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Population genetics of Australian white sharks reveals fine-scale spatial structure, transoceanic dispersal events and low effective population sizes

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Despite international protection of white sharks Carcharodon carcharias, important conservation parameters such as abundance, population structure and genetic diversity are largely unknown. The tissue of 97 predominately juvenile white sharks sampled from spatially distant eastern and southwestern Australian coastlines was sequenced for the mitochondrial DNA (mtDNA) control region and genotyped with 6 nuclear-encoded microsatellite loci. MtDNA population structure was found between the eastern and southwestern coasts (F-ST = 0.142, p < 0.0001), implying female reproductive philopatry. This concurs with recent satellite and acoustic tracking findings which suggest the sustained presence of discrete east coast nursery areas. Furthermore, population subdivision was found between the same regions with biparentally inherited micro satellite markers (F-ST = 0.009, p < 0.05), suggesting that males may also exhibit some degree of reproductive philopatry; 5 sharks captured along the east coast had mtDNA haplotypes that resembled western Indian Ocean sharks more closely than Australian/New Zealand sharks, suggesting that transoceanic dispersal, or migration resulting in breeding, may occur sporadically. Our most robust estimate of contemporary genetic effective population size was low and close to thresholds at which adaptive potential may be lost. For a variety of reasons, these contemporary estimates were at least 1, possibly 2, orders of magnitude below our historical effective size estimates. Population decline could expose these genetically isolated populations to detrimental genetic effects. Regional Australian white shark conservation management units should be implemented until genetic population structure, size and diversity can be investigated in more detail.
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MARINE ECOLOGY PROGRESS SERIES
Mar Ecol Prog Ser
Vol. 455: 229–244, 2012
doi: 10.3354/meps09659 Published May 30
© Inter-Research 2012 · www.int-res.com*Email: dean.blower@uqconnect.edu.au
Population genetics of Australian white sharks
reveals fine-scale spatial structure, transoceanic
dispersal events and low effective population sizes
Dean C. Blower1,*, John M. Pandolfi1,2, Barry D. Bruce3,
Maria del C. Gomez-Cabrera1, 2, Jennifer R. Ovenden4
1School of Biological Sciences, and 2Australian Research Council Centre of Excellence for Coral Reef Studies,
The University of Queensland, St Lucia, Queensland 4072, Australia
3Wealth from Oceans Flagship, Commonwealth Scientific and Industrial Research Organization (CSIRO)
Marine and Atmospheric Research, Hobart, Tasmania 7000, Australia
4Molecular Fisheries Laboratory, Queensland Government Department of Employment,
Economic Development and Innovation, St Lucia, Queensland 4072, Australia
ABSTRACT: Despite international protection of white sharks Carcharodon carcharias, important
conservation parameters such as abundance, population structure and genetic diversity are
largely unknown. The tissue of 97 predominately juvenile white sharks sampled from spatially
distant eastern and southwestern Australian coastlines was sequenced for the mitochondrial DNA
(mtDNA) control region and genotyped with 6 nuclear-encoded microsatellite loci. MtDNA popu-
lation structure was found between the eastern and southwestern coasts (FST = 0.142, p < 0.0001),
implying female reproductive philopatry. This concurs with recent satellite and acoustic tracking
findings which suggest the sustained presence of discrete east coast nursery areas. Furthermore,
population subdivision was found between the same regions with biparentally inherited micro -
satellite markers (FST = 0.009, p < 0.05), suggesting that males may also exhibit some degree of
reproductive philopatry; 5 sharks captured along the east coast had mtDNA haplotypes that
resembled western Indian Ocean sharks more closely than Australian/New Zealand sharks, sug-
gesting that transoceanic dispersal, or migration resulting in breeding, may occur sporadically.
Our most robust estimate of contemporary genetic effective population size was low and close to
thresholds at which adaptive potential may be lost. For a variety of reasons, these contemporary
estimates were at least 1, possibly 2, orders of magnitude below our historical effective size esti-
mates. Population decline could expose these genetically isolated populations to detrimental
genetic effects. Regional Australian white shark conservation management units should be imple-
mented until genetic population structure, size and diversity can be investigated in more detail.
KEY WORDS: Carcharodon carcharias · Population structure · Philopatry · Effective population
size · Population genetics · Conservation · Nursery areas · Reproductive strategy
Resale or republication not permitted without written consent of the publisher
INTRODUCTION
Understanding genetic diversity, population con-
nectivity and trends of abundance is crucial to the
development of conservation goals for vulnerable
species (Reed & Frankham 2003). However, these
key properties remain largely unmeasured for the
white shark Carcharodon carcharias. Many elasmo-
branchs (sharks, skates and rays) are susceptible to
rapid population depletion, as they are slow to reach
sexual maturity and have relatively low fecundity
(Stevens et al. 2000, Dulvy et al. 2008). In addition,
localized depletion rates may be increased by fidelity
to favoured sites (Hueter et al. 2005). White sharks
Mar Ecol Prog Ser 455: 229–244, 2012
exemplify these traits (Smith et al. 1998, Bruce 2008,
Dulvy et al. 2008), the species is slow to mature (7 to
9 yr [males]; 12 to 17 yr [females]), has low fecundity
(litter size: 2 to 12), has infrequent reproduction
(every 2 to 3 yr), a long life-span (up to 60 yr) and
shows indications of site-fidelity (Pardini et al. 2001,
Domeier & Nasby-Lucas 2007, 2008, 2012, Jorgensen
et al. 2010, Anderson et al. 2011). Several abundance
estimates suggest their numbers have declined dur-
ing the 20th century, alongside the rise of industrial-
ized fishing and ma rine recreation (Pepperell 1992,
Reid & Krogh 1992, Baum et al. 2003). Acknowledge-
ment of the species’ vulnerability to exploitation has
prompted regional and international protection (i.e.
threatened status in South Africa, USA, and Aus-
tralia; World Conser vation Union [IUCN] Red List
status: Vulnerable A2cd+3cd Ver. 3.1; Convention on
International Trade in Endangered Species [CITES]:
Appendix II listing). Currently, a lack of information
on abundance, genetic diversity, reproductive be -
havior and population structure prevents assessment
of the efficacy of white shark conservation.
Many oceanic teleosts and elasmobranchs are pre-
sumed to lack population subdivision owing to their
circumglobal distributions, lack of biogeographic
barriers and high dispersal capacity. However, ge -
netic analysis contradicts assumptions of panmixia in
some species, e.g. narrow-barred Spanish mackerel
Scomberomorus commerson (Sulaiman & Ovenden
2010), Atlantic bluefin tuna Thunnus thynnus (Bous-
tany et al. 2008, Riccioni et al. 2010) and whale shark
Rhincodon typus (Castro et al. 2007). Some marine
species range widely to feed; during this phase, indi-
viduals of the same species will be admixed, spatial
genetic population subdivision will be minimal and
gene flow will be assumed to be high. However, if
individuals return to their birthplaces to breed and
are sampled at that time, pronounced genetic popu-
lation subdivision may be found. This behaviour
ranging widely but returning to favoured locations to
breed —is termed reproductive philopatry (Hueter et
al. 2005) and is exemplified by anadromous fishes
such as salmonids (Narum et al. 2007). Several shark
species, such as the common blacktip Carcharhinus
limbatus and lemon shark Negaprion brevirostris
(Feldheim et al. 2002, Keeney et al. 2005, Schultz et
al. 2008) have genetically structured populations
potentially driven by philopatric behaviour centred
around nursery areas. Reproductive philopatry may
also be gender biased. When sampled prior to disper-
sal after birth, or during aggregation for reproduc-
tion, a contrasting degree of population structure
between uniparentally and biparentally inherited
genetic markers implies that one gender is more
philopatric, assuming genes are at mutation-genetic
drift equilibrium (Prugnolle & de Meeus 2002). For
instance, male-biased dispersal has been suggested
to explain the larger genetic population structure
found in maternally inherited mitochondrial DNA
(mtDNA) compared with biparentally inherited nuclear
micro satellite DNA (nDNA) for the short-fin mako
Isurus oxyrinchus (Schrey & Heist 2003) and white
sharks (Pardini et al. 2001). The genetic population
structure of white sharks has not been investigated in
Australia, and the scale at which population structure
or philopatry of either gender occurs around Aus-
tralia is unknown.
Both genetic and electronic tracking methods have
been applied to determine broad-scale population
structure and migratory habits of white sharks, but
the results are equivocal. Rare transoceanic dispersal
events have been tracked (Bonfil et al. 2005) and can
be inferred from genetic studies (Pardini et al. 2001,
Gubili et al. 2010). However, analysis of mtDNA
shows high genetic differentiation between 3 re -
gions, Australia/New Zealand, the western Indian
Ocean and the northeastern Pacific Ocean (Pardini et
al. 2001, Jorgensen et al. 2010), consistent with long-
term genetic isolation possibly resulting from female-
mediated philopatry. Despite genetic indications of
gender-biased dispersal (Pardini et al. 2001), elec-
tronic tracking suggests that both genders exhibit site
fidelity and cyclic oceanic excursions (Bous tany et al.
2002, Bruce et al. 2006, Domeier & Nasby-Lucas
2007, 2008, 2012, Jorgensen et al. 2010, Anderson et
al. 2011). Although preliminary studies had suggested
unconstrained mixture of sub-adult and adult white
sharks throughout their Australasian range (Bruce et
al. 2006), recent tracking of juveniles provides evi-
dence for population segregation between eastern
and southwestern Australia (Bruce & Bradford 2012).
Genetic effective population size (Ne) is an impor-
tant conservation measure for monitoring population
size and genetic health (Reed & Frankham 2003,
Luikart et al. 2010, Hare et al. 2011). Nerepresents
the size of a theoretical ‘Wright-Fisher population’
that reflects the observed rate of genetic drift in a real
population (Wright 1931, Hare et al. 2011). Nealso
evaluates the future evolutionary resilience of a spe-
cies; the lower Nebecomes, the greater the likelihood
of deleterious allele fixation and loss of adaptive vari-
ation through genetic drift, thereby heightening the
risk of population extinction (Frankham 2005, Hare
et al. 2011). The timescale at which Neis assessed
can be historical or contemporary. The long-term or
historical Ne(denoted HNe) is a function of popula-
230
Blower et al.: Population genetics of Australian Carcharodon carcharias
tion-wide genetic variation and the mutation rate,
which estimates the harmonic mean effective popu-
lation size per generation over approximately 4Ne
generations (Hare et al. 2011). Converting HNeinto
population estimates, Alter et al. (2007) inferred the
historical population size of eastern Pacific grey
whales Eschrichtius robustus to be 3 to 5 times higher
than previous pre-whaling demographic population
estimates, suggesting that the current population is
still substantially depleted rather than recovered.
The short-term or contemporary Ne(denoted CNe)
can be derived from the magnitude of recent genetic
drift within a population and approximates the mean
number of breeding individuals contributing off-
spring per generation (Hoarau et al. 2005, Portnoy et
al. 2009). CNeestimates for western Atlantic sandbar
shark Carcharhinus plumbeus populations were
found to be approximately half the adult population
census estimates (NC). This may serve as an example
of the relationship of CNeto NCin other elasmo-
branchs with similar life histories and indicates that
for this species a significant proportion of each popu-
lation contributes to recruitment. In contrast, highly
fecund teleost fish populations have CNe/NCratios of
10−3 to 10−6 (Hoarau et al. 2005, Portnoy et al. 2009),
which may indicate that relatively few parents
contribute recruits to the next generation. It also
suggests that this commercially harvested shark pop-
ulation may be sensitive to depletion like exploited
marine mammal populations that have similar
CNe/NCratios, e.g. Bering Sea bowhead whales Bal-
aena mysticetus (Shelden et al. 2001) and Californian
sea otters Enhydra lutris (Ralls et al. 1983). Historic
and contemporary Australian white shark population
sizes are unknown, but could be approximated from
measures of genetic diversity and genetic drift, addi-
tionally providing an assessment of their genetic
health and the effectiveness of conservation policies.
We investigated the genetic population structure
and the existence of fine-scale philopatry for Aus-
tralian white sharks within and between Australian
coastal regions using mtDNA control region (CR)
sequences and 6 nDNA microsatellite loci. Next, we
generated Neestimates from the microsatellite loci:
HNeas a coarse measure of historic white shark pop-
ulation size and CNeto indicate the current breeding
population size and genetic health of Australian
white sharks. We predicted that white shark CNe
would be significantly lower than HNe, al though not
approaching critical genetic thresholds given a his-
tory of protection from exploitation. The present
study provides data on white shark population struc-
ture and effective population sizes and fresh insight
into reproductive behaviour measures critical for
evaluating their genetic health and assessing the
effectiveness of their protection in Australian waters.
MATERIALS AND METHODS
Sample acquisition and demographics
Tissue samples (n = 97) and associated data (e.g.
gender, total length, capture location) spanning 21 yr
(Table 1) were acquired from incidental captures
(n = 62) e.g. New South Wales (NSW) bather pro-
tection program (Green et al. 2009) and commercial
and recreational fisheries by-catch —and from sharks
231
Region Sample type n Source GenBank Accession No.
Australia Tissue 68 CSIRO, Australian HQ414073−HQ414086
Federal Government
17 Griffith University, Gold Coast, Queensland
13 The University of Queensland, Brisbane
16 NSW Department of Primary Industries
Australia mtDNA CR sequences 12
New Zealand mtDNA CR sequences 4 GenBank popset (Pardini et al. 2001) AY026196−AY026224
Western Indian mtDNA CR sequences 13
Ocean
northeastern mtDNA CR sequences 20 GenBank popset (Jorgensen et al. 2010) GU002302−GU002321
Pacific
Northwest mtDNA CR sequences 2
Atlantic GenBank popset (Gubili et al. 2010) HQ540294−HQ540298
Mediterranean mtDNA CR sequences 3
Table 1. Carcharodon carcharias. Source and number (n) of tissue samples and mitochondrial DNA control region sequences
(mtDNA CR). The region represents the general area from which sharks were sampled
Mar Ecol Prog Ser 455: 229–244, 2012
tagged specifically for tracking research (n = 35) by
the Commonwealth Scientific and Industrial Re -
search Organization (CSIRO). All tagged sharks
were released alive after measurement and attach-
ment of electronic tracking devices, but the mortality
rates of these sharks and the sharks sampled from
other sources are not known. Sampling of sharks was
predominantly opportunistic from 1989 to 2004, with
approximately 2 sharks sampled per year, the major-
ity from the provinces of South Australia (SA; n = 17),
then NSW (n = 5), followed by Queensland (QLD;
n = 5), Tasmania (TAS; n = 5) and Western Australia
(WA; n = 1). From 2005 to 2009, CSIRO’s tagging pro-
gram boosted the numbers of samples by 35: 21 came
from NSW, 13 from SA and 1 from WA. A further 29
samples were obtained through bather protection
and by-catch: 9 from QLD, 17 from NSW and 3 from
an unrecorded location. Over that period (2005 to
2009) an average of 13 sharks yr−1 were sampled
from all sources. All the samples were divided by
capture province and grouped into an eastern or
southwestern region of Australia (Fig. 1). Samples of
unknown capture location (n = 3) were included in
analyses unless capture region was integral.
We defined mature sharks by the total length (TL;
all lengths are TL unless otherwise specified) at
which each gender is considered by Bruce (2008) as
sexually mature (>360 cm [males]; > 450 cm [fe -
males]). Sharks below these sizes were considered
juveniles. Sampled shark TL ranged from 138 to
510 cm (2x= 287 cm) as compared with the known TL
range of the species of 130 to 600 or 700 cm (Bruce
2008). Juvenile sharks made up 71% (n = 69) of the
total. The sex ratio of white sharks in the wild
appears to be close to parity (Domeier & Nasby-
Lucas 2007); additionally, they are thought to have a
neonate embryonic sex ratio of 1:1, similar to our
samples’ female to male sex ratio (1.2:1) (46 females,
40 males and 11 of unknown gender).
Laboratory procedures
Tissue samples consisting of muscle or fin cartilage
(15 to 20 mg) were diced and soaked overnight in 1×
TE buffer (10 mM pH 8.0; Tris-HCl, 1 mM EDTA).
Genomic DNA extraction was performed with Qia-
gen P/L DNEasy extraction kits. Extracted DNA was
standardized to 10 ng µl−1 prior to amplification. After
DNA extraction, tissue samples were archived in
20% DMSO, saturated with NaCl and frozen at −80°C.
For mtDNA amplification, we designed a forward
primer (GWSMT1F, 5’-TTA CAA CCC AGG GGG
TAT CCT-3’) to bind downstream of a heteroplasmic
thymine sequence at 173 base pairs (bp) encoun-
tered by Pardini et al. (2001). The reverse primer
(GWSMT1R, 5’-AGC CAA ACA TCC ATT TGG
CCT-3’) complemented the forward primer anneal-
ing temperature (61°C). Polymerase chain reaction
(PCR) was performed in a 10.0 µl reaction composed
232
Fig. 1. Carcharodon carcha -
rias. Number of individuals
genotyped with nuclear DNA
microsatellite loci (first number
in circles) and sequenced for
mitochondrial DNA control re-
gion (second number in circles)
from pro vinces of Australia
and their grouping by region.
Black circles represent sam-
ples comprising the southwest-
ern region. White circles rep-
resent the eastern region.
Circle positions are relative to
political provinces, as delin-
eated by the political bound-
aries shown on the land
masses, and represent the
general location where sharks
were sampled
Blower et al.: Population genetics of Australian Carcharodon carcharias 233
of 1.0 µl DNA template, 0.2 µM of both primers, 1×
PCR buffer (10× ImmoBuffer, BIOLINE P/L), 4.0 mM
MgCl2, 0.5 mM dNTP mix (25.0 mM of each dNTP)
and 0.5 U DNA polymerase (Immolase Hot Start Taq,
BIOLINE). PCR cycling (Mastercycler Pro, Eppendorf
AG) conditions were as follows: 10 min polymerase
activation at 95°C, then 30 cycles of 15 s denaturation
at 94°C, 30 s annealing at 61°C and 1 min elongation
at 72°C, followed by 7 min extension at 72°C. PCR
products were purified by combining 3.0 µl DNA
template, 0.5 µl Exonuclease I (1×, New England Bio-
Labs), 5.0 U Antarctic phosphatase (1×, New England
BioLabs) and 4.5 µl MilliQ H2O, which was heated to
37°C for 30 min, then 80°C for 15 min. Dye-termina-
tion sequencing was performed (Big Dye Terminator
v3.1 Cycle Se quencing Kit, Applied Biosystems) with
an Applied Biosystems 3130 XL capillary elec-
trophoresis genetic analyser.
Microsatellite loci Ccar1, Ccar9, Ccar13, Ccar19,
Ccar6.27x and Iox10 (Table 2) were amplified in an
8.0 µl PCR reaction of 0.17 µM fluorescent FAM-
labelled M13 primer, 0.17 µM reverse primer,
0.08 µM forward primer (with a 23 nucleotide M13
extension), 1× PCR buffer, 4.0 mM of MgCl2, 0.5 mM
dNTP mix and 0.5 U of DNA polymerase. PCR ampli-
fication conditions required 95°C for 10 min, then
35 cycles of 94°C for 15 s, 30 s at the locus-specific
annealing temperature (64°C for Ccar1, Ccar9,
Ccar19 and Iox10, 63°C for Ccar13 and 61.5°C for
Ccar6.27x), then 1 min at 72°C, and extended at 72°C
for 7 min. PCR product was diluted 1:40 with MilliQ
H2O and automated fragment separation performed
(ABI 3130 XL, Applied Biosystems).
Data analysis
Forward and reverse mtDNA sequence traces were
imported into CodonCode Aligner Version 3.5 (Co -
don Code), trimmed and edited by eye. Se quen ces
were aligned with the ClustalW algorithm imple-
mented in MEGA Version 4 (Tamura et al. 2007). The
aligned sequences, including indels, were exported
to GenAlEx Version 6.3 (Peakall & Smouse 2006) and
Arlequin Version 3.5.1.2 (Excoffier & Lischer 2010)
to identify and characterize haplotypes. Sequence
di versity indices, polymorphism statistics, nucleotide
proportions and diversity (π), and haplotype number
and diversity (h) were obtained with Arlequin using
the Tamura-Nei model (Tamura & Nei 1993) with
gamma set to 0.5 (Gubili et al. 2010).
Comparison with mtDNA CR sequences of previ-
ous studies (Table 1), trimmed to 842 bp, was per-
formed to reveal any new haplotypes. Pardini et al.
(2001) included some of the Australian tissue sam-
ples used here, so to avoid duplication or unequal
weighting of Australian haplotypes, Pardini’s Aus-
tralian mtDNA sequences were not included if they
matched haplotypes found by our study (N = 6). Phy-
logenetic relationships were assessed with a neigh-
bour-joining (Saitou & Nei 1987) bootstrap consensus
phylogram generated with MEGA 4. The relative fre-
quency of Australian/New Zealand haplotypes and
the mutational steps between global haplotypes was
assessed by generating a statistical parsimony haplo-
type network with TCS Version 1.21 (Clement et
al. 2000). The 95% parsimony haplotype connection
limit was progressively relaxed until a fully con-
nected global network was attained (sequence gaps
were assumed to represent a fifth state).
Genemapper Version 3.7 (Applied Biosystems) was
used to bin microsatellite alleles. Genotyping errors
and null allele probabilities were tested for using
Microchecker Version 2.2.3 (Van Oosterhout et al.
2004). Microsatellite genotypes were compared with
SHAZA V1.0 (Macbeth et al. 2011) to assess whether
duplicate genotypes represent the same (e.g. unin-
tentionally re-sampled) or different (e.g. shadow)
animals. Alleles were analysed for conformance to
Hardy-Weinberg equilibrium and for linkage dise-
Locus n NaHOHESuccess (%) GenBank Accession No.
Ccar1 93 6 0.634 0.725 96 AF216865 (Pardini et al. 2000)
Ccar13 87 10 0.805 0.785 90 AF184087 (Pardini et al. 2000)
Ccar19 94 3 0.426 0.519 97 AF184087 (Pardini et al. 2000)
Ccar6.27x 91 4 0.571 0.516 94 Unpublished (Gubili et al. 2009)
Ccar9 94 15 0.894 0.859 97 AF216866 (Pardini et al. 2000)
Iox10 97 5 0.742 0.702 100 AF426735 (Schrey & Heist 2002)
Table 2. Carcharodon carcharias. Summary of Australian-sourced nuclear DNA microsatellite loci data showing the number of
sharks genotyped (n), the number of alleles (Na), the average observed (HO) and expected (HE) heterozygosity, genotyping
success rate (%), and GenBank accession number
Mar Ecol Prog Ser 455: 229–244, 2012
quilibrium by Genepop web Version 4.0 (Rousset
2008). The microsatellite allelic diversity indices of
expected and observed heterozygosity and allele
numbers per locus were obtained with Arlequin.
Population structure was evaluated within and be -
tween the eastern and southwestern samples using
Wright’s F-statistic, FST (Wright 1950, 1965), with
haplotype frequencies (mtDNA) or allele frequencies
(microsatellite loci) by Arlequin (100 000 permuta-
tions). The significance of multiple comparisons was
evaluated after Bonferroni correction at a global sig-
nificance level of 0.05 (Rice 1989). To assess the pos-
sible effects of age-specific dispersal rates on popula-
tion structure, each analysis was performed with and
without adult sharks. Analyses were re peated with
and without sharks from the east coast population
that were subsequently revealed to have strong
affinities with western Indian Ocean populations.
To estimate HNe, we used the Bayesian coalescent
genealogy sampler MIGRATE-n Version 3.1.6 (Beerli
2008) with microsatellite genotypes to generate a
measure of genetic diversity, theta (θ). Initial runs of
MIGRATE-n used default parameters to establish the
potential range of θ. Convergence on the posterior
distribution of θwas then established with thorough
search parameters (10 000 recorded steps with 100
record increments, 10 heated chains with 1 as the
swapping interval and with 100 000 trees discarded
as burn-in). MIGRATE-n runs requiring intensive
processing were performed by the Computational
Biology Service Unit (CBSU) at Cornell University,
USA. In the absence of a species-specific mutation
rate, vertebrate microsatellite mutation rates (μ) of
10−3, 10−4 and 10−5 mutations gamete−1 generation−1
(Bagley et al. 1999) were substituted into the equa-
tion θ= 4Neμto produce lower, middle and upper
estimates of HNe, respectively.
CNewas estimated from the amount of pairwise
linkage disequilibrium between microsatellite loci
(Hill 1981, Waples 2006). The program LDNe Version
1.31 (Waples & Do 2008) was used to produce CNe
estimates for Australia as a whole and for each
region, since CNeestimates may be biased where
population structure exists (Palstra & Ruzzante 2008).
Random mating was chosen over monogamy for the
reproductive model, as white shark mating behav-
iour is not understood. Rare alleles upwardly bias
CNeestimates under simulated con ditions (n = 100,
20 loci with 10 alleles locus−1) (Waples & Do 2010).
Following Waples & Do (2010), who recommend a Pcrit
(allele frequency exclusion criterion) of 0.02 when
sample sizes are >25, for each sample group we
raised the Pcrit value of the LDNe software from 0.02,
in increments of 0.01, to pass through the range rec-
ommended by Waples & Do (2008, 2010) at which
there is least trade-off between bias and precision
(0.02 Pcrit 0.05). We continued to increment Pcrit
through and beyond this range until the first occur-
rence of a finite point-estimate of CNe, which was
then accepted as the best estimate.
RESULTS
Summary statistics for mtDNA and haplotype analysis
MtDNA CR sequences (842 bp) were obtained for
94 individual white sharks Carcharodon carcharias.
Fourteen unique haplotypes were found, 9 of which
were previously undescribed. The overall haplotype
diversity (h, ±SE) was 0.8776 ± 0.0148, and the nu -
cleo tide diversity (π, ±SE) was 0.00855 ± 0.00448.
There were 51 polymorphic sites, composed of 4 in -
dels, 40 transitions and 8 transversions (Tables 3 & 4).
Phylogenetic analysis revealed that 2 eastern Aus-
tralian haplotypes (Haplotypes 13 & 14; Table 4,
Fig. 2; GenBank Accession Nos. HQ414073 &
HQ414074) were substantially more similar to west-
ern Indian Ocean (WIO) white shark haplotypes
(Haplotypes 20 & 22 to 25; Fig. 2; GenBank Accession
Nos. AY026212 to AY20224); these sharks are
referred to here as WIO-like (WIOL). Only a single
nucleotide transition distinguished WIOL from WIO
haplotypes. The mean net distance between WIO
and the Australia/New Zealand clade was 0.055 ±
0.009 (base substitutions per site, ±SE), equivalent to
42 mutational steps (Fig. 2). Six sharks from both
eastern and southwestern Australia had haplotypes
identical to those originally sampled in New Zealand
(Haplotypes 11 & 8; Fig. 2; GenBank Accession Nos.
AY026209/HQ414076 and AY026210/HQ414079).
Summary statistics for microsatellite loci
Likelihood-based genotype matching by SHAZA
identified 6 pairs and 1 triplet of exact duplicate
genotypes. CSIRO confirmed that 1 duplicate pair
represented a previously unrecognized recapture
event and that 3 other duplicate pairs were dupli-
cated tissue samples. The remaining duplicates could
not be easily ex plained. Pre-PCR processing had
been conducted under sterile conditions and with
multiple negative controls during PCR processing, so
contamination was considered un likely. For each set
of duplicate samples, the sample with least demo-
234
Blower et al.: Population genetics of Australian Carcharodon carcharias 235
graphic information was excluded
from analyses.
Genotyping 97 white sharks (Fig. 1)
using 6 micro satellite loci produced be-
tween 3 to 15 alleles locus−1. Ob served
heterozygosity (HO) per locus ranged
from 0.426 to 0.894 and ex pected het-
erozygosity (HE) per locus ranged be-
tween 0.516 and 0.859 (Table 2). Mi-
crochecker highlighted a potential null
allele for locus Ccar1 at the 95% confi-
dence interval (CI). However, this locus
conformed to Hardy-Weinberg equilib-
rium expectations (p = 0.62), so it was
retained for subsequent analyses. All
other loci were within Hardy-Weinberg
equilibrium expectations, and no link-
age disequilibrium was detected after
Bonferroni correction.
Population structure
Population structure was detected with
both mtDNA and microsatellite mark-
ers. The mtDNA FST between eastern
Polymorphism position (1−842) Haplotype frequency (h)
3 8 3 3 4 4 7 7 8 New Queens- South Tasmania Western Unknown
4 7 4 8 4 4 8 8 1 South land Australia Australia region
0 8 8 9 0 9 7 Wales
1 G T G G T C T T A 0.200
2 . . . . . . . C G 0.184 0.077 0.036 0.333
3 . . . . . . . C . 0.158 0.231 0.357 0.200 0.500
4 . . . . . . A C G 0.026 0.077
5 . . A . . . . C . 0.036 0.200
6 . C . . . . . . G 0.179 0.400
7 . C . . . . . . . 0.237 0.231 0.071 0.500 0.667
8 . C . . . . . C . 0.077
9 . C . . C . . . G 0.079 0.231 0.071
10 . C . A . T . . . 0.237 0.077 0.107
11 . C . A . . . . . 0.079 0.107
12 A C . A . T . . . 0.036
Sample size (N) 38 13 28 5 2 3
Number of haplotypes 7 7 9 4 2 2
Nucleotide diversity (π, ±SE) 0.00289 0.00290 0.00285 NA NA NA
± 0.00178 ± 0.00189 ± 0.00178
Haplotype diversity (h, ±SE) 0.838 0.885 0.833 NA NA NA
± 0.025 ± 0.058 ± 0.051
Table 3. Carcharodon carcharias. Australian-sourced partial mitochondrial DNA control region haplotypes (1 to 12) showing
nucleotide position of polymorphic sites, haplotype frequencies (h), sample sizes (N), number of haplotypes and sequence di-
versity indices. Western Indian Ocean−like haplotypes 13 and 14 are excluded (see Table 4). NA: not applicable; dots: same
nucleotide as haplotype 1; dashes: haplotype not found in province
Polymorphism position (1−842) Haplotype frequency (h)
3 7 New Queens-
4 6 South land
0 4 Wales
13 A T 0.250
14 G C 0.750 1
Sample size (N) 4 1
Number of haplotypes 2 1
Nucleotide diversity (π, ±SE) NA NA
Haplotype diversity (hSE) NA NA
WIOL plus non-WIOL haplotypes (separated by 44 polymorphic sites)
Sample size (N) 42 14
Number of haplotypes 9 8
Nucleotide diversity (π, ±SE) 0.01294 0.01093
± 0.00667 ± 0.00601
Haplotype diversity (h, ±SE) 0.8641 0.9011
± 0.0226 ± 0.0523
Table 4. Carcharodon carcharias. Australian-sourced western Indian Ocean−
like (WIOL) partial mitochondrial DNA control region haplotypes (13 & 14)
showing nucleotide position of polymorphic sites, haplotype frequencies (h),
sample sizes (N), number of haplotypes, collection province and sequence di-
versity indices, including totals for WIOL plus non-WIOL haplotypes. NA: not
applicable; dashes: haplotype not found in province
Mar Ecol Prog Ser 455: 229–244, 2012
and south western sharks was 0.14174 (p < 0.0001)
(Table 5a) and was 0.17348 (p < 0.0001) for juvenile
shark samples alone (Table 5b). The microsatellite FST
between the eastern and southwestern samples was
0.00927, which was significant at 0.05 but not signifi-
cant after Bonferroni adjustment. For juvenile shark
samples the micro satellite FST was non-significant.
The mi crosatellite locus Iox10 produced the largest
locus-by-locus FST between the eastern and south-
western samples (FST = 0.03778, p = 0.00553).
Only mtDNA showed evidence of population struc-
ture between locations along the east coast. Between
NSW and QLD the mtDNA FST was 0.11933 (p <
0.0001) (Table 6). Evaluating juvenile samples alone,
the magnitude and significance of the mtDNA FST
was similar at both regional and provincial scales.
The microsatellite FST did not show significance
between NSW and QLD, but was significant at 0.05
between NSW and SA (FST = 0.01183), although not
significant after Bonferroni correction.
236
Fig. 2. Carcharodon carcharias. Network of
unique global haplotypes, based on the partial
mitochondrial DNA control region (842 base
pairs), and shaded to indicate the original cap-
ture area of each haplotype. Large numbers of
nucleotide substitutions between haplotypes
are represented by black boxes unless indi-
cated otherwise with black dots, each repre-
senting 1 substitution. Haplotypes 1 to 14, ob-
served in the present study, have circle sizes
proportional to the frequency of that haplo-
type. Haplotypes 5, 6, 8, 10 and 11 were also
observed by Pardini et al. (2001). Haplotypes
15 to 25 & 36 are exclusive to Pardini et al.
(2001). Haplotypes 26 to 35 are exclusive to
Jorgensen et al. (2010). Haplotypes 37 to 39 are
exclusive to Gubili et al. (2010). Haplotypes 13
and 14 are western Indian Ocean−like (WIOL)
sharks sampled in Australia. Haplotype 21 was
a WIOL identified by Pardini et al. (2001)
Blower et al.: Population genetics of Australian Carcharodon carcharias
The mtDNA and microsatellite FST values in -
creased slightly in magnitude when samples with
WIOL haplotypes were excluded, but the pattern of
FST significance was unchanged. For example, with-
out the WIOL samples, the mtDNA FST between the
eastern and southwestern coasts was 0.15124 (p <
0.0001) and the micro satellite FST between the east-
ern and southwestern samples was 0.00931 (p =
0.03537), and was again non- significant after correc-
tion for multiple comparisons.
Estimates of genetic effective population size
As expected, the contemporary estimates of gen -
etic effective population size (CN e) were less than
the historical estimates (HNe). Coalescent-based
estimates of HNederived from Bayesian search of
microsatellite marker genealogies gave θvalues for
individual loci ranging from 1.2 to 13.0, with a mean
of 4.9 and median of 5.2. Applying general micro -
satellite loci mutation rates and inputting the θpos-
terior distribution mode of 5.36 produced HNeesti-
mates ranging from approximately 3000, using a
faster mutation rate (10−3), to 268 000 for a slower
mutation rate (10−5) (Table 7).
Estimates of CNeresulted in a point estimate of 1512
(95% CI = 122 − , n = 97) for the Australian popula-
tion. This estimate was achieved when Pcrit was 0.06,
i.e. above the Pcrit range (0.02 to 0.05) at which simu-
lated CNeestimates are balanced in precision and
bias relative to the true CNe(Waples & Do 2008,
2010). Finite point-estimates for the eastern coast
population could not be achieved at an acceptably
low Pcrit value (CN e= 380, Pcrit = 0.18, 95% CI = 31 − ,
n = 62). The Pcrit of the southwestern estimate was
more acceptable (CN e= 693, Pcrit = 0.07, 95% CI = 28 −
, n = 32), but above the simulated optimal Pcrit range.
DISCUSSION
Population structure around the
Australian continent
We found maternal genetic popula-
tion subdivision between eastern
and southwestern coastal regions of
Australia. Previous investigations of
white shark populations had revealed
only large-scale mtDNA population
structure consistent with long-term
isolation of the Australian/New Zea -
land population from those of the
western Indian Ocean (FST = 0.81 to
0.93) and the northeastern Pacific
Ocean (FST = 0.68) (Pardini et al. 2001,
Jorgen sen et al. 2010). Immigration
rates as low as 1 in dividual per gener-
ation may obscure population struc-
ture resulting from genetic drift
(Wright 1931, Spieth 1974), but, in
reality, up to 10 migrants per genera-
tion may be required (Mills & Allen-
dorf 1996). The magnitude of mater-
237
n/N Region Eastern Australia Southwestern
(EA) Australia (SWA)
(a) All animals
62/61 EA 0.00927/0.03186*,^
32/30 SWA 0.14174/0.00000*
(b) Juvenile animals only
55/54 EA 0.00140/0.35181
13/12 SWA 0.17348/0.00003*
Table 5. Carcharodon carcharias. Genetic population struc-
ture and significance level (FST/p) for pairwise comparisons
between Australian regions. FST values below diagonal are
based on mitochondrial DNA (mtDNA), and above diagonal,
microsatellite loci (nDNA). N and n represent mtDNA and
nDNA sample sizes, respectively. *: significant at p 0.05;
^: comparison not significant after Bonferroni correction,
α= 0.0125
n/N Province New South Queensland South Australia
Wales (NSW) (QLD) (SA)
43/42 NSW 0.00601/0.21124 0.01183/0.02499*,^
14/14 QLD 0.11933/0.00013* 0.00249/0.32105
30/28 SA 0.15064/0.00000* 0.13546/0.00011*
Table 6. Carcharodon carcharias. Genetic population structure and signifi-
cance level (FST/p) for pairwise comparisons between Australian provinces.
FST values below diagonal are based on mitochondrial DNA (mtDNA), and
above diagonal, microsatellite loci (nDNA). N and n represent mtDNA and
nDNA sample sizes, respectively, and include both adult and juvenile animals.
*: significant at p 0.05; ^: comparison not significant after Bonferroni correc-
tion, α= 0.00833
θHNe(μ1)HNe(μ2)HNe(μ3)
5.36 2681 26813 268125
(4.35−6.60) (2218−3300) (21750−33000) (217500−330000)
Table 7. Carcharodon carcharias. Historic genetic effective population size
estimates (HNe) where genetic diversity (θ) is derived from Australian-sourced
nuclear DNA microsatellite alleles. Microsatellite loci mutation rates (μ: muta-
tions per gamete per generation), are arranged from faster to slower: μ1= 10−3,
μ2= 10−4 and μ3= 10−5. Ranges in brackets represent lower and upper 95%
confidence intervals
Mar Ecol Prog Ser 455: 229–244, 2012
nal population differentiation within Australian
waters was less than that between Australia and
other continents, indicating that maternal gene flow
and hence migration is greater at an Australian
regional scale, although still constrained. Following
Pardini et al. (2001), we propose that reproductive
philopatry is a behavioural barrier restricting mater-
nal gene flow around Australia, despite the high
vagility of white sharks (Bruce et al. 2006) and a lack
of current or ancient physical barriers. The mater-
nally inherited mtDNA white shark population struc-
ture evident in predominately immature Australian
populations suggests that, for many generations,
females have returned to the same coastal regions
for parturition and that the juvenile sharks sampled
have remained in the natal area. Such reproductive
philopatry is observed in several shark species
(Hueter et al. 2005), including other vagile but
coastally orientated species such as sandbar Car-
charhinus plumbeus (Heist et al. 1995) and bull C.
leucas (Tillett et al. 2012) sharks.
While the present study is consistent with female
philopatric behaviour reported for this species, it is
the first to have detected population structure in
white sharks with biparentally inherited nDNA mi -
crosatellite loci. One of 6 microsatellite loci, Iox10,
gave an FST of 0.01183 (p = 0.02499) between regions
which was significant at 0.05 but not after Bonferroni
correction. The biological significance of this result
cannot be ruled out given the conservative nature of
Bonferroni adjustment (Rice 1989). This population
structure finding suggests that it may not be just
females that exhibit philopatry, but that male white
sharks also tend to return to the same region to mate,
echoing recent tracking findings (Domeier & Nasby-
Lucas 2012). This pattern needs to be confirmed with
other genetic markers, as locus-specific effects (e.g.
selection on closely linked transcription products)
could bias results. However, if selection was operat-
ing on this locus, the results may be indicative of
adaptive genetic differences between eastern and
southwestern populations. Inclusion of locus Iox10,
developed specifically for the short-fin mako shark
Isurus oxyrinchus and experimentally amplified in
white sharks by Schrey & Heist (2002), demonstrates
the value of attempting cross-species amplification of
microsatellite loci for related elasmobranch species.
The locus was unavailable to Pardini et al. (2001) in
their microsatellite analyses, but, if utilised, it may
have revealed evidence of male population structure
and philopatry between continents.
There was an order of magnitude difference be -
tween overall microsatellite FST compared to mtDNA
CR FST, which was larger than theoretical expecta-
tions. In the absence of sex-biased dispersal and
assuming neutrality, there should only be a 4-fold
difference in the effective population size of mtDNA
relative to nuclear markers (Birky et al. 1983, 1989).
The difference observed here may have been ampli-
fied by sex-biased dispersal, where females are more
philopatric than males. Taking a random sample of
adult individuals immediately after dispersal and
before reproduction, and comparing the nuclear-
encoded population structure found for each gender,
would allow evaluation of gender bias in adult be -
haviours such as reproductive philopatry or dispersal
for feeding. The gender with the highest dispersal
should show less pronounced genetic population
structure (Goudet et al. 2002, Prugnolle & de Meeus
2002). However, the low sample size and wide tem-
poral distribution of our samples, combined with a
likely high proportion of pre-dispersal juveniles, pre-
cluded us from performing this analysis. Further
investigation is required to clarify whether the levels
of philopatry differ between genders in this species.
Founder events also have the potential to disrupt the
theoretical ratio between the magnitude of FST esti-
mated from nuclear and mtDNA markers. The pres-
ence of WIOL mtDNA haplotypes on the eastern
coast of Australia suggests that founder events may
play a role in white shark population dynamics, and
the relative importance of this should be factored into
future studies of genetic population structure in this
species.
Concordance with tracking research
The observed genetic population structure of white
sharks in Australia is broadly concordant with the
tracking research of Bruce & Bradford (2012), which
showed cyclic movements of juvenile white sharks
(175 to 260 cm) between fixed locations along the
eastern Australian coast. These authors found the
degree of juvenile site fidelity to be high and con-
stant over several years, consistent with the nursery
definition of Heupel et al. (2007). Furthermore, al -
though juvenile white sharks have been tracked to
New Zealand, indicating a capacity for wide-ranging
eastward dispersal, none have been tracked moving
west via Tasmania and Bass Strait into southwestern
Australian waters (Fig. 1) (Bruce et al. 2006, Bruce
& Bradford 2012). These tracking results identify
discrete east coast seasonal nursery areas and show
constrained movement around the Australian conti-
nent, supporting our genetic population structure
238
Blower et al.: Population genetics of Australian Carcharodon carcharias
findings of restricted gene flow between the eastern
and southwestern regions. Finding mtDNA popula-
tion structure between Queensland and New South
Wales along the east coast of Australia was surprising
due to their close geographical proximity, but may
indicate population structure linked to unidentified
Queensland nursery locations. Fine-scale tracking
data from juveniles along the Queensland coast
could be analysed to test this hypothesis. Satellite
and photo-identification tracking of white sharks in
the northwestern Pacific shows repeated male site
fidelity to aggregation sites in the region (Domeier
& Nasby-Lucas 2007, 2008, 2012, Jorgensen et al.
2010). It is not yet known if these sites are used for
breeding, but, if so, this would support the possibility
for male philopatric behaviour as indicated by our
micro satellite population structure results.
Transoceanic dispersal events
The presence of a group of WIOL haplotypes along
the Australian east coast in the Pacific Ocean was
unexpected. Transoceanic mtDNA genetic homo-
geneity would be expected if gene flow were occur-
ring on a regular and extended basis. Instead, strong
mtDNA heterogeneity is observed between trans -
oceanic populations (Pardini et al. 2001, Jorgensen et
al. 2010), indicating long-term genetic isolation,
despite the ability of white sharks to travel the dis-
tance. Pardini et al. (2001) found one instance of a
350 cm male white shark (WIOL Haplotype 21; Fig. 2;
GenBank Accession No. AY026211) captured in Tas-
mania whose mtDNA haplotype clustered with the
clade of WIO sharks sampled in South African
waters, reinforcing the author’s male-biased disper-
sal hypothesis. Here, we found 5 juvenile WIOL
sharks all smaller than 250 cm (4 female, 1 male) on
the eastern Australian coast. Two non-mutually ex -
clusive explanations are possible: (1) extant trans -
oceanic dispersal or migration, such as a pulse of
juvenile immigration from the western Indian Ocean
or occasional pupping but not recruitment of WIOL
females in Australia or (2) transoceanic immigration
in the past leading to the establishment of a mater-
nally related WIOL family along Australia’s eastern
coast. Bruce & Bradford (2012) tracked a 210 cm
juvenile that moved from eastern Australia to New
Zealand, indicating that small juvenile white sharks
can be highly vagile. A larger sub-adult female
(380 cm) was also tracked in a return excursion
across the Indian Ocean between South Africa and
Western Australia by Bonfil et al. (2005), indicating
that long-distance transoceanic dispersal in sub-
adult sharks is possible. WIO females may occasion-
ally pup in Australian waters, which could be a
manifestation of low levels of female straying, as pro-
posed for philopatric blacktip sharks (Hueter et al.
2005), a behaviour which could assist philopatric spe-
cies to colonise new territory. A substantial propor-
tion (9%) of the east coast samples comprised WIOL
sharks, supporting the conjecture that they represent
a group with recent WIO ancestry that are resident
along the eastern Australian coast. A re cently
observed similarity between Australian and Mediter-
ranean white shark mtDNA haplotypes was attrib-
uted to a past (i.e. during the Pleistocene) naviga-
tional error of Indo-Pacific sharks (Gubili et al. 2010).
Once arrived, philopatric behaviour may have pro-
moted the establishment of a Mediterranean popula-
tion. A similar scenario could have founded a WIOL
family on the Australian coastline.
The presence of haplotypes initially sampled in
New Zealand by others (Pardini et al. 2001) (com-
posed of 4 males, 1 female and 1 shark of unknown
gender, all 210 cm) in eastern and southwestern
Australian waters is consistent with electronic track-
ing evidence demonstrating movements of juvenile
sharks from Australia to New Zealand (Bruce et al.
2006, Bruce & Bradford 2012) and vice versa (Francis
et al. 2012). However, given the potential for philo -
patric reproductive behaviour and restricted gene
flow, further research is needed to test for white
shark genetic population structure between Aus-
tralia and New Zealand.
Historic effective population size
Our estimates of HNeindicate that a substantial
ancestral effective population of white sharks (mid-
range estimate of ~30 000) existed for 1000s of gener-
ations. The number of generations over which the
HNeis averaged is approximately 4HNe(Hare et al.
2011), which, for our samples, is between 11 000 and
1 073 000 generations (for mutation rates of 10−3 and
10−5, respectively), equating to 236 000 23.6 million
yr (rounded to 1000s), assuming a 22 yr generation
time (Dulvy et al. 2008). Few, if any, HNeestimates
have been derived for a shark using a coalescent
sampling technique. Whales, however, share many
life-history traits with white sharks (long life, slow
maturation and low fecundity), and have coalescent
HNeestimates in the 10 000s, similar to our mid-
range results. Roman & Palumbi (2003) used the
mitochondrial CR to estimate the female historic
239
Mar Ecol Prog Ser 455: 229–244, 2012
genetic effective population size of the humpback
whale Megaptera novaeangliae, fin whale Balaeno -
ptera physalus and minke whale Balaenoptera acu-
torostrata, as 34 000, 51 000 and 38 000, respectively,
which they translated to census sizes of 240 000,
360 000 and 265 000.
Coalescent genealogy sampling methods are gen-
erally regarded as preferable due to their ability to
account for migration as well as genealogical ambi-
guity (Kuhner 2009); however, white shark migration
was not included in our coalescent model. Neverthe-
less, historic estimates of effective sizes encompass
many 1000s of generations of mutation and genetic
drift since the most recent common ancestor of all the
lineages (represented by the allele set), and should
be treated with caution for several reasons. Fluctua-
tions in population size may require many genera-
tions to return to a mutation-drift equilibrium and
also to reduce HNeto a harmonic mean (biased
towards low values), which probably underestimates
the true HNe(Hare et al. 2011). Another great uncer-
tainty lies in the mutation rates used, which may be
highly variable between loci, and are believed to be
unusually slow in elasmobranchs (Martin et al. 1992,
Martin 1999), possibly introducing orders of magni-
tude difference between the true HNeand the
derived estimate (Hare et al. 2011). Furthermore, un -
predictable migration rates create potential for signi -
ficant HNeerror. Climate and geographical changes
over millennia may have facilitated transoceanic
gene flow, thereby inflating genetic diversity, inflat-
ing the HNesize and altering the HNescale from a
single population to a metapopulation (Portnoy
2010). Thus, our HNeestimates for white sharks are
the first evidence for a substantial historical white
shark population, but may have an indeterminate
temporal and spatial scale and are potentially unre-
lated to the CNeestimates.
Contemporary effective population size
For an idealized population to retain enough ge -
netic variability and ensure evolutionary potential,
its CNeshould be above approximately 500 to
1000 breeding individuals (Franklin 1980, Franklin
& Frankham 1998), although Lande (1995) recom-
mends a CNe> 5000. Our white shark CNeestimate
for Australia, approximately 1500 breeding indi -
viduals, is above widely accepted CNethresholds re -
quired to retain evolutionary potential, avoid accu-
mulation of deleterious alleles (CNe> 1000) and
avoid inbreeding depression (CNe> 50) (Frankham
2002). However, the CNeestimates for Australia as a
whole or the east coast alone may be unpredictably
influenced by the population structure found within
these groupings. In this respect, the southwestern
coast CNeestimate is considered the most robust as
this population of approximately 700 breeding indi-
viduals appears substantially genetically isolated, a
factor which would contribute to the loss of evolu-
tionary potential and harmful allele accumulation
should the actual CNebe closer to the lower 95% CI
boundaries of our estimates.
It is important to qualify these results, as, although
CNeis a powerful predictor when assumptions are
satisfied (non-overlapping generations, random
mating, no migration or selection, equal sex ratio and
constant population size), deviations from these
assumptions of the theoretical Wright-Fisher pop -
ulation may lead to bias or misinterpretation. Biases
associated with the linkage disequilibrium (LD)
method of CNeestimation are complex, and the
direction of bias is not easily quantified (for sum-
maries see Luikart et al. 2010, Waples & England
2011). However, the LD method shows robustness to
some real-world situations, such as variable repro-
ductive success, uneven sex ratios and selection
(Waples 2006, Araki et al. 2007). Fluctuations in
population size can influence LD CNeestimates, and
a population increasing from a genetic bottleneck
may reduce the CNeestimate for several generations
(Waples 2006). Given the low fecundity of white
sharks, it seems unlikely that the population has
increased substantially since protection of Australian
white sharks in 1997, but this is impossible to quan-
tify without historical population trend data. The
extent to which migration affects CNeestimates is
unclear but appears to be low when the migration
rate is <5 to 10% and in equilibrium (constant rate of
exchange) between genetically distinct populations
(Waples & England 2011). For Australian white
sharks, maternal population structure indicates that
gene flow is constrained around the continent and
severely restricted across oceans; however, the level
of male gene flow remains inconclusive despite the
slight biparental population structure found here. As
neither definitive biparental gene flow nor clear
genetic differentiation can be ascertained, it is un -
clear whether our CNeestimates represent a discrete
population or a broader metapopulation (Luikart et
al. 2010).
The application of the LD method to species with
overlapping generations has not been investigated in
depth, and Luikart et al. (2010) believe it can intro-
duce substantial bias. Waples & Do (2010) provision-
240
Blower et al.: Population genetics of Australian Carcharodon carcharias
ally suggest that the CNeproducing a generation is
loosely estimated if the generation length of a species
is equivalent to the number of cohorts sampled.
White sharks have an estimated generation time of
22 yr (Bruce 2008, Dulvy et al. 2008), and the present
study has samples spanning 21 yr, which theoreti-
cally approximates this requirement. In reality, the
high proportion of juveniles makes it unlikely that so
many discrete cohorts could have been sampled
here, making the impact of overlapping generations
impossible to quantify.
To better resolve transoceanic gene flow and the
effect of overlapping generations, thereby testing the
validity of our CNeestimates, future studies will
require larger sample sizes (200 or greater) taken
from clearly defined cohorts and analysed using a
more extensive nDNA loci set (>10 loci) (Tallmon et
al. 2010, Waples & Do 2010).
Obtaining a population census size (NC) for com-
parison to CNegives substantially more insight into
the status of the study species (Luikart et al. 2010),
but proved unfeasible for the present study. Sam-
pling and tracking elusive white sharks is time-con-
suming and hazardous, which restricts the amount of
currently available data. The only substantial long-
term records of Australian white shark numbers are
the shark capture logs from bather protection pro-
grams. Unfortunately, many changes over the years
in the technology used, beaches monitored and in -
formation recorded (Reid & Krogh 1992, Dudley &
Simpfendorfer 2006, Green et al. 2009) have ren-
dered population estimations from this source highly
problematic (Walker 1998), and may be further con-
founded by philopatric behaviour that can expose
these data to a localized stock depletion effect (Hueter
et al. 2005).
Conservation implications
Establishing the extent to which populations are
genetically subdivided allows their identification as
potentially demographically independent conserva-
tion management units (MUs), which may require
tailored conservation strategies (Palsbøll et al. 2007).
Our findings of white shark population structure in
Australia imply that migration rates are low between
the eastern and southwestern regions (<10 sharks
generation−1), and, consequently, these groups are
genetically isolated from each other to a substantial
degree. As a precaution, it would be advisable to
consider these populations as distinct MUs until their
genetic diversity, population structure and size can
be better resolved by a targeted rather than oppor-
tunistic white shark genetic study. Additionally, the
levels of white shark migration between oceans must
be established with higher statistical power before
gene flow can be rejected and each ocean basin
population definitively identified as separate man-
agement units.
The finding of genetically separate populations
with recurrent habitat utilization for reproduction
is of direct relevance to conservation management
strategies for white sharks in Australia. The Aus-
tralian government’s white shark recovery plan
(Environment Australia 2002, 2008, 2010) prescribes
identification of critical white shark habitat, popula-
tion size and ‘genetic status’ as key aims. The present
study contributes to these aims by highlighting the
extent of female white shark population structure,
the likelihood of reproductive philopatry and by
adding support to tracking research suggesting exis-
tence of separate nursery areas which appear to be
important juvenile habitats (Bruce & Bradford 2012).
Our study presents the first contemporary effective
population size estimates and genetic health charac-
terisation of the white shark. Our CNeestimates have
wide confidence intervals and are preliminary due to
low numbers of genetic markers and samples, so they
must be interpreted judiciously. However, the data
presented here will be valuable for comparisons
with future studies when the genetic health of white
sharks in Australian waters is further investigated.
CONCLUSIONS
Our genetic analysis of white sharks suggests pop-
ulation subdivision at a fine spatial scale and be -
havioural dynamics which were not anticipated. We
detected genetic structure in the population between
the eastern and southwestern coasts of Australia
using both mitochondrial and microsatellite markers.
The absence of substantial historical geographical
barriers between the 2 regions and the high vagility
of juvenile and adult white sharks suggest a behav-
ioural reason for the restricted gene flow. This popu-
lation differentiation may be the result of reproduc-
tive philopatry, whereby sharks return to the same
general location for breeding and parturition over
many generations. Genetic population structure seen
in biparentally inherited loci suggests that males also
exhibit reproductive philopatry and may not be pan-
mictic on a continental or a global scale. Furthermore,
detecting population structure between groups of
immature sharks concords with tracking evidence
241
Mar Ecol Prog Ser 455: 229–244, 2012
242
showing that juveniles inhabit nursery areas for
extended periods prior to dispersal, consistent with
nursery-area philopatry. We also discovered a group
of white sharks, more similar in mtDNA haplotype to
western Indian Ocean white sharks, present in east
coast Australian waters, and sharks with haplotypes
first identified from New Zealand−caught sharks
present along both the southwestern and eastern
coasts of Australia, indicating possible transoceanic
and trans−Tasman Sea dispersal or migration events.
We have also established a sizable estimate of his-
toric effective population size and made the first ini-
tial estimates of current Australian white shark effec-
tive population size, which appear to be low and may
suggest that populations could risk deleterious genetic
consequences. Populations of this species in Aus-
tralia appear to be more complicated and fragile than
previously supposed, demanding further research to
ensure conservation success.
Acknowledgements. White shark genotypes can be found
at http://www2.dpi.qld.gov.au/extra/era/index.html. This
work was supported by the Australian Research Council
Centre of Excellence for Coral Reef Studies grant to J. M.
Pandolfi. Many thanks to D. Broderick, M. Macbeth, J. Mor-
gan and R. Street, for their guidance and knowledge.
Thanks also to M. Bennett, R. Bradford, L. Marshall, N.
Otway and J. Werry for information and shark samples. V.
Peddemors and R. McAuley have also been instrumental in
supplying samples. Thanks to A. Kanani for laboratory and
writing assistance. Thanks to R. Waples who provided
advice on the estimation of CNe, and P. E. Jorde for pro -
viding useful comments on an earlier draft. Four anony-
mous reviewers provided useful feedback for which we are
grateful.
LITERATURE CITED
Alter SE, Rynes E, Palumbi SR (2007) DNA evidence for his-
toric population size and past ecosystem impacts of gray
whales. Proc Natl Acad Sci USA 104: 15162−15167
Anderson SD, Chapple TK, Jorgensen SJ, Klimley AP, Block
BA (2011) Long-term individual identification and site
fidelity of white sharks, Carcharodon carcharias, off
California using dorsal fins. Mar Biol 158: 1233−1237
Araki H, Waples RS, Blouin MS (2007) A potential bias in the
temporal method for estimating Nein admixed popula-
tions under natural selection. Mol Ecol 16: 2261−2271
Bagley MJ, Lindquist DG, Geller JB (1999) Microsatellite
variation, effective population size, and population ge -
netic structure of vermilion snapper, Rhomboplites auro -
rubens, off the southeastern USA. Mar Biol 134: 609−620
Baum JK, Myers RA, Kehler DG, Worm B, Harley SJ,
Doherty PA (2003) Collapse and conservation of shark
populations in the Northwest Atlantic. Science 299:
389−392
Beerli P (2008) Migrate Version 3.0—A maximum likelihood
and Bayesian estimator of gene flow using the coal -
escent. Available at: http: //popgen.sc.fsu.edu/Migrate/
Migrate-n.html (accessed on 17 August 2011)
Birky CW, Maruyama T, Fuerst P (1983) An approach to
population and evolutionary genetic theory for genes in
mitochondria and chloroplasts, and some results. Gene -
tics 103: 513−527
Birky CW Jr, Fuerst P, Maruyama T (1989) Organelle gene
diversity under migration, mutation, and drift: equilib-
rium expectations, approach to equilibrium, effects of
heteroplasmic cells, and comparison to nuclear genes.
Genetics 121: 613−627
Bonfil R, Meyer M, Scholl MC, Johnson R and others (2005)
Transoceanic migration, spatial dynamics, and popula-
tion linkages of white sharks. Science 310: 100−103
Boustany AM, Davis SF, Pyle P, Anderson SD, Le Boeuf BJ,
Block BA (2002) Satellite tagging Expanded niche for
white sharks. Nature 415: 35−36
Boustany AM, Reeb CA, Block BA (2008) Mitochondrial DNA
and electronic tracking reveal population structure of At-
lantic bluefin tuna (Thunnus thynnus). Mar Biol 156: 13−24
Bruce BD (2008) The biology and ecology of the white shark,
Carcharodon carcharias. In: Camhi MD, Pikitch EK, Bab-
cock EA (eds) Sharks of the open ocean: biology, fisheries
and conservation. Blackwell Scientific, Oxford, p 69−81
Bruce BD, Bradford RW (2012) Habitat use and spatial
dynamics of juvenile white sharks, Carcharodon car-
charias, in eastern Australia. In: Domeier ML (ed) Global
perspectives on the biology and life history of the great
white shark. CRC Press, Boca Raton, FL, p 225–253
Bruce BD, Stevens JD, Malcolm H (2006) Movements and
swimming behaviour of white sharks (Carcharodon car-
charias) in Australian waters. Mar Biol 150: 161−172
Castro ALF, Stewart BS, Wilson SG, Hueter RE and others
(2007) Population genetic structure of Earth’s largest
fish, the whale shark (Rhincodon typus). Mol Ecol 16:
5183−5192
Clement M, Posada D, Crandall KA (2000) TCS: a computer
program to estimate gene genealogies. Mol Ecol 9:
1657−1659
Domeier ML, Nasby-Lucas N (2007) Annual re-sightings of
photographically identified white sharks (Carcharodon
carcharias) at an eastern Pacific aggregation site (Gua -
dalupe Island, Mexico). Mar Biol 150: 977−984
Domeier ML, Nasby-Lucas N (2008) Migration patterns of
white sharks Carcharodon carcharias tagged at Gua -
dalupe Island, Mexico, and identification of an eastern
Pacific shared offshore foraging area. Mar Ecol Prog Ser
370: 221−237
Domeier M, Nasby-Lucas N (2012) Sex-specific migration
patterns and sexual segregation of adult white sharks,
Carcharodon carcharias, in the northeastern Pacific. In:
Domeier ML (ed) Global perspectives on the biology and
life history of the great white shark. CRC Press, Boca
Raton, FL, p 133–146
Dudley SFJ, Simpfendorfer CA (2006) Population status of
14 shark species caught in the protective gillnets off
KwaZulu-Natal beaches, South Africa, 1978−2003. Mar
Freshw Res 57: 225−240
Dulvy NK, Baum JK, Clarke S, Compagno LJV and others
(2008) You can swim but you can’t hide: the global status
and conservation of oceanic pelagic sharks and rays.
Aquat Conserv 18: 459−482
Environment Australia (2002) White shark (Carcharodon
carcharias) recovery plan. Available at:
www.environ-
ment.gov.au/coasts/publications/gwshark-
plan/index.html (accessed on 17 August 2011)
Blower et al.: Population genetics of Australian Carcharodon carcharias
Environment Australia (2008) Review of the white shark
recovery plan 2002. Available at: www.environment.
gov.au/biodiversity/threatened/publications/recovery/
white-shark.html (accessed on 17 August 2011)
Environment Australia (2010) Draft national recovery plan
for the white shark (Carcharodon carcharias). Available
at: www.environment.gov.au/biodiversity/threatened/
publications/recovery/white-shark.html (accessed on 17
August 2011)
Excoffier L, Lischer HEL (2010) Arlequin suite Ver 3.5: a new
series of programs to perform population genetics analy-
ses under Linux and Windows. Mol Ecol Resour 10:
564−567
Feldheim KA, Gruber SH, Ashley MV (2002) The breeding
biology of lemon sharks at a tropical nursery lagoon. Proc
R Soc B 269: 1655−1661
Francis M, Duffy C, Bonfil R, Manning MJ (2012) The third
dimension: vertical habitat use by white sharks, Carchar-
odon carcharias, in New Zealand and tropical waters of
the Southwest Pacific Ocean. In: Domeier ML (ed) Global
perspectives on the biology and life history of the great
white shark. CRC Press, Boca Raton, FL, p 319342
Frankham R (2002) Genetically viable populations. In: Bal-
lou JD, Briscoe DA, McInness KH (eds) Introduction
to conservation genetics. Cambridge University Press,
Cambridge, p 336−359
Frankham R (2005) Genetics and extinction. Biol Conserv
126: 131−140
Franklin IR (1980) Evolutionary change in small populations.
In: Soule ME, Wilcox BA (eds) Conservation biology: an
evolutionary-ecological perspective. Sinauer Associates,
Sunderland, MA, p 135−150
Franklin IR, Frankham R (1998) How large must populations
be to retain evolutionary potential? Anim Conserv 1:
69−70
Goudet J, Perrin N, Waser P (2002) Tests for sex-biased dis-
persal using bi-parentally inherited genetic markers.
Mol Ecol 11: 1103−1114
Green M, Ganassin C, Reid D (2009) Report into the NSW
shark meshing (bather protection) program. Available at:
www.dpi.nsw.gov.au/fisheries/info/sharksmart/meshing
(accessed on 17 August 2011)
Gubili C, Johnson R, Gennari E, Oosthuizen WH and others
(2009) Concordance of genetic and fin photo identifica-
tion in the great white shark, Carcharodon carcharias, off
Mossel Bay, South Africa. Mar Biol 156: 2199−2207
Gubili C, Bilgin R, Kalkan E, Karhan SÜ and others (2010)
Antipodean white sharks on a Mediterranean walka-
bout? Historical dispersal leads to genetic discontinuity
and an endangered anomalous population. Proc R Soc B
278: 1679−1686
Hare MP, Nunney L, Schwartz MK, Ruzzante DE and others
(2011) Understanding and estimating effective popula-
tion size for practical application in marine species man-
agement. Conserv Biol 25: 438−449
Heist EJ, Graves JE, Musick JA (1995) Population genetics
of the sandbar shark (Carcharhinus plumbeus) in the
Gulf of Mexico and mid-Atlantic bight. Copeia 1995:
555−562
Heupel MR, Carlson JK, Simpfendorfer CA (2007) Shark
nursery areas: concepts, definition, characterization and
assumptions. Mar Ecol Prog Ser 337: 287−297
Hill WG (1981) Estimation of effective population size from
data on linkage disequilibrium. Genet Res 38: 209−216
Hoarau G, Boon E, Jongma DN, Ferber S and others (2005)
Low effective population size and evidence for inbreed-
ing in an overexploited flatfish, plaice (Pleuronectes
platessa L.). Proc R Soc B 272: 497−503
Hueter RE, Heupel MR, Heist EJ, Keeney DB (2005) Evi-
dence of philopatry in sharks and implications for the
management of shark fisheries. J Northwest Atl Fish Sci
35: 239−247
Jorgensen SJ, Reeb CA, Chapple TK, Anderson S and others
(2010) Philopatry and migration of Pacific white sharks.
Proc R Soc B 277: 679−688
Keeney DB, Heupel MR, Hueter RE, Heist EJ (2005)
Microsatellite and mitochondrial DNA analyses of the
genetic structure of blacktip shark (Carcharhinus lim-
batus) nurseries in the northwestern Atlantic, Gulf of
Mexico, and Caribbean Sea. Mol Ecol 14: 1911−1923
Kuhner MK (2009) Coalescent genealogy samplers: win-
dows into population history. Trends Ecol Evol 24: 86−93
Lande R (1995) Mutation and conservation. Conserv Biol 9:
782−791
Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf
FW (2010) Estimation of census and effective population
sizes: the increasing usefulness of DNA-based ap proa -
ches. Conserv Genet 11: 355−373
Macbeth GM, Broderick D, Ovenden JR, Buckworth RC
(2011) Likelihood-based genetic mark-recapture esti-
mates when genotype samples are incomplete and con-
tain typing errors. Theor Popul Biol 80:185–196
Martin AP (1999) Substitution rates of organelle and nuclear
genes in sharks: implicating metabolic rate (again). Mol
Biol Evol 16: 996−1002
Martin AP, Naylor GJP, Palumbi SR (1992) Rates of mito-
chondrial DNA evolution in sharks are slow compared
with mammals. Nature 357: 153−155
Mills LS, Allendorf FW (1996) The one-migrant-per-genera-
tion rule in conservation and management. Conserv Biol
10: 1509−1518
Narum SR, Stephenson JJ, Campbell MR (2007) Genetic
variation and structure of Chinook salmon life history
types in the Snake River. Trans Am Fish Soc 136:
1252−1262
Palsbøll PJ, Bérubé M, Allendorf FW (2007) Identification of
management units using population genetic data. Trends
Ecol Evol 22: 11−16
Palstra FP, Ruzzante DE (2008) Genetic estimates of contem-
porary effective population size: What can they tell us
about the importance of genetic stochasticity for wild
population persistence? Mol Ecol 17: 3428−3447
Pardini AT, Jones CS, Scholl MC, Noble LR (2000) Isolation
and characterization of dinucleotide microsatellite loci in
the great white shark, Carcharodon carcharias. Mol Ecol
9: 1176−1178
Pardini AT, Jones CS, Noble LR, Kreiser B and others (2001)
Sex-biased dispersal of great white sharks. Nature 412:
139−140
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis
in Excel. Population genetic software for teaching and
research. Mol Ecol Notes 6: 288−295
Pepperell JG (1992) Trends in the distribution, species com-
position and size of sharks caught by gamefish anglers
off southeastern Australia, 1961−90. Aust J Mar Fresh-
water Res 43: 213−225
Portnoy D (2010) Molecular insights into elasmobranch
reproductive behavior for conservation and manage-
ment. In: Carrier JC, Musick JA, Heithaus MR (eds)
Sharks and their relatives. II. Biodiversity, adaptive
243
Mar Ecol Prog Ser 455: 229–244, 2012
physiology, and conservation. CRC Press, Boca Raton,
FL, p 435−457
Portnoy DS, McDowell JR, McCandless CT, Musick JA,
Graves JE (2009) Effective size closely approximates the
census size in the heavily exploited western Atlantic
population of the sandbar shark, Carcharhinus plum -
beus. Conserv Genet 10: 1697−1705
Prugnolle F, de Meeus T (2002) Inferring sex-biased disper-
sal from population genetic tools: a review. Heredity 88:
161−165
Ralls K, Ballou J, Brownell RL (1983) Genetic diversity in
California sea otters theoretical considerations and
management implications. Biol Conserv 25: 209−232
Reed DH, Frankham R (2003) Correlation between fitness
and genetic diversity. Conserv Biol 17: 230−237
Reid D, Krogh M (1992) Assessment of catches from protec-
tive shark meshing off NSW beaches between 1950 and
1990. Mar Freshw Res 43: 283−296
Riccioni G, Landi M, Ferrara G, Milano I and others (2010)
Spatio-temporal population structuring and genetic
di versity retention in depleted Atlantic bluefin tuna of
the Mediterranean Sea. Proc Natl Acad Sci USA 107:
2102−2107
Rice WR (1989) Analyzing tables of statistical tests. Evolu-
tion 43: 223−225
Roman J, Palumbi SR (2003) Whales before whaling in the
North Atlantic. Science 301: 508−510
Rousset F (2008) GENEPOP ‘007: a complete re-implemen-
tation of the GENEPOP software for Windows and Linux.
Mol Ecol Resour 8: 103−106
Saitou N, Nei M (1987) The neighbor-joining method: a new
method for reconstructing phylogenetic trees. Mol Biol
Evol 4: 406−425
Schrey AW, Heist EJ (2002) Microsatellite markers for the
shortfin mako and cross-species amplification in lamni-
formes. Conserv Genet 3: 459−461
Schrey AW, Heist EJ (2003) Microsatellite analysis of popu-
lation structure in the shortfin mako (Isurus oxyrinchus).
Can J Fish Aquat Sci 60: 670−675
Schultz JK, Feldheim KA, Gruber SH, Ashley MV, McGov-
ern TM, Bowen BW (2008) Global phylogeography and
seascape genetics of the lemon sharks (genus Nega -
prion). Mol Ecol 17: 5336−5348
Shelden KEW, DeMaster DP, Rugh DJ, Olson AM (2001)
Developing classification criteria under the US Endan-
gered Species Act: bowhead whales as a case study.
Conserv Biol 15: 1300−1307
Smith SE, Au DW, Show C (1998) Intrinsic rebound poten-
tials of 26 species of Pacific sharks. Mar Freshw Res 49:
663−678
Spieth PT (1974) Gene flow and genetic differentiation.
Genetics 78: 961−965
Stevens JD, Bonfil R, Dulvy NK, Walker PA (2000) The
effects of fishing on sharks, rays, and chimaeras (chon-
drichthyans), and the implications for marine ecosys-
tems. ICES J Mar Sci 57: 476−494
Sulaiman ZH, Ovenden JR (2010) Population genetic evi-
dence for the east-west division of the narrow-barred
Spanish mackerel (Scomberomorus commerson, Perci-
formes: Teleostei) along Wallace’s Line. Biodivers Con-
serv 19: 563−574
Tallmon DA, Gregovich D, Waples RS, Baker CS and others
(2010) When are genetic methods useful for estimating
contemporary abundance and detecting population
trends? Mol Ecol Resour 10: 684−692
Tamura K, Nei M (1993) Estimation of the number of
nucleotide substitutions in the control region of mito-
chondrial DNA in humans and chimpanzees. Mol Biol
Evol 10: 512−526
Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: mole-
cular evolutionary genetics analysis (MEGA) software
Version 4.0. Mol Biol Evol 24: 1596−1599
Tillett BJ, Meekan MJ, Field IC, Thorburn DC, Ovenden
J (2012) Evidence for reproductive philopatry in the bull
shark Carcharhinus leucas. J Fish Biol 80:2140–2158
Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P
(2004) MICRO-CHECKER: software for identifying and
correcting genotyping errors in microsatellite data. Mol
Ecol Notes 4: 535−538
Walker TI (1998) Can shark resources be harvested sustain-
ably? A question revisited with a review of shark fish-
eries. Mar Freshw Res 49: 553−572
Waples RS (2006) A bias correction for estimates of effective
population size based on linkage disequilibrium at un -
linked gene loci. Conserv Genet 7: 167−184
Waples RS, Do C (2008) LDNE: a program for estimating
effective population size from data on linkage disequi -
librium. Mol Ecol Resour 8: 753−756
Waples RS, Do C (2010) Linkage disequilibrium estimates of
contemporary Neusing highly variable genetic markers:
a largely untapped resource for applied conservation
and evolution. Evol Appl 3: 244−262
Waples RS, England PR (2011) Estimating contemporary
effective population size on the basis of linkage disequi-
librium in the face of migration. Genetics 189: 633−644
Wright S (1931) Evolution in Mendelian populations. Gene -
tics 16: 97−159
Wright S (1950) Genetical structure of populations. Nature
166: 247−249
Wright S (1965) The interpretation of population structure
by F-statistics with special regard to systems of mating.
Evolution 19: 395−420
244
Editorial responsibility: Philippe Borsa,
Montpellier, France
Submitted: August 23, 2011; Accepted: February 6, 2012
Proofs received from author(s): May 8, 2012
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The blue skate (Dipturus batis) has a patchy distribution across the North-East Atlantic Ocean, largely restricted to occidental seas around the British Isles following fisheries-induced population declines and extirpations. The viability of remnant populations remains uncertain, and could be impacted by continued fishing and bycatch pressure and the projected impacts of climate change. We genotyped 503 samples of D. batis, obtained opportunistically from the widest available geographic range, across 6,350 single nucleotide polymorphisms (SNPs) using a reduced-representation sequencing approach. Genotypes were used to assess the species’ contemporary population structure, estimate effective population sizes, and identify putative signals of selection in relation to environmental variables using a seascape genomics approach. We identified genetic discontinuities between inshore (British Isles) and offshore (Rockall and Faroe Island) populations, with differentiation most pronounced across the deep waters of the Rockall Trough. Effective population sizes were largest in the Celtic Sea and Rockall, but low enough to be of potential conservation concern among Scottish and Faroese sites. Among the 21 candidate SNPs under positive selection was one significantly correlated with environmental variables predicted to be affected by climate change, including bottom temperature, salinity, and pH. The paucity of well annotated elasmobranch genomes precluded us from identifying a putative function for this SNP. Nevertheless, our findings suggest that climate change could inflict a strong selective force upon remnant populations of D. batis, further constraining its already restricted habitat. Furthermore, the results provide fundamental insights on the distribution, behaviour, and evolutionary biology of D. batis in the North-East Atlantic that will be useful for the establishment of conservation actions for this and other critically endangered elasmobranchs.
... The species is described to occur in seven general regions: southern Africa, Australia/New Zealand, the western North Atlantic (WNA), the southwest Atlantic, the Mediterranean, and the northwest and northeast Pacific (NEP) (Compagno et al., 1997). While population structure is not clearly defined within and among all regions, genetically distinct groups exist at the regional level such as in the WNA and southern Africa (O'Leary et al., 2015) and at finer scales such as in southern-western and eastern Australia/New Zealand (Blower et al., 2012;Gubili et al., 2015;Hillary et al., 2018). As a highly migratory species, the white shark has been shown to undertake long-distance movements along continental shelves, forays into pelagic waters, and infrequently across ocean basins (Bonfil et al., 2005;Weng et al., 2007a;Domeier and Nasby-Lucas, 2008;Duffy et al., 2012), with no evidence of trans-equatorial movements (Jorgensen et al., 2010). ...
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