Population genetics of four heavily exploited shark species around the Arabian Peninsula

Abstract

The northwestern Indian Ocean harbors a number of larger marine vertebrate taxa that warrant the investigation of genetic population structure given remarkable spatial heterogeneity in biological characteristics such as distribution, behavior, and morphology. Here, we investigate the genetic population structure of four commercially exploited shark species with different biological characteristics (Carcharhinus limbatus, Carcharhinus sorrah, Rhizoprionodon acutus, and Sphyrna lewini) between the Red Sea and all other water bodies surrounding the Arabian Peninsula. To assess intraspecific patterns of connectivity, we constructed statistical parsimony networks among haplotypes and estimated (1) population structure; and (2) time of most recent population expansion, based on mitochondrial control region DNA and a total of 20 microsatellites. Our analysis indicates that, even in smaller, less vagile shark species, there are no contemporary barriers to gene flow across the study region, while historical events, for example, Pleistocene glacial cycles, may have affected connectivity in C. sorrah and R. acutus. A parsimony network analysis provided evidence that Arabian S. lewini may represent a population segment that is distinct from other known stocks in the Indian Ocean, raising a new layer of conservation concern. Our results call for urgent regional cooperation to ensure the sustainable exploitation of sharks in the Arabian region.

Full text

Population genetics of four heavily exploited shark species
around the Arabian Peninsula
Julia L. Y. Spaet
1
, Rima W. Jabado
2
, Aaron C. Henderson
3
, Alec B. M. Moore
4
& Michael L. Berumen
1
1
Red Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology,
23955-6900, Thuwal, Saudi Arabia
2
Gulf Elasmo Project, P.O. Box 29588, Dubai, United Arab Emirates
3
Department of Marine Science & Fisheries, College of Agricultural & Marine Sciences, Sultan Qaboos University, Muscat, Oman
4
RSK Environment Ltd, Spring Lodge, Helsby, Cheshire, WA6 0AR, UK
Keywords
Carcharhinus limbatus,Carcharhinus sorrah,
connectivity, elasmobranchs, Rhizoprionodon
acutus,Sphyrna lewini.
Correspondence
Julia Spaet, Red Sea Research Center,
Division of Biological and Environmental
Science and Engineering, King Abdullah
University of Science and Technology,
23955-6900 Thuwal, Saudi Arabia.
Tel: +966 547700019; Fax: NA;
E-mail: julia.spaet@kaust.edu.sa
Funding Information
This project was funded in part by KAUST
(award URF/1/1389-01-01 and baseline
funding to M.L.B.). Sample collections in
Oman were supported by the Ministry for
Agriculture and Fisheries (Oman) and those
from the UAE by the United Arab Emirates
University.
Received: 23 March 2015; Revised: 22 April
2015; Accepted: 23 April 2015
doi: 10.1002/ece3.1515
Abstract
The northwestern Indian Ocean harbors a number of larger marine vertebrate
taxa that warrant the investigation of genetic population structure given
remarkable spatial heterogeneity in biological characteristics such as distribu-
tion, behavior, and morphology. Here, we investigate the genetic population
structure of four commercially exploited shark species with different biological
characteristics (Carcharhinus limbatus,Carcharhinus sorrah,Rhizoprionodon acu-
tus, and Sphyrna lewini) between the Red Sea and all other water bodies sur-
rounding the Arabian Peninsula. To assess intraspecific patterns of connectivity,
we constructed statistical parsimony networks among haplotypes and estimated
(1) population structure; and (2) time of most recent population expansion,
based on mitochondrial control region DNA and a total of 20 microsatellites.
Our analysis indicates that, even in smaller, less vagile shark species, there are
no contemporary barriers to gene flow across the study region, while historical
events, for example, Pleistocene glacial cycles, may have affected connectivity in
C. sorrah and R. acutus. A parsimony network analysis provided evidence that
Arabian S. lewini may represent a population segment that is distinct from
other known stocks in the Indian Ocean, raising a new layer of conservation
concern. Our results call for urgent regional cooperation to ensure the sustain-
able exploitation of sharks in the Arabian region.
Introduction
Understanding the spatio-temporal patterns of gene flow
among geographically separated populations has long
been a major focus in ecology. Limited genetic differentia-
tion over broad spatial scales is often associated with the
high dispersal capacities of marine organisms, resulting
from either a highly dispersive larval phase affected by
ocean currents or the active movements of juvenile and
adult specimens in animals lacking a planktonic larval
stage. Yet, there are numerous well-known examples of
barriers to gene flow within and among populations that
result in higher than expected genetic structure, even in
species with presumed high levels of vagility (e.g., dol-
phins: Andrews et al. 2010; M€
oller et al. 2011; killer
whales: Foote et al. 2011; sharks: Blower et al. 2012; tuna:
Dammannagoda et al. 2008; Kunal et al. 2013).
Patterns of genetic population structure in sharks are
not uniform across species, but range from localized
genetic subdivision (e.g., leopard shark: Lewallen et al.
2007; nurse shark: Karl et al. 2012; zebra shark: Dudgeon
et al. 2009) and population structuring on relatively small
geographic scales (e.g., blacktip reef shark: Vignaud et al.
2014a; bull shark: Karl et al. 2011; dusky shark: Benavides
ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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distribution and reproduction in any medium, provided the original work is properly cited.
1

et al. 2011; grey nurse shark: Ahonen et al. 2009; lemon
shark: Schultz et al. 2008; sandbar shark: Portnoy et al.
2010), to population differentiation detectable only across
ocean basins (e.g., shortfin mako shark: Schrey and Heist
2003; whale shark: Castro et al. 2007; Schmidt et al. 2009;
Vignaud et al. 2014b) and nearly global panmixia (bask-
ing shark: Hoelzel et al. 2006). Genetic subdivision in
sharks is commonly facilitated by geographic dispersal
barriers, such as large oceanic expanses (lemon shark:
Schultz et al. 2008; spot-tail shark: Giles et al. 2014) or
environmental gradients along continuous landmasses
extending across different geographic regions (blacktip
shark: Keeney and Heist 2006). In addition, the degree of
species- and/or location-specific genetic differentiation is
typically reflected by a combination of individual vagility,
foraging habits, habitat preferences, reproductive mode,
and sensitivity toward natural and anthropogenic influ-
ences (Dudgeon et al. 2012). The wide range of life histo-
ries and movement patterns exhibited by even closely
related shark species hence hampers the a priori inference
of spatial population structure.
There is compelling evidence to investigate the genetic
population structure of sharks in the water bodies sur-
rounding the Arabian Peninsula, that is, the Arabian Sea,
the Gulf of Oman and two semi-enclosed bodies of water,
the Red Sea, and the Arabian/Persian Gulf (hereafter “the
Gulf”) (Fig. 1). First, a number of resident marine verte-
brate taxa display remarkable heterogeneity in biological
aspects, such as distribution, behavior, morphology, and
population genetics. The Arabian Sea off the Oman coast,
for instance, harbors the world’s most isolated and most
distinct population of nonmigratory humpback whales,
Megaptera novaeangliae (Pomilla et al. 2014). Hawksbill
turtles in the Gulf are significantly smaller than those in
Omani waters (Pilcher et al. 2014), and sea snakes, which
are abundant and diverse in the Gulf and present in the
Arabian Sea, are entirely absent from the Red Sea (Shepp-
ard et al. 1992). In addition, barriers to gene flow have
been indicated between the Red Sea and the western
Indian Ocean for several invertebrates (crabs: Fratini and
Vannini 2002; sponges: Giles et al. in press) and some reef
fishes (DiBattista et al. 2013), but not for others (Kochzi-
us and Blohm 2005; DiBattista et al. 2013). In the Gulf,
the large and highly mobile sailfish, Istiophorus platypterus,
was described as phylogeographically isolated (Hoolihan
et al. 2004), while another epipelagic predator, the Span-
ish mackerel, as well as the fiddler crab, does not appear
to exhibit genetic subdivision between the Gulf and the
Arabian Sea (Hoolihan et al. 2006; Shih et al. 2015).
Second, existing studies suggest variation in distribu-
tional and morphological patterns within Arabian elasmo-
branch species. Several elasmobranch species in the
Arabian region have highly localized known distributions
(e.g., C. leiodon: Moore et al. 2011) with a number of spe-
cies endemic to the Red Sea (e.g., H. bentuviai: Baranes
and Randall 1989) and the Gulf (e.g., H. randalli: Last et al.
2012). In addition, a large number of common elasmo-
branch species, which are reliably reported from the Gulf
of Oman and the Gulf of Aden, have not been reported in
the Gulf and the Red Sea, respectively (Moore 2011; Spaet
et al. 2012). Furthermore, significant morphological differ-
ences between Gulf elasmobranchs and “typical forms”
were suggested (Moore 2011), and a number of Gulf and
Red Sea taxa still remain undescribed (unpublished data).
Recent global genetic studies of elasmobranchs have
identified the Arabian region as one of four regions har-
boring a substantial proportion of taxa that are genetically
distinct from their closest relatives in neighboring regions
(Naylor et al. 2012). Moreover, global and range-wide
studies on several species that included samples from
ocean basins in the Arabian region demonstrated substan-
tial genetic differentiation between this region and widely
separated Indo-Pacific locations, as well as a strong sepa-
ration between Indo-Pacific and Atlantic clades for black-
tip reef (Vignaud et al. 2014a), silky (Clarke et al. 2015),
spot-tail (Giles et al. 2014), and whale sharks (Schmidt
et al. 2009; Vignaud et al. 2014b). Yet, in spite of the evi-
dent ecological distinctiveness of this region, no study to
date has specifically focussed on the genetic population
structure of elasmobranchs or indeed any other large
vertebrate species around the Arabian Peninsula.
Despite its ecological relevance, the Arabian region fea-
tures an alarming fisheries situation. Traditional and
industrial shark fisheries exist throughout most of the
region and for several countries have reached unsustain-
able exploitation levels (Bonfil 2003; Moore 2011; Jabado
et al. 2014a; Spaet and Berumen 2015). Nonetheless,
management strategies for shark resources are found in
only a fraction of these countries, and proper enforce-
ment of fisheries laws is essentially nonexistent (Bonfil
2003; Moore 2011; Spaet and Berumen 2015). In addition
to an apparent general lack of concern toward the conser-
vation of sharks in this region (Bonfil 2003; Spaet and
Berumen 2015), the proper assessment and management
of elasmobranch stocks has so far been hampered by
insufficient information on the biology, ecology, and fish-
eries of exploited species (Moore 2011; Spaet et al. 2012).
Only recently, efforts have been made to bridge this gap,
contributing to our knowledge on country-specific fisher-
ies and species-specific biological characteristics (Bonfil
2003; Henderson et al. 2006, 2007, 2009; Moore 2011;
Spaet et al. 2011; Moore et al. 2012; Moore and Peirce
2013; Jabado et al. 2014a; Spaet and Berumen 2015).
Patterns of dispersal and population structure can vary
significantly even among closely related species in shared
habitats (Toonen et al. 2011; DiBattista et al. 2012).
2ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.

2011). Moreover, contrasting nuclear and mitochondrial
data have been used successfully to identify sex-biased
dispersal patterns in different elasmobranch species (e.g.,
Pardini et al. 2001; Portnoy et al. 2010; Daly-Engel et al.
2012). By combining two kinds of genetic markers over
four species with variable biology, life-history characteris-
tics, and vagility, we intend to resolve intraspecific spatial
genetic patterns representative of a range of elasmo-
branchs in this region. We discuss the implications of our
findings in light of fisheries management and conserva-
tion in the Arabian Peninsula.
Materials and Methods
Sample collection and DNA extraction
Tissue samples of C. sorrah and R. acutus were collected
between 2010 and 2013 from whole sharks at fish markets
and landing sites in Saudi Arabia (Red Sea coast), Oman,
the United Arab Emirates (UAE), and Bahrain; C. limba-
tus and S. lewini were collected from all locations except
Bahrain (site 17, Fig. 1), where these species were uncom-
mon or absent in a previous landings survey (Moore and
Peirce 2013). Details of species-specific sample numbers
per landing site are given in Table S1.
Animals were initially identified based on morphologi-
cal characteristics. Saudi Arabian samples were obtained
from one fish market only (Jeddah), but landings at this
site originated from fishing grounds spanning the coun-
try’s entire Red Sea coast (Spaet and Berumen 2015)
(Fig. 1). Samples from the UAE were collected from land-
ing and market sites in Abu Dhabi, Dubai, Sharjah, and
Ras Al Khaimah as described in Jabado et al. (2014a,
2015). Samples from Oman were collected directly from
landing sites along the Omani coast; samples from Bah-
rain were obtained at the wholesale market of the capital,
Manama (Fig. 1; Table S1).
At all collection sites, special care was taken to avoid
inclusion of specimens for which catch location data were
unavailable. This was achieved by interviewing fishermen
and traders onsite and verifying the obtained information
by a thorough assessment of license plates and origin
information of transport trucks used. Based on inter-
preted assisted fishermen interviews, in the Red Sea, 90%
of all four species originated from the five main landing
sites displayed in Fig. 1, from where they were trans-
ported to the Jeddah market by trucks. The remaining
10% originated from smaller landing sites along the Saudi
Arabian Red Sea coast. Based on the limited operating
range of fishing vessels in Saudi Arabia, all fishing
grounds were assumed to lie within a 1–30 km radius of
the landing sites. While hence no exact catch location
data were available, all samples from Jeddah could defi-
nitely be assigned to the Red Sea Basin. The operational
range of vessels landing into sites in Oman tends to be
small, generally limited to within a few kilometers of the
landing site (Henderson et al. 2007). Fishermen in the
UAE remain in Gulf waters, yet they are known to travel
up to 130–185 km from their landing sites to find pro-
ductive fishing grounds (Jabado et al. 2014b). The major-
ity of Bahrain specimens were caught in local Bahraini
waters (Moore and Peirce 2013) although some may have
come from nearby Saudi Arabian or Qatari waters.
Despite extensive efforts to determine exact catch loca-
tions for more detailed seascape genetic analyses, it was
not always possible to assign the origin of samples to
their respective landing site regions with 100% certainty.
As a precautionary approach, all genetic analyses were
hence run with pooled data for the two main geographic
groups, combining all samples obtained from the Red Sea
into one group (Red Sea) and all samples obtained from
outside the Red Sea into a second group representing
other Arabian basins (OAB), that is, the Arabian Sea, the
Gulf of Oman, and the Gulf (Fig. 1).
At all market locations, small fin clips or gill tissue
were collected from each specimen and preserved in 99%
ethanol. Total genomic DNA was extracted from 10 to
20 mg of preserved tissue using the Macherey-Nagel
Genomic DNA from tissue extraction kit (Bethlehem, PA)
following the manufacturer’s instructions and subse-
quently stored at 80°C until further analysis.
Microsatellites –laboratory methods and
data analysis
Shark samples were genotyped at 8 to 12 microsatellite
loci (C. limbatus, 12 loci; C. sorrah, 9 loci; R. acutus,8
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 to target species or cross-amplified in
nontarget species. Between two and three multiplex PCRs
were performed per individual for all species. PCRs were
performed in 11 lL total volume containing 2 lL geno-
mic DNA, 5 lL Qiagen Multiplex PCR Master Mix,
3.5 lLH
2
0, and 0.5 lL of primer mix (each primer at
2lmol/L). Thermal profiles consisted of a denaturation
step at 95°C for 15 min, followed by 30 cycles of 30 sec
at 94°C, annealing for 90 sec at loci-specific temperatures
between 55°C and 60°C (Table S2), and an extension of
60 sec at 72°C, with a final extension of 30 min at 60°C.
Fragment analysis was conducted in an Applied Biosys-
tems 3730 XL genetic analyzer, and microsatellite alleles
were scored using GENEMAPPER software (v4.0 Applied
Biosystems, Foster City, CA). The null hypothesis of
Hardy–Weinberg equilibrium (HWE) was tested using
4ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.

2011). Moreover, contrasting nuclear and mitochondrial
data have been used successfully to identify sex-biased
dispersal patterns in different elasmobranch species (e.g.,
Pardini et al. 2001; Portnoy et al. 2010; Daly-Engel et al.
2012). By combining two kinds of genetic markers over
four species with variable biology, life-history characteris-
tics, and vagility, we intend to resolve intraspecific spatial
genetic patterns representative of a range of elasmo-
branchs in this region. We discuss the implications of our
findings in light of fisheries management and conserva-
tion in the Arabian Peninsula.
Materials and Methods
Sample collection and DNA extraction
Tissue samples of C. sorrah and R. acutus were collected
between 2010 and 2013 from whole sharks at fish markets
and landing sites in Saudi Arabia (Red Sea coast), Oman,
the United Arab Emirates (UAE), and Bahrain; C. limba-
tus and S. lewini were collected from all locations except
Bahrain (site 17, Fig. 1), where these species were uncom-
mon or absent in a previous landings survey (Moore and
Peirce 2013). Details of species-specific sample numbers
per landing site are given in Table S1.
Animals were initially identified based on morphologi-
cal characteristics. Saudi Arabian samples were obtained
from one fish market only (Jeddah), but landings at this
site originated from fishing grounds spanning the coun-
try’s entire Red Sea coast (Spaet and Berumen 2015)
(Fig. 1). Samples from the UAE were collected from land-
ing and market sites in Abu Dhabi, Dubai, Sharjah, and
Ras Al Khaimah as described in Jabado et al. (2014a,
2015). Samples from Oman were collected directly from
landing sites along the Omani coast; samples from Bah-
rain were obtained at the wholesale market of the capital,
Manama (Fig. 1; Table S1).
At all collection sites, special care was taken to avoid
inclusion of specimens for which catch location data were
unavailable. This was achieved by interviewing fishermen
and traders onsite and verifying the obtained information
by a thorough assessment of license plates and origin
information of transport trucks used. Based on inter-
preted assisted fishermen interviews, in the Red Sea, 90%
of all four species originated from the five main landing
sites displayed in Fig. 1, from where they were trans-
ported to the Jeddah market by trucks. The remaining
10% originated from smaller landing sites along the Saudi
Arabian Red Sea coast. Based on the limited operating
range of fishing vessels in Saudi Arabia, all fishing
grounds were assumed to lie within a 1–30 km radius of
the landing sites. While hence no exact catch location
data were available, all samples from Jeddah could defi-
nitely be assigned to the Red Sea Basin. The operational
range of vessels landing into sites in Oman tends to be
small, generally limited to within a few kilometers of the
landing site (Henderson et al. 2007). Fishermen in the
UAE remain in Gulf waters, yet they are known to travel
up to 130–185 km from their landing sites to find pro-
ductive fishing grounds (Jabado et al. 2014b). The major-
ity of Bahrain specimens were caught in local Bahraini
waters (Moore and Peirce 2013) although some may have
come from nearby Saudi Arabian or Qatari waters.
Despite extensive efforts to determine exact catch loca-
tions for more detailed seascape genetic analyses, it was
not always possible to assign the origin of samples to
their respective landing site regions with 100% certainty.
As a precautionary approach, all genetic analyses were
hence run with pooled data for the two main geographic
groups, combining all samples obtained from the Red Sea
into one group (Red Sea) and all samples obtained from
outside the Red Sea into a second group representing
other Arabian basins (OAB), that is, the Arabian Sea, the
Gulf of Oman, and the Gulf (Fig. 1).
At all market locations, small fin clips or gill tissue
were collected from each specimen and preserved in 99%
ethanol. Total genomic DNA was extracted from 10 to
20 mg of preserved tissue using the Macherey-Nagel
Genomic DNA from tissue extraction kit (Bethlehem, PA)
following the manufacturer’s instructions and subse-
quently stored at 80°C until further analysis.
Microsatellites –laboratory methods and
data analysis
Shark samples were genotyped at 8 to 12 microsatellite
loci (C. limbatus, 12 loci; C. sorrah, 9 loci; R. acutus,8
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 to target species or cross-amplified in
nontarget species. Between two and three multiplex PCRs
were performed per individual for all species. PCRs were
performed in 11 lL total volume containing 2 lL geno-
mic DNA, 5 lL Qiagen Multiplex PCR Master Mix,
3.5 lLH
2
0, and 0.5 lL of primer mix (each primer at
2lmol/L). Thermal profiles consisted of a denaturation
step at 95°C for 15 min, followed by 30 cycles of 30 sec
at 94°C, annealing for 90 sec at loci-specific temperatures
between 55°C and 60°C (Table S2), and an extension of
60 sec at 72°C, with a final extension of 30 min at 60°C.
Fragment analysis was conducted in an Applied Biosys-
tems 3730 XL genetic analyzer, and microsatellite alleles
were scored using GENEMAPPER software (v4.0 Applied
Biosystems, Foster City, CA). The null hypothesis of
Hardy–Weinberg equilibrium (HWE) was tested using
4ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.

GENEPOP on the Web (v4.2 Rousset 2008). MICRO-
CHECKER (v2.2.3 van Oosterhout et al. 2004) was used
to determine likely causes for deviations from HWE.
GENEPOP was also used to characterize genetic diversity
(expected (H
E
), observed (H
O
) and unbiased (UH
E
) het-
erozygosity, allelic richness, and mean number of alleles.
STRUCTURE (v2.3.4 Pritchard et al. 2000) was used to
infer the number of putative discrete populations in all
samples. We set K=1–10 for each run, assuming prior
population information and an admixture model allowing
for mixed ancestry of individuals. Each run was repeated
three times with independent allele frequencies, 100,000
steps, and a burn-in of 10,000 steps. We used STRUC-
TURE Harvester (Earl 2012) to determine which K best
describes the data according to the highest averaged maxi-
mum-likelihood score and Evanno’s delta K (Evanno
et al. 2005). We then re-ran STRUCTURE with pooled
data for the two main geographic groups, combining all
samples obtained from the Red Sea into one group (Red
Sea) and all samples obtained from the OABs into a sec-
ond group (Fig. 1). A hierarchical analysis of molecular
variance (AMOVA) implemented in ARLEQUIN (v3.5
Excoffier and Lischer 2010) and F
ST
(Weir and Cocker-
ham 1984) values were calculated using ARLEQUIN. All
microsatellite F
ST
values were corrected (G0
ST in Hedrick
(2005)) using SMOGD (v1.2.5 Crawford 2010) to com-
pensate for the downward bias in F
ST
associated with
highly variable microsatellites.
Mitochondrial DNA –laboratory methods
and data analysis
For each species, we examined genetic subdivision based
on sequence variation in the mtDNA CR. Approximately
1120 base pairs (bp) of the 50end of the mtDNA CR was
amplified for C. limbatus,C. sorrah, and R. acutus using
the forward primer ProL2 and the reverse primer PheCa-
caH2 (Pardini et al. 2001). A different primer set was
used for S. lewini to identify potential specimens of the
recently described cryptic species S. gilberti (Quattro et al.
2013). The forward primer CRF6 and the reverse primer
CRR10 (Pinhal et al. 2012) were shown to clearly distin-
guish between the two species and were hence used in
our study to amplify approximately 700 bp of the initial
portion of the mtDNA CR for all S. lewini specimens.
Amplification protocols were the same for both primers
and followed those described in Spaet and Berumen
(2015). For S. lewini and R. acutus, 700 bp and 1021 bp
of the CR were sequenced in the forward and reverse
direction, respectively. For C. limbatus and C. sorrah,
approximately 600 bp of the CR, respectively, was
sequenced in the forward direction only, but to ensure
accuracy of nucleotide designations, rare and questionable
haplotypes were sequenced in both directions. The pro-
gram Codon Code Aligner (v4.7.2 CodonCode Corpora-
tion, Dedham, MA) 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. Ini-
tial species identifications based on morphological charac-
ters during market sampling were confirmed by
comparison with reference CR sequences in the GenBank
database through BLAST (http://blast.ncbi.nlm.nih.gov/
Blast.cgi). In the case of R. acutus, no reference sequences
were available prior to this study. Therefore, to validate
initial species identification, all R. acutus samples were
amplified for the COI gene using the primer combination
Fish F1 and Fish R1 (Ward et al. 2005). The PCR proto-
col used was identical to the one used for the CR locus.
PCR products were purified and sequenced following
Spaet and Berumen (2015). Resultant COI sequences were
compared to reference sequences in GenBank (http://
www.ncbi.nlm.nih.gov) for species recognition. If seq-
uence data did not match the original identification,
respective specimens (C. limbatus: three, C. sorrah: four,
S. lewini: eight, R. acutus: seven) were excluded from the
data set.
For C. limbatus, C. sorrah, and S. lewini, haplotype net-
works were constructed to explore the relationships
among intraspecific haplotypes. Published haplotypes
were sourced from Keeney et al. (2003, 2005), Keeney
and Heist (2006), and Sodr
e et al. (2012) for C. limbatus;
Giles et al. (2014) for C. sorrah; and Duncan et al.
(2006), Chapman et al. (2009), and Nance et al. (2011)
for S. lewini, aligned with novel haplotypes for each spe-
cies, trimmed to one length (C. limbatus: 554 bp, C. sor-
rah: 455 bp, S. lewini: 534 bp), and subsequently assessed
using a statistical parsimony network constructed in TCS
(v1.21 Clement et al. 2000). For R. acutus, a parsimony
network was constructed based on the haplotypes
recorded in this study.
An AMOVA under the Tamura–Nei (TN) model of
sequence evolution, which was individually selected as the
most appropriate model for all four species in jModelTest
(v2.1.4. Darriba et al. 2012), was used to assess popula-
tion genetic structure in ARLEQUIN. ARLEQUIN was
also used to describe the genetic variation between the
Red Sea and OAB sampling regions by haplotype and
nucleotide diversity (hand p,respectively).
Ramos-Onsins and Rozas (2002) demonstrated that
Fu’s F
s
neutrality test (Fu 1997) has the greatest power to
detect population expansion for non-recombining regions,
such as mtDNA, under a variety of different circum-
stances, when population sample sizes are large (>50). We
hence calculated Fu’s F
s
to assess deviations from selective
sequence neutrality that could be attributed to selection
ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 5
J. L. Y. Spaet et al.Shark Population Genetics in the Arabian Region

and/or population size changes. Significance was tested
with 100,000 permutations. Recent population expansion
is indicated by negative (and significant) F
s
values. The
time since the most recent population expansion was esti-
mated by fitting the population parameter s(Rogers and
Harpending 1992) for both sampling regions and each
species. Mutation rate estimates were available from
previous studies for S. lewini: 0.8% divergence between
lineages per million years or 0.4 910
8
mutations per
site per year (Duncan et al. 2006) and for C. limbatus:
0.43% or 0.215 910
8
(Keeney and Heist 2006); no spe-
cies-specific mutation rates were available for C. sorrah
and R. acutus. For those species, we hence used the aver-
aged mutation rate (0.62%) reported for other shark spe-
cies (Galv
an-Tirado et al. 2013). Generation time
estimates were available from previous studies for all four
species, C. limbatus: 10 years, C. sorrah: 4.3 years (Cort
es
2002), and R. acutus: 2.5 years (Simpfendorfer 2003).
Generation time estimates for S. lewini are controversial
and vary among ocean basins (e.g., Branstetter 1987; Liu
and Chen 1999). As no estimates were available for the
Indian Ocean, we used the generation time estimated for
the closest ocean region for which an estimate was avail-
able, the west Pacific: 16.7 years (Cort
es 2002). We esti-
mated population expansion times assuming a constant
molecular clock and rates using the Mismatch Calculator
tool developed by Schenekar and Weiss (2011).
Results
Genetic diversity and summary statistics
Microsatellites
Microsatellite indices of genetic diversity, that is expected
(H
E
), observed (H
O
), and unbiased (UH
E
) heterozygosi-
ties, allelic richness, and mean number for each locus and
species within each sample region are provided in Table
S2. No signs of linkage disequilibrium were detected
among any pairs of loci after correction for multiple
comparisons.
In all species, several microsatellite loci showed devia-
tions from HWE in one or both of the putative popula-
tions and signs of null alleles (Table S2). To test whether
significant differences between expected vs. observed het-
erozygosities at some loci could confound population level
analyses, we removed all those loci and re-ran AMOVA
analyses. A comparison of F
ST
values calculated from the
subset of loci in HWE and from the full data set was not
significant for any of the species (paired t-tests calculated
in JMP P>0.6 in all species). To ensure that the pattern
of microsatellite structure (or lack thereof) was not being
driven by a single locus, we conducted locus-by-locus
AMOVA analyses (data not shown), which gave consistent
results across all except one locus (Cli118, C. sorrah). This
locus was solely responsible for the observed pattern of
significant population structure and was subsequently
removed from the analysis.
Mitochondrial DNA
Low haplotype (h) and nucleotide (p) diversities were
found for C. limbatus and S. lewini, while C. sorrah and
R. acutus showed slightly higher hand pvalues (Table 1).
Fu’s F
s
statistics were negative for all four species and
both sampling regions, yet significant only for C. sorrah
for both regions (Fu’s F
s
Red Sea =7.23; P=0.002;
Fu’s F
s
OAB =7.43; P=0.006) and for R. acutus for
the OAB region only (Fu’s F
s
=12.17; P=0.01)
(Table 1). The range of svalues yielded estimates of time
since last population expansion with very similar expan-
sion time estimates in all four species and both regions
(139.679–269.498 years, Table 1).
Genetic structure
The results obtained from all STRUCTURE runs yielded
K=1, indicating no differentiation among tentative pop-
ulations.
F
ST
values were small and nonsignificant for mtDNA
analyses in all four species. Very low, yet significant
genetic population subdivision was found using microsat-
ellite allele frequencies for C. limbatus (0.012; P=0.00),
R. acutus (0.002; P=0.04), and S. lewini (0.006; P=
0.001) (Table 2).
Mitochondrial DNA
Carcharhinus limbatus
A 554-bp sequence was obtained for 287 C. limbatus indi-
viduals. A total of seven haplotypes (GenBank Accession
Numbers: KR232982-KR232988) were defined, character-
ized by five polymorphic sites composed of five transi-
tions (Table S3A). Except for three singletons, all
haplotypes were found in both putative populations and
matched known Indian Ocean and Indo-Pacific mtCR
haplotypes from the global data set of Keeney and Heist
(2006). One haplotype (CL1) clearly dominated the sam-
ple set and was found in both populations in almost
identical numbers (Red Sea: n=100; OAB: n=131).
Two singletons were unique to the Red Sea and one was
unique to the OAB. Novel haplotypes were very closely
related to Indian Ocean and Indo-Pacific haplotypes
reported in Keeney and Heist (2006) and at least nine
mutational steps away from any Atlantic haplotypes
(Fig. 2A).
6ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.

Carcharhinus sorrah
A 455-bp sequence was resolved for 375 individuals and
resulted in 15 mtDNA haplotypes (GenBank Accession
Numbers: KR232989-KR233003), characterized by 12 poly-
morphic sites composed of 10 transitions, one transversion,
and one deletion (Table S3B). All common haplotypes were
observed in both putative populations. One haplotype
clearly dominated the sample set (CS1). Seven haplotypes
matched haplotypes from the range-wide data set of Giles
et al. (2014). All novel haplotypes were closely related to
Indian Ocean and South-East Asian haplotypes reported in
Giles et al. (2014) and formed a lineage distinct from Aus-
tralian and New Caledonian haplotypes (Fig. 2B).
Rhizoprionodon acutus
Variation in a 1021-bp fragment of 294 R. acutus speci-
mens defined 25 haplotypes (GenBank Accession Num-
bers: KR232957-KR232981) characterized by 22
polymorphic sites composed of 18 transitions, five trans-
versions, and two deletions (Table S3B). All common
haplotypes were separated by two mutational steps at
most. Three singletons and one haplotype, recorded from
two individuals only, were separated from the cluster of
common haplotypes by up to 10 mutational steps. Except
for one (RA7) that was unique to the OAB, all common
haplotypes were shared in both sampling regions. Haplo-
type RA17 dominated the sample set and was found in
more than half of all OAB samples (Fig. 2C).
Sphyrna lewini
A 562-bp sequence revealed low levels of diversity for 233
S. lewini specimens: five haplotypes (GenBank Accession
Numbers: KR232952-KR232956), characterized by four
polymorphic sites composed of two transitions and two
transversions (Table S3) that differed by no more than
one mutational step from each other. Two haplotypes
clearly dominated the sample set (Fig 2D). All five haplo-
types were novel, that is, not present in the global data
set of Duncan et al. (2006) or in any of the regional data
Table 1. Mitochondrial DNA control region sample size and genetic diversity indices for Carcharhinus limbatus,C. sorrah,Rhizoprionodon acutus, and Sphyrna lewini across both sampling
regions. Haplotype (h) and nucleotide (p) diversities, neutrality statistics (Fu’s F
S
), and estimates of times since last population expansion are shown. Population expansion ranges (below expansion
times) are given for 95% confidence intervals of tau.
Species
n(Red
Sea)
n
(OAB)
Time since
expansion Yrs
(Red Sea)
Time since
expansion
Yrs (OAB)
hSD
Red Sea hSD OAB pSD Red Sea pSD OAB
Fu’s F
S
Red Sea Fu’s F
S
OAB
C. limbatus 172 115 182,772
(96,129–273,696)
269,498
(4.2–1,273,612)
0.3490 0.0387 0.3054 0.0525 0.000724 0.00748 0.000755 0.000769 2.90484 1.89320
C. sorrah 159 216 214,134
(11–656,541)
178,331
(8551–478,324)
0.3270 0.0479 0.4606 0.0412 0.001030 0.000893 0.001314 0.001408 7.23390 7.43393
R. acutus 77 217 190,831
(89,096–358,124)
177,561
(48,023–378,345)
0.7365 0.0345 0.6599 0.0306 0.001397 0.000956 0.001265 0.000881 3.57008 12.17461
S. lewini 82 151 151,245
(71,174–249,110)
139,679
(64,724–194,172)
0.4998 0.0318 0.4661 0.0317 0.000086 0.000232 0.000116 0.00027 0.73386 0.13352
Numbers in bold are significant, P<0.02 for Fu’s F
S
estimates, Fu (1997).
Table 2. F
ST
results and associated P-values for both regions, charac-
terizing spatial structure with both mtDNA and microsatellites. Stan-
dardized F
ST
values (G0
ST, Hedrick 2005) are shown in brackets.
mtDNA Microsatellites
Carcharhinus
limbatus
0.0025; P=0.236 0.012; P=0.00 (0.0128)
C. sorrah 0.0057; P=0.099 0.000; P=0.58 (0.000)
Rhizoprionodon
acutus
0.0608; P=0.583 0.002; P=0.04 (0.000864)
Sphyrna lewini 0.0130, P=0.050 0.006; P=0.001 (0.009604)
ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 7
J. L. Y. Spaet et al.Shark Population Genetics in the Arabian Region

sets by Chapman et al. (2009), Nance et al. (2011), and
Castillo-Olgu
ın et al. (2012). The parsimony network
provided evidence that the haplotypes discovered in this
study form a Distinct Population Segment (DPS).
Discussion
This study is the first to assess the population structure of
large mobile marine vertebrates between the Red Sea and all
other Arabian Ocean Basins. Our analyses were based on a
comparatively large number of samples (total n=1189) of
four different shark species, from collection locations span-
ning across over 5000 km of coastline genotyped at two
types of genetic markers (mtDNA and nuclear DNA). Con-
trary to previous findings of significant population genetic
structure across the region in different taxa, our results indi-
cate that dispersal of sharks around the Arabian Peninsula is
not limited by any obvious barriers to gene flow. Further-
more, ecological, morphological, and life-history differences
among the investigated species do not appear to significantly
influence their patterns of population structure. Divergent
haplotypes in one of our study species (S. lewini), however,
are suggestive of an Arabian population that is genetically
distinct from others in the Indian Ocean.
Several previous studies have shown the existence of
historical, oceanographical, and ecological barriers to gene
29
16
9
2
25
39
15
31
30
20
32
28
25
36
CL2
CL1
CL3
CL5
CL7
34
CL4
CL6
20
11
8
H38
H45
17
6
H23 H40
H39 H48
H42
16
15
7
4
923
19
1
17
13
24
CS15
CS14
CS9 CS10
CS6
CS8
CS5
CS13
CS11
CS3
CS12
21 8
27 33
1
3
35
22 14
19
23
18
CS2
CS4
CS7
7
434
CS1
(A) Carcharhinus limbatus
(B) C. sorrah
37
Red Sea
OAB
Atlantic
Indian Ocean
Pacific
Indo-Pacific
SE Asia
Australia
New Caledonia
29
30
28
Figure 2. Mitochondrial control region
haplotype networks for Carcharhinus limbatus
(A), C. sorrah (B), Rhizoprionodon acutus (C),
and Sphyrna lewini (D) constructed by
statistical parsimony in TCS 1.21 (Clement
et al. 2000). Circles are sized in proportion to
the number of individuals with that haplotype.
Each connecting line represents a single
mutation. Black dots represent inferred
mutational steps. Ocean basins are indicated
by colors: The study region is color coded by
geographical regions displayed in Fig. 1, dark
blue (Red Sea), green (OAB). Haplotypes
sampled in previous studies are indicated by
red (Atlantic), yellow (Indian), light blue
(Pacific), yellow fading to blue (shared Indian
Pacific), gray (South-East Asia), purple
(Australia), salmon (New Caledonia) and are
numbered to match their designations in those
studies. (A) CL5–CL7 represent novel
haplotypes discovered in this study. Haplotypes
sampled in previous studies are indicated by
ovals (Keeney et al. 2003, 2005; Keeney and
Heist 2006) and rectangles (Sodr
e et al. 2012).
CL1–CL4 are identical to Indian Ocean and
Indo-Pacific haplotypes discovered by Keeney
and Heist (2006). CL1 =H33; CL2 =H24,
H26, H27, and H35; CL3 =H31; and CL4 =
H32. (B) CS4–CS10 and CS12 represent novel
haplotypes. Haplotypes sampled by Giles et al.
(2014) are represented by ovals. Haplotype
CS1 is identical to H5, CS2 to H36, CS3 to
H11, CS11 to H12, CS13 to H6, CS14 to H26,
and CS15 to H38 in Giles et al. (2014). (D)
SL1–SL5 represent novel haplotypes.
Haplotypes sampled in previous studies are
indicated by ovals (Duncan et al. 2006),
rectangles (Chapman et al. 2009), and
triangles (Nance et al. 2011).
8ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.

flow resulting in genetic subdivision in a range of marine
organisms among ocean basins surrounding the Arabian
Peninsula. Our analyses did not provide compelling evi-
dence for more than one Arabian Sea genetic stock for
C. limbatus, C. sorrah, R. acutus,orS. lewini. There was
slight evidence of genetic structure between the Red Sea
and the OAB for C. limbatus,R. acutus, and S. lewini
based on microsatellite allele frequencies; however, F
ST
values were low (0.002–0.012) and not consistent among
different statistical tests. These inconsistencies might stem
from the high number of null alleles in our data set,
which might have been caused by (1) cross-species rather
than species-specific loci used in this study due to the
limited availability of microsatellite loci for all investi-
gated species and/or (2) species-specific loci, which were
developed for specimens sampled in other ocean regions.
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 recom-
mend designing species-specific markers based on samples
originating from the targeted study region.
The homogenous population structure observed here
was not unexpected, given the contiguous shelf habitat
around the Arabian Peninsula and the high potential
mobility of our study organisms. While previous regional
and range-wide studies on C. limbatus,C. sorrah, and
SL4
RA19
RA20
RA5
RA2 RA3
RA13
RA24
RA1
RA14
RA11
RA12
RA7
RA10
RA4
RA23
RA6
RA16
RA8
RA21
RA18
RA9
RA15
RA22
RA25
RA17
(C) Rhizoprionodon acutus
2
7
3
6
22
23 21
20
18
17
13
14
15
FIN498
FIN001
FIN344
FIN551
FIN605 26
28
27
DE
SL1
SL2 SL3
SL5
16
12
(D) Sphyrna lewini
11
10
8
9
4
1
5
24
19
Figure 2. Continued.
ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 9
J. L. Y. Spaet et al.Shark Population Genetics in the Arabian Region

S. lewini demonstrated restricted dispersal across deep
ocean habitats, genetic structure along continental
margins was shown to be relatively minor (Duncan et al.
2006; Keeney and Heist 2006; Nance et al. 2011; Daly-En-
gel et al. 2012; Giles et al. 2014). Studies on all four
species across spatial scales similar to this study in Aus-
tralia and Indonesia demonstrated heterogeneous popula-
tion structure in C. sorrah and R. acutus, but not for
S. lewini and C. limbatus between central Indonesia and
northern Australia based on nuclear and mtDNA markers
(Ovenden et al. 2009, 2010, 2011). The observed subdivi-
sion in the two smaller, less vagile species was suggested
to arise from the Timor Trough acting as a deep water
dispersal barrier (Ovenden et al. 2009). While large
expanses of deep water dividing shallow habitats are
absent in our study region, potential oceanographic barri-
ers to gene flow may still exist, for example, regional
upwelling systems or local turbid water regions (Schott
1983). Present-day oceanic currents and habitat heteroge-
neity in the study area have recently been suggested to
inhibit gene flow in teleost larval dispersal (DiBattista
et al. 2013; Nanninga et al. 2014). Sharks, however, are
lacking the dispersive larval phase of most teleost fish,
and based on our results, their swimming capacities as
juveniles and especially as adults are likely too strong to
be influenced by ocean currents characteristic of the Ara-
bian region. Intermittent historical barriers like the ones
created by Pleistocene glacial cycles have also reportedly
impacted gene flow in teleost species between the Red Sea
and the Indian Ocean (Klausewitz 1989; DiBattista et al.
2013). A potential significant reduction in population size
during this period was demonstrated by negative and sig-
nificant indices of neutral evolution (Fu’s F
s
test) for
C. sorrah and R. acutus, indicating recent population
expansion events between approximately 178,000 and
214,000 years ago (Table 1). Those events likely followed
substantial bottleneck events that were caused by
re-occurring limitations of inflow and exchange of surface
water between the Red Sea and the Indian Ocean (Siddall
et al. 2003). The decrease in sea water level during those
periods likely caused increased evaporation, raising tem-
peratures, and salinity levels beyond the tolerance limits
of most marine fauna (Biton et al. 2008). Another reason
for the observed excess of low-frequency haplotypes might
be caused by positive selection. However, to unambigu-
ously discern between the effects of natural selection and
demographic population expansion would necessitate an
analysis of several unlinked loci in the genome, because
selection only acts on specific loci (Akey et al. 2004).
Additionally to the apparent homogenous population
structure, we also found no indication of differences
between male and female dispersal in any of the study
species. This finding stands in contrast to previous studies
describing marked philopatric behavior (Feldheim et al.
2014) in C. limbatus and S. lewini based on contrasting
mitochondrial and nuclear data (Keeney et al. 2003,
2005; Daly-Engel et al. 2012). We suggest that long-
shore movements of both males and females along the
continuous coastline stretching from the Red Sea all the
way into the Gulf cause panmixia over large spatial scales
across the region.
Genetic diversity for C. limbatus and S. lewini was
relatively low, with only seven and five haplotypes,
respectively. Yet, this pattern appears 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. While all
our samples of C. limbatus and C. sorrah matched pre-
viously published Indian Ocean and Indo-Pacific
mtDNA haplotypes, all haplotypes discovered for S. le-
wini were novel.
There are two possible explanations for the observed
genetic separation between S. lewini specimens sampled
around the Arabian Peninsula and specimens sampled in
other, nearby Indian Ocean regions (e.g., the Seychelles
and Madagascar, Duncan et al. 2006). First, Arabian Seas
S. lewini may have evolved to breed differently from con-
specifics outside this area. Estuaries have repeatedly been
reported as an important nursery habitat for S. lewini
elsewhere in the world (e.g., Clarke 1971; Snelson 1981;
Simpfendorfer and Milward 1993; Duncan and Holland
2006). Due to the desertification of the Arabian region in
the past few thousand years, permanent estuaries are now
entirely absent for several thousand kilometers of conti-
nental coastline from Iraq to Somalia, an area that
encompasses our study region. Suggested nursery areas
and breeding grounds for S. lewini, however, exist near
Djibouti City (Bonfil 2003) and in habitats in the central
Saudi Arabian Red Sea (J. L. Y. Spaet, unpubl. data), sug-
gesting that the species may not depend on estuarine hab-
itat in these areas. Arabian S. lewini may thus have
evolved to no longer require estuaries as breeding/nursery
grounds, eliminating the need for reproductive migrations
and thereby reducing gene flow with other populations.
Such scenarios may also explain why C. limbatus and
C. sorrah, which are not reported as being strongly
dependent on estuary nurseries, are genetically well con-
nected to other Indian Ocean populations. Second, regio-
nal oceanography and upwelling zones may form
temporary barriers between Arabian and other Indian
Ocean populations. The Somali Current, for instance,
which only operates between June and September (Schott
1983), may coincide with key migration/breeding periods
of S. lewini, but not with those of C. limbatus and C. sor-
rah. In this case, mixing with south Indian Ocean popula-
tions might be inhibited for S. lewini, but not for the
10 ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.

other two species. Additional data on migration routes,
migration times and breeding cycles, however, are needed
to confirm either of these hypotheses.
The fact that Arabian S. lewini, which comprise a large
amount of the commercial harvest in the Arabian region
(Jabado et al. 2015; Spaet and Berumen 2015), might rep-
resent a DPS raises a new layer of conservation concern
and may warrant species-specific conservation actions
under the Convention on International Trade in Endan-
gered Species (CITES), Convention on the Conservation
of Migratory Species of Wild Animals (CMS), and a
re-evaluation of its IUCN Red List conservation status.
Future research should focus on the identification of
broader scale genetic breaks by sampling all four species
further to the west and east of our sampling locations. In
addition, research on dispersal mechanisms based on
nongenetic techniques, for example, tagging or parasite
studies coupled with molecular methods would provide
interesting insights into the actual dispersal mechanisms
underlying the observed homogenous population
structure.
Conclusions
Molecular studies on a diverse range of elasmobranch
species have done much to illuminate issues that compli-
cate fisheries management and conservation (see Dudgeon
et al. 2012 for a review). Here, we provide the first multi-
species analysis of population structure between Red Sea
and Arabian Sea, Gulf of Oman, and Gulf elasmobranchs
indicating that dispersal of four different shark species is
not limited by any obvious barriers to gene flow in the
waters surrounding the Arabian Peninsula. Three broad
conclusions are apparent:
1Existing contemporary barriers such as regional upwell-
ing systems and ocean currents are likely not influenc-
ing long-shore or stepping-stone connectivity even in
smaller, less vagile shark species like R. acutus.
2A comparison of novel S. lewini haplotypes with pub-
lished western Indian Ocean haplotypes revealed the
possibility of a S. lewini population in the Arabian
region that is distinct from other Indian Ocean popula-
tions.
3Similar dispersal patterns in sharks with contrasting
ecological, morphological, life-history, and distribu-
tional patterns indicate that populations of other shark
species are likely to also function as common
stocks across all ocean basins surrounding the Arabian
Peninsula.
Overall, our results call for urgent regional cooperation
on the management of shark stocks in all countries sur-
rounding the Arabian Peninsula to ensure a sustainable
future for this vital component of the marine biodiversity
in the western Indian Ocean. Regulations on the exploita-
tion of only one part of the stock will not suffice and
management arrangements need to be implemented,
enforced, and coordinated among all responsible authori-
ties. Given current harvesting levels and the apparent con-
nectedness of stocks, unregulated exploitation in one or
several countries is likely to cause uniform depletion
across the entire stock.
Acknowledgments
We are grateful to those who helped with sample collec-
tion, particularly J.E.M. Cochran, G.B. Nanninga (Saudi
Arabia), Al Reeve, Tariq Al-Mamari (Oman), and numer-
ous others. Richard Peirce (Shark Conservation Society)
facilitated sample collection in Bahrain. We thank the
KAUST Bioscience Core Laboratory and S.P.C. Guillot for
their assistance with DNA sequencing. J.D. DiBattista, P.
Saenz-Agudelo, T.M. Vignaud, and G.B. Nanninga pro-
vided valuable comments on genetic analyses and/or the
manuscript. This project was funded in part by KAUST
(award URF/1/1389-01-01 and baseline funding to
M.L.B.). Sample collections in Oman were supported by
the Ministry for Agriculture and Fisheries (Oman) and
those from the UAE by the United Arab Emirates Univer-
sity. Finally, we gratefully acknowledge the comments of
four anonymous reviewers, which proved very helpful in
improving the manuscript.
Data Accessibility
Sequences of all haplotypes presented here have been sub-
mitted to GenBank under accession numbers: KR232952-
KR233003. In addition the entire data set used in this
study has been deposited in the Dryad Data Repository,
doi: 10.5061/dryad.4gk47
Conflict of Interest
None declared.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Table S1. Number of tissue samples obtained from all
landing sites and fish markets for Carcharhinus limbatus,
C. sorrah,Rhizoprionodon acutus, and Sphyrna lewini.
Table S2. Microsatellite loci used with their respective
annealing temperatures (°C), sample size (N), number of
alleles (Na), number of effective alleles (Ne), average
observed (Ho), expected (He) and unbiased (UHe) het-
erozygosity, and F statistics for Red Sea and other Ara-
bian basins (OAB), i.e. Arabian Sea, Gulf of Oman and
Gulf samples of (A) Carcharhinus limbatus, (B) C. sorrah,
(C) Rhizoprionodon acutus, and (D) Sphyrna lewini.
Table S3. Polymorphic nucleotide positions for mito-
chondrial DNA control region haplotypes for (A) Carcha-
rhinus limbatus, (B) C. sorrah, (C) Rhizoprionodon acutus,
and (D) Sphyrna lewini. Haplotype numbers, correspond-
ing to Figure 2 are listed in the left columns.
16 ª2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
Shark Population Genetics in the Arabian Region J. L. Y. Spaet et al.