Integrating Fisheries Dependent and Independent Approaches to Assess

Home , Knifetooth sawfish, Thuwal

Integrating Fisheries Dependent and Independent Approaches to assess Fisheries, Abundance, Diversity, Distribution and Genetic Connectivity of Red Sea Elasmobranch Populations

Dissertation by

Julia L.Y. Spät

In Partial Fulfillment of the Requirements

For the Degree of

Doctor of Philosophy

King Abdullah University of Science and Technology

Thuwal, Kingdom of Saudi Arabia

May 2014

EXAMINATION COMMITTEE APPROVALS FORM

The dissertation/thesis of Julia Spät is approved by the examination committee.

Committee Chairperson: Dr. Michael Berumen

Committee Members: Dr. Xabier Irigoyen

Dr. Timothy Ravasi

Dr. Friedhelm Krupp

Julia Spät

ABSTRACT

The Red Sea has long been recognized as a global hotspot of marine biodiversity.

Ongoing overfishing, however, is threatening this unique ecosystem, recently leading to the identification of the Red Sea as one of three major hotspots of extinction risk for sharks and rays worldwide. Elasmobranch catches in Saudi Arabian Red Sea waters are unregulated, often misidentified and unrecorded, resulting in a lack of species-specific landings information, which would be vital for the formulation of effective management strategies. Here we employed an integrated approach of fisheries dependent and independent survey methods combined with molecular tools to provide biological, ecological and fisheries data to aid in the assessment of the status of elasmobranch populations in the Red Sea. Over the course of two years, we conducted market surveys at the biggest Saudi Arabian fish market in Jeddah. Market landings were dominated by, mostly immature individuals - implying both recruitment and growth overfishing.

Additionally, we employed baited remote underwater video (BRUVS) and longline surveys along almost the entire length of the Red Sea coast of Saudi Arabia as well as at selected reef systems in Sudan. The comparison of catch per unit effort (CPUE) data for

Saudi Arabian Red Sea BRUVS and longline surveys to published data originating from non-Red Sea ocean systems revealed CPUE values several orders of magnitude lower for both survey methods in the Red Sea compared to other locations around the world.

Finally, we infered the regional population structure of four commercially important shark species between the Red Sea and the Western Indian Ocean.We genotyped nearly

2000 individuals at the mitochondrial control region as well as a total of 20 microsatellite loci. Genetic homogeneity could not be rejected for any of the four species across the spatial comparison. Based on high levels of region-wide exploitation, we suggest that, for 5 management purposes, the population structure of all four species should be considered as single stock in the three marginal seas surrounding Arabia. Overall, our combined results indicate a severe depletion of sharks in Saudi Arabian Red Sea waters, likely caused by drastic overfishing of elasmobranch populations.

TABLE OF CONTENTS

Page EXAMINATION COMMITTEE APPROVALS FORM ...... 2

ABSTRACT ...... 4

TABLE OF CONTENTS ...... 6

LIST OF ABBREVIATIONS ...... 7

LIST OF FIGURES ...... 8

LIST OF TABLES ...... 10

CHAPTER 1: INTRODUCTION & LITERATURE REVIEW ...... 11

CHAPTER 2: FISH MARKET SURVEYS ...... 48

CHAPTER 3: BRUVS AND LONGLINE SURVEYS ...... 93

CHAPTER 4: POPULATION CONNECTIVITY ...... 124

SUPPLEMENTARY MATERIAL………………………………………………… 160

GENERAL CONCLUSIONS……………………………………………………… 163

APPENDIX 1: LIST OF PUBLICATIONS ...... 169

LIST OF ABBREVIATIONS

AIMS Australian Institute of Marine Science

BRUVS Baited Remote Underwater Video Surveys

CPUE Catch Per Unit Effort

FAO Food and Agricultural Organization

HE Expected Heterozygosity

HO Observed Heterozygosity

HWE Hardy-Weinberg Equilibrium

IUCN International Union for the Conservation of Nature

IPOA International Plan of Action

LT Total Length mtDNA CR Mitochondrial DNA Control Region

MPA Marine Protected Area

TN Tamura-Nei

UAE United Arab Emirates

UVS Underwater Visual Surveys

WIO Western Indian Ocean

WD Disc Width

LIST OF ILLUSTRATIONS

Chapter 1

Figure 1: Map of elasmobranch research in the Red Sea...... 20

Chapter 2

Figure 1: Sampling locations ...... 51

Figure 2: Species composition ……...... 59

Figure 3: Cumulative species curve ………...... 60

Figure 4: Seasonal landing trends...... 61

Figure 5: Size classes ...... 65

Figure 6: Mean vs. maximum sizes..………...... 67

Figure 7: Length frequency distribution...... 68

Chapter 3

Figure 1: Sampling locations ...... 100

Figure 2: Images of BRUV units ...... 103

Figure 3: Images taken by BRUV units ...... 106

Figure 4: BRUVS CPUE …...... 107

Figure 5: Global BRUVS CPUE comparisons ...... 108

Figure 6: Global longline CPUE comparisons ...... 109

Chapter 4

Figure 1: Collection locations ...... 130

Figure 2: Parsimony network - C.limbatus ...... 141 9

Figure 3: Parsimony network - C.sorrah ...... 143

Figure 4: Parsimony network - R.acutus …...... 144

Figure 5: Parsimony network - S.lewini ...... 147

LIST OF TABLES

Chapter 1

Table 1: Literature overview - Red Sea elasmobranch diversity...... 22-23

Table 2: Literature overview - research on Iago omanensis...... 26

Chapter 2

Table 1: Sample interview questions...... 55

Table 2: Species list ...... 62-63

Table 3: Maximum sizes at maturity...... 66

Table 4: Information on pregnant individuals...... 69

Table 5: Results of fishermen interviews ...... 70

Chapter 3

Table 1: BRUV sampling regime...... 102

Chapter 4

Table 1: mtDNA marker information...... 133

Table 2: Microsatellites………...... 137

Table 3: FST values……………...... 138

Table 4: mtDNA diversity……...... 140

CHAPTER 1: GENERAL INTRODUCTION AND LITERATURE REVIEW1

Introduction

Elasmobranch populations worldwide are severely threatened, due to unsustainable levels of fishing and other human disturbances (Simpfendorfer et al. 2002; Baum et al. 2003;

Myers and Worm, 2003; Baum and Myers 2004; Myers et al. 2007; Worm et al. 2013;

Dulvy et al. 2014). Relatively recent surveys estimate that annual harvest rates of sharks could be as high as 2.29 million tons (Clarke et al. 2006) or between 63-273 million individuals per year (Worm et al. 2013). Annual global elasmobranch catch rates reported to the FAO have steadily increased to a peak of cf. 884 000 tons in 2003 and have declined by cf. 14% since (FAO 2014). True catch numbers are, however, considered to be up to four times greater than reported (Barker and Schluessel 2005; Clarke et al. 2006;

Iglesiás et al. 2010; Bornatowski et al. 2013). This extreme fishing pressure on elasmobranch stocks is worrisome because their life history characteristics (slow growth rates, late age at maturity, low fecundity) result in a slow recovery potential following disturbance, thereby making elasmobranchs especially vulnerable to total population collapse (Bonfil 1994; Berrow and Heardman 1994; Barker and Schluessel 2005).

Besides direct effects, overfishing of predators at, or near the top of marine food webs can cause a range of indirect effects at the community level with consequences as severe as species replacement and shifts in community composition (Stevens et al. 2000;

Schindler et al. 2002; Ward and Myers 2005; Ferreti et al. 2010). Whether effects caused by population declines of globally distributed species may or may not be reversible locally has yet to be determined, in either case the risk of irreversible chronic

1 The majority of this content appears in publication in Spaet et al. 2012. 12 accumulation of global marine extinction is present and should not be underestimated

(Jackson 2010; Neubauer et al. 2013). The World Conservation Union (IUCN) has officially recognized the global threat to elasmobranchs. Based on Red List criteria and the observed threat level of assessed species combined with a modeled estimate of the number of Data Deficient species that are likely to be threatened, one quarter of all condrychthian species are considered threatened due to targeted as well as incidental overfishing (Dulvy et al. 2014). In addition chondrichthyans have the lowest fraction of

Least Concern species (23.2%) of all vertebrate groups, including the marine taxa assessed to date (Hoffmann et al. 2010) as well as the lowest percentage of species considered to be safe from extinction (37.4%) (Dulvy et al. 2014).

A fundamental problem faced when attempting to manage elasmobranch populations is the lack of basic information about the population ecology and life history of most species, much less their actual abundances. For elasmobranchs, knowing their age, growth patterns, size structure, habitat distribution, and movements can prove invaluable when developing effective management strategies including harvest controls

(Brunnschweiler and Van Buskirk 2006; Southall et al. 2006). Given the increase in wealth in the Chinese middle class and the resulting rise in demand for shark fins (Clarke

2004), pressure on shark populations is likely to increase globally, including the Red Sea.

In fact, the Red Sea has been identified as one of the three main hotspots possessing greatest extinction risk of elasmobranchs worldwide and 29 out of 59 species are currently classified as threatened (Dulvy et al. 2014). The effective development of management strategies for threatened elasmobranch populations demands for a comprehensive assessment of the current status of elasmobranch assemblages in all 13 affected regions of the world but especially in the Red Sea and other hotspots of greatest extinction risk to be conducted as quickly as possible.

Review of approaches to study shark populations – key methodologies

Fisheries dependent surveys

Traditionally, population data for elasmobranchs have come from two areas, commercial fisheries and scientific surveys. Although fisheries-dependent data is one of the most resourceful tools for fisheries managers, it can be quite variable in quantity and quality because many fishermen lack the knowledge and training to accurately record the catch and bycatch of elasmobranchs (Simpfendorfer et al. 2002); in fact, in many artisanal fisheries shark landings are not recorded at all. Yet fisheries-dependent scientific surveys of artisanal fisheries can readily be conducted by trained personnel in cases where elasmobranch specimens are displayed whole at landing sites or fish markets (Castillo-

Géniz et al. 1998; Henderson et al. 2009, Moore 2012). If performed continuously, such surveys can provide important details on the population as well as at the species level.

Species composition, seasonal (Hazin et al. 1994; Castillo-Géniz et al. 1998) and non- seasonal (Martin and Zorzi 1993) catch variations respectively can easily be determined and compared to other regions. In this way, taxonomic, biological and life history data, all critical for managing elasmobranch species, can be collected with little cost and effort

(Baremore 2010; Mourato et al. 2010; White 2010). Such data can provide the foundation for estimating population sizes and ultimately form the basis for management plans

(Cortes et al. 2009; Damalas and Megalofonou 2010). Additional information is often 14 available through fishermen or traders at the markets, which can provide insights into the socio-economic situation of the fishery, utilization of the meat and fins, fishing techniques used and fishing grounds frequented (Tovar-Ávila et al. 2010; Varkey et al.

2010). Saudi Arabia has great potential for market surveys. There are 14 major publicly accessible artisanal landing sites along the Red Sea coastline and many more minor sites in between. Additionally there is a wealth of small-scale fish markets along the coast and two major ones in Jeddah and Jizan. Well-designed surveys at these locations can provide detailed descriptions of the Red Sea elasmobranch fauna not available from official sources.

Fisheries-independent surveys

Baited longlines

The alternative to fishery-dependent surveys are fishery-independent surveys carried out by survey vessels; a method that provides more accurate data and consequently, more accurate population estimates (e.g. Kohler et al. 1998; Courtney and Sigler 2007; Belcher and Jennings 2009). The current standard population assessment for both pelagic and inshore shark species involves baited longlines (Morrissey and Gruber 1993; Feldheim et al. 2002). Scientific longlines typically consist of a mainline anchored at both ends.

Attached to the mainline are gangions and buoys, spaced at certain intervals. Each gangion ends in a baited hook.

Sharks captured on the line can be identified to species, sexed, measured, tagged with acoustic, satellite and/or dart tags and tissue sampled prior to release. The resulting catch 15 per unit effort data from controlled longlining allows for accurate population estimates, as well as detailed catch composition information relating to species, size, and sex

(Pikitch et al. 2005; Garla et al. 2006). Tags can provide important information regarding long or short-term movements and facilitate the identification of recaptured sharks. All this information combined with geographic, oceanographic, and climate data can provide a comprehensive population assessment, which can then be complemented with population genetics data resulting from DNA analyses of the sampled tissues.

Besides the obvious advantages of longlining towards other less effective shark population assessment methods, e.g. underwater visual censuses (Colton and Swearer

2010), baited underwater video surveys (Brooks et al. 2011), it should be noted that the method is relatively invasive and can result in unwanted mortalities (Pikitch et al. 2005).

On certain terrains, in heavy seas or in other less than ideal situations, which make a deployment of longlines difficult it is hence suggested to employ alternative methods in order to decrease the expected mortality rate.

Baited remote under water video surveys (BRUVS)

An alternative method to longlining for fisheries-independent shark research are baited remote underwater video surveys (BRUVS). BRUVS were first developed by the

Australian Institute of Marine Science (AIMS) and have been used to quantify the relative abundance, diversity, distribution and stock structure of a variety of fish species, including elasmobranchs (Cappo et al. 2003; Cappo et al. 2004; Meekan and Cappo

2004; Watson et al. 2005; Meekan et al. 2006; White et al. 2013). 16

The technique involves lowering a baited digital video camera to the seabed for a set duration of time. Sharks are attracted to the plume of scent emitted by the bait bag and are consequently captured on film (Meekan et al. 2006). BRUVS yield similar catch per unit effort data, species, sex, and estimates of size to long line surveys, but are not invasive or destructive (Meekan et al. 2006; Brooks et al. 2011). The use of BRUVS is also relatively inexpensive, easy, quick to replicate and limited only by the number of

BRUVS units in operation (Cappo et al. 2004). One of the main limitations to BRUVS however, is that relative abundance of investigated species can only be estimated as it is usually unknown from what distance sharks are attracted to the bait source (see Willis et al. 2000 for review; Cappo et al. 2003). However, integrating the dilution factor with real time current data, this parameter could be modeled.

For elasmobranch assessments along the extensive and convoluted reef systems in

Saudi Arabia, BRUVS are the ideal alternative method to longlining. Longline operations are usually restricted to open habitats free of reef coverage or other obstacles the line could get tangled in. The use of BRUVS hence provides an opportunity to sample regions that would be completely inaccessible for longline operations.

Population genetics

In addition to data available through fisheries-dependent and independent surveys the identification of regional stock structure of elasmobranch populations is essential for conservation management (Carvalho and Hauser 1994; Keeney et al. 2003; Ovenden et al. 2010) and the achievement of sustainable fisheries (Grant and Bowen 1998; Ward 17

2000). Sharks are relatively large and highly vagile species. They lack a planktonic larval stage, which is responsible for dispersal in most marine invertebrates and teleost fishes.

Genetic population structure is thus, expected to be largely a result of juvenile and adult movements (with smaller species considered to be less mobile than larger species,

Musick 2004). Yet, other components such as habitat preference, habitat use (including distinct habitat use during reproductive behavior) periodic migration, effective population size, ocean conditions as well as historical processes also play important roles in shaping genetic structure (e.g. Pardini et al. 2001; Nance et al. 2011; Blower et al. 2012, Galván-

Tirado et al. 2013).

While cutting-edge genetic technologies are still relatively uncommon in elasmobranch research a range of established tools continues to prove successful in determining their population structure (see Dugeon et al. 2012 for review). To assess genetic variation of commercially important shark fisheries most commonly the mitochondrial (mtDNA) control region (CR) as well as nuclear DNA (microsatellites) are investigated. The mtDNA CR has a high mutation rate and uni-parental inheritance making it a useful tool to understand not only population genetic structures, but also spatial components of phylogeographical lineages and evolutionary processes of geographically related populations (Avise 2000). In addition, it is often used to assess philopatry (returning to an individual’s birthplace for parturition) among shark populations (Keeney and Heist 2006; Chabot and Allen 2009; Portnoy et al. 2010; Karl et al. 2011, Tillett et al. 2012). Nuclear DNA in contrast is inherited to equal extents from both parents. Using microsatellite markers hence gives the opportunity to investigate both maternal, as well as paternal gene flow. Many microsatellite loci can be amplified in 18 conspecifics and closely related genera (e.g. Schrey and Heist 2002, Ovenden et al. 2006,

Boomer and Stow 2010). Because microsatellites as well as mtDNA CR markers are scored using the PCR, data can be obtained from very small tissue samples, including fin clips, making non-lethal sampling possible.

A combination of molecular tools with fisheries-dependent and indepent survey data is promising to unravel patterns that cannot be identified using one method alone. An integrated approach will thus improve the possibility of achieving effective management strategies at the species and population level and eventually to ensure sustainable fisheries. This is particularly true in regions that are severely lacking data required for informed management decisions, like the Red Sea, a region that has historically been overlooked in terms of elasmobranch research.

Review of elasmobranch research in the Red Sea

Documentation of the diversity of the Red Sea elasmobranch fauna has been extremely limited (Table 1), with reports often based on chance encounters with large or distinctive species. Earlier work on elasmobranch biodiversity has been of limited use due to numerous misidentifications and incorrect synonymizations (Gohar and Mazhar 1964), confinement to the northern Red Sea (Gohar and Mazhar 1964; Baranes and Shahrabany-

Baranes 1986) and limitation to sharks (Compagno 1982) or carcharhinids (Baranes and

Shahrabany-Baranes 1986). The FAO field identification guide for the Red Sea and the

Gulf of Aden (Bonfil and Abdallah 2004) as well as a recently published book on Red

Sea sharks (Baranes 2013) provide more detailed information on most elasmobranchs 19 known to occur in the region as well as valuable, albeit sparse, background information on taxonomic, ecological and biological characteristics and fisheries. Other Red Sea elasmobranch records come from broader ichthyological investigations that have often resulted in ichthyological books and checklists including elasmobranch species (Forsskål

1775; Rüppell 1828; Klunzinger 1871; Fowler 1956; Randall 1986; Khalaf and Disi

1997). The earliest works were published before the 20th century and today are only sold as collectors’ items. While still being highly relevant to taxonomic research, they contain very limited biological information. More recent checklists on shark (Compagno 1982) and batoid species (Compagno and Randall 1987) were largely based on dated literature

(Fowler 1956; Gohar and Mazhar 1964) and are in need of revision. The limited amount of directed research on elasmobranch diversity conducted in the Red Sea raises doubts on the completeness of available elasmobranch checklists. The most recent species account

(Golani and Bogorodsky 2010) is an updated version of Dor (1984) and Goren and Dor

(1994). It lists 56 elasmobranchs (28 shark, 28 batoid species), and is mostly in agreement with the species listed in Bonfil and Abdallah (2004). The latest addition to the Red Sea elasmobranch fauna, the pigeye shark Carcharhinus amboinensis (Spaet et al. 2011) post-dates both of these lists. Yet specimens of this species have repeatedly been encountered at fish markets in Saudi Arabia (pers. obs.) as well as during trawl fishing operations in southern Sudan in 1999 and 2000 (I. El-Hassan, pers. comm.). The fact that a range of new species records has been found with minimal survey effort

(Compagno and Randall 1987; Bonfil 2003; Spaet et al. 2011) suggests that there are more new occurrences to be detected through longer-term surveys and routine market sampling by trained personnel. 20

Figure 1: Map of the Red Sea. Symbols identify sites for which scientific elasmobranch literature is available, including locations of reports on shark attacks (Randall and Levy, 1976; Clark and George, 1979; Levine et al. 2011) and a study on shark repellents (Clark and George, 1979). Circles depict observational reports (e.g., first records), triangles depict scientific studies; blue = ≥20 years old, red <20 years old. The Gulf = Arabian Gulf.

Despite the obvious incompleteness of Red Sea elasmobranch checklists, data available on Red Sea elasmobranch diversity clearly show that the Red Sea elasmobranch fauna has remarkably low diversity compared to neighboring waters (Baranes 2009). Of the 13 orders and c. 889-1124 species of living elasmobranchs (Hamlett 1999), only seven orders and 58 species have been reported from the Red Sea (Bonfil and Abdallah

2004; Golani and Bogorodsky 2010; Spaet et al. 2011), while the adjacent Gulf of Aden contains nine orders and 73 species (Bonfil and Abdallah 2004). The vast majority (97%) 21 of Red Sea elasmobranch species are also found in the western Indian Ocean. Only 18% of these species, however, are also present in the Mediterranean Sea (Serena 2005) if three Lessepsian immigrants are included (Ben-Tuvia 1966; Di Castri et al. 1990; Golani and Massuti 2002; Golani et al. 2004; Souissi et al. 2007), while c. 35% are also present in the eastern central Atlantic Ocean (Froese and Pauly 2011). Only two elasmobranch species, the Elat electric ray Heteronarce bentuviai (Baranes and Randall 1989) and the panther electric ray Torpedo panther (Olfers 1831) are thought to be endemic to the Red

Sea (Bonfil and Abdallah 2004). Differences in elasmobranch species compositions between the western Indian Ocean and the Atlantic Ocean–Mediterranean Sea reflect the

Indo-Pacific origin of the Red Sea fauna (Compagno 1982).

Table 1. Available literature on diversity and distribution of Red Sea elasmobranchs (bold type indicate where this was the primary focus of the publication)

Primary (secondary) Location Main limitation(s) Date of collection Reference data source

Museum collections Western Red Sea Restricted to Sudanese waters, not targeting Not reported Waters (1913) elasmobranchs Hook and line, museum North Western and Description of four opportunistic elasmobranch records 1957 Klausewitz (1959) collections Southern Red Sea Automatic photography Deep ocean trench, Unidentified shark species 1958 Marshall and Bourne central Red Sea (1964) Net, hook and line, Hurghada, North Dated, confined to the North-Western Red Sea Not reported Gohar and Mazhar harpoon fishing Western Red Sea (1964) Not reported Not reported Confined to the Gulf of Aqaba, lacks scientific Not reported Baranes and Ben-Tuvia information (1978c) Fish market survey, Gulf of Aqaba, Opportunistic record of a shark species 1971 Baranes and Ben-Tuvia experimental longlines Southern Sinai (1978b) Fish market Gulf of Aqaba Opportunistic record based on half-dried head of shark 1977 Baranes and Ben-Tuvia specimen (1978a) Fish market Gulf of Aqaba Description of two distinctive shark species 1977 Baranes and Ben-Tuvia (1979) Photo trap Central Red Sea Opportunistic record of a shark species 1977/1979 Klausewitz and Thiel (1982) Literature n/a Limited to sharks Not reported Compagno (1983) Not reported Central Red Sea Opportunistic depth record of a single shark species 1980 Klausewitz (1983) Literature n/a No discrimination between substantiated and Not reported Dor (1984) unsubstantiated records

Table 1 continued

Primary (secondary) Location Main limitation(s) Date of collection Reference data source Experimental hooks and Western shores of Restricted to the Northern Red Sea, confined to Not reported Baranes and lines, nets, museum Gulf of Aqaba, carcharhinids Shahrbany-Baranes collections South-Eastern Gulf (1986) of Suez Fish market, literature Gulf of Aqaba Opportunistic single record of a new guitarfish species, Not reported Compagno and Randall checklist of Red Sea batoids outdated (1987) Gill net Gulf of Aqaba New species record of a single ray species Not reported Baranes and Randall (1989) Surface observation Central Red Sea Opportunistic observation of shark aggregation 1989 Anon (1990)

Literature n/a No discrimination between substantiated and Not reported Goren and Dor (1994) unsubstantiated records Stingray spine Gulf of Aqaba Identification of ray species on the basis of the spine 1997 Spanier et al. (2000) recovered from dolphin body Literature, field surveys Red Sea, Gulf of Not specific to the Red Sea Not reported Bonfil and Abdallah Aden (2004) Snorkelling, SCUBA, Not reported Opportunistic observations of elasmobranchs between 1961- Moosleitner (2005) literature associated with fish species 2004 Literature n/a Solely based on literature Not reported Golani and Bogorodsky (2010) Fish market Jeddah Opportunistic single record of a shark species 2010 Spaet et al. (2011) SCUBA Sudan Data collected by recreational scuba divers 2007/2008 Hussey et al. (2011) SCUBA Jeddah Data obtained from shark feeding sites only 2007-2009 Clarke et al. (2011) SCUBA Jeddah Data obtained from shark feeding sites only 1995-2009 Clarke et al. (2013) Field sampling Sudan Study based on only one individual 2013 Walter et al. (2013) n/a publications which are solely based on literature and do not involve field work at specific locations 24

There are no reports on spatial variation in Red Sea elasmobranch diversity, nor are there any studies on migration patterns and population dynamics of migratory stocks available.

Yet diversity gradients and migrations are probable given the major geographical features within the Red Sea (e.g. the shallow Gulf of Suez and the deep, narrow-shelved Gulf of

Aqaba). In other systems (e.g. the North Atlantic Ocean), significant biogeographical gradients exist (Muñoz-Chápuli 1985). Reef use and residency patterns of a baited population of silky sharks Carcharhinus falciformis have been investigated in the central

Red Sea of Saudi Arabia since 1995 and clearly highlighted not only the importance of

Red Sea reef habitats for recruitment into local shark populations (Clarke et al. 2011) but also the potential drastic impact of local fisheries on grey reef Carcharhinus amblyrhynchos as well as silky shark populations (Clarke et al. 2013).

Most information critical for elasmobranch management simply does not exist in the scientific literature. For example, local nursery areas remain unidentified and life history aspects have largely been overlooked for Red Sea populations. Published records make no mention of shark nursery areas in the Red Sea. The nature of the coastline [extensive shallow areas, often fringed by mangrove and seagrass habitats (Khalil 2004; FAO

2007)], and the large amount of newborn and juvenile sharks offered for sale in regional fish markets (Spaet et al. 2011) suggest a high likelihood of local nursery areas. Close examination of potential nursery sites, following the criteria of Heupel et al. (2007) is clearly warranted.

Very limited quantitative data exist on elasmobranch life history variables (Baranes and Ben-Tuvia 1978a; Baranes and Wendling 1981; Fishelson and Baranes 1998a;

Hamlett et al. 2002), although they have been relatively well studied in nearby Omani 25 waters (Henderson et al. 2006, 2007, 2009). Farther afield, there are reproductive data on several species that occur in the Red Sea (White 2007; White and Dharmadi 2007; White et al. 2008; Marshall and Bennett 2010). While some of this information could potentially be extrapolated for use in management of Red Sea fisheries, geographical variability in life history characters within a species’ range is common, e.g. reproductive variables (Yamaguchi et al. 2000; Lombardi-Carlson et al. 2003) and growth rates

(Lombardi-Carlson et al. 2003; Driggers et al. 2004; Neer and Thompson 2005). Given the isolation and environmental conditions of the Red Sea, such differences from non-

Red Sea populations may be expected. Thus, there is a clear need for accurate, up-to-date, and Red Sea-specific life history information covering various species.

To date, only one elasmobranch species has received considerable attention in the Red

Sea: the bigeye houndshark Iago omanensis, also known as the Oman shark. A relatively small shark, I. omanensis forms dense and stationary populations in the Gulf of Aqaba at depths of 150–1500 m (Waller and Baranes 1994). On the basis of over 2000 specimens collected near Eilat, Israel, in the Gulf of Aqaba, several studies focusing on the biology of this species were published between 1991 and 2004 (Table 2). Some of these studies were the first to investigate ontogenetic changes in sharks’ sensory systems and have since served as reference points for subsequent research on the sensory biology of elasmobranchs and coral-reef fishes (Leis and McCormick 2002; Schluessel et al. 2008;

Harahush et al. 2009). Iago omanensis and the Arabian smooth-hound Mustelus mosis, the only other Red Sea triakid, are the only two Red Sea elasmobranchs for which information on feeding ecology exist. While the diet of I. omanensis mainly consists of cephalopods (and myctophids during the summer months, Table 2), M. mosis seems to 26 feed equally on cephalopods, decapods and terrestrial food items (Goldschmidt et al.

1996).

Table 2: Publications on Iago omanensis (exclusively from the Gulf of Aqaba, except for Goldschmidt et al. (1996)) Subject Reference Diet Baranes (1982) Morphology Waller and Baranes (1991) Diet Waller and Baranes (1994) Diet/Trophic relationships Goldschmidt et al. (1996) Diet/ Fisheries biology Abdallah (1997) Sensory biology Fishelson and Baranes (1997) Sensory biology Fishelson and Baranes (1998a) Sensory biology Fishelson and Baranes (1998b) Sensory biology Fishelson and Baranes (1999) Reproductive aspects Fishelson and Baranes (1998c) Reproductive aspects Hamlett et al.( 2002) Morphogenesis Fishelson et al.( 2004) Parasites Ivanov and Lipshitz (2006)

Feeding ecology of other Red Sea species could also potentially be extrapolated from studies on parasites of elasmobranchs (Caira 1990). Except for one study on I. omanensis and M. mosis (Ivanov and Lipshitz 2006) and another on the black tip reef shark

Carcharhinus melanopterus (Bakhrebah 2008), studies on parasites conducted throughout the Red Sea are, however, exclusively based on the ribbontail ray Taeniura lymma (Saoud 1963; Saoud and Hassan 1981; Hassan 1982; Saoud et al. 1982; Ramadan

1984; Moosleitner 2005). Of the four dietary studies on I. omanensis (Table 2), one also focused on aspects of its fishery biology (Abdallah 1997). In this work growth and mortality coefficients were obtained from I. omanensis specimens caught during experimental fishing over a period of 12 months. On the basis of these data the author calculated and classified the maximum exploitation rate as well below the maximum sustainable yield and hence recommended an increase in commercial fishing efforts. The 27 fact that no details are given on how many individuals were caught and included in the analysis, however, renders the results questionable. More recent investigations suggest that the current exploitation rate of I. omanensis, through commercial fishery on deep-sea fishes in the Gulf of Aqaba will put local populations at high risk, if continued at the same level (Baranes 2005).

Red Sea fisheries generally lack reliable and species-specific data (Sanders and

Morgan 1989; Morgan 2006) and there are no published studies addressing directed elasmobranch fisheries (Bonfil 2003). Several studies specific to individual countries

(Tesfamichael et al. 2014; Gladstone 2002; Morgan 2006; Tsehaye and Nagelkerke 2006) mention elasmobranchs as being part of the targeted fisheries, but species-specific information or actual catch numbers are not presented. Global statistics on elasmobranch landings reported to the FAO (2014) are difficult to interpret for the region because all

Red Sea countries, except for Eritrea, Jordan and Sudan, share coastlines with the

Mediterranean Sea, the Arabian Gulf or the Indian Ocean. FAO statistics do not partition by regional catchment and catch statistics are solely separated by country. Although Red

Sea-specific fisheries statistical yearbooks exist for several countries, e.g. Saudi Arabia

Marine Fisheries Department 2007 and Eritrea (D. Habteselassie and I. Habte, unpubl. data), there are no means of measuring the relationship between such statistics and actual catches of elasmobranchs. The lack of Red Sea-specific elasmobranch landing statistics is cause for concern. For instance, Yemen is one of the top 20 elasmobranch fishing nations globally (Lack and Sant 2009), but the total biomass and number of species landed in

Yemen and caught in the Red Sea remain unknown.

Use of shark meat and liver oil in the Red Sea has been the subject of several studies 28

(Amer 1972; Moharram 1983; Moharram and Awadallah 1983; Sachithananthan 1983;

El-Tanahy 1990). The value of elasmobranch products seems to be region-specific, e.g. in several provices in Yemen sharks are an important and highly valued food source (Krupp

2004) while in other Red Sea regions, e.g. Saudi Arabia elasmobranchs seem to be considered of low value as food fishes by locals (pers. obs.). On the contrary, sharks in particular have been shown to provide one of the most economic and profitable fisheries of the region (Hariri et al. 2002; Tesfamichael and Pitcher 2006), a fact, which is most likely attributable to the trade in fins to East Asia (Hariri et al. 2002). The fin trade is most likely also responsible for significant decreases in catch per unit effort (CPUE) for sharks noticed in Eritrea between 1996 and 2002 (Tsehaye et al. 2007), a time frame in which the fin trade in Hong Kong alone grew > 5% a year (Clarke et al. 2006). Up until

1996 fishing in these and adjacent waters, e.g. Djibouti (Künzel et al. 1996) did not appear to represent a threat to the local shark populations (Marshall 1996). This situation has almost certainly changed, although the absence of data prevents any determination of the magnitude of this threat.

According to the 2011 IUCN Red List of threatened species, 44% of Red Sea elasmobranch species are globally classified as Threatened (Endangered and Vulnerable),

26% are of conservation concern (Threatened and Near Threatened) and 19% are considered data deficient (IUCN, 2014). The high presence of threatened species, together with possible local depletions of shark diversity (Gallagher and Hammerschlag

2011) and potentially increasing markets for elasmobranchs and their products are areas of concern for which currently available management (and enforcement) measures appear to offer only limited, if any, protection. The lack of information available to carry out 29 proper evaluations of the state of Red Sea elasmobranch stocks currently precludes any effective long-term management of the elasmobranch fishery (Hariri 2000).

This situation is no isolated case for the Arabian Seas region. Elasmobranch populations in the Arabian Gulf for example have also been largely overlooked to date.

The scattered and often outdated information available has hampered detailed assessments of even their current status, not to mention any future predictions (Moore

2011). As a first step towards a better understanding of elasmobranchs in this ecosystem, recent efforts have been made to document the diversity of the Arabian Gulf’s extant elasmobranch fauna (Moore et al. 2007, 2010; Moore, 2010; Moore et al. 2012). As for the Red Sea, however, the lack of quantitative data on ecological and biological aspects of Arabian Gulf populations renders sustainable management of these resources unachievable at this point. Clearly, there is an urgent need for relevant information on

Red Sea elasmobranchs.

Research priorities should include routine systematic fishery surveys in combination with species-level monitoring and reporting of landings by each of the Red Sea riparian countries. This would provide useful information on diversity, patterns of abundance, sex and maturity composition for those species taken directly or as by-catch in marine- capture fisheries. There is also a pressing need to identify spatial and temporal sensitivities, such as nursery areas or seasons of high by-catch, and the relationship between elasmobranch populations in the Red Sea with those of other oceans (Bonfil

2003). Further work should aim to identify species, areas of origin and the countries involved in the Red Sea fin trade. 30

Aims and objectives of this PhD thesis

Based on an integrated approach of fisheries-dependent and fisheries-independent survey methods, the general aim of this thesis was to provide biological, ecological and fisheries data to aid in the assessment of the status of elasmobranch populations in the Red Sea.

Chapter 2 aimed to provide data on elasmobranch fisheries in the Saudi Arabian Red Sea by determining the species, size, and sex composition, seasonal variations, and sustainability of landings. The aim of chapter 3 was to determine the diversity and abundance of elasmobranchs in the Saudi Arabian Red Sea by integrating BRUVS and longline surveys and to compare the results to published elasmobranch abundance and diversity in protected and unprotected non-Red Sea environments. Chapter 4 aimed to investigate the genetic connectivity of four commercially harvested shark species between the Red Sea, the Indian Ocean and the Arabian Gulf by genotyping the mitochondrial control region and a range of microsatellite loci.

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A fisheries-dependent survey of elasmobranch landings in the Saudi Arabian Red

Sea

Abstract

Elasmobranch populations worldwide are severely threatened due to overexploitation and unregulated fisheries. Despite the fact that sharks and rays are captured in fisheries operating along the Saudi Arabian Red Sea coast, information on any aspects of these fisheries are very limited. Here we document the structure, composition and biological characteristics of eastern Red Sea elasmobranch fisheries based on anecdotal accounts, interviews with fishermen, genetic barcoding and fisheries landings data over an intensive two-year sampling period at one of the biggest fish markets in the Kingdom of

Saudi Arabia. Bi-monthly market surveys between 2011 and 2013 revealed that 24 of the

56 known elasmobranch species in the Red Sea were landed by fishermen and offered for sale. Barcoding revealed two previously undescribed batoid species as well as four batoid species and one shark species not previously reported from the Red Sea. Five coastal carcharhinid species - Carcharhinus sorrah, C. amblyrhynchos, C. falciformis, C. limbatus, Rhizoprionodon acutus, dominated the landings, together comprising 73% by number of specimens of the total landings. Targeted shark fisheries reportedly exist in shark nursery areas. Most elasmobranchs outside of these areas were reportedly landed as bycatch. Most strikingly, the large majority of landed elasmobranchs were immature

2 The majority of this content is currently under review in Fisheries Research. Part of it appears in Spaet et al. 2011. 48 males or females below their reported size of sexual maturity, which suggests potential for both growth and recruitment overfishing and emphasizes the urgent need to implement region-specific management and conservation strategies to avoid the loss of these critical predators.

Introduction

Elasmobranch fisheries are highly susceptible to overexploitation, largely due to their K- selected life-history strategies (low fecundity, late-maturity and/or longevity, Hoenig et al. 1990). Nonetheless, sharks and batoids have been fished for millennia in many regions around the world. Demand for seafood has been increasing due to a rapidly growing human population and this demand has outstripped wild-caught supply since the 1980s

(Pauly et al. 2002). Elasmobranchs are often specifically targeted for pharmaceuticals and cultural practices such as shark fin soup (Vannuccini 1999). Fisheries impacts on elasmobranchs can range from alterations of population size structures, demography (age of maturity, fecundity) and abundance (Olsen 1959; Holden 1973; Walker 1998) on the species level to large-scale changes impacting the entire ecosystem (Stevens et al. 2000;

Jackson et al. 2001; Ruppert et al. 2013). Avoiding such negative effects requires baseline species-specific information of exploited elasmobranch populations to implement effective conservation and management plans.

Sharks and batoids in the Western Indian Ocean are thought to be heavily exploited by a wide variety of fisheries, yet there is a distinct paucity of specific fisheries data

(Anderson and Simpfendorfer 2005). Monitoring and management of Red Sea 49 elasmobranch fisheries is undertaken by various agencies (e.g. the UNDP (United

Nations Development Program)-FAO Red Sea Project) and these agencies have provided some training in elasmobranch stock identification for local fisheries observers, species identification guides and detailed strategic action plans to promote sustainable elasmobranch fishing (Bonfil 2003). However, several issues still face Red Sea elasmobranch fisheries, key among these being a general lack of data on elasmobranch ecology, species diversity, catch size and composition, and effort (Bonfil 2003; Spaet et al. 2012).

A recent preliminary analysis of the Saudi Arabian artisanal fishery suggests that targeted teleost species have been overexploited since 1990 (Jin et al. 2012). While official fisheries statistics indicate limited exploitation of Saudi Arabian Red Sea elasmobranch species (Fisheries Statistics of Saudi Arabia, Department of Marine

Fisheries), historical data suggests that elasmobranch catches are much higher than reported by national and FAO statistics (Sanders and Morgan 1989). Indeed, overfishing has been suggested as the cause for recent shark population declines along the eastern

Red Sea coast (Clarke et al. 2013). Sharks are typically taken as bycatch in mixed-species artisanal reef fisheries, which operate gillnets, hand-lines and traps from small open speedboats. Industrial fisheries operating bottom-trawls and purse seines are of minor importance (Jin et al. 2012) but are the primary fishery responsible for batoid catches

(Bonfil 2003).

As part of his consultancy work in the Arabian Seas region, Bonfil (2003) provided the first baseline data on Saudi Arabian elasmobranch fisheries. There has been 50 no update on this data in over a decade. In 2008, a royal decree prohibiting all shark- fishing activities was enacted by the Ministry of Agriculture in an effort to protect Saudi

Arabian fisheries resources. Details on sanctions for violations to the law, however, are not specified and enforcement appears to be virtually non-existent. In addition, Saudi

Arabia is not participating in the International Plan of Action for the Conservation and

Management of Sharks (IPOA-Sharks) as suggested for all countries involved in targeted or incidental shark fisheries by the United Nations Food and Agriculture Organization

(FAO) in 1999 (FAO 1999). Over a two-year study period from 2011 to 2013 we conducted a survey of the biggest Saudi Arabian Red Sea fish market (Jeddah). This study aimed to bridge the gap in knowledge of elasmobranch landings in the eastern Red

Sea by determining the species, size and sex composition, and seasonal variation, of landings, and whether this was sustainable. Collectively, this data provides the first detailed evaluation of Saudi Arabian Red Sea elasmobranch landing data.

Materials and Methods

Sampling sites

The Saudi Arabian Red Sea coastline extends over a length of approximately 1760 km from the border with Jordan in the North to the border with Yemen in the South (Fig. 1).

Out of all Red Sea countries, Saudi Arabia has the largest shelf area (c. 70 000km2). In the northern and central part, the coastal shelf is characterized by shallow fringing reefs that often have steep, wall-like drop-offs (Edwards 1987). The southern region, in contrast, is characterized by nutrient-rich water and soft-bottomed communities, 51 including shallow bays and lagoons, which are often fringed by mangroves (Khalil 2004).

Compared with the Indian Ocean, environmental conditions in the Red Sea are relatively extreme, with salinities up to 40 parts per thousand in the northern Red Sea and annual sea surface temperatures ranging from 24°C in spring to 35°C in summer (Raitsos et al.

2011). The present study focused on elasmobranch landings at major Saudi Arabian Red

Sea fish markets, primarily at the Jeddah fish market. Daily auctions are held at this market in the early morning hours, during which elasmobranch catches from all along the

Saudi Arabian Red Sea coast are

The

Figure 1: Map of the Red Sea - locations where surveys were conducted to assess the status of Saudi Arabian Red Sea elasmobranch landings. Other main landing sites for elasmobranch catches, which area mettioned in the text are also shown. 52

offered. Data on past and current Red Sea elasmobranch fisheries were obtained through interviews with fishermen in two coastal fishing villages (Farasan Kebir and Thuwal) and at the fish market in Jeddah.

Data collection

Market surveys

Bi-monthly market surveys were conducted between May 2011 and May 2013 at the

Jeddah fish market. Visits started between 4am and 6am in the morning and lasted until the end of the morning auction (between 3-8 hours, depending on the season). All elasmobranchs offered during each survey day were identified to the species level and measured to the nearest centimeter using total length (LT = the distance between the tip of the snout and the tip of the upper caudal fin (in a relaxed position), with the measuring tape stretched alongside the body axis) for sharks and guitarfishes or disc width (WD) for rays and sexed through the presence/absence of claspers. Whenever possible a small tissue sample was collected from the left pelvic fin of each encountered specimen. Tissue samples were preserved in 99% ethanol for subsequent genetic analysis. On rare occasions very large numbers of juvenile sharks were landed, which rendered the collection of measurements, biological data and tissue samples of all individuals unfeasible. In such cases only the number of each species auctioned was recorded.

Reproductive data were obtained opportunistically due to the limited time given for 53 measurements and identification by the auctioneers. Male maturity was determined based on the calcification stage of the claspers. If the cartilage within the claspers was not calcified, males were considered juveniles. Males having partially calcified claspers were classified as sub-adults and only animals with fully calcified claspers were considered fully mature adults. Elasmobranchs were purchased and taken to the laboratory for dissection in cases where there were doubts regarding species identification or when pregnant females were encountered. In case of pregnant females, sex and size of embryos and LT (to the nearest cm) of the mother was recorded.

Interviews with fishermen

Whenever possible, the market personnel (including fishermen, laborers and vendors) were interviewed regarding the catch location and sales price of the auctioned specimens.

In addition, semi-structured interviews of local Saudi fishermen from three different fishing areas were conducted to obtain insights into Saudi Red Sea elasmobranch fisheries based on the methods of Lam and Sadovy de Mitcheson (2011). Questions asked during the interviews were intended to provide information on the interviewee’s career history, details of the past and current elasmobranch fisheries, details of targeted species, seasonality, catch location, gear and vessel types used, utilization of caught specimens and the personal appraisal of each fisherman regarding the current exploitation level of, and possible future management strategies for Elasmobranchs (Table 1). The majority of fishermen employed in Saudi Arabian fisheries are non-Saudi nationals who are hired for a limited time and do not have any historical knowledge of elasmobranch fisheries in the 54 region. Hence, only traditional Saudi fishermen who had worked in the fishing sector for a minimum of 30 years were interviewed. These individuals were identified by fishermen, who were asked to name colleagues, who they considered to have good knowledge on elasmobranchs and elasmobranch fisheries (Berg 2004). In total 50 interviews were conducted between June 2011 to January 2013 at three major fishing locations along the Saudi Red Sea coast (Fig. 1): Thuwal (n = 9), Jeddah (n = 16) and

Farasan Kebir (n = 25). Interviews were conducted in Arabic with the help of local translators who had no background in fisheries. Except for three retired fishermen, all interviewees were still active in the traditional fisheries sector. Each interview lasted between 0.5 and 3 hours.

Table 1: Selected sample questions presented to Saudi fishermen during the interviews to assess the present and historical state of elasmobranch fisheries in the Saudi Arabian Red Sea. Sub theme Sample questions (a select list) Career history In what year or at what age did you start fishing in the area? Do you have any occupation other than fishing? Current and past elasmobranch fisheries Indicate on the map in which area you usually catch sharks/rays? What is the average duration of your fishing trips? At which times of the day do you usually fish? At which times of the year do you usually fish? If fishing is seasonal indicate start and end date of each season and determine the seasons with most fishing effort Please identify all sharks that you have caught throughout your fishing career (flashcards) Personal perception of the status of Saudi Red How many species of sharks are found in the Sea elasmobranchs Red Sea? Is there a tradition of shark/ray fishing in your community? Do you think there are more, less or the same number of sharks in your fishing areas now compared to 20, 10 or 5 years ago? If less or more what do you think are the reasons for an increase/decrease in abundance? Do you remember a particular time or event when the numbers started to increase/decrease? Do you think people should be concerned about the future of sharks? Do you think the catching of sharks should be regulated? Do you know of any laws protecting sharks in the Red Sea? Gear characteristics What gear do you use to target sharks? What gear types catch sharks incidentally? What do you use as bait? Are the species you catch dependent on the gear you use?

Genetic analysis

All tissue samples collected during the survey were barcoded using the cytochrome oxidase barcode marker 1 (COI) to confirm initial species identification based on morphology. DNA was extracted using the Machery-Nagel Genomic DNA from tissue

(Bethlehem, PA, USA) extraction kit following the manufacturers’ instructions. Total amplification volumes for PCR reactions were 12.5uL, and contained 1uL of the template

DNA, 1uL of the primer mix (10 pmol/uL), 6.25 uL of the Qiagen Master Mix (Qiagen

Inc.) and 4.25uL of RNAse-free water. The primer combination FishF1 and FishR1

(Ward et al. 2005) was used for initial amplification and amplified the barcode region for the majority of samples. When these primers failed to produce a PCR product, primers

FishF2 and FishR2 (Ward et al. 2005) were used. If this PCR was still unsuccessful, primer combination FishF1 and HCO2198 (Folmer et al. 1994) was used. The PCR thermal cycling employed was: 95°C initial heating for 15 min to activate the hot start

DNA polymerase, followed by 35 cycles of 94°C for 30 seconds, 58°C for 1 min, 72°C for 1 min, and a 10 min final extension step at 72°C. Amplifications were performed using Veriti 96-well thermal cyclers (Applied Biosystems). PCR products were visualized on 0.8% TBE agarose gels containing ethidium bromide for DNA quality and concentration, viewed on Gel Doc IT Imaging System (Mitsubishi) and purified using the

Exonuclease I method (ExoSap, USB, Cleveland, USA). All sequences were sequenced in the forward and reverse direction and have been submitted to the GenBank database.

In addition to COI, the faster evolving NADH2 fragment was amplified for two shark species and all batoid species in order to distinguish between recently evolved 57 elasmobranch sister species which cannot be resolved based on COI alone (Wong et al.

2009; Naylor et al. 2012). The universal primer combination ASNM, ILEM (Naylor et al. 2005) was successful in amplifying the targeted fragment in 151 out of 163 cases. The

PCR protocol used was identical to the one used for COI. All sequences were sequenced in the forward and reverse direction. The program Codon Code Aligner (CodonCode

Corporation, Dedham, USA) was used to assemble and edit forward and reverse sequences. Species identifications were made using GenBank through BLAST

(http://blast.ncbi.nlm.nih.gov/Blast.cgi) based on a 95% match criteria. All NADH2 sequences have been submitted to the GenBank database. If sequence data did not match the original identification based on morphological characters, the respective species were reassessed morphologically when available.

Statistical analyses

Seasonal changes in shark landings composition were explored via a one-way analysis of similarity (ANOSIM) with time of year (four seasons) as a fixed factor. Months were pooled across years to provide replicates of their respective seasons. The hypothesis of parity in the sex ratio of all species was tested with chi-square statistics (χ2) at a 95% confidence level. Observed mean total lengths of the most common 15 shark species were divided by and then plotted against the maximum known length obtained from previous studies of the respective species and displayed graphically. For each species ontogenetic patterns of abundance were assessed by graphically displaying the relative contribution

(%) of each 10 cm size class to the total landings; species with a sample size of less than 58

80 individuals were excluded. An Anderson-Darling test (Anderson and Darling 1954) was performed for species with sample sizes of less than 80 individuals to assess if sex- specific size-frequency distributions conformed to a normal distribution. A two-tailed t- test (normally distributed data) and a Mann-Whitney U-test (not normally distributed data) (Zar 1999) respectively were used to assess possible sex-based differences through investigation of inter-gender size differences.

Results

Species composition

In total 2724 individuals comprising 37 elasmobranch species of 11 families were recorded during the fish market survey: 23 shark and 14 ray species (Table 2). Landings were predominantely comprised of sharks (c. 94%, cf. c. 6% rays) of the family

Carcharhindae, which accounted for c. 91% of the total numbers and 15 out of the 37 species. Five species of carcharhinids comprised c. 73% of all elasmobranch individuals

(Fig. 2), with the most abundant being the spot-tail shark, Carcharhinus sorrah, with c.

35% of the total number of sharks landed. Grey reef shark (C. amblyrhynchos), silky shark (C. falciformis) and milk shark (Rhizoprionodon acutus) were the next most abundant in landings, each contributing 11% to total shark landings, as well as blacktip shark (C. limbatus) (with c. 10%). 59

Figure 2: Species composition (%) of 2724 elasmobranchs recorded between May 2011 to May 2013 during 48 visits to the Jeddah fish market. Carcharhinus amblyrhynchos, C. falciformis, C. sorrah, C. limbatus

Rhizoprionodon acutus, Sphyrna lewini, Loxodon macrorhinus, C. melanopterus, S. mokarran, Other, Batoids

Five species new for the Red Sea were recorded during the surveys: the pigeye shark, which was recorded during a pilot study in 2010 (Spaet et al. 2011); the Oman cownose ray, Rhinoptera jayakari; the flapnose ray, R. javanica; the shortfin devil ray, Mobula kuhlii; and the banded eagle ray, Aetomylaeus nichofii. Genetic identification based on

NADH2 sequences of these species confirmed initial morphological assessments for M. kuhlii and A. nichofii. Rhinoptera jayakari was the most abundant batoid species comprising nearly half of all landings (c. 48 %). Interestingly, no additional elasmobranch species were recorded after a survey period of nine months until the end of the survey, when four ray species were discovered 21 and 23 months into the study (Fig.

3). Stability of the species richness throughout most of the survey period suggests that the study adequately recorded species richness of Saudi Arabian Red Sea elasmobranch landings between 2011 and 2013. 60

Cumulativeelasmobranchdiversity 5

0 0 10 20 30 40 50 60 Number of market visits

Figure 3: Cumulative number of elasmobranch species recorded in 48 bi-monthly visits to the Jeddah fish market between May 2011- and May 2013.

Seasonality

The global ANOSIM demonstrated that the shark composition was not significantly different among seasons (global r = 0.1244, p = 0.166). The majority of shark species were present during all months of the year, however, numbers of recorded landings varied significantly between seasons (Fig. 4). The lowest number of shark landings was observed during the summer months (June, July, August) with almost four times less sharks observed than during winter (December, January, February).

100% 494 305 582 1179 90% 80%

70%

60%

50%

40%

30%

20% 10% 0% Spring Summer Fall Winter

Figure 4: Seasonal trends in Red Sea shark landings into Saudi Arabia from 2011 to 2013 (n = 2404). Percent composition values relate to all sharks recorded during the respective season and numbers above bars represent the total number of sharks recorded in each season. Infrequently caught species were combined into the category “other”. Carcharhinus amblyrhynchos, C. sorrah , C. falciformis, C. limbatus Rhizoprionodon acutus, C. melanopterus, Loxodon macrorhinus, Sphyrna lewini, Sphyrna mokarran, Other.

Table 2: Elasmobranch species, recorded during 48 visits to the Jeddah fish market between May 2011 and May 2013: total number of individuals, percentage of total number of all species, size range by sex (sharks and guitarfishes LT, rays WD; mean  S.D. cm) and percentage of mature males based on clasper calcification status. + indicates potentially undescribed species * indicates species recorded for the first time from the Red Sea.

Family/Species Total number of Male and female size range Percentage individuals (mean  S.D. cm) mature males (%) (% total of all species combined) Sharks Ginglymostomatidae Nebrius ferrugineus 2 (<1) F: 117 (n/a) 0 M: 136 (n/a) Stegostomatidae Stegostoma fasciatum 1 (<1) F: 135 (n/a) n/a M: n/a Alopiidae Alopias pelagicus 5 (<1) F: 280 (n/a) 0 M: 215-242 (229.3 ± 13.7) Lamnidae Isurus oxyrinchus 1 (<1) F: 205 (n/a) n/a M: n/a Triakidae Iago omanensis 2 (<1) F: 85-89 (87.0 ± 2.8) n/a M: n/a Hemigaleidae Hemipristis elongata 15 (<1) F: 76-170 (108.3 ± 33.5) 62.5 M: 85-170 (117.4 ± 21.2) Carcharhinidae Carcharhinus albimarginatus 3 (<1) F: 81-93 (88.7 ± 6.7) n/a M: n/a Carcharhinus altimus 24 (<1) F: 134-240 (157.7 ± 63.9) 18 M: 67-215 (144.3 ± 51.3) Carcharhinus amblyrhynchos 287 (10.61) F: 56-170 (101.5 ± 31.1) 35 M: 60-170 (101.9 ± 29.4) Carcharhinus amboinensis * 13 (<1) F: 93-205 (141.5 ± 45.0) 0 M: 91-223 (149.8 ± 48.3) Carcharhinus brevipinna 38 (<1) F: 63-244 (104.8 ± 61.3) 34 M: 65-237 (134.9 ± 69.0) Carcharhinus falciformis 272 (10.05) F: 72-250 (127.3 ± 47.7) 4 M: 77-233 (130.4 ± 43.8) Carcharhinus limbatus 276 (9.13) F: 60-250 (93.2 ± 30.3) 5 M: 55-215 (96.4 ± 33.0) Carcharhinus melanopterus 127 (4.69) F: 41-144 (32.6 ± 35.0) 13 M: 44-127 (80.1 ± 28.9) Carcharhinus plumbeus 21 (<1) F: 90-208 (163.0 ± 40.5) 50 M: 90-206 (150.8 ± 47.7) Carcharhinus sorrah 880 (32.52) F: 47-150 (87.9 ± 25.2) 10 M: 38-130 (89.1 ± 21.6) Galeocerdo cuvier 6 (<1) F: 130-208 (174.4 ± 5.4) 0 M: 203-230 (216.5 ± 19.1) Loxodon macrorhinus 102 (3.77) F: 30-96 (69.3 ± 9.7) 95 M: 30-80 (71.0 ± 9.5) Negaprion acutidens 7 (<1) F: 76-86 (81.0 ± 7.1) 75 M: 81-265 (195.8 ± 83.5)

Table 2 continued Rhizoprionodon acutus 262 (10.01) F: 27-76 (51.4 ± 15.1) 39 M: 30-91 (57.6 ± 17.0) Triaenodon obesus 29 (1.07) F: 80-140 (106.9 ± 18.2) 46 M: 70-120 (89.5 ± 16.9) Sphyrnidae Sphyrna lewini 105 (3.88) F: 40-260 (89.2 ± 53.5) 8 M: 45-210 (100.9 ± 50.7) Sphyrna mokarran 83 (3.14) F: 48-250 (140.9 ± 43.4) 5 M: 70-250 (135.9 ± 40.4) Rays Rhinidae Rhina ancylostoma 4 (<1) F: 153-180 (170.8 ± 12.1) n/a M: n/a Rhynchobatidae Rhynchobatus sp1+ 10 (<1) F: 178-210 (161.5 ± 39.5 ) 85 M: 115-136 (118.25 ± 28.5) Rhynchobatus sp2+ 15 (<1) F: 163-210 (186.5 ± 23.5) 50 M: 76-118 (97 ± 29.7) Rhinobatidae Glaucostegus halavi 12 (<1) F: 130-150 (148.2 ± 6.0) n/a M: n/a Dasyatidae Himantura gerrardi 1 (<1) F: 78 (n/a) n/a M: n/a Himantura uarnak 1 (<1) F: 125 (n/a) n/a M: n/a Pastinachus atrus 1 (<1) F: 76 (n/a) n/a M: n/a Taeniura lymma 2 (<1) F: 34 (n/a) n/a M: n/a

Myliobatidae Aetobatus ocellatus 14 (<1) F: 48-100 (88.9 ± 29.2) 16 M: 16-121 (74.8 ± 24.5) Aetomylaeus nichofii * 3 (<1) F: 41-43 (42.0 ± 1.4) n/a M: 43 (n/a) Rhinopteridae Rhinoptera jayakari * 82 (3.01) F: 37-99 (41.1 ± 12.7) 0 M: 49-72 (47.9 ± 13.0) Rhinoptera javanica* 11 (<1) F: 41-77 (51.1 ± 15.0) 0 M: 42-50 (46.0 ± 5.7)

Mobulidae Mobula thurstoni 6 (<1) F: 130-137 (134 ± 3.6) 67 M: 136-160 (152.3 ± 15.0) Mobula kuhlii * 1 (<1) F: 62 (n/a) n/a M: n/a

Size and sex composition and reproduction

Size ranges for males and females, percentage of mature individuals and species abundance data are presented in Table 2. Sex ratio differed significantly from parity for 64 three sharks, C. falciformis (p = 0.01, χ2), the sliteye shark, Loxodon macrorhinus (p =

0.02, χ2), the spinner shark, C. brevipinna, (p = 0.02, χ2), and one batoid species, the halavi ray, Glaucostegus halavi (p < 0.0001, χ2). C. brevipinna was the only species with a departure from parity towards males. The other four species showed a departure from parity towards females.

The bulk (c. 71%) of all elasmobranchs landed were small (LT < 100 cm) (Fig. 5) and consisted mostly of juveniles of larger-sized species (reported size max: 160 cm and 300 cm). Small-sized elasmobranchs of <120 cm reported maximum LT comprised only c.

14% of the total landings, which is less than half of the number of large elasmobranchs with reported maximum LT >200 cm (c. 29%). Of the 2724 individual elasmobranchs recorded, 83% were below the reported size of maturation for the species based on direct observations and maturity data sourced from the literature (Table 3). The majority of large (maximum size >200 cm) sharks were typically landed at less than half of their reported maximum size, and 90% below their reported size of maturation. Shark species of smaller body size were typically landed closer to their reported maxium lengths (Fig.

6). Size frequency distributions for all shark species with a sample size >80 individuals are indicated in Fig. 7. Landings of all shark species were strongly biased towards juvenile size classes (Fig. 7). This bias was particularly pronounced in species attaining bigger maximum sizes, i.e. C. falciformis, the scalloped hammerhead shark, Sphyrna lewini, and the great hammerhead shark, Sphyrna mokarran, which showed almost a complete lack of all sizes above maturation (Fig. 7). Rhinoptera acutus was the only species of all elasmobranchs with sample sizes of >80 that showed a significant difference between the average size of males and females landed (Mann-Whitney U-test, 65 p = 0.033), with males being larger than females (Fig. 7).

100 90

70 60 50 40

Frequency (%) Frequency 30 20 10 0 30-100 100-200 200-300 Size classes (cm)

Figure 5: The percent contribution of size classes for all 2724 elasmobranch specimens (sharks n = 2561; batoids n = 163) examined from Saudi Arabian Red Sea landings based on bi-monthly visits to the Jeddah fish market between 2011 and 2013. Grey represents the percentage of mature elasmobranchs within each size class.

Seven pregnant females of four different shark species were dissected. Species, size and sex details of mothers and embryos are shown in Table 4.

Barcoding

In total 2056 tissue samples were collected during the survey and barcoded. Of these

1870 (91%) yielded DNA that could be amplified and sequenced. Of the 1870 samples that yielded barcodes, 1590 gave 100% matches to elasmobranch species in GenBank.

The average best match was 98.67% (second best 99.43%). A few specimens showed an unequivocal GenBank match at 100% sequence identity to the species pair C. altimus/C. plumbeus.

Table 3: Maximum size and size at maturity from published data from the Indian Ocean for all shark species recorded in 48 visits to the Jeddah fish market between 2011-2013.

Species Maximum Size (LT) at maturity References size LT (cm) (cm) Alopias pelagicus 365 M: 267-276 Liu et al. 1999 F: 282-292 Carcharhinus albimarginatus 275 M: 160-180 Fourmanoir 1961, F: 170-200 Bass 1973 Carcharhinus altimus 277 F: 234; M: 216 Springer 1960 Carcharhinus amblyrhynchos 193 M: 110-130; F: 120 Compagno 1984 Carcharhinus amboinensis 280 M: 195 Bass et al. 1973, F: 200-223 Fourmanoir 1961, Carcharhinus brevipinna 278 M: 176-200 Bass et al. 1973, F: 200 Wheeler and Ommanney 1953 Carcharhinus falciformis 283 F: 248-260 Bass et al. 1973, M: 240 Fourmanoir et al. 1961 Carcharhinus limbatus 280 M: 160-180 Bass et al. 1973, F: 170-180 Bonfil 2003 Carcharhinus melanopterus 180 M: 109 Bass et al. 1973, F: 112 Fourmanoir et al. 1961 Carcharhinus plumbeus 218 M: 160-170; F: 170 Bass et al. 1973 Carcharhinus sorrah 160 M: 106; F: 110-118 Bass et al. 1973 Galeocerdo cuvier 550 M: 250 Wintner and Dudley F: 293 2000, Compagno 1984 Loxodon macrorhinus 91 M: 62-66; F: 79 Compagno 1984 Negaprion acutidens Rhizoprionodon acutus 110 M: 68-70; F: 70-81 Compagno 1984 Triaenodon obesus not available Nebrius ferrugineus 320 M: 250 Compagno 1984 F: 230-290 Hemipristis elongata 240 not available Compagno et al. 1989 Isurus oxyrinchus 400 M: 160-170 Compagno et al. 1989, F: 220 Cliff et al. 1990 Sphyrna lewini 370-420 M: 189; F: 210 Liu and Chen 1999 Stegostoma fasciatum 354 M: 150-180 White et al. 2006, F: 169-172 Compagno 2002 Iago omanensis 85 M: 31; F: 35 Henderson et al. 2006, Wheeler and Ommanney 1953

These two species, however, are fairly easy to distinguish morphologically and additional amplification for NADH2 clearly distinguished the sequences when blasted on GenBank, confirming our initial identification based on morphological characters. All batoid tissue samples were amplified and sequenced for COI as well as NADH2. Sequence data of two guitarfish species did not match any sequences on BLAST and the collected specimens showed previously undescribed morphological traits.

0.9 a 0.8 b

0.7 c

0.6 l d e

f 0.5 g h j i 0.4 k

m n 0.3 o 0.2

Average total length /maximum total length total /maximum length total Average 0.1

0 0 100 200 300 400 500 600 700 Maximum total length (cm)

Figure 6: Recorded mean total lengths/reported maximum size against maximum length (sourced from the literature, see Table 3) and standard deviation for all shark species with sample sizes > 10 recorded in visits to the Jeddah fish market from 2011 to 2013 (a) Loxodon macrorhinus (n = 102); (b) Carcharhinus plumbeus (n = 21); (c) Triaenodon obesus (29); (d) Carcharhinus amblyrhynchos (287); (e) Carcharhinus amboinensis (13); (f) Hemipristis elongata (15); (g) Carcharhinus melanopterus (n = 127); (h) Carcharhinus sorrah (n = 880); (i) Carcharhinus altimus (n = 24); (j) Carcharhinus falciformis (n = 272); (k) Carcharhinus brevipinna (n = 38); (l) Rhizoprionodon acutus (n = 271); (m) Carcharhinus limbatus (n = 247); (n) Sphyrna lewini (n = 105); (0) Sphyrna mokarran (n = 83). 68 Rhizoprionodon acutus Loxodon macrorhinus Carcharhinus sorrah 25 70 80 (c) (a) (b) 60 20 70 50 60 40 15 50 30 10 40 30 20 5 20 10 10 0

0 0

30-39 50-59 70-79 90-99

110-119 130-139 150-159

30-39 40-49 50-59 60-69 70-79 80-89 90-99 Carcharhinus melanopterus Carcharhinus amblyrhynchos Carcharhinus limbatus 45 35 (e) 25 (d) 40 (f) 30 35 20 25 30 20 25 15 15 20 10 10 15 10 5

% Frequency % 5 5 0 0

30-39 60-69 90-99

30-39 50-59 70-79 90-99

50-59 60-69 70-79 80-89 90-99

120-129 150-159 180-189 210-219 240-249

110-119 130-139

100-109 110-119 120-129 130-139 140-149 150-159 160-169 170-179

Carcharhinus falciformis Sphyrna mokarran Sphyrna lewini

35 18 30 (g) 16 (h) (i) 30 25 14 25 12 20 20 10 15 15 8 10 6 10 4 5 5 2

0 0 0

70-79 90-99 40-49 60-69 80-89 40-49 60-69 80-89

110-119 130-139 150-159 170-179 190-199 210-219 230-239 250-259 100-109 120-129 140-149 160-169 180-189 200-209 220-229 240-249 100-109 120-129 140-149 160-169 180-189 200-209 220-229 240-249

Total length (TL)

Figure 7: Total length (LT)-frequency distributions for all sharks with sample sizes >80 from observations at the Jeddah fish market from 2011 to 2013. (a) Rhizoprionodon acutus (male (white) and female (black)), (b) Loxodon macrorhinus, (c) Carcharhinus sorrah, (d) C. melanopterus, (e) C. amblyrhynchos, (f) C. limbatus, (g) C. falciformis, (h) Sphyrna mokarran, (i) S. lewini. Fine dashed lines indicate average published size (or range) at birth, dotted lines indicate female size at maturity (see Table 2 for reference details on reported maturity size ranges). 69

Table 4: Species, total length (LT) of pregnant sharks, sex and number of embryos, and observation date of sharks examined from landings at the Jeddah fish market. Sex LT embryos Species LT (cm) (embryos) (cm) Date Carcharhinus altimus 215 M (4) 35-36 20.09.2012 F (3) 35 213 M (2) 67 06.12.2012 F (3) 67-70 Carcharhinus brevipinna 244 M (3) 46-53 31.01.2013 F (4) 50-53 Loxodon macrorhinus 79 F (2) 42, 44 04.04.2013 77 M (1) 30 11.04.2013 F (1) 30 78 M (4) 30-32 22.11.2012 F (1) Triaenodon obesus 124 2 10 24.07.2013

Interviews with fishermen

All fishermen interviewed were involved in multi-species fisheries and in general did not target elasmobranchs during their fishing operations. About half of the Farasani fishermen, however stated that they heavily targeted neonate blacktip reef sharks, C. melanopterus, in their nursery areas on a seasonal basis. All elasmobranchs caught are routinely brought to the nearest fish market for onward sale. Only a small number of neonates are kept for personal consumption. Most (98%) interviewees were unaware of the royal decree prohibiting shark fishing but the large majority (80%) considered it important to implement management strategies for shark fisheries in the region, due to an observed decline in shark abundance over the past 20 years (Table 5). Two main explanations were given by the interviewees regarding the cause of this decline: (1) an increase in fishermen, resulting in less prey availability for shark species and (2) the improvement of fishing gear resulting in higher shark catches (Table 5). In addition all fishermen interviewed in Thuwal and Jeddah pointed out that in 2008 longline fishing 70 fleets were instructed by the Saudi Ministry of Agriculture to target sharks in the northern and central Saudi Arabian provinces between Yanbu and Rabigh. One particular dhow, operating longlines over the period of one year to catch substantial quantities of larger semi-pelagic (mainly tiger sharks, Galeocerdo cuvier) and reef sharks, was consistently blamed for the drastic decline of shark populations in the area (Table 5). Sharks caught during this operation were either sold in Jeddah or transported to the Dubai fish market, which serves as major hub in the shark fin trade (Rose 1996).

Table 5: Percentage of Saudi Arabian fishery interview respondents to selected questions.

Question related to Answers (in %) (A) Awareness of royal aware (2%) unaware (98%) decree prohibiting sharks as by-catch (B) Sharks targeted all sizes neonates only (25%) by-catch only (10%) (65%) (C) Perceived abundance of declined same (8%) increased (20%) sharks compared to 20 years (72%) ago (D) Probable reason for shark less prey finning more longlining fleet decline availability (1%) fishermen of 2008 (12%) (68%) (19%) (E) Perceived importance of high (80%) medium (5%) low (15%) management strategies for sharks

Fisheries

Based on information received from local elasmobranch traders and market personnel, the bulk of specimens observed in this study were landed in the southern region of Saudi

Arabia at three particular landing sites - Al Qunfudhah, Al Birk and Jizan (Fig. 1). No 71 finned speciemens were observed in the landings and all specimens were sold whole.

Most larger sharks and guitarfishes were finned following the auction, upon sale for consumption at the retail stores. Fins were usually purchased by two individual Yemeni traders who indicated that fins would be dried in Jeddah and subsequently be shipped to

Yemen for onward sale to South East Asia. At only one occasion dried and salted fins

(between 35-50 sphyrnid fins, each measuring approximately 30cm) were offered for US

$40 separately from the auction.

Retail prices of elasmobranchs differed by species and size and, compared with most other fishes available at the market, shark meat was very cheap. High priced groupers such as the roving coral grouper, Plectropomus pessuliferus, are frequently 10-20 times more expensive than any of the elasmobranch species offered. Species fetching the highest prices during the auction were large sphyrnids and rhynchobatids. Specimens of

2-3 m were offered for c. US$250 while other shark species (e.g. C. falciformis and C. altimus) of the same size were sold for up to 50% less. A correlation between price and fin size was obvious. Smaller sharks, regardless if they were juveniles of larger species or adults of smaller species, were mostly sold in bundles of up to seven sharks for less than

US $5 per bundle. Rays were of similar low value, independent of species or size.

Discussion

An active, unregulated fishery for elasmobranchs persists in the eastern Red Sea, with

Saudi Arabian landings heavily concentrated on carcharhinid sharks, despite a recent governmental decree prohibiting all shark-fishing activities. Most concerning is the 72 evidence that most individuals are immature, suggesting that both growth and recruitment overfishing are occurring in this fishery. In comparison with elasmobranch landings reported from other locations in the Western Indian Ocean (Henderson et al. 2007; Moore et al. 2012; Robinson and Sauer 2013) we found noticeable differences concerning species, size and seasonal compositions. We documented 24 of the 56 Red Sea chondrichthyan species (Golani and Bogorodsky 2010) in landed catches, plus one new species record of large carcharhinid (Spaet et al. 2011), four new batoid species records, two previously undescribed guitarfish species and four species belonging to species complexes needing revision (Peter Last, pers. comm.). Most tissue samples yielded high quality sequences for genetic barcoding, which are now publicly available as a reference tool for Red Sea elasmobranch identification. Information gained through fishermen interviews indicated that targeted shark fisheries are very limited and almost exclusively concentrated on neonates of six species while the large majority of sharks is taken as bycatch. Although the large majority of fishermen are unaware of the royal decree prohibiting all shark fishing activities, they did notice declines in shark abundances over the past two decades and consider it necessary to implement management strategies towards the conservation of sharks.

Species composition

Few elasmobranch fisheries studies exist for the Western Indian Ocean. Compared to artisanal elasmobranch landings from Madagascar (Robinson and Sauer 2013) species diversity was greater for the Red Sea (37 compared to 23 species), while landings in both 73 regions were dominated by a similar species assemblage. Frequencies of neonates and juveniles in Red Sea shark landings were overall much higher than in Madagascar.

Species diversity of Red Sea elasmobranch landings was equivalent to that of the Arabian

Gulf (Moore et al. 2012) but it was noticeably lower than in Oman (37 cf. 56 species)

(Henderson and Reeve 2011). The majority of sharks recorded in Red Sea, Oman and

Arabian Gulf landings was small (LT < 100 cm) (Henderson et al. 2009, Moore et al.

2012). Yet, landings in the Arabian Gulf and Oman, were dominated by small but mostly mature individuals of species with a small reported maximum size, i.e. R. acutus and I. omanensis (Oman) and R. acutus and C. dussumieri (Arabian Gulf) (Henderson et al.

2006, Henderson et al. 2009, Moore et al. 2012), whereas the majority of small individuals recorded in the Red Sea consisted of juveniles of species with larger reported maximum size (between 160 and 300 cm). While such size variation may potentially be attributed to general differences in the occurrence and abundance of species between these ocean basins, we suspect that regional variations in size-selective fishing gear might also play an important role in creating the observed patterns.

In contrast to other locations in the Arabian Seas region (Henderson et al. 2007) and elsewhere (Bizzarro et al. 2009), seasonal variation in landings composition was not evident (Fig. 2) despite seasonal fluctuations of temperature and oceanographic condition within the study area (Patzert 1974; Johns et al. 1999). The variability in the number of observed landings (individuals) between the summer months and the rest of the year (Fig.

2) is most likely due to lower fishing efforts (and hence lower catch rates) during the month of Ramadan, which fell into the months of July and August respectively during the two years of the survey. 74

Six sharks and 19 batoid species previously reported from the Red Sea were not found during the survey. Of particular concern is the absence of the sand tiger shark, C. taurus, and the dusky shark, C. obscurus, in the landings. The very strong K-selected life history characteristics of these species make them particularly vulnerable to exploitation and populations in several locations around the world have already been severely depleted

(Musick et al. 1993; Lucifora et al. 2002; Otway et al. 2004). None of the fishermen interviewed have encountered either of the two species in the past 20-25 years, even though they described relatively stable abundance levels of the sand tiger shark in the

1970s. This trend is worrisome and may indicate marked population declines of these species. Of equal concern is the absence of the only two sawfish species known from the

Red Sea, the knifetooth sawfish, Anoxypristis cuspidata as well as the green sawfish,

Pristis zijsron. The current International Union for the Conservation of Nature Red list status of both species is endangered and critically endangered respectively (IUCN 2013).

Anecdotes from fishermen and relatively large amounts of dried saws displayed in the homes of retired fishermen indicate formerly high abundance of both species, particularly in southern Saudi Arabian Red Sea waters, but fishermen interviewed consistently mentioned their rarity nowadays. The absence of 16 ray species from the landings, as well as the overall low numbers of ray species observed, may be explained by the extremely low financial value of this group, leading to disposal of large amounts of rays during industrial trawl fishing operations (T. Abushusha, pers. comm.). The only batoid species of relatively high value were guitarfish species, emphasizing the need to describe the two new species and evaluate their status. 75

In contrast to other locations in the Arabian Seas region (Henderson et al. 2007) and elsewhere (Bizzarro et al. 2009), seasonal variation in catch composition was not evident

(Fig. 4) despite seasonal fluctuations of temperature and oceanographic condition within the study area (Patzert 1974; Johns et al. 1999). The variability in the number of observed landings (individuals) between the summer months and the rest of the year (Fig. 4) is most likely due to lower fishing efforts (and hence lower catch rates) during the month of

Ramadan.

New species records

The five new species records in this study showed different frequencies of occurrence and demographic characteristics in the landings. Carcharhinus amboinensis was only occasionally observed and consisted of young-of-the-year, juvenile and adult sharks, indicating that all life-stages of this species occur in Saudi Arabian Red Sea waters and all are caught in the fisheries. Rhinoptera jayakari, which previously had only been reported from Oman (Randall 1995) and possibly the Arabian Gulf (Moore et al. 2012) was observed in relatively large quantities throughout the year and was mostly landed in schools of 10-40 juvenile specimens: no mature or neonate specimens were observed.

The same patterns were recorded for R. javanica, even though landings were less frequent. Field observations of two Rhinoptera schools consisting of 15-30 individuals were made during scientific longline operations in the Farasan islands in May 2012, confirming the presence of species belonging to the genus Rhinoptera in the Red Sea.

Fishermen interviewed also frequently identified Rhinoptera specimens as one of the most common Red Sea batoid species. It is hence surprising that neither of the two 76 species identified during this survey have been recorded in any Red Sea species checklists (Compagno and Randall 1987; Goren and Dor 1994). Mobula kuhlii, as well as

Aetomylaeus nichofii, were only encountered at one occasion during the survey period at very low numbers (one and three individuals respectively).

Exploitation biology of elasmobranchs landed

Sexual segregation is a common characteristic in elasmobranchs (Sims 2005) and appears to be occurring in three shark and one guitarfish species in this study. Among sharks, C. falciformis and L. macrorhinus were significantly biased towards females while C. brevipinna was significantly biased towards males. For C. falciformis sexual segregation has previously been reported for the Red Sea based on field observations (Clarke et al.

2011) and for L. macrorhinus and C. brevipinna in Northern Australia (Stevens and

McLoughlin 1991). Glaucostegus halavi landings consisted exclusively of mature females. Although sexual segregation in this species has not been previously reported.

Rhinobatids from other regions are known to undergo seasonal migrations for breeding and make use of nursery areas in shallow embayments (Fennessy 1994; Echwikhi et al.

2012) similar to those found in southern Saudi Arabian Red Sea waters.

Differences in size distributions among the dominant shark species in the landings were evident (Fig. 7). Loxodon macrorhinus, C. sorrah, C. melanopterus and S. lewini showed a strong dominance for one particular size class (Fig. 7). Except for L. macrorhinus all of these species are known to occupy nursery areas during the first stages of life (Clarke 1971; Simpfendorfer and Milward 1993; Heupel and Simpfendorfer 2002; 77

Speed et al. 2011). Interviewees repeatedly emphasized that nursery grounds were the main areas for targeted shark fisheries during certain times of the year. In early February

2013 more than 500 individual neonate C. sorrah specimens, all measuring between 50-

60 cm and with the umbilical scars partially open were landed in one morning. Based on the information obtained from local traders, these specimens originated from one particular nursery area near Al Qunfudhah (Fig. 1). Based on a reported birth size of 45-

60 cm (Compagno et al. 2005) it can be assumed that these specimens were within their first month of life. Birth in this species in the Red Sea thus appears to occur in January, which would be consistent with time of birth in Northern Australia (Stevens and Wiley

1986) and in contrast to time of birth reported from Indonesia (late September-October)

(White 2007). Two peak reproductive seasons were observed for C. melanopterus with birth occurring in January and again in late May/June as previously suggested for the northern Red Sea by Gohar and Mazhar (1964). During both seasons, large amounts of neonates, measuring 41-59 cm in length, which were harvested in nursery areas in the

Jizan area and the Farasan Islands, were landed and offered for less than US$0.20 each.

Relatively large quantities of neonate S. lewini, measuring between 50-60 cm with open umbilical scars were landed in December and January of both years, which suggests the existence of S. lewini nursery areas in Saudi Red Sea waters and that parturition occurs once every year in November in contrast to the majority of regions where parturition mainly occurs during the summer months (see summary in Hazin et al. 2001). Loxodon macrorhinus was the only species with the majority of individuals in one of the mature size classes (75% between 70-79 cm) (Fig. 7), indicating segregation by size, which has previously been reported for Western Indian Ocean populations of this species (Anderson 78 and Ahmed 1993). Carcharhinus limbatus showed a relatively even distribution of individuals within the juvenile size classes (Fig. 7). In contrast to C. sorrah, C. melanopterus and S. lewini, for which nursery areas seem to be targeted right after parturition and depleted within a few days, nursery fishing for C. limbatus seems to be conducted on a more frequent basis throughout the year. Heavy fishing of juvenile C. sorrah and C. limbatus has already been noted over a decade ago (Bonfil 2003). Based on our observations, this trend seems to persist at similar levels today. Landings of C. falciformis and S. mokarran showed similar patterns in size distribution as S. lewini (Fig.

7), yet with much lower numbers of neonates in the landings and no indication of targeted fishing within potential nursery areas. Carcharhinus amblyrhynchos showed the most homogenous distribution of individuals within all size classes below 160 cm and the highest number of mature individuals after L. macrorhinus (Fig. 7). This might be explained by the higher site-fidelity and association with coral reefs, especially in the juvenile life-stages of this species (Heupel et al. 2010) in comparison to larger species

(Speed et al. 2010), making all life-stages more vulnerable to inshore fisheries. While R. acutus showed a relatively even distribution of individuals in all but the largest size classes, males in the landings were significantly larger than females (Fig. 7), indicating a separation by sex and size similar to that observed in L. macrorhinus.

Pregnant females of four species were infrequently observed in the survey and opportunistically examined (Table 4). Two pregnant C. altimus, measuring 213 and 215 cm, as well as one mature male measuring 190 cm were observed, indicating that Red

Sea females and males may mature at similar sizes as the same species in Northern

Australia (Stevens and McLoughlin 1991; Last and Stevens 2009) and at smaller sizes 79 compared to representatives of this species in Indonesia (Compagno et al. 2005, White

2007). The observed litter size of five and seven respectively lies within the known range of 3 to 15 as reported previously for this species (Compagno et al. 2005, Last and Stevens

2009). The largest embryo measured 70 cm, while the smallest neonate with a fully closed umbilical scar was recorded at 96 cm. Size at birth of this species in the Red Sea hence lies within the reported range of 60-90 cm (Bass et al. 1973; Stevens and

McLoughlin 1991). Based on five full-term embryos observed in late-September 2012 we can assume that parturition for C. altimus in the Red Sea occurs sometime between

September and November, similar to waters off Madagascar and in the Mediterranean

(August-October) (Fourmanoir 1961; Moreno and Hoyos 1983). One pregnant C. brevipinna female, measuring 244 cm LT was observed in late January 2013. The litter size of seven specimens found in this study is less than that previously reported for the

Western Indian Ocean (8 to 13) (Stevens 1984) but within the range of litter sizes reported from off Taiwan (3 to 14) (Stevens 1984; Joung et al. 2005). In addition, several mature males, measuring less than 200 cm in length (196 cm to 198 cm) were observed, indicating a size of first maturity similar to the one observed in Indonesia (White 2007) and smaller than the one observed in Taiwan (Joung et al. 2005). One pregnant whitetip reef shark (Triaenodon obesus) specimen was sampled in late July 2012, carrying two early-term embryos, each measuring 10 cm in length. The observed litter size of only two embryos lies within the known range of one to five for this species (Compagno et al.

2005). The highest number of pregnant females was observed for L. macrorhinus. Litter sizes of two to four were within the range of those reported from Northern Australia

(Stevens and McLoughlin 1991) and Indonesia (White 2007). Based on the high 80 variability of embryo LT within the same month, L. macrorhinus specimens of the Red

Sea seem to be characterized by a year-round reproductive cycle in contrast to specimens from Indonesia (White 2007) and Northern Australia (e.g. Stevens and McLoughlin

1991), which were suggested to have a distinct annual reproductive cycle with parturition occurring only once a year.

The noticeable dominance of individuals measuring between 40-90 cm LT in the shark landings (Fig. 7) is a reason for concern. The majority of landings are heavily centered on juveniles and newborns, indicating growth and recruitment overfishing (Pauly 1988).

While nursery areas are poorly known by fisheries officials and researchers, they are heavily targeted by local fishermen. Unless urgent measures are put in place to protect these areas recruitment collapse for several species but most noticeably C. sorrah might be inevitable. Recruitment and growth overfishing are most likely also the cause for the low numbers of larger sharks in the landings, according to fishermen and fisheries- independent surveys (Spaet et al. unpublished data). Gear and fisheries bias, combined with naturally low abundances, could potentially influence the size distribution as well.

Sample sizes for the majority of ray species were either too small or maturity data was unavailable to draw conclusions on size distributions.

DNA barcoding

A substantial number of taxa in the Arabian Seas Region has been found to be genetically differentiated from their closest relatives in neighboring oceans (Naylor et al. 2012).

NADH2 sequencing in this study was chosen as the most appropriate method to 81 distinguish among potential geographic variants, very closely related species, and cryptic species of batoids exhibiting very similar morphological characters. Based on sequencing results two undescribed species as well as several new species records might have been overlooked if identification was based on morphological criteria alone. Several specimens belonging to genera or species that only recently have been expanded into species complexes (i.e., Pastinachus, Aetobatus narinari, Aetomylaeus nichofii,

Himantura uarnak and Taeniura lymma) (Richards et al. 2009; Last et al. 2010; White et al. 2010; Naylor et al. 2012) were identified, highlighting the need for a full revision of the respective genera. While sequences of several of these specimens matched reference specimens from the Arabian Gulf (Aetobatus cf. ocellatus 2, Aetomylaeus cf. nichofii 1) in the GenBank database several species for which the Saudi Arabian Red Sea is the type locality (Pastinachus sephen, T. lymma and H. uarnak) did not match to any of their respective sequences in GenBank. Instead the Pastinachus specimen collected during this study, matched to the sequence of a P. atrus specimen from Indonesia our Himantura specimen matched to a H. leoparda sequence from Indonesia while our T. lymma sequence did not match to any of the T. lymma sequences provided in GenBank. This indicates that records in the GenBank database have been misidentified and non- conspecific populations from outside the Red Sea need to re-named.

Identities of Rhinoptera specimens obtained were difficult to determine using morphological criteria, due to the very poor taxonomic knowledge of this genus.

Sequences of 82 samples however, unambiguously matched to R. jayakari, while 11 samples matched to R. javanica. Yet, identifications in GenBank need to be treated with caution, especially if sequences refer to samples from outside the type localities as was 82 the case here. The greatest taxonomic difficulties were found in the identification of

Rhynchobatus specimens. The majority of these showed highly variable morphological forms and genetically did not match any records on BLAST based on NADH2 sequences.

Two unique sequences were shared by 20% and 80% of all collected specimens.

Taxonomic clarification and description of these new Rhynchobatus species is required and currently in progress.

Fisheries and Management

Our data suggests that Saudi Arabian Red Sea shark fisheries are currently concentrated in the southern Red Sea. Shallow embayments, nutrient-rich waters, and extensive mangrove habitats make this area an ideal environment for juvenile elasmobranch species, which are heavily targeted in local gillnet fisheries. In addition, the majority of shrimp and multi-species trawl fishing operations take place in the southern Red Sea, resulting in bycatch of larger elasmobranchs. Despite the apparent concern about the current status of Saudi Arabian Red Sea shark populations, which resulted in a royal decree prohibiting all shark-fishing activities, awareness concerning sustainable exploitation of marine resources in general and elasmobranchs in particular is severely lacking. Back in 2003, Bonfil proposed a detailed set of immediate actions as initial steps towards long-term sustainability in Red Sea elasmobranch fisheries. Up to date, none of these suggestions have been implemented in Saudi Arabia.

In many countries overfishing is driven by extreme poverty and lack of alternatives

(Pauly et al. 1989). Overfishing in the Saudi Arabian Red Sea, however, appears to be 83 unrelated to sustenance or economic need, but is rather related to the social community structure of Saudi Arabian fisheries. While most fleet owners are Saudi nationals, the majority of skippers and fishermen are foreigners, usually originating from developing countries (e.g. Bangladesh, the Philippines, Somalia). Those immigrant-workers aim to make maximum profit during their stay in Saudi Arabia, regardless of the damage they may cause to natural resources. In addition, little or no effective management in Saudi

Arabian Red Sea waters appears to exist for any fisheries (see Berumen et al. 2013) and revenue sharing agreements between Saudi boat owners and non-Saudi crews aggravate the damage to fish stocks.

In some countries, such as Egypt, elasmobranchs are considered to have greater value alive in terms of attracting tourists (i.e., scuba divers) than in fisheries (e.g. Dicken and

Hosking 2009; Catlin and Jones 2010; Vianna et al. 2010). While Saudi Arabia has long been characterized by a very limited tourism industry (apart from religious pilgrims), increasing efforts are currently underway to expand marine-based tourism. Yet still, an apparent lack of political will in combination with a general dearth of public awareness seem to be preventing the implementation of effective fisheries management plans for the elasmobranch resources of Saudi Arabia.

Paradoxically, financial and human resources for the adequate management of elasmobranch fishes in Saudi Arabia are, unlike in the majority of the other countries bordering the Red Sea, likely to be at hand. Saudi Arabia, possessing the world’s largest proven oil reserves, clearly has the financial means. Current border control regulations require every boat to report to one of the numerous coast guard stations along the Saudi 84

Arabian coast every time they leave or return to port. An investigation of each boat’s catch by coast guard authorities could therefore be implemented without incurring major additional costs. Even without intensive training of personnel, such monitoring of catches would be a first step towards effective conservation of Red Sea elasmobranchs, particularly if this included effective sanctions for violations to the royal decree forbidding shark fishing.

Assessment methods

As demonstrated in the present chapter, fish market surveys can offer a cost and effort efficient method to obtain large quantities of biological and fisheries data on exploited elasmobranch stocks. Yet, basing estimations of abundance trends on fisheries-dependent data alone can lead to misinterpretation thereof and thus to false assessments of the status of animals investigated. To obtain accurate and precise results it is essential to get details on the fishing effort, which can then be used to calculate species-specific catch per unit effort (CPUE). Accurate CPUE estimates over a broad geographic range are key for the interpretation of abundance data. In the present study it was not possible to obtain accurate effort estimates from the interviewed fishermen. Chapter 3 hence aimed to obtain fisheries-independent CPUE and abundance estimates by employing a combination of two fisheries independent methodologies, baited remote underwater video surveys (BRUVS) and longline surveys.

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CHAPTER 3: BRUVS AND LONGLINE SURVEYS

Fisheries-independent abundance and diversity assessment of Red Sea

elasmobranchs using two key methodologies: Longline and Baited Remote

Underwater Video Surveys (BRUVS

Abstract

Information on the abundance and diversity of Red Sea elasmobranch species is notoriously scarce. To collect such data from beyond the range of the commercial fishery, we conducted a dedicated longline and baited remote underwater video survey (BRUVS) sampling program along the entire Red Sea coast of Saudi Arabia over a time frame of two years. Both survey techniques were employed on a seasonal basis at central and southern Saudi Arabian Red Sea reef systems. In addition BRUVS were conducted opportunistically in the northern Saudi Arabian Red Sea and at selected reef-systems in

Sudan. Catch per unit effort (CPUE) data for BRUVS and longline surveys was compared to published data originating from non-Red Sea ocean systems. This comparison revealed CPUE values several orders of magnitude lower for BRUVS and longline surveys in the Saudi Arabian Red Sea compared to other locations around the world. CPUE of BRUVS on Sudanese reefs on the other hand was within the range of estimates from different places where elasmobranchs are considered common. The results of this study are discussed in relation to fisheries dependent studies indicating overfishing of Saudi Arabian Red Sea shark populations. We argue that the apparent degree of overfishing of Saudi Arabian Red Sea reefs and the low abundance of sharks could make 94 this the ideal model system to determine the effects of predator removal on coral reef ecosystems and fish assemblages.

Introduction

Quantifying the spatial distribution and abundance of mobile species is an essential step in the development of effective conservation strategies (Heagney et al. 2007; Brooks et al. 2011). Yet in highly mobile species like elasmobranchs, naturally low abundances, complex population structures, widespread geographic distribution and cross- jurisdictional fisheries (Simpfendorfer et al. 2002) severely complicate the assessment and management of populations (Knip et al. 2012). Difficulties to overcome these challenges have resulted in a state of data deficiency for many elasmobranch species worldwide (Ferretti et al. 2010; Dulvy et al. 2014). This shortage of data is especially pronounced in regions that are difficult to access, hampering efforts to survey populations appropriately.

Traditionally, data on elasmobranch species abundance and distribution has originated from fisheries-dependent surveys at fish markets (Castillo-Géniz et al. 1998; Bizzaro et al. 2009; Robinson and Sauer 2013), from catch and effort statistics derived through fisheries observer programs (Stevens 1992; Burgees et al. 2005; Harry et al. 2013), or from fisheries logbooks (Stobutzki et al. 2002; Walsh et al. 2002; Baum et al. 2003).

While fisheries-dependent surveys are often considered a cost-efficient method to obtain large quantities of data on biological characteristics of targeted or bycatch species with relatively little effort, such data has its limitations due to unknown fishing effort, actual 95 species specific catch numbers, as well as potential gear selectivity among life history stages (Thorson and Simpfendorfer 2009).

An alternative to fisheries-dependent surveys are fisheries-independent sampling methods that commonly either consist of field surveys based on relatively robust sampling designs and commercially used gears (e.g. longline, gillnet, seine, trawl) (Clark et al. 1994; Bertrand et al. 1997; Simpfendorfer et al. 2002; Heupel et al. 2006), non- extractive sampling designs like Baited Remote Underwater Video Surveys (BRUVS)

(Cappo et al. 2003; Langlois et al. 2006), or Underwater Visual Surveys (UVS)

(Samoilys and Carlos 2000; Castro and Rosa 2005). Fisheries-independent survey methodologies offer the possibility to calculate catch per unit effort (CPUE) estimates, which are frequently used by fisheries scientists to estimate relative abundance of targeted or bycatch species based on the assumption that abundance is proportional to

CPUE (Hilborn and Walters 1992). In order to obtain the most accurate and precise results on the status of elasmobranchs, the collection of CPUE data over broad geographic ranges by a variety of survey methods is required. Additionally, data on migratory patterns, biology and population structure of the stock needs to be obtained to facilitate interpretation of CPUE data.

Standardized fisheries independent techniques have been used to examine the abundance and distribution of mobile elasmobranchs (Simpfendorfer et al. 2002; Robbins et al. 2006; Brooks et al. 2011; White et al. 2013), however, each of them has its own limitations. Employment of commercial gear, like longlines in fisheries independent surveys, constrains the sampling through gear selectivity (see Løkkeborg et al. 1992 for a 96 review) and limitations to the range of habitats where the gear can operate. On the other hand, longline surveys offer the opportunity to collect data on certain biological and ecological characteristics beyond the range of BRUVS and UVS. The physical capture of animals allows for more accurate species identification compared to BRUVS and UVS, detailed assessment of physiological parameters, accurate determination of sex and size, collection of tissue samples and the attachment of tagging devices to captured specimens in order to gain further insights into their movement ecology.

The limitations of UVS compared to longlines and BRUVS are manifold and include potential behavioral responses of surveyed species to divers (Watson and Harvey 2007), short survey times as well as depth and visibility constraints. BRUVS entail the risk of repeatedly counting the same individual (Langlois et al. 2006) and exhibit potential difficulties in the determination of observed species, sex and size under certain conditions. Yet, issues like gear selectivity in longline surveys and biases of behavioral avoidance associated with UVS are less problematic in this approach. In addition the non- extractive nature of BRUVS makes this method suitable for protected areas. Survey depth and time are only limited by the technical features of the equipment. Despite differences in limitations inherent to each survey method, data from studies using various combinations of the three techniques have compared relatively well to each other (e.g.

Meekan and Cappo 2004; Watson et al. 2005; Cappo et al. 2007 Harvey et al. 2007;

Heagney et al. 2007; Brooks et al. 2011). While individually none of these approaches may provide the most precise high-resolution data on all characteristics necessary to define species abundance and distribution, a combination of methods might be able to 97 resolve some of the issues caused by the specific limitations inherent to each survey technique.

Recent efforts have been made to investigate elasmobranch fisheries along the coast of

Saudi Arabia based on landing surveys at Saudi Arabian Red Sea fish markets, like

Jeddah (Spaet and Berumen, unpublished data). These surveys have indicated unsustainable targeted and bycatch elasmobranch fisheries and a significant lack of mature specimens for the majority of species in the landings. To stabilize exploited elasmobranch populations and to rebuild potentially depleted populations in Saudi

Arabian Red Sea waters, it is essential to combine data obtained through fish market surveys with data collected beyond the range of the commercial fisheries. In the present study we combined long-term, temporally and spatially stratified BRUVS and longline sampling to examine the relative abundance and distribution of elasmobranchs along the

Saudi Arabian Red Sea coast.

In the present study we combined long-term BRUVS sampling and discrete, temporally stratified longline sampling to examine the relative abundance and distribution of elasmobranchs along the Saudi Arabian Red Sea coast. Based on Saudi

Arabian Red Sea fisheries data, indicating unsustainable elasmobranch fisheries for the study region, we predicted that (1) the relative abundance of elasmobranchs in Saudi

Arabian Red Sea waters would be low compared to other parts of the world and (2) the relative abundance of elasmobranchs would be higher in the southern Red Sea compared to the central and northern region. The latter prediction was based on the fact that the 98 majority of elasmobranch landings observed during market surveys originated from the south.

Materials and Methods

Study system

Stretching over a length of approximately 1760 km, the Saudi Arabian Red Sea coast exhibits marked latitudinal and seasonal changes in environmental and physical parameters. North-south gradients are visible in primary productivity and turbidity (low- high) (Raitsos et al. 2011, 2013), sea surface temperature (summer: 24-35°C; winter: 20-

28°C) (Ngugi et al. 2012) and salinity (42-37‰). Well-developed coral reef systems are found along most of the Saudi Arabian Red Sea coast. In the northern and central part those systems are characterized by fringing reefs with often steep drop-offs (Edwards

1987) and interrupted offshore barrier reefs. The southern region is characterized by less developed fringing reefs, extensive shallow soft-bottom communities and mangrove habitat (Khalil 2004), highlighting a distinct shift in habitat along the north-south gradient.

For the present study we separated the Saudi Arabian Red Sea coastline into three broad geographical regions (north, central, south), based on the aforementioned gradients

(Raitsos et al. 2011, 2013; Ngugi et al. 2012). The central and southern region was sampled using both BRUVS and longline surveys, while sampling in the northern region was restricted to BRUVS. Fieldwork was conducted between March 8th 2011 and March 99

19th 2013. Table 1 depicts geographic details of all sampling locations. Work in the central Saudi Arabian Red Sea took place 1-24 km offshore of Thuwal and 4-49 km offshore of Al Lith. Eight coral reef systems were surveyed in this region (Table 1, Fig.

1). Work in the northern Red Sea took place on reef systems 6-42 km offshore of the coastline stretching from the entrance of the Gulf of Aqaba south to Umluj. Five individual coral reef systems were surveyed in this region (Table 1, Fig. 1). Work in the southern Red Sea took place 70-86 km offshore of Jizan at three reef systems surrounding the Farasan Islands (Table 1, Fig. 1). All surveyed reef systems experience fishing pressure through multi-species fisheries, which often take elasmobranchs as bycatch. In addition to sampling locations along the Saudi Arabian Red Sea coast we conducted

BRUVS in the eastern Red Sea in Sudan at four coral reef systems 30-41 km offshore of

Port Sudan and 27-30 km offshore of Suakin respectively (Table 1, Fig. 1). Surveyed reef systems in Sudan are considered relatively undisturbed with only moderate recreational and extractive use of resources (Klaus et al. 2007).

BRUVS

Equipment design BRUVS

The construction of BRUVS units was geared towards the design of units previously operated by AIMS (Meekan and Cappo 2004) (Fig. 2) with adaptions for use on vessels with space limitations and for nighttime deployments. 100

Bur

AN Nuw

North Mas AM

AB MI SN CQ

Central SR NN SH AJ San Jum Mar SA

FK South FQ FZ

Figure 1: Sampling locations of baited remote underwater video surveys (BRUVS) and longline surveys along the Red Sea coast of the Kingdom of Saudi Arabia and the Republic of Sudan, conducted between March 8th 2011 and March 19th 2013. See Table 1 for details on each site. 102

Table 1: Sampling regime of baited remote underwater video surveys (BRUVS) and longline surveys conducted between March 8th 2011 and March 19th 2013 at reef systems in Saudi Arabia and Sudan. Sampling areas are divided by sampling regions, from top to bottom - north, central, south, Sudan. Original site names, abbreviation codes, GPS coordinates, distance to shore, sampling effort, sampling depth and captured species are listed.

Location [code] Latitude Longitude Distance Effort Effort Depth Depth Captured to BRUVS longline (min) (max) Species BRUVS roman mainland (hours of surveys (m) (m) Longline surveys bold (km) tape) (soak time x (n sightings) number of [n individuals in one frame] hooks) {capture depth} Burcan [Bur] 27°54'35.46" 35°03'55.20" 21 32 n/a 8 100 Himantura fai (1) {15m} An Numan [AN] 27°08'11.64" 35°45'03.06" 6 16 n/a 12 140 Nuwayshiziyah [Nuw] 26°37'26.82" 36°05'43.14" 13 32 n/a 5 100 Mashabi [Mas] 25°34'56.70" 36°32'55.08" 42 32 n/a 20 41 Abu Matari [AM] 24°43'23.82" 37°09'04.20" 8 16 n/a 27 230 Mangrove Island [MI] 22°17'38.52" 39° 4'38.10" 1 15 420 1 6 n/a Abu Shoosha [AS] 22°18'12.43" 39° 2'52.05" 4 32 840 3 50 n/a Qita al Qirsh [QQ] 22°25'55.51" 38°59'33.87" 9 50 540 4 160 n/a Sh’ib Nazer [SN] 22°19'52.73" 38°51'24.19" 24 50 1620 8 130 Triaenodon obesus (1) {27m} Carcharhinus falciformis (3) {34,53,64m} Carcharhinus altimus (2) {22,57m} Sphyrna lewini (2) {64m, 4m} Sh’ib Habil [SH] 20°6'37.00" 40°13'32.00" 4 32 420 5 12 Nebrius ferrugineus (1) {8m} No name reef [NN] 20°1'36.12" 40°11'35.22" 12 32 840 15 20 n/a Marmar [Mar] 19°50'15.00" 39°55'39.00" 47 32 540 27 160 Carcharhinus falciformis (1) {3m} Al Jadir [AJ] 19°47'18.00" 39°57'11.00" 49 32 540 16 230 n/a Farasan Kebir [FK] 16°48'6.25" 41°46'6.69" 86 50 1260 4 32 Nebrius ferrugineus (1) {4m} Taeniura lymma (2) [1] {4m} Rhynchobatus sp. (1) {5m} Himantura spp. (1) {6m} Himantura fai (1) {6m} Farasan Qummah [FQ] 16°30'53.29" 42°1'38.15" 70 50 1260 5 34 Nebrius ferrugineus (1) {8m} Taeniura lymma (1) {8m} Stegostoma fasciatum (1) {8m} Himantura sp. (1) {12m} Farasan Zifaf [FZ] 16°41'17.51" 41°45'33.33" 81 50 1260 3 140 Nebrius ferrugineus (1) {7m} Stegostoma fasciatum (1) {7} Glaucostegus halavi (1) {16m} Taeniura lymma (1) {16m} Sh’ab Rumi [SR] 19°55'43.62'' 37°24'47.52'' 41 25 n/a 32 74 Carcharhinus amblyrhynchos (39) [3] {25m} Sphyrna lewini (22) [5] {25m} Triaenodon obesus (1) {45m} Sanganeb [San] 19°44'34.00" 37°26'52.00" 30 25 n/a 4 77 Carcharhinus amblyrhynchos (72) [6] {30m} Sphyrna lewini (15) [7] {34m} Sha’ab Jumna [Jum] 19°10'59.06" 37°36'40.46" 27 5 n/a 40 40 Sh’ab Amre [SA] 19°9'3.66" 37°37'58.30" 30 25 n/a 23 72 Triaenodon obesus (1) {33m} Sphyrna lewini (1) {33m} 103

HD-video camcorders (Sony HDR-XR520, Sony HDR-HC9, Sony HDR-CX130) set to wide-angle recording mode were enclosed in underwater housings (Light and Motion

Inc.,AquaCam Inc.) and mounted on welded aluminum frames (Fig. 2). An aluminum bait arm of 250 cm length (diameter: 25 mm) held an aluminum live rat trap, which served as bait cage, away from the front of the camera housing (Fig. 2). The live rat trap had a mesh size of 5 mm, allowing for bait odor and particle distribution into the water column. Supporting legs as well as the bait arm were dismounted for storage during navigation and were reassembled before deployment. For nighttime sampling underwater

LED lights (Greenforce Inc.) were mounted onto each frame. Four separate BRUVS units for simultaneous deployment were constructed.

(a) (b) Camera

housing

light

Detachable supporting legs

Bait cage

Figure 2: Baited remote underwater video survey units employed at Red Sea reef systems: (a) full unit deployed on the seafloor during day time surveys, (b) unit with detached supporting legs hung from the side of a research vessel during nighttime drift deployments.

103

Experimental design

Surveys in the central Red Sea area (as classified by Fig. 1) were conducted within two major sampling areas (Thuwal and reef systems off Al Lith). Based on accessibility and suitable habitat, four reefs within each of these regions were chosen as study sites (Table

1, Fig. 1). To test for seasonal patterns of abundance and diversity at these reef systems,

BRUVS were uniformly distributed over four periods throughout the year (spring:

March/April/May, average water temperature Jeddah: 27°C; summer: June/July/August, avg. temp.: 31°C; autumn: September/October/November, avg. temp.: 29°C; winter:

December/January/February, avg. temp.: 26°C) (see Table 1 for reef specific effort data).

Exact locations for deployment were, wherever possible, randomly selected within these reef systems based on navigation coordinates. At certain reefs this was impossible due to wall-like drop offs surrounding the entire reef area. In such instances BRUVS were deployed at particular locations on reef plateaus. Wherever possible, BRUVS units were deployed from the boat using a rope and in-water personnel to guide it away from corals or other obstacles and to orient the bait arm away from the reef, achieving the best field of vision possible. Occasionally deployment required the use of SCUBA to ensure stability and orientation of the sampling unit away from the reef. Relocation and manual retrieval of each unit was facilitated by marker buoys attached to each rope. Depths greater than 72 m were sampled by drift deployments. In this sampling mode the research vessel drifted in deep (50-1000 m +) water in sufficient distance to the reef drop off and

BRUVS units, with supporting legs detached, were hung from the side of the boat (Fig.

2). All deployments, including drift deployments were conducted at equal time intervals 104 throughout 24h periods, except for deployments in Sudan, which due to logistical constraints could only be conducted during day light hours (5 am-7 pm).

One kilogram of freshly ground bonito tuna (Sarda orientalis) served as standardized bait and was deployed in the rat traps attached to each bait arm (Fig. 2).

Occasionally fresh tuna was unavailable due to logistical constraints. In such cases a range of available species, consisting mostly of species belonging to the genera Caranx and Lutjanus were obtained through spearfishing and prepared as bait. At each deployment site, substrate type, weather conditions, water depth, and an estimate of current speed and direction were recorded. Standardized deployment time was 180 min.

Opportunistic sampling of reef systems in the northern Saudi Arabian Red Sea and

Sudan occurred on a non-seasonal basis. Sampling techniques at those reef systems were identical to the techniques employed in the central region.

Results

A total of 186 BRUV (total duration 33,480 min) and 33 longline deployments (total duration 23,760 min) were conducted between March 8th 2011 and March 19th 2013 across the four study regions (Table 1). In total, 31 sharks belonging to four species and four families and ten batoids belonging to four species and two families were observed in

BRUVS (Table 1, Table 2, Fig. 3). Seven sharks, belonging to four species were captured in longline operations (Table 1). In the central Red Sea, BRUVS and longline deployment were conducted on a seasonal basis throughout the study period (1-2 105 deployments per study reef per season) (BRUVS n = 92), longline surveys (n = 32).

Sharks in this region were observed on two BRUVS (2% of deployments) and seven sharks were caught on longlines (21% of longline deployments). Deployments of

BRUVS at reef systems of the northern section of the Saudi Arabian Red Sea coast were conducted between August 8th 2011 and August 15th 2011 (BRUVS n = 42) with a batoid observed on one BRUVS (2% of deployments). BRUVS and longline surveys in the southern region of the Saudi Arabian Red Sea coast occurred between May 1st - 5th 2012

(BRUVS n = 25, longline surveys n = 4) and November 12th - 19th 2012 (BRUVS n = 25, longline surveys n = 5) with five sharks (10% of deployments) and 8 batoids (8% deployments) on BRUVS and no elasmobranch catches on longlines. BRUV deployments in Sudan occurred between February 13th 2013 and February 22nd 2013

(BRUVS n= 16) with sharks observed on eight BRUVS (50% of deployments at these sites), seven of which recorded between three to seven individuals. Overall, 20 of the 186

BRUV deployments (10% of total number of deployments) recorded at least one elasmobranch. All but three of these occurred in the Southern Red Sea or in Sudan.

106

(a) (b)

Figure 3: Images of elasmobranch encounters from baited remote underwater video surveys (BRUVS) conducted in the Saudi Arabian Red Sea between March 8th 2011 and March 19th 2013. (a) Nebrius ferrugineus; (b) Stegostoma fasciatum; (c) Pastinachus sp.; (d) Rhinobatos

Because the vast majority of BRUV and longline deployments resulted in zero elasmobranchs being observed, it was not possible to statistically evaluate the impact of environmental variables, depth, seasonality, habitat type, region or survey method on patterns of abundance and distribution.

CPUE

BRUVS at Saudi Arabian sampling sites in the southern Red Sea showed by far the highest CPUE (0.186 elasmobranchs per hour cf. 0.0078 in the northern Red Sea and

0.0072 in the central Red Sea) (Fig. 4). The total CPUE for all sampling sites in Sudan 107 was almost four times higher than the highest value obtained in the Saudi Arabian Red

Sea (0.4 elasmobranchs per hour cf. 0.186 in the southern Red Sea) (Fig. 4).

0.6

) 0.5

1 -

0.4

0.3

0.2

0.1 CPUE (Elasmobranchs h (Elasmobranchs CPUE

0 NorthNorth CentralCentral SouthSouth CentralSudan (Sudan)

Figure 4: Comparison of baited remote underwater video surveys (BRUVS) elasmobranch catch per unit effort (CPUE) data in the northern, central and southern Red Sea of Saudi Arabia and the central Red Sea of Sudan. Grey in the South-column indicates CPUE for this region for sharks only. Error bars represent standard error.

CPUE at Sudanese reefs was strongly influenced by aggregations of grey reef sharks

(Carcharhinus amblyrhynchos) and scalloped hammerhead sharks (Sphyrna lewini), which were observed in groups of up to eight animals. Compared to published BRUVS

CPUE data obtained in shark abundance surveys in Australia (Meekan et al. 2006), Fiji

(Goetze and Fullwod 2012), the Marianna Islands (Lindfield unpublished data) and the

Bahamas (Brooks et al. 2011), shark CPUE for all Saudi Arabian Red Sea regions combined was lower by one to nearly two orders of magnitude (Fig. 5).

Saudi Arabian Red Sea longline surveys yielded a total CPUE for shark catches

(0.0013) that was lower than published values obtained from Belize, Hawaii and the 108

Bahamas by one to almost two orders of magnitude (Fig. 6).

1.2 Australia Fiji Marianna Bahamas Saudi Islands Arabian Red

) 1

1 Sea -

0.8

0.6

CPUE (Sharks h (Sharks CPUE 0.4

0.2

0 unfished fished unfished fished unfished fished unfished fished

Figure 5: Comparison of total catch per unit effort (CPUE) obtained from published and unpublished baited remote underwater video surveys (BRUVS) of shark abundance in unfished and fished regions of Australia, Fiji, The Marianna Islands and The Bahamas to total shark CPUE obtained by BRUVS along the northern, central and southern Red Sea coast of Saudi Arabia between March 8th 2011 and March 19th 2013. Published data sourced from, Meekan et al. 2006 (Australia); Goetze and Fullwod 2012 (Fiji); Brooks et al. 2011 (Bahamas). Unpublished data from Lindfield (Marianna Islands); The error bar for the Red Sea represents standard error.

109

0.04 0.035

0.03 0.025

0.02

0.015

0.01

0.005

0 HawaiiBelize HawaiiBelize Bahamas Saudi KSA Arabian Red Sea

Figure 6: Comparison of total catch per unit effort (CPUE) obtained from published shark abundance longline surveys in Belize and The Bahamas to total shark CPUE obtained by longline surveys along the central and southern Red Sea coast of the Kingdom of Saudi Arabia between March 8th 2011 and March 19th 2013. Published data sourced from Pikitch et al. 2005; Dale et al. 2011; Brooks et al. 2011. The error bar for the Red Sea represents standard error.

Discussion

General discussion

We employed two fisheries-independent survey methods to determine the relative abundance and diversity of elasmobranch species at a range of fished inshore and offshore Saudi Arabian reef systems and four relatively undisturbed offshore reef systems in Sudan. Both approaches yielded catch per unit effort (CPUE) values that were at least one order of magnitude lower than those reported from similar surveys outside of the Red

Sea (Meekan et al. 2006, Brooks et al. 2011, Bond et al. 2012, Goetze and Fullwood

2012). Sampling efforts and the survey time frames for both methods in this study were 110 in range or higher than those reported for most published BRUVS and longline surveys investigating elasmobranchs (Meekan et al. 2006, Brooks et al. 2011, Bond et al. 2012,

Goetze and Fullwood 2012). We are thus confident that our relative abundance estimates were accurately recorded and would not increase significantly if the study were to be continued for longer periods. At the same time, we do believe that continuing the survey with both techniques may have eventually resulted in an increase of the reported species diversity.

Due to logistical constraints, this study was limited in providing seasonal or long-term variation data for the northern Saudi Arabian Red Sea and Sudan. Based on seasonal data obtained in BRUVS and longline surveys from the central and southern Saudi Arabian

Red Sea and elasmobranch landings data obtained through fisheries dependent surveys

(Spaet and Berumen unpublished data), however, we believe that seasonal differences in elasmobranch assemblages along the Saudi Arabian Red Sea coast are minor, if present at all. We would hence not expect significant changes in species abundance and diversity, if surveys were to be conducted on a seasonal basis in the northern Saudi Arabian Red Sea or Sudan.

Previous studies conducted in the Red Sea with the aim to provide baseline data on the

Red Sea elasmobranch fauna have focused on a maximum of three reefs (Hussey et al.

2011; Clarke et al. 2012). The present study is the first to survey multiple reefs, stretching along nearly the entire coast of the Saudi Arabia as well as four reefs in Sudan.

The lack of observed sharks during UVS in previous surveys has been attributed to vertical movement patterns on a diurnal basis and sampling that was restricted to daylight 111 hours (Dennis et al. 2011). In the present study we conducted BRUVS during day and nighttime, thus avoiding missing data due to diurnal/nocturnal movement patterns. By integrating BRUVS with longline surveys, our study has provided the benefits of both techniques, which is reflected in the differences of species composition as well as abundance recorded by each survey method.

Abundance and mean CPUE

Overall, mean BRUVS CPUE for sharks at Saudi Arabian Red Sea reef systems was between 11- 82 times lower than mean BRUVS CPUE of unfished or fished regions outside the Red Sea, where sharks are considered common (Meekan et al. 2006; Brooks et al. 2011; Goetze and Fullwod 2012; Lindfield unpublished data) (Fig 5). While we expected relatively strong differences in shark abundance between Saudi Arabian reef systems and well protected, unfished regions elsewhere, the extreme differences observed in mean CPUE between Saudi Arabian Red Sea reefs and fished reef areas in non-Red

Sea ocean systems were unexpected. We believe that such strong discrepancies are most likely attributable to the lack of any properly managed marine reserve areas in the Saudi

Arabian Red Sea. In contrast, fished areas of all other countries used in this comparison, are located adjacent to relatively well-managed marine reserves (Meekan et al. 2006;

Brooks et al. 2011; Goetze and Fullwod 2012; Lindfield unpublished data). The benefits of marine protected areas (MPAs) towards shark abundance have been sufficiently proven by demonstrating that marine reserves, especially no-entry zones, harbor significantly more sharks than nearby sites of similar habitat, but open to fishing (Meekan 112 and Cappo 2004; Meekan et al. 2006; Robbins et al. 2006; Heupel et al. 2009; Goetze and Fullwood 2012). In addition to benefits observed within the reserve areas, MPAs are also expected to replenish shark stocks exploited in adjacent areas open to fishing

(Palumbi 2002). It seems unlikely that spill-over effects from relatively small MPAs have the capacity to sufficiently replenish surrounding reefs (Robbins et al. 2006). Yet, we believe that spill-over might explain the higher mean CPUEs observed in fished areas adjacent to MPAs (as observed in the published studies used in our comparison) compared to mean CPUEs of the Saudi Arabian Red Sea, where all coastal and open ocean environments are open to unregulated fishing activities. At the same time we cannot rule out the possibility that the observed patterns are due to naturally low abundances of sharks in Saudi Arabian Red Sea waters. Fisheries independent baseline data on abundance and species composition is not available for any areas of the Saudi

Arabian Red Sea, rendering a comparison of the data collected in this study to a historical dataset impossible. We thus cannot draw a definitive conclusion regarding this hypothesis. Yet, Sudanese reef systems exhibit a very similar habitat structure to that of central Saudi Arabian Red Sea reefs at equivalent latitudes. We may hence have expected similar CPUE values for both sides of the basin. Yet Sudan yielded an overall mean

BRUVS CPUE for sharks that was 13 times greater than the highest mean CPUE obtained for sharks in the Saudi Arabian Red Sea (Fig 4). The main difference between these habitats is that the Sudanese reef systems investigated in the present study are largely unaffected by fishing on sharks (Klaus et al. 2007), indicating that overfishing may have indeed seriously depleted the abundance of shark populations in Saudi Arabian

Red Sea waters, as suggested in previous studies based on fisheries-independent and 113 fisheries-dependent surveys (Clarke et al. 2012; Spaet and Berumen unpublished data).

Discrepancies in mean BRUVS CPUE also exist between the present study and a previous survey investigating shark abundances in the Saudi Arabian Red Sea. Clarke et al. 2012 conducted BRUVS at three Saudi Arabian Red Sea reef systems 10-30 km offshore from Jeddah. Their study reported an overall mean CPUE for these reef systems of 1.5, which is higher than any BRUVS CPUE value reported from any location in the scientific literature. Such a high value might be explained by the fact that their survey sites included a shark observation point that had been baited on a semi-regular basis since

1995 (Clarke et al. 2012). In addition, their reported mean CPUE value is based on only

19.7 hours of video material. It is thus questionable whether the mean CPUE reported in their study is representative of the majority of Saudi Arabian Red Sea reef systems, or rather an artifact of shark aggregations around a reliably food source.

Elasmobranch diversity

We observed strong differences in the estimates of elasmobranch diversity obtained by

BRUVS between the southern area and the central and northern areas combined (Fig. 4).

Most strikingly, a number of elasmobranch species were observed exclusively in BRUVS in the southern Saudi Arabian Red Sea, including the tawny nurse shark (Nebrius ferrugineus), zebra shark (Stegostoma fasciatum), as well as several larger batoid species

(Table 1). These differences in the spatial distribution of elasmobranch diversity along the Saudi Arabian Red Sea may be a product of the pronounced north- south gradients in environmental parameters and habitat structure that characterize this coastline. The 114 shallow, nutrient-rich waters surrounding the Farasan Islands provide the perfect habitat for benthic-orientated elasmobranchs, while more oceanic or reef-associated species seem to be less common in these areas.

Shark diversity observed at Saudi Arabian Red Sea reefs in this study matched well to that observed in Clarke et al. 2012. All shark species reported in their study were also observed in this survey, either in longline surveys - C. albimarginatus, C. falciformis, S. lewini, or BRUVS - C. amblyrhynchos, T. obesus (Table 1). Only one shark species in this study was captured by both survey methods, S. lewini. All other species where recorded by only one of the two survey methods. Factors contributing to the discrepancies of species diversity observed in the two survey methods might stem from differences in the sampled habitat types and general differences in gear design. Typically,

BRUV units are placed on the seabed, allowing the bait odor to distribute relatively close to the seafloor. Accordingly, this approach has a greater potential to attract elasmobranchs exhibiting a benthic life style, like batoids, which made up the majority of elasmobranchs captured by BRUVS in the southern Saudi Arabian Red Sea. The longline set-up used in this study, on the other hand, was designed to have hooks dispersed in the water column, rather than distributed on the seafloor. This likely explains why none of the benthic species captured in BRUVS were caught on longlines and why several reef- associated, pelagic species were captured on longlines and not BRUVS. We supplemented our seafloor BRUV deployments with a sampling method in which BRUV units were were suspended in the pelagic off of the edge of reef drop offs. 115

This method has resulted in relatively high CPUE of reef-associated species like C. amblyrhynchos and C. albimarginatus in other studies (Meekan and Cappo 2004). In the

Saudi Arabian Red Sea, however, zero elasmobranchs were captured by this sampling mode and observations of C. albimarginatus and C. amblyrhynchos were restricted to longline surveys and BRUVS in Sudan respectively. This lack of correlation between both survey techniques for rare species has previously been reported (Brooks et al. 2011).

Longlines in general send out a much stronger odor signal compared to BRUV units due to the larger amount of bait in the water (up to 35 pieces stretched across 500m cf. one bag of fish in BRUVS). Elasmobranchs are far more likely to locate the odor source of several pieces of bait attached to a longline than the single bait source associated with the

BRUV unit. If abundances of sharks are very low, as assumed for the Saudi Arabian Red

Sea, it is hence not surprising that several reef-associated, pelagic species were captured on longlines and not BRUVS. We assume that odor plumes of most BRUVS deployments simply did not cross the olfactory fields of predators because none were present in sufficient vicinity to the unit. For future surveys investigating the abundance and diversity of elasmobranch species, when abundances are expected to be low, it is recommended to employ a combination of at least two survey methods to get the most accurate and precise estimates of diversity.

Conclusion

The alarmingly low numbers of elasmobranch and especially sharks recorded in this survey are reason for concern. There is an urgent need to further investigate the 116 abundance and structure of exploited Saudi Arabian Red Sea elasmobranch stocks with the aim to establish management strategies for species of highest conservation concern.

Our results have the potential to be used as an appropriate baseline, if such management strategies were to be established at some point in the future.

In addition we propose the use of the Saudi Arabian Red Sea as a model system to determine the effects of predator removal on coral reef ecosystems and fish assembages.

The Sudanese reef systems investigated in the present study are largely unaffected by fishing on sharks, but show habitat and environmental characteristics that are very similar to exploited Red Sea reef systems, they hence may then serve as proper control sites.

Such comparisons between depleted and nearly pristine reef systems have previously been suggested for a region in the Timor Sea (Meekan et al. 2006). We believe that the comparison of a range of such studies across different ocean systems could significantly advance our knowledge of the effects of predator removal on an ecosystem basis.

As demonstrated in the two preceding chapters a range of exploited Red Sea shark stocks are obviously fished at unsustainable levels and overfishing is likely the reason for the low abundance observed in fisheries-independent surveys. Overexploitation of shark resources coupled with a severe lack of protection strategies and enforcement are with very few exceptions (e.g. Oman) a common scenario in the Arabian Seas region. Recent research in the Arabian Sea, Gulf of Oman and Arabian Gulf has revealed exploitation levels similar or greater than those observed in the Saudi Arabian Red Sea (Moore et al.

2012; Henderson et al. 2007). 117

In addition to comprehensive information of biological, ecological as well as fisheries parameters, the identification of regional stocks is vital in achieving sustainable exploitation for sharks and marine resources in general. Population genetic analyses have been proven as an effective tool in defining stock structures and subsequently developing conservation strategies for exploited species (McCook et al. 2009). In the following chapter we have hence investigated the population structure and genetic connectivity of four commercially harvested shark species that are occurring sympatrically in the Red

Sea, the Arabian Sea, and the Arabian Gulf. Our goal was to determine, if fishing nations in the Arabian Seas region exploit the same shared stocks, entailing regional cooperation to maintain and stabilize depleted stocks at an optimum levels.

118

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CHAPTER 4: POPULATION CONNECTIVITY

Genetic structure and population connectivity of four shark species among three ocean basins in the Arabian Seas region

Abstract

The Arabian Peninsula is a region that has historically been underrepresented in terms of elasmobranch research, while at the same time ever increasing pressure from unmanaged fisheries is putting these predators at high risk. Data pertaining to shark conservation is comparatively scarce and the few existent management measures are at the national level ignoring transboundary issues. In sharks, the dynamics of demographic connectivity and gene flow among spatially separated populations have particularly critical effects on the resilience and persistence of stocks due to the vulnerable life history traits characterizing this taxon. Here we sampled populations of four commercially important shark species

(Carcharhinus limbatus, C. sorrah, Rhizoprionodon acutus and Sphyrna lewini) from the

Arabian Seas region: the Red Sea (Saudi Arabia), the Arabian Sea and the Gulf of Oman

(Oman) and the Arabian Gulf (United Arab Emirates and Bahrain). We genotyped a total of 1189 specimens at the mitochondrial control region as well as a total of 20 microsatellite loci to test the null hypothesis of population genetic homogeneity. We were unable to reject genetic homogeneity in all four species based on both types of markers.

Our results suggest that for management purposes, each of the four shark species in the

Arabian Seas region should be considered as a single unit.

125

Introduction

Traditional as well as industrial elasmobranch fisheries exist in almost all Arabian countries. For several countries these fisheries have reached unsustainable exploitation levels (Bonfil 2001; Moore 2011; Spaet and Berumen unpublished data). Nonetheless, management strategies for elasmobranch resources are found in only a fraction of these countries and proper enforcement of fisheries laws is almost non-existent (Bonfil 2002;

Moore 2011). In addition to an apparent general lack of interest towards conservation of sharks and rays (Bonfil 2001), assessment and management of elasmobranch stocks has so far been hampered by insufficient information on the biology, ecology and fisheries of exploited species (Moore 2011; Spaet et al. 2011) and a complete lack of country- or region-specific stock status information (Dulvy et al. 2014). Only recently, efforts have been made to bridge this gap and they have contributed to our knowledge on country- specific fisheries and species-specific biological characteristics (Bonfil 2001; Henderson et al. 2006, 2007, 2009; Moore 2011; Moore et al. 2012; Spaet and Berumen unpublished). To what extent sympatric elasmobranchs are genetically connected among sea basins in the Arabian Seas region, however, remains untested. Knowledge of stock structure is critical in determining effective management strategies that are appropriate to the degree of genetic subdivision within a species range. If elasmobranch species were found to disperse regularly among the Red Sea, the Arabian Sea, the Gulf of Oman and the Arabian Gulf (Fig. 1), countries in this region may in fact exploit the same stocks.

Patterns of high among-basin connectivity may hence render national conservation measures ineffective, if mobile populations are exploited elsewhere, thus requiring action at the international level. 126

The general aim of this study was to determine whether the species investigated show genetically homogenous populations throughout the study region or, whether stocks exhibit limited gene flow among sea basins. To provide a high degree of genetic resolution for each species, we used a combination of genetic markers, which are both neutral in regards to selection: the maternally inherited mitochondrial control region and high resolution, bi-parentally inherited microsatellite DNA markers. Previous studies employing combinations of these markers, or each of them individually suggest relatively low genetic variation in vagile elasmobranch species reaching large body-sizes and have demonstrated nearly global panmixia in two planktivorous oceanic species (e.g. Castro et al. 2007; Lieber et al. 2013). On the other hand, more coastal-oriented, smaller and less mobile species were shown to exhibit genetic structure on large spatial scales (e.g.

Schultz et al. 2008; Portnoy et al. 2010; Vérissimo et al. 2010; Benavides et al. 2011;

Karl et al. 2011). Such genetic subdivision of shore-associated species is often facilitated by dispersal barriers, e.g. large oceanic expanses (Schultz et al. 2008; Karl et al. 2012;

Giles et al. 2014) or continuous landmasses that extend into geographic regions unsuitable for species that are adapted to environmental characteristics associated with certain longitudes (Keeney and Heist 2006). Localized genetic subdivision has been shown in several demersal species (e.g. Dudgeon et al. 2009; Karl et al. 2011; Lewallen et al. 2012).

The four species investigated in this study were chosen based on their sympatry in the study area and their dominance in elasmobranch landings throughout the study region

(Henderson et al. 2007; Moore et al. 2012, Spaet and Berumen unpublished data). They belong to two families: (1) Carcharhinidae: C. limbatus, C. sorrah, R. acutus; and (2) 127

Sphyrnidae: S. lewini. Carcharhinus limbatus and S. lewini are globally distributed species encountered in coastal tropical and subtropical waters (Compagno 1984; Castro

1996). Distribution of C. sorrah is restricted to coastal pelagic waters throughout the tropical and subtropical Indo-West Pacific (Compagno 1984). Rhizoprionodon acutus is found along the continental shelf in the Arabian Seas region, east and west Africa, the

Indo-West Pacific and north Australia (Gallo et al. 2000). It is the smallest of the four study species and commonly reaches lengths of 110 cm (Compagno et al. 2005).

Reported maximum sizes for C. sorrah, C. limbatus and S. lewini are 160 cm, 275 cm and 340 cm respectively (Ebert et al. 2013). Based on IUCN Red List criteria R. acutus is globally categorized as Least Concern, C. limbatus and C. sorrah are classified as Near

Threatened, and S. lewini is listed as Endangered (IUCN 2014). Dispersal capacities for at least three of the species are considered very high. Tagging studies of C. sorrah demonstrated an individual maximum travel distance of 1116 km, although almost half of the tagged specimens were recaptured within 50km of the tagging location (Stevens et al.

2000). Movements of up to 2,148 km have been observed for C. limbatus (Kohler et al.

1998) and an individual S. lewini has reportedly traversed 1600 km of deep ocean habitat

(Kohler and Turner 2001), yet strong swimming capabilities alone are no guarantee for dispersal (Oveneden 2009).

Previous studies on all four species have demonstrated genetic variation on regional scales or even across the species’ range. Carcharhinus sorrah and R. acutus were shown to exhibit significant genetic subdivision between central Indonesian and northern-

Australian populations based on mtDNA and microsatellite markers, but appeared homogenous across sampling locations in northern Australia (Ovenden et al. 2009; 128

2011). Significant population structure associated with the division of shelf habitat by deep water has been observed for C. sorrah across the Indo-West Pacific (Giles et al.

2014). Sphyrna lewini and C. limbatus in contrast appeared genetically homogenous and minimally structured across the Indo-Australian archipelago and along the Australian east coast based on mtDNA and microsatellite loci (Ovenden et al. 2010; 2011).

Phylogeographic characteristics of S. lewini and C. limbatus have been thoroughly examined (Duncan et al. 2006; Keeney and Heist 2006) and stock structure of both species has been investigated worldwide and at regional scales (Lim et al. 2010; Nance et al. 2011; Daly-Engel et al. 2012). Genetic variation has been demonstrated for S. lewini populations among major ocean basins (Atlantic, Pacific and Indian Oceans), while populations along continental margins seem to be more homogenous (Duncan et al. 2006; and Quattro et al. 2006). A previously discovered cryptic species close to S. lewini

(Duncan et al. 2006; Quattro et al. 2006) has recently been described as a new species

Sphyrna giberti sp. nov. (Quattro, 2013). Morphologically this new species is distinguishable from S. lewini solely by the number of vertebrae and its geographic range is yet to be discovered (Quattro et al. 2013). Carcharhinus limbatus has been shown to exhibit philopatric behavior, which might explain the substantial genetic subdivision associated with nursery areas (Keeney et al. 2003; Keeney et al. 2005).

Previous molecular studies on marine fauna in the Arabian Seas region have indicated barriers to gene flow between the Red Sea and the WIO for invertebrates (Benzie 1999,

Fratini and Vannini 2002) and several reef fish species (Froukh and Kochzius 2008; Di

Battista et al. 2013). Sailfish (Istiophorus platypterus) populations have been demonstrated to exhibit genetic differentiation between the Arabian Gulf and the Indian 129

Ocean. At the same time no differentiation was found between lionfish (Pterois miles) populations (Kochzius and Blohm 2005) and seveal reef fish species (Di Battista et al.

2013) between the Red Sea and the WIO.

The lack of traditionally recognized global marine barriers (Rocha et al. 2007) or other obvious barriers to movement in the study region combined with the documented mobility of our study species lead us to expect little to no genetic subdivision for any of the species studied throughout the Arabian Seas region. By combining the use of two kinds of genetic markers over a range of species with variable life history characteristics, we aim to thoroughly assess the genetic population structure of elasmobranchs among the

Arabian sea basins. Information on gene flow among national coastal waters is important for the implementation of effective, regional, transboundary stock management.

Materials and Methods

Sample collection and DNA extraction

Tissue samples of C. limbatus, C. sorrah, R. acutus and S. lewini were collected between

2010 and 2013 from whole sharks at fish markets and landing sites in Saudi Arabia,

Oman, the United Arab Emirates, and Bahrain (Fig. 1).

130

The

Figure 1: Map of the Arabian Seas region, displaying collection locations of Carcharhinus limbatus, C. sorrah, Rhizoprionodon acutus and Sphyrna lewini. Numbers indicate fish markets or landing sites in Saudi Arabia, Oman, the United Arab Emirates and Bahrain from where samples were obtained. (1) Jeddah, (2) Salalah, (3) Mirbat, (4) Masirah, (5) Sur, (6) Muscat, (7) Seeb, (8) Barka, (9) Sohar, (10) Shinas, (11) Dibba, (12) Khasab, (13) Ras Al Khaima, (14) Sharkjah, (15) Dubai, (16) Abu Dhabi, (17) Bahrain.

Specimens were identified based on morphological characteristics. In Bahrain, Oman and the UAE specimens were sampled primarily from landing sites of local inshore fisheries, which operate in fishing grounds of less than 100km distance to the sampling location. Collections in Dubai were an exception, as this fish market serves as regional hub for shark products (Rose 1996) and offers specimens from various catch locations throughout the Arabian Sea, Gulf of Oman and Arabian Gulf. In this case only sharks of 131 catch locations within the study area that were confirmed by market personnel were included in the study (Rima Jabado unpublished data). Saudi Arabian samples were obtained from one fish market only (Jeddah), but landings at this site originated from fishing grounds stretching the country’s entire Red Sea coast (see chapter two for details).

A small fin clip was collected from each specimen and preserved in 99% ethanol. Total genomic DNA was extracted from 10 to 20mg of preserved tissue DNA using the

Machery-Nagel Genomic DNA from tissue extraction kit (Bethlehem, PA, USA) following the manufacturers’ instructions and subsequently stored at -80 °C.

Mitochondrial DNA – laboratory methods and data analysis

For each species we examined intra-specific genetic subdivision based on sequence variation in the mtDNA CR. Approximately 1121 base pairs (bp) were amplified for C. limbatus, C. sorrah and R. acutus using the forward primer ProL2 (5’

CTGCCCTTGGCTCCCAAAGC 3’) and the reverse primer PheCacaH2 (5’

CTTAGCATCTTCAGTGCCAT 3’) (Pardini et al. 2001). A different primer set was used for S. lewini to identify potential specimens belonging to the recently described S. gilbert (Quattro et al. 2013). The forward primer CRF6 (5’

AAGCGTCGACTTTGTAAGTC 3’) and the reverse primer CRR10 (5’

CTTAGAGGACTGGAAATCTTGATCGAG 3’) (Pinhal et al. 2012) were shown to clearly distinguish the two species and were hence used in our study to amplify a 562 bp fragment of the mtDNA CR for all S. lewini specimens. Details on species-specific sample sizes, fragment length and primers used are displayed in Table 1. Amplification 132 protocols were the same for both primer combinations used. Total amplification volumes for PCR reactions were 12.5uL, and contained 1uL of the template DNA, 1uL of the primer mix (10 pmol/uL), 6.25 uL of the Qiagen Master Mix (Qiagen Inc.) and 4.25uL of

RNAse-free water. The PCR thermal cycling employed was: 95°C initial heating for 15 min to activate the hot start DNA polymerase, followed by 35 cycles of 94°C for 30 seconds, 58°C for 1 min, 72°C for 1 min, and a 10 min final extension step at 72°C.

Amplifications were performed using Veriti 96-well thermal cyclers (Applied

Biosystems). PCR products were visualized on 0.8% TBE agarose gels containing ethidium bromide for DNA quality and concentration, viewed on Gel Doc IT Imaging

System (Mitsubishi) and purified using the Exonuclease I method (ExoSap, USB,

Cleveland, USA). For S. lewini and R. acutus 562 bp and 1021 bp of the control region were sequenced in the forward and reverse direction. For C. limbatus and C. sorrah 553 and 622 bp respectively of the control region were sequenced in the forward direction only. The program Codon Code Aligner 4.7.2 (CodonCode Corporation, Dedham, USA) was used to assemble, check, manually edit and subsequently align sequences using the

MUSCLE algorithm. Aligned sequences were exported to FaBox (Villesen 2007) and collapsed into haplotypes. Unique mtDNa haplotypes have been submitted to the

GenBank database. Initial species identifications based on morphological characters during market sampling were confirmed by comparison with reference sequences on the

GenBank database through BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi). In case of R. acutus no mtDNA CR reference sequences were available, but species identification was confirmed by barcodes obtained in chapter two. If sequence data did not match the original identification, respective specimens were excluded from the dataset. To account 133 for differences in sample size for the various sample locations in Oman, the UAE and

Bahrain as well as uncertainties regarding the exact catch location of specimens sampled, all Red Sea and all Arabian Sea, Seas of Oman and Arabian Gulf samples were pooled into two separate regions (Red Sea and WIO).

Table1: Species studied, sample size, fragment length and primers used for the initial portion of the mtDNA control region. All novel haplotypes have been deposited to the GenBank database. Species Fragment Red WIO Primer set length (bp) Sea Rhizoprionodon acutus 1021 77 217 ProL2-PheCacaH2 (1) Caracharhinus sorrah 622 159 216 ProL2-PheCacaH2 (1)

Carcharhinus limbatus 553 172 115 ProL2-PheCacaH2 (1)

Sphyrna lewini 562 82 151 CRF6-CRR10 (2) References: (1) Pardini et al. 2001; (2) Pinhal et al. 2012

For C. limbatus, C. sorrah and S. lewini species evolutionary relationships of novel haplotypes (not previously reported for the respective species) to published haplotypes were investigated. Published haplotypes sourced from Keeney and Heist (2006) for C. limbatus, Giles et al. (2013) for C. sorrah and Duncan et al. (2006), Chapman et al.

(2009), Pinhal et al. (2012), Nance et al. (2012), Castillo-Olguín et al. (2012) for S. lewini were aligned with novel haplotypes for each species, trimmed to one length (C. limbatus: 554 bp, C. sorrah: 455 bp, S. lewini: 471 bp) and subsequently assessed by phylogenetic analysis. No published mtDNA CR haplotypes were available for R. autus.

We hence chose two closely related sister species: R. lalandei and R. terranovae and added both as outgroups to the phylogenetic tree. Phylogenies were constructed using 134

MrBayes 3.1 (Ronquist and Huelsenbeck 2003) and the RAxML Black Box webserver

8.0.0 (Stamatakis 2006) employing Bayesian and maximum likelihood (ML) methods respectively. For Bayesian and ML analyses, the most appropriate model of DNA evolution was selected using Akaike and Bayesian Information Criteria in jModelTest

2.1.4 (Guindon and Gascuel 2003; Darriba et al. 2012). The Tamura-Nei (TN) (Tamura and Nei 1993) model was preferred for R. acutus with gamma distributed among site variation (gamma 0.3850), HKY + 1 and HKY (Hasegawa and Kishino 1985) were selected for C. sorrah and C. limbatus respectively and F81 (Felsenstein 1981) for S. lewini. In MrBayes 3.1 (Ronquist and Huelsenbeck 2003) four heated chains were run, with five million generations each that were sampled every 100 generations. The first 250 trees were discarded as burn-in and the final consensus phylogeny was generated using the remaining trees. RAxML (Stamatakis 2006) was run under ML phylogenetic methods with the models selected by jmodeltest 2.1.4 and 100 ML bootstrap replicates. The program network 4.5.1.0 (http://www.fluxus-engineering.com/network_terms.htm) was used to investigate genealogical relationships of our haplotypes by constructing unrooted intraspecific median joining networks using default settings (Bandelt et al.1999).

A hierarchical analysis of molecular variance (AMOVA) implemented in Arlequin 3.5

(Excoffier et al., 2005) was used to assess population genetic structure between the Red

Sea and WIO regions under the TN model of sequence evolution. TN was equivalent or most similar to the original models selected for each species in jmodeltest 2.1.4 (the selected models themselves were not available in Arlequin 3.5). Arlequin 3.5 was also used to describe the genetic variation between the two sampling regions by haplotype and nucleotide diversity (h and π respectively), to calculate species-specific FST values (Weir 135 and Cockerham 1984) and Fu’s Fs haplotype frequency neutrality test.

Microsatellites – laboratory methods and data analysis

Shark samples were genotyped at eight to 12 microsatellite loci (C. limbatus, 12 loci; C. sorrah nine loci; R. acutus, eight loci; S. lewini, 12 loci). Microsatellite loci were adopted from Feldheim et al. (2001), Keeney and Heist (2003), Ovenden et al. (2006) and Nance et al. (2009) and were directly applied directly to target species or cross-amplified in none-target species. Between two to three multiplex PCRs were preformed per individual for all species. PCRs were performed in 11 µl total volume containing 2 µl genomic

DNA, 5 µl Qiagen Multiplex PCR Master Mix, 3.5 µl H20, and 0.5 µl of primer mix

(each primer at 2µM). Thermal profiles consisted of a denaturation step at 95 °C for

15 min, followed by 30 cycles of 30 s at 94 °C, annealing for 90 s at a loci-specific temperatures between 55 °C - 60 °C (Table 2) and extension of 60 s at 72 °C, with final extension of 30 min at 60 °C. Fragment analysis was conducted in an Applied

Biosystems 3730 XL genetic analyzer and microsatellite alleles were scored using

Genemapper version 4.0 software (Applied Biosystems, Foster City, CA). The null hypothesis of Hardy-Weinberg Equilibrium (HWE) was tested using Genepop 4.2

(Rousset 2008). Microchecker v2.2.3 (van Oosterhoot et al. 2004) was used to determine likely causes for deviations from HWE. For each species, between two to seven microsatellite loci originally selected for the study were excluded from the analysis based on the presence of null alleles in both study regions and deviations from HWE; LS24,

Cs09, Ct05, Cs08, Cli55, Cli13, Cli102 for C. limbatus; Cli12; Cs07; Cli100, Cli107, 136

Cli118 for C. sorrah; LS11, Ct06, Cli07 for R. acutus and SLE13 and SLE18 for S. lewini. Genepop 4.2 (Rousset 2008) was used to characterize genetic diversity (expected

(HE), observed (HO) and unbiased (UHE) heterozygosity, allelic richness and mean number of alleles. AMOVA and FST (Weir & Cockerham 1984) values were calculated using Arlequin 3.5 (Excoffier & Lischer 2010).

Although genetic subdivision was low, we also used Structure 2.3.4 (Pritchard et al.

2000) to estimate the number of genetically differentiated populations (K). We predefined K to 1-20 for each run and assuming a correlation of allele frequencies among populations used an admixture model with sampling site as location prior. Default settings were used for alpha and prior FST. Each run was repeated 20 times with 100 000 repetitions each and burn-in set to 100 000 iterations. To visually assess the number of inferred populations we used Structure Harvester (Earl 2012) that describes our data according to the highest averaged maximum log-likelihood.

Wilcoxon signed rank tests under the infinite alleles model (IAM), the two-phase model (TPM), and the single step model (SSM) implemented in Bottleneck 1.2.02

(Cornuet and Luikart 1996; Piry et al. 1999) were used to check for microsatellite heterozygote excess in all four species to test for evidence of a recent reduction in effective population size (Ne).

137

Table 2: Description of microsatellite loci used to genotype C.limbatus, C. sorrah, R. acutus and S. lewini Locus Reference Species T a Cli07 Keeney and Heist (2003) C. limbatus, R.acutus 55 Cli12 Keeney and Heist (2003) C. sorrah 55 Cli13 Keeney and Heist (2003) C. limbatus 57 Cli55 Keeney and Heist (2003) C. limbatus 57 Cli100 Keeney and Heist (2003) C. limbatus, R.acutus 55 Cli102 Keeney and Heist (2003) C. limbatus 55 Cli103 Keeney and Heist (2003) C. limbatus 57 Cli107 Keeney and Heist (2003) C. limbatus, R. acutus 57; 55 Cli118 Keeney and Heist (2003) C. sorrah 55 Cli119 Keeney and Heist (2003) C. limbatus 57 Ct03 Ovenden et al. (2006) R. acutus 58 Ct05 Ovenden et al. (2006) C. limbatus 57 Ct06 Ovenden et al. (2006) C. limbatus, C. sorrah 55 Cs01 Ovenden et al. (2005) C. sorrah 55 Cs02 Ovenden et al. (2005) C. sorrah 55 Cs07 Ovenden et al. (2005) C. sorrah 55 Cs08 Ovenden et al. (2005) C. limbatus 57 Cs09 Ovenden et al. (2006) C. limbatus 57 LS11 Feldheim et al. (2001) C. sorrah 55 LS24 Feldheim et al. (2001) C. limbatus 57 SLE13 Nance et al. (2009) S. lewini 60 SLE18 Nance et al. (2009) S. lewini 60 SLE25 Nance et al. (2009) S. lewini 60 SLE27 Nance et al. (2009) S. lewini 60 SLE28 Nance et al. (2009) S. lewini, R. acutus 60; 55 SLE33 Nance et al. (2009) S. lewini 60 SLE38 Nance et al. (2009) S. lewini 60 SLE45 Nance et al. (2009) S. lewini 60 SLE53 Nance et al. (2009) S. lewini 60 SLE54 Nance et al. (2009) S. lewini 60 SLE71 Nance et al. (2009) R.acutus 60 SLE86 Nance et al. (2009) S. lewini 60 SLE89 Nance et al. (2009) S. lewini 60

138

Results

Genetic structure

For C. limbatus, C. sorrah and R. acutus, FST values were small and non-significant for

both mtDNA as well as microsatellite analyses (Table 3). Sphyrna lewini showed small

but significant FST values for mtDNA CR sequences (Table 3). STRUCTURE indicated

no genetic subdivision between samples and determined that K=1 had the highest

probability in all four species (no evidence of population structure was visible in plots of

assignment for K = 1-20, plots not shown). Yet, the quality of information provided by

STRUCTURE based on such low FST estimates may be questionable (Hubisz et al. 2009).

Table 3: F results for both regions, characterizing spatial structure with both ST mtDNA and microsatellites mtDNA microsatellites Carcharhinus limbatus 0.0025; P= 0.236 0.0097; P= 0.999 Carcharhinus sorrah 0.0057; P= 0.099 - 0.000094; P= 0.3178 Rhizoprionodon acutus 0.0608; P= 0.583 0.0035; P= 0.07 Sphyrna lewini 0.0130, P= 0.049 0.0034; P = 0.07

Microsatellite and mtDNA diversity

For C. limbatus and S. lewini, relatively few haplotypes, separated by no more than one

mutational step, were revealed by mtDNA CR sequences (Fig. 2 and 5). This pattern

resulted in low haplotype and nucleotide diversity for both species within each region

(Table 4). Similarly low haplotype and nucleotide diversity was observed for C. sorrah, 139 while the number of haplotypes was higher for this species as well as for R. acutus, which also showed the highest haplotype and nucleotide diversities of all species (Table 4).

Microsatellite indices of genetic diversity, i.e. expected (HE), observed (HO) and undbiased (UHE) heterozygosities, allelic richness and mean number for each locus and species within each sample region are provided in the supplementary material.

Fu’s Fs statistics were negative for all four species and both regions, yet significant only for S. lewini (for both regions) and for C. sorrah (for the WIO region only), suggesting population expansions for those species in the respective regions. Bottleneck

1.2.02 did not detect statistical evidence for any severe recent bottleneck and populations of all four species showed a normal L-shaped distribution. Species-specific parsimony,

ML and statistical parsimony reconstructions produced comparable topologies (Fig 2, 3,

4, 5).

Carcharhinus limbatus

Sequences of 287 individuals revealed seven C. limbatus haplotypes. Three of these were novel haplotypes, i.e. not included in the global data set of Keeney and Heist (2006) or the western Atlantic dataset of Sodré et al. (2012); CLI 03, CLI05, CLI07 (Fig. 2). All of these were singletons represented by only one individual each. Haplotype frequencies of the other four haplotypes varied between the two regions, but all were common to both study regions (Fig. 2A). Carcharhinus limbatus microsatellite genotypes were obtained from five loci (supplementary material). Nucleotide and haplotype diversity for C. limbatus were low and did not vary noticeably between the two sea area (Table 4).

140

Table 4: Mitochondrial DNA control region genetic diversity indices for C. limbatus, C. sorrah, R. acutus and S. lewini across both sampling area. Nucleotide (π) and haplotype (h) diversities and neutrality statistics (Fu’s Fs) are shown.

Species h Red Sea h WIO π Red Sea π WIO Fu’s Fs Fu’s Fs Red Sea WIO C. limbatus 0.3490 0.3054 0.000724 0.000755 -2.90484 -1.89320 P: 0.07633 P: 0.16805 C. sorrah 0.3270 0.4606 0.001030 0.001314 -7.23390 -7.43393 P: 0.00333 P: 0.00844 R. acutus 0.7365 0.6599 0.001397 0.001265 -3.57008 -12.17461 P: 0.05836 P: 0.00068 S. lewini 0.4998 0.4661 0.000086 0.000116 0.73386 - 0.13352 P: 0.61279 P: 0.46034

The maximum parsimony tree for C. limbatus displayed two clearly separated clades. The first one was formed by the haplotypes observed in the present study, nested between

Indian Ocean haplotypes sampled by Keeney and Heist (2006) (Fig. 2B), the second one consisted exclusively of Atlantic haplotypes sampled by Keeney and Heist (2006) (Fig.

2B). 141

82/98

83/96

84/86

100/100 83/76 CLI04

CLI07 CLI03 CLI06

CLI02 CLI01 100/100

CLI05

74/76

Figure 2: Statistical parsimony network among haplotypes for C. limbatus based on the partial mitochondrial DNA control region (554 bp) (A) and phylogenetic relationships among C. limbatus haplotypes based on the BI and ML analysis of partial mitochondrial DNA control region sequences (554 bp) (B). Haplotypes are indicated by circles, with diameters of circles being proportional to haplotype frequencies. Each line represents a single nucleotide difference. Ocean basins are indicated by colors: WIO (blue), Red Sea (pink). Haplotypes CLI01, CLI02, CLI03, CLI04 were also observed by Keeney and Heist (2006) in Indian Ocean samples. Haplotypes CLI05, CLI06 and CLI07 were discovered in the present study. All other haplotypes were sourced from Keeney and Heist (2006) and are represented by their designation in that study. Maximum parsimony/maximum likelihood bootstrap values are indicated at nodes: only nodes supported by > 50% are shown.

142

Carcharhinus sorrah

For C. sorrah, mtDNA CR sequences were resolved for 375 individuals and resulted in

16 mtDNA haplotypes. Except for five singletons and three haplotypes, observed in two and three individuals respectively, all haplotypes were common in both regions (Fig. 3A).

As in C. limbatus, haplotype and nucleotide diversities were low for both regions (Table

4). Microsatelite genotypes were obtained from four loci (supplementary material). All haplotypes were connected by one mutational step only, except for haplotype CSO11, which was three mutational steps away from CSO1. In the maximum parsimony tree, all haplotypes obtained in the present study were nested within Indian Ocean haplotypes sourced from Giles et al. 2013 and formed a separate clade to any of the Atlantic C. sorrah haplotypes observed by Giles et al. 2013 (Fig. 2B).

Rhizoprionodon acutus

Variation in a 1021 bp fragment of the mtDNA CR defined 26 haplotypes for R. acutus.

All common haplotypes formed cluster and were separated by one mutational step only.

A second cluster was formed by four rare haplotypes that was separated by up to nine mutational steps from the main cluster (Fig. 4A). All common haplotypes were common to both regions (Fig. 4A). Microsatelite genotypes were obtained from five loci

(supplementary material).

143

83/100

CSO5 CSO10 CSO13 85/96 CSO11 CSO9

CSO15 CSO1 85/96 84/98 CSO4

84/98 CSO3 CSO8 73/60 85/76 CSO2 CSO12 CSO6 CSO9 CSO14 84/57 CSO7

Figure 3: Statistical parsimony network 86/77 among haplotypes for C. sorrah based on the partial mitochondrial DNA control 82/99 region (622 bp) (A) and phylogenetic relationships among C. sorrah haplotypes based on the BI and ML analysis of partial mitochondrial DNA control region sequences (455 bp) (B). Haplotypes are 83/95 indicated by circles, with diameters of circles being proportional to haplotype frequencies. Each line and cross bar 80/60 respectively represents a single nucleotide 85/85 difference. Ocean basins are indicated by 93/57 colors: WIO (blue), Red Sea (pink). Haplotypes CSO1, CSO2, CSO4, CSO6- CSO8 and CSO12 were identical to Indian Ocean haplotypes observed by Giles et al. (2013). Haplotypes CSO3, CSO5, CSO9- CSO11 and CSO13-CSO15 were discovered in the present study. All other haplotypes were sourced from Giles et al. 2013 and are represented by their designation in that study. Maximum parsimony/maximum likelihood bootstrap values are indicated at nodes: only nodes supported by > 50% are shown.

144

100/100

56/77 100/100 93/87

RAC1 85/93 RAC2 RAC26 83/66

84/87 RAC25 85/97

59/64

RAC21 86/94

RAC23 RAC3 84/92

RAC22 83/52 RAC18 RAC12 RAC14 RAC17 RAC20 RAC15 RAC10 RAC19

RAC13 RAC11 RAC9 RAC8 RAC5

RAC7 RAC4 RAC16 RAC6

Figure 4: Statistical parsimony network among haplotypes for R. acutus based on the partial mitochondrial DNA control region (1021 bp) (A) and phylogenetic relationships among R. acutus haplotypes based on the BI and ML analysis of partial mitochondrial DNA control region sequences (844 bp) (B). Haplotypes are indicated by circles, with diameters of circles being proportional to haplotype frequencies. Red diamonds represent hypothetical missing ancestors. Each line and cross bar respectively represents a single nucleotide difference. Ocean basins are indicated by colors: WIO (blue), Red Sea (pink). The phylogeny was rooted with Rhizoprionodon lalandei and R. terranovae. Maximum parsimony/maximum likelihood bootstrap values are indicated at nodes: only nodes supported by > 50% are shown.

145

S. lewini

Mitochondrial DNA CR sequences of 233 S. lewini did not reveal any individuals belonging to the newly described S. gilberti. Five novel haploptypes were discovered in the present study. In the maximum parsimony tree haplotypes obtained in the present study were nested within Indian Ocean haplotypes sourced from Duncan et al. 2006 and formed a separate clade to any of the Atlantic or Pacific S. lewini haplotypes obtained by

Duncan et al. (2006), Chapman et al. (2009), Nance et al. (2012) and Castillo-Olguín et al. (2012) (Fig. 2B). S. lewini microsatelite genotypes were obtained from 10 loci

(supplementary material).

Discussion

Genetic structure

This study represents the first comprehensive, regional analysis of genetic connectivity in elasmobranch species throughout the Arabian Seas region. Our analyses were based on a comparatively large number of samples from collection locations encompassing over

5000km of coastline, genotyped at two types of genetic markers (mtDNA and nuclear

DNA). Based on our results from both markers, we were unable to reject the null- hypothesis of genetic homogeneity between Red Sea and WIO samples for C. limbatus,

C. sorrah and R. acutus. Mitochondrial DNA as well as microsatellite values were non- significant in all three species (p = 0.7 - 0.999). 146

Sphyrna lewini showed statistically significant differentiation for mtDNA (p = 0.049), however, the actual Fst value is very low (Fst = 0.013). Genetic structure in S. lewini populations has only been demonstrated among major ocean basins (Atlantic, Pacific and

Indian Ocean), while there seems to be panmixia in populations along the continental shelf (Duncan et al. 2006; and Quattro et al. 2006; Ovenden et al. 2009). We are hence doubtful about the biological significance of the very low Fst value at mtDNA observed for this species in the present study, specifically considering the non-significant value for microsatellites (p = 0.07). The statistical significance may rather be caused by measuring- artifacts (Bird et al. 2011).

Each of our study species has previously been assessed for the degree of genetic subdivisions across similar scales in Australia and Indonesia. In these regions, structure has been demonstrated in C. sorrah and R. acutus between central Indonesia and northern

Australia based on nuclear and mtDNA markers, but not for S. lewini and C. limbatus

(Ovenden et al. 2009; 2010; 2011). The suggested reason for the apparent subdivision in the two smaller shark species in these studies was the Timor Through, which may act as a deep-water dispersal barrier preventing gene flow of less vagile species between

Australia and Indonesia (Ovenden et al. 2009). Our study region in contrast is characterized by a continuous coastline, lacking any obvious barriers to gene flow (e.g. expanses of deep water). The apparent absence of genetic structure across

147

98/96 65/52

62/77

54/57

65/77 SL4

SL2

SL3 98/75

SL1 SL5

91/87

72/87

Figure 5: Statistical parsimony network among haplotypes for S. lewini based on the partial mitochondrial DNA control region (562 bp) (A) and phylogenetic relationships among S. lewini haplotypes based on the BI and ML analysis of partial mitochondrial DNA control region sequences (471 bp) (B). Haplotypes are indicated by circles, with diameters of circles being proportional to haplotype frequencies. Each line represents a single nucleotide difference. Ocean basins are indicated by colors: WIO (blue), Red Sea (pink). All five SL haplotypes were discovered in the present study, i.e. have not been observed in any of the previous studies by Duncan et al. (2006), Chapman et al. (2009), Nance et al. (2012), Castillo-Olguín et al. (2012). Haplotypes HE and HD represent Pacific haplotypes sourced from Nance et al. 2011. All other haplotypes are Atlantic, Pacific and Indian Ocean haplotypes sourced from Duncan et al. (2006), Chapman et al. (2009) and Castillo-Olguín et al. (2012) and are represented by their designation in Duncan et al. (2006). The tree was rooted with the newly discovered S. gilbert sp. nov (SICR6) (Pinhal et al. 2012). Maximum parsimony/maximum likelihood bootstrap values are indicated at nodes: only nodes supported by > 50% are shown.

148 broad geographical scales in this study is hence not surprising and concordant with homogenous population structures demonstrated in all four study species across a similar continuous coastline in eastern and northern Australia encompassing approximately

3000km (Ovenden et al. 2009; 2010; 2011). Although sharks are lacking the dispersive larval phase of most teleost fish, their capacity to actively move over large distances as juveniles and especially as adults is very high. We suggest that long-shore dispersal along the continuous coastline stretching from the Red Sea all the way into the Arabian Gulf causes panmixia over large spatial scales in this region.

Failure to detect genetic structure may often be caused by limited sampling effort. In the present study, however, sample sizes for all four species are higher than those of comparable studies. We are hence convinced that the patterns of panmixia observed across our study region are not caused by artifacts. Due to challenges in the application of cross-species as well as species-specific microsatellite loci, our study is currently limited in the number of markers employed in each species. Although, most loci chosen for this study seem to work well for the same or similar species in other ocean basins, we had to exclude a large number of loci based on deviations from HWE and the presence of null alleles, leading to much lower sample sizes than anticipated. For future studies on elasmobranch species from regions that have not previously been included in samples used for the design of microsatellite markers we hence recommend designing species- specific markers based on samples originating from the targeted study region. In addition, research on dispersal mechanisms based on non-genetic techniques, e.g. tagging or parasite studies coupled with molecular methods would provide interesting insights into the actual dispersal mechanisms underlying the observed homogenous structure. Future 149 studies should also test for broader scale genetic breaks by sampling all four species further to the west and east of our sampling locations.

Microsatellite and mtDNA diversity

We detected low genetic diversity for C. limbatus and S. lewini, distinguishing only seven and five haplotypes respectively. Yet, this pattern seems to be typical for both species throughout their global range (Duncan et al. 2006; Keeney and Heist 2006) and hence may not necessarily be a function of overexploitation. In addition, low levels of genetic variation have been attributed to historical demographic processes, such as founder or bottleneck effects followed by demographic expansion (Stow et al. 2006). Here, recent population expansions were detected by indices of neutral evolution (Fu’s Fs) for S. lewini for both study regions and for C. sorrah for the WIO region. No statistical evidence for any severe recent bottleneck events was detected for any of the species studied. Expansion events and the lack of bottleneck events, however, do not necessarily indicate healthy, expanding populations. Bottlenecks often remain undetected based on specific population and sampling characteristics (Heller et al. 2013) and the fact that both species are heavily exploited throughout the study region makes it unrealistic to expect healthy expanding populations.

Structures of reconstructed maximum parsimony trees support the finding of strong genetic subdivision of C. limbatus, C. sorrah and S. lewini on a global scale (Duncan et al. 2006; Keeney and Heist 2006; Giles et al. 2013) and show all novel Red Sea/WIO haplotypes nested within Indian Ocean haplotypes, clearly separated from clades 150 consisting of Atlantic and Pacific haplotypes (Fig. 2,3,5).

Implications for fisheries and management

In some countries of the Arabian Peninsula fisheries and environmental legislation is inadequate for the management of the four study species. Our results indicate that regional cooperation in managing these species is urgently required. Elasmobranch fisheries throughout the region are largely focused on a limited number of species. Four of these species have now been proven to be exploited from shared stocks across several countries. In general, a lack of stock structure over broad geographical scales is considered favorable for exploited populations, because stocks have the capacity to be replenished by individuals from unexploited areas. If, however, fisheries stocks are exploited at similarly high levels throughout their range, this paradigm might not hold true. Fisheries may then not only risk the loss of local sub-populations, but a collapse of the population as a whole. Further research on stock structure and biological characteristics of other species belonging to the largely unstudied, but exploited elasmobranch fauna important to the Arabian elasmobranch fishery is warranted to ensure a sustainable future for this vital component of the marine biodiveristy in the

Middle East.

Conclusions

Based on mitochondrial as well as nuclear molecular data, dispersal of C. limbatus, C. 151 sorrah, R. acutus and S. lewini among the Red Sea, the Arabian Sea, the Gulf of Oman and the Arabian Gulf is not limited by any obvious barriers to gene flow. Different

Arabian countries are thus most likely exploiting shared stocks of these species. In a region that is almost completely lacking adequate fisheries management and enforcement of existing legislation regarding the sustainable exploitation of elasmobranchs and other marine resources, this result calls for urgent regional collaboration in assessing sustainable exploitation levels and catch limits not only for the four study species but for elasmobranch species in general.

152

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Supplementary material: The population, sample size (N), number of alleles per microsatellite locus (Na), number of effective alleles per microsatellite locus (Ne), average observed heterozigosity (Ho), expected (He) and unbiased (UHe) heterozygosity, and F statistic for Red Sea and Western Indian Ocean samples of Carcharhinus limbatus (A) C. sorrah (B) Rhizoprionodon acutus (C) and Sphyrna lewini (D). Significant deviations from Hardy-Weinberg Equilibrium are indicated with an asterisk.

(A) Carcharhinus limbatus

Region Locus N Na Ne I Ho He UHe F

WIO Cli103 97 6.000 3.156 1.420 0.701 0.683 0.687 -0.026

Cli107 104 12.000 6.161 2.026 0.788 0.838 0.842 0.059 *

Cli119 116 9.000 4.619 1.754 0.698 0.784 0.787 0.109

Cli100 112 8.000 4.359 1.631 0.732 0.771 0.774 0.050

Ct06 112 12.000 6.274 2.016 0.768 0.841 0.844 0.087

Red Sea Cli103 179 9.000 3.609 1.545 0.620 0.723 0.725 0.142 *

Cli107 158 14.000 6.422 2.065 0.804 0.844 0.847 0.048

Cli119 168 11.000 5.116 1.844 0.732 0.805 0.807 0.090 *

Cli100 160 10.000 3.945 1.648 0.688 0.747 0.749 0.079 *

Ct06 160 12.000 6.229 2.023 0.713 0.839 0.842 0.151 *

(B) Carcharhinus sorrah

Region Locus N Na Ne I Ho He UHe F

WIO Ct06 158 23.000 12.304 2.696 0.968 0.919 0.922 -0.054

Cs02 169 34.000 13.738 2.929 0.917 0.927 0.930 0.011

Cs01 156 37.000 23.277 3.347 0.949 0.957 0.960 0.009 *

LS11 197 4.000 2.111 0.902 0.523 0.526 0.528 0.007

Red Sea Ct06 137 22.000 13.645 2.756 0.927 0.927 0.930 0.000

Cs02 147 32.000 15.191 2.982 0.891 0.934 0.937 0.046 *

Cs01 138 40.000 25.025 3.393 0.906 0.960 0.964 0.057

LS11 160 4.000 2.049 0.870 0.500 0.512 0.513 0.023 *

161

Region Locus N Na Ne I Ho He UHe F

WIO Cli100 209 9.000 6.259 1.945 0.780 0.840 0.842 0.072

Cli107 206 17.000 5.444 2.115 0.757 0.816 0.818 0.072

SLE28 223 7.000 4.328 1.558 0.798 0.769 0.771 -0.038

Ct03 221 7.000 2.502 1.170 0.615 0.600 0.602 -0.025

SLE071 218 8.000 2.223 1.131 0.578 0.550 0.551 -0.051 *

Red Sea Cli100 74 11.000 7.126 2.072 0.905 0.860 0.866 -0.053

Cli107 75 15.000 5.230 2.113 0.747 0.809 0.814 0.077

SLE28 87 7.000 4.985 1.681 0.736 0.799 0.804 0.080 *

Ct03 89 5.000 2.387 1.108 0.607 0.581 0.584 -0.044

SLE071 83 8.000 2.545 1.273 0.518 0.607 0.611 0.147

(D) Sphyrna lewini

Region Locus N Na Ne I Ho He UHe F

WIO SLE038 190 14.000 5.646 1.930 0.863 0.823 0.825 -0.049

SLE086 197 8.000 1.833 1.046 0.437 0.454 0.456 0.039

SLE033 197 14.000 4.240 1.836 0.807 0.764 0.766 -0.056

SLE089 192 14.000 7.935 2.217 0.870 0.874 0.876 0.005

SLE027 187 12.000 5.089 1.842 0.829 0.803 0.806 -0.032

10.41 SLE025 199 34.000 6 2.795 0.804 0.904 0.906 0.111

SLE054 206 7.000 3.016 1.374 0.641 0.668 0.670 0.041

14.57 SLE028 202 31.000 3 2.923 0.866 0.931 0.934 0.070

10.32 SLE053 192 21.000 9 2.556 0.849 0.903 0.906 0.060

SLE045 204 6.000 2.535 1.088 0.647 0.606 0.607 -0.069

Red Sea SLE038 79 11.000 5.658 1.949 0.785 0.823 0.829 0.047

SLE086 83 11.000 2.181 1.307 0.530 0.542 0.545 0.021

SLE033 82 13.000 5.255 1.980 0.817 0.810 0.815 -0.009

162

SLE089 80 15.000 8.573 2.304 0.888 0.883 0.889 -0.005

SLE027 76 9.000 5.176 1.792 0.882 0.807 0.812 -0.093

10.50 SLE025 76 24.000 2 2.635 0.882 0.905 0.911 0.026

SLE054 80 10.000 2.639 1.365 0.600 0.621 0.625 0.034

16.04 SLE028 76 32.000 4 3.039 0.855 0.938 0.944 0.088

SLE053 74 18.000 9.203 2.486 0.838 0.891 0.897 0.060

SLE045 81 6.000 2.646 1.139 0.704 0.622 0.626 -0.131

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GENERAL CONCLUSIONS

The integrated approach of fisheries-dependent and independent survey methods employed in this thesis has resulted in three main findings: (1) potential growth and recruitment overfishing of commercially important shark species, (2) a severe paucity of sharks in Saudi Arabian Red Sea reef ecosystems, and (3) an open population structure for four commercially harvested and regionally fished shark species shared by the Red

Sea and the Western Indian Ocean. Together this dataset provides the most thorough and up-to-date baseline of information about the status of Red Sea elasmobranchs.

Although the extent and degree of impact of fisheries upon marine resources is highly contentious, there is plenty of evidence that overfishing has profoundly altered shark and ray populations in many regions around the world (Baum et al. 2003, 2004; Stevens et al.

2000). In addition to overfishing, pressure on marine ecosystems is generally caused by habitat degradation, climate change and pollution (Dulvy et al. 2014). Localized pollution of coral reef areas is a problem faced by several Red Sea countries (e.g. Walker and

Ormond 1982; Hanna and Muir 1990; Idris et al. 2007), yet, Red Sea reef systems in general and Saudi Arabian Red Sea reef systems in particular are overall still considered healthy (Kotb et al. 2008). The effects of climate change on sharks and rays are yet to be determined, but a depletion of elasmobranchs in this region due to elevated CO2 levels is highly unlikely, especially since no other marine ecosystems around the world are known to have experienced the loss of sharks resulting from this phenomenon. A hypothesis of naturally low abundances of sharks in the Red Sea due to geographical or environmental factorsis is also unconvincing. Healthy reef shark populations are found in unexploited

Red Sea regions displaying identical habitat characteristics to those of exploited reef 164 systems in Saudi Arabia (Hussey et al. 2011). We hence believe that the extremely low numbers of sharks observed along the coast of Saudi Arabia are the result of decades of unregulated overfishing of not only sharks but marine resources in general. The reduction of sharks may either be caused by direct fishing on this group, or by the depletion of their prey species (bottom-up effects; e.g. Hunt et al. 2006). Clarification of the exact effects or combinations of effects will require detailed data on fishing effort and species composition of fisheries catches.

Unsustainable fishing and overexploitation of shark resources coupled with a severe lack of enforced protection strategies seems to be a common scenario in the Arabian Seas region (Moore et al. 2012; Henderson et al. 2007). Such regional overexploitation of marine resources is especially worrisome in light of the shared stock structure that was genetically determined for four of the most dominant shark species in elasmobranch landings throughout the region. If shark stocks do not have the capacity to be replenished by individuals from adjacent unexploited areas, fisheries may risk the loss of the population as a whole. Altogether, the data obtained in this thesis call for immediate conservation measures with a strong focus on upgrading management and enforcement capacities of the Saudi Arabian fisheries sector.

The fact that a Royal Decree prohibiting all shark-fishing activities issued six years ago has thus far been ignored, by fishermen and fishery authorities, underlines the dysfunctio of the enforecement system. As long as sanctions for violationing to the law are not specified and Saudi Arabia continues to choose not to participate in the

International Plan of Action for the Conservation and Management of Sharks (IPOA- 165

Sharks, FAO 1999), any proposals of specific actions aiming at sustainable exploitation are rendered meaningless. Any management strategies formulated at this stage are doomed to fail, as long as the existing large-scale problems of the Saudi Arabian fisheries sector are not solved. Thus instead of proposing a set of country-specific management measures, which have already been outlined in detail over a decade ago (Bonfil 2001), we rather call for an urgent general revision of the current fisheries management systems and institutions and the enforcement of existing legislation. Bonfil (2001) proposed a set of low-cost, low-research-effort management recommendations to solve immediate problems in the regional fisheries sector. He stressed that those recommendations should be treated urgently and be implemented within a maximum of two years. He speculated that a failure to do so within the next few years would severly threaten elasmobranch resources and the very future of fisheries that depend on these resources. We can now say that the detoriation of elasmobranch resources has continued unhindered since Bonfil’s proposal. The introduction of meaningful management and conservation of Saudi

Arabian Red Sea elasmobranchs has so far failed and we believe that at this point in time only a strict and immediate moratorium on shark fishing will have the potential to secure a future for sharks roaming Saudi Arabian Red Sea reefs, once again which is of vital importance for the future of fishermen depending on these resources. A suspension of fishing activities would provide a time window for proper stock assessment, which should form the basis for catch quota for individual species. The moratorium could be lifted if continuing studies show that Saudi Arabian shark resources have the capacity to sustain implemented controlled fishing pressure. We believe that the immediate assessment of the stock status of all exploited shark species and the implementation of 166 strictly enforced catch limits are the only way to achieve long-term sustainability of

Saudi Arabian elasmobranch fisheries.

167

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APPENDIX 1: LIST OF PUBLICATIONS BY THE AUTHOR

Publications directly arising from this PhD thesis:

Published:

Spaet, J. L. Y., Cochran, J. E. M. and Berumen, M. L. (2011) First record of the Pigeye Shark, Carcharhinus amboinensis (Müller & Henle, 1839) (Carcharhiniformes: Carcharhinidae), in the Red Sea. Zoology in the Middle East, 52: 118-121. doi:110.1080/09397140.09392011.10638488.

Spaet, J. L. Y., Thorrold, S. R. and Berumen, M. L. (2012) A review of elasmobranch research in the Red Sea. Journal of Fish Biology, 80: 952-965. doi: 910.1111/j.1095-8649.2011.03178.x.

In review:

Spaet, J. L. Y., Berumen, M. L. In review. Fish market surveys indicate unsustainable practices for elasmobranch fisheries in the Saudi Arabian Red Sea. Fisheries Research.

In preparation:

Spaet, J. L. Y., Berumen, M. L. In prep. Fisheries independent surveys indicate low abundance of sharks at Saudi Arabian coral reef systems. Coral Reefs. Spaet, J. L. Y., Jabado, R.W., Henderson, A.C., Moore, A.B.M., Berumen, M. L. In prep. Genetic structure and population connectivity of four shark species among three ocean basins in the Arabian Seas region. Molecular Ecology. Spaet, J. L. Y., Berumen, M. L. In prep. Fisheries independent surveys indicate low abundance of sharks at Saudi Arabian coral reef systems. Coral Reefs. Spaet, J. L. Y., Braun C., Skomal, G. Thorrold, S.R., Berumen, M. L. In prep. Diel vertical movements of a scalloped hammerhead, Sphyrna lewini, in the central Red Sea. Journal of Fish Biology.

170

Additional publications:

Published:

Berumen M.L., Hoey A.S., Bass W.H., Bouwmeester J., Catania D., Cochran J.E.M., Khalil M.T., Miyake S., Mughal M.R., Spaet J.L.Y., Saenz-Agudelo, P. (2013) The status of coral reef ecology research in the Red Sea. Coral Reefs, 32: 1-12. Spaet, J.L.Y. Predictable annual aggregation of longnose parrotfish (Hipposcarus harid) in the Red Sea. Marine Biodiversity, 43: 179-180.

In review:

O’Bryhim, Spaet, J.L.Y., Hyde, J.R., Jones, K.L., Lance, S.L. In press. Development of microsatellite markers for globally distributed populations of the threatened Silky Shark, Carcharhinus falciformis. Journal of Fish Biology. Vignaud, T.M., Mourier, J., Maynard, J.A., Leblois, R., Spaet, J.L.Y., Clua, E., Neglia, V. Planes, S. Blacktip reef sharks, Carcharhinus melanopterus, have high genetic structure and varying demographic histories in their Indo-Pacific range. Molecular Ecology.