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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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Mar Ecol Prog Ser
Vol. 455: 229–244, 2012
doi: 10.3354/meps09659 Published May 30
© Inter-Research 2012 ·*Email:
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
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-
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.
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
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
northeastern mtDNA CR sequences 20 GenBank popset (Jorgensen et al. 2010) GU002302−GU002321
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
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
CCT-3’) complemented the forward primer anneal-
ing temperature (61°C). Polymerase chain reaction
(PCR) was performed in a 10.0 µl reaction composed
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 (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.
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-
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.
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.
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-
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
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
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
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
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-
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.
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
Mar Ecol Prog Ser 455: 229–244, 2012
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 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
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Editorial responsibility: Philippe Borsa,
Montpellier, France
Submitted: August 23, 2011; Accepted: February 6, 2012
Proofs received from author(s): May 8, 2012
... Esto tiene importantes consecuencias debido a que ambos contaminantes pueden afectar negativamente la salud de los tiburones (Mull et al., 2012(Mull et al., , 2013 (Tanaka et al., 2011). También en áreas más pequeñas en los dos extremos de un país (Australia), se han encontrado diferencias genéticas entre Tiburones Blancos juveniles (Blower et al., 2012). ...
... Incluso individuos, entre los extremos de una misma cuenca oceánica, Pacífico Noreste y Pacífico Noroeste, presentan diferenciación genética entre ellos(Tanaka et al., 2011). También en áreas más pequeñas en los dos extremos de un país (Australia), se han encontrado diferencias genéticas entre Tiburones Blancos juveniles(Blower et al., 2012). tiburones del Pacífico Noreste, en particular los de California, Estados Unidos, forman un grupo monofilético separado genéticamente de tiburones de otras regiones(Jorgensen et al., 2010; Díaz-Jaimes et al., 2014) como Sudáfrica, Japón, Australia y Nueva Zelanda(Pardini et al., 2001;Tanaka et al., 2011). ...
... Esto tiene importantes consecuencias debido a que ambos contaminantes pueden afectar negativamente la salud de los tiburones (Mull et al., 2012(Mull et al., , 2013 (Tanaka et al., 2011). También en áreas más pequeñas en los dos extremos de un país (Australia), se han encontrado diferencias genéticas entre Tiburones Blancos juveniles (Blower et al., 2012). ...
... Incluso individuos, entre los extremos de una misma cuenca oceánica, Pacífico Noreste y Pacífico Noroeste, presentan diferenciación genética entre ellos(Tanaka et al., 2011). También en áreas más pequeñas en los dos extremos de un país (Australia), se han encontrado diferencias genéticas entre Tiburones Blancos juveniles(Blower et al., 2012). tiburones del Pacífico Noreste, en particular los de California, Estados Unidos, forman un grupo monofilético separado genéticamente de tiburones de otras regiones(Jorgensen et al., 2010; Díaz-Jaimes et al., 2014) como Sudáfrica, Japón, Australia y Nueva Zelanda(Pardini et al., 2001;Tanaka et al., 2011). ...
El objetivo general del Programa de Acción para la Conservación de la Especie Tiburón Blanco (PACE) consiste en establecer una estrategia integral de investigación, protección y conservación del Tiburón Blanco en aguas mexicanas, que permita incrementar el conocimiento de la especie, robustecer las medidas de manejo para su aprovechamiento no extractivo sustentable y prevenir y mitigar las posibles amenazas para la especie y su hábitat.
... Red Stingray, Dasyatis akajei) species (Blower, Pandolfi, Bruce, Gomez-Cabrera, & Ovenden, 2012;Chapman, Pinhal, & Shivji, 2009;Li, Chen, et al., 2015). Although philopatric behaviour is mostly identified on a regional level (~100 km), females from two species (Largetooth Sawfish and Speartooth Shark) showed a strong mtDNA population signal, indicating philopatry to their site of birth (i.e. ...
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Globally, elasmobranch populations (sharks and rays) are declining due to increasing anthropogenic and climate pressures. Genetic connectivity between elasmobranch populations is crucial to ensure their persistence and sustain the ecological integrity of ecosystems. Genetic connectivity implies gene flow among discrete populations occurring via the dispersal of individuals outside their population of origin, followed by reproduction — a process that can be biased between sexes (i.e. sex-biased dispersal or SBD). In this thesis, I first examine the current knowledge of population structure and SBD in elasmobranchs, and the tools that are commonly used. Next, this thesis uses novel genomic approaches (kinship, nuclear single nucleotide polymorphisms, and mitochondrial genomes) to provide insights into the patterns of (i) population structure, (ii) sex-chromosome systems, and (iii) SBD in elasmobranchs. My thesis focuses on three shark species that allow the study of dispersal patterns based on life history, local ecology, population size and different seascape features: Northern River Shark, Glyphis garricki; School Shark, Galeorhinus galeus; and Bull Shark, Carcharhinus leucas. Overall, male-biased dispersal (MBD) was observed in 25 of the 50 studied species. Population structure was found at both broad (Bull Shark) and fine (Northern River Shark) spatial scales. I demonstrated that 19 out of the 21 studied elasmobranch species contain X and Y chromosomes using the R function I developed. Combined, the sex-linked markers and kinship data supported the evidence of MBD in the Northern River Shark and the Bull Shark. My final discussion synthesised the observed dispersal patterns and examines the potential ecological and evolutionary drivers for these patterns. I critically compared the genetic and analytical approaches for the detection of population structure and SBD. Finally, potential implications of these quantitative findings for management were highlighted.
... Population genomic data, including estimates of effective size, have been used as a monitoring tool in many conservation and management situations for other species, such as translocations and reintroductions (Hess et al., 2015;Roques et al., 2018;Whitlock et al., 2017), quantifying genetic diversity to prevent extinctions (Faulks et al., 2017), and identifying ecologically significant units (Blower et al., 2012). Parentage has been used to evaluate the size of invading populations in species like the Asian swamp eel (Monopterus albus; Taylor et al., 2021). ...
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The sea lamprey (Petromyzon marinus) is an invasive species in the Great Lakes and the focus of a large control and assessment program. Current assessment methods provide information on the census size of spawning adult sea lamprey in a small number of streams, but information characterizing reproductive success of spawning adults is rarely available. We used RAD‐capture sequencing to genotype single nucleotide polymorphism (SNP) loci for ~1600 sea lamprey larvae collected from three streams in northern Michigan (Black Mallard, Pigeon, and Ocqueoc Rivers). Larval genotypes were used to reconstruct family pedigrees, which were combined with Gaussian mixture analyses to identify larval age classes for estimation of spawning population size. Two complementary estimates of effective breeding size (Nb), as well as the extrapolated minimum number of spawners (Ns), were also generated for each cohort. Reconstructed pedigrees highlighted inaccuracies of cohort assignments from traditionally used mixture analyses. However, combining genotype‐based pedigree information with length‐at‐age assignment of cohort membership greatly improved cohort identification accuracy. Population estimates across all three streams sampled in this study indicate a small number of successfully spawning adults when barriers were in operation, implying that barriers limited adult spawning numbers but were not completely effective at blocking access to spawning habitats. Thus, the large numbers of larvae present in sampled systems were a poor indicator of spawning adult abundance. Overall, pedigree‐based Nb and Ns estimates provide a promising and rapid assessment tool for sea lamprey and other species.
... In the north-eastern Pacific (NEP), white sharks perform seasonal migrations from inshore seal colonies to offshore areas where they likely forage on deep mesopelagic prey (Le Croizier et al., 2020a;Jorgensen et al., 2010). In Australian waters, white sharks are divided into two populations, namely the eastern Australasian (EA) and south-western Australasian (SWA) populations (Blower et al., 2012). In the SWA population, although occasional offshore movements were observed, immature and adult sharks mainly occupy coastal waters on the continental shelf where they primarily target locally abundant pinnipeds Bruce et al., 2006;Meyer et al., 2019). ...
Large marine predators exhibit high concentrations of mercury (Hg) as neurotoxic methylmercury, and the potential impacts of global change on Hg contamination in these species remain highly debated. Current contaminant model predictions do not account for intraspecific variability in Hg exposure and may fail to reflect the diversity of future Hg levels among conspecific populations or individuals, especially for top predators displaying a wide range of ecological traits. Here, we used Hg isotopic compositions to show that Hg exposure sources varied significantly between and within three populations of white sharks (Carcharodon carcharias) with contrasting ecology: the north-eastern Pacific, eastern Australasian, and south-western Australasian populations. Through Δ200Hg signatures in shark tissues, we found that atmospheric Hg deposition pathways to the marine environment differed between coastal and offshore habitats. Discrepancies in δ202Hg and Δ199Hg signatures among white sharks provided evidence for intraspecific exposure to distinct sources of marine methylmercury, attributed to population and ontogenetic shifts in foraging habitat and prey composition. We finally observed a strong divergence in Hg accumulation rates between populations, leading to three times higher Hg concentrations in large Australasian sharks compared to north-eastern Pacific sharks, and likely due to different trophic strategies adopted by adult sharks across populations. This study illustrates the variety of Hg exposure sources and bioaccumulation patterns that can be found within a single species and suggests that intraspecific variability needs to be considered when assessing future trajectories of Hg levels in marine predators.
... Some species demonstrate high levels of gene flow across ocean basins (Lieber et al., 2020), while others are divided into smaller subpopulations with limited gene flow (Le Port & Lavery, 2012;Thorburn et al., 2018). A wide range of behaviours such as site fidelity and natal philopatry (Corrigan et al., 2018;Feutry et al., 2017;Pardini et al., 2001;Thorburn et al., 2018), long-distance migrations (Blower et al., 2012;Cameron et al., 2018;Corrigan et al., 2018) and aggregating behaviour among closely related individuals (Lieber et al., 2020;Thorburn et al., 2018) can shape patterns of elasmobranch population connectivity and genetic diversity. In addition, environmental discontinuities such as bathymetric barriers (Le Port & Lavery, 2012) and temperature gradients (Griffiths et al., 2010) can influence species distributions and population connectivity, especially for less vagile species. ...
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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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Understanding how mobile, marine predators use three-dimensional space over time is central to inform management and conservation actions. Combining tracking technologies can yield powerful datasets over multiple spatio-temporal scales to provide critical information for these purposes. For the white shark ( Carcharodon carcharias ), detailed movement and migration information over ontogeny, including inter- and intra-annual variation in timing of movement phases, is largely unknown in the western North Atlantic (WNA), a relatively understudied area for this species. To address this need, we tracked 48 large juvenile to adult white sharks between 2012 and 2020, using a combination of satellite-linked and acoustic telemetry. Overall, WNA white sharks showed repeatable and predictable patterns in horizontal movements, although there was variation in these movements related to sex and size. While most sharks undertook an annual migratory cycle with the majority of time spent over the continental shelf, some individuals, particularly adult females, made extensive forays into the open ocean as far east as beyond the Mid-Atlantic Ridge. Moreover, increased off-shelf use occurred with body size even though migration and residency phases were conserved. Summer residency areas included coastal Massachusetts and portions of Atlantic Canada, with individuals showing fidelity to specific regions over multiple years. An autumn/winter migration occurred with sharks moving rapidly south to overwintering residency areas in the southeastern United States Atlantic and Gulf of Mexico, where they remained until the following spring/summer. While broad residency and migration periods were consistent, migratory timing varied among years and among individuals within years. White sharks monitored with pop-up satellite-linked archival tags made extensive use of the water column (0–872 m) and experienced a broad range of temperatures (−0.9 – 30.5°C), with evidence for differential vertical use based on migration and residency phases. Overall, results show dynamic inter- and intra-annual three-dimensional patterns of movements conserved within discrete phases. These results demonstrate the value of using multiple tag types to track long-term movements of large mobile species. Our findings expand knowledge of the movements and migration of the WNA white shark population and comprise critically important information to inform sound management strategies for the species.
... While it is possible that some connectivity occurs through northern Australia, this could not be determined in our study due to the limited number of receivers deployed in far north WA and Northern Territory. Bass Strait has previously been identified as a provincial zoogeographic boundary and a region of significant clustering of breaks (Dawson, 2005), with several marine species showing genetic divergences in the vicinity of this region (e.g., white shark, Carcharodon carcharias; Blower et al., 2012;sawsharks, Pristiophorus spp.;Nevatte et al., 2021). Such divergence is likely related to the historical total barrier to gene flow during the late Pliocene, when periods of cold climate and low sealevel segregated warm temperate organisms east or west of the emergent Bassian Isthmus resulting in population divergence and speciation (Waters, 2008). ...
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Understanding the movement ecology of marine species and connectivity of populations is required for effective fisheries management. This is especially the case for species with wide-ranging distributions for which movement can span across several jurisdictions with different management regulations. We used the Australian national network of acoustic receivers facilitated by the Integrated Marine Observing System (IMOS) to describe the extent and frequency of movements for two large epipelagic shark species, the bronze whaler (Carcharhinus brachyurus) and dusky shark (Carcharhinus obscurus). A total of 210 sharks (117 bronze whalers and 93 dusky sharks) were tracked for a 10-year period during which 21% and 9% of detected bronze whalers and dusky sharks, respectively, moved between Australian states. Bronze whalers showed more variable inter-state movements, mostly between Western Australia and South Australia but also eastwards to New South Wales (NSW). Although no dusky sharks tagged in Western Australia undertook inter-state movements, ∼50% of the sharks tagged in South Australia went to Western Australia. Five of the 14 dusky sharks tagged in NSW (36%) were detected across different states but remained on the east and southeast coasts (Queensland, NSW, Victoria, and Tasmania). The IMOS receivers also detected six bronze whalers in Ningaloo Reef, representing an extension of the previously known Australian distribution. Our findings highlight the value of collaboration between researchers and the value of national infrastructure, by providing a more accurate understanding of inter-state movements. This new information will allow the development of more adequate population dynamic models for stock assessment and management advice, requiring collaboration among state agencies for coordinating research activities, sharing data and resources, and establishing appropriate cross-jurisdictional policies. This is essential to achieve successful management and conservation outcomes for highly migratory species.
... Symmetric dispersal suggests that males of some species have similar patterns of reproductive philopatry, in this case to breeding sites, as observed in females. In a study of the White Shark, Carcharodon carcharias, in Australia, Blower et al. (2012) found genetic structure between juveniles in nursery areas using mtDNA and nDNA markers, suggesting biparental reproductive philopatry, whereby males also return to the same area to breed. Biparental philopatry has been documented for the American Cownose Ray, Rhinoptera bonasus, in the northwest Atlantic, where both females and males migrate to Chesapeake Bay each summer for parturition and subsequent mating (Fisher et al. 2013), although genetic studies investigating SBD are not available. ...
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Dispersal in many organisms is asymmetric by sex, a pattern that is often identified through the use of genetic tools. Sex-biased dispersal (SBD) is thought to derive from the varying fitness needs of females and males, as mediated by local ecology and life history. SBD is frequently reported in elasmobranchs (sharks, rays), long-lived fishes that often give live birth to well-developed young and are capable of dispersing thousands of kilometers. While many studies point to male-biased dispersal (MBD) being common, results are highly variable and no clear trends have yet emerged, even as the number of case studies has grown over the past decade. Here, we evaluated patterns in sampling regime, molecular marker type, and analysis method for every genetic structure study published to date that allowed for an assessment of SBD in elasmobranchs. We find that while some degree of MBD in elasmobranchs is likely, factors such as the pooling of life stages during data analysis and the inherent characteristics of different marker types, may lead to an overemphasis on male dispersal and potentially obscure genetic signals of female and male reproductive philopatry. The role of life history and biogeography in determining patterns of SBD in sharks and rays is also discussed.
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Background: The interplay of animal dispersal and environmental heterogeneity is fundamental for the distribution of biodiversity on earth. In the ocean, the interaction of physical barriers and dispersal has primarily been examined for organisms with planktonic larvae. Animals that lack a planktonic life stage and depend on active dispersal are however likely to produce distinctive patterns. Methods: We used available literature on population genetics and phylogeography of elasmobranchs (sharks, rays and skates) to examine how marine barriers and dispersal ecology shape genetic connectivity in animals with active dispersal. We provide a global geographical overview of barriers extracted from the literature and synthesize the geographical and hydrological factors, spatial and temporal scales to characterize different types of barriers. The three most studied barriers were used to analyse the effect of elasmobranch dispersal potential and barrier type on genetic connectivity. Results: We characterized nine broad types of marine barriers, with the three most common barriers being related to ocean bathymetry. The maximum depth of occurrence, maximum body size and habitat of each species were used as proxies for dispersal potential, and were important predictors of genetic connectivity with varying effect depending on barrier type. Environmental tolerance and reproductive behaviour may also play a crucial role in population connectivity in animals with active dispersal. However, we find that studies commonly lack appropriate study designs based on a priori hypotheses to test the effect of physical barriers while accounting for animal behaviour. Main conclusions: Our synthesis highlights the relative contribution of different barrier types in shaping elasmobranch populations. We provide a new perspective on how barriers and dispersal ecology interact to rearrange genetic variation of marine animals with active dispersal. We illustrate methodological sources that can bias the detection of barriers and provide potential solutions for future research in the field.
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Under the U.S. Endangered Species Act, a species is classified as endangered, threatened, or recovered based on the extent to which its survival is affected by one or more of five subjective factors. A key criticism of the act is that it makes no reference to quantitative or even qualitative parameters of what constitutes "danger of extinction." Without objective standards to guide decisionmakers, classification decisions fall prey to political and social influences. We recommend the development of species-specific, status-determining criteria as a means to rationalize and expedite the listing process and reclassification decisions, independent of the requirement for delisting criteria in recovery plans. Such criteria should (1) clearly define levels of vulnerability, (2) identify gaps in information on life-history Parameters, and (3) address uncertainty in existing data. As a case study, we developed preliminary criteria for bowhead whales (Balaena mysticetus). Thresholds for endangered and threatened status were based on World Conservation Union (IUCN) Red List criteria and population viability analyses. Our analysis indicates that particular attention must be focused on population structure within the species to appropriately classify the degree to which one or more components of a species are vulnerable to extinction. A similar approach could be used in the classification of other species. According to our application of the IUCN criteria and those developed for similar species by Gerber and DeMaster (1999), the Bering Sea population of bowhead whales should be delisted, whereas the other four populations of bowheads should continue to be considered endangered.
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Allozyme electrophoresis and restriction fragment length polymorphism analysis of mitochondrial DNA (mtDNA) were performed on sandbar sharks (Carcharhinus plumbeus) from coastal waters of Virginia, including the Chesapeake Bay, and the Gulf of Mexico to test the hypothesis that individuals from the two locations comprise a single gene pool. Both techniques revealed a very low degree of genetic variability within the species (allozyme mean heterozygosity = 0.005, mean nucleotide sequence diversity = 0.036%). The small amount of genetic variation present appeared to be evenly distributed between sampling locations; and therefore, the null hypothesis of a single gene pool could not be rejected (contingency χ 2=1.344, P > 0.5, chi-square significance of mtDNA haplotype distribution = 0.81). This conclusion is consistent with the known life-history characteristics of the species, as well as the results of tagging studies.
Molecular approaches in elasmobranch research have been used predominantly for phylogenetic inference or to define population structure. However, a variety of high-resolution molecular markers allow for the interpretation of complex patterns of molecular variance as well as the fast and accurate reconstruction of individual molecular profiles. Such techniques can be useful in augmenting current understanding of the reproductive biology of elasmobranchs and, where experimental or observational approaches may be difficult, can provide novel insights. The increased access and ease of utilizing molecular approaches makes the techniques and concepts discussed in this chapter useful for any elasmobranch researcher interested in the study of reproductive biology.
A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
A method is proposed for estimating effective population size (N) from data on linkage disequilibrium among neutral genes at several polymorphic loci or restriction sites. The efficiency of the method increases with larger sample size and more tightly linked genes; but for very tightly linked genes estimates of N are more dependent on long-term than on recent population history. Two sets of data are analysed as examples.