Available via license: CC BY 4.0
Content may be subject to copyright.
RESEARCH ARTICLE
Population genetics of Southern
Hemisphere tope shark (Galeorhinus galeus):
Intercontinental divergence and constrained
gene flow at different geographical scales
Aletta E. Bester-van der Merwe
1☯
*, Daphne Bitalo
1☯
, Juan M. Cuevas
2
, Jennifer Ovenden
3
,
Sebastia
´n Herna
´ndez
4,5
, Charlene da Silva
6
, Meaghen McCord
7
, Rouvay Roodt-Wilding
1
1Department of Genetics, Stellenbosch University, Stellenbosch, South Africa, 2Universidad Nacional de
La Plata (UNLP), Divisio
´n Zoologı
´a Vertebrados, Museo de La Plata, La Plata, Argentina, 3Molecular
Fisheries Laboratory, Queensland Government, St Lucia, Queensland, Australia, 4Sala de Colecciones
Biolo
´gicas, Facultad de Ciencias del Mar, Universidad Cato
´lica del Norte, Coquimbo, Chile, 5Molecular
Biology Laboratory, Center for International Programs, Veritas University, San Jose
´, Costa Rica, 6Fisheries
Research, Department of Agriculture, Forestry and Fisheries, Cape Town, South Africa, 7South African
Shark Conservancy, Old Harbour Museum, Hermanus, South Africa
☯These authors contributed equally to this work.
*aeb@sun.ac.za
Abstract
The tope shark (Galeorhinus galeus Linnaeus, 1758) is a temperate, coastal hound shark
found in the Atlantic and Indo-Pacific oceans. In this study, the population structure of
Galeorhinus galeus was determined across the entire Southern Hemisphere, where the
species is heavily targeted by commercial fisheries, as well as locally, along the South Afri-
can coastline. Analysis was conducted on a total of 185 samples using 19 microsatellite
markers and a 671 bp fragment of the NADH dehydrogenase subunit 2 (ND2) gene. Across
the Southern Hemisphere, three geographically distinct clades were recovered, including
one from South America (Argentina, Chile), one from Africa (all the South African collec-
tions) and an Australia-New Zealand clade. Nuclear data revealed significant population
subdivisions (F
ST
= 0.192 to 0.376, p<0.05) indicating limited gene flow for tope sharks
across ocean basins. Marked population connectivity was however evident across the
Indian Ocean based on Bayesian clustering analysis. More locally in South Africa, F-statis-
tics and multivariate analysis supported moderate to high gene flow across the Atlantic/
Indian Ocean boundary (F
ST
= 0.035 to 0.044, p<0.05), with exception of samples from
Struisbaai and Port Elizabeth which differed significantly from the rest. Discriminant and
Bayesian clustering analysis indicated admixture in all sampling populations, decreasing
from west to east, corroborating possible restriction to gene flow across regional oceano-
graphic barriers. Mitochondrial sequence data recovered seven haplotypes (h= 0.216, π=
0.001) for South Africa, with one major haplotype shared by 87% of the individuals and at
least one private haplotype for each sampling location except Port Elizabeth. As with many
other coastal shark species with cosmopolitan distribution, this study confirms the lack of
both historical dispersal and inter-oceanic gene flow while also implicating contemporary
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 1 / 20
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Bester-van der Merwe AE, Bitalo D,
Cuevas JM, Ovenden J, Herna
´ndez S, da Silva C, et
al. (2017) Population genetics of Southern
Hemisphere tope shark (Galeorhinus galeus):
Intercontinental divergence and constrained gene
flow at different geographical scales. PLoS ONE 12
(9): e0184481. https://doi.org/10.1371/journal.
pone.0184481
Editor: Tzen-Yuh Chiang, National Cheng Kung
University, TAIWAN
Received: October 5, 2016
Accepted: August 24, 2017
Published: September 7, 2017
Copyright: ©2017 Bester-van der Merwe et al. This
is an open access article distributed under the
terms of the Creative Commons Attribution
License, which permits unrestricted use,
distribution, and reproduction in any medium,
provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This study was funded by the National
Research Foundation of South Africa. The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
factors such as oceanic currents and thermal fronts to drive local genetic structure of G.
galeus on a smaller spatial scale.
Introduction
Elasmobranchs are currently regarded as one of the most vulnerable extant vertebrate groups
and many of the species are threatened with extinction [1]. The exploitation of elasmobranchs
has been steadily increasing raising concerns over the sustainability of this marine resource
and the impacts to the marine ecosystem globally [2]. Most elasmobranchs (especially sharks)
are vulnerable to fishing pressures due to the relatively K-selected traits they exhibit such as
low fecundity, late sexual maturity and a long lifespan with slow growth rates [3]. Further, lim-
ited baseline data exists for species-specific landings since historically elasmobranchs were of
low economic value and a lesser priority in terms of fisheries management. In general, the
assessment of the spatial extent of populations has been hampered by a lack of fisheries inde-
pendent data, species-specific assessments and limited understanding of transoceanic move-
ment patterns [4]. In order to implement regional management strategies for exploited
elasmobranchs, information on migration patterns and genetic population structure is needed
to monitor the effect of fishing on individual species along a given stretch of coastline [5]. This
could lead to a more integrated approach to fisheries management, where species showing dif-
ferent levels of population subdivision over similar spatial scales, should be co-managed [6,7].
The tope shark (Galeorhinus galeus Linnaeus 1758) is a commercially important shark spe-
cies distributed in temperate waters around the world [8]. Tope is harvested for its high value
fillets, sold as flake and is one of the most commercially valuable sharks in South Africa [4].
Across the Southern Hemisphere, the species is heavily targeted in demersal shark fisheries
and is therefore listed as vulnerable globally by the International Union for the Conservation
of Nature (IUCN) [9]. Despite its commercial importance, limited data on landings exist and
Tope is often lumped with similar species. In Chilean waters, for example, landings of G.
galeus,Mustelus mento,M.whitney and Squalus acanthias, are lumped under the generic local
name “tollo” [10,11]. In the south-western Atlantic (SWA), G.galeus is ranked as critically
endangered and was subject to intensive fishing throughout its distribution. Though drastic
declines have occurred, the population continues to be fished without restraint in Argentina
and Uruguay [12,13,14]; indeed, new access was granted to a large number of artisanal fishers
in the late 1990. Declines in tope shark have been most marked in Brazil and Uruguay, where
the Catch Per Unit Effort (CPUE) has declined to nearly zero. In Argentina, the CPUE for the
trawler fleet has declined by around 80%, attributed to recruitment overfishing during the
1990s [9]. It is believed that G.galeus comprises only one population extending across Brazil,
Uruguay and Argentina with large aggregations of sharks moving in to closed bays of northern
Argentina during spring for parturition [15]. Galeorhinus galeus also has an Indo-South Pacific
distribution in the Southern Hemisphere wherein it occupies the temperate waters of Australia
and New Zealand [9]. In Australia, the species is landed primarily in southern waters, includ-
ing Tasmania, and is considered overfished and is afforded protected species status [9,16,17].
The species occurs throughout New Zealand’s entire exclusive economic zone (EZZ) where it
is considered a sustainable fishery. The New Zealand fisheries mandated numerous restrictions
on the commercial harvesting of G.galeus and as of 1986, implemented eight quota manage-
ment areas (QMAs). Despite this and genetic evidence for one panmictic population [18], G.
galeus in Australia and New Zealand is currently managed as separate stocks [19].
In South Africa, the commercial fishery for G.galeus has existed since the 1930s with major
landing sites occuring off the south-west coast at Saldanha Bay, Cape Town, Hout Bay, Gans
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 2 / 20
Competing interests: The authors have declared
that no competing interests exist.
Bay and Struisbaai [20]. Heavy unmanaged fishing resulted in a decline in catches by the
1940s, catches have not returned to pre–World War II levels [21]. The species is listed as vul-
nerable in South Africa and is threatened by over-exploitation while management is made dif-
ficult by a lack of species-specific catch data and non-cohesive fishing regulations across
different coastal management zones [21,22]. With the exception of preliminary population
genetic data [23], very little information exists for the migratory patterns of this species in
South African waters.
The marine realm is a dynamic environment with fluctuating ocean currents and tempera-
tures, all of which can act as drivers of specific dispersal patterns and hence population struc-
ture. Despite high dispersal abilities for some coastal sharks, several studies have confirmed
different levels of population genetic subdivision over various spatial scales [6,18,24,25]. There
is increasing evidence of deep divergences between ocean basin lineages related to paleoce-
anographic changes, including closure of corridors such as evidenced by the Tethys Seaway
[26,27,28]. Futhermore, genetic breaks may be shaped by known biogeographic barriers includ-
ing ocean currents and temperatures. Certain life history traits, such as philopatric behaviour,
have also explained population structure observed in coastal species [29,30]. There are a number
of traditionally recognised biogeographic barriers across the Southern Hemisphere: most nota-
bly the Eastern South Pacific Barrier (EPB), the Mid-Atlantic Barrier (MAB) and the Benguela
Barrier (BB) [31]. The EPB and MAB encompass over 5000km and 2800km of oceanic expanse
respectively and have resulted in the complete isolation of populations of coastal species associ-
ated with continental shelves [27,32,33,34]. Conversely, these barriers show no to little effect in
pelagic species that are highly vagile [26,35,36,37]. Around southern Africa, the BB across the
southern tip of Africa, resulting from the cold-water upwelling of the Benguela Current has
been reported to restrict gene flow between southern Atlantic and southern Indian Ocean pop-
ulations of tropical and subtropical sharks such as Sphyrna lewini [26] and M.mustelus [7]. In
addition, thermal barriers created by contrasting oceanic currents such as the sharp transition
zone along the SWA where the warm Brazil current from the north meets the cold Malvinas
current from the south impact gene flow in coastal sharks [38,39].
Previous population genetic studies have supported distinct continental populations of G.
galeus, which are structured along a latitudinal gradient. These studies suggested G.galeus to
have a strong affinity for cool temperate waters limiting their ability to cross warm temperate
waters [18,33]. However, none of these studies resolved the genetic connectivity of G.galeus
across all the known barriers and transition zones of the Southern Hemisphere. Here, patterns
of gene flow were assessed between geographic samples separated by apparent regions of
unsuitable environmental conditions along the species’ range based on the following hypothe-
ses: (1) genetic discontinuity exists across the Southern Hemisphere oceans including the
south Pacific, south Atlantic and south Indian Oceans and (2) genetic discontinuity exists
across the Indian/Atlantic Ocean boundary with differentiation found between Atlantic and
Indian G.galeus. A dual-marker approach was applied where variation in the mitochondrial
ND2 gene and 19 microsatellite markers were used to assess genetic diversity and population
connectivity of G.galeus across the Southern Hemisphere and the South African coastline.
Material and methods
Sample acquisition and DNA extraction
Across the Southern Hemisphere, 185 fin clips or muscle biopsies were collected including 22
from Chile, 10 from Argentina, 124 from South Africa, nine from Australia and 20 from New
Zealand (Fig 1,S1 Table). Genetic samples collected specifically for this study included those
from Argentina, Australia and South Africa. The samples from Chile and New Zealand were
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 3 / 20
acquired in the course of other research and were also included in a previous study on tope
[18]. In Argentina, sampling was carried out in compliance with the fishery act # 217/07 for
sustainable fishing of coastal sharks in the Province of Buenos Aires. Sharks were all captured
and released inside the Bahı
´a San Bla
´s Marine Protected Area by anglers of the Conservar
Tiburones en Argentina project. The Australian samples were collected under Department of
Primary Industries Parks Water and Environment permit # 11055 and with approval from the
University of Tasmania Animal Ethics Committee (# A0011882). More locally, the South Afri-
can samples were collected by research and commercial vessels according to protocols and per-
mits (# RES2012/59) approved by the Department of Agriculture, Fisheries and Forestry
(DAFF), South Africa and covered most of the species’ South African range over which exploi-
tation occurs. Fin clips collected by DAFF scientists were taken from the trailing edge of the
second dorsal fin using small surgical scissors and sharks were released with efforts taken to
minimize stress and mortality. The majority of the sharks were mature adults (>100cm) and
collected between May and September 2012. All sampling populations were mixed-sex. The
samples from Struisbaai were collected from dead animals already captured by a commercial
fishing company. All genetic samples were handeled according to guidelines of the Research
Ethics Committee for Animal Care and Use at Stellenbosch University for work involving tis-
sue samples and not live animals. This included 26 samples from Robben Island (RI), 11 from
False Bay (FB), 39 from Kleinmond (KL), ten from Agulhas Bank (AB), 28 from Struisbaai
(SB) and ten from Port Elizabeth (PE). All samples except those from Struisbaai originated
from fishery observer programs operated by DAFF and some of these samples were included
in a previous study on G.galeus [23]. Genomic DNA was extracted from fin clips or tissue
samples using a modified CTAB extraction method with minor modifications [40].
Fig 1. Sampling locations of Galeorhinus galeus.Map showing the major biogeographic barriers and
oceanic currents across the Southern Hemisphere and South Africa. The main biogeographic barriers
indicated by the dashed lines are the Eastern South Pacific Barrier (EPB) and the Mid-Atlantic Barrier (MAB).
Sampling codes: Chile (CHI), Argentina (ARG), South Africa (SA), Australia (AUS), New Zealand (NZ);
Robben Island (RI), False Bay (FB), Kleinmond (K), Agulhas Bank (AB), Struisbaai (SB) and Port Elizabeth
(PE).
https://doi.org/10.1371/journal.pone.0184481.g001
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 4 / 20
Laboratory procedures
Mitochondrial DNA sequencing. Sequences of the mitochondrial gene NADH dehydro-
genase subunit 2 (ND2) were analysed for a total of 81 samples of G.galeus using the species-
specific primers of Farrell et al., 2009 [41]. Southern Hemisphere (SH) samples included Chile
(6), Argentina (10), South Africa (53), Australia (9) and New Zealand (3) while South African
(SA) samples included Robben Island (11), False Bay (7), Kleinmond (12), Agulhas Bank (5),
Struisbaai (13) and Port Elizabeth (5). PCR was performed in a 20 μl total volume containing
100 ng template DNA, 1X GoTaq buffer (Anatech, South Africa), 200 μM dNTPs, 0.4 μM of
each primer (Integrated DNA Technologies, IDT, South Africa), 2 mM MgCl
2
(Promega, Wis-
consin, USA) and 0.5 U GoTaq DNA polymerase (Anatech, South Africa). PCR amplifications
were performed in an Applied Biosystems (ABI) (Life Technologies, California USA) thermal
cycler version 2.09 using cycling conditions as described by [41]. Amplicons were sequenced
bi-directionally using the BigDye
1
Terminator 3.1 Cycle Sequencing Kit (Life Technologies,
California USA) and a ABI 3730xl Genetic Analyser. All mtDNA sequences were manually
edited and aligned using the MUSCLE alignment algorithm available in MEGA 6 [42]. Aligned
sequences were trimmed to 599 bp and exported to DNASP 5.10.01 [43] for further analysis.
Microsatellite genotyping. A total of 185 G.galeus individuals were genotyped using ten
species-specific microsatellites developed by Chabot et al., 2011 [44] and nine cross-species
markers previously developed for Mustelus henlei and M.canis [45,46]. Southern Hemisphere
samples included Chile (22), Argentina (10), South Africa (124), Australia (9) and New Zea-
land (20). South African samples included Robben Island (26), False Bay (11), Kleinmond
(39), Agulhas Bank (10), Struisbaai (28) and Port Elizabeth (10). Three multiplex PCRs were
conducted based on primer pair combinations and multiplex panels previously optimised for
use in G.galeus [23]. The PCR cycling profile recommended in the Qiagen Multiplex kit user’s
manual was used. Subsequent to capillary electrophoresis, microsatellite allele sizes were
scored manually using the LIZ
1
600 internal size standard and GeneMapper
1
4.0 software
(ABI, Life Technologies, California, USA). Particular care was taken with allele scoring and
control samples were added with each independent capillary electrophoresis run.
Data analysis
Mitochondrial data. The software DNASP and ARLEQUIN 3.5 [47] were used to calcu-
late molecular diversity indices such as the number of segregating sites (K), number of haplo-
types (H), haplotype diversity (h) and nucleotide diversity (π). Genetic structure across
sampling sites was investigated using two different approaches. Firstly, an analysis of molecu-
lar variance (AMOVA) [48] was conducted in ARLEQUIN using 1,000 permutations to deter-
mine the variance components and fixation indices (Ф-statistics) at a single level followed by
testing hierarchical subdivision between the three Southern Hemisphere oceans: among
groups (Ф
CT
), among populations (Ф
SC
), and within populations (Ф
SC
). The Kimura-2 (K2)
model selected according to the Bayesian Information Criterion (BIC) generated in MEGA
[42] was employed for both the Southern Hemisphere (regional) and South African (local)
datasets. Secondly, pairwise genetic differences (F
ST
) based on haplotype frequencies were
estimated across the Atlantic, South Indian and South Pacific oceans. Pairwise F
ST
values were
computed in ARLEQUIN using 20,000 permutations for both Southern Hemisphere and
South African datasets. Sequential false discovery rate (FDR) corrections of the significant val-
ues were performed following the Benjamini and Hochberg (B–H) method [49]. The recon-
struction of genealogies was performed using phylogenetic algorithms in order to estimate
the relationship between haplotypes without ambiguities or unresolved connection [50]. A
phylogenetic tree of the mtDNA sequences was estimated using a maximum likelihood (ML)
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 5 / 20
approach in PHYML 3.0 [51] based on the Kimura-2 (K2) model. For tree searching and level
of branch support, default settings were used. The ML tree was imported into HAPLO-
VIEWER [50] to create a haplotype network.
To assess the demographic history of the populations, past demographic and population
expansions were evaluated using two methods. Firstly, using the neutrality test, computation
of Tajima’s D[52] and Fu’s F
S
[53] statistics and significance values were tested by 20,000 coa-
lescent simulations (significance at α0.05) under the infinite-sites model in ARLEQUIN.
Secondly, nucleotide mismatch distributions of the pairwise differences were obtained for
each sampling population (20,000 permutations). The observed distribution was assessed
against models of constant population size and population growth-decline to corroborate the
significance between observed and expected mismatch distribution patterns using DnaSP [43].
In addition, corresponding Harpending’s raggedness (H
R
) and sum of squared deviations
(SSD) indices [54] were calculated in ARLEQUIN to determine whether any observed mis-
match distributions were drawn from an expanded population (small values) or a stationary
one (large values).
Microsatellite data. Departure from the expectations of Hardy-Weinberg Equilibrium
(HWE) was examined locus by locus and across each geographic sample in ARLEQUIN. Link-
age disequilibrium (LD) between all pairs of loci was also tested in ARLEQUIN, followed by
correction for multiple comparisons. Microsatellite scoring errors due to stuttering, large allele
dropouts and null alleles were assessed in MICROCHECKER 2.2.3 [55]. Indices of genetic
diversity such as mean number of alleles (NA), the effective number of alleles (NE), unbiased
expected heterozygosity (uHE) and inbreeding coefficient (FIS) were estimated for each sam-
pling population in GENALEX 6.5 [56]. Given the uneven sample sizes, rarefied private allelic
richness (П
s
) was computed in HP-RARE 1.1 [57] using the rarefaction method with a mini-
mum sample size of n = 20 gene copies. To test for genetic homogeneity across the Southern
Hemisphere and South Africa, a single level AMOVA was conducted in ARLEQUIN for both
datasets. In addition, AMOVA was conducted to test for genetic subdivision across the three
ocean basins and within South Africa testing a priori defined grouping of Atlantic- (RI, FB,
KL, AB) versus South Indian Ocean sampling populations (SB, PE). The genetic distance
matrix for all AMOVAs was estimated by pairwise differences and the significance levels of the
variance components and F-statistics values were tested by 20,000 nonparametric permuta-
tions. Pairwise F
ST
was estimated for all pair of samples and significance was tested using
20,000 permutations in ARLEQUIN. A false discovery rate was determined for multiple tests
using the B–H method and applied to minimise type I errors. Tests for isolation-by-distance
(IBD) were performed for the South African samples using the web interface isolation by dis-
tance web service (IBDWS) 3.23 [58] by plotting linearized F
ST
values against corresponding
minimum geographical distances. Geographic distances were measured with the path tool
option in GoogleEarth 6.2.2 (Google Inc.) using the shortest path, via sea, between any two
sampling locations and assuming that G.galeus travels along the coast. Significance was tested
by 30,000 randomisations of the data.
Discriminant analysis of principal components (DAPC), a non-model-based (multivariate)
clustering method, was implemented in the R package ADEGENET [59]. The DAPC analysis
was used here for visual representation of genotypic partitioning of Southern Hemisphere and
South African populations, respectively. First, the find.clusters function, which runs successive
K-means clustering with increasing number of clusters (k), was used to assess the number of
clusters which maximizes between group variance and minimizes within group variance [60].
For selecting the optimal k, we applied the Bayesian Information Criterion (BIC) for assessing
the best supported model. Then, DAPC was performed on the pre-defined clusters based on
geographical sampling location (i.e. k=K) using the dapc function. Finally, a Bayesian
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 6 / 20
clustering analysis was performed in STRUCTURE 2.3.4 [61] to detect the most likely number
of ancestral genetic clusters (K). Fifteen iterations were run for each expected cluster setting K
from 1 to 6 for the regional dataset and 1 to 7 for the local dataset. Markov chain Monte Carlo
(MCMC) simulation runs of 10
6
iterations were made with 10
5
burn-in periods using an ad-
mixture model with correlated allele frequencies. The web-based STRUCTURE HARVESTER
0.6.93 software [62] was used to determine the number of K first by plotting the mean log
probability of each successive K and then using the Delta K method following Evanno et al.,
2005 [63]. The program CLUMPAK [64] was used for the graphical representation of the
STRUCTURE results. As the Evanno method of each K revealed hierarchical structure of the
regional data set (K = 2), STRUCTURE was rerun separately on each of the main identified
clusters which were the South Pacific cluster (Group 1 = CHI + NZ) and the Indo-Atlantic
cluster (Group 2 = ARG + SA + AUS). For the South African dataset, simulations were also
run with prior information on sampling location and applying the non-admixture model.
Results
Mitochondrial and nuclear descriptive statistics of G.galeus
Regional (Southern Hemisphere). A 599 bp fragment of the ND2 gene was sequenced
and analysed for a total of 96 G.galeus samples. Across the Southern Hemisphere, this resulted
in a total of 15 haplotypes ranging from one (NZ) to six (SA) per geographical location. The
overall haplotype diversity (h) was 0.626 ±0.057 with a nucleotide diversity (π) of 0.004 ±
0.001 (Table 1). A single haplotype was shared between Chile and Argentinia and another one
between New Zealand and Australia, while all other haplotypes were unique to their geograph-
ical locations. No haplotypes were shared between Argentina and South Africa and therefore
across the Atlantic Ocean. The Atlantic Ocean collections (ARG) also showed the highest hap-
lotype diversity (h= 0.822 ±0.097). The haplotype network indicated that haplotypes were
almost exclusively associated with one of three ND2 lineages linked to geographical origin, one
including all South African samples, one including all Australian and New Zealand samples
and a lineage of South American origin (Fig 2).
For the microsatellites, all 19 loci conformed to HWE with the exception of locus Mca33
and analyses for LD showed that 13 out of 171 locus pairwise comparisons were significant
(P<0.002). None of the loci showed evidence of scoring errors due to stuttering, large allele
dropouts or the presence of null alleles in MICRO-CHECKER. All diversity estimates for each
location are presented in Table 1. Across sampling sites, the total number of alleles (N
A
) and
unbiased expected heterozygosity (uH
E
) ranged from 3(ARG) to 11(SA) and 0.373(ARG) to
Table 1. Genetic diversity estimates for all Southern Hemisphere sampling populations of Galeorhinus galeus.
Location Mitochondrial DNA Microsatellites
N H H
P
K h (±s.d.) π(±s.d.) N N
A
N
E
uH
E
ПsF
IS
CHI 6 3 2 3 0.600 ±0.215 0.002 ±0.001 22 10 6.04 0.807 2.66 0.166
ARG 10 5 4 4 0.822 ±0.097 0.002 ±0.001 10 3 1.95 0.373 0.39 0.040
SA 53 7 6 6 0.181 ±0.071 0.001 ±0.001 124 11 3.33 0.681 0.77 0.028
AUS 9 2 1 1 0.222 ±0.166 0.001 ±0.001 9 4 2.55 0.506 0.38 -0.190
NZ 3 1 0 0 0.000 ±0.000 0.000 ±0.000 20 8 5.52 0.763 1.83 0.349
All/Avg 81 18 13 14 0.626 ±0.057 0.004 ±0.001 185 36 3.88 0.655 1.21 0.082
For mtDNA ND2 sequence data: number of samples (N), number of haplotypes (H), private haplotypes (H
P
), polymorphic sites (K), haplotype- (h) and
nucleotide diversity (π). For microsatellite data: the number of alleles (N
A
), number of effective alleles (N
E
), unbiased expected heterozygosity (uH
E
),
rarefied number of private alleles (Пs) and inbreeding coefficient (F
IS
).
https://doi.org/10.1371/journal.pone.0184481.t001
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 7 / 20
0.807(CHI) respectively. Unbiased expected heterozygosity (uH
E
) and the effective number of
alleles (N
E
) shows nuclear genetic diversity to be higher in the Pacific Ocean (NZ, CHI) rela-
tive to the rest of the Southern Hemisphere locations. Mean rarefied private allelic richness per
locus and per location averaged 1.21 (Table 1).
Local (South Africa). A total of 53 mtDNA ND2 sequences from six sampling sites across
the coastline of South Africa were analysed. The genetic diversity estimates are summarised in
(Table 2). The sequences generated a total of seven haplotypes, with very low levels of haplo-
type- (h= 0.216 ±0.076) and nucleotide (π= 0.001 ±0.000) diversity overall. One major haplo-
type was shared amongst 87% of individuals and all sampling sites except Port Elizabeth
exhibited at least one unique haplotype (Fig 2). For the microsatellites, all sampling popula-
tions were in HWE, with the exception of Agulhas Bank showing significant departure from
HWE (P<0.05) at seven (Mca33,McaB39,McaB22,Gg3,Gg11,Gg12,Gg23) of the 19 loci. No
LD was present for any of the loci pairwise comparisons. MICROCHECKER indicated no
scoring errors due to stuttering, large allele dropout or the presence of null alleles. Nuclear
genotypic diversity such as unbiased expected heterozygosity and allelic richness were compa-
rable for G.galeus across the Atlantic and Indian Ocean. The overall number of alleles ranged
from N
A
= 5 to 6 in the Indian Ocean samples, and from N
A
= 4 to 8 in the Atlantic Ocean
samples. Expected heterozygosity was highest for Robben Island (uH
E
= 0.707) and lowest for
Struisbaai (uH
E
= 0.600) (Table 2).
Fig 2. Global and local haplotype genealogy of Galeorhinus galeus based on a maximum likelihood
tree of ND2.Circles represent the haplotypes with area being equivalent to frequency. Each line indicates
one mutational step between haplotypes and small dark blue circles indicate hypothetical missing haplotypes.
https://doi.org/10.1371/journal.pone.0184481.g002
Table 2. Genetic diversity estimates for all South African sampling populations of Galeorhinus galeus.
Location Mitochondrial DNA Microsatellites
NHH
P
K h (±s.d.) π(±s.d.) N N
A
N
E
H
O
uH
E
F
IS
RI 11 3 2 3 0.345 ±0.030 0.001 ±0.001 26 8 3.686 0.615 0.707 0.141
FB 7 2 1 2 0.286 ±0.196 0.001 ±0.001 11 5 3.050 0.630 0.641 0.021
KL 12 2 1 2 0.167 ±0.134 0.001 ±0.001 39 7 3.232 0.692 0.679 -0.025
AB 5 2 1 3 0.400 ±0.237 0.002 ±0.001 10 4 2.851 0.705 0.615 -0.134
SB 13 2 1 1 0.154 ±0.126 0.002 ±0.001 28 6 2.726 0.648 0.600 -0.045
PE 5 1 1 0 0.000 ±0.000 0.000 ±0.000 10 5 3.003 0.786 0.646 -0.210
All/Avg 53 12 7 11 0.216 ±0.076 0.001 ±0.000 124 35 3.091 0.679 0.669 -0.042
For mtDNA ND2 sequence data: number of samples (N), number of haplotypes (H), private haplotypes (H
P
), polymorphic sites (K), haplotype- (h) and
nucleotide diversity (π). For microsatellite data: number of alleles (N
A
), number of effective alleles (N
E
), observed heterozygosity (H
O
), unbiased expected
heterozygosity (uH
E
), and inbreeding coefficient (F
IS
).
https://doi.org/10.1371/journal.pone.0184481.t002
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 8 / 20
Population connectivity of G.galeus
Regional (Southern Hemisphere). Based on the ND2 gene, genetic differentiation was
evident among geographic sampling populations since based on AMOVA, only a small per-
centage of variation was explained by the within-population level of subdivision while a signifi-
cant level of variation amongst the geographic populations existed (Ф
ST
= 0.895, P<0.05).
Further grouping hypotheses to test for structuring between ocean basins were not significant
(Ф
CT
= 0.113 to 0.460, P>0.05), irrespective of South Africa being grouped with the Atlantic-
or Indian Ocean (Table 3). All of the pairwise comparisons of Ф
ST
values showed statistically
significant differentiation after correcting for multiple tests (Ф
ST
= 0.151 to 0.934, P<0.05)
except for between New Zealand and Australia (Table 4). Overall, this indicated strong inter-
continental structure with the highest genetic differentiation found between samples from
South Africa and Argentina (Ф
ST
= 0.934, P<0.05). Substantial population isolation was evi-
dent within the Atlantic (ARG, SA), Indian (SA, AUS) and Pacific Ocean (NZ, CHI) samples.
Table 3. Analysis of molecular variance (AMOVA) across the Southern Hemisphere of Galeorhinus galeus based on mtDNA ND2 sequence and
microsatellite data.
Marker Hypothesis tested Source of variation % variation Fixation index
mtDNA Panmixia Among locations 87.64 Φ
ST
= 0.895*
Inter-oceanic (SA with Atlantic) Within locations 17.68
Among groups 11.26 Φ
CT
= 0.113
Among locations 58.63 Φ
SC
= 0.661*
Within locations 30.11 Φ
ST
= 0.699*
Inter-oceanic (SA with Indian) Among groups 64.96 Φ
CT
= 0.460
Among locations 22.68 Φ
SC
= 0.831*
Within locations 17.68 Φ
ST
= 0.909*
Microsatellites Panmixia Among locations 13.65 F
ST
= 0.137
Within locations 86.35
Inter-oceanic (SA with Atlantic) Among groups 5.70 F
CT
= 0.057
Among locations 8.88 F
SC
= 0.094*
Within locations 85.42 F
ST
= 0.146*
Inter-oceanic (SA with Indian) Among groups 8.060 F
CT
= 0.081
Among locations 6.810 F
SC
= 0.074*
Within locations 85.130 F
ST
= 0.149*
*Statistically significant at P<0.05
https://doi.org/10.1371/journal.pone.0184481.t003
Table 4. Pairwise Φ
ST
values for mtDNA (below diagonial) and pairwise F
ST
values for microsatellite data (above diagonial) among sampling loca-
tions across the Southern Hemisphere (left) and South Africa (right).
CHI ARG SA AUS NZ RI FB KL AB SB PE
CHI 0.236*0.136*0.171*0.050*RI 0.011 0.000 0.016*0.030*0.030*
ARG 0.151*0.138*0.330*0.287*FB 0.002 0.017*0.048*0.045*0.023*
SA 0.933** 0.934** 0.097*0.131*KL -0.053 0.018 0.003 0.028*0.040*
AUS 0.839** 0.844** 0.873** 0.163*AB 0.050 0.023 0.086 0.020*0.073*
NZ 0.770** 0.798** 0.871** -0.180 SB 0.008 0.047 0.003 0.141 0.091*
PE -0.089 -0.055 -0.093 0.000 -0.096
**Statistically significant after a false discovery rate (P0.027)
*Statistically significant at P<0.05
https://doi.org/10.1371/journal.pone.0184481.t004
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 9 / 20
Genetic differentiation across the Southern Hemisphere was further investigated using
microsatellite nuclear data. The global AMOVA showed high molecular variation amongst
sampling populations (F
CT
= 0.137, P<0.05) while none of the a priori grouping hypotheses
tested was significant (Table 3). Similarly, pairwise F
ST
values indicated high levels of genetic
differentiation on an inter-oceanic and intra-oceanic level across the Southern Hemisphere.
Pairwise F
ST
values ranged from 0.050 to 0.330, P<0.05; with the lowest genetic differentia-
tion found between NZ and CHI on opposite sides of the South Pacific Ocean (Table 4).
Population structuring was further investigated by ascertaining the relationship between
individual genotypes through discriminal analysis of principal components (DAPC). For the
K-means method, a k value of nine (the lowest BIC value) represented the best summary of
the data that maximized the variance between groups while minimizing the variance within
groups. When using the pre-defined clusters based on geographical sampling location, the
DAPC plot confirmed strong separation between the five Southern Hemisphere populations
of G.galeus with NZ and CHI as well as SA and AUS showing some overlap (Fig 3). Finally,
the true number of populations (K) was investigated using Bayesian clustering analysis. Prior
to the application of the Evanno method, the normal distribution of the mean likelihood score
(Ln(K)) did not reach a plateau for values of K tested while two clusters (K = 2) were identified
and statistically supported based on the Delta K method (S1 Fig). The assignment plot associ-
ated with K = 2 implied a strong relationship between the population samples and two genetic
groups: 1) CHI + NZ, and 2) ARG, SA and AUS with further structure evident for K >2 (Fig
4). For this reason, STRUCTURE was run hierarchically for the South Pacific (Group 1 = CHI
+ NZ) and the Indo-Atlantic clusters (Group 2 = ARG + SA + AUS) respectively. Further sub-
division was detected within Group 1 (Delta K was maximum for K = 3) while no further sub-
structure was evident for Group 2 (Delta K was maximum for K = 2) (S2 Fig). The assignment
bar plots were investigated for the respective groups and within group 1 assignment was
mainly to a single cluster for NZ while shared assignment to three clusters (admixture) was evi-
dent in CHI. For group 2, assignment plots indicated almost full membership for ARG and
AUS to different clusters with SA showing admixture between the two clusters (S3 Fig).
Local (South Africa). Analysis of the ND2 sequence variation across six local populations
resulted in seven haplotypes, with one common haplotype shared among all the sampling pop-
ulations. The AMOVA analysis showed no significant molecular variation amongst the sam-
pling populations (F
ST
= 0.013, P= 0.255) with most of the variation attributed to amongst
individual variation within populations. Also, no significant variation was detected between
Indian and Atlantic Ocean samples (F
CT
= -0.018, P= 0.752) (Table 5). The pairwise F
ST
val-
ues shown in Table 4 corroborated this haplotype genealogy, reflecting high connectivity
Fig 3. Cluster composition and population differentiation of Galeorhinus galeus.Scatterplots
generated by the DAPC analysis for sampling populations from (A) the Southern Hemisphere and (B) South
Africa respectively.
https://doi.org/10.1371/journal.pone.0184481.g003
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 10 / 20
across the South African populations (P>0.05). For the nuclear data, the single level AMOVA
testing for population differentiation, was significant (F
ST
= 0.025, P<0.05) while the hierar-
chical AMOVA showed no differentiation between oceanic basins tested for a priori (F
CT
=
0.000, P= 0.273) (Table 5). Pairwise F
ST
values ranged from 0.003 to 0.091, P0.0363 with
the highest pairwise value between the two Indian Ocean populations (PE and SB) (Table 5).
Low but significant differentiation between both Struisbaai and Port Elizabeth and the rest of
the populations was detected. This could however not be explained by isolation-by-distance as
genetic distance was not significantly correlated with geographic distance in South African G.
galeus (R
2
= 0.238, P= 0.1478) (S4 Fig). We therefore continued with tests to detect population
genetic structure.
With the DAPC, the sampling population of Struisbaai clustered separately while the Port
Elizabeth population also showed less overlap with the rest of the sampling populations (Fig
3). In STRUCTURE using sampling locations as priors, the mean likelihood score (Ln(K))
increased more slowly from K= 3–7 while Delta K supported three clusters (S1 Fig). The
assignment plots associated with K = 3 showed no clear correspondence between geographical
origin and cluster membership across sampling populations. On the individual level, admix-
ture was evident in the majority of samples (Fig 4), confirming high levels of gene flow
between Atlantic and Indian Ocean G.galeus.
Fig 4. Individual cluster assignments generated from STRUCTURE analysis. This is illustrated by
sampling location for K = 2 to K = 6 for both the Southern Hemisphere and the South African sampling
populations.
https://doi.org/10.1371/journal.pone.0184481.g004
Table 5. Analysis of molecular variance (AMOVA) across South Africa of Galeorhinus galeus based on mtDNA ND2 sequence and microsatellite
data.
Marker Hypothesis tested Source of variation % variation Fixation index
mtDNA Panmixia Among locations 1.31 Φ
ST
= 0.013
Within locations 98.69
Inter-oceanic (Atlantic vs. Indian) Among groups -1.89 Φ
CT
= -0.018
Among locations 2.37 Φ
SC
= 0.023
Within locations 99.52 Φ
ST
= 0.005
Microsatellites Panmixia Among locations 2.48 F
ST
= 0.025*
Within locations 97.52
Inter-oceanic (Atlantic vs. Indian) Among groups -0.01 F
CT
= 0.000
Among locations 2.48 F
SC
= 0.025*
Within locations 97.53 F
ST
= 0.024*
*Statistically significant at P<0.05
https://doi.org/10.1371/journal.pone.0184481.t005
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 11 / 20
Demographic history of Galeorhinus galeus
Overall the parameters of neutrality for G.galeus presented by sampling location were indica-
tive of population expansion rather contraction throughout the Southern Hemisphere and
South Africa. Across the Southern Hemisphere, both South Africa and Argentina showed sig-
natures of population expansion with statistically significant negative Tajma’s D and/or Fu’s
values. This was corroborated by results of goodness-of-fit tests for the observed mismatch dis-
tributions, which were non-significant (P>0.05) for all of the geographic sampling popula-
tions (Table 6), suggesting past population expansion. However, the expansion model was
rejected for Chile as well as for the pooled Southern Hemisphere samples as indicated by the
multimodal curve of mismatch distributions while a significant deviation from mutation-drift
equilibrium (D = 0.338, P= 0.652) was also not evident. Since the haplotype genealogies depict
three clades most likely linked to continental shelves, the analysis of demographic history was
also presented by clade rather than by sampling location. Tests for neutrality indicated a depar-
ture from mutation-drift equilibrium for all three clades and the unimodal curves detected for
the mismatch distribution was also indicative of populations having passed through a recent
demographic expansion. Although the observed mismatch distribution was compared to two
models of population development, the expansion and decline model versus the constant
model, these observations were carefully interpreted as observed mismatch distributions may
be a consequence of several demographic processes.
On a local scale, only the collection of Robben Island showed significant Tajima’s D value
(D = -1.600, P= 0.040) reflecting an excess of rare polymorphisms and population expansion
in the past (Table 6). Significant deviation was also observed overall populations (D = -2.299,
P= 0.010). This was further supported by the non-significance for the sum of squares distribu-
tion (SSD) and relatively low levels of Harpending’s raggedness index obtained for all sampling
populations. For the entire South African dataset, a process of expansion is suggested by the
unimodal curve of mismatch distributions but was not statistically supported (F
S
= -0.493,
P= 0.490 and SSD = 0.050, P= 0.192). The latter observation of deviation from neutrality for
the pooled South African dataset could well be an artefact of sampling in that the South
Table 6. Demographic analysis parameters for mtDNA ND2 sequences of all sampling populations of Galeorhinus galeus.
Site/clade Neutrality tests
D F
S
SSD H
R
CHI 0.338 (P= 0.652) 0.381 (P= 0.508) 0.064 (P= 0.320) 0.222 (P= 0.580)
ARG -0.521 (P= 0.321) -1.758 (P= 0.039) 0.028 (P= 0.240) 0.191 (P= 0.190)
SA -1.946 (P= 0.003) -5.109 (P= 0.000) 0.012 (P= 0.180) 0.571 (P= 0.680)
AUS -1.088 (P= 0.200) -0.264 (P= 0.169) 0.307 (P= 0.080) 0.358 (P= 0.310)
NZ no polymorphism no polymorphism n.d. n.d.
SH all -1.030 (P= 0.100) -2.343 (P= 0.050) 0.082 (P= 0.164) 0.268 (P= 0.352)
RI -1.600 (P= 0.040) -0.537 (P= 0.117) 0.004 (P= 0.600) 0.262 (P= 0.650)
FB -1.237 (P= 0.125) 0.856 (P= 0.598) 0.045 (P= 0.260) 0.673 (P= 0.730)
KL -1.451 (P= 0.069) 0.432 (P= 0.358) 0.015 (P= 0.240) 0.750 (P= 0.690)
AB -1.048 (P= 0.148) 1.688 (P= 0.767) 0.102 (P= 0.090) 0.680 (P= 0.780)
SB -1.149 (P= 0.163) -0.537 (P= 0.020) 0.000 (P= 0.380) 0.503 (P= 0.750)
PE no polymorphism no polymorphism n.d. n.d.
SA all -2.299 (P= 0.010) -4.213 (P= 0.020) 0.094 (P= 0.173) 0.478 (P= 0.390)
Demographic indices: Neutrality test estimates Tajima’s test (D) and Fu’s test (F
S
), sum of squared distribution (SSD), Harpending’s raggedness index (H
R
),
n.d. Not determined due to lack of polymorphism.
https://doi.org/10.1371/journal.pone.0184481.t006
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 12 / 20
African samples do not necesarrily represent a panmictic population assumed not to be
affected by local, rapid demographic processes [65].
Discussion
Population genetics of Southern Hemisphere Galeorhinus galeus
Patterns of contemporary and historical gene flow were determined for G.galeus across the
South Pacific, South Atlantic and the Indian Ocean. Both mitochondrial and nuclear data indi-
cate that the species is highly divergent across the three ocean basins and the hypothesis of
panmixia can be rejected based on statistical support. Since only a small number of individuals
were assessed per sampling population, results are placed in context by comparing the overall
genetic diversity estimates obtained for G.galeus in this study with other elasmobranch species
exhibiting similar life history patterns. Overall, the results show relatively low genetic diversity
for G.galeus across the Southern Hemisphere. The overall ND2 haplotypic diversity (h=
0.626) is comparable to those reported for other commercially exploited shark species such as
Mustelus mustelus,Sphyrna lewini,Carcharhinus brachyurus and C.falciformis [7,26,27,66].
Although lower haplotype and nucleotide diversities are expected for coastal sharks with
smaller distribution ranges than for pelagic shark species with wider distribution ranges (e.g.
Prionace glauca and Carcharhinus falciformis), recent studies have reported low levels of
genetic diversity also for highly migratory pelagic species including Pseudocarcharias kamo-
harai [67] and Carcharhinus longimanus [68].
Based on mtDNA ND2 haplotypes, this study confirms historical dispersal for G.galeus
along continental shelves and over short geographic distances with CHI and ARG as well as
NZ and AUS sharing a single haplotype. This is supported by pairwise Ф
ST
values and haplo-
type genealogy showing association with geographical distance. These results are in agreement
with previous findings for this species, suggesting a lack of historical gene flow across the large
open expanses of the South Atlantic, South Pacific and South Indian oceans [33]. The study by
Chabot and Allen., 2009 [33] also postulated that South America had only one historical popu-
lation but placed uncertainty on the interpretation of results due to low sample sizes used e.g.
one sample from Argentina pooled with 11 samples from Peru. Divergent lineages with geo-
graphic correspondence, as was seen with G.galeus, can result from two alternative scenarios;
vicariance or lineage sorting [69]. Since it is difficult to confidently ascertain lineage sorting,
two models of vicariance were considered; the closure of the Tethyan corridor (12 to 20 mil-
lion years ago) [70] and the emergence of the Isthmus of Panama (3.5 million years ago) [71].
The closure of the Tethyan corridor occurred at a time when the African and Eurasian plates
converged, resulting in the elimination of the warm coastal Tethyan corridor between the
Atlantic and Indian oceans [72]. The network genealogies indicated three clades which corre-
spond to a South American, African and an Australian-New Zealand lineage, with shallow
divergence between the latter two lineages. This is in accordance with both the global and
regional studies of tope shark based on the mtDNA control region [18,33] suggesting vicari-
ance as a result of the emergence of the Isthmus of Panama rather than an ancient divergence
due to closure of the Tethyan corridor. Demographic analysis based on the mtDNA data
set also suggested and confirmed recent population expansions for all of the Southern Hemi-
sphere collections except Chile. The study by Herna
´ndez et al., 2015 [18] reported a similar
demographic event for Australian and New Zealand tope shark that is characterised by a long
historical period of population expansion that most likely began before the last glacial Pleisto-
cene (19,000 years before present). It is likely that after the rise of the Isthmus of Panama, and
the subsequent warmer interglacial period, new habitats opened up and promoted population
expansion in the Southern Hemisphere countries within Atlantic and Indian waters. This
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 13 / 20
demographic pattern is also observed in other shark species such as S.lewini [26], Carcharhinus
limbatus [36], C.brachyurus [27] and C.leucas [73], which showed dramatic population expan-
sion trends during the Pleistocene. Despite the evidence for population expansion in many spe-
cies, coordinated expansion events across populations are not expected to be observed unless
shared environmental and historical factors obscured evidence of lineage specific adaptation, as
seen in elasmobranchs inhabiting a similar environment across a small spatial scale [e.g. for G.
galeus in southern Australia and New Zealand [18] and P.glauca in the Pacific Ocean [37]. This
synchrony in population expansions supports the argument that current genetic variation may
be the result of a major regional event over all populations. However, as one can not assume
that samples were drawn from a panmictic population but most likely from locally adapted pop-
ulations [66], this study does not necassarily have samples from the appropriate spatial and tem-
poral scales to determine the environmental changes associated with the historical events that
influenced population dynamics of G.galeus across the Southern Hemisphere.
The microsatellite data did not support contemporary gene flow between all geographic
sampling sites implying that known biogeographic barriers in the Southern Hemisphere can
hinder gene flow for G.galeus over smaller and larger spatial scales. For example, the Mid-
Atlantic and Benguela barriers together with the presence of gyres and straits possibly restricts
gene flow between sampling populations of Argentina and South Africa while the Tasman Sea
and the Great Australian Bight (GAB) are likely barriers between Australian samples and that
of New Zealand. It should be noted that a panmictic population of G.galeus was previously
found to exist between Australia and New Zealand [18] and did not include samples west of
the GAB barrier. The high connectivity observed between South African and Australian sam-
ples in the current study is in accordance with recent studies of highly migratory sharks such
as white shark (Carcharodon carcharias) [74] and the tiger shark (Galeocerdo cuvier) [75] and
highlights the high dispersal ability for this relatively smaller bodied shark. Hierarchical Bayes-
ian clustering assignment further supported a connection of Indian and Atlantic Ocean G.
galeus with migration around the tip of South Africa, most likely associated with the Agulhas
leakage [67]. Previous tagging efforts across the Southern Hemisphere have shown that G.
galeus exhibits extensive migratory patterns within the Indian and Pacific ocean basins [19,21].
On a local scale, McCord 2005 [21] showed that G.galeus aggregates during autumn (March
to May) and spring (September to November) when water temperatures are slightly cooler.
Across the Tasman Sea, Herna
´ndez et al., 2015 [19] showed that G.galeus migrates between
New Zealand and southern Australia and that these migrations occur more often over time.
Similar to aggregation patterns noted in South Africa, G.galeus tends to aggregate in large
numbers during spring and summer within the South West Atlantic (SWA), in closed gulfs
and bays of northern Patagonia, and are believed to be the primary nursery grounds for the
species [76]. Also, Cuevas et al., 2014 [77] studied the diving behaviour of G.galeus in the main
nursery ground for the SWA and showed that the species prefers cool temperate waters rang-
ing from temperatures between 17˚C and 19˚C and exhibits a yo-yo oscillatary movement
within the water column. The seasonal migratory patterns exhibited by the aforementioned
studies seem to indicate that G.galeus habours nursery grounds within coastal bay areas and
seasonally aggregates towards these sites. Further understanding these migratory patterns will
play an important role in the development of informed fishing and regulatory policies, particu-
larly regarding protection measures for critical habitats such as G.galeus nursery grounds.
Local population connectivity and management implications
On a local scale, no inter-oceanic ND2 divergence was observed across the Atlantic/ Indian
Ocean boundary, illustrating high levels of connectivity across the South African coastline. A
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 14 / 20
single genealogical clade was detected indicating historical admixture between the Indian and
Atlantic Ocean or quite possibly incomplete lineage sorting due to recent co-ancestry. The
overall low haplotypic diversity in combination with a single haplotype shared by most of the
individuals is similar to what was found for Mustelus mustelus [7] and Carcharodon carcharias
[74] assessed along the South African coastline. Given that the latter studies are based on dif-
ferent mitochondrial genes, it is possible that the low haplotype diversity observed here simply
reflects the inherent properties of the mitochondrial ND2 gene or the relatively short gene
region sequenced (599bp). However, the haplotype network for the Southern Hemisphere mir-
rored the haplotype geneology previously obtained for the control region [18,33] and recently
a few studies have demonstrated strong intraspecific divergence based on the ND2 gene
[78,79]. The presence of only a few private haplotypes could therefore well indicate the lack of
localized haplogroups expected for a species that shows philopatric behavior of females [80].
Bigger sample sizes and movement studies could help to confirm the presence or absence of
philopatry in South African G.galeus.
The microsatellite dataset, and pairwise F
ST
and standardized G”
ST
values in particular,
confirmed low but significant levels of genetic differentiation amongst local populations. Very
similar levels of heterogeneity in microsatellite allelic distributions has recently been reported
for smaller coastal shark species over range-wide and more restricted distributions including
M.mustelus,M.henlei and Carcharhinus isodon [7,81,82]. Strong intra-oceanic differentiation
was evident amongst samples of the Indian Ocean (SB en PE), illustrating contemporary
restriction to gene flow along the south-east coast of South Africa. The Bayesian cluster analy-
sis showed high levels of admixed assignment across all sampling populations with Port Eliza-
beth as the only population showing a more distinct membership. These findings support the
hypothesis that an additional barrier besides the Atlantic/Indian Ocean boundary might be at
play [83] and that the fragmentation of the PE population could be as a result of the cold water
pockets found at the thermal front in this region. Although it seems the Bayesian clustering
analysis was unable to resolve population genetic structure on a local scale, we hypothesize
that the genetic differentiation across the South African coastline are probably a result of a
combination of habitat preference, thermal fronts that generate cold water pockets and upwell-
ing currents. In a previous study including only one collection of Indian Ocean samples, the
varying levels of genetic admixture found across the South African coastline for G.galeus were
predicted to occur as a result of habitat preference [23] and could therefore also be linked to
the bioregions found along the South African coastline. More recently, Maduna et al., 2017
[84] also implicated the Cape Agulhas boundary as the main barrier to gene flow in four
coastal shark species including G.galeus. Most noteworthy in the aforementioned study is that
the coalescent analysis of migration supported assymetric gene flow of G.galeus from the
Indian to the Atlantic Ocean, concordant with the Atlantic Ocean–Indian Ocean connection
of G.galeus via Agulhas leakage proposed in the current study.
The outcomes of this study could have immediate implications for the local and more
global management of Galeorhinus galeus. On a Southern Hemisphere scale, all sampled popu-
lations comprise distinct genetic groups and therefore management units in fisheries terms.
This implies that any form of replenishment in the Pacific, Atlantic and Indian oceans will
have to be done locally, without any genetic input from geographically distant populations.
For local samples of G.galeus, the genetic data suggests that there could be more than one con-
temporary barrier affecting gene flow along the South African coastline. Although this do not
result in fully differentiated ‘stocks’ in the classic fisheries sense, local managers should recog-
nise the existence of a highly admixed population along the south-west coast and possibly
more discrete populations on the east coast. We therefore suggest that locally, G.galeus should
be managed, not just on an ecosystems-based approach in line with the marine bioregions of
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 15 / 20
South Africa, but it should be taken into account that since most of the fishery efforts are cen-
tered on the southwestern coast, G.galeus of Atlantic origin might be most vulnerable. Fur-
thermore, differences exhibited in mitochondrial haplotypes and microsatellite genotypes,
between these and other populations included from the Southern Hemisphere, could facilitate
trade-monitoring efforts for internationally traded products such as fins and meat which are
known to be exported from South Africa to other countries.
Supporting information
S1 Table. Regional and local sampling populations, collection site, sample numbers (N)
and sampling year.
(DOCX)
S1 Fig. L (K) distributions using the “log probability of data” (Mean of LnP±1) approach
prior to application of Evanno method (above) and Delta K analysis of the true number of
clusters following the Evanno method (below) across the Southern Hemisphere (left) and
across South African (right).
(DOCX)
S2 Fig. L (K) distributions using the “log probability of data” (Mean of LnP±1) approach
prior to application of Evanno method (above) and Delta K analysis of the true number of
clusters following the Evanno method (below) for the two main genetic clusters Group 1
(left) and Group 2 (right) identified using STRUCTURE.
(DOCX)
S3 Fig. Individual cluster membership of the Southern Hemisphere samples following
hierarchical structure performed on the two main genetic clusters (Group 1 and Group 2)
identified using STRUCTURE.
(TIF)
S4 Fig. Plots of the isolation-by-distance (IBD) analysis of the South African sampling
populations showing regression linearized F
ST
and geographic distance (R
2
= 0.238,
P= 0.1478).
(DOCX)
Acknowledgments
The authors wish to thank the following individuals, organisations and institutions for pro-
viding biological samples: Jayson Semmens from Australia, South African Department of
Agriculture, Forestry and Fisheries (DAFF), and the Viking Fishing Group. Simo Maduna is
especially thanked for assistance with clustering analyses as well as two anonymous reviewers
for many helpful comments on an earlier draft of this manuscript. This study was funded by
the National Research Foundation of South Africa.
Author Contributions
Conceptualization: Aletta E. Bester-van der Merwe, Juan M. Cuevas, Jennifer Ovenden, Char-
lene da Silva, Meaghen McCord, Rouvay Roodt-Wilding.
Data curation: Aletta E. Bester-van der Merwe, Daphne Bitalo.
Funding acquisition: Aletta E. Bester-van der Merwe.
Investigation: Daphne Bitalo.
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 16 / 20
Methodology: Aletta E. Bester-van der Merwe.
Project administration: Aletta E. Bester-van der Merwe.
Resources: Juan M. Cuevas, Jennifer Ovenden, Sebastia
´n Herna
´ndez, Charlene da Silva, Mea-
ghen McCord.
Supervision: Aletta E. Bester-van der Merwe, Rouvay Roodt-Wilding.
Validation: Aletta E. Bester-van der Merwe.
Writing – original draft: Aletta E. Bester-van der Merwe, Daphne Bitalo.
Writing – review & editing: Juan M. Cuevas, Jennifer Ovenden, Sebastia
´n Herna
´ndez, Char-
lene da Silva, Meaghen McCord, Rouvay Roodt-Wilding.
References
1. Dulvy NK, Fowler SL, Musick JA, Cavanagh RD, Kyne PM, Harrison LR et al. Extinction risk and conser-
vation of the world’s sharks and rays. Elife 2014; 3:e00590. https://doi.org/10.7554/eLife.00590 PMID:
24448405
2. Worm B, Davis B, Kettemer L, Ward-Paige CA, Chapman D, Heithaus MR et al. Global catches, exploi-
tation rates, and rebuilding options for sharks. Mar Policy. 2013; 40:194–204.
3. Corte
´s E. Life history patterns and correlations in sharks. Rev Fish Sci. 2000 1; 8(4):299–344.
4. da Silva C, Booth AJ, Dudley SF, Kerwath SE, Lamberth SJ, Leslie RW et al. The current status and
management of South Africa’s chondrichthyan fisheries. Afr J Mar. Sci 2015; 37(2):233–48.
5. Ovenden JR, Berry O, Welch DJ, Buckworth RC, Dichmont CM. Ocean’s eleven: a critical evaluation of
the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish
Fish. 2015; 16(1):125–59.
6. Ovenden JR, Kashiwagi T, Broderick D, Giles J, Salini J. The extent of population genetic subdivision
differs among four co-distributed shark species in the Indo-Australian archipelago. BMC Evol Biol.
2009; 9(1):40.
7. Maduna SN, da Silva C, Wintner SP, Roodt-Wilding R, Bester-van der Merwe AE. When two oceans
meet: regional population genetics of an exploited coastal shark, Mustelus mustelus. Mar Ecol Prog
Ser. 2016; 544:183–96.
8. Compagno LJ. FAO species catalogue. Vol. 4. Sharks of the World. FAO Fisheries Synopsis. 1984(125
pt 1): 2.
9. Walker TI, Cavanagh RD, Stevens JD, Carlisle AB, Chiramonte G, Domingo A et al. Galeorhinus
galeus. The IUCN Red List of Threatened Species 2006: e.T39352A10212764. Available: http://dx.doi.
org/10.2305/IUCN.UK.2006.RLTS.T39352A10212764.en.
10. Herna
´ndez S, Haye PA, Shivji MS. Characterization of the pelagic shark-fin trade in north-central Chile
by genetic identification and trader surveys. J Fish Biol. 2008; 73(10):2293–304.
11. Herna
´ndez S, Gonza
´lez MT, Villaroel JC, Acuña E. Seasonal variation in fish bycatch associated with
an artisanal flounder fishery on Coquimbo Bay, Chile. Rev Biol Mar Oceanog. 2010; 45(S1):695–703.
12. Chiaramonte GE. 1998. Shark fisheries in Argentina. Mar Freshwater Res. 1998; 49:601–9.
13. Elı
´as I, Rodriguez A, Hasan E, Reyna MV, Amoroso R. Biological observations of the tope shark,
Galeorhinus galeus, in the northern Patagonian gulfs of Argentina. J Northwest Atl Fish Sci. 2004;
37:261–5.
14. Chiaramonte GE. 2015. El cazo
´n o tiburo
´n vitamı
´nico Galeorhinus galeus (Linnaeus, 1758) (Pisces
Elasmobranchii: Triakidae) en Aragentina. Ph.D. Thesis. Universidad de Buenos Aires. 286 pp.
15. Lucifora LO, Menni RC, Escalante AH. Reproductive biology of the school shark, Galeorhinus galeus,
off Argentina: support for a single south western Atlantic population with synchronized migratory move-
ments. Environ Biol Fishes. 2004; 71(2):199–209.
16. Punt AE, Pribac F, Walker TI, Taylor BL, Prince JD. Stock assessment of school shark, Galeorhinus
galeus, based on a spatially explicit population dynamics model. Mar Freshwater Res. 2000; 51
(3):205–20.
17. Punt AE, Pribac F, Taylor BL, Walker TI. Harvest strategy evaluation for school and gummy shark. J
Northwest Atl Fish Sci. 2005; 35:387–406.
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 17 / 20
18. Herna
´ndez S, Daley R, Walker T, Braccini M, Varela A, Francis MP et al. Demographic history and the
South Pacific dispersal barrier for school shark (Galeorhinus galeus) inferred by mitochondrial DNA and
microsatellite DNA markers. Fish Res. 2015; 167:132–42.
19. Francis MP. Movement of tagged rig and school shark among QMAs, and implications for stock man-
agement boundaries. New Zealand Fisheries Assessment Report 2010; 2010(3):1–24.
20. von Bonde C. Shark fishing as an industry. Fisheries and Marine Biology Division, Union of South Africa,
Investigational Report 1934; 2:1–19.
21. McCord ME. Aspects of the ecology and management of the soupfin. Rhodes University; 2005.
22. Best LN, Attwood CG, da Silva C, Lamberth SJ. Chondrichthyan occurrence and abundance trends in
False Bay, South Africa, spanning a century of catch and survey records. Afr Zool. 2013; 48(2):201–27.
23. Bitalo DN, Maduna SN, da Silva C, Roodt-Wilding R, Bester-van der Merwe AE. Differential gene flow
patterns for two commercially exploited shark species, tope (Galeorhinus galeus) and common smooth-
hound (Mustelus mustelus) along the south–west coast of South Africa. Fish Res. 2015; 172:190–6.
24. Geraghty PT, Williamson JE, Macbeth WG, Wintner SP, Harry AV, Ovenden JR et al. Population
expansion and genetic structure in Carcharhinus brevipinna in the southern Indo-Pacific. PLoS One.
2013; 8(9):e75169. https://doi.org/10.1371/journal.pone.0075169 PMID: 24086462
25. Spaet JL, Jabado RW, Henderson AC, Moore AB, Berumen ML. Population genetics of four heavily
exploited shark species around the Arabian Peninsula. Ecol Evol. 2015; 5(12):2317–32. https://doi.org/
10.1002/ece3.1515 PMID: 26120422
26. Duncan KM, Martin AP, Bowen BW, DE Couet HG. Global phylogeography of the scalloped hammer-
head shark (Sphyrna lewini). Mol Ecol. 2006; 15(8):39–51.
27. Benavides MT, Feldheim KA, Duffy CA, Wintner S, Braccini JM, Boomer J et al. Phylogeography of the
copper shark (Carcharhinus brachyurus) in the southern hemisphere: implications for the conservation
of a coastal apex predator. Mar Freshwater Res. 2011; 62(7):861–9.
28. Chabot CL. Microsatellite loci confirm a lack of population connectivity among globally distributed popu-
lations of the tope shark Galeorhinus galeus (Triakidae). J Fish Biol. 2015; 87(2):371–85. https://doi.
org/10.1111/jfb.12727 PMID: 26179946
29. Keeney DB, Heupel MR, Hueter RE, Heist EJ. Microsatellite and mitochondrial DNA analyses of the
genetic structure of blacktip shark (Carcharhinus limbatus) nurseries in the northwestern Atlantic, Gulf
of Mexico, and Caribbean Sea. Mol Ecol. 2005; 14(7):1911–23. https://doi.org/10.1111/j.1365-294X.
2005.02549.x PMID: 15910315
30. Ashe JL, Feldheim KA, Fields AT, Reyier EA, Brooks EJ, O’Connell MT et al. Local population structure
and context-dependent isolation by distance in a large coastal shark. Mar Ecol Prog Ser. 2015;
520:203–16.
31. Briggs JC. Global biogeography. Elsevier; 1995.
32. Schultz JK, Feldheim KA, Gruber SH, Ashley MV, McGovern TM, Bowen BW. Global phylogeography
and seascape genetics of the lemon sharks (genus Negaprion). Mol Ecol. 2008; 17(24):5336–48.
https://doi.org/10.1111/j.1365-294X.2008.04000.x PMID: 19121001
33. Chabot CL, Allen LG. Global population structure of the tope (Galeorhinus galeus) inferred by mitochon-
drial control region sequence data. Mol Ecol. 2009; 18(3):545–52. https://doi.org/10.1111/j.1365-294X.
2008.04047.x PMID: 19161473
34. Daly-Engel TS, Seraphin KD, Holland KN, Coffey JP, Nance HA, Toonen RJ et al. Global phylogeogra-
phy with mixed-marker analysis reveals male-mediated dispersal in the endangered scallopedhammer-
head shark (Sphyrna lewini). PLoS One. 2012; 7(1):e29986. https://doi.org/10.1371/journal.pone.
0029986 PMID: 22253848
35. Schrey AW, Heist EJ. Microsatellite analysis of population structure in the shortfin mako (Isurus oxy-
rinchus). Can J Fish Aquat Sci. 2003; 60(6):670–5.
36. Keeney DB, Heist EJ. Worldwide phylogeography of the blacktip shark (Carcharhinus limbatus) inferred
from mitochondrial DNA reveals isolation of western Atlantic populations coupled with recent Pacific dis-
persal. Mol Ecol. 2006; 15(12):3669–79. https://doi.org/10.1111/j.1365-294X.2006.03036.x PMID:
17032265
37. Taguchi M, King JR, Wetklo M, Withler RE, Yokawa K. Population genetic structure and demographic
history of Pacific blue sharks (Prionace glauca) inferred from mitochondrial DNA analysis. Mar Fresh-
water Res. 2015; 66(3):267–75.
38. Piola AR, Matano RP. 2001. Brazil and Falklands–Malvinas Currents. In Encyclopedia of Ocean Sci-
ences, vol. 1, Steele JH, Thorpe SA, Turekian KK (eds). Academic Press: London; 340–9.
39. Garcı
´a G. Phylogeography from South-Western Atlantic Ocean: Challenges for the Southern Hemi-
sphere. INTECH Open Access Publisher; 2012.
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 18 / 20
40. Saghai-Maroof MA, Soliman KM, Jorgensen RA, Allard RW. Ribosomal DNA spacer-length polymor-
phisms in barley: mendelian inheritance, chromosomal location, and population dynamics. Proc Natl
Acad Sci U S A. 1984; 81(24):8014–8. PMID: 6096873
41. Farrell ED, Clarke MW, Mariani S. A simple genetic identification method for Northeast Atlantic smooth-
hound sharks (Mustelus spp.). ICES J Mar Sci. 2009; 66:561–5.
42. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analy-
sis version 6.0. Mol Biol Evol. 2013; 30(12):2725–9. https://doi.org/10.1093/molbev/mst197 PMID:
24132122
43. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bio-
informatics. 2009; 25(11):1451–2. https://doi.org/10.1093/bioinformatics/btp187 PMID: 19346325
44. Chabot CL, Nigenda S. Characterization of 13 microsatellite loci for the tope shark, Galeorhinus galeus,
discovered with next-generation sequencing and their utility for eastern Pacific smooth-hound sharks
(Mustelus). Conserv Genet Resour. 2011; 3:553–5.
45. Chabot CL. Characterization of 11 microsatellite loci for the brown smooth-hound shark, Mustelus hen-
lei (Triakidae), discovered with next-generation sequencing. Conserv Genet Resour. 2012; 4:23–5.
46. Giresi M, Renshaw MA, Portnoy DS, Gold JR. Isolation and characterization of microsatellite markers
for the dusky smoothhound shark, Mustelus canis. Conserv Genet Resour. 2012; 4(1):101–4.
47. Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics
analyses under Linux and Windows. Mol Ecol Resour. 2010; 10(3):564–7. https://doi.org/10.1111/j.
1755-0998.2010.02847.x PMID: 21565059
48. Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances
among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992; 131
(2):479–91. PMID: 1644282
49. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to mul-
tiple testing. J R Stat Soc Series B Stat Methodol. 1995; 1:289–300.
50. Salzburger W, Ewing GB, Von Haeseler A. The performance of phylogenetic algorithms in estimating
haplotype genealogies with migration. Mol Ecol. 2011; 20(9):1952–63. https://doi.org/10.1111/j.1365-
294X.2011.05066.x PMID: 21457168
51. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to
estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;
59(3):307–21. https://doi.org/10.1093/sysbio/syq010 PMID: 20525638
52. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genet-
ics. 1989; 123(3):585–95. PMID: 2513255
53. Fu YX. Statistical tests of neutrality of mutations against population growth, hitchhiking and background
selection. Genetics. 1997; 147(2):915–25. PMID: 9335623
54. Harpending HC. Signature of ancient population growth in a low-resolution mitochondrial DNA mis-
match distribution. Hum Biol. 1994; 66(4):591–600. PMID: 8088750
55. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. Micro-checker: software for identifying and
correcting genotyping errors in microsatellite data. Mol Ecol Notes 2004; 4:535–538.
56. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teach-
ing and research- an update. Bioinformatics 2012; 28(19):2537–9. https://doi.org/10.1093/
bioinformatics/bts460 PMID: 22820204
57. Kalinowski ST. hp-rare 1.0: a computer program for performing rarefaction on measures of allelic rich-
ness. Mol Ecol Notes. 2005; 5:187–9.
58. Jensen JL, Bohonak AJ, Kelley ST. Isolation by distance, web service. BMC Genet. 2005; 6(1):13.
59. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics.
2008; 24(11):1403–5. https://doi.org/10.1093/bioinformatics/btn129 PMID: 18397895
60. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the
analysis of genetically structured populations. BMC Genet. 2010; 11(1):94.
61. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype
data. Genetics. 2000; 155(2):945–59. PMID: 10835412
62. Earl DA. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and
implementing the Evanno method. Conserv Genet Resour. 2012; 4(2):359–61.
63. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software
STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611–20. https://doi.org/10.1111/j.1365-
294X.2005.02553.x PMID: 15969739
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 19 / 20
64. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: a program for identifying
clustering modes and packaging population structure inferences across K. Mol Ecol Resour. 2015; 15
(5):1179–91. https://doi.org/10.1111/1755-0998.12387 PMID: 25684545
65. Sta
¨dler T, Haubold B, Merino C, Stephan W, Pfaffelhuber P. The impact of sampling schemes on the
site frequency spectrum in nonequilibrium subdivided populations. Genetics. 2009; 182(1):205–16.
https://doi.org/10.1534/genetics.108.094904 PMID: 19237689
66. Clarke CR, Karl SA, Horn RL, Bernard AM, Lea JS, Hazin FH et al. Global mitochondrial DNA phylogeo-
graphy and population structure of the silky shark, Carcharhinus falciformis. Mar Biol. 2015; 162
(5):945–55.
67. Ferrette BLD, Mendonca FF, Coelho R, de Oliveira PGV, Hazin FHV, Romanov EV, et al. High connec-
tivity of the crocodile shark between the Atlantic and Southwest Indian Oceans: Highlights for Conserva-
tion. Plos One. 2015; 10(2).
68. Camargo SM, Coelho R, Chapman D, Howey-Jordan L, Brooks EJ, Fernando D et al. Structure and
genetic variability of the Oceanic whitetip shark, Carcharhinus longimanus, determined using mitochon-
drial DNA. PloS One. 2016; 11(5):e0155623. https://doi.org/10.1371/journal.pone.0155623 PMID:
27187497
69. Cunha RL, Coscia I, Madeira C, Mariani S, Stefanni S, Castilho R. Ancient divergence in the trans-oce-
anic deep-sea shark Centroscymnus crepidater. PLoS One. 2012; 7(11):e49196. https://doi.org/10.
1371/journal.pone.0049196 PMID: 23145122
70. Briggs JC. Marine Zoogeography. New York: McGraw-Hill; 1974.
71. Coates AG, Collins LS, Aubry MP, Berggren WA. The geology of the Darien, Panama, and the late Mio-
cene-Pliocene collision of the Panama arc with northwestern South America. Bull Geol Soc Am. 2004;
116:11–2.
72. Musick JA, Harbin MM, Compagno LJ. Historical zoogeography of the Selachii. In ‘Biology of Sharks
and their Relatives’. (Eds JC Carrier, JA Musick and MR Heithaus.) 2004. pp. 33–78.
73. Tillett BJ, Meekan MG, Field IC, Thorburn DC, Ovenden JR. Evidence for reproductive philopatry in the
bull shark Carcharhinus leucas. J Fish Biol. 2012; 80(6):2140–58. https://doi.org/10.1111/j.1095-8649.
2012.03228.x PMID: 22551174
74. Andreotti S, Heyden S, Henriques R, Rutzen M, Mey¨er M, Oosthuizen H et al. New insights into the evo-
lutionary history of white sharks, Carcharodon carcharias. J Biogeogr. 2016; 43(2):328–39.
75. Bernard AM, Feldheim KA, Heithaus MR, Wintner SP, Wetherbee BM, Shivji MS. Global population
genetic dynamics of a highly migratory, apex predator shark. Mol Ecol. 2016; 25(21):5312–29. https://
doi.org/10.1111/mec.13845 PMID: 27662523
76. Walker T. Galeorhinus galeus fisheries of the world. FAO Fisheries Technical Paper (FAO). 1999.
77. Cuevas JM, Garcı
´a M, Di Giacomo E. Diving behaviour of the critically endangered tope shark Galeorhi-
nus galeus in the Natural Reserve of Bahia San Blas, northern Patagonia. Anim Biotelem. 2014; 2(1):1.
78. Naylor GJ, Caira JN, Jensen K, Rosana KA, White WT, Last PR. A DNA sequence-based approach to
the identification of shark and ray species and its implications for global elasmobranch diversity and par-
asitology. Bull Am Mus Nat Hist. 2012; 21:1–262.
79. Feutry P, Kyne PM, Pillans RD, Chen X, Naylor GJ, Grewe PM. Mitogenomics of the Speartooth Shark
challenges ten years of control region sequencing. BMC Evol Biol. 2014; 14(1):232.
80. Chapman DD, Feldheim KA, Papastamatiou YP, Hueter RE. There and back again: a review of resi-
dency and return migrations in sharks, with implications for population structure and management. Ann
Rev Mar Sci. 2015; 7:547–70. https://doi.org/10.1146/annurev-marine-010814-015730 PMID:
25251267
81. Chabot CL, Espinoza M, Mascareñas-Osorio I, Rocha-Olivares A. The effect of biogeographic and phy-
logeographic barriers on gene flow in the brown smoothhound shark, Mustelus henlei, in the northeast-
ern Pacific. Ecol Evol. 2015; 5(8):1585–600. https://doi.org/10.1002/ece3.1458 PMID: 25937903
82. Portnoy DS, Hollenbeck CM, Bethea DM, Frazier BS, Gelsleichter J, Gold JR. Population structure,
gene flow, and historical demography of a small coastal shark (Carcharhinus isodon) in US waters of
the Western Atlantic Ocean. ICES J. Mar. Sci. 2016. https://doi.org/10.1093/icesjms/fsw098
83. Teske PR, Von Der Heyden S, McQuaid CD, Barker NP. A review of marine phylogeography in south-
ern Africa. S Afr J Sci. 2011; 107(5–6):43–53.
84. Maduna SN, Rossouw C, Silva C, Soekoe M, Bester-van der Merwe AE. Species identification and
comparative population genetics of four coastal houndsharks based on novel NGS-mined microsatel-
lites. Ecol Evol. 2017; 7(5):1462–86. https://doi.org/10.1002/ece3.2770 PMID: 28261458
Population genetics of Southern Hemisphere tope shark (Galeorhinus galeus)
PLOS ONE | https://doi.org/10.1371/journal.pone.0184481 September 7, 2017 20 / 20
