Baselines and Comparison of Coral Reef Fish Assemblages in the Central Red Sea

Thesis by

Alexander Kattan

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, 2014 2

The thesis of Alexander Kattan is approved by the examination committee.

Committee Chairperson: Dr. Michael Lee Berumen

Committee Member: Dr. Xabier Irigoien

Committee Member: Dr. Simon Thorrold

Alexander Kattan

ABSTRACT

Baselines and Comparison of Coral Reef Fish Assemblages in the Central Red Sea

Alexander Kattan

In order to properly assess human impacts and appropriate restoration goals, baselines of pristine conditions on coral reefs are required. In Saudi Arabian waters of the central Red

Sea, widespread and heavy fishing pressure has been ongoing for decades. To evaluate this influence, we surveyed the assemblage of offshore reef fishes in both this region as well as those of remote and largely unfished southern Sudan. At comparable latitudes, of similar oceanographic influence, and hosting the same array of species, the offshore reefs of southern Sudan provided an ideal location for comparison. We found that top predators

(jacks, large snappers, groupers, and others) dominated the reef fish community biomass in Sudan’s deep south region, resulting in an inverted (top-heavy) biomass pyramid. In contrast, the Red Sea reefs of central Saudi Arabia exhibited the typical bottom-heavy pyramid and show evidence for trophic cascades in the form of mesopredator release.

Biomass values from Sudan’s deep south are quite similar to those previously reported in the remote and uninhabited Northwest Hawaiian Islands, northern Line Islands, Pitcairn

Islands, and other remote Pacific islands and atolls. The findings of this study suggest that heavy fishing pressure has significantly altered the fish community structure of Saudi

Arabian Red Sea reefs. The results point towards the urgent need for enhanced regulation and enforcement of fishing practices in Saudi Arabia while simultaneously making a strong case for protection in the form of marine protected areas in the southern Sudanese

Red Sea. 5

ACKNOWLEDGEMENTS

I would like to begin by thanking my advisor, Dr. Michael Berumen, who provided the funding, guidance, and support needed to carry out this thesis work. I am also indebted to him for the opportunity he offered to travel to and dive in the incredible Sudanese Red

Sea. Without that chance, this project simply would not have been possible. Indeed, I never would have experienced any of this crazy life-changing KAUST rollercoaster if it were not for Mike. He created more opportunities for study and research (both in the lab and field), diving and travel (both domestically and abroad), and networking and collaboration (both in KAUST and with other institutions) over the course of this program than I ever would have imagined, for which I am exceedingly grateful and consider myself very lucky.

I owe many thanks to all of the members of the Coral Reef Ecology Lab, who each provided assistance in their own way and made their personal contribution towards my uniquely awesome graduate experience over the past 18 months. Their camaraderie was cornerstone during my KAUST tenure. I owe particular thanks to Maha Khalil and Dr.

Julia Spaet, who helped with data handling and preparation for the Sudan expedition, respectively. I would also like to acknowledge May Roberts, my number one dive buddy.

May’s incessant enthusiasm and keen eye helped me develop the fish identification skills needed to conduct my surveys. Her baking prowess was also instrumental during the writing and analysis process. I thank Dr. Stein Kaartvedt, Dr. Darren Coker, Dr. Julia 6

Spaet, Maha Khalil, and my committee members, whose comments significantly improved this manuscript.

Sincere gratitude is extended to the staff and crew of KAUST’s Coastal and Marine

Resources Core Lab (CMOR), who made surveying in Thuwal possible (and safe). I recognize the Dream Divers team, who facilitated my surveys in the Al Lith region. And

I owe particular thanks to the staff and crew of the Don Questo liveaboard company.

Special recognition goes to Captain Lorenzo (Lory) Segalini for his warm Italian hospitality (and cooking) as well as his enthusiasm for working with scientists and his leadership in the Don Questo’s voluntary contributions towards marine conservation in

Sudan. I thank Sara Valla for her efforts in making the Sudanese visa process completely painless. I also want to acknowledge the dive guide Mauritzio Chiarenza, who first introduced me to our “friends” (scalloped hammerhead sharks). Seeing nearly one hundred of these magnificent creatures while narced at 45 meters is not an experience I will soon forget.

Finally, I would like to pay tribute to the late Hans Hass, early marine biologist, ingenious diving pioneer, and fearless adventurer. His book Manta (1952) was a particularly great source of inspiration during the course of my fieldwork in Sudan. Hass, you were definitely better than Jacques.

TABLE OF CONTENTS

EXAMINATION COMMITTEE APPROVAL FORM ...... 2

ABSTRACT ...... 4

ACKNOWLEDGEMENTS ...... 5

TABLE OF CONTENTS ...... 7

LIST OF FIGURES ...... 8

LIST OF TABLES ...... 10

SECTION 1 – INTRODUCTION ...... 11

1.1 – The Anthropocene………………………………………………………….12

1.2 – Human Impacts on the Marine Environment and the Shifting Baseline…..13

1.3 – Pristine Coral Reefs………………………………………………………..16

1.4 – Concept and Justification of the Study…………………………………….17

SECTION 2 – MATERIALS AND METHODS ...... 21

2.1 – Study Sites and Sampling Design………………………………………….21

2.2 – Fish Sampling Methodology……………………………………………….22

2.3 – Data Handling and Statistical Analysis…………………………………….24

SECTION 3 – RESULTS ...... 26

3.1 – Recorded Fishes and Species Richness, Evenness, and Diversity…………26

3.2 – Fish Biomass, Abundance, and Average Size……..………………………32

3.3 – Shark Sightings…..…………………………..…………………………….36

3.4 – A Proxy for Fishing Pressure…..…………………………..………………38

SECTION 4 – DISCUSSION ...... 39

4.1 – Sudan’s Deep South…………………………………………………..……39 8

4.2 – South Sudan, Al Lith, and Thuwal…………………………………………40

4.3 – Shark Sightings………………………………………...…………………..42

4.4 – Study Limitations and Considerations…………………………….……….43

SECTION 5 – CONCLUSION ...... 46

APPENDICES ...... 49

REFERENCES ...... 53

LIST OF FIGURES

Figure 2.1 - Map of the central Red Sea showing location of the four study regions and reefs: Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles). Map is super-imposed on a bathymetric map taken from Raitsos et al. (2013)………………………………………………………………………….….22

Figure 3.1 - Biomass of fishes in four major tropic guilds from four study regions in the central Red Sea. Stacked vertical bars represent mean biomass estimated from visual surveys of the length and number of fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2). Trophic guilds are color- coded (see Table 3.1 for species' guild assignments). Results are grouped by study region (horizontal axis). Error bars represent standard error of mean top predator biomass……..33

Figure 3.2 - Numerical abundance of fishes in four major trophic guilds from four study regions in the central Red Sea. Stacked vertical bars represent mean numerical abundance estimated from visual surveys of fishes recorded in belt transects. Trophic guilds are color-coded (see Table 3.1 for species’ guild assignments). Results are grouped by study region (horizontal axis). Error bars represent standard error of mean fish abundance…..34

Figure 3.3 - Average weight of fishes in four major trophic guilds from four study regions in the central Red Sea. Vertical bars represent mean weight in kilograms estimated from visual surveys of the length and number of fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2). Trophic guilds are color-coded (see Table 3.1 for species' guild assignments). Results are grouped by study region (horizontal axis). Error bars represent standard error of mean weight for each guild and region………………………………………………………………………..….35

Figure 3.4 - Biomass of two prominent top predator species, Caranx sexfasciatus and Lutjanus bohar, from four study regions in the central Red Sea. Vertical bars represent mean biomass estimated estimated from visual surveys of the length and number of fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2). Error bars represent standard error of mean biomass for both species in each of the four study regions……………………………………………………………..….36

Figure 3.5 - Number of each shark species sighted during survey dives in four study regions in the central Red Sea. Vertical bars represent mean number of sharks counted over the course of individual dives by the surveyor (AK). Results are grouped by species (horizontal axis). Error bars represent standard error of mean shark sightings………….37

Figure 3.6 - Relationship between distance from nearest port (fish market) and top predator biomass for all surveyed reefs in four study regions. Biomass was estimated from visual surveys of the length and number of top predator fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2). Reefs are shape- and color-coded by region: blue circles: deep south; red diamonds: south Sudan; green triangles: Al Lith; purple circles: Thuwal…………………………..38

Figure A1 - Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a sea surface temperature (SST) map taken from Ngugi et al. (2012): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles)……………………………………………………………..49

Figure A2 - Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a chlorophyll-a map taken from Raitsos et al. (2013): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles). ……………………………………………………………………………………..50

Figure A3: Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a salinity map taken from Ngugi et al. (2012): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles). ……………………………………………………………………………………………..51

Figure A4: Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a phosphate concentration map taken from Ngugi et al. (2012): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles). ……………………………………………………………………….52

LIST OF TABLES

Table 3.1 - List of all diurnally active reef fish species greater than 3cm (organized alphabetically and grouped by family) recorded during belt transect surveys in four study regions in the Red Sea. Fishes are assigned to one of the four trophic guilds used by Sandin et al. (2008): top predator, carnivore, herbivore, and planktivore. Trophic assignments follow species dietary information derived from FishBase (Froese and Pauly 2014). a and b values, also obtained from FishBase, are species-specific coefficients used to determine weight of individual fish using the weight-length allometric formula W=aLb, where W is weight in kilograms and L is total length in centimeters.…………...... ……..26

Table 3.2 - Species richness, evenness, and diversity of fishes surveyed in visual belt transects among four study regions in the Red Sea. Species Richness is the mean (+\- SE) number of species recorded on each transect. See Section 2 for details on calculations for evenness as well as Shannon's and Simpson's diversity indices……………………………..…..31

Table 4.1 Comparison of Sudanese deep south top predator biomass to that of other pristine regions found in the literature. Total community and top predator biomass are estimated from visual surveys of the length and number of fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2)………40

SECTION 1 – INTRODUCTION

1.1 – The Anthropocene

There is no doubt that man’s impact on the natural world is dominant and entirely global in scope (Vitousek et al. 1997, Sanderson et al. 2002). Advances in technology, namely agriculture and medicine, allowed for a booming human population that is placing increasing pressure on natural resources (Diamond 1997). The world population,

7.2 billion individuals as of mid-2013, continues to grow and is expected to reach 9.6 billion the middle of the century (United Nations 2013). The amount of natural resources required to feed and fuel the livelihoods of such an immense population has put 38 percent of the planet’s terrestrial surface under agriculture, the single largest use of land on Earth (Foley et al. 2011). Vitousek et al. (1997) estimate that human action has transformed between one-half and one-third of the world’s terrestrial surface. So great is the human footprint in the natural world, that its influence rivals that of geophysical processes; we now live in what can be defined as a new epoch, the Anthropocene (Steffen et al. 2011).

This massive transformation of the Earth’s land surface has resulted in extreme losses of natural habitats. Combined with the effects of overexploitation and invasive species, habitat loss, degradation, and fragmentation has put the world’s wildlife in a major crisis aptly termed “Anthropocene defaunation” (Dirzo et al. 2014). Despite international commitments to preserve biodiversity, the rate of biodiversity loss does not appear to be slowing (Butchart et al. 2010, Hoffmann et al. 2010), with current rates of extinction estimated to be 1000 times the likely background rate (Pimm et al. 2014). In fact, according to a World Wildlife Fund for Nature report, the planet has lost half of its 13 wildlife in the last 40 years (McLellan 2014). The loss of biodiversity carries major consequences for vital ecosystem functioning and services, such as pollination, seed dispersal, water quality, and nutrient cycling (Dirzo et al. 2014). Climate change is expected to further put the Earth’s animal and plant life at risk, with 15 to 37 percent of terrestrial species expected to be “committed to extinction” under mid-range climate- warming scenarios for 2050 (Thomas et al. 2004). The scale and pace of global change by man means the entire global ecosystem is at risk of approaching a planetary-scale tipping point, transforming Earth into a state unknown in human experience (Barnosky et al. 2012). “Pristine” wildernesses still harboring historic levels of wildlife populations have become far and few between (Meyer 2006).

1.2 – Human Impacts on the Marine Environment and the Shifting Baseline

Man’s global impact unquestionably extends into the marine realm. Today our ocean is a great deal different from that of centuries ago (Jackson 2001). Marine habitats have been altered and destroyed due to a variety of human activities. Mangroves, critical habitat for different life stages of a multitude of marine species (Mumby et al. 2004,

Aburto-Oropeza et al. 2008), are being cleared for urban development, aquaculture, mining, and timber at a global rate of 1 to 2 percent per year (Duke et al. 2007), with approximately one-third of mangroves lost in the past 50 years (Alongi 2002). Loss of seagrass meadows, which provide such essential ecosystem services as nutrient cycling, sediment stabilization, enhancement of biodiversity, and trophic transfer to adjacent habitats, has increased by an order of magnitude in the last 40 years (Orth et al. 2006).

The widespread construction of dams has fragmented riverine ecosystems, preventing 14 some anadromous and catadromous species from making their requisite migrations

(Morita, Morita, and Yamamoto 2009). Even the great expanses of the deep sea have not been able to avoid the damaging influence of man. Destructive practices such as bottom trawling pose a serious threat to vast areas of slow-growing and long-lived deep-sea benthic communities (Roberts, Wheeler, and Freiwald 2006).

The loss of marine habitat coupled with overexploitation has led to massive reductions in the abundance of marine life. Historical accounts by voyagers in previous centuries tell of marine animal abundances that are unfathomable by today’s standards

(Jackson 1997). As in terrestrial environments, the loss of species in the ocean has focused on large top predators and keystone species, rendering them functionally extinct as marine systems are “fished down the food web” (Jackson et al. 2001, Pauly et al.

1998). Whales, sharks, and large predatory fish communities exist today at mere fractions of their former plenty (Roman and Palumbi 2003, Myers and Worm 2005, Baum and

Myers 2004, Christensen et al. 2014). Such “trophic downgrading” results in a loss of top-down forcing in ecosystem structure and function (Estes et al. 2011) and promotes changes in population structure of lower-trophic species (Baum and Worm 2009, Myers et al. 2007). Fueled by massive global fisheries subsidies (between 25 and 29 billion USD in 2003), the global community is “effectively funding the over-exploitation of marine resources” (Sumaila et al. 2010). Past and present pressure on traditional fisheries is so strong that stocks of all species currently fished are expected to collapse by mid-century

(Worm et al. 2006).

Declines in marine life are truly global in scale and even impact the most remote parts of the ocean, leaving essentially no “pristine” sanctuaries (Myers and Worm 2003, 15

Pandolfi et al. 2003). A large-scale analysis of 17 anthropogenic drivers of ecological change across 20 marine ecosystems concluded that “no area of the ocean is unaffected by human influence” and that the only large areas of relatively little human interference that remain are close to the poles (Halpern et al. 2008). This loss of marine biodiversity

“is increasingly impairing the ocean’s capacity to provide food, maintain water quality, and recover from perturbations” (Worm et al. 2006).

The pace at which change in the ocean is occurring puts marine scientists and the general public at risk of suffering from the “shifting baselines” syndrome. As Pauly

(1995) describes:

“Essentially, this syndrome has arisen because each generation of fisheries

scientists accepts as a baseline the stock size and species composition that

occurred at the beginning of their careers, and uses this to evaluate changes.

When the next generation starts its career, the stocks have further declined, but it

is the stocks at that time that serve as a new baseline. The result obviously is a

gradual shift of the baseline, a gradual accommodation of the creeping

disappearance of resource species, and inappropriate reference points for

evaluating economic losses resulting from overfishing, or for identifying targets

for rehabilitation measures.”

Increasingly for many ecosystems across a range of spatial scales, we simply do not know what baseline conditions once were. This poses a serious challenge with respect to management, and we risk becoming complacent about the rarity of species (Pauly 1995).

In the context of coral reefs, few “pristine” reefs persist to this day, with essentially all reef environments degraded to some extent (Jackson et al. 2001). Reefs 16 face a host of stressors, which often act simultaneously and thereby intensifying their impacts. Overfishing, destructive fishing practices, agricultural and urban runoff, sedimentation, coral bleaching, invasive species, and coral mining pose serious threats to the health of coral reef ecosystems (Bryant et al. 1998). Today’s coral reefs have suffered major losses (Gardner et al. 2003). If current trends continue, all corals will likely be threatened by 2050, with 75 percent of reefs facing a threat-level categorization of “high” to “crucial” (Burke et al. 2011).

1.3 – Pristine Coral Reefs

With marine ecosystems and wildlife, particularly those of coral reefs, so drastically altered from historic levels, there is much difficulty in knowing what is

“natural” and how to manage in this context. Fortunately, there remain select coral reefs that have experienced relatively little change from the activities of man and that exist in a

“near-pristine” state (the terms “near pristine” and “pristine” are interchangeably used hereafter to describe environmental conditions that can be interpreted as being currently and historically detached from anthropogenic stressors). Studies conducted in the

Northern Line Islands (Sandin et al. 2008), Northwest Hawaiian Islands (Friedlander and

DeMartini 2002), Pitcairn Islands (Friedlander et al. 2014), and various remote islands and atolls across the central and western Pacific (Williams et al. 2010, Stevenson et al.

2007) have reported the highest values for top predator biomass on coral reefs. These studies reveal a common and significant characteristic in the trophic structure of pristine coral reefs: reef fish biomass pyramids are top-heavy or “inverted”. In these unique 17 locations, sharks and other top predators (jacks, snappers, groupers) overwhelm the fish assemblages.

The 2008 study conducted by Sandin et al., in particular, demonstrates a compelling shift in the assemblage and trophic structure of coral reef fishes across a gradient of fishing intensity within the same biogeographic region and highlights that human impacts to coral reefs, primarily fishing, have major implications for the overall health of the system. Trophically bottom-heavy reefs exhibit higher instances of coral disease and lower coral recruitment. Other studies have revealed further cascade effects for coral reefs under even modest fishing pressure, including changes in the abundance, body condition, length, and reproductive potential of prey species as well as changes in the timing of ontogenetic sex change in parrotfishes (Walsh et al. 2012, Ruttenberg et al.

2011, DeMartini et al. 2008) Ruttenberg et al. 2011, Walsh et al. 2012).

1.4 – Concept and Justification of the Study

With so much of the world traveled, documented, explored, and exploited, is it possible that more, currently undocumented pristine reefs still exist in other parts of the world? The few studies in the literature documenting inverted biomass pyramids were all located in the Pacific Ocean basin. Do “baseline” conditions exist for other biogeographic regions? The largely understudied Red Sea is a region with potential areas under weak pressure from man, and it thus potentially contains baseline sites for the rest of the basin.

The remoteness and general political instability of the region, coupled with complicated visa and permitting regulations as well as a lack of marine infrastructure, have traditionally made the Red Sea a difficult region to travel and study. Moreover, what 18 work has been done has been spatially skewed, with more than half of recent research in the Red Sea originating from the Gulf of Aqaba, an area representing less than 2% of the

Red Sea area (Berumen et al. 2013).

Baseline conditions on coral reefs are naturally variable within the Red Sea and differ, along with environmental gradients, across latitudes (Raitsos et al. 2013, Kürten et al. 2014, Ngugi et al. 2012, Sawall et al. 2014). Within latitudes, however, researchers in the King Abdullah University of Science and Technology’s Reef Ecology Lab have observed that Red Sea fish assemblages appear quite similar on an east-west basis, particularly in the central region bordered by Saudi Arabia to the east and Sudan to the west. The apparent similarity of coral reefs between the east and west central Red Sea offers a unique opportunity to assess potential dissimilarities of trophic structures and fish community assemblages between the two regions, especially in light of differences in fishing intensity (Hariri et al. 2002).

Saudi Arabia, home to 28.83 million people in 2013 (Central Intelligence Agency

2013) and containing 1840 km of Red Sea coastline, has exerted much greater fishing pressure on its Red Sea resources than Sudan. Until 1981, the Kingdom’s fisheries were exploited exclusively by small artisanal wooden boats and sanbuks (PERSGA/GEF

2003). At that time, however, Saudi Arabia’s industrial fishing fleet was established and has since grown consistently. The Kingdom’s artisanal fishing vessels have likewise increased in number, nearly tripling from 3,491 in 1990 up to 10,102 boats in 2005

(Department of Marine Fisheries. 2008). According to the United Nations Environmental

Program, Saudi Arabia lands 1500 metric tons of finfish from Red Sea and Gulf coasts combined, with over 74 percent of annual Red Sea landings coming from the southern 19 section between Al Lith and the Yemeni border (United Nations Environment

Programme). Jin et al. (2012) estimate that aggregate traditional fisheries in Saudi Arabia have been overfished since the 1990s.

Sudan, with a population of 37.96 million (Central Intelligence Agency 2013), has a largely underdeveloped fishery. Despite owning roughly half (853 km) as much Red

Sea coastline as Saudi Arabia, Sudanese landings of Red Sea fishes amount to just over 8 percent of that of its eastward neighbor (Hariri et al. 2002). This is chiefly due to the fact that Sudan’s industrial fisheries are underdeveloped, while the nation’s artisanal fishery is seriously limited in its access to modern boat and fishing gear technologies (Hariri et al.

2002). The disparity between fishing pressure on Saudi Arabian and Sudanese Red Sea reefs is paralleled by the large gap in wealth between the two countries; per capita GDP in Saudi Arabia is over 25,000 USD while that of Sudan amounts to approximately 1,750

USD (Central Intelligence Agency 2013). Whether related to income or not, per capita fish consumption is also five times higher in Saudi Arabia: 8.0 kilograms per year for

Saudis in 1993 compared to 1.6 kilograms per year for Sudanese in 1997 (Hariri et al.

2002).

At a fundamental level, the comparability of the reef ecosystems of Sudan and east-central Saudi Arabia provides a unique opportunity to explore the concept of baselines for the coral reefs of the central Red Sea. The purpose of this study is to compare the standing stock, trophic structure, and species assemblages of central Saudi

Arabian and southern Sudanese Red Sea coral reefs. Based on personal communications and observations, demographic information, and the limited fisheries data available for the region, it is hypothesized that low fishing pressure on Sudanese coral reefs results in 20 an inverted biomass pyramid and high overall biomass, while heavy and sustained pressure on the reefs of Saudi Arabia produces a bottom-heavy trophic structure typical of most modern-day coral reefs. It is anticipated that the relatively intact food webs of southern Sudanese reefs can serve as baselines for their degraded Saudi Arabian counterparts, whose communities are likely to be experiencing the impacts of trophic cascades as sharks and other top predators are systematically removed.

SECTION 2 – MATERIALS AND METHODS

2.1 – Study Sites and Sampling Design

Four distinct regions were surveyed in the central Red Sea: Thuwal, Saudi Arabia;

Al Lith; Saudi Arabia; and the “south” and “deep south” regions of Sudan (Figure 2.1).

Surveys in each region were conducted at reefs classified as “offshore”. Offshore reefs were defined as those in close proximity to the outer margin of the continental shelf and characterized by steep profiles (particularly on the exposed side) as well as plateaus at around 25-meter depth on both oblong ends of the reef structure.

In Thuwal, 3 visual belt transects (VBT) were conducted at three reefs (Abu

Madafi, Shark Reef, and Shib Nazar) for a total of n=9. In Al Lith, three transects were conducted at five reefs (Dohra, Long Reef, Malathu, Mar Mar, and Al Jadir) for a total of n=15. Reefs in Sudan were surveyed opportunistically as a participant in a recreational liveaboard charter. The region of south Sudan was considered less remote than the deep south. Therefore, delineation was made between the two. In the region defined as south

Sudan, two transects were conducted at four reefs (Bara Musa Saqir, Pinnacle, Preserver, and Sha’ab Anbar) for a total of n=8. In the designated deep south region, two transects were conducted at eight reefs (Dahrat Abid, Dahrat Qab, Darraka, Ed Donesh Shesh,

Habily Lory, Karam Masamarit, Loka, and Masamarit) for a total of n=16 (Figure 2.1).

All surveys took place during morning and midday hours between the months of May and

June, 2014.

n=9

n=15

n=8

n=16

Figure 2.1 - Map of the central Red Sea showing location of the four study regions and reefs: Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles). Number of transects (n) per region is displayed. Map is super-imposed on a bathymetric map taken from Raitsos et al. (2013).

2.2 – Fish Sampling Methodology

Reef fish surveys were conducted using the underwater visual belt transect survey method described in Sandin et al. (2008). This method has been used in a number of other peer-reviewed publications (DeMartini et al. 2008, Friedlander et al. 2014, Friedlander and DeMartini 2002, Friedlander et al. 2010, Ruttenberg et al. 2011, Williams et al.

2010)All diurnally active reef fishes along the fixed-length (25 meter) transects greater 23 than 3 centimeters were counted and size-estimated to the nearest centimeter. Size- estimation was practiced regularly throughout the study period using objects of known length to maintain accuracy. Fishes were documented to the species level or, if necessary, to the lowest reliably identifiable taxon. Surveys were conducted by a single diver (AK) to help eliminate bias and provide consistency between surveys and locations.

Transects were located at 10-meter depth, typically above the plateau of the reefs’ exposed side, and were set following the contour of the reef. The transect width differed depending on the size of the fish. On an initial swim-out laying the transect line, large- bodied, vagile species greater than 20 centimeters were surveyed within 4 meters of the tape, focusing observations ahead in a 5-meter-long moving window. On the return swim, small-bodied, more site-attached species were surveyed within 2 meters of the transect line. Mapstone and Ayling (1998) demonstrated that these transect dimensions optimize data precision and accuracy, minimize surveyor bias, and compensate for differences in size- and species-specific differences in density for this type of in situ underwater visual census. Replicate transects, whose number depended on the time constraints at individual sites, were separated by a distance of approximately 10 meters.

As sharks in the study regions were very rarely encountered within transect boundaries, simple presence/absence and abundance data were also collected for any species of shark seen by the surveyor (AK) during the entire course of the survey dives

(varying between 60 and 90 minutes).

2.3 – Data Handling and Statistical Analysis

Transects provided the input to calculate estimates of species richness, evenness, diversity, and densities of numerical abundance and biomass. Species richness was determined by the total number of species recorded by the surveyor on each transect.

Species diversity was calculated using two common indices, the Shannon and Simpson diversity indices. Shannon’s index (H’) is computed from the formula

! ! � = �! ln �! !!! where pi is the proportion of all individuals counted belonging to species i and R is the total number of species (Shannon and Weaver 1949). Simpson’s index (D) is calculated from the formula

! � (� − 1) � = ! ! �(� − 1) !!! where ni is the number of individuals belonging to species i, N is the total number of individuals in all species summed, and S is the total number of species (Simpson 1949).

Species evenness was determined for each region by dividing each zone’s Shannon’s index value by the natural log of the region’s species richness value (Shannon and

Weaver 1949).

Fish species were allocated into four functional trophic guilds based on diet information obtained from FishBase (Froese and Pauly 2014). These groupings were top predators (sharks, jacks, large groupers and snappers, as well as any other fish at or near the end of the food chain), carnivores (smaller groupers and snappers, benthic invertivores, etc.), planktivores, and herbivores (including coralivores and detritivores; see Table 3.1 for complete list of species’ trophic allocation). Fish lengths were 25 converted to biomass using the allometric length-weight conversion W = aLb, where W is weight in kilograms, L is total length in centimeters and a and b are species-specific constants, obtained from FishBase (Froese and Pauly 2014). These converted weights coupled with numerical densities supplied from transects provided the input required to generate biomass pyramids per study region. This information was also used to assess differences in the average weight of fish per guild and variations in biomass of individual species among study regions. Shark sighting information was used to measure average shark sightings on a regional basis and therefore provide a rough comparison of the presence of these top predators between the four zones. Finally, distance from each study reef to the nearest port for fisheries landing (Al Lith and Thuwal in Saudi Arabia and Port

Sudan in Sudan) was measured in Google Earth to test the hypothesis that top predator biomass on central Red Sea reefs rises with increased distance from market.

SECTION 3 – RESULTS

3.1 – Recorded Fishes and Species Richness, Evenness, and Diversity

Across all regions and reefs, a total of 30,494 individual fish from 129 species of

27 families were recorded (Table 3.1). The only instances wherein a fish could not be identified to the species level involved individuals of the genera Scarus and Parupeneus.

Carnivores represented the most species-diverse trophic guild with 50 species of fish in the category, followed by herbivores (43 species), top predators (20 species) and planktivores (17 species). The family Labridae had the highest number of species (21) recorded in the entire data set, with the families Pomacentridae (13 species), Serranidae

(12 species), Chaetodontidae (11 species), Acanthuridae (10 species), and Carrangidae,

Lutjanidae, and Scaridae (7 species each) also well represented. Seven families were represented by only two species, while seven more families in the whole data set consisted of a single recorded species.

Trophic Guild a b Species Assignment value value Acanthuridae Acanthurus gahhm (Forsskål, 1775) Herbivore 0.023 3.060 Acanthurus nigrofuscus (Forsskål, 1775) Herbivore 0.023 3.060 Acanthurus sohal (Forsskål, 1775) Herbivore 0.023 3.060 27

Table 3.1 (Continued) Ctenochaetus striatus (Quoy & Gaimard, 1825) Herbivore 0.023 3.060 Naso brevirostris (Cuvier, 1829) Herbivore 0.060 2.743 Naso elegans (Rüppell, 1829) Herbivore 0.023 3.060 Naso hexacanthus (Bleeker, 1855) Planktivore 0.042 2.854 Naso unicornis (Forsskål, 1775) Herbivore 0.028 2.980 Zebrasoma desjardinii (Bennett, 1835) Herbivore 0.034 2.861 Zebrasoma xanthurum (Blyth, 1852) Herbivore 0.034 2.861 Balistidae Balistapus undulates (Park, 1797) Carnivore 0.026 3.010 Balistoides viridescens (Bloch & Schneider, 1801) Carnivore 0.024 3.018 Odonus niger (Rüppell, 1836) Carnivore 0.036 2.875 Pseudobalistes flavimarginatus (Rüppell, 1829) Carnivore 0.073 2.760 Rhinecanthus assasi (Forsskål, 1775) Carnivore 0.022 3.000

Caesionidae Caesio lunaris (Cuvier, 1830) Carnivore 0.011 3.039 Caesio suevica (Klunzinger, 1884) Planktivore 0.010 3.050

Carangidae Carangoides bajad (Forsskål, 1775) Top Predator 0.020 2.869 Carangoides ferdau (Forsskål, 1775) Top Predator 0.037 2.851 Caranx ignobilis (Forsskål, 1775) Top Predator 0.023 3.060 Caranx melampygus (Cuvier, 1833) Top Predator 0.025 2.940 Caranx sexfasciatus (Quoy & Gaimard, 1825) Top Predator 0.028 2.980 Scomberoides lysan (Forsskål, 1775) Top Predator 0.017 2.835 Trachinotus blochii (Lacepède, 1801) Carnivore 0.088 2.572

Carcharhinidae Triaenodon obesus (Rüppell, 1837) Top Predator 0.001 3.382

Chaetodontidae Chaetodon auriga (Forsskål, 1775) Herbivore 0.032 2.920 Chaetodon austriacus (Rüppell, 1836) Herbivore 0.023 3.130 Chaetodon fasciatus (Forsskål, 1775) Herbivore 0.023 3.130 Chaetodon larvatus (Cuvier, 1831) Herbivore 0.026 3.100 Chaetodon lineolatus (Cuvier, 1831) Herbivore 0.069 2.622 Chaetodon melannotus (Bloch & Schneider, 1801) Herbivore 0.027 3.049 Chaetodon mesoleucos (Forsskål, 1775) Herbivore 0.023 3.130 Chaetodon paucifasciatus (Ahl, 1923) Herbivore 0.023 3.130 Chaetodon semilarvatus (Cuvier, 1831) Herbivore 0.023 3.130 28

Table 3.1 (Continued) Chaetodon trifascialis (Quoy & Gaimard, 1824) Herbivore 0.035 2.860 Heniochus intermedius (Steindachner, 1893) Planktivore 0.017 3.070

Chanidae Chanos chanos (Forsskål, 1775) Planktivore 0.017 2.987

Cirrhitidae Cirrhitichthys oxycephalus (Bleeker, 1855) Carnivore 0.014 3.117 Paracirrhites forsteri (Schneider, 1801) Carnivore 0.009 3.070

Ephippidae Platax orbicularis (Forsskål, 1775) Herbivore 0.043 2.975 Platax teira (Forsskål, 1775) Herbivore 0.043 2.975

Haemulidae Plectorhinchus albovittatus (Rüppell, 1838) Carnivore 0.017 3.040 Plectorhinchus gaterinus (Forsskål, 1775) Carnivore 0.017 3.040

Kyphosidae Kyphosus cinerascens (Forsskål, 1775) Herbivore 0.018 3.000 Kyphosus vaigiensis (Quoy & Gaimard, 1825) Herbivore 0.020 3.037

Labridae Anampses meleagrides (Valenciennes, 1840) Carnivore 0.010 3.080 Anampses twistii (Bleeker, 1856) Carnivore 0.020 3.000 Bodianus anthioides (Bennett, 1830) Carnivore 0.011 3.039 Bodianus axillaris (Bennett, 1831) Carnivore 0.011 3.039 Bodianus Diana (Lacepède, 1801) Carnivore 0.011 3.039 Cheilinus quinquecinctus (Rüppell, 1835) Carnivore 0.015 3.000 Cheilinus undulates (Rüppell, 1835) Carnivore 0.015 3.070 Coris aygula (Lacepède, 1801) Carnivore 0.003 3.489 Epibulus insidiator (Pallas, 1770) Carnivore 0.016 3.081 Gomphosus caeruleus (Lacepède, 1801) Carnivore 0.024 2.703 Halichoeres hortulanus (Lacepède, 1801) Carnivore 0.012 3.064 Labroides dimidiatus (Valenciennes, 1839) Carnivore 0.006 3.231 Larabicus quadrilineatus (Rüppell, 1835) Carnivore 0.011 3.039 Macropharyngodon bipartitus (Smith, 1957) Carnivore 0.010 3.040 Oxycheilinus digramma (Lacepède, 1801) Carnivore 0.049 2.450 Oxycheilinus mentalis (Rüppell, 1828) Carnivore 0.049 2.450 Pseudocheilinus evanidus (Jordan & Evermann, 1903) Carnivore 0.012 3.115 Pseudocheilinus hexataenia (Bleeker, 1857) Carnivore 0.012 3.115 29

Table 3.1 (Continued) Pseudodax moluccanus (Valenciennes, 1840) Carnivore 0.011 3.039 Thalassoma lunare (Linnaeus, 1758) Top Predator 0.006 3.000 Thalassoma rueppellii (Klunzinger, 1871) Carnivore 0.021 2.814

Lethrinidae Lethrinus harak (Forsskål, 1775) Carnivore 0.017 3.037 Lethrinus mahsena (Valenciennes, 1830) Carnivore 0.016 3.077 Lethrinus microdon (Valenciennes, 1830) Carnivore 0.021 2.900 Lethrinus xanthochilus (Klunzinger, 1870) Top Predator 0.022 2.940 Monotaxis grandoculis (Forsskål, 1775) Carnivore 0.027 2.960

Lutjanidae Lutjanus argentimaculatus (Forsskål, 1775) Top Predator 0.034 2.792 Lutjanus bohar (Forsskål, 1775) Top Predator 0.016 3.059 Lutjanus ehrenbergii (Peters, 1869) Carnivore 0.003 3.335 Lutjanus gibbus (Forsskål, 1775) Carnivore 0.023 3.060 Lutjanus kasmira (Forsskål, 1775) Carnivore 0.011 3.154 Lutjanus monostigma (Cuvier, 1828) Top Predator 0.022 2.913 Macolor niger (Forsskål, 1775) Planktivore 0.015 3.000

Monacanthidae Aluterus scriptus (Osbeck, 1765) Herbivore 0.823 1.814 Amanses scopas (Cuvier, 1829) Herbivore 0.022 2.922 Cantherhines pardalis (Rüppell, 1837) Herbivore 0.017 3.070

Mullidae Mulloidichthys vanicolensis (Valenciennes, 1831) Carnivore 0.012 3.167 Parupeneus cyclostomus (Lacepède, 1801) Carnivore 0.010 3.110 Parupeneus forsskali (Fourmanoir & Guézé, 1976) Carnivore 0.010 3.110 Parupeneus sp. Carnivore 0.010 3.110

Muraenidae Gymnothorax javanicus (Bleeker, 1859) Top Predator 0.001 3.100

Ostraciidae Ostracion cyanurus (Rüppell, 1828) Carnivore 0.115 2.550

Pomacanthidae Centropyge multispinis (Playfair, 1867) Herbivore 0.031 2.885 Pomacanthus imperator (Bloch, 1787) Herbivore 0.034 2.968

Table 3.1 (Continued) Pomacanthus maculosus (Forsskål, 1775) Herbivore 0.034 2.968 Pygoplites diacanthus (Boddaert, 1772) Herbivore 0.031 2.885

Pomacentridae Abudefduf sexfasciatus (Lacepède, 1801) Planktivore 0.023 3.130 Abudefduf vaigiensis (Quoy & Gaimard, 1825) Planktivore 0.020 3.034 Amblyglyphidodon indicus (Allen & Randall) Planktivore 0.023 3.130 Amphiprion bicinctus (Rüppell, 1830) Planktivore 0.020 3.000 Chromis dimidiate (Klunzinger, 1871) Planktivore 0.057 2.650 Chromis flavaxilla (Randall, 1994) Planktivore 0.057 2.650 Chromis pembae (Smith, 1960) Planktivore 0.048 2.710 Chromis viridis (Cuvier, 1830) Planktivore 0.048 2.710 Chromis weberi (Fowler and Bean, 1928) Planktivore 0.057 2.650 Dascyllus trimaculatus (Rüppell, 1828) Planktivore 0.060 2.850 Neoglyphidodon melas (Cuvier, 1830) Herbivore 0.018 3.182 Plectroglyphidodon lacrymatus (Quoy & Gaimard, 1824) Herbivore 0.061 2.635 Pomacentrus sulfureus (Klunzinger, 1871) Herbivore 0.030 2.870

Scaridae Cetoscarus bicolor (Rüppell, 1829) Herbivore 0.020 3.000 Chlorurus gibbus (Rüppell, 1829) Herbivore 0.019 3.100 Chlorurus sordidus (Forsskål, 1775) Herbivore 0.019 3.100 Hipposcarus harid (Forsskål, 1775) Herbivore 0.013 3.050 Scarus ferrugineus (Forsskål, 1775) Herbivore 0.020 3.000 Scarus ghobban (Forsskål, 1775) Herbivore 0.019 3.003 Scarus niger (Forsskål, 1775) Herbivore 0.018 3.130 Scarus sp. Herbivore 0.032 3.060

Scombridae Gymnosarda unicolor (Rüppell, 1836) Top Predator 0.011 3.065

Serranidae Aethaloperca rogaa (Forsskål, 1775) Carnivore 0.030 3.000 Cephalopholis argus (Bloch & Schneider, 1801) Carnivore 0.012 3.120 Cephalopholis hemistiktos (Rüppell, 1830) Carnivore 0.022 3.000 Cephalopholis miniata (Forsskål, 1775) Carnivore 0.017 2.990 Cephalopholis sexmaculata (Rüppell, 1830) Carnivore 0.027 3.000 Diploprion drachi (Roux-Estevève, 1955) Carnivore 0.010 3.040 Epinephelus fuscoguttatus (Forsskål, 1775) Top Predator 0.014 3.117 31

Table 3.1 (Continued) Epinephelus tauvina (Forsskål, 1775) Top Predator 0.023 3.021 Epinephelus tukula (Morgans, 1959) Top Predator 0.106 2.560 Plectropomus pessuliferus (Fowler, 1904) Top Predator 0.012 3.060 Pseudanthias squamipinnis (Peters, 1855) Planktivore 0.057 2.650 Variola louti (Forsskål, 1775) Top Predator 0.014 3.117

Siganidae Siganus luridus (Rüppell, 1829) Herbivore 0.019 2.956 Siganus stellatus (Forsskål, 1775) Herbivore 0.014 3.138

Sparidae Acanthopagrus bifasciatus (Forsskål, 1775) Carnivore 0.023 3.130

Sphyraenidae Sphyraena barracuda (Edwards, 1771) Top Predator 0.050 2.517 Sphyraena qenie (Klunzinger, 1870) Top Predator 0.006 3.000

Tetraodontidae Arothron diadematus (Rüppell, 1829) Herbivore 0.017 2.960

On average, study sites in Saudi Arabia exhibited higher species richness (Al

Lith: 88.0 +/- 4.6, Thuwal: 88 +/- 10.1) in the surveys as compared to study sites in

Sudan (deep south: 78.8 +/- 2.0, south Sudan: 68.0 +/- 8.7). However, no significant trends were apparent in terms of species evenness or both Shannon’s and Simpson’s diversity indices (Table 3.2).

Deep South 16 78.8 (2.0) 0.402 1.757 0.657 South Sudan 8 68.0 (8.7) 0.395 1.666 0.622 Al Lith 15 88.0 (4.6) 0.371 1.660 0.669 Thuwal 9 88.0 (10.1) 0.395 1.770 0.664 32

3.2 –Fish Biomass, Abundance, and Average Size

Average total community biomass (Figure 3.1) within the deep south region of

Sudan (6.91 +/- 8.4 tons hectare-1) was greater than that of any other study region, being approximately three times that of the south Sudan (2.19 +/- 5.9 tons hectare-1) and Al Lith

(2.20 +/- 5.5 tons hectare-1) regions and more than nine times that of Thuwal (0.75 +/- 1.4 tons hectare-1). Moreover, average top predator biomass in the south Sudan region (2.99

+/- 4.2 tons hectare-1) was significantly higher than that of the other three regions (0.85

+/- 2.4, 0.40 +/- 1.5, and 0.04 +/- 0.2 tons hectare-1 for south Sudan, Al Lith, and Thuwal, respectively), comprising 39.1 percent of the total fish assemblage biomass (compared to

33.3, 17.7 and 4.8 percent in the 3 other regions, respectively). The relative proportion of carnivore biomass remained similar among the deep south, south Sudan, and Al Lith

(14.2, 17.5 and 15.1 percent, respectively), but was significantly higher in the Thuwal region with an average of 31.6 percent of total community biomass. 33

10 Apex Predator Carnivore 8 Herbivore Planktivore 6

Estimated Biomass (tons/hectare) 2

0 Deep South South Sudan Al Lith Thuwal (16) (8) (15) (9) Region

Figure 3.1 - Biomass of fishes in four major tropic guilds from four study regions in the central Red Sea. Stacked vertical bars represent mean biomass estimated from visual surveys of the length and number of fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2). Trophic guilds are color- coded (see Table 3.1 for species' guild assignments). Results are grouped by study region (horizontal axis). Italicized numbers in parentheses indicate number of belt transects conducted in each respective region. Error bars represent standard error of mean top predator biomass.

Average fish abundance was also greatest at the community level for Sudan’s deep south region at 8.63 +/- 0.9 individuals square meter-1 (Figure 3.2). This exceeded the numerical abundance of all other regions by more than three individual fish (60 percent) per square meter (south Sudan: 4.60 +/- 1.0 individuals square meter-1; Al Lith:

5.41 +/-0.5 individuals square meter-1; Thuwal: 3.82 +/- 0.8 individuals square meter-1).

Planktivorous fishes dominated the reef community assemblages in all four regions (deep south: 87.4 percent; south Sudan: 88.4 percent; Al Lith: 90.07 percent; Thuwal: 87.7 34 percent). Top predators, on average, gradually decreased in relative numerical abundance from the deep south (2.39 percent) to south Sudan (1.71 percent) to Al Lith (0.35 percent) to Thuwal (0.31 percent).

(16) (8) (15) (9)

Figure 3.2 - Numerical abundance of fishes in four major trophic guilds from four study regions in the central Red Sea. Stacked vertical bars represent mean numerical abundance estimated from visual surveys of fishes recorded in belt transects. Trophic guilds are color-coded (see Table 3.1 for species’ guild assignments). Results are grouped by study region (horizontal axis). Italicized numbers in parentheses indicate number of belt transects conducted in each respective region. Error bars represent standard error of mean fish abundance.

The average size of fish in each of the four guilds also exhibited trends along the deep south – south Sudan – Al Lith – Thuwal gradient (Figure 3.3). The average weight of each guild in Sudan’s deep south, on average, exceed that of the three other regions, particularly dwarfing that of the Thuwal region. Carnivores and herbivores decreased in 35 size along the aforementioned continuum, while top predators and planktivores tended to increase in weight slightly from south Sudan to Al Lith.

While many individual species exhibited higher abundances and sizes along the deep south – Thuwal gradient, two in particular stand out: the big eye trevally (Caranx sexfasciatus) and the two-spot red snapper (Lutjanus bohar) (Figure 3.4). While the biomass of these fishes could vary greatly from site to site, they were largely responsible for the large proportion of top predators in the Sudanese regions.

8 Apex Predator 7 Carnivore 6 Herbivore 5 Planktivore 4

Weight (kilograms) 2

0 Deep South South Sudan Al Lith Thuwal (16) (8) (15) (9) Region

Figure 3.3 - Average weight of fishes in four major trophic guilds from four study regions in the central Red Sea. Vertical bars represent mean weight in kilograms estimated from visual surveys of the length and number of fishes counted in belt transects and calculated using the allometric weight-length formula W=aLb, (see Section 2). Trophic guilds are color-coded (see Table 3.1 for species' guild assignments). Results are grouped by study region (horizontal axis). Italicized numbers in parentheses indicate number of belt transects conducted in each respective region. Error bars represent standard error of mean weight for each guild and region. 36

8.00 Deep South 7.00 South Sudan 6.00 Al Lith 5.00 Thuwal 4.00

3.00

Biomass (tons/hectare) 2.00

1.00

0.00 Caranx sexfasciatus Lutjanus bohar Species

3.3 – Shark Sightings

Only four individuals of one species of shark, the whitetip reef shark (Triaenodon obesus) were recorded within transect boundaries. These four individuals were sheltered under the same table coral in Dohra, one of the reefs in the Al Lith region. All other sharks encountered during the survey dives were sighted some distances (>10 meters) away from the survey area. Average shark sightings (Figure 3.5) of all species (gray reef

(Carcharhinus amblyrhynchos); whitetip reef shark; silky shark (Carcharhinus falciformis); and scalloped hammerhead shark (Sphyrna lewini)) declined significantly from the deep south (6.1 +/- 1.8 sharks dive-1) to south Sudan (4.0 +/- 2.3 sharks dive-1) 37 to Al Lith (1.5 +/- 1.6 sharks dive-1) and Thuwal (0.1 +/- 0.3 sharks dive-1). With the exception of slightly more whitetip reef sharks seen in south Sudan (1.4 +/- 0.5 sharks dive-1) compared to the deep south (1.1 +/- 0.3 sharks dive-1), sightings of all four shark species followed a pattern of decrease along the deep south – Thuwal gradient. While all four species were encountered in the deep south, south Sudan, and Al Lith regions (with the exception of no silky sharks in Al Lith), only one specimen of a single shark species

(whitetip reef) was sighted during the survey dives in Thuwal.

8 Deep South 7 South Sudan 6 Al Lith 5 Thuwal 4

Shark Sightings per Dive 2

0 Gray Reef Whitetip Silky Scalloped All Sharks Hammerhead Shark Species

3.4 – A Proxy for Fishing Pressure

Top predator biomass increased with distance from the nearest port, peaking at a distance of 157 kilometers (Figure 3.6). All Saudi Arabian survey reefs lay within 60 kilometers of the nearest fish market, while south Sudanese study sites are less than 93 kilometers from Suakin. The deep south reefs, a cluster quite separated from the rest of the study sites, are situated at distances of at least 143 kilometers from the port of Suakin.

All reefs in the Sudanese deep south exhibit top predator biomass greater than the even the least impacted south Sudan study site.

R² = 0.56807 20

0 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0 Estimated Apex Predator Biomass (tons/hectare) -10

Distance from Nearest Port (kilometers)

SECTION 4 – DISCUSSION

4.1 – Sudan’s Deep South

The high total community and top predator biomasses recorded in Sudan’s deep south region rank among some of highest reported in the available literature (Table 4.1).

All reefs in the Sudanese deep south exhibit top predator biomass greater than even the least impacted south Sudan study site. The regional average total community and top predator biomass of the deep south are three and seven times greater than comparable Al

Lith reefs, respectively. These large differences are due to the combination of containing both more and larger fishes across trophic guilds in the Sudanese deep south, but particularly and importantly for top predators. It is most likely that the seemingly near- pristine status of the fish assemblages in Sudan’s deep south, at least compared to the south Sudan and Al Lith regions, is not due to a unique set of environmental characteristics but in fact due to very low fishing pressure. The reefs of this region are upwards of 174 kilometers from the nearest Sudanese port and fish market, Suakin. Most typical Sudanese artisanal fishermen simply do not have the means to reach the remote deep south reefs, and those who do venture the great distances risk their lives doing so.

There also exists the challenge of keeping fish catches fresh during the long voyages.

This data highlights the distant deep south region as exceedingly rare and valuable, one that warrants protection in the form of marine protected areas (MPAs).

Total Community Top Predator Biomass Biomass Site Region Study (tons/hectare) (tons/hectare)

Kingman Reef South Pacific Sandin et al. 2008 5.3 4.3 Pearl & NW Hawaiian Friedlander and 4.7 3.8 Hermes Atoll Islands DeMartini 2002 Kure Atoll NW Hawaiian Williams et al. 3.5 2.3 Islands 2011 Jarvis Reef South Pacific Williams et al. 2.5 1.7 2011 French Frigate NW Hawaiian Friedlander and 2.6 1.6 Shoals Islands DeMartini 2002 Palmyra Atoll South Pacific Sandin et al. 2008 2.5 1.6 Ducie Island South Pacific Friedlander et al. 1.6 1.0 2014

Sudanese Deep Red Sea This study 4.3 2.9 South

4.2 – South Sudan, Al Lith, and Thuwal

The high degree of similarity with respect to community-wide biomass between the Al Lith and south Sudan regions suggests somewhat similar intensity of fishing pressure amongst these regions, although top predators comprise a higher proportion of south Sudan mean biomass. The reefs of these two regions are appreciably closer to the ports of Suakin and Al Lith and thus much more accessible to fishermen. As the south

Sudan and Al Lith regions experience environmental conditions essentially the same as that of the deep south (see Appendix A), it is highly likely that fishing pressure (both 41 current and historical), although difficult to quantify in this part of the world, is the driving force in structuring these lower community-wide and top predator biomasses.

Interpretation of the results at Thuwal are complicated by two primary problems.

Foremost, we lack reliable baseline data of population abundance or community structure, particularly at offshore sites. Although the problem of shifting baselines is a global problem, in the Red Sea region the problem is particularly acute as there is simply no indication of historical community structure at these sites. Did Thuwal reefs ever sustain significant numbers of sharks and other top predators? As overall community biomass in Thuwal was a fraction of that of the three other study regions, this is a critical question. The second complication is the potential effect of environmental differences.

Annual mean levels of ecosystem productivity (using remote sensing of chlorophyll-a as a proxy) are generally higher in the deep south, south Sudan, and Al Lith, compared to a more oligotrophic system in Thuwal (see (Raitsos et al. 2013).

Nevertheless, high fishing intensity in this region has undoubtedly been a force shaping the overall low fish community biomass presently observed in Thuwal, which has a long tradition as a fishing village. Although a formal assessment of spatial fishing pressure in Saudi Arabia has not been conducted (i.e., a determination of which reefs fishermen target), on all of the surveyed reefs in the Thuwal area, lost fishing gear is apparent (e.g., fishing lines, fishing nets, fish traps, etc.) and several fishing boats are routinely seen, even at the reefs farthest offshore. The extremely small proportion of top predators in this region is particularly worrying. It appears that the virtual absence of top predators in Thuwal has resulted in a trophic cascade. Carnivores in Thuwal, on average, represented 31.6 percent of community biomass, approximately double that of any of the 42 other 3 study regions. This may be an indication of mesopredator release in the general absence of top predators. With respect to species richness, the higher values reported from the Thuwal and Al Lith regions of Saudi Arabia also suggest a form of trophic cascade, as areas with weakened top-down regulating forces are known to exhibit higher number of species, focused in the lower trophic guilds, compared to their higher top predator biomass counterparts (Sandin et al. 2008).

4.3 – Shark Sightings

The shark sighting data of this study suggest that populations in Saudi Arabia, as compared to Sudan, are at a worryingly low level. The survey data presented here adds to the work of Spaet and Berumen (2015) who demonstrate through a long time series of fish market surveys that a range of elasmobranch species in the Saudi Arabian Red Sea suffer from both severe growth and recruitment overfishing despite a 2008 royal decree by the Kingdom’s Ministry of Agriculture prohibiting all shark-fishing activities in the country’s waters. Spaet and Berumen (2015) provide several explanations for this unfortunate situation, including poorly specified repercussions and lack of enforcement of the 2008 decree, a near-total unawareness of the royal ban on shark fishing (98% of interviewees), the fact that most fishermen in the country are non-Saudis hired for short durations with no long-term interest, incentives, or care for the sustainability of the fisheries, and the 2008 commissioning of a long-line vessel by the Ministry of

Agriculture that specifically targeted semi-pelagic sharks (mostly tiger sharks

(Galeocerdo cuvier)).

4.4 – Study Limitations and Considerations

While the data of this study offers a compelling story, it is important to recognize the range of limitations at play when interpreting the results. First of all, the surveys were quite limited in their spatial scope. While it is possible that conducting more visual surveys may have reduced variability of the data, some variability may also be explained by typical patchiness of many marine fishes. Jacks (Carangidae) and other schooling fishes in particular fall into this category, as do sharks (Robbins et al. 2006).

Unfortunately there is no "one size fits all" survey method. The method used in this project, however, allows for comparison to similar studies. While trends can nevertheless be ascertained from the current data set, it would have been beneficial to expand the number of reefs visited as well as to add to the number of replicates conducted at each site. Logistic and time limitations were primarily responsible for limiting the size of the data set.

The four trophic guilds used in this study mirrored those of Sandin et al. (2008).

While alternate approaches for trophic groupings could have been utilized (Friedlander and DeMartini 2002, Williams et al. 2010, Stevenson et al. 2007, DeMartini et al. 2008,

Ruttenberg et al. 2011), the Sandin et al. (2008) study, clearly capturing human impacts to coral reefs, was selected due to the significance of the results, the publication’s substantial and widely-recognized impact in the academic study of marine science, and for the ease of comparison. A related point of discussion concerns the allocation of species to the selected trophic guilds. While this study based its assignments from information obtained from FishBase (Froese and Pauly 2014), assignments were not always straightforward. In some cases with limited available information, sister taxa 44 assignments were utilized (particularly for unavailable a and b values) and subjective decisions made (largely in the assignment of trophic guilds for some species).

Contributing further to this matter is the fact that all individuals of a species, regardless of size, were considered in the same trophic guild. For example, an individual Lutjanus bohar, was recorded as an top predator whether it was 20 or 70 cm total length. Some species’ dietary ontogenetic changes were not accounted for. Future studies may consider more detailed methods for data handling to address this issue.

The lack of available and reliable coral reef fisheries data for Saudi Arabia,

Sudan, and the greater Red Sea poses another limitation. Had such fishery effort and landing information been accessible and detailed, it would have allowed for further justification of this study’s conclusion: that Saudi Arabia is overfished, particularly in comparison to Sudanese reefs at similar latitudes. What information was discovered was largely outdated; current coral reef fisheries dynamics in both Saudi Arabia and Sudan are highly likely to be more intense today than 20 years ago. Incorporation of benthic cover data in the survey regions and reefs also could have provided further justification for the comparison and conclusions drawn in this study. However, the benthic composition and cover among all the offshore reefs of this study (in all four regions) appear very similar (personal observation).

Still, the lack of historical data remains the most glaring issue. In most parts of the world, marine resource managers and scientists are struggling with a problem of a shifting baseline. In this Red Sea region, however, the problem goes a step further: baseline information simply does not exist at all. It could be entirely possible that Thuwal has simply always had exceedingly low populations of sharks and top predators. The Al 45

Lith region may never have had the top predator biomass reported from the Sudanese deep south region. While these statements are likely untrue, the virtual nonexistence of accessible data and the difficulty of historic baseline reconstruction makes them hard to assess. Despite this, the results of this study provide a rough benchmark for how conditions on central Saudi Arabian Red Sea reefs may have once been.

SECTION 5 – CONCLUSION

Man has significantly altered the natural world and continues to do so at an accelerating pace. Pristine natural conditions have largely been lost across ecosystem types, with few rare relics of undisturbed conditions still available as baselines. This is particularly true for the majority of the world’s coral reefs, which have been severely degraded through a synergy of multiple anthropogenic stressors. For the central Red Sea, however, it appears that the reefs of southern Sudan persist today in as near to a pristine state as can be found in the basin. They are, at a minimum, the best representations of a natural state for the region. As such, they provide an invaluable comparison for the fish assemblages of other coral reefs within the same biogeographic locality. Remote reefs of the Sudanese deep south, however, will not remain immune to anthropogenic stressors indefinitely. In fact, recreational dive operators such as the Don Questo liveaboard company venturing the Sudanese deep south region each spring have reported dramatic decreases in juvenile silky sharks in recent years, citing illegal finning by Yemeni fisherman as the cause (L. Segalini, personal communication). Enforced protection in the form of marine reserves is necessary to safeguard these precious resources from new extractive pressures. Nevertheless, southern Sudanese coral reefs should be considered as locations for baseline conditions for future studies assessing other aspects of coral reef ecology in the region.

Fish assemblages of comparable Saudi Arabian coral reefs contain one-third the amount of community biomass and 15 percent of the total top predator biomass of that of

Sudan’s deep south province. While natural variation may result in some differences 47 between reefs and within regions, fishing down top predators in the central Saudi Arabian

Red Sea very likely explains the observed structural differences in this study. Direct targeting and incidental removal of sharks is also largely to blame for the very low sightings of sharks in Saudi Arabian waters as compared to those of Sudan. Future work focusing on expanding the spatial and temporal scope of surveys along the Saudi Arabian coastline, particularly in the Farasan Banks region, would help to better explore these hypotheses.

The results of this study have important and pressing implications for Saudi

Arabia in the management of their Red Sea marine resources, which at current levels are well overexploited. It is recommended that the Kingdom urgently adopt comprehensive conservation strategies to allow for recovery of depleted predatory coral reef fish communities and safeguard the future of their Red Sea fisheries, particularly in the face of susceptibility to ongoing climate change impacts. These new and revised policies should take the form of regulated species-specific catch quotas, minimum and maximum legal sizes, seasonal closures, and, if necessary, complete moratoriums as well as the legal mechanisms and repercussions in place to enforce these regulations. Most importantly, however, is the recommended establishment of strategically located no-take marine reserves where fish assemblages can be allowed to recover. The progress of such protected areas can be monitored and restoration goals targeted according to the baseline southern Sudanese reefs less than 200 kilometers away. Implementation of these management strategies should theoretically not be difficult for a country that does not rely on subsistence fishing and that certainly has the financial wherewithal to do so. For the Kingdom of Saudi Arabia, however, the greatest challenge for the recovery and 48 management of coral reefs appears to require a comprehensive shift in cultural attitudes and behaviors towards the conservation of the country’s living resources.

APPENDICES

APPENDIX 1 (Figure A1): Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a sea surface temperature (SST) map taken from Ngugi et al. (2012): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles).

n=9

n=15

n=8

n=16

APPENDIX 2 (Figure A2): Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a chlorophyll-a map taken from Raitsos et al. (2013): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles).

n=9

n=15

n=8

n=16

APPENDIX 3 (Figure A3): Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a salinity map taken from Ngugi et al. (2012): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles).

n=9

n=15

n=8

n=16

APPENDIX 4 (Figure A4): Map of the central Red Sea showing location of the four study regions and reefs super-imposed on a phosphate concentration map taken from Ngugi et al. (2012): Thuwal (purple circles), Al Lith (green triangles), south Sudan (red diamonds), and the Sudanese deep south (blue circles).

n=9

n=15

n=8

n=16

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