors represent different reefs, and larger bubbles represent higher densities of Diadema antillarum.

Figure 2-11. Ordination based on the macroalgal community on each patch reef. Colors represent different reefs, and larger bubbles represent higher densities of Diadema antillarum.

Figure 2-12. Ordination based on the full benthic community grouped according to density classes for Diadema antillarum.

Figure 2-13. Ordination based on macroalgal communities and grouped according to density classes for Diadema antillarum.

CHAPTER 3 THE EFFECT OF HERBIVOROUS FISHES AND Diadema antillarum ON THE ESTABLISHMENT OF A MACROALGAL COMMUNITY ON A SHALLOW BELIZEAN CORAL REEF

Introduction

Herbivorous fishes and sea urchins, particularly Diadema antillarum, historically played key roles in the control and regulation of macroalgae on Caribbean coral reefs (Jackson et al.

2014). In the past, large herbivorous reef fishes and D. antillarum had the capacity to consume over 90% of algae produced daily and together maintained cropped "lawns" of macroalgae and algal turf (Carpenter 1986). Unfortunately, the loss of D. antillarum due to a mass die-off and the loss of herbivorous fishes due to overexploitation have contributed to the proliferation of macroalgae on reefs and associated declines in coral cover (Lessios 2016; Shantz et al. 2020).

Caribbean coral reefs exist in a markedly different state now than before the loss of these herbivores with average coral cover declining from 35% in the 1970s to 16% today and macroalgal cover increasing from 7% to 24% (Jackson et al. 2014).

To combat the loss of herbivores and restore degraded coral reef ecosystems, some

Caribbean countries have created marine protected areas and banned the fishing of large herbivorous reef fish. A meritorious example of this approach is the Exuma Cays Land and Sea

Park (ECLSP) in the Bahamas which was created in 1958 and has banned fishing since 1986

(Chiappone and Sealey 2000; Mumby et al. 2007). The status of the ECLSP as a no-take zone has led to a 2 to 3-fold increase in parrotfish abundance, a concomitant decrease in macroalgal cover from between 20 to 25% to between 1 to 5%, and 2 to 3 times more coral recruitment

(Jackson et al. 2014). Overall, a 2003 review of 89 studies on the effect of no take marine reserves found that 53% of reserves increased the density of herbivorous fishes while 63% increased biomass of herbivorous fishes (Halpern 2003). Along the Mesoamerican reef,

parrotfish are legally protected everywhere except coastal Honduras (McField et al. 2020). This protection has resulted in increases in herbivorous fish biomass and Belize receiving a “good” score in regard to the status of the herbivorous fishes (McField et al. 2020).

Along with increases in herbivorous reef fish, the modest increase of D. antillarum in some areas has been linked to decreases in macroalgal abundance and an increase in coral recruitment. Along the north coast of Jamaica, where sea urchin densities had been reduced to ~0 individuals/m2, areas where D. antillarum increased to ~5 individuals/m2 showed 10x lower percent macroalgal cover and 11x more coral recruits as compared to nearby sites where urchins had not returned (Edmunds and Carpenter 2001). The Healthy Reefs Initiative found that

Mesoamerican reefs with sea urchin densities of 0.5-1.0 D. antillarum/m2 have ~10% fleshy macroalgae whereas reefs with sea urchin densities greater than 1.0 D. antillarum/m2 have less than 5% macroalgae (Kramer et al. 2015). Comparatively, reefs with <0.5 D. antillarum/m2 or no

D. antillarum had ~25% macroalgal cover (Kramer et al. 2015). Surveys in Discovery Bay,

Jamaica indicated distinctly different benthic assemblages in sea urchin and algal zones. Urchin zones with 4.1 D. antillarum/m2 contained 10.6% hard corals, 6.2% macroalgae and 73.5% crustose coralline algae, turf algae, and bare space (CTB) while algal zones with 0.02 D. antillarum/m2 contained 4.2% hard corals, 67.6% macroalgae and 16.4% CTB (Idjadi et al.

2010). Overall, areas with a D. antillarum density above 1/m2 have lower macroalgal cover as compared to areas without D. antillarum and the decreases in macroalgae are greater in areas with higher densities.

In Caribbean areas where densities of herbivorous reef fishes and D. antillarum are increasing due to protection or natural recovery, macroalgal abundance is predicted to decrease and the macroalgal community structure is likely to change. Herbivore exclusion experiments on

shallow reefs have shown that D. antillarum plays a substantial role in controlling the abundance of macroalgae, but herbivorous fishes may play a larger role in determining the structure (species composition) of the macroalgal community due, in large part, to dietary preference and selection of food items (Morrison 1988; Burkepile et al. 2017). Diadema antillarum feed primarily at night by scraping algae from the substrate that they move over and are known to consume all types of fleshy macroalgae (red, green, brown) as well as coralline algae and even the substrate itself

(Lewis 1964; Tuya et al. 2004). Herbivorous reef fish in comparison are primarily classified as either croppers, scrapers, excavators, or browsers (Bonaldo et al. 2014; Adam et al. 2018).

Croppers remove the top portion of algae without making any contact with the substrate, scrapers remove the selected algae, primarily turf, from the reefs while leaving the substrate predominately intact, excavators remove chunks of the substrate during the process of feeding, and browsers remove mature fleshy macroalgae without disturbing the substrate (Bonaldo et al.

2014; Adam et al. 2018). Surgeonfish and small parrotfish are known to feed via cropping while parrotfish are known to act as scrapers, excavators or browsers (Bonaldo et al. 2014; Marshell and Mumby 2015; Adam et al. 2018)

Historically, D. antillarum were known to create and maintain algal communities comprised primarily of low standing crops of highly productive algal turfs (Carpenter 1986).

Herbivorous reef fish, on the other hand, created algal communities dominated by high biomass algal turfs and chemically rich algae (Carpenter 1986; Morrison 1988). These differences in algal community structure are likely due to differences in feeding preferences and available foraging range. Herbivorous fishes have the capacity to swim relatively large distances (up to 1400 m2) in search of preferred food and will graze selectively in one area before moving on (Carpenter

1986; Mumby and Wabnitz 2002). In comparison, D. antillarum have a smaller foraging range

of only ~0.5-1 m2 and graze more heavily within this range, which has led to the evolution of an ability to handle secondary metabolites (Littler et al. 1983; Carpenter 1986; Craft et al. 2013). A variety of previous studies have found that herbivorous fishes often are deterred by chemical defenses and some physical defenses, but in comparison to other herbivores, such as D. antillarum, parrotfishes are less deterred by calcium carbonate (Hay et al. 1994; Burkepile et al.

2017). Feeding assays conducted with D. antillarum have found that ”jointed-calcareous” and

“crustose” algae were the least preferred algal types followed by “thick-leathery” algae while softer “sheets” and “coarsely branched” algae were consumed the most (Littler et al. 1983).

Assays looking into the feeding preferences of D. antillarum on calcareous Halimeda species found that there were significant decreases in feeding with increasing calcium carbonate concentrations (Campbell et al. 2014). Diadema antillarum, however, have been shown to consume algae containing calcium carbonate if the nutritional quality of the food was great enough (Hay et al. 1994).

Although D. antillarum densities continue to increase in some areas, whether they will return to 1983 densities remains in doubt (Levitan et al. 2014). Therefore, it is vital to understand how current D. antillarum and herbivorous fish communities interact with algal cover on present day Caribbean reefs. To address this question a caging experiment was created to examine how herbivorous fishes alone, herbivorous fishes and D. antillarum together, and the exclusion of all large herbivores shape both the cover of macroalgae as well as the structure of algal communities. This experiment was followed by experiments looking into the feeding preferences of natural communities of herbivorous fishes and D. antillarum.

Methods

To better understand the role of different herbivores in the establishment of an algal community on a shallow Caribbean reef, a caging experiment was conducted on a shallow patch 62

reef (16˚ 45.108’, -88˚ 5.923’) near Carrie Bow Cay, Belize. This reef was chosen because it had the highest recorded D. antillarum density among reefs near Carrie Bow Cay during previous surveys. Cages were deployed via SCUBA at a depth < 3 m and the experiment was carried out from August to December 2016. For this experiment, natural fish and D. antillarum densities were used rather than manipulating these densities. To determine the density of D. antillarum, surveys (n = 8) along a 2-m belt transects were conducted across the study reef. Transect lengths ranged from 25 to 50 m depending on the extent of the reef, and all D. antillarum found within 1 m of either side of a transect line were counted. These counts were then used to determine the average density of D. antillarum per m2 at the study site. Fish surveys were conducted by a snorkeler swimming 50 m transects along the experimental reef and recording video of fishes in a 2-m wide swath using a GoPro®. A total of three transects were run, one before set-up of the caging experiment and two at the conclusion of the experiment. Videos were reviewed, and all fish throughout the water column were counted and identified to the species level. Density of each fish species per m2 was calculated for the reef, along with overall fish density and density of herbivorous fishes as a group.

Caging Experiment

Four different treatment types were established (n = 10 per treatment) for this caging experiment; full herbivore exclusion cages, fence cages that allowed fish access, partial cages

(shade control) accessible by fishes and D. antillarum, and open plot treatments that allowed access by fish and sea urchins (Figure 3-1). Each cage was created out of 2.5-cm diameter wire mesh and covered 0.25 m2 of substrate. The dimensions of the full and fence cages were 0.5 m x

0.5 m x 0.4 m with a 5 cm lip at the bottom to assist with attachment to the substrate. The partial cages had the same dimensions as the full cages and fences but had only 2 sides and a top. All cages were attached by hammering 3.8-cm masonry nails into the bare substrate through large 63

washers placed over the caging material (0.48 cm x 3.175 cm zinc plated fender washer). The open cages were a 0.25 m2 plots with corners differentiated by orange flagging tape on 3 nails and one piece of rebar. All plots were scrubbed at the beginning of the experiment using wire brushes to remove all visible macroalgae. After scrubbing some fine turf remained, but all plots had >80% bare substrate. Cage location was determined based on availability of bare pavement for attachment. Photographs of scrubbed plots were taken at the beginning of the experiment to determine the community structure within each plot.

Cages were checked after 7 weeks, 11 weeks, and 20 weeks and at each time point pictures were taken of the inside of each cage. At the end of the experiment, photographs also were taken of ten haphazardly placed 0.25-m2 quadrats on the experimental reef to help understand the community structure of the reef at that time. Data from these “unmanipulated” plots were used for all analyses involving data from the week 20 sampling. All photographs were analyzed using Coral Point Count (150 points) (Kohler and Gill 2006) to identify and quantify all algae and sessile organisms living within each plot as well as to determine the percent bare substrate (pavement, rubble, and sand). All corals, gorgonians, zoanthids, and sponges were identified to the species level, where possible, or to genus level while all macroalgae were identified to the genus level. Macroalgae were defined as any algae large enough to be seen with the naked eye excluding turf and crustose coralline algae (CCA). Turf and CCA were not identified to a lower taxonomic level. Percent cover of each taxa was calculated based on the

Coral Point Count data. Living organisms found in the treatment plots were grouped as corals, gorgonians, zoanthids, sponges, macroalgae, CCA, or turf and their percent covers were graphed.

In addition to determining percent cover of macroalgae, the diversity of the macroalgal community was calculated for each plot at each time point. The experiment concluded after 20

weeks, at which time canopy heights were measured and vouchers of the predominant algae were collected. Canopy heights were measured to the nearest 0.5 cm at 16 equally spaced points within each cage, and results were averaged.

Statistical analysis. Percent covers of macroalgae and bare substrate were compared across treatments each month using two-way ANOVAs followed by TukeyHSD tests (RStudio version 3.2.2). All macroalgae also were grouped according to type of defenses employed as chemically rich, structurally defended, chemically and structurally defended, palatable, or unknown. Data for macroalgae grouped by these defenses as well as turf and CCA were graphed for each month. In addition, percent covers of macroalgae within these macroalgal groups were compared using a two-way ANOVA followed by TukeyHSD tests to examine the effect of time and treatment.

Community structure within each treatment type was compared across months using a permutational multivariate analysis of variance based on distance matrices. To do this, Euclidean distances were used in the function adonis from the vegan package in R (Oksanen et al. 2017).

For this experiment, the macroalgal community structure was analyzed. Initial PERMANOVAs were followed by the post-hoc pairwise.adonis function from the pairwiseAdonis package in R

(Martinez Arbizu 2017). Nonmetric multi-dimensional scaling (nMDS) was used to visualize differences in macroalgal communities for each month across treatment types (Oksanen et al.

2017). Each nMDS was performed across three dimensions to assure an acceptable level of stress

(< 0.2).

Canopy heights at the end of the experiment were compared using a one-way ANOVA across treatment types followed by TukeyHSD tests in R using package stats (R Core Team

2015). Shannon diversity was calculated for each cage using the diversity function in the R

program vegan (Oksanen et al. 2017). These diversities were compared using a two-way

ANOVA looking at time and treatment followed by TukeyHSD tests.

In Cage Feeding Experiment

After the caging experiment concluded, each full cage was divided in half using the lid of the cage (n = 6) to explore the effects of herbivory by fishes and D. antillarum. Photographs were then taken of each side of the cage. Half of each cage was opened to herbivory by roaming herbivorous fishes while one Diadema antillarum was confined in the other half. Each in-cage feeding experiment was set up as the initial caging experiment concluded and terminated on

December 20, 2016 (2-4 days later). At this time, pictures were taken of each side of the cage and canopy heights were measured on each side. To determine the average canopy height, eight points were measured to the nearest 0.5 cm. All pictures were analyzed within Coral Point Count using 150 points to determine percent cover of each alga and the composition of the macroalgal community. In addition to overall change in percent cover of macroalgae, change in percent cover of each species was calculated. The change in percent cover of macroalgae grouped by type of defense employed along with CCA and turf also was examined.

Statistical analysis. Change in percent cover of macroalgae between the side of the cage open to herbivorous fishes and the side with a D. antillarum was compared using a paired t-test

(R Core Team 2015). Algal communities subject to grazing were compared to those in fences, partial cages, and open plots using a distance based permutational multivariate analysis of variance performed with function adonis in R, and differences among communities were visualized via nMDS (Oksanen et al. 2017). Percent cover of macroalgae and bare substrate were compared between treatments characterized as fish side, urchin side, fences, partial cages, and open plots at the conclusion of the in-cage feeding experiment using a one-way ANOVA

followed by TukeyHSD tests. In addition, canopy heights were compared using a one-way

ANOVA followed by TukeyHSD tests at the conclusion of the in-cage feeding experiment.

Herbivore Feeding Assays

In order to better understand the role of dietary preference in shaping macroalgal communities within the experimental cages and on the surrounding reef, a series of feeding assays were conducted with both herbivorous fishes and D. antillarum. Fish feeding assays were conducted in-situ and relied on herbivory by the natural assemblage of resident fishes. Feeding assays with D. antillarum were conducted in the laboratory and consisted of no-choice assays where sea urchins were given only one macrophyte species per feeding trial. These feeding assays were conducted at two different time periods, July and December 2016, with two different sets of macrophytes being tested.

For all no-choice assays, D. antillarum were collected from the experimental reef and their test diameters were measured to nearest 0.5 cm using a ruler. For each assay an individual urchin was placed into a 5-gallon bucket with a constant flow of sea water. For each alga tested, a known amount (g) was placed in a bucket containing a single urchin and left for 24 hours. The methodology for weighing algae was to place a piece of appropriate size in a salad spinner, spin it 15 times to remove water, weigh it using a balance and then immediately place it into the container full of sea water. At the end of 24 hours, all remaining algae were removed from each bucket, spun 15 times in a salad spinner, and weighed to determine amount that was not consumed. Controls for each trial involved similar sized pieces of algae of that were placed in individual containers with running water and no urchins. These control algae were weighed at the beginning and end of the experiment to account for any natural growth or loss during the trial. To determine the amount consumed and correct for changes not due to consumption,

Equation 3-1 was used: 67

Cf Ti ∗ ( ) − Tf (3-1) Ci

Where Ti is the initial algal mass, Tf is the final algal mass, Ci is the initial control mass, and Cf is the final control mass (Lockwood III 1998; Erickson et al. 2006). The amount (g) and proportion consumed in each no-choice assay were compared using a one-way ANOVA followed by TukeyHSD tests (R Core Team 2015).

Assays in July 2016 used Amphiroa sp. collected from Golden Reef, a patch reef near

Carrie Bow Cay, Turbinaria sp. and two species of Dictyota collected from the experimental reef, and Halimeda opuntia and Halimeda tuna collected from the reef on the southern side of

Carrie Bow Cay. Whole pieces of each species between 1-3 g were used in each feeding trial, with a total of 12 urchins used in the trials. For Turbinaria sp., ~ 3 g was used for trial one and

~1.5 g for trail two. Assays for Dictyota sp. 1, Amphiroa sp., H. tuna and H. opuntia assays used

~2 g while assays for Dictyota sp. 2 used ~1 g. Feeding assays with Turbinaria sp. were done twice due to the high variability of consumption, and data from these two trials were combined for further analysis.

In addition to using whole pieces, Turbinaria sp. also was ground up and served as a gel on window screen to test if the leathery structure of the Turbinaria sp. played a role in deterring feeding by D. antillarum. These samples of Turbinaria sp. were created by spinning algae 20 times in a salad spinner to remove excess water and then blending into a fully homogenized puree. A 20.05-gram aliquot of this blended Turbinaria was then combined with 0.625 g agar,

0.625 g carrageenan, and 40 ml water (Capper et al. 2016). This mixture was poured over window screen and allowed to cool and solidify. The strips were then cut into 2 by 2.5 cm pieces, weighed and placed in buckets.

In December 2016, macrophytes were collected from within experimental cages and on the surrounding reef for use in fish and D. antillarum feeding assays. The macrophytes collected were Turbinaria sp., Dictyota sp., Padina sp., Halimeda opuntia, Laurencia dendroidea, and

Thalassia testudinum. These species were chosen due to their prevalence within the experimental cages and on the adjacent reef.

Of the macrophytes collected in December, L. dendroidea, Padina sp., Turbinaria sp. and

H. opuntia were used for the December no-choice D. antillarum feeding assays. These feeding assays followed the same protocols set forth previously for no-choice feeding assays, and results were calculated using equation 3-1. For these assays 2.0 g of algae were used for Padina sp., 3.0 g for Turbinaria sp. and H. tuna, and 4.0 g for L. dendroidea.

Fish feeding assays were conducted at ~10 m depth on a patch reef called Golden Reef

(16°48.575’, -88°05.138’) near Carrie Bow Cay in December 2016. Previous surveys (n = 2) of

Golden Reef were used to characterize the natural fish assemblage on the reef. Surveys were conducted by a diver slowly swimming along a transect placed down the middle of the patch reef while recording with a GoPro®. The videos were then analyzed and all fish in the water column within 1 m of either side of the transect line were counted and identified to species level.

Transect lengths were 15 m and 23 m to cover the length of the reef at the surveyed locations.

Fish counts were used to calculate density of each fish species per m2.

Fish feeding assays were conducted by attaching pieces of each macrophyte to a braided

3-strand yellow polypropylene line and then attaching this line to the substrate. Attachment to the line was accomplished by untwisting the braided line and placing the pieces of macrophyte within this opening. Five different types of macrophytes were placed on each line; Turbinaria sp., Dictyota sp., Padina sp., Laurencia dendroidea, and Thalassia testudinum. The lines and

macrophytes were suspended into the water column where they were visible and accessible to herbivorous fishes. The location of each macrophyte on each line was haphazardly determined to limit the role of location in water column or placement near another species. A total of 18 lines were placed on the reef. All pieces of macrophyte were scored as either present or eaten

(completely gone) after 1 h and 20 h. These data were analyzed in RStudio using a G-test (Hervé

2019) followed by a Fisher’s exact test (R Core Team 2015) comparing eaten versus uneaten at 1 and 20 h.

Results

Surveys conducted on the experimental reef found an average of 0.61 D. antillarum per m2. The herbivorous fish abundance was found to be 0.20 per m2. The herbivorous fishes present in the environment included parrotfish, specifically stoplight (Sparisoma viride) and redband parrotfish (Sparisoma aurofrenatum), and acanthurid species, specifically ocean surgeonfish

(Acanthurus bahianus), and doctorfish (Acanthurus chirurgus), as well as various other small herbivores (Table 3-2). Sparisoma viride made up 12.1±3.9% of all fish on the reef (37.3±13.2% of herbivores) while A. chirurgus made up 12.7±7.0% of all fish on the reef (31.9±10.2% of herbivores).

During the course of the experiment some cages were dislodged or lost and at the 11- week time period some open plots were not found. Thus, the final sample sizes were reduced below 10. Photographs of each experimental plot were analyzed using 150 points in coral point count and all organisms were identified to at least the genus level. Percent cover of corals, gorgonians, zoanthids, sponges, macroalgae, CCA, and turf algae were graphed for each month

(Figure 3-2). Similar graphs were created for cover of macroalgae grouped by type of defenses employed as chemically rich, structurally defended, chemically and structurally defended, palatable, unknown, turf or CCA (Figure 3-3). Table 3-1 shows the different algal genera that 70

made up the algal community in each month in each type of cage, their abundance, their palatability based on published data.

In addition to looking at the living components of the benthos, the percent cover of bare substrate also was examined. A two-way ANOVA found there was a significant interaction between treatment and time (df = 9, p-value = 0.003). At the beginning of the experiment, there was no significant difference in the percent bare substrate among the different treatments. At 7 weeks, the full cages had significantly less bare substrate compared to the fences and partial cages (TukeyHSD, p-value < 0.02). At 11 weeks, only the fences and full cages were significantly different, with the fences having higher cover of bare substrate (p-value < 0.001).

At 20 weeks, the full cages had significant lower cover of bare substrate compared to the fences, partial cages, and open plots (p-value < 0.01). In addition, the full cages had significantly lower cover of bare substrate compared to the unmanipulated experimental reef (p-value = 0.03), and the unmanipulated reef was not significantly different from the fences, partial cages, and open plots.

In regard to the cover of macroalgae, there was a significant interaction between time and treatment (df = 9, p-value < 0.001) as illustrated in Figure 3-4. Specifically, after scrubbing at the beginning of the experiment there was no significant difference between any of the treatments.

At 7 weeks, the full cages had significantly higher macroalgal cover compared to the fences, open plots, and partial cages (TukeyHSD, p-value < 0.001). At 11 weeks, the full cages had significantly higher percent cover compared to the fences and partial cages (p-value < 0.001). At

20 weeks, the full cages had significantly higher cover compared to all other treatments (p-value

< 0.001). In addition, the unmanipulated experimental reef had significantly lower cover of

macroalgae compared to the full cages (p-value = 0.001), but significantly higher cover than the fences and partial cages (p-value < 0.05).

When looking at percent cover of algae with different defenses, turf, and CCA (Figure 3-

3), there were significant differences found by two-way ANOVA. At the beginning of the experiment, the only significant difference found was with the CCA where the open plots had significantly higher cover compared to fences and partial cages (TukeyHSD, p-value < 0.01). At

7 weeks, there were significant differences between treatments in regard to chemically rich (df =

3, p-value = 0.004) and palatable algae (df = 3, p-value < 0.001). Specifically, the full cages had significantly higher cover of chemically rich algae as compared to fences and partial cages

(TukeyHSD, p-value < 0.05). The full cages also had significantly greater cover of palatable algae compared to all other treatments (TukeyHSD, p-value < 0.001). At 11 weeks, there were significant differences in regard to the turf algae (df = 3, p-value = 0.02), palatable algae (df = 3, p-value = 0.03), and chemically rich algae (df = 3, p-value < 0.001). For the turf algae, the partial cages had significantly higher cover compared to the fences (TukeyHSD, p-value = 0.014). For the palatable algae the full cages had significantly higher cover compared to the fences and partial cages (TukeyHSD, p-value < 0.05). Similarily, the full cages had significantly higher cover of chemically rich algae as compared to the fences and partial cages (TukeyHSD, p-value

< 0.001). At 20 weeks, there were significant differences in the percent cover of palatable algae

(df = 3, p-value < 0.001), chemically rich algae (df = 3, p-value < 0.001), structurally defended algae (df = 3, p-value < 0.01), and algae with both chemical and structural defense (df = 3, p- value < 0.01). At this point, the full cages had significantly higher cover of palatable algae compared to all other treatments (TukeyHSD, p-value < 0.001). The full cages also had significantly higher cover of chemcially rich algae as compared to fences and partial cages

(TukeyHSD, p-value < 0.001), and the open plots had higher cover compared to the fences

(TukeyHSD, p-value = 0.01). In regard to cover of structurally defended algae, the full cages had significantly higher cover as compared to all other treatments (TukeyHSD, p-value < 0.01). The fences had the highest cover of algae that employ both chemical and structural defense, significantly higher that the fences and partial cages (TukeyHSD, p-value < 0.05).

Macroalgal community structure analyzed using a two-way PERMANOVA after removing August due to the lack of macroalgal community at the initial time point showed significant differences in the algal community by treatment type (df = 3, p = 0.001). At 7 weeks, the full cages had a significantly different macroalgal community compared to the fences and partial cages (TukeyHSD, p-value = 0.006) (Figure 3-5A). At 11 weeks, the full cages were still significantly different from the fences and partial cages (p-value = 0.006) (Figure 3-5B). At 20 weeks, the full cages were significantly different from the fences and partial cages (p-value <

0.05), and the open plot was significantly different from the partial cages (p-value = 0.01)

(Figure 3-6A). In addition, at this time the surrounding experimental unmanipulated reef had a significantly different algal community as compared to the fences and partial cages (p-value =

0.01) (Figure 3-6B).

When looking at the Shannon diversity index as related to the macroalgal community, there were significant differences found at 7 and 11 weeks. At 7 weeks, the partial cages and full cages were significantly different (TukeyHSD, p-value = 0.05) with the full cages having a greater diversity. At 11 weeks, the full cages had a significantly greater diversity as compared to the fences, partial cages, and open plots (p-value < 0.05). At 20 weeks, there was no significant difference between the diversity of the full cages, fences, partial cages, open plots, and unmanipulated reef.

Along with differences in the percent cover and community structure of macroalgae within each treatment type, there was a significant difference in the canopy height of algae in each treatment at the end of the experiment after 20 weeks (Figure 3-7). Full cages had significantly higher canopy heights than all other treatments (df = 3, p < 0.001).

In Cage Feeding Experiment

The cage sections open to fish and the cage sections with D. antillarum did not exhibit significantly different values for percent eaten over the 2-4 days (paired t-test, p = 0.196) (Figure

3-8). The side open to herbivorous fishes had a mean ± standard error of 19.9 ± 5.7% of algae removed while the side with D. antillarum had 13.5 ± 4.3% removed. Herbivorous fishes reduced macroalgae cover to levels not significantly different than fence, open or partial treatments (TukeyHSD, p-value > 0.2) while D. antillarum reduced macroalgae to levels significantly different than partial and fences (TukeyHSD, p-value < 0.01), but not open plots (p- value = 0.36) (Figure 3-9). After exposure to these two types of herbivores, there was no significant difference in canopy height (1-way ANOVA, df = 1, p-value = 0.2). There also were no significant differences in the percent cover of bare substrate between any of the cage treatments, D. antillarum side and herbivorous fish side (one-way ANOVA, df = 4, p-value =

0.13) or any significant differences in Shannon diversity (one-way ANOVA, df = 4, p-value =

0.35).

Distance-based PERMANOVAs showed significant differences between treatments after the in-cage feeding experiment (df=4, p-value=0.001). Specifically, the side open to fish grazing had an algal assemblage similar to fences, open plots, partial cages, unmanipulated reefs, and the side open to D. antillarum (TukeyHSD, p-value > 0.68) Diadema antillarum created an algal assemblage similar to open plots, and unmanipulated reefs (p-value > 0.38), but significantly

different from fences or partial cages (p-value = 0.015). This difference can be seen via nMDS

(Figure 3-10).

Feeding Assays

For all no-choice Diadema antillarum feeding assays, the mean test diameter of the urchins ± standard error was 6 ± 2 cm. In July 2016 there was a statistically significant difference in the proportion of macroalgae eaten (df = 6, p-value = 0.006) and amount of macroalgae eaten

(df = 6, p-value < 0.001) by D. antillarum when given Amphiroa sp., two species of Dictyota sp.,

Halimeda opuntia, H. tuna, whole Turbinaira sp. or Turbinaira sp. ground up and presented as an agar on window screen. The results of two assays run with whole Turbinaira sp. were combined. When looking at the mean amount eaten ± standard error, significantly more

Turbinaria sp. agar was eaten (2.12 ± 0.25g) compared to Amphiroa sp. (1.24 ± 0.14g), Dictyota sp. 1 (0.92 ± 0.27g), Dictyota sp. 2 (0.82 ± 0.04g), H. tuna (0.96 ± 0.19g), and whole Turbinaria sp. (0.74 ± 0.13g) (Figure 3-11A).

In December 2016, D. antillarum were given either Laurencia dendroidea, Padina sp., H. opuntia, or Turbinaria sp. (Figure 3-11B). There was once again a statistically significant difference in both the amount eaten (df=3, p-value= 0.023) and proportion eaten (df=3, p-value=

0.002). When looking at mean amount eaten (g) ± standard error, there was a gradient of consumption, with H. opuntia (0.83 ± 0.29g) eaten significantly less than Turbinaria sp. (2.14 ±

0.28g) (TukeyHSD, p-value = 0.02).

Fish feeding assays also were conducted in December 2016 using Turbinaria sp.,

Dictyota sp., L. dendroidea, Thalassia testudinum, and Padina sp. (Figure 3-12). Surveys of the fish community found three species of roving herbivorous fish on the reef, A. chirurgus, A. coeruleus, and S. viride (Table 3-3). Acanthurus chirurgus made up 70% of herbivores present,

A. coeruleus made up 20% and S. viride was 10%. The density of herbivorous fishes was found 75

to be 0.35 per m2. There were statistically significant differences at both the 1-hour and 20-hour time points. At 1 hour, there was a significant difference in consumption among the different species (G-test, df = 4, p-value < 0.001). Specifically, there was a significant difference in the eaten vs uneaten ration between L. dendroidea and both Dictyota sp. and T. testudinum (Fisher’s exact test, p-value = 0.018). After 20 hours, all macrophytes except L. dendroidea were completely eaten resulting in a significant difference among species (G-test, df = 4, p-value <

0.001). Specifically, significantly less L. dendroidea was eaten compared to all other species

(Fisher’s exact test, p-value < 0.001).

Discussion

Historically, herbivores have acted as a structuring force on Caribbean coral reefs, keeping macroalgae in check and allowing for corals, sponges, and other sessile organisms to grow and thrive (Hughes et al. 2003). The decline of herbivorous fishes from overfishing and

Diadema antillarum from the 1983 mass die-off have been linked to increases in macroalgae and decline in coral reef health (Lessios 2016). As expected, this experiment showed that the exclusion of large herbivorous fishes and D. antillarum led to significantly higher percent cover of macroalgae and higher canopy height of macroalgae as compared to other treatments that allowed access by different herbivores. In particular, Padina sp., Dictyota spp., and Turbinaria sp. all increased in abundance over the course of the experiment. This result is consistent with previous caging experiments done off Carrie Bow Cay, Belize in 1983 which also showed an increase in these algal genera (Lewis 1986).

There was no significant difference in percent cover of algae or canopy height between treatments that allowed only fish access (fence cages) and treatments that allowed access by both fish and sea urchins (partial cages and open plots). This finding is inconsistent with historic and modern studies that indicate that D. antillarum is a more effective herbivore than herbivorous 76

fishes (Mumby et al. 2006; Lessios 2016). Based on published research it was hypothesized that partial and open treatments would have significantly lower percent cover than fence treatments.

These incongruent findings may be due to the lower density of D. antillarum on the experimental reef as compared to the pre-die-off densities (0.61 individuals/m2 versus 0.8-14.4 individuals/m2) or the difference in herbivorous fish density on the experimental reef compared to reefs historically used (Lessios 2016). As noted by Hay (1984), many of the reefs studied before the die-off were severely overfished and may not have been an accurate representation of the roles of

D. antillarum and herbivorous fishes elsewhere in the Caribbean. This study was conducted in

Belize where parrotfish and surgeonfish have been protected since 2009, and this management may be responsible for the presence of larger herbivorous parrotfish and roaming schools of surgeonfish. Specifically, herbivorous fishes (particularly Sparisoma parrotfish) were consistently found within the fence and partial cages, which may have increased the grazing within those cages. Another factor that may have contributed to the relatively lower influence of

D. antillarum is their capacity and willingness to roam through the reef. The location of cages was primarily influenced by the availability of substrate for anchoring which may have put some cages farther from shelter than the D. antillarum were willing to roam. Under the conditions observed on the experimental reef, it appears that herbivorous fishes alone are equally capable of maintaining a low percent cover of macroalgae as fishes and urchins together.

Along with comparing the overall percent cover of macroalgae within the different treatment types, comparisons of algal community structure also were conducted using distance based PERMANOVAs. As expected, treatments that excluded large herbivores had different algal communities relatively early in the experiment (after 7 weeks). Some palatable algae types grew exclusively within the full cage treatments, including Padina sp. and Acanthophora

spicifera. Unexpectedly, partial and open treatments, which allowed access by fish and D. antillarum, did not diverge from fence treatments which allowed only fish access. Herbivores can be deterred from eating specific types of algae due to chemical defenses, structural differences, and combinations of these defenses (Paul and Hay 1986). Herbivorous fishes have been shown to be less tolerant of these defenses as compared to D. antillarum, likely due to their more mobile nature and more selective feeding behavior (Morrison 1988; Capper et al. 2016).

Due to differences in tolerance between herbivorous fishes and D. antillarum, it was expected that partial and open treatments would diverge from fences, but instead fences and partial treatments were most comparable to each other and open treatments differed from these treatments. Open treatments, however, were found to be very similar to the unmanipulated areas of the experimental reef indicating that this treatment most accurately reflected the surrounding reef. This grouping may indicate that fishes exerted a major influence and were attracted to plots with structure. Large herbivorous fishes often were found within the fences and partial cages, and it is possible that they were the dominant herbivore in these plots even though D. antillarum had access to the partial cages.

The experiment comparing feeding by herbivorous fishes and D. antillarum after opening the full treatments to herbivores indicated that herbivorous fishes and D. antillarum consumed a similar amount of macroalgae. This result was unexpected due to previous research that indicated

D. antillarum can graze a larger amount of the available macroalgal community compared to herbivorous fishes (Mumby et al. 2006). This discrepancy indicates that, on this Belizean reef, herbivorous fishes alone may have the capacity to consume algae to the same level as D. antillarum present at a density of 8 per m2. Of note, the cages used for this experiment had a high cover of palatable algae, which likely contributed to the high level of consumption over 2-4 days.

At the end of the experimental grazing, the algal community created by grazing by fish overlapped all treatments and the natural reef, as expected because fish could access all experimental cages and surrounding reef. In contrast, the algal community created by D. antillarum herbivory alone was comparable to the unmanipulated reef and open plots but did not overlap with the fence or partial treatments. These findings support the previous conclusion that herbivorous fishes can shape the structure of algal communities.

In feeding assays, herbivorous fishes apparently avoided the L. dendroidea. This result is consistent with previous research that showed herbivorous fishes avoid chemically rich algae

(Paul and Hay 1986). In comparison, D. antillarum ate less of one species of Dictyota and

Turbinaria sp. in July 2016 and H. opuntia in December. Lower consumption of Turbinaria sp. and H. opuntia is consistent with previous findings that D. antillarum eat less of thick-leathery species or calcareous species (Littler et al. 1983). This conclusion is further supported by the observation that Turbinaria sp. is readily consumed after its structural defenses were removed. It is, however, important to note that at least 25% of each alga was eaten, which indicated D. antillarum can eat all the types of algae that were offered. Overall, these feeding assays support previous research showing differences in feeding preferences of herbivorous fishes and D. antillarum.

The results from this series of experiments are consistent with previous research showing the importance of herbivores in controlling and structuring macroalgal populations on coral reefs. However, these results bring into question some previous hypotheses about the importance of herbivorous fishes versus the sea urchin Diadema antillarum. From current work, it does not appear that one type of herbivore serves as a better control of macroalgae than the other. At sufficient densities, it appears that herbivorous fishes alone can maintain macroalgal cover at the

same level as fishes and D. antillarum together. However, feeding by these different herbivores does create different algal communities. This finding is important because macroalgae are known to affect the settlement of coral reef organisms differentially, specifically coral larvae (Kuffner et al. 2006). Further experiments examining the role of herbivorous fishes and Diadema antillarum at current densities and realistic possible future densities is necessary to gain a fuller understanding of the role of herbivores on reefs and the future of coral reef ecosystems.

Table 3-1. Algal assemblage by treatment and month sorted by percent cover. Palatability: C- chemically rich, S- structurally defended, P- palatable, U-unknown. (Littler et al. 1983; Paul and Hay 1986; Lhullier et al. 2009; Sudatti et al. 2011; Capper et al. 2016; Guiry and Guiry 2020) 0 Weeks

Fence Full Partial Open

N = 10 N = 10 N = 10 N = 10

Turf- 4.22% U Turf- 6.39% U Turf- 3.22% U CCA- 2.30% S

Dictyota sp.- 0.88% C CCA- 0.77% S Dictyota sp.- 0.54% C Turf- 2.26% U

Turbinaria sp.- 0.08% Cyanobacteria- 0.38% Sargassum sp.- 0.17% P Dictyota sp.- 0.35% C S C

Thalassia testudinum-0.17% Sargassum sp.- 0.21% Dictyota sp.- 0.30% C P S

Sargassum sp.- 0.07% S

Table 3-1 cont.

7 Weeks Fence Full Partial Open N = 8 N = 9 N = 10 N = 9

Turf- 17.12% U Turf- 18.09% U Turf- 19.71% U Turf- 21.12% U

Halimeda sp.- Laurencia dendroidea- Dictyota sp.- 10.48% C Dictyota sp.- 7.27% C 1.18% C, S 1.62% C

Laurencia dendroidea- Padina sp.- 8.46% P Dictyota sp.- 0.63% C Dichothrix sp.- 1.49% C 0.71% C

Acanthophora sp.- 7.19% Thalassia testudinum- Dictyota sp.- 0.55% C P 0.42% P

Laurencia dendroidea- Cyanobacteria- 0.46% C Halimeda sp.- 0.22% C, S 2.20% C

Sargassum sp.- 0.09% S Halimeda sp.- 0.86% C, S Turbinaria sp.- 0.21% S

Turbinaria sp.- 0.09% S Cyanobacteria- 0.79% C

Gelidiella sp.- 0.09% U Turbinaria sp.- 0.62% S

Sargassum sp.- 0.16% S

Thalassia testudinum-

0.08% P

Table 3-1 cont.

11 Weeks

Fence Full Partial Open

N = 7 N = 6 N = 7 N = 2

Okeania sp.- Turf- 5.08% U Dictyota sp.- 16.65% C Turf- 20.50% U 11.41% C

Halimeda sp.- Laurencia dendroidea- Padina sp.- 11.94% P Turf- 7.34% U 2.01% C, S 2.62% C

Laurencia dendroidea- Dichothrix sp.- Turf- 9.89% U Halimeda sp.- 0.51% C, S 1.28% C 2.34% C

Dictyota sp.- Cyanobacteria- 0.40% C Acanthophora sp.- 2.98% P Cyanobacteria- 0.20% C 1.01% C

Laurencia dendroidea - Halimeda sp.- Okeania sp.- 0.10% C 2.28% C 0.33% C, S

Cyanobacteria- 1.61% C

Halimeda sp.-

1.41% C,S

Turbinaria sp.- 0.98% S

CCA- 0.49% S

Table 3-1 cont.

20 Weeks Fence Full Partial Open N = 7 N = 6 N = 10 N = 9

Turf- 6.41% U Dictyota sp.- 15.22% C Turf- 14.98% U Dictyota sp.- 10.90% C

Laurencia dendroidea- Laurencia dendroidea- Padina sp.- 12.42% P Turf- 9.98% U 3.48% C 5.48% C

Halimeda sp.- Laurencia dendroidea- Turf- 9.54 % U Cyanobacteria- 2.26% C 2.11% C, S 2.83% C

Laurencia dendroidea- Cyanobacteria- 0.65% C Dictyota sp.-0.57% C Dichothrix sp.- 1.56% C 6.57% C

Dictyota sp.- 0.10% C Turbinaria sp.-1.73% S Halimeda sp.-0.43% C, S CCA- 0.07% S

Halimeda sp.- Thalassia testudinum- Cyanobacteria- 0.07% C 0.93% C, S 0.27% P

Halimeda sp.- Laurencia sp.- 0.61% P Turbinaria sp.- 0.07% S 0.07% C, S

Cyanobacteria- 0.35% C

Liagora sp.- 0.34% C

Table 3-2. Average density of fish species per m2 ± standard error (SE) on the experimental reef. * herbivorous species. Species Average SE Abudefduf saxatilis 0.03 0.02 Acanthurus chirurgus * 0.07 0.04 Acanthurus coeruleus * 0.02 0.02 Chaetodon ocellatus 0.01 0.01 Chromis cyanea 0.03 0.03 Haemulon flavolineatum 0.05 0.04 Halichoeres garnoti 0.02 0.01 Holocanthus ciliaris 0.00 0.00 Holocentrus sp. 0.00 0.00 Sparisoma aurofrenatum * 0.04 0.01 Sparisoma viride * 0.07 0.03 Stegastes partitus 0.02 0.02 Stegastes variabilis 0.20 0.05 Thalassoma bifasciatum 0.02 0.01

Table 3-3. Average density of fish species per m2 ± standard error (SE) on Golden Reef where feeding assays were conducted. * represent herbivorous species. Species Average SE Acanthurus chirurgus * 0.22 0.05 Acanthurus coeruleus * 0.09 0.09 Bodianus rufus 0.07 0.07 Canthigaster rostrate 0.04 0.04 Chaetodon capistratus 0.04 0.04 Chromis cyanea 0.77 0.43 Clepticus parrae 0.04 0.04 Haemulon flavolineatum 1.32 0.01 Holocentrus sp. 0.09 0.09 Pomanthus paru 0.04 0.04 Pterois volitans 0.04 0.04 Sparisoma viride * 0.04 0.04 Stegastes variabilis 0.48 0.05 Thalassoma bifasciatum 0.48 0.05

A B

C D

Figure 3-1. Photographs of different experimental treatments. Images show full herbivore exclusion cage (A), lidless fence cages (B), partial cages with two sides and a top (C), and open plots (D).

Figure 3-2. Stacked bar plot dividing percent cover of living portion of each treatment by month. n= indicates the number of replicates of that treatment.

Figure 3-3. Stacked bar plots showing percent cover of algae employing different defense strategies found in each treatment type by month.

Figure 3-4. Percent cover of macroalgae excluding CCA and turf for each sampling event. Error bars represent ± 1 standard error.

Figure 3-5. Plots of results of non-metric multidimensional scaling indicating the community structure in different cage types at 7 weeks (A) and 11 weeks (B). Points that are closer together are more similar.

Figure 3-6. Plots of results of non-metric multidimensional scaling indicating the community structure in different cage types at 20 weeks without (A) and with (B) unmanipulated reefs. Points that are closer together are more similar.

Figure 3-7. Comparison of canopy heights between four cage treatment types. Error bars represent ± 1 standard error. Letters represent significant differences.

Figure 3-8. Comparison of percent cover of macroalgae eaten in cage sections open to herbivorous fishes and with sections containing Diadema antillarum. Error bars represent ± 1 standard error.

Figure 3-9. Percent cover of macroalgae excluding CCA and turf during the exclusion and in- cage feeding experiments. Error bars represent ± 1 standard error.

Figure 3-10. Plots of results of non-metric multidimensional scaling comparing algal assemblages in fences, partial cages, open plots, fish cages, urchin cages, and unmanipulated plots.

B B

Figure 3-11. Percent macroalgae eaten by D. antillarum in no-choice feeding assays in July (A) and December (B) 2016. Number of replicates represented by numbers at the bottom of the bar. Error bars represent ± 1 standard error. Letters indicate significant differences.

Figure 3-12. Proportion of algae eaten by herbivorous fish during feeding assays. Letters indicate significant differences

CHAPTER 4 HOW HERBIVORES RESHAPE A MACROALGAL COMMUNITY ON A LITTLE CAYMAN CORAL REEF: THE ROLE OF HERBIVORE TYPE AND DENSITY

Introduction

Coral reefs are in decline worldwide in large part due to the compounding effects of a variety of global and local stressors and negative feedback loops (Hughes et al. 2017b). On

Caribbean coral reefs, a major consequence of these stressors is a high abundance of macroalgae and, in some areas, the subsequent shift to an algal-dominated system (Mumby et al. 2007). The increase in macroalgae on reefs is influenced by a number of different factors including increases in nutrients from anthropogenic and natural sources, increases in open substrate due to coral mortality, and decreases in herbivory (Burke et al. 2011; Fong and Paul 2011; Roff and Mumby

2012). Decreases in herbivory can be linked to overfishing of herbivorous fishes, especially large-bodied parrotfishes, and the mass die-off of the long-spined sea urchin Diadema antillarum

(Jackson et al. 2014; Holbrook et al. 2016; Shantz et al. 2020).

Historically, D. antillarum was a voracious herbivore and large densities were known to create 2 to 10 m wide “halos” of heavily grazed sea grass around patch reefs (Ogden et al. 1973).

Prior to the die-off, D. antillarum was considered to be the most effective Caribbean herbivore and much of the structuring of Caribbean coral reef ecosystems was attributed to them (Lessios

2016). This structuring came via the consumption of macroalgae, competition with herbivorous fish and other sea urchins, the consumption of adult and juvenile corals, and the erosion of calcium carbonate during grazing activity (Lessios et al. 1984; Ogden and Carpenter 1987).

Between 1983 and 1984 a putative pathogen spread throughout the Caribbean and killed between

93% and 99% of all D. antillarum which shifted densities from ~7.5 individuals/m2 to < 1 individual/m2 (Lessios 1988; 2016; Levitan 1988). These die-offs on reefs throughout the

Caribbean have been linked to significant increases in algal biomass. In some areas, algal

biomass increased by ~30% just five days after mortality, with further increases to 3,000% of initial biomass by six months post-mortality (Carpenter 1988; Lessios 1988). Further surveys showed decreases in coral cover and cover of crustose coralline algae that coincided with D. antillarum mortality and subsequent increases in percent cover of fleshy macroalgae (Liddell and

Ohlhorst 1986). On modern reefs, macroalgal cover continues to be elevated as compared to that found in the 1980s with percent cover throughout the Caribbean being on average double that found prior to 1983 (Jackson et al. 2014). In combination with the decreases in D. antillarum density, parrotfish and other large herbivorous fish were considered overfished in most of the

Caribbean by the time comprehensive quantitative assessments were conducted in the 1970s and

1980s (Jackson et al. 2014). The continued overfishing of parrotfish, which selectively removes larger individuals, has been associated with increases in macroalgal cover and coral disease as well as decreases in coral recruitment (Holbrook et al. 2016; Steneck et al. 2018; Shantz et al.

2020).

Increases in macroalgal cover negatively affect corals in a variety of direct and indirect ways. Interactions between corals and algae have been categorized into seven main types of interactions; shading, allelopathy, abrasion, basal encroachment, preemption of space, settlement of coral larvae on ephemeral algal surfaces, and increased sedimentation due to reduced water flow (Chadwick and Morrow 2011). The type of interaction depends on the identity of both the algae and the coral. Brown algae such as Lobophora variegata and Dictyota spp., have been shown to damage corals through abrasion, shading, and allelopathy while benthic cyanobacteria have been shown to chemically deter settlement of coral larvae and use both abrasion and allelopathy to damage adult corals (Chadwick and Morrow 2011; Paul et al. 2011; Ritson-

Williams et al. 2016; Vieira 2019). Dictyota spp., L. variegata, Padina sp., Sargassum sp.,

Halimeda tuna and algal turfs have all been linked to decreases in coral settlement (Kuffner et al.

2006; Chadwick and Morrow 2011; Lee et al. 2012). Conversely, many algal species have no known effect on corals and still others, primarily crustose coralline algae (CCA), have a positive effect on larval settlement (Chadwick and Morrow 2011; Ritson-Williams et al. 2016; Vieira

2019). Overall, it can be concluded that increases in overall algal cover can significantly affect corals, with increases in certain algal species being especially detrimental.

Much like interactions between corals and algae depend on species involved, the nature of herbivore-algal interactions also is species dependent. To prevent herbivory, algae display different kinds of defenses that can be broadly categorized as chemical defenses, structural defenses, or a combination of chemical and structural defenses (Fong and Paul 2011). Analysis of previous feeding and caging experiments has led to the conclusion that, in general, herbivorous fishes are most deterred by chemical defenses while D. antillarum are most deterred by structural defenses and neither group prefers algae that employ both chemical and structural defenses (Littler et al. 1983; Paul and Hay 1986; Hay et al. 1994; Fong and Paul 2011; Campbell et al. 2014). These differences in feeding preference can lead to differences in algal communities. For example, experimental removal of D. antillarum from a shallow backreef in the U.S. Virgin Islands (USVI) left herbivorous fishes as the only large herbivore, and this manipulation resulted in algal biomass that was 2-4 times higher than that found on reefs grazed by fishes and D. antillarum and this algal community was dominated by one algal species,

Sphacelaria tribuloides (Carpenter 1986). Exclusion of both herbivorous fishes and D. antillarum on a Jamaican reef resulted in the increase of erect and filamentous algae dominated by those susceptible to herbivory by fishes, but the exclusion of only D. antillarum resulted in increases in erect algal species that were resistant to fish herbivory (Morrison 1988). In

particular, Lobophora variegata and Dictyota spp. significantly increased, but they were consumed quickly once D. antillarum were placed back in the fenced treatment plots (topless cages), which supports hypotheses regarding differing preferential feeding (Morrison 1988). Due to the higher grazing rate and the more generalist diet of D. antillarum compared to herbivorous fishes, their loss has been categorized as one of the major factors that led to the decline and continued poor state of coral reef health (Mumby et al. 2007; Jackson et al. 2014).

Although D. antillarum densities have remained low compared to pre die-off densities, there has been limited recovery in certain areas to ~5 individuals/m2 (Lessios 2016). These recoveries led to a new set of observational and manipulative experiments to examine the ability of D. antillarum as an herbivore. Observational studies have shown that D. antillarum densities of approximately 4/m2 resulted in areas with ten times less macroalgae, 67.6% versus 6.2%, increased cover of CCA and bare space, and an eleven times higher rate of coral settlement as compared to areas without D. antillarum (Edmunds and Carpenter 2001; Idjadi et al. 2010).

Manipulative experiments that moved D. antillarum to previously unoccupied reefs to create densities of ~0.7 individuals/m2 resulted in decreases in abundance of brown algae, specifically

Dictyota spp., as well as increases in bare substrate and CCA (Chiappone et al. 2003; Maciá et al. 2007).

Along with experiments manipulating D. antillarum, there also have been experiments that caged different species of herbivorous fishes to determine their effects on algal communities.

These experiments found that different species of herbivorous fish play different roles depending on the successional stage of the algal community. On newly disturbed reefs Scarus taeniopterus and Acanthurus bahianus created a community of closely cropped filamentous algae and crustose coralline algae, while Sparisoma aurofrenatum created a community of tall filamentous

algae and later successional stage macroalgae (Burkepile and Hay 2010). In contrast, on established reefs, S. taeniopterus and A. bahianus allowed late successional macroalgae to flourish while S. aurofrenatum reduced cover of upright macroalgae (Burkepile and Hay 2008).

It is these distinctions in the types of algae consumed by different herbivores, the subsequent changes in the structure of algal communities, and the variation in how algal species affect corals that can lead to differences in the structure of benthic communities on reefs. To examine the roles of D. antillarum and herbivorous fishes in determining benthic community structure, a caging experiment was conducted. Treatments included cages that excluded D. antillarum but not herbivorous fishes, cages with only D. antillarum, cages open to herbivorous fishes and D. antillarum naturally occurring on the reef, and cages that excluded both fish and sea urchins. Cages with only D. antillarum were created to mimic both modern densities of 1/m2 and past high densities of 4/m2 (Lessios 2016). It was hypothesized that different herbivore treatments would lead to differences in the overall abundance of macroalgae, due to differing grazing pressures, as well as different algal communities, due to differing abilities and preferences of herbivores encountering chemically and/or structurally defended macroalgae.

Methods

This extensive caging experiment was set up in the shallow waters near the Central

Caribbean Marine Institute off Little Cayman, Cayman Islands. Little Cayman was chosen because herbivorous fish are abundant and diverse due to low fishing pressure, overall high coral cover compared to the rest of the Caribbean, and a relatively high percentage of the nearshore waters being in a marine protected area (Creary et al. 2008). Little Cayman’s protected areas were established in 1986, and they cover approximately 15% of the island’s shelf (Dromard et al.

2011). The site chosen for this experiment was a reef named ICON (19°41.982’ -80°03.582’ to

19°41.952’ -80°03.693’) which ranged in depth from 8 to 10 m. The spur and groove reef 100

formation at ICON becomes more defined and rugose as depth increases. Cages were set up along the southern shallower portion of the spurs. For this experiment, all cages were deployed over an established algal community to test the effect of the treatments on an existing macroalgal community and the associated benthic community. Algae present in the experimental area included Stypopodium zonale, Halimeda tuna, Dictyota spp., and Lobophora spp. Turbinaria sp.,

Sargassum sp. or Padina sp. also were present in some treatments. These algae were targeted due to their abundance at the experimental site and their differing defenses.

For this experiment, five different treatments were used with 10 replicates per treatment.

The treatments were four D. antillarum/m2 (pre-mortality density and current “high” density), one D. antillarum/m2 (current common density), fence treatments open to fish (4 sides but no top), full herbivore exclosures (allowed access only to organisms smaller than the caging material) and open, no cage, controls (nails at the 4 corners, open to all organisms). The high D. antillarum treatment (4/m2) was hypothesized to completely clear the substrate of macroalgae by the end of the experiment beginning with the palatable algae then the chemically defended algae and finally the structurally defended algae. The herbivorous fish only and the low D. antillarum treatment (1/m2) were predicted to leave the chemically defended and structurally defended algae, respectively. The cages that completely excluded large herbivores were predicted to increase in overall percent cover of algae especially palatable algae. The open plots should be reflective of the surrounding reef.

All treatments covered 1m2 of substrate, and cage dimensions were 1 m x 1 m x 0.5 m with a 0.1 m lip along the bottom that allowed for attachment to the substrate. Full herbivore exclusion cages, 4 individuals/m2 cages, and 1 individual/m2 cages all had 4 sides and a lid to keep sea urchins in and other herbivores out. Cages were built using galvanized chicken wire

101

with a 1.27-cm mesh. All cages were attached to the substrate with masonry nails (predominately

3.8 cm in length) and large washers (0.48 cm x 3.175 cm zinc plated fender washer). The washers were placed between the nail and the caging material to ensure the caging material was held down. A sufficient number of nails were used to ensure that there were no holes along the bottom that would allow sea urchins to escape or large organisms to enter. A half cinderblock was placed inside each cage or open plot to act as a shelter for the D. antillarum because sea urchins were consumed in previous caging experiments due to their habit of trying to use the corners of cages as shelter and exposing their undersides to predators. For this experiment, all sea urchins were collected from a shallow rocky site known as McCoy’s (19°40.463’ -

80°05.848’). This site was chosen due to its accessibility from shore and high abundance of D. antillarum. In an effort to limit bias, after all cages were deployed and numbered the treatment for each cage was determined using a random number generator, after which an appropriate number of urchins were placed within the cages and the lids were secured if needed.

This experiment was carried out from July 2018 to November 2018 with maintenance checks being done monthly. Each month, the sides and lids of the cages were cleaned, pictures were taken of the benthos inside the cages, and fish surveys were conducted on the reef. Fish surveys were conducted using five transects spaced out across the entire experimental reef, approximately 50 m apart, to encompass the entirety of the experimental site from east to west.

Each fish transect was conducted along a 30-m line with all fish in the water column extending 1 m from either side of the transect recorded. Throughout the experiment, cages were checked every other week to ensure the appropriate number of urchins were present. Urchins were replaced immediately if missing from cages. Between the October and November sampling periods, rough weather destroyed some of the cages (three exclosures, one fence, three 1 D.

102

antillarum/m2 treatments, and three 4 D. antillarum/m2 treatments). These cages were excluded from analysis of data from November.

Monthly pictures of the benthos within experimental plots were analyzed in Coral Point

Count with 150 points identified (Kohler and Gill 2006). To compare differences in community structure of macroalgal assemblages across treatment types in each month, a permutational multivariate analysis of variance based on distance matrices was preformed using the function adonis in the vegan package in R (version 3.2.2) (Oksanen et al. 2017). This test was followed by post-hoc tests using the pairwise.adonis function from the pairwiseAdonis package in R

(Martinez Arbizu 2017). Nonmetric multi-dimensional scaling (nMDS) was used to visualize differences in algal community structure (Oksanen et al. 2017). The distance matrix used for these three tests was created using Euclidean distances. Macroalgae were defined as all algae large enough to be seen clearly with the naked eye excluding crustose coralline algae (CCA) and turf.

Percent cover of macroalgae was compared in three different ways, overall total percent cover of macroalgae, change in overall percent cover, and percent cover of algae classified as chemically rich macroalgae, structurally defended macroalgae, macroalgae with both chemical and structural components, and cyanobacteria. Effects of both time (month) and treatment were examined for both overall macroalgal percent cover and change in percent cover of macroalgae from time point zero using a repeated measures two-way ANOVA followed by TukeyHSD tests using package stats (R Core Team 2015). The percent cover of chemically rich macroalgae, structurally defended macroalgae (calcification and general toughness), macroalgae with both chemical and structural components, and cyanobacteria also were compared using a two-way

ANOVA followed by TukeyHSD tests to examine the effect of time and treatment. Type of

103

defense (chemical, structural or both) of each alga was drawn from the literature. The percent cover of two ubiquitous reef macroalgae, Dictyota spp. and Lobophora spp., in each treatment was compared using a two-way ANOVA looking at time and treatment followed by TukeyHSD tests. In addition to examining percent cover of these two chemically rich algae, percent cover of crustose coralline algae (CCA) also was examined using a two-way ANOVA looking at time and treatment followed by TukeyHSD tests.

In addition to examining the difference in percent cover and community structure of macroalgae in the five treatments, differences in macroalgal diversity were examined each month. Shannon diversity indices were calculated using the diversity function found in the program vegan (Oksanen et al. 2017). These diversities were compared using a two-way

ANOVA looking at time and treatment followed by TukeyHSD tests.

At the conclusion of the experiment, 25 canopy heights measured to the nearest 0.5 cm were taken inside each cage in a grid pattern. Canopy heights were compared using a one-way

ANOVA across treatment types. Also at this time, D. antillarum inside each cage were collected and taken back to the laboratory for further analysis. Previous studies have indicated a changing relationship between test diameter and weight depending on food availability (Levitan 1989). In the laboratory, all sea urchins were weighed, and pictures were taken of each individual with a scale for reference. These pictures were analyzed using ImageJ to acquire 3 different diameters.

Multiple diameters were measured because D. antillarum are not perfectly round. These diameters were then averaged and the ratio of diameter to weight was calculated. Any sea urchins that had been replaced since the beginning of the experiment were not used in this analysis. Sea urchins from the 1 D. antillarum/m2 and 4 D. antillarum/m2 were compared to each

104

other and to sea urchins newly collected from the original collection site (McCoy’s) by a one- way ANOVA to compare treatment types followed by Tukey HSD tests.

Results

A large variety of fish species as well as some other organisms, such as green sea turtles and Caribbean spiny lobsters, were recorded in the monthly transects. The average number of individuals of each species per month provided an idea of the species present on the reef (Table

4-1). Of the large herbivorous fishes found, Acanthurus coeruleus, Scarus taeniopterus,

Sparisoma aurofrenatum and Sparisoma viride were the only species present every month.

Kyphosus sp., Melichythys niger, and Scarus vetula were present in five of the six surveys.

When looking at the total percent cover of all macroalgae (Figure 4-1A) there was a significant an interaction between the factors (df=20, p-value<0.001). The mean percent cover of each algae each month (± standard error or SE) can be found in Table 4-2. In June, at the beginning of the experiment, the percent cover of macroalgae ranged from 38.9% to 55.9% in the different treatments. At this time, the 4 individuals/m2 treatment (38.9 ± 1.2%) 1 individual/m2 treatment (43.7 ± 3.2%) and the open plots (41.5 ± 2.7%) were all significantly different from the exclosures (55.9 ± 2.4%) (Tukey HSD, p-value < 0.01). The percent cover in the fence treatments was 47.1 ± 2.8% and it was not significantly different from the other treatments. In

July, the percent cover in the open plots (13.9 ± 2.1%) and 4 individuals/m2 treatment (17.1 ±

1.9%) were significantly different than the percent covers in exclosures (33.5 ± 2.2%), fence treatments (26.6 ± 1.9%), and 1 individual/m2 treatment (29.1 ± 2.6%) (Tukey HSD, p-value <

0.05). In August, the percent cover in the 4 individuals/m2 treatment had dropped to 8.9 ± 1.8% which was significantly different from all other treatments (Tukey HSD, p-value < 0.03). The percent cover in the exclosures was 39.8 ± 3.2%, which also was significantly different from all other treatments (Tukey HSD, p-value < 0.04). The percent cover was 20.8 ± 1.9% in fences, 105

25.5 ± 2.8% in 1 individual/m2 treatment cages, and 28.5 ± 3.3%in open plots. In September, the exclosures were at 44.8 ± 2.6% macroalgal cover which was significantly different from the fence treatments (33.5 ± 2.5%), 1 individual/m2 treatments (26.0 ± 2.1%), 4 individuals/m2 treatments (14.7 ± 1.6%), and open plots (19.6 ± 2.7%) (Tukey HSD, p-value < 0.001). The 4 individuals/m2 treatments and the open plots were both significant different from the fence treatments (Tukey HSD, p-value < 0.01), and the 4 individuals/m2 treatments and 1 individual/m2 treatments were significantly different from each other (Tukey HSD, p-value =

0.01). In October, the exclosures had 62.6 ± 2.9% macroalgal cover, the highest cover reached during the study, the 1 individual/m2 treatments, 4 individuals/m2 treatments, fence treatments and open plots had 19.3 ± 0.9%, 10.6 ± 1.2%, 34.8 ± 2.8%, and 37.6 ± 2.2% cover, respectively.

All treatments were significantly different from each other (Tukey HSD, p-value < 0.05) except for open plots and fence treatments (Tukey HSD, p-value = 0.89). In November, the macroalgal cover had decreased in all cages, presumably due to rough weather, before measurements were conducted. The exclosures had 43.8 ± 3.6% macroalgal cover, the 1 individual/m2 treatments had

16.2 ± 2.2% cover, the 4 individuals/m2 treatments had 9.1 ± 0.9% cover, the fence treatments had 23.1 ± 2.3% cover, and the open plots had 23.6 ± 1.9% cover. The exclosure treatment cages were once again significantly different from all other treatments (Tukey HSD, p-value < 0.0001).

Cover in the 4 individuals/m2 treatments was significantly lower than cover in both fence treatments and open plots (Tukey HSD, p-value < 0.001).

Due to unequal percent cover of the macroalgae in the different cages at the start of the experiment, the change in percent cover of macroalgae also was compared (Figure 4-1B). There was a significant interaction between treatment and month according to a 2-way ANOVA (df =

16, p-value < 0.001). In July, cover in the open plots was reduced significantly more than cover

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in the 1 individual/m2 treatments (Tukey HSD, p-value = 0.004). In August, cover in the 4 individuals/m2 treatments was reduced significantly more (-30.0 ± 2.1%) than cover in the exclosures and open plots (Tukey HSD, p-value < 0.02). In addition, there was a significant difference between change in cover in the open plots and fence treatments (Tukey HSD, p- value=0.02) with cover in the fences significantly more reduced. In September, the only significantly different treatments were the 4 individuals/m2 treatments and exclosures (Tukey

HSD, p-value = 0.02) with percent cover of macroalgae more reduced in the 4 individuals/m2 treatments. In October, the exclosures had increased in percent cover of macroalgae as compared to the beginning of the experiments (5.6 ± 4.7%), and this change was significantly different from changes in the fence treatments, 1 individual/m2 treatments, and 4 individuals/m2 treatments (Tukey HSD, p-value < 0.001). In addition, the 1 individual/m2 treatments and 4 individuals/m2 treatments had been reduced significantly more than the open plots and fence treatments (Tukey HSD, p-value < 0.05). In November, the 1 individual/m2 treatments and 4 individuals/m2 treatments were significantly different from the exclosures (Tukey HSD, p-value

< 0.05) but none of the other treatments were significantly different.

The percent cover of chemically rich macroalgae, structurally defended macroalgae, macroalgae with both chemical and structural components, and cyanobacteria was compared across every month of the experiment (Figure 4-2). In June, the exclosures had significantly more chemically rich algae than the open plots, 1 individual/m2 treatments, and 4 individuals/m2 treatments (Tukey HSD, p-value <0.04). The 1 individual/m2 treatments also had significantly less algae that employ both chemical and structural defenses as compared to fence treatments

(Tukey HSD, p-value = 0.03). In July the open plots and 4 individuals/m2 treatments had significantly less chemically rich algae as compared to the exclosures and 1 individual/m2

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treatment (Tukey HSD, p-value < 0.01), and the open plots also had significantly less cover of these algae than the fence treatments (Tukey HSD, p-value = 0.006). The 4 individuals/m2 treatments, 1 individual/m2 treatments, and open plots all had significantly less structurally defended algae as compared to the fence treatments (Tukey HSD, p-value < 0.04).

In August, the 4 individuals/m2 treatments had significantly less chemically rich algae as compared to the exclosures, fence treatments, 1 individuals/m2 treatments and open plots (Tukey

HSD, p-value < 0.03). In addition, the fences had significantly less chemically rich macroalgae compared to the exclosures (Tukey HSD, p-value = 0.04). The exclosures had a significantly higher cover of cyanobacteria as compared to all other treatments (Tukey HSD, p-value < 0.001).

In September, the exclosures had significantly more chemically rich algae compared to the 4 individuals/m2 treatments, 1 individual/m2 treatments and open plots (Tukey HSD, p-value

< 0.001). The 4 individuals/m2 treatment cages and open plots had less chemically rich algae than the fence treatments (Tukey HSD, p-value < 0.002), and the 4 individuals/m2 treatments had significantly less chemically rich algae than the 1 individual/m2 treatments (Tukey HSD, p-value

< 0.03). In addition, the fence treatments had significantly higher cover of macroalgae with both chemical and structural defenses compared to the 4 individuals/m2 treatments, 1 individual/m2 treatments and open plots (Tukey HSD, p-value <0.04).

In October, the exclosure treatments had significantly higher cover of chemically rich algae compared to all other treatments (Tukey HSD, p-value < 0.001) and the fences and open plots had significantly higher cover of chemically rich algae compared to the 1 individual/m2 treatments and 4 individuals/m2 treatments (Tukey HSD, p-value < 0.001). Exclosures also had significantly higher cover of cyanobacteria than all other treatments (Tukey HSD, p-value <

0.04). The 1 individual/m2 treatments and 4 individuals/m2 treatments had significantly lower

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cover of macroalgae with both chemical and structural defenses compared to the exclosures

(Tukey HSD, p-value < 0.01).

In November, the exclosures still had significantly higher cover of chemically rich macroalgae compared to all other treatments (Tukey HSD, p-value < 0.001), and 4 individuals/m2 treatments had significantly less chemically rich macroalgae than the open plots and fence treatments (Tukey HSD, p-value< 0.03). At this time, the open plots had significantly more cyanobacteria than the fences and 4 individuals/m2 treatments (Tukey HSD, p-value <

0.01).

When looking at variation in CCA (Figure 4-3A), there was a significant interaction between treatment and month (df= 20, p < 0.001). When looking at how CCA cover varied by treatment each month, there were no significant differences between treatments in June, July, or

August. In September, the 4 individuals/m2 treatments had significantly higher cover as compared to all other treatments (Tukey HSD, p-value < 0.001). In October, the 4 individuals/m2 treatments had significantly greater cover than the exclosures, fence treatments and open plots

(Tukey HSD, p-value < 0.03). In November, the 4 individuals/m2 treatments had significantly higher cover of CCA as compared to the open plots (Tukey HSD, p-value < 0.05).

In regard to the chemically rich Lobophora spp. (Figure 4-3B), there were significant differences in percent cover by treatment (df = 4, p-value < 0.001) and month (df = 5, p-value <

0.001) but no significant interaction between treatment and month (df = 20, p-value = 0.33).

There was no significant difference between treatments in June or July, but in August the 4 individuals/m2 treatments had significantly lower cover than the open plots (Tukey HSD, p <

0.03). In September, the 4 individuals/m2 treatments had significantly less Lobophora spp. as compared to the exclosures, 1 individual/m2 treatments, and fence treatments (Tukey HSD, p-

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value < 0.02). In addition, cover in the fence treatments was significantly higher than cover in the open plots (TukeyHSD, p-value ≤ 0.05). In October, the percent cover of Lobophora spp. was significantly less in the 4 individuals/m2 treatments as compared to the exclosures and fence treatments (TukeyHSD, p-value < 0.01), and the cover in the 1 individual/m2 treatments was significantly less than the cover in the exclosures (TukeyHSD, p-value = 0.003). In November, overall reduction in cover of Lobophora spp. was seen, presumably due to storm the came through the area, but significant difference in percent cover persisted. The fence treatments had significantly higher percent cover of compared to the 1 individual/m2 treatments, 4 individuals/m2 treatments, and open plots (TukeyHSD, p-value ≤ 0.005). Furthermore, the 4 individuals/m2 treatments had significantly lower cover as compared to the exclosures

(TukeyHSD, p-value < 0.05).

In regard to another ubiquitous chemically rich alga, Dictyota spp. (Figure 4-3C), there was a significant interaction between treatment and month (df = 20, p-value < 0.001).

Specifically, the exclosures had significantly higher cover as compared to the 1 individual/m2 treatments, 4 individuals/m2 treatments and open plots in June (Tukey HSD, p-value < 0.02). In

July, the open plots had significantly lower percent cover compared to the exclosures, fence treatments, 1 individual/m2 treatments and 4 individuals/m2 treatments (Tukey HSD, p-value <

0.01), and the 4 individuals/m2 treatments had significantly lower cover compared to exclosures

(Tukey HSD, p-value < 0.05). In August, the exclosures had significantly higher cover of

Dictyota spp. compared to the 4 individuals/m2 treatments and fence treatments (Tukey HSD, p- value < 0.01). In September, the exclosures had significantly higher cover compared to all other treatments (Tukey HSD, p-value < 0.01). In October, the exclosures continued to have higher cover than all other treatments (Tukey HSD, p-value < 0.001). In addition, the 1 individual/m2

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treatments and the 4 individuals/m2 treatments had significantly lower percent cover as compared to the open plots and fence treatments (Tukey HSD, p-value < 0.05). In November, the exclosures continued to have a higher percent cover of Dictyota spp. compared to all other treatments (Tukey HSD, p-value < 0.0001), and the 4 individuals/m2 treatments had significantly lower cover of Dictyota spp. compared to the open plots (Tukey HSD, p-value < 0.05).

The PERMANOVA analyzing macroalgal community structure within the cages showed a significant interaction between treatment and month (df = 20, p = 0.001). The variations in community structure were visualized using nMDS graphs (Figure 4-4). Based on the pairwise adonis tests following the PERMANOVA, at the beginning of the experiment (June) the exclosure cages were significantly different from the 4 individuals/m2 cages and the open plots

(pairwise.adonis, p-value = 0.02). In July, at the 4-week measurements, the open plots were significantly different from the exclosures, fence treatments, and 4 individuals/m2 treatments

(pairwise.adonis, p-value ≤ 0.03). At this time point, the 4 individuals/m2 treatments were also significantly different from the exclosures (pairwise.adonis, p-value < 0.05). In August (8- weeks), the 4 individuals/m2 treatments were significantly different from the exclosures, fence treatments, open plots, and 1 individual/m2 treatments (pairwise.adonis, p-value≤0.02). The exclosures and fence treatments were also significantly different from each other

(pairwise.adonis, p-value = 0.03). In September, the 4 individuals/m2 treatments were still significantly different from the exclosures, fence treatments, and 1 individual/m2 treatments

(pairwise.adonis, p-value=0.03). In addition, the exclosure cages were significantly different from 1 individual/m2 treatments, fence treatments and open plots (pairwise.adonis, p-value =

0.03), and the fence treatments and open plots were significantly different from each other

(pairwise.adonis, p-value = 0.03). In October, the 4 individuals/m2 treatments, 1 individual/m2

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treatments and exclosures were significantly different from the fence treatments and open plots and from each other (pairwise.adonis, p-value ≤ 0.03). Only the fences and open plots were not significantly different. At the final time point, in November, the exclosures were still significantly different from all other treatments (pairwise.adonis, p-value ≤ 0.03) and the 4 individuals/m2 treatments were significantly different from the fence treatments and open plots

(pairwise.adonis, p-value = 0.01).

When examining differences in Shannon diversity index values between experimental cages, a two-way ANOVA found a significant interaction between treatment and month (df=20, p-value < 0.01). There were no significant differences in diversity among treatment in June or

July. In August, the 4 individuals/m2 treatments had significantly lower diversity compared to the exclosures (TukeyHSD, p-value = 0.01). In September and October, the 4 individuals/m2 treatments had significantly lower diversity than all other treatments (TukeyHSD, p-value <

0.05). In November, the 4 individuals/m2 treatments had significantly lower diversity than the fence cages (TukeyHSD, p-value = 0.003). In addition to differences among treatments in each month, the diversity in the 4 individuals/m2 treatments was significantly lower in August,

September, October, and November as compared to June (TukeyHSD, p-value < 0.05).

A comparison of algal canopy heights within all cages in November found that the mean canopy height of macroalgae was 0.83 ± 0.25 cm for fence treatments, 0.89 ± 0.38 cm for 1 individuals/m2 treatments, 0.42 ± 0.12 cm for 4 individuals/m2 treatments, 1.98 ± 0.43 cm for exclosures, and 1.03 ± 0.15 cm for open plots (Figure 4-5). A one-way ANOVA found significant differences among the treatments (df = 4, p-value = 0.008), with exclosures being significantly different from fence treatments (Tukey HSD, p-value = 0.04) and the 4 individuals/m2 treatments (Tukey HSD, p-value = 0.003) but similar to other treatments.

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The ratio of weight to diameter was compared for a total of 10 wild urchins, 21 urchins that had been in the 4 individuals/m2 treatments, and 8 urchins from the 1 individual/m2 treatments (Figure 4-6). Using a one-way ANOVA, significant differences were found among the three treatments (df=2, p-value<0.001). Specifically, the urchins in the 4 individuals/m2 treatments weighed significantly less per cm diameter, than those in the 1 individuals/m2 treatments (Tukey HSD, p-value = 0.035) and the newly collected wild urchins (Tukey HSD, p- value = 0.001), but the wild and 1 individual/m2 treatment urchins were not different from each other (Tukey HSD, p-value = 0.75).

Discussion

The results from this caging experiment showed that D. antillarum more thoroughly removed macroalgae from a reef as compared to roaming, herbivorous fishes. Furthermore, both percent cover and composition of algal communities differed and grouped according to the type of herbivore present in a given experimental plot; herbivorous fishes, D. antillarum, or exclusion of all but small herbivores. Higher densities of D. antillarum appeared to hasten these changes.

By the eighth week of the experiment, August, the sea urchins in the high-density D. antillarum treatments (4/m2) had reduced the percent cover of macroalgae by 30% to 8.9%, a significantly lower percent cover than both the fish only fence treatment and the low density 1 D. antillarum/m2 treatments. This result is consistent with previous research that found that D. antillarum had a faster grazing rate than herbivorous fish (Carpenter 1986). In addition, a reduction of macroalgae to <10% cover by urchin densities of 4 D. antillarum/m2 is consistent with observations from Jamaica (Edmunds and Carpenter 2001; Idjadi et al. 2010).

At the eight-week time point, the 4 individuals/m2 treatment also had a significantly different algal community as compared to all other treatments. This trend is reflected in the significantly lower amount of chemically rich algae in the high-density treatments. Based on 113

analysis of the different major algae present in the cages, these differences are likely due to the high consumption of Lobophora spp. as well as the grazing of Dictyota spp., both of which were grazed significantly more in the 4 individuals/m2 treatments than any other treatment. In addition to overall reduction of macroalgae, the significant decrease in Dictyota spp. and increase in CCA in the D. antillarum treatments (particularly the high-density treatment) is consistent with previous manipulative experiments in Jamaica and the Florida Keys that showed these same trends at ~1 D. antillarum/m2 (Chiappone et al. 2003; Maciá et al. 2007). This selective grazing of specific algal species led to a significant decrease in the algal diversity within the 4 individuals/m2 treatments by the eighth week. The trend for 4 individuals/m2 treatments to be significantly different from the fish only fence treatments persisted throughout the experiment, but by October (17 weeks) the 4 individuals/m2 treatments were also significantly different from the open plots and no longer different from the 1 individual/m2 treatments. This result was attributed to the very low natural abundance of D. antillarum on the experimental reef (< 0.01 individuals/m2) making grazing pressure on the open plots and the fences very similar. At this time, the two D. antillarum treatments had significantly lower cover of both chemically rich and chemically and structurally defended algae as compared to the other treatments. Furthermore, the

4 individuals/m2 treatments had significantly higher cover of CCA. In October, sea urchins in the

4 individuals/m2 treatments had reduced the cover of Lobophora sp. from 9.4% to 0.4% and sea urchins in the 1 individual/m2 treatment had reduced the Lobophora sp. from 13.5% to 2.9%. In comparison, the fence treatments and open plots had 9.5% and 7.4% Lobophora sp., a slight reduction from 12.6% and 15.2% at the beginning. However, the percent cover of Dictyota sp. had increased in the fences and open plots while it had been slightly reduced in the D. antillarum treatments. This finding is of importance due to the documented ability of Dictyota and

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Lobophora species to negatively impact settlement of coral species (Kuffner et al. 2006; Vieira

2019). In addition, the CCA which increased in the 4 individuals/m2 treatment has been shown to be a preferred settlement substrate for many Caribbean coral species including the endangered

Acropora species (Ritson-Williams et al. 2016). These differences can be attributed to differences in herbivore community. Diadema antillarum are known consumers of fleshy, chemically defended algae, such as Dictyota spp. and Lobophora spp., while herbivorous fishes are known to avoid these types of algae (Littler et al. 1983; Paul and Hay 1986).

Although herbivorous fishes in this experiment did not decrease the overall percent cover of macroalgae, the fish only fence treatments did have a lower canopy height as compared to exclosures, and the fence canopy height was not significantly different from the high 4 D. antillarum/m2 treatments. A lower canopy height can be attributed to the abundance of herbivorous fishes that employ a cropping mode of feeding which only removes the top portion of algae, e.g., A. bahianus, A. chirurgus, A. coeruleus and S. taeniopterus (Purcell and Bellwood

1993; Dromard et al. 2015; Adam et al. 2018). Although many parrotfish species, especially

Sparisoma species such as S. aurofrenatum and S. viride, are known to reduce the cover of upright macroalgae, in this experiment it appeared they only maintained a relatively constant cover of macroalgae (Burkepile and Hay 2010; 2011; Adam et al. 2018). This finding also may be attributed to the lack of large Scarus, such as S. coelestinus and S. quacamaia, which are important for the complete removal of many robust macroalgae (Burkepile and Hay 2011; Adam et al. 2018). This maintenance of macroalgal cover is not entirely unexpected because it was herbivorous fishes that created the original macroalgal community.

In general, the trend of macroalgal cover within the D. antillarum cages followed the hypothesized changes, with the high-density sea urchin treatment almost completely clearing the

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substrate by the end of the experiment. The same was not the case in the full herbivore exclosures, where instead of becoming dominated by palatable species of macroalgae, the established macroalgal community became more abundant and there were no new macroalgal species that established themselves, as reflected in the consistent algal diversity in the exclosures. It is likely that the established algae prevented the settlement of new algae. In fact,

Dictyota spp., the dominant alga in the cages, has been shown to use allelopathic interactions to prevent the settlement of other sessile organisms (Paul et al. 2011). In addition, macroalgae in this experiment did not increase in percent cover at the rate (1-4 weeks) or to the extent

(increases to 90% cover) that might have been expected based on previous studies (Roff and

Mumby 2012). These results were attributed to the presence of small herbivorous fishes, primarily initial phase Scarus iseri, which were often seen on the reef and even found within the cages. In Panama where large parrotfishes had been removed by overfishing, S. iseri have shown the capacity to prevent a phase shift to algal dominance (Kuempel and Altieri 2017). They, i.e. the smallest fish, did not succeed in preventing algal overgrowth in these exclosures, likely because the mesh size prevented all but the smallest fish from entering, but it is possible that they consumed detectable new amounts of algae.

In addition to differences between the herbivore types, it is also important to look at how living in different densities affects the D. antillarum themselves. Although there was no difference in the average test diameter of the urchins in the different experimental cages, there was a significant difference in the weights and ratios of test diameters to weights. Previous experiments found that D. antillarum will shrink their test size when starved (Levitan 1989). It is unclear if shrinking occurred in this experiment due to the inability to gather pre-experiment test diameters, but what is clear is that the sea urchins in the high-density treatments weighed less.

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This result, in combination with the almost entirely cleared macroalgal community, indicates that it is likely that these urchins were being starved by the end of the experiment. This finding may serve as a cautionary message in regard to the detrimental effects of urchins living in high densities. In this experiment, the “starving” urchins were observed consuming organisms other than algae; primarily Briareum asbestinum and in one case another D. antillarum. These results indicate that although recovery of D. antillarum and increases in density may have immediate positive effects in clearing more substrate of macroalgae for possible coral settlement, it also is possible that D. antillarum may consume any newly settled corals, either purposefully or inadvertently. This behavior would make it necessary for corals to settle and grow at a rate that allows them to escape consumption, a feat that may not be possible in all areas of the Caribbean or by all coral species.

Although this and other studies have expounded on the positive influences of increases in

D. antillarum, it is important to examine interactions between herbivorous fishes and sea urchins.

For example, increases in D. antillarum populations in St. Croix, USVI led to herbivorous fishes, both parrotfish and surgeonfish, avoiding the area (Onufryk et al. 2018). This relationship also was observed in the 1980s with parrotfish moving onto a reef and beginning to fill the role of primary herbivore when D. antillarum were experimentally removed and leaving when sea urchins returned (Hay and Taylor 1985). The threshold for coexistence of herbivorous fishes and

D. antillarum appears to be ~7 sea urchins/m2 (Hay and Taylor 1985). The potential for density effects should be incorporated into future research elucidating how different herbivores may differentially affect algal removal and settlement of corals and other sessile organisms.

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Table 4-1. Mean numbers of fish ± standard errors (SE) for each species found along transects. *indicates roaming herbivores Fish Species June July August September October November

Abudefduf saxatilis 0.0 0.0 0.8 ± 0.6 1.0 ± 0.6 0.0 0.0

Acanthurus bahianus* 0.0 0.8 ± 0.6 0.8 ± 0.6 0.2 ± 0.2 0.0 0.4 ± 0.4

Acanthurus chirurgus* 1.8 ± 0.8 0.0 0.0 0.0 3.0 ± 1.3 0.2 ± 0.2

Acanthurus coeruleus* 2.4 ± 0.9 3.6 ± 1.0 3.6 ± 1.6 4.8 ± 1.0 1.4 ± 0.4 1.8 ± 0.6

Aluterus scriptus 0.2 ± 0.2 0.0 0.0 0.0 0.0 0.0 Archosargus 0.0 0.2 ± 0.2 0.0 0.0 0.0 0.0 probatocephalus Aulostomus maculatus 0.2 ± 0.2 0.0 0.2 ± 0.2 0.4 ± 0.4 0.0 0.0

Balistes vetula 0.2 ± 0.2 0.2 ± 0.2 0.4 ± 0.2 0.0 0.2 ± 0.2 0.0

Bodianus rufus 0.0 0.0 0.2 ± 0.2 0.0 0.0 0.0

Cantherhines macrocerus 0.0 0.0 0.0 0.2 ± 0.2 0.0 0.0

Cantherhines pullus 0.0 0.0 0.4 ± 0.4 0.0 0.0 0.0

Caranx ruber 0.6 ± 0.2 0.4 ± 0.2 2.4 ± 0.4 1.2 ± 0.4 0.2 ± 0.2 0.0

Cephalopholis cruentata 0.4 ± 0.2 0.2 ± 0.2 1.4 ± 0.5 1.0 ± 0.3 0.6 ± 0.4 0.4 ± 0.2

Chaetodon capistratus 0.6 ± 0.4 0.4 ± 0.2 1.8 ± 0.5 2.4 ± 0.8 0.8 ± 0.4 0.6 ± 0.6

Chaetodon striatus 1.4 ± 0.7 0.4 ± 0.2 0.0 0.2 ± 0.2 0.4 ± 0.4 0.0

Chromic cyanea 9.6 ± 4.0 0.0 0.0 2.6 ± 0.6 2.6 ± 0.9 7.0 ± 3.5

Dasyatis americana 0.0 0.0 0.0 0.2 ± 0.2 0.0 0.0

Epinephelus guttatus 0.0 0.4 ± 0.2 0.2 ± 0.2 0.2 ± 0.2 0.0 0.0

Epinephelus striatus 0.0 0.0 0.0 0.2 ± 0.2 0.4 ± 0.2 0.4 ± 0.2

Gramma loreto 4.0 ± 1.4 0.0 0.0 0.0 0.0 4.2 ± 2.9

Haemulon flavolineatum 0.6 ± 0.6 1.0 ± 0.6 1.0 ± 0.5 1.2 ± 0.6 0.0 0.0

Haemulon plumierii 0.0 0.2 ± 0.2 0.0 0.0 0.0 0.0

Haemulon sciurus 1.2 ± 1.2 0.6 ± 0.4 0.2 ± 0.2 0.6 ± 0.4 0.4 ± 0.4 0.2 ± 0.2

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Table 4-1 cont. Fish Species June July August September October November

Halichoeres bivittatus 0.4 ± 0.4 0.0 0.0 0.0 0.0 0.0

Holacanthus ciliaris 0.0 0.0 0.2 ± 0.2 0.0 0.0 0.0

Holocentrus adscensionis 0.6 ± 0.4 1.8 ± 0.4 2.0 ± 0.8 2.8 ± 1.0 1.2 ± 0.5 0.2 ± 0.2 Kyphosus 0.6 ± 0.4 0.2 ± 0.2 0.6 ± 0.2 1.4 ± 1.4 0.8 ± 0.8 0.0 sectatrix/bigibbus* Lactophrys sp. 0.0 0.0 0.0 0.0 0.2 ± 0.2 0.0

Lutjanus analis 0.0 0.0 0.2 ± 0.2 0.0 0.0 0.0

Lutjanus apodus 0.0 4.2 ± 2.0 5.0 ± 3.8 6.0 ± 2.9 0.0 0.0

Melichthys niger* 1.2 ± 1.0 1.0 ± 0.2 0.2 ± 0.2 1.0 ± 0.6 0.2 ± 0.2 0.0

Microspathodon chrysurus 0.0 0.0 0.0 0.4 ± 0.2 0.0 0.0

Mycteroperca tigris 0.0 0.0 0.2 ± 0.2 0.2 ± 0.2 0.2 ± 0.2 0.0

Ocyurus chrysurus 0.2 ± 0.2 0.8 ± 0.4 0.0 0.0 1.2 ± 0.8 0.0

Pomacanthus paru 0.4 ± 0.4 0.4 ± 0.2 0.0 0.0 0.4 ± 0.4 0.0

Pseudupeneus maculatus 2.8 ± 1.9 2.2 ± 0.9 0.8 ± 0.8 0.0 0.0 0.0

Scarus iseri* 0.0 0.4 ± 0.4 5.4 ± 2.2 7.0 ± 3.7 0.8 ± 0.4 0.0

Scarus taeniopterus* 3.0 ± 1.1 2.2 ± 0.7 3.4 ± 1.0 0.2 ± 0.2 10.0 ± 4.2 9.0 ± 2.6

Scarus vetula* 0.0 0.4 ± 0.2 1.2 ± 0.2 0.2 ± 0.2 1.0 ± 0.5 0.4 ± 0.2

Sparisoma aurofrenatum* 3.0 ± 0.4 3.6 ± 1.1 4.2 ± 0.9 2.4 ± 0.9 1.4 ± 0.4 3.4 ± 1.3

Sparisoma rubripinne* 0.0 0.0 0.0 0.2 ± 0.2 0.8 ± 0.8 0.0

Sparisoma viride* 2.0 ± 0.9 2.0 ± 0.8 1.0 ± 0.5 1.8 ± 0.5 1.4 ± 0.9 1.4 ± 0.5

Sphyraena barracuda 0.2 ± 0.2 0.4 ± 0.2 0.0 0.0 0.0 0.0

Stegastes adustus 4.6 ± 1.2 0.0 0.0 1.0 ± 0.5 0.0 0.0

Stegastes partitus 2.6 ± 0.9 0.4 ± 0.2 0.0 0.2 ± 0.2 0.0 0.0

Stegastes planifrons 0.0 0.0 0.2 ± 0.2 1.6 ± 0.4 0.0 0.0

Stegastes variabilis 0.0 1.4 ± 0.5 2.4 ± 0.7 0.0 1.6 ± 0.7 3.8 ± 3.8

Thalassoma bifasciatum 1.2 ± 0.7 0.6 ± 0.4 0.6 ± 0.4 0.0 0.6 ± 0.4 0.4 ± 0.4

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Table 4-2. Percent cover of macroalgae by genera for each month. Superscripts represent defenses: 1. Cyanobacteria, 2. Chemically rich, 3. Chemically rich and structurally defended, 4. Structurally defended, 5. Unknown June Macrolgae Exclosure Fence Lid 1/m2 Lid 4/m2 Open Cyanobacteria1 0.87 ± 0.54 0.07 ± 0.07 0.14 ± 0.14 0.00 0.13 ± 0.09 Dichothrix spp.1 0.00 0.00 0.00 0.00 0.00 Dictyota spp.2 29.00 ± 3.91 18.39 ± 2.57 16.32 ± 2.43 9.56 ± 2.42 10.02 ± 1.44 Laurencia spp.2 0.00 0.00 0.00 0.00 0.00 Stypopodium zonale2 11.57 ± 3.06 13.87 ± 2.04 13.13 ± 3.16 18.69 ± 3.17 14.75 ± 0.93 Lobophora spp.2 13.61 ± 3.43 12.56 ± 1.71 13.52 ± 4.44 9.42 ± 1.69 15.22 ± 2.85 Galaxaura spp.3 0.07 ± 0.07 0.57 ± 0.18 0.00 0.00 0.00 Halimeda spp.3 0.62 ± 0.22 1.62 ± 0.49 0.56 ± 0.25 1.13 ± 0.35 1.14 ± 0.36 Turbinaria spp.4 0.14 ± 0.14 0.00 0.07 ± 0.07 0.00 0.00 Sargassum spp.4 0.00 0.00 0.00 0.07 ± 0.07 0.27 ± 0.27 Chrysophyte5 0.00 0.00 0.00 0.00 0.00 Filamentous Green5 0.00 0.00 0.00 0.00 0.00 CCA 0.54 ± 0.26 0.56 ± 0.49 0.36 ± 0.19 0.07 ± 0.07 0.20

Table 4-2 cont. July Macrolgae Exclosure Fence Lid 1/m2 Lid 4/m2 Open Cyanobacteria1 0.00 0.07 ± 0.07 0.00 0.00 0.00 Dichothrix spp.1 0.00 0.00 0.00 0.00 0.00 Dictyota spp.2 17.17 ± 2.58 10.75 ± 1.73 12.89 ± 1.77 9.52 ± 1.22 1.80 ± 0.28 Laurencia spp.2 0.00 0.00 0.00 0.00 0.00 Stypopodium zonale2 2.38 ± 0.91 2.06 ± 0.61 1.45 ± 0.65 1.12 ± 0.74 1.73 ± 0.57 Lobophora spp.2 11.93 ± 3.13 12.62 ± 1.70 14.14 ± 4.01 6.35 ± 1.79 10.07 ± 2.20 Galaxaura spp.3 0.07 ± 0.07 0.14 ± 0.09 0.34 ± 0.21 0.00 0.00 Halimeda spp.3 0.97 ± 0.33 0.54 ± 0.22 0.27 ± 0.15 0.14 ± 0.10 0.27 ± 0.18 Turbinaria spp.4 0.07 ± 0.07 0.21 ± 0.11 0.00 0.00 0.00 Sargassum spp.4 0.00 0.20 ± 0.20 0.00 0.00 0.00 Chrysophyte5 0.89 ± 0.52 0.00 0.00 0.00 0.00 Filamentous Green5 0.00 0.00 0.00 0.00 0.00 CCA 0.00 0.41 ± 0.15 0.27 ± 0.15 0.42 ± 0.15 0.00

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Table 4-2 cont. August Macrolgae Exclosure Fence Lid 1/m2 Lid 4/m2 Open Cyanobacteria1 8.71 ± 1.95 0.14 ± 0.09 1.41 ± 0.95 0.00 0.07 ± 0.07 Dichothrix spp.1 0.00 0.00 0.00 0.00 0.40 ± 0.18 Dictyota spp.2 17.801 ± 2.46 7.82 ± 0.96 13.05 ± 2.52 5.89 ± 0.98 13.09 ± 2.59 Laurencia spp.2 0.00 0.00 0.00 0.00 0.00 Stypopodium zonale2 1.54 ± 0.92 0.48 ± 0.27 0.48 ± 0.23 0.72 ± 0.40 0.74 ± 0.45 Lobophora spp.2 10.91 ± 2.84 11.51 ± 1.99 9.82 ± 2.80 1.87 ± 0.84 12.49 ± 2.91 Galaxaura spp.3 0.00 0.07 ± 0.07 0.00 0.00 0.00 Halimeda spp.3 0.49 ± 0.23 0.48 ± 0.23 0.47 ± 0.33 0.21 ± 0.21 1.60 ± 0.61 Turbinaria spp.4 0.28 ± 0.19 0.28 ± 0.15 0.28 ± 0.16 0.21 ± 0.15 0.07 ± 0.07 Sargassum spp.4 0.00 0.00 0.00 0.00 0.07 ± 0.07 Chrysophyte5 0.07 ± 0.07 0.00 0.00 0.00 0.00 Filamentous Green5 0.00 0.00 0.00 0.00 0.00 CCA 0.00 0.07 ± 0.07 0.00 0.35 ± 0.35 0.00

Table 4-2 cont. September Macrolgae Exclosure Fence Lid 1/m2 Lid 4/m2 Open Cyanobacteria1 3.21 ± 1.08 0.84 ± 0.42 1.96 ± 1.13 0.79 ± 0.36 0.07 ± 0.07 Dichothrix spp.1 0.00 0.00 0.00 0.00 1.94 ± 0.56 Dictyota spp.2 25.87 ± 2.84 13.58 ± 2.30 14.28 ± 1.44 12.51 ± 1.69 9.80 ± 1.62 Laurencia spp.2 0.00 0.00 0.00 0.00 0.00 Stypopodium zonale2 0.61 ± 0.26 0.62 ± 0.35 0.48 ± 0.20 0.00 0.13 ± 0.13 Lobophora spp.2 10.88 ± 2.01 14.83 ± 1.98 8.19 ± 1.75 0.64 ± 0.23 6.60 ± 1.63 Galaxaura spp.3 0.00 0.14 ± 0.14 0.07 ± 0.07 0.14 ± 0.09 0.00 Halimeda spp.3 2.12 ± 0.58 3.37 ± 0.73 0.92 ± 0.41 0.57 ± 0.36 1.07 ± 0.55 Turbinaria spp.4 0.07 ± 0.07 0.14 ± 0.14 0.07 ± 0.07 0.00 0.00 Sargassum spp.4 0.00 0.00 0.00 0.00 0.00 Chrysophyte5 1.99 ± 1.17 0.00 0.00 0.00 0.00 Filamentous Green5 0.00 0.00 0.00 0.00 0.00 CCA 0.27 ± 0.11 0.48 ± 0.15 0.71 ± 0.28 5.13 ± 0.96 0.27 ± 0.20

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Table 4-2 cont. October Macrolgae Exclosure Fence Lid 1/m1 Lid 4/m2 Open Cyanobacteria1 6.84 ± 1.87 0.21 ± 0.11 1.66 ± 0.64 0.35 ± 0.15 0.47 ± 0.26 Dichothrix spp.1 0.00 0.00 0.00 0.00 2.40 ± 0.78 Dictyota spp.2 39.07 ± 3.12 22.77 ± 3.14 13.845 ± 1.06 9.20 ± 1.24 25.75 ± 1.49 Laurencia spp.2 0.00 0.00 0.00 0.00 0.00 Stypopodium zonale2 0.28 ± 0.15 0.07 ± 0.07 0.00 0.00 0.00 Lobophora spp.2 13.36 ± 3.01 9.50 ± 1.75 2.86 ± 1.02 0.42 ± 0.18 7.40 ± 2.18 Galaxaura spp.3 0.48 ± 0.21 0.21 ± 0.15 0.00 0.07 ± 0.07 0.07 ± 0.07 Halimeda spp.3 2.58 ± 0.54 2.01 ± 0.45 0.89 ± 0.27 0.51 ± 0.36 1.47 ± 0.42 Turbinaria spp.4 0.00 0.07 ± 0.07 0.00 0.00 0.00 Sargassum spp.4 0.00 0.00 0.00 0.00 0.07 ± 0.07 Chrysophyte5 0.00 0.00 0.00 0.00 0.00 Filamentous Green5 0.00 0.00 0.00 0.00 0.00 CCA 0.62 ± 0.21 2.23 ± 0.72 3.19 ± 0.92 5.71 ± 1.37 0.27 ± 0.15

Table 4-2 cont. November Macrolgae Exclosure Fence Lid 1/m1 Lid 4/m2 Open Cyanobacteria1 0.98 ± 0.77 0.17 ± 0.17 1.40 ± 0.82 0.00 0.40 ± 0.29 Dichothrix spp.1 0.00 0.00 0.00 0.00 3.07 ± 0.79 Dictyota spp.2 36.13 ± 3.98 14.112 ± 2.21 12.54 ± 2.25 7.92 ± 0.64 17.95 ± 1.92 Laurencia spp.2 0.59 ± 0.38 0.00 0.00 0.11 ± 0.11 0.00 Stypopodium zonale2 0.00 0.34 ± 0.23 0.00 0.00 0.13 ± 0.09 Lobophora spp.2 3.96 ± 1.95 5.33 ± 1.02 0.10 ± 0.10 0.00 0.73 ± 0.23 Galaxaura spp.3 0.00 0.00 0.00 0.00 0.00 Halimeda spp.3 1.97 ± 0.59 2.84 ± 0.67 1.72 ± 0.62 1.01 ± 0.46 1.27 ± 0.46 Turbinaria spp.4 0.20 ± 0.13 0.26 ± 0.18 0.40 ± 0.30 0.00 0.00 Sargassum spp.4 0.00 0.00 0.00 0.00 0.00 Chrysophyte5 0.00 0.00 0.00 0.00 0.00 Filamentous Green5 0.00 0.00 0.00 0.10 ± 0.10 0.00 CCA 0.30 ± 0.21 2.64 ± 0.90 0.70 ± 0.22 3.44 ± 1.42 0.13 ± 0.09

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Figure 4-1. Total percent cover (A) and change in percent cover (B) of macroalgae in every month of the 5-month caging experiment. Colors represent different experimental treatments. Error bars represent ± standard error.

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Figure 4-2. Percent cover of different types of algae divided into CCA, chemically and structurally defended, chemically defended, structurally defended, and cyanobacteria in each month in each experimental treatment. n= number of cages for each treatment.

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Figure 4-2 cont.

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A B

Figure 4-3. Percent cover of CCA (A), Lobophora spp. (B), and Dictyota spp. (C), over time in each experimental treatment. Error bars represent ± standard error.

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Figure 4-4. Nonmetric multidimensional scaling representations of the algal communities in month and treatment type.

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Figure 4-4 cont.

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Figure 4-5. Height of algae found in each treatment type at the conclusion of the experiment. Letters indicate statistically significant differences. Error bars represent ± standard error.

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Figure 4-6. Ratio of Diadema antillarum mass to test diameter at the conclusion of the experiment. Urchin came from the 4 individuals/m2 treatments, 1 individual/m2 treatments, and “wild” urchins from a nearby reef. Letters represent statistically significant differences. Error bars represent ± standard error.

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CHAPTER 5 ANALYSIS OF FEEDING CHOICE OF HERBIVOROUS FISH AND Diadema antillarum IN LITTLE CAYMAN

Introduction

Throughout the world, coral reefs are in decline with an approximate 30% of reefs already either lost or degraded, and a further 75% of reefs under threat by local and global stressors (Barbier et al. 2011; Burke et al. 2011). Regionally, there have been differences in the level of threat with nearly twice as many reefs in the Atlantic considered threatened or highly threatened as compared to the Indo-Pacific (Burke et al. 2011). Specifically, the Caribbean has experienced losses in coral cover ranging from 50% to 80% since the 1970s, and these losses are only expected to increase as stressors persist and increase (Cramer et al. 2020; de Bakker et al.

2017). Furthermore, there have been drastic, regional differences in the ability of reefs to recover. While studies have shown recovery in 46% of Indo-Pacific reefs following disturbances, there have been no observations of recovery on Caribbean coral reefs (Roff and Mumby 2012).

Worldwide, and particularly in the Caribbean, the loss of herbivores has been implicated as one of the largest threats to coral reef health, and it has even been called the primary cause of reduced ecosystem function on coral reefs (Holbrook et al. 2016; Shantz et al. 2019). The loss of herbivores in the Caribbean, both herbivorous fishes and the sea urchin Diadema antillarum, has been particularly devastating due to the inherently lower herbivore biomass, and lower herbivore diversity that leads to lower functional redundancy as compared to the Indo-Pacific (Bonaldo et al. 2014; Burkepile and Hay 2011; Mouillot et al. 2014; Roff and Mumby 2012). The biomass of parrotfish and surgeonfish in the Caribbean is half and a quarter of that found in the Indo-Pacific, respectively (Roff and Mumby 2012). Furthermore, while the Indo-Pacific is home to 3,689 reef fish species the Caribbean only has 891 species total, leading to the Indo-Pacific having double the number of species for each functional role as compared to the Caribbean (Mouillot et al.

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2014). This disparity applies to the herbivorous fishes, with the Indo-Pacific being home to 57 species of parrotfishes while the Caribbean has only 28 species (Kulbicki et al. 2018). Of the many influences, it is the lack of functional redundancy that has been credited as one of the primary causes for the lower resilience of Caribbean coral reefs (Bonaldo et al. 2014; Roff and

Mumby 2012). Functional redundancy differs not only on the regional scale but also between individual Caribbean reefs based on the composition of the herbivore community.

The two main types of herbivorous fishes in the Caribbean are parrotfishes, particularly

Sparisoma parrotfish, and surgeonfish within the genus Acanthurus. The reliance on fishing for subsistence throughout the Caribbean has led to widespread overfishing and contributed to declines in coral reef health (Jackson et al. 2014). Unfortunately, the fishes that are the most heavily targeted also are the largest bodied fishes, including many parrotfish. These fishes are the most effective herbivores due to their ability to remove greater portions of macroalgae and sediment (Adam et al. 2018; Edwards et al. 2014; Holbrook et al. 2016). According to modeling studies, in ecosystems where parrotfish are not exploited, they are capable of grazing 40% of a coral reef, the level that supports a trajectory towards high coral cover, but when overfished, they can only graze 5% of the reef (Mumby et al. 2007).

In addition to herbivorous fishes, D. antillarum were historically a key herbivore on

Caribbean coral reefs and, prior to their mortality, they were known to entirely clear habitats of algae (Lessios 2016; Ogden et al. 1973). Beginning in Panama in 1983, a putative pathogen began to affect D. antillarum and proceeded to spread throughout the Caribbean over the next 13 months (Lessios 2016). Upon reaching a new area this pathogen quickly killed urchins and by the time the pathogen dissipated between 93% and 100% of all D. antillarum were dead (Lessios

1988). The loss of the majority of D. antillarum in combination with the overfishing of large

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herbivorous fishes had detrimental effects on Caribbean coral reefs (Jackson et al. 2014; Kramer et al. 2015).

The primary consequence of the loss of both herbivorous fishes and D. antillarum was an increase in macroalgal cover (de Bakker et al. 2017; Edwards et al. 2014). Prior to the loss of these herbivores, macroalgae was considered controlled, with herbivores consuming all of the algal biomass produced on a daily basis (Fong and Paul 2011; Jackson et al. 2014; Steneck et al.

2018). Once D. antillarum were reduced on coral reefs where overfishing had lowered abundances of herbivorous fishes, macroalgal cover more than doubled at many sites, and macroalgal biomass increased by 300-3,000% (Carpenter 1988; Lessios 1988; Levitan 1988;

Liddell and Ohlhorst 1986). This increased macroalgal cover and biomass has persisted in modern surveys (de Bakker et al. 2017; Jackson et al. 2014).

Increases in macroalgae have been linked to a variety of detrimental effects on coral reefs, both direct and indirect. Direct effects include increased mortality and decreased growth, fecundity, and recruitment of corals (Box and Mumby 2007; Fong and Paul 2011; McCook et al.

2001; Jones et al. 2016). Indirect effects include the loss of suitable substrate for recruitment of corals and other sessile organisms further impeding recovery of coral reefs (Ritson-Williams et al. 2009; Kuffner et al. 2006). All of this stress has contributed to declines in coral cover from~33% to ~16% and increases in macroalgal cover from ~7% to 24% across the Caribbean since the 1970s (Jackson et al. 2014).

Each type of herbivore in the Caribbean is essential to the functioning of the reef. On a

“healthy” Caribbean coral reef, D. antillarum act as the generalists of the system by eating most of the algae they encounter and eating the macroalgae the fishes find unpalatable (Adam et al.

2015; Littler et al. 1983). Among fishes, Sparisoma parrotfish primarily target macroalgae,

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Scarus parrotfish suppress turf algae, and surgeonfish will eat many things including detritus, turf and macroalgae (Adam et al. 2015; Dromard et al. 2015). Models suggest that before the mass die off of D. antillarum, Caribbean coral reefs had the resilience to recover from disturbance and did not shift to an algal-dominated state but since the 1980s this resilience has been lost (Mumby et al. 2007).

Although coral reefs remain in decline, there have been some positive changes in herbivore populations. Some areas have seen increases in D. antillarum, and even these slight increases have been linked to decreases in macroalgal cover and increases in coral cover and recruitment (Edmunds and Carpenter 2001; Idjadi et al. 2010; Kramer et al. 2015). Along with increases in D. antillarum, increased prevalence of marine protected areas and bans on the take of large herbivorous fishes have been linked to increases in both biomass and density of herbivorous fish which in some areas has led to decreases in macroalgae (Edwards et al. 2014;

Halpern 2003; Jackson et al. 2014).

Increases in herbivores have led to questions about the capacity of different herbivores to consume different types of macroalgae. In an effort to deter consumption, algae employ a number of defenses including structural defenses, such as calcification, leathery consistency, and general toughness, as well as secondary metabolites that act as a chemical defense (Fong and

Paul 2011; Littler et al. 1983). Furthermore, many species employ both types defenses that act in a synergistic manner (Hay et al. 1994). Herbivores, in turn, react variably to these defenses with many herbivorous fishes selectively avoiding chemically defended algae, D. antillarum often avoiding structurally defended species, and all herbivores avoiding algae that employ both chemical and structural defenses (Campbell et al. 2014; Fong and Paul 2011; Hay et al. 1994;

Littler et al. 1983). Understanding how specific Caribbean herbivores interact with macroalgae is

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particularly important due to the differing capacities of macroalgae to affect sessile organisms, particularly corals. Many macroalgal species, particularly Lyngbya, Dictyota, and Lobophora species, have been shown to be detrimental to the settlement and survival of coral larvae

(Chadwick and Morrow 2011; Kuffner et al. 2006; Vieira 2019). These differences in interactions carry on to interactions between established corals and macroalgae, with strength of interaction varying among species (Fong and Paul 2011). Because of these differences, it is vital to understand how the composition of macroalgal communities is related to the variety and abundance of herbivores in the ecosystem.

One important step toward understanding the key interactions is to study differences in choice of foods among herbivorous species. Better understanding of feeding preferences allows for better predictions of algal communities under different herbivore regimes. In addition to looking directly at the feeding preferences of different herbivores, experiments described here allow for examination of complementarity and redundancy.

Methods

In an effort to better understand dietary preferences of different herbivores, a series of feeding assays were conducted. Feeding assays were conducted using nine macroalgal species and one species of seagrass (collectively referred to as macrophytes) collected around Little

Cayman, Cayman Islands (Figure 5-1). Lobophora sp. and Red algae 1 were collected on the south side of the island in Head O’Bay at a depth of 1 to 2 m (19°40'29.3"N 80°03'31.4"W). Red algae 1 was chosen due to its similarity to Acanthophora sp., a known palatable alga. The seagrass Thalassia testudinum and the macroalgae Dictyota sp., Galaxaura sp., Halimeda tuna,

Laurencia sp. 1, Laurencia sp. 2, Padina sp. and Turbinaria sp. were collected at a depth of 1 to

3 meters in Grape Tree Bay (19°41.777’ -80°03.685’). These macrophytes were selected due to their high abundance in the area as well as their varying defenses. To examine the role of 135

defenses on feeding preferences of herbivorous fish and D. antillarum, these species were categorized as palatable, chemically defended, structurally defended, or both chemically and structurally defended. These determinations were made based on previously published literature

(Table 5-1). The chemically defended algae consisted of Dictyota sp., Lobophora sp., Laurencia sp. 1, and Laurencia sp. 2 while Turbinaria sp. was the only structurally defended alga and

Galaxaura sp. and Halimeda tuna were both chemically and structurally defended (Littler et al.

1983; Paul and Hay 1986). Padina sp. and Thalassia testudinum were considered palatable while

Red algae 1 was unknown (Paul and Hay 1986). Feeding assays were conducted both in-situ with naturally occurring herbivorous fish populations and in the laboratory with D. antillarum.

Feeding assays with D. antillarum were divided into no-choice and choice assays.

Fish Feeding Assays

Fish feeding assays were conducted at a shallow site (1-2 m) in Grape Tree Bay

19°41.697’ -80°03.867’) and at a deep site (16-18 m) near a deep coral nursery (19°42.022’

-80°03.660’) in September and October 2017. The shallow site was on the shoreward side of a reef crest near a natural channel. This site saw daily temporal surges in abundance of parrotfish, chubs, and surgeonfish beginning in the late afternoon. Shallow feeding assays were set up along a 35-m-long area at the reef edge where the transition to a sandy bottom began. This location allowed lines to remain underwater throughout the tidal cycle. The makeup of the deep site was a spur and groove reef with no observed temporal differences in fish abundances. Feeding assays were placed at the top of reef spurs so that foods were easily accessible.

The taxa used for the fish feeding assays were Dictyota sp., Laurencia sp. 1, Laurencia sp. 2, Lobophora sp., Turbinaria sp., Galaxaura sp., Halimeda tuna, Red algae 1, and the seagrass Thalassia testudinum. Fish feeding assays were conducted by attaching pieces of algae or seagrass to 80 cm of braided, 3-strand, light blue polypropylene line and then attaching this 136

line to the substrate. Lines were attached to the substrate by twisting open a portion of the line and placing this open portion over a piece of dead coral or heavy rubble. All pieces of macrophyte were approximately 4 cm in length. Four different types of macrophytes were placed on each line in each assay with the exception of one assay where it was necessary to place 5 pieces on the lines to ensure all species were used in multiple assays. The pieces on the line were suspended vertically in the water column where they were accessible to herbivorous fishes.

Pieces were evenly spaced along the top two thirds of the line, and location of each species was haphazardly chosen to limit bias due to placement in the water column or proximity of another macrophyte that may have attracted herbivores. Each type of algae and the seagrass were used in

3-4 feeding assays at each depth in different combinations. A total of 7 feeding assays were conducted at each depth and 15 lines were used in each feeding assay.

After 1 and 24 h the macrophyte pieces on each line were examined and scored as either eaten (completely gone) or uneaten and after 24 h all lines were collected. These time periods were chosen to determine the most preferred macrophyte, those eaten after 1 h, and all macrophytes that herbivorous fishes ate in 24 h. Consumption after 24 h was analyzed in R (R

Core Team 2015) using a G-test (Hervé 2019) followed by a Fisher’s exact test (R Core Team

2015). This methodology was chosen as it is a standard laid out in the literature (Paul and Hay

1986).

In October 2018, GoPro® cameras were set up at each site next to a set of three lines.

Two cameras were set up for each feeding trial so that a total of six lines were observed. These cameras recorded approximately 1 h of video to document what fishes were eating each type of macrophyte and the number of bites each fish took on each macrophyte (standardized to bites per h). With these values, the primary herbivore on each macrophyte was identified (which fish ate

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the most of each macrophyte) and which macrophyte each fish species ate the most of

(percentage of total bites taken standardized to 1 h). This information was used to examine dietary preference for each species. Surveys were done at each site to determine the natural fish assemblage. Surveys were conducted by laying out a transect along the portion of the reef on which the fish feeding assays were conducted and documenting all fish within 1 m of each side of the transect line and in the water column that extended approximately 3 m from the bottom at the deep site or to the surface at the shallow site. At the shallow site, a set of surveys were conducted over one day along 42 m of the reef. Two surveys were run at 10:30 am and two at

4:00 pm to examine the fish migrating over the reef during the day. At the deep site, three 30 m belt transects were conducted one afternoon at 2:00 pm.

Diadema antillarum Feeding Assay

A series of feeding assays were conducted using D. antillarum with the purpose of better understanding dietary preferences of this sea urchin. These feeding assays were divided into no- choice assays which presented a piece from a single macrophyte for consumption and choice feeding assays that gave pieces from four different species. The majority of feeding assays were conducted in December 2017, with a smaller series of choice assays being run in May 2018. All

D. antillarum for feeding assays were collected from a shallow rocky site known as McCoy’s

(19°40.463’ -80°05.848’). This site was chosen because it had a high abundance of D. antillarum and could be accessed from the beach. The mean test diameter ± standard error of all D. antillarum was 6.5 ± 1.5 cm. A total of 30 urchins was collected from McCoy’s and held in the outdoor laboratory at the Central Caribbean Marine Institute (CCMI). This area was covered by shade cloth that created a light regime similar to what would be found at approximately 10 m underwater. D. antillarum feeding assays were conducted in 4-liter, flow-thru tanks with running seawater. During the course of the feeding experiments, 15 urchins were used for each daily trial, 138

with no urchin used on two consecutive days. While in the holding tank, urchins were fed a mix of palatable algae to ensure they were not starving, which has been shown to make them less selective (Cronin and Hay 1996).

To set up the experiment, each urchin was collected from the holding tank, its test diameter was measured to the nearest half centimeter using calipers, and then it was placed in a newly cleaned, flow-through container. Once all urchins were placed in their containers and all macrophytes were weighed, the appropriate pieces of macrophyte were placed in the container.

After 24 hours the trial was considered complete, each urchin was removed, and any remaining macrophytes were collected and reweighed. No-herbivore controls were set up for each trial in which pieces of macrophyte similar to those used in the trial were placed in individual containers with running water and no urchins. All species were weighed at the beginning and end of the experiment to account for any natural growth or loss during the trial. The methodology used for weighing macrophytes was to collect a piece of macrophyte of approximately the correct size and weight, spin it 15 times in a salad spinner, weigh it using a balance to ensure all pieces were of desired weight, and then immediately place it in a container full of sea water. To determine the amount consumed and correct for possible natural changes, we used Equation 5-1:

퐶푓 푇푖 ∗ ( ) − 푇푓 (5-1) 퐶푖

In this equation, Ti is the initial algal mass, Tf is the final algal mass, Ci is the initial control mass, and Cf is the final control mass (Lockwood III 1998; Erickson et al. 2006).

No-choice feeding assays

No-choice feeding assays with D. antillarum were conducted using the same macrophytes used in the fish feeding assays; Dictyota sp., Laurencia sp. 1, Laurencia sp. 2,

Lobophora sp., Turbinaria sp., Galaxaura sp., Halimeda tuna, Red algae 1, and the seagrass

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Thalassia testudinum. For these no-choice assays, each urchin was given a single species of algae or seagrass each day, and all urchins were given the same species each day. This macrophyte piece, once weighed, was placed in the corner of the tank furthest away from the urchin. After use in a feeding trial, urchins were exchanged for urchins that had been resting and feeding in the large holding tank. Four g (± 0.3 g) of each species were used in each trial except for the Laurencia sp. 1 and Red Algae 1 which were completely consumed when only 4 g was provided so 20 g were used and the T. testudinum trials where 8 grams were used. The smaller amounts of macrophytes were used because they were never fully consumed, and some of macrophytes had limited abundances. In fact, 4 g was chosen as the starting weight based on previous literature that stated D. antillarum could eat ~3 g per day (Lewis 1964; Tuya et al.

2001). The amounts of each macrophytes consumed (g) in the no-choice assays were compared with a one-way ANOVA followed by Tukey Tests (R Core Team 2015). The possible influence of D. antillarum size was examined using Pearson’s Correlation tests (R Core Team 2015).

Choice feeding assays

For all D. antillarum choice feeding assays, pieces of four different macrophyte species of equal weight were placed in each container with an urchin. The four different taxa were haphazardly placed in the corners of the tank before the urchin was added to the middle of the tank to limit the influence of distance on choice. Pieces of the four taxa also were placed in control tanks with no urchins.

Two sets of choice feeding assays were conducted. The first set conducted in December

2017 used the same macrophytes as those used in the fish feeding assays and no-choice D. antillarum feeding assays; Dictyota sp., Laurencia sp. 1, Laurencia sp. 2, Lobophora sp.,

Turbinaria sp., Galaxaura sp., Halimeda tuna, Red algae 1, and the seagrass Thalassia testudinum. In this experiment 4 g (± 0.4g) of each species was used for each trial. A total of 7 140

different D. antillarum feeding assays were conducted to ensure that all experimental macrophytes were used three times and to allow for a variety of combinations. The combinations for each trial were determined using a random number generator. A total of 15 urchins were used in each trial. The second set of assays were conducted in May 2018 using one palatable alga,

Padina sp., one chemically defended, Dictyota sp., one structurally defended, Turbinaria sp., and one alga that employs both chemical and structural defenses, Halimeda tuna. For these experiments 20 g (±0.6g) of each alga was used to ensure no alga was completely consumed.

These trials involved 4 runs with 10 new urchins collected from McCoy’s for a total of 40 urchins.

Equation 5-1 was used to correct for possible natural changes and determine the amount consumed after 24 h. All choice feeding assays were compared with Friedman’s Test (R Core

Team 2015) followed by Student-Newman-Keuls tests (Ferreira et al. 2018) to determine if there was any significant difference in the amount consumed between species. A Pearson’s Correlation test (R Core Team 2015) was used for each assay to examine the relationship between D. antillarum test diameter and the total amount of all macrophytes eaten.

Results

Fish Feeding Assays

Surveys of herbivorous fishes at the shallow site found Halichoeres radiatus, Acanthurus spp., and parrotfish (Table 5-2). Acanthurus spp. represented 46.8% of all herbivorous fishes, with a further 36.9% being parrotfish. Sparisoma rubripinne (initial and terminal phase) accounted for 21.6%, Sparisoma viride (primarily initial phase) accounted for 9.9%, and

Sparisoma aurofrenatum (initial phase) accounted for 5.4%.

When offered the 9 different species of macrophytes, these herbivorous fish showed a clear inclination to eat certain species over others. At the shallow site, herbivorous fishes

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completely or almost completely consumed (over 70% eaten) Lobophora sp., Red algae 1,

Turbinaria sp. and Thalassia testudinum in every feeding assay and ~50% or more of Laurencia sp. 1, Laurencia sp. 2 (Figure 5-2). In comparison, Galaxaura sp. and Halimeda tuna were untouched in every assay and between 13% and 47% of Dictyota sp. was eaten.

Number of bites by each type of herbivorous fish across the trials at the shallow site provided insights into dominant herbivores for each macrophyte (Table 5-4). Of the macrophytes that appeared to be more highly preferred, Lobophora sp. was primarily eaten by initial

Sparisoma rubripinne (96.6% of bites), Red algae 1 was primarily eaten by Acanthurus spp.

(97.4% of bites), Turbinaria sp. was primarily bitten by both initial phase Sparisoma rubripinne

(28.4%) and Acanthurus spp. (71.6%) and Thalassia testudinum was primarily eaten by

Sparisoma viride (initial phase) (64.7%) and initial phase Sparisoma rubripinne (29.6%).

When the data was grouped for different herbivorous fishes, they targeted different macrophytes (Figure 5-3). Acanthurus spp. took 47.8% of their bites on Laurencia sp. 1, 18.1% of bites on Laurencia sp. 2, and 23.1% of bites on Red Algae 1. Acanthurus spp. also took bites of all other macrophytes offered with the exception of H. tuna. Sparisoma rubripinne (initial phase) took 52.3% of its bites out of Lobophora sp., 18.8% out of Thalassia testudinum, 5.7% out of Turbinaria sp. and 12.3% out of Laurencia sp. 1 along with a few bites out of Dictyota sp. and Galaxaura sp. Sparisoma viride (initial phase) only ate two macrophytes with 93.4% of bites on T. testudinum and 6.6% of bites on Laurencia sp. 1. The two other fishes seen biting the macrophytes were Haemulon sciurus and Kyphosus sectatrix/bigibbus which took 100% of bites from Galaxaura sp. and Red Algae 1, respectively.

Surveys conducted at the deep site found primarily Acanthurus spp. (26.7% of all herbivorous fishes), initial phase Scarus iseri (22.7%), and initial phase Sparisoma aurofrenatum

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(18.7%), along with terminal phase Scarus iseri, initial phase Sparisoma viride, and terminal phase Sparisoma aurofrenatum in lower percentages (Table 5-3). Along with these parrotfish, the Black Durgon, Melichthys niger, also was found at the site and observed eating macrophytes, but it only made up 2.7% of the herbivorous fish population.

The types of macrophytes consumed at the deep site were similar to those at the shallow site with a few differences (Figure 5-4). At the deep site herbivorous fishes ate ~70% or more of

Laurencia sp. 1, Lobophora sp., Galaxaura sp. (in 3 of the 4 assays), Red algae 1, and Thalassia testudinum. Around 57% of Laurencia sp. 2 was consumed and a variable amount of Turbinaria sp. was consumed (27-86%). Once again, Halimeda tuna was untouched and only 0-7% of

Dictyota sp. was consumed.

Number of bites by each type of herbivorous fish across the trials at the deep site gave insights into dominant herbivores (Table 5-5). Of the more highly consumed macrophytes,

Laurencia sp. 1. was eaten by both Acanthurus spp. (33.6%) and Melichthys niger (66.4%).

Lobophora sp. was eaten by Acanthurus spp. (37.8%), initial phase Sparisoma aurofrenatum

(37.3%) and initial phase Sparisoma rubripinne (24.9%). Red algae 1 was primarily eaten by

Acanthurus spp. (85.1%) with lesser consumption by Sparisoma aurofrenatum (11.4%) and

Melichthys niger (3.5%). Galaxaura sp. was primarily eaten by Acanthurus spp. (43.2%) with lesser consumption by Thalassoma bifasciatum (30.1%) and initial phase Sparisoma rubripinne

(26.7%). In addition, consumption of Galaxaura sp. by Melichthys niger was observed by divers outside of recordings. Thalassia testudinum was primarily eaten by Sparisoma aurofrenatum, both initial (50.2%) and terminal phases (12.7%), and initial phase Sparisoma rubripinne

(31.4%).

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When looking at the number of bites on different macrophytes by each herbivorous fish, there were differences (Figure 5-5). Acanthurus spp. took bites of all macrophytes except

Dictyota sp. and Laurencia sp. 2 which were untouched. They took 50.7% of bites out of Red algae 1, 15.7% out of Laurencia sp. 1, 15.2% out of Galaxaura sp., and 7.1% out of Lobophora sp. In addition, 7.5% of bites were taken out of Turbinaira sp., although no appreciable loss of macrophyte was recorded. Initial phase Sparisoma aurofrenatum ate predominately T. testudinum (36.4% of bites) and Turbinaria sp. (37.7%), as well as taking 10.5% of bites out of

Lobophora sp. and 10.1% out of Red algae 1. Sparisoma rubripinne took 44.8% of bites out of T. testudinum, 27.6% of bites out of Galaxaura and 13.8% of bites out of both Dictyota sp. and

Lobophora sp. Melichthys niger took 93.9% of observed bites out of Laurencia sp. 1 and 6.1% of bites out of Red algae 1.

Diadema antillarum Feeding Assay-No-choice

In the no-choice feeding assays, D. antillarum showed clear preference for certain macrophytes over others according to the one-way ANOVA (df = 8, p-value < 0.001) (Figure 5-

6). On average, they ate 15.74 ± 0.92 g of the Laurencia sp. 1 and 15.12 ± 1.23 g of the Red algae 1 which is significantly more than the consumption of 1 to 5 g eaten of all the other macrophytes (Tukey HSD test, p-value< 0.001). After the two macrophytes listed above, the consumption of Thalassia testudinum was next highest at 4.56 ± 0.47 g. Urchins ate significantly more of this seagrass than the Halimeda tuna, Laurencia sp. 2, Lobophora sp. and Turbinaria sp.

(Tukey HSD test, p-value < 0.05). The species eaten least were Laurencia sp. 2 (1.04 ± 0.48 g) and Halimeda tuna (1.14 ± 0.26 g), but consumption of these macrophytes was not significantly less than Dictyota sp. (2.61 ± 0.34 g), Lobophora sp. (1.69 ± 0.23 g), or Turbinaria sp. (1.83 ±

0.38 g) (Tukey HSD, p-value > 0.20). Only H. tuna (1.14 ± 0.26 g) was eaten significantly less than Galaxaura sp. (3.86 ± 0.05 g) (Tukey HSD, p-value = 0.041). There was no significant 144

correlation between the total amount eaten and the diameter of the D. antillarum according to

Pearson correlation (df = 127, p-value = 0.14).

Diadema antillarum Feeding Assay-Choice

There were significant differences in the amounts of the different macrophytes that were eaten in all choice feeding assays (Figure 5-7). In the first assay, significantly more Galaxaura sp. and Laurencia sp. 1 were eaten as compared to Turbinaira sp. and T. testudinum (df = 3, p- value < 0.001). Furthermore, significantly more of the Turbinaria sp. was eaten compared to the

T. testudinum. In the second assay, it was Lobophora sp. that was eaten significantly less than all other macrophytes (df = 3, p-value < 0.001). In addition, T. testudinum was eaten significantly less than Galaxaura sp. and Red algae 1. In the third assay, significantly more Red algae 1 was eaten as compared to all other macrophytes (df = 3, p-value < 0.001). Additionally, significantly more Dictyota sp. was eaten compared to H. tuna. In the fourth assay, Dictyota sp., Lobophora sp., and H. tuna were all eaten significantly less than Red algae 1, and once again, significantly more Dictyota sp. was eaten compared to H. tuna (df = 3, p-value < 0.001). Laurencia sp. 2,

Dictyota sp. and Turbinaria sp. were all eaten significantly less that Laurencia sp. 1 in the fifth assay (df = 3, p-value < 0.001). In the sixth assay, significantly more Galaxaura sp. was eaten as compared to Turbinaira sp. (df = 3, p-value = 0.032). In the final assay, H. tuna, Laurencia sp. 2 and Lobophora sp. were all eaten significantly less than Laurencia sp. 1 (df = 3, p-value <

0.001). There was no significant relationship between amounts eaten and test diameters in trials

1, 3, 4, 5, 6 or 7 (Pearson correlation, df = 13, p-value > 0.05). In Trial 2, the amount eaten increased with greater D. antillarum diameter (Pearson correlation, r (13) = 0.73, p-value =

0.0021). Specifically, larger diameter D. antillarum ate more of Lobophora sp. (Pearson correlation, r (13) = 0.58, p-value = 0.024).

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The D. antillarum feeding assay in May 2018 used larger quantities of Padina sp.,

Dictyota sp., Turbinaria sp., and Halimeda tuna (Figure 5-8). When comparing the amount eaten of these different algae, significantly more of Padina sp. (4.19 ± 0.52 g) and Dictyota sp. (3.66 ±

0.48 g) were eaten in comparison to Turbinaria sp. (1.95 ± 0.18 g) and H. tuna (1.89 ± 0.37 g)

(Friedman’s test, df = 3, p-value = 0.04). There was no significant correlation between the amount eaten and test diameter in these assays (Pearson correlation, r (38) = 0.13, p-value =

0.40).

Discussion

The goal of this study was to examine the feeding choices of herbivorous fishes and the sea urchin Diadema antillarum for the dual purposes of predicting the prevalence of different macroalgae, given knowledge of the dominant herbivore in the area, and examining any redundancy or complementarity in feeding preference between the herbivores. There appeared to be redundancy between the herbivorous fishes and D. antillarum in regard to the most palatable macrophyte, Red algae 1, which was almost completely eaten in every fish feeding assay, consumed in large quantities (over 15 g) by D. antillarum in 24-h, no-choice assays, and consumed in significantly larger quantities than all but one macrophyte in choice assays. There was also high consumption of Laurencia sp. 1 by both herbivorous fishes and D. antillarum in all feeding assays. Due to the uncertain taxonomy and defenses of Red Algae 1 and Laurencia sp. 1, these results have been attributed to the fact that both of these species were collected in the seagrass bed. Previous studies that have shown that algae collected from areas with low herbivore pressure, such as the seagrass beds, are highly susceptible to grazing presumably due to being less chemically rich (Lewis 1985; Bolser and Hay 1996). There was also overlap among the macrophytes least consumed by herbivores with H. tuna completely uneaten in all fish feeding assays and of consumed very little in D. antillarum feeding assays. This result is 146

consistent with previous studies that have found Halimeda species to be rich with secondary metabolites that act as a powerful deterrent to herbivory along with their CaCO3 structure that can act synergistically with chemical defenses as a deterrent (Hay et al. 1994; Paul and Van

Alstyne 1988). It is important to note that unlike fish, D. antillarum did eat a portion of H. tuna in all assays indicating a capacity to eat the algae although it is not preferred.

Far more relevant than the similarities between the herbivores are the differences that could affect the structure of macroalgal communities. The most significant difference between the herbivore groups was seen for brown algae, Dictyota sp., Lobophora sp. and Turbinaria sp., with Dictyota sp. eaten more by D. antillarum and Lobophora sp. and Turbinaria sp. eaten more by herbivorous fishes. Species within the genus Dictyota are known to produce a variety of secondary metabolites that have been shown to be effective in deterring herbivorous fishes (Fong and Paul 2011; Vallim et al. 2005). Turbinaria sp. can be categorized as structurally defended due to its thick leathery/rubbery branches and a thick-walled, heavily corticated structure (Littler et al. 1983). Although this structure did not deter the herbivorous fishes, particularly Sparisoma parrotfish, it did deter feeding by D. antillarum. In addition to Sparisoma parrotfish, Acanthurus spp. were also seen biting at the Turbinaira sp., especially at the deep site, but due to the lack of appreciable consumption it is likely these fish were consuming epiphytes off the alga rather than eating the alga itself. The high consumption of Lobophora sp. by herbivorous fishes but not by

D. antillarum was unexpected due to the presence of secondary metabolites within this genus and lack of obvious structural defenses (Targett et al. 1992), which would predict deterrence of fish but not urchins (Coen and Tanner 1989; Paul and Hay 1986). The consumption by herbivorous fishes was attributed to the collection location of Lobophora sp. in seagrass beds and ruffled morphological form, both of which have been linked to decreased herbivorous fish

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deterrence (Coen and Tanner 1989; Bolser and Hay 1996). It is not entirely clear why Lobophora sp. was less preferred by D. antillarum, but it is predicted that this preference was due to its ruffled structure which has a thicker thallus as well as being a larger ball like structure (Coen and

Tanner 1989).

It is also necessary to examine Galaxaura sp. which was untouched by fish at the shallow site but almost completely consumed at the deep site. The reason for this difference is not entirely clear, but it may be linked to the presence of Black Durgon (Melichthys niger) which were only seen at the deep site and were observed by divers biting Galaxaura sp. on the lines, although this was not caught on camera. This report is not the first observation of M. niger consuming Galaxaura sp., and in previous assays M. niger appeared to preferentially consume

Galaxaura sp. (~80% of M. niger bites) (Tebbett et al. 2020). In fact, this study cited M. niger as the dominant herbivore, a condition not replicated in our feeding assays (Tebbett et al. 2020). Of note, both this study and the one mentioned above were conducted off Little Cayman Island and were in fact geographically close to each other on the north side of the island. It is possible that the role of M. niger as a major herbivore is unique to Little Cayman. In our study, Acanthurus species also were seen feeding on Galaxaura sp., although they did not appear to completely consume it like they did other algal species. Galaxaura species are known to be both chemically rich and calcified, but in these assays, this combination of defenses was not as effective in deterring D. antillarum and herbivorous fishes as might have been expected from previous studies (Paul and Hay 1986).

This study also examined functional complementarity in herbivory. Although, D. antillarum showed the ability to eat all types of macrophyte, there were less preferred algae that typically employed some sort of structural defense. Herbivorous fish, particularly parrotfishes,

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were more selective in what they consumed and avoided algae that were known to employ chemical defenses. Acanthurus species were more willing to consume chemically rich genera such as Galaxaura sp. and Laurencia spp. but they did not graze to the same extent on thick- bladed species such as Turbinaria sp. and T. testudinum. This difference in feeding by herbivorous fishes can be attributed to differences in mouth structure. Parrotfish have large beaklike mouths that allow them to engulf and consume large portions of macrophytes, and they are known to completely tear algae away from substrate (Adam et al. 2018; Bonaldo et al. 2014).

In comparison, Acanthurus species have relatively small mouths that do not open wide, which leads them to crop algae through quick nips at their top portions (Adam et al. 2018; Dromard et al. 2015; Purcell and Bellwood 1993). This difference in mouth size is consistent with the type of macrophytes that these two groups of herbivorous fishes consumed. Acanthurus species were seen eating almost entirely filamentous red algae, some of which were calcified and/or chemically rich. In comparison, parrotfish ate Turbinaria sp. and large bladed T. testudinum.

These differences in feeding preferences among herbivorous fishes are consistent with previous experiments that showed dietary complementarity between Acanthurus spp. and Sparisoma parrotfish, specifically S. aurofrenatum and S. rubripinne (Adam et al. 2018; Burkepile and Hay

2011).

These results indicate that although there may be some functional redundancy between D. antillarum and herbivorous fishes, it is incomplete with some types of algae only eaten by one or the other type of herbivore. There was also incomplete redundancy within the herbivorous fishes with Sparisoma parrotfish targeting different species than Acanthurus species. The complementarity and redundancy among herbivores suggests that it will be important to have all of these herbivores on coral reefs if the goal is controlling macroalgae in order to facilitate

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recovery of coral reefs (Lefcheck et al. 2019). The lack of diversity in herbivore communities will likely result in the dominance of certain macroalgae on a reef. Reefs dominated by

Sparisoma parrotfish may be dominated by chemically rich species while reefs with Acanthurus species may have predominately thick-bladed macrophytes. Finally, D. antillarum dominance may lead to reefs dominated by structurally defended species such as Turbinaria sp. As a whole, dominance of macroalgae on reefs, no matter the type, will continue to prevent the settlement of sessile organisms, especially corals (Ritson-Williams et al. 2020).

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Table 5-1. List of macrophytes used in feeding assays along with their known defenses and known responses of herbivores to congeners. Defense Reaction of Reaction of D. Macrophyte Source Employed Herbivorous Fishes antillarum

Hay et al. 1987, Littler et al. 1983, Dictyota sp. Chemical Variable Consumption Paul and Hay 1986

Hay et al. 1987, Littler et al. 1983, Laurencia sp. 1 Chemical Variable Variable Paul and Hay 1986

Hay et al. 1987, Littler et al. 1983, Laurencia sp. 2 Chemical Variable Variable Paul and Hay 1986

Coen and Tanner Lobophora sp. Chemical Variable Eaten 1989, Paul and Hay 1986

Turbinaria sp. Structural Consumption Avoidance Littler et al. 1983

Chemical & Paul and Hay Galaxaura sp. Variable Not tested Structural 1986

Littler et al. 1983, Chemical & Halimeda tuna Avoidance Avoidance Paul and Hay Structural 1986

Thalassia Hay 1984, Paul None/Palatable Consumption Consumption testudinum and Hay 1986

Paul and Hay Padina sp. Chemical Consumption Not Tested 1986

Red Algae 1 Unknown Unknown Unknown

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Table 5-2. Average number per m2 (± standard error, SE) of fish of each species found during 4 transects run at shallow site. Fish Species Average SE Scarus vetula (Initial) 0.012 0.009 Sparisoma viride (Initial) 0.033 0.012 Sparisoma viride (Terminal) 0.003 0.002 Sparisoma aurofrenatum (Initial) 0.018 0.008 Sparisoma rubripinne (Initial) 0.065 0.023 Sparisoma rubripinne (Terminal) 0.006 0.003 Scarus iseri (Initial) 0.021 0.013 Scarus iseri (Terminal) 0.006 0.003 Acanthurus spp. 0.155 0.014 Halichoeres radiatus 0.012 0.009

Table 5-3. Average number per m2 (± standard error, SE) of fish of each species found during 3 transects run at deep site. Fish Average SE Sparisoma viride (Initial) 0.050 0.030 Sparisoma aurofrenatum (Initial) 0.078 0.010 Sparisoma aurofrenatum (Terminal) 0.017 0.007 Scarus iseri (Initial) 0.094 0.026 Scarus iseri (Terminal) 0.022 0.004 Acanthurus spp. 0.111 0.10 Chaetodon capistratus 0.033 0.007 Melichthys niger 0.011 0.008

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Table 5-4. Number of bites per hour by each herbivorous fish species on each macrophyte species across all trials at the shallow site. Number Sparisoma Sparisoma Haemulon Kyphosus Macrophyte Acanthurus spp. of lines viride (I) rubripinne (I) sciurus sectatrix/bigibbus Dictyota sp. 18 4.90 ± 3.14 0 0.31 ± 0.31 0 0

Galaxaura sp. 15 3.24 ± 1.66 0 0.61 ± 0.61 0.31 ± 0.31 0

Halimeda tuna 18 0 0 0 0 0

Laurencia sp. 1 18 105.27 ± 22.94 0.44 ± 0.31 0.94 ± 0.94 0 0

Laurencia sp. 2 15 39.76 ± 11.00 0 0 0 0

Lobophora sp. 18 0.28 ± 0.18 0 7.95 ± 7.80 0 0

Red algae 1 18 42.32 ± 14.58 0 0 0 0.44 ± 0.31

T. testudinum 21 0.47 ± 0.34 2.68 ± 2.68 3.68 ± 2.06 0 0

Turbinaria sp. 15 1.31 ± 1.31 0 2.61 ± 2.61 0 0

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Table 5-5. Number of bites per hour by each herbivorous fish species on each macrophyte species across all trials at the deep site. Number Acanthurus Thalassoma Sparisoma Sparisoma Sparisoma Melichthys Macrophyte of lines spp. bifasciatum aurofrenatum (I) aurofrenatum (T) rubripinne (I) niger

Dictyota sp. 12 0 0 0.88 ± 0.88 0 1.18 ± 1.18 0

Galaxaura sp. 21 6.51 ± 3.90 1.51 ± 1.51 0 0 1.34 ± 1.34 0

Halimeda 15 0.21 ± 0.21 0 0 0.41 ± 0.41 0 0 tuna Laurencia sp. 12 8.10 ± 6.10 0 0 0 0 8.02 ± 8.02 1 Laurencia sp. 15 5.97 ± 5.32 0 0 0 0 0 2

Lobophora sp. 12 1.79 ± 1.79 0 1.76 ± 1.76 0 1.18 ± 1.18 0

42.23 ± Red algae 1 18 0 1.31 ± 1.31 0 0 0.34 ± 0.34 25.22

T. testudinum 18 0.91 ± 0.59 0 20.38 ± 12.11 1.03 ± 1.03 2.55 ± 2.55 0

Turbinaria sp. 9 2.50 ± 2.50 0.93 ± 0.93 16.90 ± 14.81 0 0 0

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D C

E F

H I J

Figure 5-1. Photographs of macrophytes used in feeding assays. Photographs show Dictyota sp. (A), Laurencia sp. 1 (B), Laurencia sp. 2 (C), Lobophora sp. (D), Turbinaria sp. (E), Galaxaura sp. (F), Halimeda tuna (G), Red Algae 1 (H), Padina sp. (I) and the sea grass Thalassia testudinum (J). Insets are included to show details of some of the macroalgae.

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Figure 5-2. Proportion of each macrophyte eaten by fish at the shallow sites over 24 h. n = number lines per assay. Results analyzed using a G-test followed by Fisher’s exact tests, and letters indicate significant differences.

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Figure 5-2 cont.

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Figure 5-3. Percent of bites by each herbivorous fish on each macrophyte across all shallow sites. For parrotfishes, (I) indicates intermediate stage fish and (T) indicates terminal phase.

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Figure 5-4. Proportion of each macrophyte eaten by fish at the deep feeding sites over 24 h. n = number lines per assay. Results analyzed using a G-test followed by Fisher’s exact tests, and letters indicate significant differences.

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Figure 5-4 cont.

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Figure 5-5. Percent of bites by each herbivorous fish on each macrophyte across all deep sites. For parrotfishes, (I) indicates intermediate stage fish and (T) indicates terminal phase.

Figure 5-6. Amount of each macrophyte eaten by Diadema antillarum in no-choice feeding assays. Different letters indicate significant differences according to an ANOVA followed by Tukey tests.

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Figure 5-7. Amount in grams of each macrophyte eaten by Diadema antillarum in choice feeding assays in December 2017. Different letters indicate significant differences according to Friedman’s test followed by Student-Newman-Keuls tests.

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Figure 5-7 cont.

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Figure 5-8. Amount of each algal species eaten by Diadema antillarum in choice feeding assays in May 2018. Different letters indicate significant differences according to Friedman’s test followed by Student-Newman-Keuls tests.

CHAPTER 6 CONCLUSION: FINAL THOUGHTS ON THE ROLE OF HERBIVORES ON CARIBBEAN CORAL REEFS

Historically, herbivorous fishes and sea urchins, particularly Diadema antillarum, played key roles in the regulation of macroalgae on Caribbean coral reefs (Jackson et al. 2014; Lessios

2016; Shantz et al. 2020). Prior to their exploitation and die-off respectively, large herbivorous reef fish and D. antillarum together maintained cropped "lawns" of macroalgae and algal turfs

(Carpenter 1986). The loss of these important herbivores throughout the broader Caribbean region likely has contributed to the proliferation of macroalgae on coral reefs and associated decline in coral cover (Jackson et al. 2014; Lessios 2016). Caribbean coral reefs today are markedly different than before the 1983 D. antillarum die-off with average coral cover having declined from 35% in the 1970s to 16% today and macroalgae having increased from 7% to 24%

(Jackson et al. 2014). It is this phase shift from a coral dominated state to a macroalgal dominated state that has been of particular interest to coral reef scientists. Gaining an understanding of mechanisms that may help reverse this shift is vital to ensuring healthy coral reef ecosystems in the future.

With the return of D. antillarum in some locations and the protection of large herbivorous fishes through legislation and the establishment of MPAs in different countries, it is necessary for scientists to re-evaluate the role of herbivores on coral reefs. One widely held view is that a return of D. antillarum populations to near pre-1983 densities may facilitate a shift back to a coral dominated system. However, many characteristics of Caribbean coral reefs beyond herbivore density have changed significantly since the 1980s. Coral reefs are under increasing stress due to decreases in herbivore abundance as well as increasing nutrification, sedimentation, disease prevalence, and elevated seawater temperature (Burke et al. 2011; Ban et al. 2014;

Zaneveld et al. 2016). Furthermore, although D. antillarum densities continue to increase in

some localized areas, their return to pre-mortality densities throughout the broader Caribbean region remains in doubt (Levitan et al. 2014). Historic densities of D. antillarum were ~6 per m2 before the die-off, but these densities dropped to less than 0.2 per m2 immediately following the die-off and have only risen to around 1.2 per m2 throughout the Caribbean (Lessios 2016). The status of herbivorous fish populations is highly variable with protections being enacted in some areas and overfishing continuing in many others (Jackson et al. 2014). As a consequence of these herbivore dynamics, it is increasingly important to understand more fully the combined effects of

D. antillarum and herbivorous fish on algal communities over a range of densities. Thus far, many studies have focused on the effect of herbivorous fishes and D. antillarum on overall algal percent cover. Beyond the effects of herbivores on the abundance of macroalgae on reefs, it is also important to understand how the composition of the algal assemblages on reefs varies with the presence of different herbivores; an aspect of coral reef ecosystem ecology that has not been studied extensively. Examining this relationship between herbivores and the structure of macroalgal communities is important because different algae have differing propensities to overgrow benthic organisms, or aid or inhibit settlement of sessile organisms, like corals and sponges, and motile organisms, like D. antillarum and fish. As indicated above, the composition of herbivores on Caribbean coral reefs vary in different locations and this composition will likely continue to change quite markedly. Thus, it is important to differentiate the impacts that D. antillarum alone, herbivorous fish alone, or sea urchins and fish in combination have on overall macroalgal abundance as well as composition of macroalgal communities on reefs.

To examine how herbivore type and density affects cover and structure of macroalgal communities on coral reefs, three types of experiments were conducted. Benthic surveys

(Chapter 2) provided a snapshot of the current state of coral reefs in Belize in regard to cover and

community structure of benthic organisms. Caging experiments allowed me to manipulate herbivore access and assess potential effects on the community structure and percent cover of macroalgae on newly cleared (Chapter 3, Belize) and established reef areas (Chapter 4, Little

Cayman) with varying natural herbivore communities. Finally, feeding assays allowed for a better understanding of feeding preferences of herbivorous fishes and D. antillarum and the potential for selective grazing to influence algal structure on coral reefs (Chapter 3 & 5).

Based on the results of these studies, it is possible to draw some conclusions about the differing roles of herbivorous fishes and D. antillarum on reefs. When an established macroalgal community was exposed to different herbivores during a caging experiment carried out off Little

Cayman Island, D. antillarum exhibited a greater capacity to consume macroalgae compared to herbivorous fishes, and this capacity appeared to increase with density. In the high-density treatment (4 D. antillarum/m2), sea urchins were able to almost entirely clear the substrate.

Furthermore, D. antillarum appeared to remove chemically rich macroalgae preferentially, specifically Lobophora spp. and Dictyota spp., two ubiquitous macroalgae on Caribbean coral reefs that are known to detrimentally affect corals (Chadwick and Morrow 2011; Fong and Paul

2011). Herbivores appeared to play a different role on the newly cleared substrate in Belizean caging experiments. In that case, herbivorous fishes were able to keep the macroalgae grazed below ~10% cover. Noteworthy was the observation that in treatments dominated by herbivorous fishes, chemically rich algae were kept at low percent cover despite previous reports that herbivorous fishes normally avoid consuming chemically rich macroalgae (Littler et al. 1983;

Paul and Hay 1986). These results indicate the complexity in trying to predict how reefs will respond to the presence of different herbivores.

In comparison to caging experiments, particularly those conducted in Little Cayman, benthic surveys suggested very different relationships between D. antillarum and macroalgae.

On the reefs surveyed, higher D. antillarum density coincided with the highest cover of macroalgae. Most of the difference can be attributed to the chemically rich species such as

Dictyota spp., but reefs with higher D. antillarum density also had higher percent cover of structurally defended species, specifically, Turbinaira sp. Some of the results from these caging experiments and benthic surveys can be explained by feeding assays in which herbivorous fishes avoided chemically rich algae (like Dictyota spp.), D. antillarum avoided structurally defended species (like Turbinaria spp.) and both herbivores did not prefer species that employed both chemical and structural defenses (H. tuna).

Inconsistences between caging experiments and benthic surveys conducted in Belize also may indicate that factors other than top-down controls by herbivores are playing a significant role in shaping benthic communities, specifically algal communities on Caribbean coral reefs.

Additional controls include light availability as affected by depth and water clarity, nutrient inputs, and behavioral differences due to the presence of predators. Further studies are necessary to help isolate and evaluate the effects of individual factors.

Additionally, it is necessary to test the hypothesis that herbivorous fishes and D. antillarum will differentially affect settlement of corals due to the differences they create in algal communities. Previous research into the effect of different macroalgae on coral settlement suggests that the algal community created under conditions where D. antillarum are dominant

(low abundance of chemically rich algae and higher CCA cover) may be better than those created by herbivorous fishes (algal community dominated by chemically rich algae) (Kuffner et al. 2006; Ritson-Williams et al. 2016; Vieira 2019; Ritson-Williams et al. 2020). However, it is

necessary to test if differing densities or size class structures of D. antillarum may have unexpected consequences for coral settlement, as was seen when the experimental exclusion of large parrotfish resulted in increased coral recruitment (Shantz et al. 2020). Although D. antillarum generally act as herbivores, they are omnivores and have been shown to consume benthic invertebrates, either intentionally or incidentally, during the act of scraping macroalgae off the substrate (Lewis 1964; Rodríguez-Barreras et al. 2015a). It is likely that at high enough densities, D. antillarum could completely clear the substrate of all organisms including recently settled coral recruits.

This research provided a more comprehensive understanding of the state of Caribbean coral reefs under current herbivore conditions rather than historic conditions. This understanding was vital to answering the overarching question of how different herbivores differentially affect both the abundance of macroalgae on coral reefs and the structure of macroalgal communities.

Herbivores are capable of reducing algal dominance if present in sufficient numbers and variety, but the capacity of the remaining corals to respond positively remains an unanswered question.

Overall, a return of herbivores may be necessary for coral reef ecosystems to recover, but it is also essential that other stressors are reduced significantly.

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BIOGRAPHICAL SKETCH

Lindsay Spiers was born in Guam and resided there until 2002 when she moved to Fort

Pierce, Florida. Lindsay graduated from John Carroll High School in spring 2009 and enrolled in

Elon University that fall. At Elon university, Lindsay was a part of the Elon College Fellows program. Lindsay took part in the inaugural “Marine Biodiversity and Conservation” study abroad class through the Sea Education Association between April and June 2012. Lindsay received a Bachelor of Science in Biology from Elon University in May 2013. Lindsay began her

Ph.D. in the School of Fisheries and Aquatic Sciences at University of Florida in August 2014.

She received her Ph.D. from the University of Florida in 2020.