Nutritional Basis of Butterflyfish Corallivory in the Red Sea a Thesis

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Nutritional Basis of Butterflyfish Corallivory in the Red Sea

A Thesis by

Jessica Masterman

In Partial Fulfillment of the Requirements

For the Degree of

Master of Science

King Abdullah University of Science and Technology, Thuwal,

Kingdom of Saudi Arabia

December 2012

EXAMINATION COMMITTEE FORM

The thesis of Jessica Masterman is approved by the examination committee.

Committee Chairperson: Michael Berumen

Committee Co-Chair: Randi Rotjan

Committee Member: David Raubenheimer

ABSTRACT

The overall goal of this study was to elucidate the relationship between coral nutrition and the observed prey preferences exhibited by corallivorous butterflyfishes. Fifteen species of coral (thirteen hard, two soft) and stomach/hindgut contents from six species of butterflyfish were analyzed in this study, all collected from the central Saudi Arabian Red Sea. All samples were analyzed for lipid, total-nitrogen (proxy for protein), and ash (proxy for minerals and when combined with lipid data, allows for calculation of carbohydrate).

Unfortunately, substantial errors were encountered in the experimental lipid data, precluding the use of this data set. Using the value of (protein/ash) as a proxy for potential nutritional quality, it was determined that Pocillopora cf. verrucosa and P. damicornis have the highest nutritional quality, while Acropora hyacinthus and

Stylophora pistillata have intermediate nutritional quality, and all remaining 11 species have low nutritional quality. This suggests that the high nutritional quality of Pocillopora damicornis and Acropora hyacinthus may be the cause of the well documented predator preferences for these two species. Fish gut content samples were, on average, twice as rich in protein and half as rich in minerals as the coral tissue samples, suggesting either selective consumption of especially rich parts of the coral colony, or consumption of other food sources (facultative corallivores). In all six butterflyfish species, stomach content samples were consistently richer in protein and poorer in mineral content than the hindgut content samples; this suggests significant and measureable uptake of protein in the butterflyfish digestion process. 4

ACKNOWLEDGEMENTS

I would like to first thank my MS thesis advisors Dr. Michael Berumen, Dr.

Randi Rotjan, and Dr. David Raubenheimer. Without their support and guidance, I would never have been able to complete this work. Particularly, I would like to thank Mike and Randi for allowing me to utilize the parrotfish and butterflyfish samples that they had collected from the Red Sea in February 2011. Many thanks also go to Randi for the dedication and enthusiasm she has shown toward this project. Without Randi’s constant encouragement, I surely would not have gotten as far as I did in this work. Unfortunately, time constraints did not permit me to take advantage of David’s expertise as much as I would have liked in this project, although I hope to work with David much more in the future.

I would also like to thank KAUST for providing all the resources and financial support for this project. Thanks also to the University of Wisconsin Soil and Plant

Analysis Lab. Their work on the ash measurement of my samples was not only well planned, but also highly meticulous.

I must also thank Mae Noble for acting as my dive buddy and helping me to collect all my coral fragments. I’d also like to thank Jessica Bouwmeester for her help in confirming my coral species identifications.

Lastly, I’m very grateful to all of the amazing friends I’ve had during my stay at KAUST. Some of you have graduated already and some of you will continue attending KAUST, but I will never forget the time we’ve spent here and all the wonderful experiences we’ve shared together. Thank you all for joining me in this

Saudi Arabian adventure! 5

TABLE OF CONTENTS

EXAMINATION COMMITTEE FORM ...... 2 ABSTRACT ...... 3 ACKNOWLEDGEMENTS ...... 4 TABLE OF CONTENTS ...... 5 LIST OF FIGURES ...... 7 LIST OF TABLES ...... 9 CHAPTER ONE: INTRODUCTION ...... 11 1.1 Importance and Degradation of Coral Reefs ...... 11 1.2 Corallivory ...... 12 1.3 Corallivory in Butterflyfish (f: Chaetodontidae) ...... 13 1.4 Butterflyfish Species Studied ...... 14 1.4.1 Chaetodon auriga ...... 14 1.4.2 Chaetodon austriacus ...... 14 1.4.3 Chaetodon fasciatus ...... 15 1.4.4 Chaetodon larvatus ...... 15 1.4.5 Chaetodon paucifasciatus ...... 15 1.4.6 Chaetodon trifascialis ...... 15 1.5 A New Approach ...... 16 CHAPTER TWO: MATERIALS AND METHODS ...... 17 2.1 Sample Collection and Preparation ...... 17 2.2 Lipid Analysis ...... 19 2.2.1 Modified Folch method for lipid analysis ...... 20 2.2.2 Preliminary standardization tests ...... 22 2.3 Protein Analysis ...... 26 2.3.1 Preliminary standardization tests ...... 27 2.4 Carbohydrate Analysis ...... 28 2.4.1 Preliminary attempts to measure ash content ...... 29 2.4.2 Successful measurement of ash content ...... 30 CHAPTER THREE: RESULTS ...... 31 3.1 Prey: Coral Tissue Macronutrient Content ...... 31 3.1.1 Protein Results ...... 31 3.1.2 Ash Content Results ...... 35 3.1.3 Combination of Protein Content and Ash Content Results ...... 37 3.2 Predator: Stomach/Hindgut Macronutrient Content in Butterflyfishes ...... 44 3.2.1 Protein Content Results ...... 45 3.2.2 Ash Content Results ...... 48 3.2.3 Combination of Protein and Ash Content Results ...... 51 3.3 Predator vs. Prey ...... 54 3.3.1 Chaetodon auriga: Stomach Content vs. Feeding Preferences ...... 55 3.3.2 Chaetodon austriacus: Stomach Content vs. Feeding Preferences ...... 56 3.3.3 Chaetodon fasciatus: Stomach Content vs. Feeding Preferences ...... 57 3.3.4 Chaetodon larvatus: Stomach Content vs. Feeding Preferences ...... 58 6

3.3.5 Chaetodon trifascialis: Stomach Content vs. Feeding Preferences ...... 59 3.4 Lipid Results: Issues and Errors ...... 60 CHAPTER FOUR: DISCUSSION ...... 65 4.1 Prey: Coral Tissue Macronutrient Content ...... 65 4.2 Predator: Stomach/Hindgut Macronutrient Content in Butterflyfishes ...... 69 4.3 Lipids: Hypotheses as to Source of Error ...... 74 4.4 Future Work ...... 75 BIBLIOGRAPHY ...... 77 APPENDICES ...... 81

LIST OF FIGURES

Figure 1. Results of standardization test #4 using foodstuff standards: A) banana, B) chicken, and C) cashew nuts. The majority of evaporation had finished after approximately 2-3 weeks of evaporation, although allowing for at least a month is preferable...... 25

Figure 2. Scatter plot of average protein content vs. average ash content per coral species. Numbers correspond as follows: 1) Acropora hyacinthus, 2) A. pharaonis, 3) Echinopora forskaliana, 4) E. fruticulosa, 5) Favia stelligera, 6) Galaxea fascularis, 7) Goniastrea edwardsi, 8) Montipora millepora, 9) Montipora tuberculosa, 10) Pocillopora cf. verrucosa, 11) Pocillopora damicornis, 12) Porites spp., 13) Sinularia spp., 14) Stylophora pistillata, and 15) Xenia spp...... 40

Figure 3. Average potential nutritional quality (PNQ) per species; error bars represent standard deviation over all colonies sampled. Based on apparent grouping of potential nutritional quality values, three groups are advocated and differentiated by color: Group 1, dotted black (PNQ <0.50); Group 2, hatching (0.50< PNQ <1.00); and Group 3, gray (PNQ >1.00)...... 41

Figure 4. Plot of protein content in butterflyfishes' gut content samples, differentiated by sample type. Different sample types are denoted as (black triangle: stomach, gray circle: hindgut). Note that no stomach content sample was obtained for C. paucifasciatus...... 46

Figure 5. Plot of ash content in butterflyfishes' gut samples, differentiated by sample type. Different sample types denoted as (black triangle: stomach, gray circle: hindgut). Note that no stomach sample was obtained for C. paucifasciatus...... 49

Figure 6. Plot of percent protein content vs. percent ash content for coral (prey) and butterflyfishes stomach content (predator). All data points represent averages. Letters denote butterflyfishes stomach content data points. Numbers denote coral species data points...... 54

Figure 7. Repeat of Figure 6, but with emphasis on Chaetodon auriga. The average protein content vs. ash content for C. auriga stomach content samples is marked as a solid red circle. Transparent red circles mark the coral species that are preferred prey items for this butterflyfish species...... 56

Figure 8. Repeat of Figure 6, but with emphasis on Chaetodon austriacus. The average protein content vs. ash content for C. austriacus stomach content samples is marked as a solid red circle. Transparent red circles mark the coral species that are preferred prey items for this butterflyfish species...... 57

Figure 9. Repeat of Figure 6, but with emphasis on Chaetodon fasciatus. The average protein content vs. ash content for C. fasciatus stomach content samples is marked as a solid red circle. Transparent red circles mark the coral species that are preferred prey items for this butterflyfish species...... 58

Figure 10. Repeat of Figure 6, but with emphasis on Chaetodon larvatus. The average protein content vs. ash content for C. larvatus stomach content samples is marked as a solid red circle. Transparent red circles mark the coral species that are preferred prey items for this butterflyfish species...... 59

Figure 11. Repeat of Figure 6, but with emphasis on Chaetodon trifascialis. The average protein content vs. ash content for C. trifascialis stomach content samples is marked as a solid red circle. Transparent red circles mark the coral species that are preferred prey items for this butterflyfish species...... 60

Figure 12. Plot of calculated lipid content in banana standard samples as a function of evaporation duration. Six replicates were performed (Rep 1 - 6). Note that the expected value for banana lipid content (as based on previous standardization runs) is between 5-7%; this expected value is denoted by the dotted line: CorrectBananaLipid...... 62

Figure 13. Plot of calculated lipid content in chicken standard samples as a function of evaporation duration. Five replicates were performed (Rep 1 - 5). Note that the expected value for chicken lipid content (as based on previous standardization runs) is between 9-14%; this expected value is denoted by the dotted line: CorrectChickenLipid...... 63

Figure 14. Plot of calculated lipid content in seven coral tissue samples (Coral Rep 1 - 7) and one butterflyfish gut content sample (dotted line: Chaet - Rep 1) as a function of evaporation duration...... 64

LIST OF TABLES

Table 1. Count of butterflyfish gut content samples before and after pooling the lower- mass samples...... 18

Table 2. Count of colonies sampled per coral species. Shaded rows are soft coral species; all others are hard coral species (scleractinian)...... 19

Table 3. Results of standardization test #1. Samples in Run 4 had methanol added following Step 10 (see "Modified Folch method for lipid analysis"); Runs 2,3 did not. No significant difference in resulting percent lipid content values was observed...... 23

Table 4. Results of standardization test #2. Changing the pore size of filter used in Step 6 (see "Modified Folch method for lipid analysis") had no effect on resulting lipid content...... 23

Table 5. Results of standardization test #3. No effect on resulting lipid content was observed in changing the sample mass from 75 to 30mg...... 24

Table 6. Results of preliminary protein analysis using foodstuff standards. “n” number of replicates for each foodstuff (banana, chicken, and cashew nuts) were analyzed for total-nitrogen content using a CHNS/O analyzer; these values were then multiplied by n=6.25 to find percent protein content. Percent protein content values were then averaged per foodstuff...... 27

Table 7. Summarized results of protein content for all coral tissue samples. Averages are taken over all samples per species (1 sample per colony), regardless of reef at which sample was taken. The total number of colonies sampled per species is listed under “n”. Note that Echinopora fruticulosa was only sampled once, precluding standard deviation calculations...... 32

Table 8. Results of Mann-Whitney U tests to determine significant difference in protein content between reefs for nine species of coral. A resulting (P>0.05) indicated a significant difference; these species are marked in bold. Average protein content values were calculated over (n(AF)) samples for Al-Fahal Reef, and over (n(SN)) samples for Shib Nazar Reef...... 34

Table 9. Summarized results of ash content for all coral tissue samples. Averages are taken over “n” colonies (1 sample per colony), with associated standard deviations between the colonies. Note that Echinopora fruticulosa was only sampled once, precluding standard deviation calculations...... 35

Table 10. Average percent protein content was divided by average percent ash content for each coral species to obtain the average "potential nutritional quality" values. Standard deviations are calculated as among the “n” colonies sampled...... 38 10

Table 11. Mann-Whitney tests confirmed that each of three groups of coral species (separated based on PNQ values) is significantly different from each other group. Mann Whitney tests confirmed that each group is significantly different from each other group by comparing PNQ values in each group to group 1 (Re:G1), group 2 (Re:G2), and group 3 (Re:G3)...... 43

Table 12. Summary of number of butterflyfish gut content samples that underwent each macronutrient analysis. Samples were analyzed for protein content (n (Protein)), ash content (n (Ash)), and lipid content (n (Lipid)) per butterflyfish species per sample type (hindgut vs. stomach). Bottom row contains total number of butterflyfish gut content samples analyzed for each of the three macronutrients...... 44

Table 13. Average ash content and average protein content values, per butterflyfish species per sample type. A total of (n (ash)) and (n (protein)) butterflyfish gut content samples were analyzed for protein content and ash content, respectively...... 45

Table 14. Summary of potential nutritional quality (PNQ) values for butterflyfish gut content samples. Averages are taken as per sample type per fish species, over (n(PNQ)) samples, with associated standard deviation...... 52

CHAPTER ONE: INTRODUCTION

1.1 Importance and Degradation of Coral Reefs

Coral reefs hold a special place in our world as one of the most diverse ecosystems on the planet (e.g. Connell, 1978; Reaka-Kudia & Wilson, 1997). Besides their high level of biodiversity, coral reefs also provide invaluable goods and services to millions of coastal populations. (Cote et al., 2005) Slightly more than one hundred countries have coral reefs along their coasts, with the overwhelming majority of them being developing countries. With alternative resources often lacking in these areas, coral reefs become a critical source of human subsistence; for example, it is thought that up to 90% of the animal protein consumed on Pacific islands is derived from marine resources. (Salvat, 1992)

With the impressive biodiversity provided by coral reefs and the heavy dependence of human populations on these ecosystems, it is highly distressing to find that coral reefs around the world are facing imminent extinction. (e.g.

Richmond, 1993) This is due to a variety of both local and global anthropogenic influences. Nutrient loading and pollution deriving from increased urbanization and agriculture combine with overharvesting of marine organisms and destructive fishing methods to create a potent local forcing that destroys nearby coral reefs. (e.g

Johannes 1975, McClanahan et al. 1999) Rising temperatures and ocean acidification effect a powerful global forcing that is also quickly destroying the world’s reefs.

Following the industrial revolution, CO2 inputs into the environment have been steadily increasing at an alarming rate. During the 20th century, this has resulted in 12 an increase in global average ocean temperature by 0.74°C and sea level by 17cm, as

-1 well as a decrease in seawater CO3 concentrations by ~30 mmol*kg of seawater causing pH to decrease by 0.1 pH units (Solomon et al., 2007). As coral organisms are highly sensitive to changes in both water temperature and salinity, these rapid changes present a serious threat.

1.2 Corallivory

Considering the continual degradation of this important ecosystem, it is increasingly important to understand trophic dynamics of coral reefs so that we may predict how reef-dependent organisms will respond to changes in coral communities. It is therefore rather surprising to find that corallivory (the feeding mode in which live coral is the prey), one of the most fundamental trophic interactions in reef habitats, is still poorly understood. Only recently have corallivores begun to receive significant attention in the scientific community (Cole et al, 2008; Rotjan & Lewis, 2008).

Corallivores include both vertebrates and invertebrates (Rotjan & Lewis,

2008), but the most conspicuous of these are fishes. Primarily over the last 30 years, various studies have reported a total of 128 species of fishes that at least occasionally consume live coral. (Cole et al., 2008) However not all coral species are grazed equally. In fact, only 18 out of the 111 total coral genera (Veron, 2000) have been reported as being even occasionally consumed by fishes, with the most common being Acropora, Pocillopora, Montipora, and Porites (Pratchett 2005;

Rotjan, 2007). This suggests strong prey preferences in corallivorous fishes. 13

Of the 128 corallivore fish species, 69 of them (53.9%) belong to the butterflyfish family (Chaetodontidae) (Cole et al. 2008). Compared to other corallivores, most butterflyfishes can be considered less destructive since they do not damage the underlying skeleton as they graze, but rather remove individual coral polyps using the bristle teeth from which their family name derives (Motta

1988, Motta 1989).

1.3 Corallivory in Butterflyfish (f: Chaetodontidae)

Butterflyfish as a whole are known to consume a wide variety of food sources, however the majority of the species feed primarily, if not exclusively, on hard coral (Scleractinia) (e.g. Anderson et al., 1981; Harmelin-Vivien & Bouchan-

Navaro, 1983; Pratchett, 2005) Like other corallivores, butterflyfish also exhibit strong prey preferences. (Gore, 1984; Irons, 1989; Tricas, 1989; Cox, 1994; Berumen et al., 2005; Pratchett, 2005) For example, in a study by Pratchett (2007), it was found that Chaetodon trifascialis selectively consumed Acropora corals (especially A. hyacinthus), avoiding every other genus of prey coral. More generally, in an earlier study done by Pratchett (2005) on the feeding preferences of 20 species of butterflyfish on the Great Barrier Reef, Australia, it was found that virtually all 20 species fed predominantly on just two species of coral: Acropora hyacinthus and

Pocillopora damicornis. It is notable that the basis for this selectivity or preference is not known. 14

1.4 Butterflyfish Species Studied

Six species of butterflyfishes have been included in this study. Each of six species was sorted into a feeding category: an obligate corallivore (three species studied) that exclusively feeds on live coral, or a facultative corallivore (three species studied) that consumes live coral in addition to other food sources.

1.4.1 Chaetodon auriga Chaetodon auriga is a facultative corallivore. C. auriga is a wide-ranging species and can be found throughout the Indo-west and central Pacific (Allen et al.,

1998). In previous studies, between 18-60% of the gut contents of this species have been found to be of live coral (Randall et al., 1996; Sano et al., 1984; Harmelin-

Vivien, 1989; Harmelin-Vivien & Bouchon-Navaro, 1983). In general, C. auriga is considered to be less heavily dependent on coral as a food source than other butterflyfish species. As an example, in the aforementioned study done by Pratchett

(2005), 104 individuals of C. auriga were observed feeding, resulting in the diet being determined as 3.61% live hard coral and 95.27% “imperceptible prey items” based on bite rates.

1.4.2 Chaetodon austriacus Chaetodon austriacus is an obligate corallivore with a relatively narrow habitat range, found only in the Red Sea and the adjacent Gulf of Aden, with rare excursions also to the coast of Oman (Allen et al., 1998). Coral genera on which it has been observed feeding include Acropora, Favia, Montipora, Pocillopora, Porites,

Seriatopora, and Stylophora; C. austriacus has been observed feeding at rates of 4.4- 15

6.4 bites per minute (Harmelin-Vivien, 1989; Wrathall et al., 1992; Righton et al.,

1998; Alwany et al., 2003).

1.4.3 Chaetodon fasciatus Chaetodon fasciatus is a facultative corallivore, as determined by studies of gut samples from the Red Sea (Harmelin-Vivien & Bouchon-Navaro, 1982).

Chaetodon fasciatus exhibits a relatively restricted habitat range, and can be found only in the Red Sea and the neighboring Gulf of Aden (Allen et al., 1998).

1.4.4 Chaetodon larvatus Chaetodon larvatus is also an obligate corallivore, observed feeding at an average rate of 10 bites per minute (Zekeria et al., 2002). This species is found only in the Red Sea and in the adjacent Gulf of Aden (Allen et al., 1998).

1.4.5 Chaetodon paucifasciatus Chaetodon paucifasciatus is a facultative corallivore (Bouchon-Navaro, 1986) and can be found only in the Red Sea and in the adjacent Gulf of Aden (Allen et al.,

1998).

1.4.6 Chaetodon trifascialis Chaetodon trifascialis is an obligate corallivore and is a highly widespread species, with habitat extending throughout the Indo-west and central Pacific, from the Red Sea to the Hawaiian Islands (Allen et al., 1998). Chaetodon trifascialis has been observed feeding on Acropora, Pocillopora, Porites, and Montipora at rates ranging from 4.58-10.74 bites per minute (Sano et al., 1984; Harmelin-Vivien, 1989;

Sadovy & Cornish, 2000; Irons, 1989). Although C. trifascialis has been observed to consume the aforementioned coral genera, this species is actually very selective in its coral consumption, feeding on an extremely limited range (<1%) of available 16 coral species. In particular, C. trifascialis strongly prefers to consume just one coral species, Acropora hyacinthus; this extreme dietary preference is well-documented

(Irons, 1989; Pratchett, 2005).

1.5 A New Approach

A significant number of studies have been conducted on the subject of prey selectivity in butterflyfishes (e.g. Reese, 1981; Tricas, 1989; Berumen & Pratchett,

2008; Pratchett 2007; Pratchett & Berumen, 2008), however most of these studies have used feeding observations alone as a method of determining the corallivores’ diets. These studies result in descriptions of what the corallivores are eating or preferentially selecting but leave unclear the question of why they are selecting one prey item over another. Two studies have also taken a nutritional approach to understanding the various corallivores’ dietary selectivity, however these have also had limited success in explaining the dietary choices (Keesing, 1990; Pratchett,

1995).

It is suggested here that previous nutritional studies of corallivory have had limited success because they did not assess all three of the main macronutrients

(lipid, protein, carbohydrate) simultaneously in their studies. This study will evaluate all three macronutrients for each sample; this data will later be used to build a model that will, for the first time, consider the interactive effects between them.

CHAPTER TWO: MATERIALS AND METHODS

2.1 Sample Collection and Preparation

All fish gut samples were collected in February 2011 from four offshore reefs

(Al-Fahal, Mar Mar Gorgonia II, Saut, and Shib Nazar) along the central Saudi

Arabian Red Sea coast. A number of specimens from six species of butterflyfishes

(Chaetodontidae) were speared and put on ice until dissection (Table 1). Prior to dissection, the weight, total length, and fork length were recorded for each fish. The contents from the stomach and from the last 5cm of the gut were then collected separately and kept at -20°C. Freeze-dried for approximately two days, smaller samples were then pooled according to species and sample type (i.e. stomach vs. hindgut) to achieve a mass sufficient for all three macronutrient measurements, a total mass of at least 70mg. All fish gut content samples were then homogenized by placing one stainless steel ball bearing per vial and then putting the vials in the

Qiagen® Tissue Lyser II at a frequency of 30 Hz for t = 10 minutes.

Table 1. Count of butterflyfish gut content samples before and after pooling the lower-mass samples.

No. samples No. samples Species Sample Type collected after pooling hindgut 2 1 Chaetodon auriga stomach 3 2 hindgut 18 5 C. austriacus stomach 9 3 hindgut 3 3 C. fasciatus stomach 2 2 hindgut 23 9 C. larvatus stomach 20 7 hindgut 1 1 C. paucifasciatus stomach 0 0 hindgut 7 2 C. trifascialis stomach 10 3

All coral samples were collected in July 2011 from two reefs (Al-Fahal and

Shib Nazar) along the central Saudi Arabian Red Sea coast. Thirteen species of scleractinian coral and two species of soft coral were collected, with fragments collected separately from up to sixteen colonies per species (Table 2). Fragments were collected and temporarily kept in seawater to avoid bleaching; within 3 hours, they were frozen at -20°C. Throughout the following month, all tissue was removed from the fragments using pressurized air, with a pre-rinse of distilled water to avoid salts contamination. During tissue removal, care was taken to avoid the inclusion of boring organisms in the collection vial. The slurry of coral tissue plus water was collected separately by colony, frozen at -20°C, then freeze-dried for approximately two days. All coral tissue samples were then homogenized using stainless steel ball bearings and the Qiagen® Tissue Lyser II, same as for the fish gut content samples. 19

Table 2. Count of colonies sampled per coral species. Shaded rows are soft coral species; all others are hard coral species (scleractinian).

Species No. colonies sampled Acropora hyacinthus 2 Acropora pharaonis 10 Echinopora forskaliana 11 Echinopora fruticulosa 1 Favia stelligera 6 Galaxea fascularis 12 Goniastrea edwardsi 16 Montipora millepora 4 Montipora tuberculosa 8 Pocillipora cf. verrucosa 6 Pocillipora damicornis 4 Porites spp. 10 Stylophora pistillata 10 Sinularia spp. 14 Xenia spp. 10

Following homogenization, all fish gut content samples and coral tissue samples were subsampled for the three macronutrient analyses, with a target of

15mg, 30mg, and 40mg for the protein, lipid, and carbohydrate analyses, respectively. These target weights were assigned based on technical limitations of the respective analytical methods used.

2.2 Lipid Analysis

All fish gut samples and coral tissue samples were analyzed in KAUST’s Red

Sea Research Center for total lipid content as a percentage of dry weight, using a 2:1 20

(v/v) chloroform-methanol mixture, as according to the Folch method (Folch, 1957).

In previous similar studies, the specific protocol called for a relatively large sample size (~250mg), requiring an unfavorably large degree of sample pooling. For this study, the specific protocol was modified to accommodate smaller sample sizes. This allowed for less sample pooling, which increased the resolution and the number of data points in the data set. The specific protocol is detailed below.

2.2.1 Modified Folch method for lipid analysis

1. Remove pre-weighed subsamples designated for lipid analysis from -20°C

freezer. Allow ~20 minutes to thaw.

2. Shake extensively the stock solution of chloroform:methanol (2:1 by v/v) as

the solution begins to dissociate with time.

3. Add volumes of chloroform:methanol to each vial such that:

(!"#$%& !ℎ!"#"$"#%: !"#ℎ!"#$ !" ) = 20×(!"## !" !"#!$%&'( !"#$% !" )

4. Place one stainless steel ball bearing in each vial and then homogenize

solutions by placing vials in the Qiagen® Tissue Lyser II at frequency of 17 Hz

for t = 10 minutes. Note that preliminary standardizations using higher

frequency caused pieces of the vial’s cap to chip off into the solution, yet

using lower frequency caused the subsample solid to condense into small

hard chunks, preventing the total solubilizing of lipids. Thus a frequency of

17 Hz has been found to be ideal.

5. Remove the ball bearings from each vial using a large magnet.

6. Filter the homogenized solution into pre-weighed empty vials using a syringe

with a 0.45μm Nylon membrane filter attachment. Each vial was 21

homogenized again immediately prior to filtration using a small vortex

mixer.

7. Add volumes of reverse osmosis water such that:

!!"#$% !" !"#$% !" = 0.25 × (!"#$%& !" !"#$%&%' !"#$%&"' !" )

The volume of filtered solution may be determined by using a vial that has

graduation markings, with the accuracy of the markings having already been

tested. In this study, the volume of filtered solution was estimated for each

vial in this way, with an accuracy of ±50 μL.

8. Homogenize new solutions in Qiagen® Tissue Lyser II at frequency of 17 Hz

for t = 5 minutes.

9. Centrifuge all vials at 12,000 rpm for t = 30 minutes, in order to promote

separation of solution into two density layers.

10. Solutions should now be composed of two phases. Remove and dispose of the

upper phase, which contains dissolved sugars and salts.

11. Place vials with lower phase (containing dissolved lipids) into 25inHg

vacuum at room temperature and allow solution to evaporate for

approximately two weeks, or preferably up to a month if time constraints

allow.

12. Weigh and re-weigh vials over the course of evaporation to determine when

the weight becomes steady, suggesting that the evaporation has essentially

finished. Determine “percent lipid content” values by:

!"## !" !"#$ !"#ℎ !"#$%&'(! − (!"## !" !"#$% !"#$ ) % !"#"$ !"#$%#$ = × 100% (!"## !" !"#!"#$% !"#!$%&'( !"#$%) 22

2.2.2 Preliminary standardization tests

The modified protocol outlined above was tested using foodstuffs as standards, namely banana, ground chicken, and cashew nuts. These foods were chosen as standards so that experimental lipid values might be compared to those given by the U.S. Department of Agriculture (USDA). The three foods were chosen in particular in an effort to test with a range of lipid contents, with the expectation that

“% lipid content” values would range as banana < ground chicken < cashew nuts.

Stocks of homogenized foodstuffs were prepared by placing a stainless steel ball bearing in each vial of foodstuff and homogenizing in a Qiagen® Tissue Lyser II at frequency of 30 Hz for t = 20 minutes.

1. Effect of adding methanol to lower phase before evaporation:

It has been suggested that one might add methanol to the

lower phase solution before beginning the evaporation process, in

order to homogenize with any remaining upper phase.

By doing so, the differences in resulting percent lipid content

values were negligible. (Table 3)

Methanol Avg. lipid content Run # Standard added? (± std.dev.) 2 Banana No 6.4±0.2% 3 Banana No 5.7±0.2% 4 Banana Yes 6.9±0.1% 2 Chicken No 9.6±0.6% 3 Chicken No 10.7±0.5% 4 Chicken Yes 13.1±1.0% 2 Cashew nuts No 65.5±2.2% 3 Cashew nuts No 81.6±9.4% 4 Cashew nuts Yes 63.0±2.9%

2. Effect of filter pore size:

Results from very first lipid analyses of foodstuffs were higher

than values given by USDA. It was thought this might have been due to

the filter pore size being too large, allowing unwanted particles to

pass through. Comparing results with a smaller filter pore size (both

are Nylon membrane) reveal no significant difference. (Table 4)

Table 4. Results of standardization test #2. Changing the pore size of filter used in Step 6 (see "Modified Folch method for lipid analysis") had no effect on resulting lipid content.

Filter pore Avg. lipid content Standard size (μm) (± std.dev.) Banana 0.2 5.0±0.2% Banana 0.45 5.5±0.5% Chicken 0.2 10.8±1.9% Chicken 0.45 9.5±0.2%

3. Effect of smaller sample sizes:

Many of the fish gut content samples to be analyzed were very

small. All previous standardization tests had used 75±5mg of

standards, so a run of standards with masses of 30±5mg was analyzed

to test for effects due to sample size. There was no significant

difference. (Table 5)

Table 5. Results of standardization test #3. No effect on resulting lipid content was observed in changing the sample mass from 75 to 30mg.

Sample mass Avg. lipid content Run # Standard (±5mg) (± std. dev.) 4 Banana 75 6.9±0.1% 6 Banana 30 6.9±0.3% 4 Chicken 75 13.1±1.0% 6 Chicken 30 10.7±0.6% 4 Cashew nuts 75 63.0±2.9% 6 Cashew nuts 30 64.1±4.8%

4. Time necessary to complete evaporation:

All vials from standardization runs were weighed several times

throughout the course of about a month to monitor their progress in

the evaporation procedure and to determine at which point their

weights stabilized. It was determined that the majority of the

evaporation process had finished after about two weeks, yet allowing

a month or more for evaporation is preferable. (Figure 1)

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5. Investigation of green appearance in coral samples:

In running a preliminary lipid analysis of coral tissue from the

coral Montipora millepora, a dark green color was observed in the

final lower phase solution, quite different from the usual yellowish

color observed in the lower phases of foodstuff standards. We

hypothesized the coloration might either be due to chlorophyll or to

intact zooxanthellae in the lower phase. If intact zooxanthellae were

the cause, this would be a problem because it would suggest that the

zooxanthellae in our samples were not being homogenized properly,

meaning that our resulting percent lipid content values were not

including the lipids found in zooxanthellae, which likely have a

significant contribution to the total amount.

To determine the cause, samples were observed under a

microscope; no physical structures were observed, indicating that

there were no intact zooxanthellae in the lower phase. The sample

was then observed under microscope with UV light and a double filter

specific to chlorophyll; an excitation was observed, indicating that the

green coloration was indeed due to chlorophyll.

2.3 Protein Analysis

All samples were analyzed by the Analytical Core Lab at KAUST for crude protein content as a percentage of dry weight. This was done by measuring total- 27 nitrogen content using a Flash 2000 Thermo Scientific CHNS/O Analyzer, as according to U.S. EPA Method 440.0 (Zimmerman, 1997). Multiplying the determined concentration of total-nitrogen by n=6.25 then resulted in the concentration of crude protein in the sample, with n=6.25 being the generally accepted scaling factor deriving from the observation that most animal proteins contain about 16% nitrogen (Lusk, 1928; Brody, 1945; Winberg, 1971).

2.3.1 Preliminary standardization tests

Prior to running targeted samples on CHNS/O Analyzer, foodstuff standards were analyzed for protein content, with sample sizes ranging from 14-140mg.

Results for the three different foodstuffs are listed in Table 6.

Standard n Average protein content Std. dev. Relative std. dev. Banana 12 9.7% 4.7% 48.4% Chicken 12 93.3% 5.2% 5.5% Cashew nuts 12 29.8% 9.4% 31.5%

Resulting average standard deviation for all three types was 6.4±2.6%; resulting average relative standard deviation for all three types was 28.5±21.6% (as percentage of average protein concentration per food type). Due to the relatively large breadth of relative standard deviation values, the more consistent precision indicator across all food types is the absolute standard deviation. This suggests that 28 precision in this protein analysis is not dependent on the protein content of the sample; rather, there appears to be a relatively constant error (6.4±2.6%) in all measurements, suggesting the error derives from some other factor, perhaps inadequacy in the sample homogenization process. Thus, a standard deviation of

±9.0% (the maximum standard deviation as estimated by this observed constant error) will be applied to all protein concentration results for the targeted coral tissue and fish gut content samples.

2.4 Carbohydrate Analysis

All samples were measured for ash content at the Soil and Plant Analysis

Laboratory in the University of Wisconsin, USA. The determined percent ash content represents the percentage of the sample that is inorganic minerals by weight. Using this and previous measurements, the percent carbohydrate content of each sample can be determined by subtraction using the following equation:

%!"#$%ℎ!"#$%& = 100% − %!"#"$ − %!"#$%&' − %!"ℎ

where “%ash” is calculated by dividing the mass of the ash obtained after burning by the mass of the original sample prior to burning.

2.4.1 Preliminary attempts to measure ash content

We first attempted to perform the ash content analyses at KAUST, using a muffle furnace in the campus furnace lab. Four crucibles were borrowed from the lab: two ceramic crucibles and two quartz crucibles. Crucibles were weighed empty and then between 20-50mg of standard (banana or chicken) was placed in each one.

All crucibles were then fired in a Thermo Scientific Lindberg® Blue M® muffle furnace at 550°C for three hours, with three hours each of warming time and cooling time. The results were unreasonable, with the final weight of the crucible plus ash being less than the weight of the empty crucible. It was assumed this error was due to differences in water content of the crucibles before versus after ashing. Thus, the crucibles were fired empty at 200°C for 3 hours to remove all water, and then placed in a desiccator to cool before obtaining the empty weight. Crucibles were weighed, standards were added in amount ranging from 25-100mg, and then were fired in the muffle furnace same as before. Results were still inconsistent, giving negative ash content values as before.

It was thus assumed that the large size of the crucibles (weighing about 15g each) allows for too large a variation in weight, which overshadows the small change in mass of the standard sample as it becomes ash (maximum change of

100mg, which is only 0.67% the weight of the crucible).

2.4.2 Successful measurement of ash content

Unable to complete the ash content analysis at KAUST, all fish gut content and coral tissue samples were instead sent to the Soil and Plant Analysis Laboratory

(SPAL) in the University of Wisconsin, USA.

At SPAL, samples were analyzed following their proposed procedure, modified in particular for these small samples, using NIST (National Institute of

Standards and Technology) Standard Reference Material 1547 (peach leaves). This standard was chosen due to its low ash content, near to expected minimum values for the fish gut content and coral tissue samples; this standard is also dry and uniformly ground, similar to the targeted samples. The standard was tested twice.

First, using 12-14mg, resulting ash content was 7.41±0.16% (n=5). Second, using

14-18mg, resulting ash content was 7.42±0.12% (n=5). With an average of

7.42±0.14%, this was an acceptable degree of precision.

The ashing was done in a Sybron Thermoline model FA1740 muffle furnace for a minimum of 3 hours. The average time was 8-12 hours at 550°C. Heating time was approximately 1 hour and cooling time was about 3 hours.

For samples of 65mg or less, 0.25 dram Type 1 borosilicate shell vials were used and for samples larger than 65mg, 0.5 dram Type 1 borosilicate shell vials were used. This division was arbitrary but the intent was to minimize the depth of the sample material, to allow for complete burning. Prior to use, all vials were fired under the same conditions used for ashing and then stored in a desiccator.

CHAPTER THREE: RESULTS

3.1 Prey: Coral Tissue Macronutrient Content

A total of 124 coral tissue samples were analyzed for protein, ash, and lipid content. Thirteen species of hard coral and two species of soft coral were included; these spanned eleven genera. A special note on Pocillopora spp.: this genus was sampled from two different reefs (Al-Fahal and Shib Nazar). The six colonies sampled at Al-Fahal were understood to be Pocillopora verrucosa upon collection.

However, there is known taxonomic complexity with Pocillopora damicornis and P. verrucosa, and these species may actually form a species complex (Erin Jhen, personal communication). For this reason, the six samples from Al-Fahal reef are marked as “Pocillopora cf. verrucosa”, as it is not completely certain which species was actually sampled.

3.1.1 Protein Results

Experimental results for protein content in all coral tissue samples are listed in Appendix 1. A summary of these results, including averages per species, is presented in Table 7.

Average protein Standard Relative Standard

Species n content per species deviation Deviation

Acropora hyacinthus 2 29.41% 6.58% 22.39%

Acropora pharaonis 10 18.03% 7.53% 41.77%

Echinopora forskaliana 11 16.91% 5.05% 29.86%

Echinopora fruticulosa 1 8.13% N/A N/A

Favia stelligera 6 19.46% 6.17% 31.73%

Galaxea fascularis 12 12.13% 5.31% 43.80%

Goniastrea edwardsi 16 19.41% 6.25% 32.21%

Montipora millepora 4 10.39% 3.25% 31.30%

Montipora tuberculosa 8 14.62% 2.38% 16.26%

Pocillipora cf. verrucosa 6 42.74% 3.79% 8.86%

Pocillipora damicornis 4 29.80% 1.21% 4.05%

Porites spp. 10 12.14% 3.85% 31.73%

Sinularia spp. 14 11.21% 4.38% 39.04%

Stylophora pistillata 10 31.70% 4.76% 15.02%

Xenia spp. 10 18.96% 3.38% 17.84%

In the coral tissue samples, protein content ranged from 4.1-48.7% (SD:

±9.0%), with an average of 18.8±9.4%, by weight. 33

The protein concentrations found in coral tissue samples were not normally distributed (Kolmogorov-Smirnov, df=124, F=0.090, P=0.015). For this reason, non- parametric tests were used for this data set.

A Kruskal-Wallis test was employed to determine if there was a significant difference in protein content among different species. Results indicated that there was a highly significant difference in protein content among different species of coral (χ2 = 76.063, P<0.0005).

Next, Mann-Whitney U tests were performed consecutively to determine if there was a significant difference in protein content between the two reefs, per species. Six species (Acropora hyacinthus, Echinopora fruticulosa, Montipora millepora, Montipora tuberculosa, Pocillopora damicornis, and Pocillopora cf. verrucosa) were sampled from only one of the two reefs, precluding the analysis.

Results for the nine species tested are summarized in Table 8. For those species that were found to have significant differences in protein content between the two reefs, average protein content per species per reef is also included in Table 6. Significant differences were found for four of the nine species tested: Galaxea fascularis,

Goniastrea edwardsi, Sinularia spp., and Xenia spp. No significant differences were found for the five remaining species: Acropora pharaonis, Echinopora forskaliana,

Favia stelligera, Porites spp., and Stylophora pistillata. Note that both soft coral species (Sinularia spp. and Xenia spp.) were found to have significant differences in protein content between the two reefs.

3$--4 /;<=&(#"7)+-& /;<=&(#"7)+-& !"#$%&'()*+), -&./01 -&.'21 56+7-)8&9 : ./%40$6$%1 '7>=&>);= .'6+?&2$@$#1 '7>=&>);= !"#$%$#&'%(&#&$)*+ ! " "#$$$ $#$%! &'#(&) *#'") (+#*$) '#,$) ,"(*)$%$#&'-$#+.&/*&)& ' * ,#$$$ $#(!" &%#!$) "#,$) &'#+") '#!') 0&1*&'+23//*43#& & ' $#$$$ $#&+" ,#*") -. (&#+") +#"() !"#"$%"&'"()*#"+,( A A B=CCC C=CDE F=CBG D=HIG JE=DJG E=EAG !-.,"(/+%"&%01"+0(, I F K=CCC C=CCK JB=BDG B=EHG DK=DHG B=KBG 5$#*23+'/00# ' ' !#$$$ $#('& &"#%*) +#$,) &$#+&) "#$") 2,.*#"+,"&,((= I I K=CCC C=CCA JB=EBG D=KDG I=HHG K=DJG 627/$%($#&'%*+2*//&2& ' ' +#$$$ $#$!* "+#+$) +#"$) (,#$$) "#!!) 3%.,"&,((= E E J=CCC C=CJA DJ=DHG J=DFG JA=AEG K=DIG

12/345/67869:;;<=>?56@652/56576/226?86A?86?;60B752?;6C5D6(6B228/ C74A6E6F2/G6B2H34:BE;7Mann-Whitney U tests were also used to compare differences within a genus between species. Out of all eleven genera sampled in this study, only four genera were sampled from two different species, thereby allowing for this test: Acropora,

Echinopora, Montipora, and Pocillopora. Out of these four, Pocillopora spp. showed significant difference (U=0.000, P=0.011) in protein content between the two species Pocillopora cf. verrucosa and Pocillopora damicornis. The average protein content in Pocillopora spp. per species can be found in Table 7. The remaining three genera (Acropora spp., Echinopora spp., and Montipora spp.) showed no significant differences in protein content between species.

3.1.2 Ash Content Results

Experimental results for ash content in all coral tissue samples are listed in

Appendix 1. A summary of these results, as averages per species, is presented in

Table 9.

Average Relative

Species n ash content Std. dev. std.dev.

Acropora hyacinthus 2 48.33% 16.09% 33.29%

Acropora pharaonis 10 57.25% 10.47% 18.30%

Echinopora forskaliana 11 67.69% 8.66% 12.80%

Echinopora fruticulosa 1 80.52% N/A N/A

Favia stelligera 6 62.78% 9.77% 15.56%

Galaxea fascularis 12 69.22% 9.98% 14.42%

Goniastrea edwardsi 16 56.61% 10.32% 18.23%

Montipora millepora 4 73.61% 10.47% 14.22%

Montipora tuberculosa 8 72.53% 5.17% 7.13%

Pocillipora cf. verrucosa 6 39.63% 7.55% 19.06%

Pocillipora damicornis 4 20.22% 11.48% 56.79%

Porites spp. 10 77.97% 3.79% 4.87%

Sinularia spp. 14 78.18% 10.14% 12.97%

Stylophora pistillata 10 44.34% 7.24% 16.33%

Xenia spp. 10 44.07% 6.09% 13.81%

In the coral tissue samples, ash content (as percentage of sample weight) ranged from 13.97-90.31%, with an average of 60.96 ±16.83%.

A test of normality of the ash content distribution resulted in a non-normal distribution (Kolmogorov-Smirnov value of 0.079, P=0.057). For this reason, non- parametric tests were used for this data set.

A Mann-Whitney U test was used to determine if there was a significant difference in ash content between the two reefs sampled (Al-Fahal and Shib Nazar).

Results showed that there was no significant difference (U=1868, P=0.806). The interpretation of this result is a bit problematic, however, since this test did not account for differences among coral species; some coral species were sampled from only one of the reefs, further biasing the result of this test.

A Kruskal-Wallis test was employed to determine if there was a significant difference in ash content depending on species. Results indicated that there was a highly significant difference in ash content among different species of coral (χ2 =

89.905, P<0.0005).

Next, Mann-Whitney U tests were performed consecutively to determine if there was a significant difference in ash content between the two reefs, per species.

Six coral species (Acropora hyacinthus, Echinopora fruticulosa, Montipora millepora,

Montipora tuberculosa, Pocillopora cf. verrucosa, and Pocillopora damicornis) were sampled from only one of the two reefs, precluding this particular test. Out of the nine species tested (Acropora pharaonis, Echinopora forskaliana, Favia stelligera,

Galaxea fascularis, Goniastrea edwardsi, Porites spp., Sinularia spp., Stylophora 37 pistillata, and Xenia spp.), none were found to have significant difference in ash content between the two reefs.

Similar to the testing done for protein content, the ash content values were also tested for differences within genera but between species, again using Mann-

Whitney U tests. Tests were done on the same four genera: Acropora spp.,

Echinopora spp., Montipora spp., and Pocillopora spp. Similar to the results for protein content, again Pocillopora spp. exhibited significant difference (U=2.000,

P=0.033) in ash content between the two species (Pocillopora cf. verrucosa vs.

Pocillopora damicornis). The average ash content in Pocillopora spp. per species is given in Table 9. The three remaining genera showed no significant differences in ash content between species.

3.1.3 Combination of Protein Content and Ash Content Results

Due to unexpected issues with the lipid analyses of the samples, the percent lipid content values of the coral tissue and butterflyfish gut content samples were not found. However, upon combining the results from the protein analysis and the ash content analysis, several interesting trends became apparent. We propose that the ratio obtained by dividing the percent protein content by the percent ash content of a sample can be considered to represent the “potential nutritional quality” (PNQ) of the sample. This is because protein is one of the three main macronutrients; a higher protein content suggests a higher nutritional quality.

Conversely, ash content essentially measures mineral content; minerals are of a lesser importance to the nutritional quality of a food item, compared with protein, 38 lipids, and carbohydrates. Note that the modifier “potential” is included in this

“potential nutritional quality” value, since the lipid and carbohydrate content of the samples remain unknown, but may affect the ranking of nutritional quality in the coral tissue samples. Average potential nutritional quality values per coral species are listed in Table 10, along with the average ash content and average protein content values that were used in their calculation. Potential nutritional quality values for each individual sample are listed in Appendix 2.

Table 10. Average percent protein content was divided by average percent ash content for each coral species to obtain the average "potential nutritional quality" values. Standard devi!"#$%&' ations are calculated as among the “n” colonies sampled.()*+,&-.//01&/+23,1/4&/02&5+,01/&+67&80)-.16-&5+,01/9&+/&+51*+:1/&31*&/31;.1/

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

The data set of potential nutritional quality values for all individual samples formed a non-normal distribution (Shapiro-Wilk result = 0.719, P< 0.0005). Thus, non-parametric tests were used in the study of this data set. The potential nutritional quality values ranged from (0.0469-2.1635), with an average of (0.3976

±0.3866).

To determine if there was a significant difference in potential nutritional quality values for coral samples from one reef versus the other reef, a Mann-

Whitney U test was performed (U=1784.500, P=0.506). There was no significant difference in the potential nutritional quality values of coral tissue between the two reefs sampled.

Next, to determine if there was a significant difference in potential nutritional quality depending on the coral species, a Kruskal Wallis test was performed (χ2=82.888, P<0.0005). Thus, there are highly significant differences in the potential nutritional quality among the various coral species sampled.

To determine between which species these significant differences lay, the average protein content and ash content for each species was plotted in Figure 2.

(!"# 10

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14 11 &!"# 1 7 5 3 %!"# 9 12 !"#$%&'()"*%)&')&" 15 2 13 $!"# 6 8 4 !"# !"# $!"# %!"# &!"# '!"# (!"# )!"# *!"# +!"# ,!"# !"+,-"*%)&')&"

Figure 2 shows that the coral species sampled span a wide range of both protein content values and ash content values. Coral species plotted near the upper left side of Fig. 2 have higher potential nutritional quality, while species plotted near the lower right side of Fig. 2 have lower potential nutritional quality. Looking at this plot, it was suggested that the coral species may be able to be split into groups with similar potential nutritional quality and so the average PNQ values for each species were plotted in bar graph form in Figure 3.

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Based on this, all species were separated into three apparent groups, color coded in Figure 3 and listed in Table 12. Note that all three groupings can be generally described as: Group 1 (PNQ <0.50), Group 2 (0.50< PNQ <1.00), and Group

3 (1.00< PNQ). Group 1 contained PNQ values ranging from 0.10-0.44 and included the 11 following species, ordered from smallest PNQ value to largest: Echinopora fruticulosa, Montipora millepora, Sinularia spp., Porites spp., Galaxea fascularis,

Montipora tuberculosa, Echinopora forskaliana, Favia stelligera, Acropora pharaonis, 42

Goniastrea edwardsi, and Xenia spp. Group 2 contained PNQ values ranging from

0.67-0.74 and included just two species: Acropora hyacinthus and Stylophora pistillata. Group 3 included just Pocillopora cf. verrucosa and Pocillopora damicornis, with PNQ values of 1.11 and 1.76, respectively.

The aforementioned three groups were then tested to confirm if there was a significant difference between them. A Kruskal-Wallis test gave (χ2=49.861,

P<0.0005), confirming that there was a significant difference between these three groups.

Multiple Mann Whitney U tests were then performed to test differences between each of the three groups. Results are listed in Table 11. All three groups were found to be significantly different from each other. A within-group Mann

Whitney U test was done on Group 2 (U=8.000, P=0.758) and on Group 3 (U=6.000,

P=0.199). Both results showed no significant differences within the groups, supporting the decision to group them together.

Species Avg. PNQ Std. dev. Group Re:G1 Re:G2 Re:G3 U=48.500, U=2.000, Echinopora fruticulosa 0.10 N/A 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Montipora millepora 0.14 0.04 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Sinularia spp. 0.15 0.08 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Porites spp. 0.16 0.05 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Galaxea fascularis 0.18 0.10 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Montipora tuberculosa 0.20 0.05 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Echinopora forskaliana 0.26 0.11 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Favia stelligera 0.32 0.14 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Acropora pharaonis 0.35 0.21 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Goniastrea edwardsi 0.37 0.20 1 P<0.0005 P<0.0005 U=48.500, U=2.000, Xenia spp. 0.44 0.12 1 P<0.0005 P<0.0005 U=48.500, U=10.000, Acropora hyacinthus 0.67 0.36 2 P<0.0005 P<0.001 U=48.500, U=10.000, Stylophora pistillata 0.74 0.20 2 P<0.0005 P<0.001 U=2.000, U=10.000, Pocillopora cf. verrucosa 1.11 0.24 3 P<0.0005 P<0.001 U=2.000, U=10.000, Pocillopora damicornis 1.76 0.67 3 P<0.0005 P<0.001

3.2 Predator: Stomach/Hindgut Macronutrient Content in

Butterflyfishes

The number of fish gut content samples that were analyzed for protein, ash, and lipid content were N=52,50,51 respectively; two samples (one C. auriga hindgut and one C. fasciatus hindgut) were too small in mass to allow for all three analyses.

These 52 samples include six species of butterflyfish, with N=23 stomach samples and N=29 hindgut samples. These totals are summarized in Table 12.

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3.2.1 Protein Content Results

Complete results of protein analysis of each fish gut content sample can be found in Appendix 3. A summary of this data set can be found in Table 13, where average protein content and average ash content has been calculated per sample type per species.

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A plot of the protein content for each butterflyfish species is shown in Figure

4, with samples differentiated according to sample type (stomach vs. hindgut).

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As can be seen in the above figure, for all species, protein content in hindgut samples consistently fell below that of stomach samples. The only exception was for

C. auriga, in which the one hindgut sample had a protein content of 50.44%, which is above two of the five stomach samples. However, in this case, the stomach sample that was collected from the same individual fish as this hindgut sample is the sample that gave a protein content of 54.38%, and so this still follows the trend of finding higher protein content in the stomach as compared to the hindgut.

The protein content in all butterflyfish gut samples ranged from 9.06-

55.50%, with an average of 36.56 ±11.61%, given as a percentage of the total sample 47 weight. Although the range found in the fish gut samples is approximately the same as that found for the coral tissue samples ((9.06-55.50%) vs. (4.1-48.7%)), the average protein content in the fish gut contents is approximately twice that of the coral tissue ((36.56 ±11.61%) vs. (18.8±9.4%)).

The protein data set forms a non-normal distribution (Shapiro Wilk = 0.952,

P=0.026), so non-parametric tests were used for this data set.

A Kruskal-Wallis test (χ2=15.960, P=0.007) revealed there is a significant difference in the protein content of gut content among different species, without differentiating between sample types (stomach vs. hindgut).

In only considering the stomach samples from all species, a Kruskal-Wallis test (χ2=5.386, P=0.250) revealed that there is no significant difference in protein content between the stomach samples of all species.

In only considering the hindgut samples from all species, a Kruskal-Wallis test (χ2=13.549, P=0.019) revealed that there is a significant difference in protein content between the hindgut samples of all species. To determine between which species this difference lay, multiple Mann Whitney tests were performed to compare the hindgut protein content of each species to all other species. Since this resulted in five tests done for each species, the minimum acceptable significance (α = 0.05) was lowered, as according to the Bonferroni Correction; the new minimum acceptable significance was (α = 0.05/5 = 0.01). After applying the Bonferroni

Correction, none of the species’ hindgut samples were found to have significantly different protein content from any of the other species’ hindgut samples. 48

Protein content of the three different sample types were found to be significantly different from each other (regardless of species) (Kruskal-Wallis test,

χ2=33.160, P<0.0005).

3.2.2 Ash Content Results

Complete results of ash content analysis for each fish gut sample can be found in Appendix 3. This data is also summarized in Table 13, given above. A plot of the ash content for each species is plotted in Figure 5, with samples differentiated according to sample type (stomach vs. hindgut).

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It is clear in the above figure that with regards to ash content, stomach samples consistently fall below hindgut samples; note that this is opposite of the trend observed for protein content in the samples.

The resulting ash content values for all species and over both sample types ranges from (7.05-55.56%), with an average ash content of (25.91 ±11.77%). This range is significantly smaller than that found for the coral tissue samples ((7.05-

55.56%) vs. (13.97-90.31%)). Recall that the average protein content found in the butterflyfishes gut samples was observed to be approximately twice that of the coral 50 tissue samples ((36.56 ±11.61%) vs. (18.8±9.4%)). The opposite relationship is observed here with regards to ash content. That is, the average ash content found in the butterflyfishes gut samples is approximately half of the average ash content found in the coral tissue samples ((25.91 ±11.77%) vs. (60.96 ±16.83%)).

The data set of ash content in butterflyfishes’ gut contents forms a non- normal distribution (Shapiro-Wilk = 0.933, P=0.005). Thus, non-parametric tests were used on this data set.

A Kruskal-Wallis test was performed to search for significant differences in the ash content values between butterflyfish species, without differentiating between sample type (stomach vs. hindgut). The result was (χ2=16.982, P=0.005), indicating that there was a significant difference between the ash content values of different species.

In only considering the stomach samples from all species, a Kruskal-Wallis test revealed (χ2=3.100, P=0.541) that there was no significant difference in the ash content values of stomach samples among the various butterflyfish species.

In only considering the hindgut samples from all species, a Kruskal-Wallis test revealed (χ2=20.242, P<0.0005) that there was a highly significant difference in the ash content values of hindgut samples among the various butterflyfish species. This result of the only significant difference lying within the hindgut data set (and not in the stomach data set) is the same as was obtained for the protein content data.

To determine between which species this significant difference lies, multiple

Mann Whitney tests were performed to compare the ash content found in hindgut 51 samples of one species to all other species. Since C. auriga hindgut samples were not analyzed for ash content (insufficient sample size), this left five butterflyfish species to test. Therefore four Mann Whitney tests were performed for each; with the

Bonferroni Correction, the corrected acceptable significance was (α = 0.05/4 =

0.013). The only significant difference found was between the ash content values of

C. austriacus hindgut samples (n=8) and C. larvatus hindgut samples (n=13); the

Mann Whitney result was (U=0, P<0.0005).

3.2.3 Combination of Protein and Ash Content Results

Upon combining the protein content and ash content results for all butterflyfish gut content samples, several interesting trends became readily apparent. Potential nutritional quality (PNQ) values were calculated for the butterflyfish gut content samples in the same way as for the coral tissue samples, by dividing percent protein content by percent ash content per sample. A complete list of PNQ values for each sample can be found in Appendix 4. A summary of those results is listed in Table 14.

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The PNQ values for all samples formed a non-normal distribution (Shapiro

Wilk = 0.872, P<0.0005). The PNQ values ranged from 0.28-7.44, with an average value of 2.02 ±1.51. This was an approximately three times larger range and an approximately five times higher average value than for the coral tissue samples; recall that coral tissue PNQ values ranged from 0.05-2.16, with an average of 0.40

±0.39.

In comparing the potential nutritional quality values for all samples, some highly interesting patterns emerged.

A Kruskal-Wallis test confirmed (χ2=37.037, P<0.0005) that there was a significant difference in the potential nutritional quality value between different sample types (stomach vs. hindgut). 53

Considering only stomach samples, a Kruskal-Wallis test (χ2=3.641,

P=0.457) revealed that there was no significant difference in PNQ value of stomach content samples between the six different butterflyfish species.

Considering only hindgut samples, a Kruskal-Wallis test (χ2=18.032,

P=0.001) revealed that there was a highly significant difference in the PNQ values of hindgut content samples between the six different butterflyfish species.

To elucidate exactly where this difference lay, multiple Mann Whitney tests were performed on the hindgut PNQ value data set. Chaetodon auriga was not included in this test, since the hindgut sample for this species was not able to be analyzed for ash content. The remaining five species (C. austriacus, C. fasciatus, C. larvatus, C. paucifasciatus, and C. trifascialis) were all compared to each other. This meant four tests each; applying Bonferroni Correction, this called for an acceptable significance value of (α = 0.05/4 = 0.013). The only significant difference found was between C. austriacus and C. larvatus (U=7.000, P=0.001). This is the same finding as that concerning the ash content of hindgut samples. Recall that no significant differences were found between the protein content in hindgut samples among any of the species. Thus, it is suggested here that this difference in the PNQ values of hindgut samples from C. austriacus and C. larvatus is due mainly to a significant difference in the hindguts’ ash content and not protein content.

3.3 Predator vs. Prey

Previous sections have compared results among the fifteen coral species studied and among the six butterflyfish species studied. We now look to compare the data for the prey (coral) to that of the predator (butterflyfishes).

Figure 6 shows how the protein content vs. ash content data set for the coral species compares to that of the butterflyfishes’ stomach content. Note that only five of the six butterflyfish species studied are shown here, since no stomach samples were obtained for C. paucifasciatus.

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From Figure 6, it is clear that for all five butterflyfish species, the stomach content was enriched in protein and poorer in minerals (using ash content as a proxy for mineral content), as compared to all fifteen coral species.

It is also useful to look at Figure 6 in regards to each butterflyfish species and its feeding preferences. Feeding preferences described here are derived from preliminary feeding observations performed by M. L. Berumen in the Red Sea in

2008 (Berumen, unpublished data).

3.3.1 Chaetodon auriga: Stomach Content vs. Feeding Preferences

Chaetodon auriga has been observed to feed primarily on Xenia spp. (>50% of diet); its diet is also composed of <10% each of Pocillopora verrucosa, P. damicornis, and Sinularia spp. (Berumen, unpublished data). This is shown in Figure

3.3.2 Chaetodon austriacus: Stomach Content vs. Feeding Preferences

Chaetodon austriacus has been observed to have a diet composed of >50%

Pocillopora verrucosa, >10% Porites spp., and <10% each of Pocillopora damicornis,

Acropora hyacinthus, and A. pharaonis (Berumen, unpublished data). This is shown in Figure 8.

3.3.3 Chaetodon fasciatus: Stomach Content vs. Feeding Preferences

Chaetodon fasciatus has been observed to have a diet composed of about

20% each of Xenia spp. and Sinularia spp. (Berumen, unpublished data). This is shown in Figure 9.

3.3.4 Chaetodon larvatus: Stomach Content vs. Feeding Preferences

Chaetodon larvatus has been observed to feed primarily on Pocillopora verrucosa (>60% of diet), P. damicornis (~20% of diet), and <10% Galaxea fascularis

(Berumen, unpublished data). This is shown in Figure 10.

3.3.5 Chaetodon trifascialis: Stomach Content vs. Feeding Preferences

Chaetodon trifascialis has been observed to have a diet composed of about

35% each of Acropora hyacinthus and Pocillopora verrucosa, as well as ~15%

Pocillopora damicornis and ~5% Acropora pharaonis (Berumen, unpublished data).

This is shown in Figure 11.

3.4 Lipid Results: Issues and Errors

Combining N=124 samples of coral tissue, N=52 samples of butterflyfish gut content, and N=11 samples of standards (banana, chicken), a total of 187 samples were analyzed for lipid content using the modified Folch method described in

“Materials and Methods”. All samples were run over the course of three days. A complete list of all resulting lipid values can be found in Appendix 5.

Quite surprisingly, despite the fact that the samples were run in the same manner as all previous standards and “test run” coral tissue samples, the results for these latest samples came out with apparent errors. Upon first weighing the 61 samples following between 3-6 days of evaporation in the 25inHg vacuum, it was found that many of the samples had lipid content values far above reasonable estimates, including 53 samples with calculated lipid content greater than 100%.

Altogether, the lipid content values ranged from (28.18-244.03%), with an average lipid content of (86.12±36.75%).

At that point, we considered the possibility that perhaps that samples simply needed more time to evaporate. Seven representative samples of coral tissue and one sample of butterflyfish gut content were chosen, along with all 11 standards, to be weighed several times over the course of two weeks. With past standardization tests, after two weeks of evaporation, the majority of the solution had evaporated and the weight of the extracted lipids became relatively constant (Figures 12, 13, and 14). Note that in Figures 12 and 13, a line is plotted in each displaying the expected lipid content for the two standards (banana and chicken), based on previous standardization experiments.

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With the banana and chicken standards giving such inaccurate results, we are forced to assume that the rest of the data set should be inaccurate as well.

CHAPTER FOUR: DISCUSSION

4.1 Prey: Coral Tissue Macronutrient Content

First, based on the tests done in this study, it appears that the Pocillopora cf. verrucosa samples collected from Al-Fahal reef may indeed be of the species

Pocillopora verrucosa, as they were originally estimated to be. This is because both the ash content and protein content in these six samples are significantly different

(protein: (U=0.000, P=0.011), ash: (U=2.000, P=0.033)) than those from the four

(identity confirmed) Pocillopora damicornis samples. However, note that Pocillopora cf. verrucosa was sampled from only Al-Fahal reef, while P. damicornis was sampled only from Shib Nazar reef. Hence, one might suggest that the true identity of P. cf. verrucosa may actually be P. damicornis, and that these aforementioned significant differences in protein and ash content are due to differences between the two reefs

(e.g. nutrient availability, light intensity, temperature, etc.). Since the true identity of these samples remains uncertain, we will continue to refer to these samples as

“Pocillopora cf. verrucosa.”

Considering differences in protein and ash content of coral tissue depending on the reef at which the coral was sampled, it appears that there are some differences, but these are rather small. In Table 8, we see that four out of five coral species (Galaxea fascularis, Goniastrea edwardsi, Sinularia spp., and Xenia spp.) all exhibited significant differences in their protein content between the two reefs sampled (Al-Fahal vs. Shib Nazar). The interpretation of the two soft coral “species” 66 being included in this list is a bit problematic, in that they were not actually identified down to the species level. Hence, this difference between reefs may actually represent a difference between species; unfortunately, we are unable to test this hypothesis at this point. Overall, the difference in protein and ash content between reefs does not appear to be highly pronounced. The largest difference in protein content found between the two reefs was in species Goniastrea edwardsi and was a difference of ±8.86%; no significant difference was found for coral tissue ash content between the two reefs.

Although there is not too much difference in macronutrient content between reefs, there are highly significant and clear differences between different coral species. Kruskal-Wallis tests revealed highly significant differences in protein content (χ2 = 76.063, P<0.0005) and in ash content (χ2 = 89.905, P<0.0005) between different species of coral. A significant difference (χ2=82.888, P<0.0005) was also found in the potential nutritional quality (PNQ) values between different coral species. This observation is quite favorable in that it supports the proposed future plans to relate macronutrient content of coral prey to macronutrient content of corallivore gut content, with the hope of being able to use these measures to recognize different prey coral species in the gut contents.

Now recognizing that the coral species are significantly different in macronutrient content, we would like to know how different they are and, in particular, which species stand out as especially high/low in nutritional quality.

Looking at protein, the coral species with the lowest average concentration is

Echinopora fruticulosa (8.13% by weight); note that this value could be biased due 67 to low sampling volume, as only one sample was taken for this species. Following E. fruticulosa, the average protein contents of coral species gradually rise up to

19.46±6.17% as (E. fruticulosa < M. millepora < Sinularia spp. < Ga. fascularis <

Porites spp. < M. tuberculosa < E. forskaliana < A. pharaonis < Xenia spp. < Go. edwardsi < F. stelligera). Following this group of 11 species with protein content <

20%, there is a sudden jump up to a second group of 3 species ranging from (29.41-

31.70%), as (A. hyacinthus < P. damicornis < S. pistillata). Finally, there is another large jump up to 42.74±3.79% with species P. cf. verrucosa occupying the position as the species with the highest average protein content.

In looking at the ash content of coral tissue samples (Table 9), we find that E. fruticulosa has the highest average ash content at 80.52% (with possible bias due to only one sampling). From there, the average ash content values decrease gradually as (Sinularia spp. > Porites spp. > M. millepora > M. tuberculosa > Ga. Fascularis > E. forskaliana > F. stelligera > A. pharaonis > Go. edwardsi > A. hyacinthus > S. pistillata >

Xenia spp. > P. cf. verrucosa), with P. cf. verrucosa having an average ash content of

39.63±7.55%. Following P. cf. verrucosa, there is a sudden jump to the species with the lowest average ash content, P. damicornis at 20.22±11.48%.

As ash content represents the mineral content of a sample and since these minerals can be considered less critical to a consumer than the major macronutrients (lipid, protein, and carbohydrate), we suggest that a lower ash content in a sample represents a more desirable, “higher” nutritional quality. That is, the higher the ash content of a sample, the less “percent content value” there is left for protein, lipid, and carbohydrate. Considering this, we can say that the two 68

Pocillopora species (P. cf. verrucosa and P. damicornis) have the highest nutritional quality out of all the coral species tested, since they have among the highest protein content values and the lowest ash content values. A. hyacinthus and S. pistillata can also be considered to have notably high nutritional quality, since they have among the highest protein content values and among the lowest ash content values out of all fifteen species tested. With both the lowest protein content and the highest ash content, E. fruticulosa can be considered as having the lowest nutritional quality out of all species tested.

An overall important point to note concerning the coral tissue data set is that there is a wide range in potential nutritional quality among the fifteen species of coral. Ranging from (PNQ = 0.10 to 1.76±0.67), we see that corals are highly variable in terms of nutritional quality; this may explain the strong feeding preferences observed in many corallivores.

Looking at Table 10, we see that the potential nutritional quality of the coral species starts at (PNQ = 0.10) and gradually increases to (PNQ = 0.44±0.12) as (E. fruticulosa < M. millepora < Sinularia spp. < Porites spp. < Ga. fascularis < M. tuberculosa < E. forskaliana < F. stelligera < A. pharaonis < Go. edwardsi < Xenia spp.).

Following this, there is a jump in potential nutritional quality to A. hyacinthus

(0.67±0.36) and S. pistillata (0.74±0.20). A third jump takes us to the two

Pocillopora species. A Mann Whitney test revealed that the average PNQ values of these two species are not significantly different, thus we cannot consider one as having significantly higher nutritional quality than the other. So, the two coral 69 species with the highest nutritional quality are P. cf. verrucosa (1.11±0.24) and P. damicornis (1.76±0.67).

There are a few interesting things to note on this potential nutritional quality hierarchy. Both soft coral species (Sinularia spp. and Xenia spp.) fall in the lower nutritional quality category, though the former is lower than the latter. It seems that within genera, there is some kind of homogeneity in nutritional quality; both

Montipora species and both Echinopora species fall in the lower category of nutritional quality, while both the Pocillopora species form the highest category.

However, the two Acropora species are split between the lower and middle categories of nutritional quality, so not all genera are homogenous in nutritional quality.

4.2 Predator: Stomach/Hindgut Macronutrient Content in

Butterflyfishes

Firstly, we saw that protein content had the same approximate range for both coral tissue samples and fish gut samples (fish gut: (9.06-55.50%) vs. coral tissue: (4.1-48.7%)). However, the average protein content in fish gut content samples was approximately twice that of the coral tissue (fish gut: (36.56 ±11.61%) vs. coral tissue: (18.8±9.4%)). The trend observed in the ash content results was notably contrasting. Ash content was found to be more constrained in fish gut samples, with a range of (7.05-55.56%), versus a range of (13.97-90.31%) found in coral tissue samples. Additionally, the average ash content found in the 70 butterflyfishes gut samples is approximately half of the average ash content found in the coral tissue samples (fish gut: (25.91 ±11.77%) vs. coral tissue: (60.96

±16.83%)). Hence, we can say here that fish gut content samples were, on average, of notably higher nutritional quality than the coral tissue samples.

Looking at Figure 4, we see that protein content in hindgut samples consistently falls below that of stomach samples for all six butterflyfish species

(Chaetodon auriga, C.austriacus, C.fasciatus, C.larvatus, C.paucifasciatus, and

C.trifascialis).

Looking at Figure 5, we see the opposite trend in ash content of fish gut samples as compared to protein content. That is, hindgut samples lie consistently above stomach samples in terms of ash content. This is true for all six butterflyfish species. Both trends lead us to conclude that the butterflyfishes appear to be absorbing a notable amount of protein while they digest their food. This explains why protein makes up a higher percentage of the stomach content samples, and why the minerals then make up a higher percentage of the hindgut content samples, due to uptake of protein. What is not entirely clear is whether or not the fishes are also absorbing some of the minerals as they digest their food. Additionally, the notable increase in ash content from stomach to hindgut is likely also due to lipids and carbohydrates being absorbed during digestion as well as the protein; we are unable to test this hypothesis without the lipid data for these samples.

Recall that in that Kruskal-Wallis tests that searched for differences in the ash content or protein content per sample type among the six different butterflyfish species, only one sample type revealed any significant differences between the 71 species; that is, the hindgut samples. This test revealed a significant difference (χ

2=13.549, P=0.019) in protein content of hindgut samples, however investigatory

Mann Whitney tests failed to determine between which species these differences lay. The Kruskal-Wallis test also revealed a significant difference (χ2=20.242,

P<0.0005) in ash content of hindgut samples among the fishes. The only interspecies significant difference found was between the ash content values of C. austriacus hindgut samples (n=8) and C. larvatus hindgut samples (n=13); the Mann Whitney result was (U=0, P<0.0005). This may suggest that these two fish species have considerably different dietary preferences; however, this should also mean that the stomach samples should be significantly different in protein and mineral content, which is not the case. Therefore, a more likely explanation is that these two fish species achieve similar protein and mineral intake, but absorb the nutrients differently, leading to significantly different ash content values in their hindgut samples. Since C. austriacus has an average hindgut ash content value of

42.42±3.33% and C. larvatus has an average hindgut ash content value of

33.09±3.81%, a further interpretation would be to estimate that C. larvatus may absorb macronutrients (note this does not include minerals) more efficiently than C. austriacus. Note that this is a highly tentative estimation; more samples are needed in order to confirm or disprove this hypothesis.

Recall that in the above Kruskal-Wallis tests, all four other species (C. auriga,

C. fasciatus, C. paucifasciatus, and C. trifascialis) failed to present significant differences in either ash content or protein content when comparing species to each other per sample type. With no significant difference found between the stomach 72 samples of these species, this suggests that food intake is similar in protein and mineral composition across all species. This could either mean they consume the same food sources, or that the assortment of food choices that each species makes combines to be essentially equal in protein/mineral content across all species. This also suggests that these species’ uptake of protein and minerals is also relatively homogenous across all four species, since no significant differences were found between their hindgut samples either.

Lastly, in comparing the potential nutritional quality (PNQ) values for coral tissue samples to those of butterflyfish stomach samples, we see that those of the fish stomach samples are consistently and significantly higher than those of the coral tissue samples. Average PNQ values for stomach samples per butterflyfish species range from (2.2920±0.8218) to (4.4018±2.6353), while average PNQ values per coral species range from (0.10) to (1.76±0.67). Thus, we can see that the nutritional quality of the stomach samples is much higher than that of the coral prey. This could mean that these butterflyfishes are eating other coral species not tested in this study. A more likely explanation might be that the fishes are targeting the macronutrient-rich parts of the prey coral; unfortunately, this study analyzed the total tissue from a large chunk of coral colony, thereby obscuring this feeding behavior. Another likely explanation relates from the fact that half of these species are facultative corallivores (C. auriga, C. fasciatus, and C. paucifasciatus), meaning they consume other non-coral food sources that likely provide different macronutrient compositions. 73

Finally, we consider the observation that the most commonly preferred corals among corallivorous fishes are Acropora, Montipora, Pocillopora, and Porites

(Rotjan, 2007). We have found that Acropora hyacinthus, Pocillopora damicornis, and

P. verrucosa occupy three out of the four highest potential nutritional quality places, out of all fifteen coral species in this study. Thus, we suggest that these coral species may be preferred due to their nutritional quality, as hypothesized. However,

Montipora millepora, M. tuberculosa, and Porites spp. were all found to have low potential nutritional quality. This may mean these coral are preferred by corallivores for alternative reasons (e.g. ease of access, abundance, digestibility, etc.). Another possibility is that corals from these genera have higher lipid or carbohydrate content, making them favorable to consume for those reasons. This hypothesis is not able to be tested here, without the lipid or carbohydrate data for our samples, but will hopefully be tested in the proposed future work.

One especially interesting point that came out of this study concerns the results for Chaetodon trifascialis. As mentioned previously, C. trifascialis is well documented (Irons, 1989; Pratchett, 2005) as having an extreme preference for only one species of prey coral, Acropora hyacinthus. However, according to preliminary feeding observations performed in the Red Sea in 2008 (Berumen, unpublished data), C. trifascialis appears to feed on Pocillopora verrucosa in equal amounts to

Acropora hyacinthus in this location. The pertinent question is then if this is due to a difference in C. trifascialis in the Red Sea vs. the rest of the world or if this is due to a difference in the prey items (A. hyacinthus and P. verrucosa) in the Red Sea vs. the rest of the world. Interestingly, P. cf. verrucosa was found in this study to have a 74 higher potential nutritional quality as compared to A. hyacinthus (1.08 vs. 0.61, respectively). Perhaps in other locations, this relationship is switched, suggesting that C. trifascialis feeds on P. verrucosa more frequently than usual in the Red Sea due to its relatively higher nutritional quality there.

4.3 Lipids: Hypotheses as to Source of Error

It is unclear what has caused the unfortunate errors in the lipid data. One trend that is quite clear to see is that the error decreases with the “lipid ID number”.

Since samples were run over the course of three days, according to this lipid ID number (that is, starting from L001 and ending at L235 three days later), this indicates that time may be an important factor in this error. Perhaps samples that were run on the first day were tested more slowly than those that were run on the later two days, due to increasing experimenter familiarity with the assay over time.

This difference in “waiting time” could have meant that some samples were allowed to evaporate off some filtrate after Step 6 of the modified Folch procedure (see

“Materials and Methods” section), which meant that an incorrect amount of water was then added to the samples in Step 7.

Another possible explanation for these inaccurate results is contamination.

The solution used during this 3-day analysis was a solution freshly prepared, and so potentially different from the solution used in past standardization experiments.

It is unlikely that these errors arise from flaws in the modified method itself, since both standard and coral samples were analyzed successfully on previous occasions using the same modified method. Nevertheless, the modified method itself will be scrutinized and tested again with standards, in an attempt to determine what 75 caused the substantial errors for this data set, as well as to assure that the method can and will work before running target samples again.

4.4 Future Work

The first step will be to determine the source of error in the lipid analyses.

Once the method is again producing correct results using standards, the coral tissue samples will be analyzed for lipid content again, as there is leftover sample for almost all the lipid samples. Unfortunately, essentially all of the butterflyfish gut content samples were used completely in the macronutrient analyses for this study; therefore, no re-analysis of the lipid content in these samples will be possible.

Due to the lack of lipid (a critical macronutrient) data for the current samples, new samples will have to be collected. In order to control for all external factors (seasonal changes, local variations, etc.), an attempt will be made to collect all new butterflyfish gut content samples and coral tissue samples (and, should time allow, new parrotfish gut content samples as well) from one location off the coast of

Thuwal, Saudi Arabia during the course of approximately 2-3 weeks. Now that all analytical methods have been tested and verified, we estimate that the macronutrient analyses of these new samples will go smoothly.

In addition to the butterflyfish samples that were collected in the Red Sea in

February 2010, twenty-seven samples of parrotfish were also collected and prepared for the three macronutrient analyses. These will also be analyzed for the three major macronutrients, in addition to the aforementioned butterflyfish gut content and coral tissue samples that we propose to collect in the near future. 76

When all three macronutrient analyses are complete for all the new butterflyfish gut content, parrotfish gut content, and coral tissue samples, this data will be used to build a model. It has been clearly demonstrated that many nutrients have interactive effects; therefore, complete nutritional studies should take these interactions into account (Raubenheimer and Simpson 2004). Thus, results of these macronutrient analyses will be used to build a model through the geometric framework (GF), a systematic analysis of the nutritional needs of an organism that accounts for these complex interactions. The GF provides a detailed understanding of dietary choices and their effect on fish performance and can also elucidate foraging behaviors, nutritional demands, resource availability, etc.

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APPENDICES

Appendix 1. Complete list of coral tissue samples, a total of 124 samples from 15 species. Each sample was taken from a different colony, from either Al- Fahal Reef on July 6, 2011 or from Shib Nazar Reef on July 26, 2011. Each sample was subsampled: a mass of “Protein Sample Mass” was used in measuring protein content (via CHNS/O analyzer), while a mass of “Ash !""#$%&'() Sample Mass” was used in measuring ash content!"#$%&'$()*+,%$-.+)-,/0%,#%0)$&(,10()$(),*(2,*-3,10()$(),&(,*++,10%*+,)&--.$,-*'#+$-4 (via muffle furnace).

*+,,#-.&+$( 305",#( 78+.#&$( 78+.#&$(305",#( 78+.#&$( !4=(305",#( !4=( /0.# 1##2 3"#- 6/ 6/ 9044(:5;< *+$.#$. !4=(6/ 9044(:5;< *+$.#$. 5678759:: ;3&<,=*>*% !"#$%$#&'()&"*+,(-. ?7:@ A988 BC4:: ?@496D E988 6:4F8 ?64FBD 5678759:: ;3&<,=*>*% !"#$%$#&'()&"*+,(-. ?7:B A98C 68485 5@48BD E98C :??455 BF489D 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 57:F A9:C 564CB 5:4C:D E9:C C:4?5 6@489D 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 5759 A9:F 594?C 6499D E9:F B84@: 854@6D 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 575: A959 BB4F6 F4@@D E959 @?4FC 66486D 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 5755 A95: :C45F 594C:D E95: @:495 @8469D 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 575? A955 ::4F8 :94B6D E955 ?@4@F BB4:FD 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 575@ A95? BC45C :64?CD E95? B84F5 6:4BCD 678759:: G+7H*3*+ !"#$%$#&'%(&#&$+*. 575B A95@ B6495 5:4B9D E95@ @C4?6 @F4C:D 5678759:: ;3&<,=*>*% !"#$%$#&'%(&#&$+*. ?7:: A98@ 8548: 5?46?D E98@ BF458 BF4?8D 5678759:: ;3&<,=*>*% !"#$%$#&'%(&#&$+*. ?7:5 A98B ??46? :F45BD E98B @@46: BC46?D 5678759:: ;3&<,=*>*% !"#$%$#&'%(&#&$+*. ?7:? A986 ?F49: ?94F@D E986 BB4?8 ?64@BD 678759:: G+7H*3*+ /"(*+$%$#&'0$#.1&2*&+& 57@@ A9@B 654?@ :C4C:D E9@B F845B 8?4BBD 678759:: G+7H*3*+ /"(*+$%$#&'0$#.1&2*&+& 57@B A9@6 5F485 5?4?:D E9@6 @:46B 6?466D 678759:: G+7H*3*+ /"(*+$%$#&'0$#.1&2*&+& 57@6 A9@8 B@4:@ 594C:D E9@8 @B4F6 6B4F6D 678759:: G+7H*3*+ /"(*+$%$#&'0$#.1&2*&+& 57@8 A9@C 5845@ :846FD E9@C @@4:B 6F4:FD 678759:: G+7H*3*+ /"(*+$%$#&'0$#.1&2*&+& 57@C A9@F 5846@ :54CCD E9@F @84:B C:4CBD 5678759:: ;3&<,=*>*% /"(*+$%$#&'0$#.1&2*&+& ?7@@ A:9F ?@48C :64B9D E:9F @:46F 684?5D 5678759:: ;3&<,=*>*% /"(*+$%$#&'0$#.1&2*&+& ?7@B A::9 ?54CB 5@4?:D E::9 @94:? B54F@D 5678759:: ;3&<,=*>*% /"(*+$%$#&'0$#.1&2*&+& ?7@6 A::: :F4? :546?D E::: B94:B 8:4@9D 5678759:: ;3&<,=*>*% /"(*+$%$#&'0$#.1&2*&+& ?7@8 A::5 564@F :946FD E::5 6?4B@ 8B48FD 5678759:: ;3&<,=*>*% /"(*+$%$#&'0$#.1&2*&+& ?7@C A::? 5C4@F F4:?D E::? @B46B 6F45:D 5678759:: ;3&<,=*>*% /"(*+$%$#&'0$#.1&2*&+& ?7@F A::@ 5B4C5 :F4?:D E::@ ?546@ B?48@D 678759:: G+7H*3*+ /"(*+$%$#&'0#-,*"-2$.& 57: A99: :F4B? C4:?D E99: B949B C94B5D 678759:: G+7H*3*+ 3&4*&'.,522*65#& 575 A995 :F4BF F46?D E995 ?C4C: 8@465D 5678759:: ;3&<,=*>*% 3&4*&'.,522*65#& ?76 A96F 88495 :F4?:D E96F :9@486 8545:D 5678759:: ;3&<,=*>*% 3&4*&'.,522*65#& ?78 A989 @:4@ 5B45BD E989 BC4@@ B949BD 5678759:: ;3&<,=*>*% 3&4*&'.,522*65#& ?7C A98: @F4?: 554C:D E98: 8546? 664?:D 5678759:: ;3&<,=*>*% 3&4*&'.,522*65#& ?7F A985 B645F :@4F@D E985 894FB B84BBD 5678759:: ;3&<,=*>*% 3&4*&'.,522*65#& ?7:9 A98? @?4B: 5@4C:D E98? :?B486 BB4FBD 678759:: G+7H*3*+ 7&2&85&'0&."-2&#*. 57C A998 :@?4FB C45BD E998 :5548F C546CD 678759:: G+7H*3*+ 7&2&85&'0&."-2&#*. 57F A99C 5B948 :94?CD E99C 55B4@B 8C4@8D 678759:: G+7H*3*+ 7&2&85&'0&."-2&#*. 57:9 A99F BC46B 846?D E99F @F4FC 684B5D 678759:: G+7H*3*+ 7&2&85&'0&."-2&#*. 57:: A9:9 :5B4F? :@45BD E9:9 :5@49C 8?49CD 678759:: G+7H*3*+ 7&2&85&'0&."-2&#*. 57:5 A9:: ?64BB 64@@D E9:: 6?456 BB4F6D 678759:: G+7H*3*+ 7&2&85&'0&."-2&#*. 57:? A9:5 F:4FF 84?:D E9:5 6F4:@ 8C469D 5678759:: ;3&<,=*>*% 7&2&85&'0&."-2&#*. ?7?C A:9? @@4CC :8499D E:9? @C4?8 6:4F8D 5678759:: ;3&<,=*>*% 7&2&85&'0&."-2&#*. ?7?F A:9@ 6@4B@ 5?496D E:9@ BF4FC B64B:D 5678759:: ;3&<,=*>*% 7&2&85&'0&."-2&#*. ?7@9 A:9B 6B4@ C46FD E:9B :?C4CB C?4F5D 5678759:: ;3&<,=*>*% 7&2&85&'0&."-2&#*. ?7@: A:96 B845: :F4B6D E:96 BB49B 6949:D 5678759:: ;3&<,=*>*% 7&2&85&'0&."-2&#*. ?7@5 A:98 ?F48F ::4F@D E:98 8:4BB 6C458D 5678759:: ;3&<,=*>*% 7&2&85&'0&."-2&#*. ?7@? A:9C 664C6 ::499D E:9C @:4CC 6?465D 82

Appendix 1. continued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

Appendix 1. continued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�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�#12 3&$$ 4%0!# 4#0&&2 $!"#"$%&& 7+89:;*<*= 2&+$.(,--0 ."16 /&$. $!0%6 &&0!.2 3&$. .606. 4#0$.2

Appendix 2. Complete list of coral tissue samples, a total of 124 samples from 15 species. Each sample was taken from a different colony, from either Al- Fahal Reef on July 6, 2011 or from Shib Nazar Reef on July 26, 2011. Each sample was !""#$%&'()analyzed for protein content (via CHNS/O analyzer) and ash content (via muffle furnace).!"#$%#&'()*+#,&#&"%'()-+'(&#.)/!*-0)1'(+$2)3",)$'45)4",'()#&22+$)2'67($8)!*-)9'2)4'(4+('#$:)'2)/7$,4$%#)7,"#$&%)4"%#$%#);)7$,4$%#)'25)4"%#$%#08 “Potential Nutritional Quality” (PNQ) was then calculated as protein content divided by ash content.

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

Appendix 2. continued !"##$%&'"() 3"&$(&'+#)56&4'&'"(+#) *+&$ ,$$- ./$%'$0 102)!"(&$(& 34"&$'()!"(&$(& 76+#'&8)9357: !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ ,,-."/ !0-$1/ %-01 !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ 2%-""/ !0-1"/ %-$% !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ 2,-&,/ !0-0&/ %-"$ !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ 12-,%/ !&-$1/ %-2% !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ 1.-.&/ !,-1%/ %-,. !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ 11-!$/ !%-22/ %-,$ !"#$#!%&& '(#)*+*( !"#$%&'()%*)+,%(+&$ 1"-"&/ !%-1"/ %-," "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ "&-!%/ !%-&./ %-,, "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ 1$-1$/ &0-00/ %-,, "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ 12-,0/ &$-%"/ %-,& !"#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ ",-&!/ &0-%"/ %-!. !"#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ $1-$"/ &.-&,/ %-!1 "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ "1-.!/ &2-.2/ %-!, "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ ",-!1/ &&-!1/ %-&0 "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ "2-1%/ &%-1"/ %-&" "#$#!%&& 3+4567*8*9 !"#$%&'()%*)+,%(+&$ 1"-&"/ 0-%"/ %-&2 "#$#!%&& '(#)*+*( -"#'$."(%*/$00)."(% ",-$,/ &&-&,/ %-&$ "#$#!%&& '(#)*+*( -"#'$."(%*/$00)."(% 0"-%%/ &2-&,/ %-&" "#$#!%&& '(#)*+*( -"#'$."(%*/$00)."(% ""-!1/ &%-%"/ %-&1 "#$#!%&& '(#)*+*( -"#'$."(%*/$00)."(% $0-2"/ "-!1/ %-%0 !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% "2-.!/ &0-%%/ %-!0 !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% "0-&"/ &"-,&/ %-!2 !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% $!-0./ &1-$1/ %-!! !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% ".-$1/ &2-0&/ %-!& !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% $!-$2/ &2-",/ %-!% !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% $!-2$/ &,-"./ %-&. !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% $.-!1/ &,-00/ %-&0 !"#$#!%&& 3+4567*8*9 -"#'$."(%*'12)(310"&% 0%-%./ .-00/ %-&! "#$#!%&& '(#)*+*( 4"3$00$."(%*:;-*5)((13"&% !0-!0/ 2,-00/ &-11 "#$#!%&& '(#)*+*( 4"3$00$."(%*:;-*5)((13"&% ,"-,0/ 2!-,&/ &-&" "#$#!%&& '(#)*+*( 4"3$00$."(%*:;-*5)((13"&% ,0-&&/ 22-%"/ &-&" "#$#!%&& '(#)*+*( 4"3$00$."(%*:;-*5)((13"&% 1&-&2/ 20-"./ %-.1 "#$#!%&& '(#)*+*( 4"3$00$."(%*:;-*5)((13"&% 2!-2,/ ,.-1"/ %-., "#$#!%&& '(#)*+*( 4"3$00$."(%*:;-*5)((13"&% 2&-2"/ ,$-.2/ %-.! !"#$#!%&& 3+4567*8*9 4"3$00$."(%*+%/$3"(#$& &2-22/ ,&-!1/ !-&" !"#$#!%&& 3+4567*8*9 4"3$00$."(%*+%/$3"(#$& &,-.$/ !.-&./ !-%. !"#$#!%&& 3+4567*8*9 4"3$00$."(%*+%/$3"(#$& &1-%2/ ,%-!1/ !-%& !"#$#!%&& 3+4567*8*9 4"3$00$."(%*+%/$3"(#$& ,$-22/ !0-1%/ %-$"

Appendix 2. continued !"##$%&'"() 3"&$(&'+#)56&4'&'"(+#) *+&$ ,$$- ./$%'$0 102)!"(&$(& 34"&$'()!"(&$(& 76+#'&8)9357: !"#"$%&& '(")*+*( !"#$%&'(,--) ##.%/0 $%.1!0 %.$# $!"#"$%&& '(")*+*( !"#$%&'(,--) #2.�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

Appendix 3. Complete list of all fish gut content samples, a total of 52 samples from 6 species of butterflyfish. Stomach samples are denoted by gray shading; hindgut samples are in white. Each sample listed represents a combined mass of samples from (n) individual fish. Under the “Ash” heading, the listed (Sample Mass) was used to measure (Ash Content) using a muffle furnace. Under the “Protein” heading, the listed (Sample Mass) was used to measure (Protein Content) using a CHNS/O analyzer.!""#$%&'() Several samples were run in duplicate or triplicate, denoted by hyphenated values under (Sample ID); e.g. >?0&@'081A8BC1'?"#841#'?#'83#$830!841#'?#'8A1C8?34!8A"0!8%&'8032B@?,8D!?8EF8032B@?08B11@?$E8@"0'08 sample #118 was run in triplicate (118'!?8#&2G?C81A8"#$"H"$&3@08I!10?8%&'841#'?#'08I?C?8B11@?$8"#81C$?C8'18%?'81#?8032B@?8@3C%?8-1, 118-2, 118-3). The resulting ash content and protein content values for these replicates were then averaged?#1&%!8A1C83#3@J0"0,8<12?81A8'!?0?8B11@?$8032B@?08I?C?8C"#8C?B@"43'?08K#1'?$8GJ8EC?B@"43'?8 as (Ash (Avg. over Rep.s)) #&2G?CELM8'!?0?8C?B@"43'?08I?C?83H?C3%?$8K0??8E3H?C3%?81H?C8C?B,0EL,8<'1234!8032B@?083C?8and (Protein (Avg. over Rep.s)), respectively.

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

Appendix 3. continued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

Appendix 4. Complete list of all fish gut content samples, a total of 52 samples from 6 species of butterflyfish. Stomach samples are denoted by gray shading; hindgut samples are in white. Each sample listed represents a combined mass of samples from (n) individual fish. For each pooled sample, (Ash Content) was measured using a muffle furnace and (Protein Content) was measured using a CHNS/O analyzer. (Potential Nutritional Quality (PNQ)) was then calculated for each sample as protein content divided by ash content. !""#$%&'() Note that several samples were run in duplicate or triplicate, denoted by hyphenated values >3'?#'"5@A+&'B"'"3#5@AC&5@"'DAE>+CFAG5@&?2AH3BA?56!AH"2!A%&'A254I@?/A>+CAG5@&?2A under (Sample ID);J?B?A65@6&@5'?$A52AEIB3'?"#A63#'?#'AKA52!A63#'?#'F/A<'3456!A254I@?2A5B?A e.g. sample #118 was run in triplicate (118-1, 118-2, 118- 3). 2!5$?$LA!"#$%&'A254I@?2A5B?A#3'/

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

Appendix 4. continued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