Population Connectivity of the Highly Migratory Shortfin Mako (Isurus oxyrinchus Rafinesque 1810) and Implications for Management in the Southern Hemisphere

Abstract

In this paper we combine analyses of satellite telemetry and molecular data to investigate spatial connectivity and genetic structure among populations of shortfin mako (Isurus oxyrinchus) in and around Australian waters, where this species is taken in recreational and commercial fisheries. Mitochondrial DNA data suggest matrilineal substructure across hemispheres, while nuclear DNA data indicate shortfin mako may constitute a globally panmictic population. There was generally high genetic connectivity within Australian waters. Assessing genetic connectivity across the Indian Ocean basin, as well as the extent that shortfin mako exhibit sex biases in dispersal patterns would benefit from future improved sampling of adult size classes, particularly of individuals from the eastern Indian Ocean. Telemetry data indicated that Australasian mako are indeed highly migratory and frequently make long-distance movements. However, individuals also exhibit fidelity to relatively small geographic areas for extended periods. Together these patterns suggest that shortfin mako populations may be genetically homogenous across large geographical areas as a consequence of few reproductively active migrants, although spatial partitioning exists. Given that connectivity appears to occur at different scales, management at both the national and regional levels seems most appropriate.

Full text

ORIGINAL RESEARCH
published: 20 November 2018
doi: 10.3389/fevo.2018.00187
Frontiers in Ecology and Evolution | www.frontiersin.org 1November 2018 | Volume 6 | Article 187
Edited by:
David Jack Coates,
Department of Biodiversity,
Conservation and Attractions (DBCA),
Australia
Reviewed by:
Viorel Dan Popescu,
Ohio University, United States
Melissa Ann Millar,
Department of Biodiversity,
Conservation and Attractions (DBCA),
Australia
*Correspondence:
Shannon Corrigan
scorrigan@floridamuseum.ufl.edu
Specialty section:
This article was submitted to
Conservation,
a section of the journal
Frontiers in Ecology and Evolution
Received: 18 August 2018
Accepted: 26 October 2018
Published: 20 November 2018
Citation:
Corrigan S, Lowther AD,
Beheregaray LB, Bruce BD, Cliff G,
Duffy CA, Foulis A, Francis MP,
Goldsworthy SD, Hyde JR,
Jabado RW, Kacev D, Marshall L,
Mucientes GR, Naylor GJP,
Pepperell JG, Queiroz N, White WT,
Wintner SP and Rogers PJ (2018)
Population Connectivity of the Highly
Migratory Shortfin Mako (Isurus
oxyrinchus Rafinesque 1810) and
Implications for Management in the
Southern Hemisphere.
Front. Ecol. Evol. 6:187.
doi: 10.3389/fevo.2018.00187
Population Connectivity of the Highly
Migratory Shortfin Mako (Isurus
oxyrinchus Rafinesque 1810) and
Implications for Management in the
Southern Hemisphere
Shannon Corrigan 1
*, Andrew D. Lowther 2, Luciano B. Beheregaray 3, Barry D. Bruce 4,
Geremy Cliff 5,6 , Clinton A. Duffy 7, Alan Foulis 8, Malcolm P. Francis 9,
Simon D. Goldsworthy 10, John R. Hyde 11 , Rima W. Jabado12 , Dovi Kacev 11 ,
Lindsay Marshall 13, Gonzalo R. Mucientes 14, 15, Gavin J. P. Naylor 1, Julian G. Pepperell 16 ,
Nuno Queiroz 14, William T. White4, Sabine P. Wintner5, 6 and Paul J. Rogers 10
1Florida Museum of Natural History, University of Florida, Gainesville, FL, United States, 2Norwegian Polar Institute, Fram
Centre, Tromsø, Norway, 3College of Science and Engineering, Flinders University, Adelaide, SA, Australia, 4CSIRO National
Research Collections Australia, Hobart, TAS, Australia, 5KwaZulu-Natal Sharks Board, Umhlanga Rocks, South Africa,
6School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa, 7Department of Conservation, Auckland, New
Zealand, 8Oceanographic Research Institute, University of KwaZulu-Natal, Durban, South Africa, 9National Institute of Water
and Atmospheric Research, Wellington, New Zealand, 10 South Australia Research and Development Institute – Aquatic
Sciences, Henley Beach, SA, Australia, 11 Southwest Fisheries Science Center, National Marine Fisheries Service, La Jolla,
CA, United States, 12 Gulf Elasmo Project, Dubai, United Arab Emirates, 13 Stick Figure Fish Illustration, Peregian Beach, QLD,
Australia, 14 Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO/InBIO), Universidade do Porto, Porto,
Portugal, 15 Centro Tecnológico del Mar-Fundación CETMAR, Vigo, Spain, 16 Pepperell Research and Consulting Pty Ltd.,
Noosaville DC, QLD, Australia
In this paper we combine analyses of satellite telemetry and molecular data to investigate
spatial connectivity and genetic structure among populations of shortfin mako (Isurus
oxyrinchus) in and around Australian waters, where this species is taken in recreational
and commercial fisheries. Mitochondrial DNA data suggest matrilineal substructure
across hemispheres, while nuclear DNA data indicate shortfin mako may constitute
a globally panmictic population. There was generally high genetic connectivity within
Australian waters. Assessing genetic connectivity across the Indian Ocean basin, as
well as the extent that shortfin mako exhibit sex biases in dispersal patterns would
benefit from future improved sampling of adult size classes, particularly of individuals from
the eastern Indian Ocean. Telemetry data indicated that Australasian mako are indeed
highly migratory and frequently make long-distance movements. However, individuals
also exhibit fidelity to relatively small geographic areas for extended periods. Together
these patterns suggest that shortfin mako populations may be genetically homogenous
across large geographical areas as a consequence of few reproductively active migrants,
although spatial partitioning exists. Given that connectivity appears to occur at different
scales, management at both the national and regional levels seems most appropriate.
Keywords: telemetry, tracking, population structure, mitochondrial DNA, microsatellites, conservation, fisheries

Corrigan et al. Shortfin Mako Population Connectivity
INTRODUCTION
Implementing practical and effective management for highly
migratory species (HMS) of pelagic sharks is challenging because
they have vast ranges that are often spatiotemporally dynamic.
For example, some HMS of pelagic sharks move among favorable
foraging, breeding, and pupping grounds, sometimes using
specific migration pathways (Hueter et al., 2005; Kinney and
Simpfendorfer, 2009; Chapman et al., 2015). Recognizing such
movement patterns is important for devising suitably scaled
management plans, particularly when a species range spans
multiple, or extends beyond, national jurisdictions (Worm
and Vanderzwaag, 2007). However, propensity to migrate is
multifaceted and different movement types variously influence
population persistence. This means that management plans
for HMS must consider more than simply their mobility. For
instance, the extent of genetic and spatial connectivity among
regions are each relevant for management, but are not always
positively correlated (Palumbi, 2003). Some migration patterns
are driven by prey availability or environmental change and
are unrelated to gene flow (Heupel et al., 2003; Campana
et al., 2011; Hueter et al., 2013; Doherty et al., 2017). Habitat
preference, philopatric behavior or physical and ecological
barriers to dispersal can promote genetic structure in species with
high mobility (Schultz et al., 2008; Portnoy et al., 2010; Daly-
Engel et al., 2012; Feldheim et al., 2014; Sandoval-Castillo and
Beheregaray, 2015; Corrigan et al., 2016; Bester-Van Der Merwe
et al., 2017; Guttridge et al., 2017). Conversely, regions may be
genetically homogenized by a few reproductively active migrants
despite considerable spatial partitioning (Waples, 1998; Gagnaire
et al., 2015).
Combining analyses of satellite telemetry and molecular
data can provide information about both spatial connectivity
and genetic linkages among populations of HMS of pelagic
sharks. Satellite telemetry methods are particularly useful for
determining mobility and for identifying migration pathways
or habitat preferences (Block et al., 2011). Complementing this
with population genetic analysis can inform about connectivity
via reproductively effective migration. The combination of these
approaches allows assessments at a range of spatiotemporal
scales, informing about contemporary population dynamics and
dispersal patterns as well as connectivity that is relevant for long-
term population fitness (Frankham et al., 2010; Gagnaire et al.,
2015).
We employed both satellite telemetry and molecular
approaches to study spatial connectivity and population genetic
structure in shortfin mako Isurus oxyrinchus Rafinesque 1810
(Rogers et al., 2015a). Shortfin mako exhibit red myotomal
endothermy and are able to maintain their body temperature
above ambient levels. This adaptation is thought to allow this
species to occupy a broad thermal niche, sustain high swimming
speeds, and ultimately be very highly migratory (Carey, 1973;
Dickson and Graham, 2004). Shortfin mako are oceanic,
coastal, and pelagic. They are an economically lucrative fisheries
resource, taken as bycatch and targeted both recreationally and
commercially worldwide. Declines in relative abundance of
shortfin mako have been recorded in the Mediterranean and the
northern Atlantic Ocean (Chang and Liu, 2009). This prompted
their listing on the Convention on the Conservation of Migratory
Species of Wild Animals Appendix II (Dulvy et al., 2008) and
as globally Vulnerable according to the International Union for
Conservation of Nature Red List of Threatened Species criteria
(Cailliet et al., 2009). Recent stock assessment confirmed that
the North Atlantic stock remains overfished and that current
regulations will neither promote future growth, nor prevent
further decline (Sims et al., 2018).
Available fisheries and tracking data suggest that shortfin
mako combine phases of fidelity in neritic regions with
characteristic broad-scale, highly directional, transitory
movements across both neritic, and oceanic environments.
It also appears that warm water masses, such as thermal
equatorial fronts, act as a dispersal barrier resulting in Northern
and Southern Hemisphere stock differentiation (Holts and
Bedford, 1993; Abascal et al., 2011; Block et al., 2011; Musyl
et al., 2011; Sippel et al., 2011; Rogers et al., 2015a,b; Holdsworth
and Saul, 2017). Consistent with these patterns, previous genetic
studies have shown cross-equatorial matrilineal sub-structure
(Heist et al., 1996; Schrey and Heist, 2003; Taguchi et al., 2015).
Significant matrilineal sub-structure between the southeastern
and southwestern Pacific has also been reported (Michaud et al.,
2011; Taguchi et al., 2015). Nuclear data, on the other hand,
indicate that shortfin mako are globally panmictic, possibly
as a result of male-mediated gene flow (Schrey and Heist,
2003; Taguchi et al., 2015). While these studies have provided
important insights regarding the movement ecology of shortfin
mako, more information is needed to determine the appropriate
spatial scale at which to manage this species. Specifically, the
Southern Hemisphere has previously been sparsely sampled and
the extent of connectivity among locations within this region
is poorly understood. This region thus forms the geographical
focus of the current study, particularly in and around Australian
waters, where shortfin mako are regularly targeted by commercial
and recreational fishers and proposed protection measures have
been the subject of substantial conjecture.
Shortfin mako were previously listed in Australia under
the Environment Protection and Biodiversity Conservation Act
(EPBC Act 1999). Uncertainty regarding regional connectivity
within Australian waters, and among Australian stocks and
declining populations in the Northern Hemisphere, ultimately
resulted in this listing being amended to allow recreational
fishing for shortfin mako to continue. Developing appropriately
scaled management strategies for shortfin mako in the Southern
Hemisphere therefore requires further information regarding
spatial and genetic connectivity within Australian waters, and
among neighboring regions. We thus focused satellite tracking
effort and intensified the geographic coverage of sampling for
molecular analyses within these areas (Rogers et al., 2015a). Our
specific aims were to (1) use genetic data to assess the extent of
regional population genetic structure within Australian waters,
and between Australian waters and neighboring regions within
the Southern Hemisphere, and (2) compare the spatial scale
of genetic connectivity with movement and dispersal patterns
determined using satellite telemetry data collected over multiple
years.
Frontiers in Ecology and Evolution | www.frontiersin.org 2November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
MATERIALS AND METHODS
Satellite Telemetry
Thirteen dorsal-fin mounted satellite tags were deployed in
continental shelf and slope waters of southern Australia between
2008 and 2013. These included position-only Sirtrack KiwiSat
202 and K2F161A tags, Wildlife ComputersTM Smart Position or
Temperature (SPOT) tags, and data-collecting Argos SPLASH
and Mk10A tags. Sirtrack 202 and WC SPOT tags were
programmed to transmit daily signals, whereas the SPLASH
and Sirtrack K2F161A tags were duty-cycled to transmit at a
2-day frequency to optimize battery life. Capture and satellite
tagging techniques are described in Rogers et al. (2015b). Shark
total length, sex, and tag deployment locations were recorded
(Table S1).
Satellite tags transmitted signals to the low polar orbiting
environmental satellite network receiver stations, which were
forwarded to Argos centers in France and the USA. Argos
position estimates were accessed using Telnet and Tera Term
Pro software, downloaded in seven location classes ranging
from the highest to the lowest quality between 3, 2, 1, 0, A,
B, and Z with predicted accuracies of 3 =<250 m, 2 =250–
500 m, 1 =500–1,500 m, classes 0 – B =>1,500 m, and
Z=no position (http://www.argos-system.org). Raw data were
mapped in MapInfo v. 11.5. to remove positions on land.
Argos data were filtered by estimating locations using a Kalman
filter under a continuous-time state-space framework using the
(C)orrelated (RA)andom (W)alk (L)ibrary “CRAWL” package in
R v. 2.15.2 (Johnson et al., 2008; R Core Team, 2013). Locations
were interpolated along each filtered track to reduce sampling
bias due to irregular transmission of Argos location data. To
establish a set of spatial scale-based movement parameters, we
estimated mean rate of movement (ROM) per day, minimum
cumulative distance traveled and distal displacement distances
(linear distance between tagging location and most distant
location) for each individual (Table S1).
Population Genetics—Sample and Data
Collection
Tissue samples were obtained from 389 shortfin mako collected
opportunistically from recreational and commercial fisheries
catches or through collaboration with international research
organizations. Samples were collected from six regions
throughout the Southern Hemisphere (N=275: Indo-Pacific,
eastern Australia, southern Australia, Western Australia, New
Zealand, and South Africa; Figure 1). Two regions from the
Northern Hemisphere (N=114: Northern Atlantic and northern
Indian Oceans) were also sampled to assess trans-equatorial
connectivity. Individuals were sampled at several locations
within these broad regions to ensure that fine-scale geographic
structure would be detected, if present (Figure 1). Tissue was
preserved in either 95% ethanol or salt-saturated 20% dimethyl
sulfoxide, and genomic DNA was extracted using a modified
salting out protocol (Sunnucks and Hales, 1996).
The mitochondrial control region and portions of the flanking
tRNAs (∼1142 bp) were amplified by the Polymerase Chain
Reaction (PCR) using primers Shark tPheR 5′-TYTCATC
TTAGCATCTTCAGTGC-3′and Shark tProF 5′-AGCCAAG
ATTCTGCCTAAACTG-3′. Reactions were conducted in 25
µL volumes comprised of 15–30 ng template DNA, 2.5 mM
MgCl2, 1×MangoTaqTM reaction buffer (Bioline, Taunton USA),
0.25 mM dNTPs, 30 pmol forward and reverse primers and
1.25 U MangoTaqTM DNA polymerase (Bioline, Taunton USA).
PCR cycling consisted of initial denaturation at 94◦C, followed by
“touchdown” cycling of 30 s denaturation at 94◦C, 45 s annealing,
and 1 min extension at 72◦C. Annealing temperatures began
at 59◦C and decreased by 2◦C at each touchdown, stabilizing
at 51◦C for 30 cycles. Amplified products were purified using
ExoSAP-IT (Affymetrix USB R
Products, Affymetrix, Inc.,
Cleveland USA), according to the manufacturer’s protocol.
Sanger sequencing was performed bi-directionally using internal
primers mako405F 5′-GCCCGCTAGTTCCCTTTAATG-3′and
mako572R 5′-CCTTTCAGTTATGGTCAACTTGACAATC-3′,
and BigDye R
Terminator chemistry on an ABI 3730xl genetic
analyzer (Applied Biosystems R
, Life Technologies, Grand Island
USA). DNA sequences were edited and aligned using Geneious R

Pro v. 6.1.7 (Biomatters Ltd, Auckland New Zealand http://
www.geneious.com). Sequences were cropped to 791bp for
downstream analyses due to variability in sequence quality on
either end.
Ten microsatellite loci were amplified using primers Iox-
12 and Iox-30 (Schrey and Heist, 2002) Iox-M01, Iox-M110,
Iox-M115, Iox-M192, Iox-M36, Iox-M59, Iox-B3, and Iox-
D123 (via GenBank accession numbers KJ454433, KJ454434,
KJ454435, KJ454436, KJ454437, KJ454438, KJ454439, KJ454440,
respectively). Forward primers were tailed with a fluorescently
labeled M13 tag (Schuelke, 2000). Reactions were conducted
in 5 µL volumes comprising 15–30 ng template DNA, 3 mM
MgCl2, 1×MangoTaqTM reaction buffer (Bioline, Taunton USA),
0.1 mM each dNTP, 0.1 pmol M13 tailed forward primer, 0.3
pmol reverse primer, 0.1 pmol fluorescently labeled M13 primer,
0.5 µg bovine serum albumin, and 0.25 U MangoTaqTM DNA
polymerase (Bioline, Taunton USA). PCR cycling consisted of
initial denaturation at 94◦C, followed by “touchdown” cycling of
30 s denaturation at 94◦C, 45 s annealing, and 1 min extension
at 72◦C. Annealing temperature began at 65◦C and decreased
by 2◦C at each touchdown, stabilizing at 57◦C for 30 cycles.
Products were separated on an ABI 3730xl genetic analyzer
(Applied Biosystems R
, Life Technologies, Grand Island USA).
Reference samples for each locus were included in all PCR
programs and during capillary separation of fragments to ensure
consistent genotype calling. Microsatellite alleles were visually
inspected, binned, and sized according to the GeneScanTM 500
LIZTM size standard (Applied Biosystems R
, Life Technologies,
Grand Island USA) using the Third Order Least Squares
algorithm in the microsatellite plugin for Geneious R
Pro v6.1.7
(Biomatters Ltd, Auckland New Zealand. http://www.geneious.
com). Genotypes were checked for signatures of possible scoring
errors due to null alleles, short allele dominance, and stutter
peaks using Microchecker v. 2.2.3 (Van Oosterhout et al.,
2004).
Population Genetics—Genetic Diversity
and Structure
To avoid biases associated with limited sampling, samples from
Western Australia and southern Australia, the Indo-Pacific and
Frontiers in Ecology and Evolution | www.frontiersin.org 3November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
FIGURE 1 | Sampling locations and sample sizes of shortfin mako for genetic analyses. Regions include the Northern Atlantic, South Africa, Northern Indian, Western
Australia, Indo-Pacific, southern and eastern Australia, and New Zealand. Western and southern Australia were grouped to comprise southwestern Australasia and
the Indo-Pacific and eastern Australia were grouped to comprise eastern Australia for some analyses.
eastern Australia, were pooled for all frequency-based analyses
of both mitochondrial and microsatellite data. Analysis of
molecular variance (AMOVA) did not indicate any significant
difference among these sampling locations, confirming the
validity of this pooling scheme.
Mitochondrial DNA sequence variation and the extent
of population differentiation were explored in Arlequin v.
3.5.1.2 (Excoffier and Lischer, 2010). Number of haplotypes,
haplotypic, and nucleotide diversities were calculated assuming
the Jukes and Cantor model of DNA substitution (Jukes and
Cantor, 1969). An exact test of population differentiation was
performed and population pairwise estimates of the parameters
FST and 8ST were calculated. Significance was assessed via
permutation (100,000 permutations) and interpreted following
non-parametric Bonferroni correction (Rice, 1989). Hierarchical
AMOVA was conducted partitioning total variance into within
population, among population, and among regional covariance
components (Cockerham, 1973) and testing for significance via
permutation (10,100 permutations). A median-joining network
(Bandelt et al., 1999) was constructed in Network v. 5.0 (Fluxus
Technology Ltd) and illustrated in Network Publisher v. 2.0.0.1
(Fluxus Technology Ltd). Epsilon was set to 0 and hyper-variable
sites were down weighted.
Microsatellite diversity was characterized using GenAlEx
v. 6.5 (Peakall and Smouse, 2012) by calculating allele
frequencies, number of alleles, effective number of alleles,
and average observed, expected and unbiased expected
heterozygosities per sampling location. Allelic richness was
calculated in FSTAT v. 2.9.3.2 (Goudet, 2001). Genepop v. 4.2
(Raymond and Rousset, 1995) was used to assess whether the
data conformed to expectations under Hardy-Weinberg and
linkage equilibrium models. Bonferroni corrections for multiple
comparisons were applied prior to interpretation.
Powsim v. 4.1 (Ryman and Palm, 2006) was used to
determine the alpha error and statistical power with which
significant genetic differentiation could be determined using our
microsatellite dataset. Datasets were simulated with the same
sample size, number of loci, and average allele frequencies as our
observed and populations allowed to drift for a user-specified
number of generations in order to attain a pre-defined level
of differentiation (FST =0.0005 to 0.05, 500 replicates per
value). Statistical power was determined as the proportion of
Frontiers in Ecology and Evolution | www.frontiersin.org 4November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
simulations for which Fisher’s exact and Chi-square tests showed
significant differentiation. Statistical α(type I) error was assessed
by calculating the probability of rejecting H0when it is true for
simulations omitting the drift step (i.e., FST =0).
Population differentiation was investigated in GenAlEx v.
6.5. Pairwise fixation indices, Nei’s GST, and Hedrick’s GST′′ ,
were calculated following Meirmans and Hedrick (2011). Allelic
differentiation, DEST, was calculated following Jost (2008).
Arlequin v. 3.5.1.2 was used to conduct an AMOVA of
microsatellite data, partitioning total variance into within
population, among population, and among regional covariance
components. Significance was assessed via permutation (10,100
permutations).
Model-based clustering of genotypic data was conducted
using Structure v. 2.3.4 (Pritchard et al., 2000). Since mobility
is high in shortfin mako, allele frequencies were assumed to be
similar across populations (Falush et al., 2003) and individuals
were assigned using the admixture model of ancestry. Prior
information regarding sampling location was allowed to inform
ancestry in order to assist clustering (Hubisz et al., 2009).
Inference was conducted over one million iterations (100,000
burn-in). Five independent runs were performed for each value
of K, which varied from one to the number of sampled
localities. Priors for the average and standard deviation of F
(drift within populations) were set to 0.01 and 0.05, respectively,
following Falush et al. (2003). A uniform prior (0, 10) on α
(the parameter shaping the distribution of admixture proportion)
was assumed. Following Evanno et al. (2005),1K(the second
order rate of change of the log probability of the data given
K(Ln P(X|K)) was calculated using Structure Harvester v.
0.6.93 (Earl and Vonholdt, 2012) and used to guide inference
regarding the number of populations. Replicate clustering
analyses were aligned using CLUMPP v. 1.1.2 (Jakobsson and
Rosenberg, 2007) and visualized using distruct v. 1.1 (Rosenberg,
2004).
Population Genetics—Sex-Biased
Movement
Analyses of sex-biased dispersal were conducted on a reduced
dataset consisting only of individuals for which sex data were
available (152 females (F) and 151 males (M) total; northern
Indian 41 F: 40 M, South Africa 34 F: 57 M, eastern Australia 28
F: 20 M, southern Australia 21 F: 22 M, and New Zealand 28 F:
12 M).
The likelihood that an individual originates from its sampled
location was calculated following Paetkau et al. (1995) using
GeneClass2 v.2.0 (Piry et al., 2004). Log transformed likelihood
values were corrected for population effects following Favre
et al. (1997) resulting in corrected Assignment Indices (AIc)
that averaged zero per population and whereby negative values
indicate lower than average probability of being born locally
(migrants). The distributions of AIcwere calculated and
compared for males and females, with the expectation that the
more dispersive sex would show a more negative frequency
distribution (Favre et al., 1997; Mossman and Waser, 1999).
Following Goudet et al. (2002), the parameters FST,FIS , and the
mean (mAIc) and variance (vAIc) of AIcwere calculated and
compared among sexes by taking the difference between the more
dispersive and philopatric sex for FIS (FISd –FISp), the difference
between the more philopatric and dispersive sex for mAIcand
FST (mAIcp –mAIcd,FSTp –FSTd); or the ratio of the more
dispersive to philopatric sex for vAIc(vAIcd /vAIcp ). Significant
bias was detected using a randomization approach in FSTAT v.
2.9.3.2.
Following Banks and Peakall (2012), multivariate spatial
autocorrelation analyses (Smouse and Peakall, 1999; Peakall
et al., 2003) were compared across sexes to look for any sex-
bias in fine-scale spatial patterns of genetic structure. Pairwise
genetic distances were calculated following Peakall et al. (1995)
and Smouse and Peakall (1999). Autocorrelation coefficients
(Smouse and Peakall, 1999) were calculated across a range of
distance classes that varied so as to incorporate comparisons
within sampling localities, among adjacent localities, and more
distant comparisons. Confidence intervals (95% CIs) about r
were calculated by bootstrapping (Peakall et al., 2003) and
the null hypothesis of no sex-bias was accepted if there was
overlap in the CIs between sexes. The alternative hypothesis
predicts that rvalues are significantly greater in the more
philopatric sex. Heterogeneous autocorrelation across sexes was
also assessed using single- (t2) and multi-distance (ω) class
criteria as implemented in the non-parametric heterogeneity tests
described by Smouse et al. (2008). These analyses were conducted
in GenAlEx v. 6.5 and assessed for significance using 10,000
permutations and 10,000 bootstrap replicates.
RESULTS
Movement Patterns Based on Satellite
Telemetry
Shortfin mako exhibited fidelity to the neritic waters of the
Great Australian Bight, Bass Strait, southern Western Australia
and the broad oceanic area to the west of Tasmania along
the Sub-Tropical Front (Figure 2). Some sharks exhibited
oceanic transit phases, leaving continental shelf waters to
travel northward into the tropical waters of the northeastern
Indian Ocean. During these migrations, three sharks traveled
via the Perth and Carnarvon Canyons to the Bartlett and
Karma Seamounts, located to the south of Indonesia. Other
long-distance movements included four sharks that traveled
southward to the Sub-Tropical Front. One shark traveled to
the Coral Sea and another individual crossed the Tasman
Sea to New Zealand shelf waters, followed by a northward
migration to tropical waters near New Caledonia. A single
individual moved as far west as the Crozet Plateau in the Indian
Ocean.
Thirteen individuals were tracked for 249–672 days (mean 418
±37 per individual). Six individuals were tracked for more than
one year. Figure 2 shows the spatial scale of movements by all
individuals according to CRAWL model fits to the Argos data.
Total cumulative distances traveled by shortfin mako ranged
from 8,776 km in 262 days to 24,213 km in 551 days (Table S1).
Distal displacement distances ranged from 1,500 to 7,520 km
Frontiers in Ecology and Evolution | www.frontiersin.org 5November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
FIGURE 2 | CRAWL model fits to tag data showing the spatial range occupied by shortfin mako over 249–672 days. Individuals were tagged in continental shelf and
slope waters of southern Australia between 2008 and 2013.
(mean 3356 ±509 km per individual), with 69% of individuals
exhibiting distal displacements greater than 2,000 km and 38%
of individuals moving more than 4,000 km from their tagging
locations. Many of these movements however, represented return
migration events (Figure 2). There were no apparent sex-biases
in scale of movement. The two longest (M5, M8) and shortest
(M9, M12) movements were undertaken by both a male and
a female. Males and females traveled an average of 40.8 and
37.5 km, respectively, per day.
Population Genetics—Genetic Diversity
and Structure
Mitochondrial DNA data suggest matrilineal substructure across
hemispheres, while nuclear DNA data indicate shortfin mako
may constitute a globally panmictic population. There was
generally high genetic connectivity within Australian waters.
The mitochondrial control region was sequenced for 365
shortfin mako resulting in 48 unique haplotypes, defined by
31 polymorphic sites, sampled across eight broad geographic
regions (Table 1,Figure 1). Overall, haplotypic diversity was high
(0.894 ±0.013) while nucleotide diversity was very low (0.004
±0.003). Diversity metrics including sample size, number of
haplotypes, haplotypic, and nucleotide diversity are shown in
Table 1.
The haplotype network (Figure 3A) is characterized by a
single, abundant haplotype that was sampled from all locations
and in approximately 30% (108/365) of individuals. Other
haplotypes are closely related, mostly separated by only a single
substitution. Three, or fewer, substitutions were required to link
any two haplotypes using parsimony. The network does not
indicate any apparent geographic partitioning of haplotypes.
Frequencies differ across sampling sites but most haplotypes are
found at several, often geographically disparate, locations. One
third of haplotypes (16/48) were unique to a single location, 13 of
which were singletons.
Pairwise values of FST and 8ST based on mitochondrial
data were low to moderate. There was significant differentiation
among both Northern Hemisphere locations (Northern Atlantic
and Northern Indian Ocean) and all Southern Hemisphere
localities. There was significant differentiation between South
Africa and all other locations based on exact tests of population
differentiation. Weak but significant differentiation was also
detected between South Africa and southern Australia based on
FST, but this result was not corroborated by 8ST estimates, which
showed no significant differentiation between South Africa and
any of the Australasian locations (southern Australia, eastern
Australia, or New Zealand). Within Australasia, significant
differentiation was detected between southern Australia and
New Zealand based on FST,8ST and exact tests of population
differentiation (Table 2A).
The results from AMOVA based on FST and 8ST were
qualitatively similar. Interpretations presented herein are thus
based on 8ST only. The global 8ST estimate was low, but
significant (8ST =0.080; P=0.000). Total variation in the
dataset could be separated into five major regions: the northern
Atlantic, the northern Indian, South Africa, western Australasia
(western and southern Australia), and eastern Australasia (Indo-
Pacific, eastern Australia, and New Zealand). While most of
the total variation in the dataset was found within populations
(91.5%, 8ST =0.085, P=0.000), among region variance
accounted for a significant 8.2% (8CT =0.082, P=0.009).
Ten microsatellite loci were genotyped for 355 shortfin mako
sampled across the eight broad geographic locations (Figure 1,
Table 1). There was no evidence of scoring errors, although Iox-
12 and Iox-D123 showed evidence of null alleles in samples
from a single location. The frequency of null alleles was low
overall (<10%) and all loci and populations conformed to
Frontiers in Ecology and Evolution | www.frontiersin.org 6November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
TABLE 1 | Genetic diversity at mitochondrial DNA and nuclear microsatellite markers.
Mitochondrial DNA Microsatellites
Sampling region n N h πn N NeArHoHeuHe
Northern Atlantic 30 11 0.846 0.005 28 12.500 7.873 6.683 0.828 0.815 0.830
Northern Indian (Oman) 77 16 0.574 0.002 84 15.600 8.925 6.842 0.856 0.842 0.848
South Africa 92 24 0.911 0.004 91 15.800 8.991 6.789 0.852 0.845 0.850
Indo-Pacific (Indonesia/Taiwan) 22 14 0.918 0.004 13 9.200 6.543 6.657 0.839 0.791 0.826
Eastern Australia 60 28 0.940 0.005 44 14.500 9.336 6.924 0.862 0.844 0.853
Southern Australia 36 16 0.927 0.005 46 14.100 9.165 6.846 0.813 0.830 0.839
Western Australia 9 8 0.972 0.003 7 7.100 5.272 6.699 0.748 0.742 0.802
New Zealand 39 18 0.912 0.005 42 15.500 9.420 7.166 0.838 0.855 0.865
Total 365 48 0.894 0.004 355 13.038 8.191 6.902 0.839 0.838 0.847
Data were obtained from n number of individuals. Mitochondrial diversity is summarized by the number of haplotypes (N), haplotypic diversity (h) and nucleotide diversity (π). Microsatellite
diversity is summarized by the number of alleles per locus (N), effective number of alleles (Ne), allelic richness (Ar), observed heterozygosity (Ho), expected heterozygosity (He) and unbiased
expected heterozygosity (uHe). All estimates for microsatellite data are averaged over loci.
FIGURE 3 | (A) Median joining network containing 10 equally parsimonious trees. Haplotypes are shown as pie charts indicating geographical distribution with size
proportional to observed haplotype frequency. Small solid red circles are intermediate states that were not observed. Light gray, dark gray, and black lines represent 1,
2, and 3 mutational steps between haplotypes, respectively (B) Plot of the estimated membership coefficients for each individual in each of two genetic clusters
(K=2). Individuals are represented by vertical columns and grouped according to sampling region.
Hardy-Weinberg expectations following Bonferroni correction.
Linkage disequilibrium was detected between Iox-M110 and Iox-
B3, Iox-12 and Iox-30, and Iox-M192, and Iox-D123, also in
samples from a single location. All loci were therefore included
in final analyses.
Genetic diversity at microsatellite loci was moderate to high.
The number of alleles per locus ranged between 9 and 30,
with means per population ranging from 7.1 to 15.8. The
effective number of alleles per locus ranged between 5.3 and
9.4 across populations. Allelic richness was relatively consistent
Frontiers in Ecology and Evolution | www.frontiersin.org 7November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
TABLE 2 | Pairwise measures of population differentiation based on (A) mitochondrial DNA and (B) microsatellite data.
Northern Atlantic Northern Indian South Africa Eastern Australia Southern Australia New Zealand
(A) MITOCHONDRIAL DNA
Northern Atlantic – 0.396* 0.114* 0.139* 0.147* 0.152*
Northern Indian 0.257λ* – 0.100* 0.073* 0.186* 0.077*
South Africa 0.041λ* 0.119λ* – 0.004 0.027 0.020
Eastern Australia 0.072λ* 0.109λ* 0.007λ– 0.021 0.002
Southern Australia 0.080λ* 0.197λ* 0.029λ* 0.011 – 0.063*
New Zealand 0.077λ* 0.115λ* 0.016λ−0.002 0.032λ* –
(B) MICROSATELLITES
Northern Atlantic – 0.014 0.021 0.007 0.026 0.014
Northern Indian 0.017 – 0.014 0.006 0.013 0.013
South Africa 0.025 0.016 – 0.013 0.037* 0.009
Eastern Australia 0.008 0.007 0.016 – 0.000 −0.004
Southern Australia 0.031 0.015 0.043* 0.000 – 0.021
New Zealand 0.017 0.015 0.011 −0.005 0.025 –
Mitochondrial DNA data are presented with FST below the diagonal and 8ST above the diagonal. Microsatellite data are presented with GST ′′ below the diagonal and DEST above the
diagonal. A *indicates significant differentiation at the 95% confidence level following Bonferroni correction. A λindicates significant comparisons based on exact tests of population
differentiation.
across populations, ranging between 6.7 and 7.2. Observed
heterozygosity ranged from 0.75 to 0.86 (unbiased expected
heterozygosity =0.80–0.87; Table 1).
The microsatellite dataset has good statistical power with a
100% probability of detecting a true FST as low as 0.0025, and
a high probability (65–70%) of detecting an FST as low as 0.001.
The alpha error was ≤5%. The majority of microsatellite variation
was within populations (99.8%) and the global multilocus FST
estimate was very low (FST =0.002), but significant (P=0.020).
This result was driven by significant FST values at just two of the
10 loci (Iox-M192, FST =0.005, P=0.004; Iox-M36 FST =0.009,
P=0.001). Population pairwise estimates of GST,GST′′ , and
DEST were low and only a single pairwise comparison, South
Africa vs. southern Australia, indicated significant differentiation
(Table 2B,Table S2). The among-region variance component
of AMOVA was not significant (FCT =0.004, P=0.060),
accounting for <1% of total variation in the dataset.
High connectivity among sampling locations was also
supported by model-based clustering analyses (Figure 3B). The
mean estimated log probability of the data was highest for K=1,
while the modal value of the distribution of 1K(Evanno et al.,
2005) suggested that two clusters could be identified in the
data. The 1Kmetric cannot be estimated for K=1 and so
panmixia could not be assessed as a possible scenario using this
approach. Further, this metric does not take into account the
scale of 1K. Observed values were two orders of magnitude
smaller than is typical of cases of real structure and bar plots of
the estimated cluster membership coefficients for each individual
did not support K=2 (Figure 3B). There was considerable
variance in parameter estimates across runs for each individual
K, suggesting non-convergence of the analysis despite running
for a sufficient length of time. Together these observations are
consistent with there being no signal of population structure in
the data.
Population Genetics—Sex-Biased
Movement
Patterns of molecular variation across sexes trended toward a
signal of male-biased dispersal, however, this was not statistically
supported.
Pairwise fixation indices (FST) based on microsatellite markers
were low overall, but higher in females (FST =0.003) than
males (FST =0.000). This difference bordered on significance
(P= ∼0.050), however the observed values of the test statistics
for these parameters were within the range of the null distribution
that dispersal is not biased by sex (Figure S1). FIS was higher
in males (FIS =0.009) than females (FIS =0.001), but this
difference was not statistically significant (P=0.203) and the
observed value of the test statistic was also within the null
distribution. Corrected assignment (AIc) values ranged between
−8.0 and 7.9 for males and −6.2 and 10.8 for females. The
frequency distributions of AIcvalues for males and females
were largely overlapping and both sexes showed a similar
proportion of negative values (54% for females and 52% for
males). The mean and variance of AIcwere higher for females
(mAIc=0.19, vAIc=11.87) than for males (mAIc= −0.19,
vAIc=9.11), however, these differences were not statistically
significant (P=0.158 and P=0.838, respectively). The observed
value of the test statistics for both mAIcand vAIcfell within the
range of the null distribution representing the probability that
dispersal is not biased by sex (Figure S1).
Spatial patterns of genetic structure were similar across sexes.
The male and female 95% bootstrap confidence intervals about r
overlapped in all distance classes (Figure 4). The single distance
class t2-tests were all non-significant, as was the multi-class ωtest
of overall correlogram heterogeneity (ω=6.2, P=0.411). Low
but significant positive autocorrelation among genotypes was
detected for both males and females at small (100 km) distance
classes (Table S3;Figure 4).
Frontiers in Ecology and Evolution | www.frontiersin.org 8November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
FIGURE 4 | Correlogram plot of the spatial autocorrelation coefficient, r, as a function of geographic distance for males (dark gray) and females (light gray). Black
dotted lines represent the 95% confidence interval for the null hypothesis of no spatial structure (r=0) based on 10,000 random permutations of the data among
distance classes. 95% confidence intervals about rwere determined using 10,000 bootstrap replicates. Geographic distances (km) are the maximum distance of each
class.
DISCUSSION
The vast ranges of pelagic HMS make it challenging to assess
population connectivity at spatial scales that are appropriate
for informing policy. The pelagic ocean is consequently one
of the most under-regulated ecosystems on the planet (Wood,
2008; Game et al., 2009), evidenced by widespread declines in
pelagic biodiversity (Verity et al., 2002; Dulvy et al., 2008; Worm
and Tittensor, 2011). We aimed to address knowledge gaps
regarding connectivity among populations of highly migratory
shortfin mako from the Southern Hemisphere, particularly in and
around Australian waters. We improved sampling throughout
the region and examined spatial and genetic connectivity based
on information from long-term satellite telemetry and molecular
data.
Movement Patterns Based on Satellite
Telemetry
Satellite telemetry data indicated that shortfin mako in Australian
waters exhibit multiple movement phases. Periods of residency
in neritic habitats are probably indicative of time spent foraging.
These contrasted with highly directional transitory movements,
within neritic waters, and across vast oceanic expanses including
among seamounts, ridges, and adjoining basins (Figure 2). For
example, we hypothesize that features such as the eastward
flowing South Indian Current, Sub-Tropical Front, and east–
west running bathymetric features such as the Naturaliste
Plateau, Diamantina Fracture Zone, and Broken Ridge may have
facilitated the long-distance movement into the Indian Ocean
that was recorded for one individual (Figure 2).
Overall, the geographical scale of the telemetry dataset
spanned over 10,700 km from east to west. Observed movement
patterns aligned closely with those documented in a previous
study of juveniles (Rogers et al., 2015b) and both males and
females exhibited similar scales of movement. While movement
at these spatial scales indicate that shortfin mako in the
Australian region are among the most HMS of pelagic sharks
(Benavides et al., 2011; Rogers et al., 2013a,b; Holmes et al., 2014),
it is important to also note that many of these movements were
return events and some individuals exhibited fidelity to particular
areas for extended periods.
Notably, no satellite tagged individuals traversed the lower
tropical latitudes, nor passed through equatorial thermal frontal
systems. Observed northernmost turning points of directional
migrations aligned with surface water temperatures of 28–
29◦C. Southernmost latitudinal turning points coincided with
the Southern subtropical frontal zone and were generally
characterized by 9–11◦C surface water temperatures. Although
a single individual tagged with a standard tag as part of the
New South Wales Department of Primary Industries Game
Fish Tagging Program was recaptured after having apparently
traversed the equator from the east coast of Australia to the
Philippines (Rogers et al., 2015a), such transequatorial migration
events appear to be uncommon in this species. Long-term
telemetry studies of shortfin mako in the northeast Pacific Ocean
also reported tropical thermal fronts aligned with turning points
during similarly vast return migrations to shelf waters of the
California Current ecosystem (Block et al., 2011). This apparent
thermal preference has been documented by other studies (Holts
and Bedford, 1993; Abascal et al., 2011; Musyl et al., 2011; Rogers
et al., 2015b), providing further evidence that warm water may
act as a potential barrier to dispersal among hemispheres.
Trans-Equatorial Matrilineal Substructure
We found considerable mitochondrial DNA diversity in
shortfin mako. Haplotypic diversity was close to, or higher
than, 0.9 at most sampling sites (Figure 3,Table 1), which
is similar to that observed in previous studies of this
species (Heist et al., 1996; Michaud et al., 2011; Taguchi
et al., 2011, 2015) and toward the higher end of the range
typically observed for elasmobranchs (Hoelzel et al., 2006;
Dudgeon et al., 2008; Schultz et al., 2008; Chabot and
Allen, 2009; Benavides et al., 2011; Blower et al., 2012;
Corrigan et al., 2016). Also consistent with previous work, our
mitochondrial DNA data showed evidence of trans-equatorial
matrilineal substructure. Although haplotype sharing was
observed among all locations, both Northern Hemisphere
sampling locations (North Atlantic and northern Indian)
showed significant differentiation from all other sampling
sites (Table 2A). This indicates reduced matrilineal gene flow
between hemispheres, consistent with the observation that
trans-equatorial migration events appear to be infrequent
according to tracking data.
Frontiers in Ecology and Evolution | www.frontiersin.org 9November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
Based on observed haplotype sharing between the Atlantic
ocean basin and Australia/New Zealand, Michaud et al. (2011)
hypothesized that gene flow between Pacific and Atlantic
populations of shortfin mako occurs primarily via the Indian
Ocean. Taguchi et al. (2011) were similarly unable to distinguish
western Indian Ocean sampling sites from those in the eastern
Indian or Pacific Oceans. Our analyses provide some support for
this hypothesis. Pairwise 8ST estimates between South Africa and
the Australasian sampling sites were low relative to comparisons
between Northern and Southern Hemisphere sampling sites
and not statistically significant, indicating gene flow across
the Indian Ocean basin (Table 2A). A distal displacement
distance of 7,520 km was recorded for one tracked individual
tagged off southern Australia, representing a return westward
movement to a location about 2,000 km east of South Africa. This
suggests that suitable oceanic migratory pathways exist that could
facilitate trans-Indian Ocean linkages between Australasian
and South African populations (Figure 2,Table S1). However,
exact tests of population differentiation indicated significant
differentiation between Australasian and South African sampling
sites, and a single pairwise comparison between South Africa and
southern Australia was also statistically significant based on FST
(Table 2A).
Taguchi et al. (2011) reported that the eastern Indian
Ocean sample was highly differentiated from most other
sampling sites, although this was based on limited sampling
from the region. We are unable to comment on the validity
of their finding because, despite extensive efforts, we too
obtained only few samples from the eastern Indian Ocean
off Western Australia. Taguchi et al. (2011) also indicated
possible population structure between the eastern and western
coasts of Australia. The Bassian Isthmus in southern Australia
is a well-characterized biogeographic barrier that is thought
to have promoted bicoastal population subdivision in several
marine taxa (Teske et al., 2017). For example, Blower et al.
(2012) reported matrilineal subdivision between eastern and
southwestern coastal regions of Australia in the white shark,
Carcharodon carcharias, a close relative of shortfin mako (Naylor
et al., 2012). In contrast, we did not find any evidence of
matrilineal population structure in shortfin mako sampled
from around the Australian continent (Table 2A). Interestingly,
however, the comparison between southern Australia and New
Zealand indicated significant differentiation. The 8ST estimate
between southern Australia and New Zealand is lower than those
observed between Northern and Southern Hemisphere sampling
sites, indicating that gene flow between these locations is less
constrained than across the equator, but nevertheless restricted
enough to represent significant differentiation (Table 2A). It is
possible that matrilineal gene flow occurs in a “stepping stone”
fashion throughout the region whereby southern Australia and
New Zealand are connected via the east coast of Australia.
Alternatively, the statistical significance of this single comparison
may be artefactual (discussed below).
Further investigations into the extent of connectivity between
Australian and South African waters would benefit from tracking
information from additional adult individuals, as this may
uncover links between neritic habitats on either coastline
and reveal how these animals may use bathymetric features
during transoceanic movements. Genetic data from an improved
sampling of individuals from the southeastern Indian Ocean
region would also help clarify the extent of gene flow across the
Indian Ocean Basin and between the east and west coasts of
Australia.
Nuclear DNA Data Suggest Global
Panmixia
Schrey and Heist (2003) report very weak evidence of population
structure between the North Atlantic and North Pacific Oceans
according to their analysis of microsatellite DNA. Based on an
analysis of a larger number of microsatellite markers, Taguchi
et al. (2015) report that shortfin mako lack differentiation across
their Pacific Ocean range. Sampling from Australasia and the
Indian Ocean were limited in both of their studies, allowing little
prior inference regarding nuclear genetic structure across the
region.
Similar to Schrey and Heist (2003) and Taguchi et al. (2015),
we inferred little evidence of population structure based on our
microsatellite data. Only a single pairwise comparison, South
Africa vs. southern Australia, indicated significant differentiation
(Table 2B). The model-based clustering analysis suggested only
subtle differences in allele frequencies across regions (Figure 3)
and no apparent population structure.
The biological relevance of significant pairwise comparisons
of fixation indices should generally be interpreted with caution
given their observed small magnitude. FST and analogs measure
the effects of gene flow on population differentiation and are
thus influenced by both population size and migration rate.
The magnitude of FST and analogs decreases non-linearly with
increasing migration rate, such that a similar signal of weak to
no genetic differentiation can be produced under a scenario of
panmixia as well as when populations are large but sufficiently
independent to warrant separate management (Waples and
Gaggiotti, 2006; Waples et al., 2008; Gagnaire et al., 2015). This
makes it difficult to precisely estimate these parameters, and
interpret the significance of their magnitude, when population
sizes are large and dispersal potential is high. This is likely the
case of most marine species, including shortfin mako. Moreover,
restricted sampling from a highly diverse set of genotypes can
mean that some low estimates of pairwise differentiation are
spuriously rendered statistically significant due to minor allele
frequency differences (Waples, 1998; Waples et al., 2008).
Given that a high percentage of our tracked individuals
showed long distance movements and that there was no
evidence of genetic structure based on clustering analyses, it
seems plausible that the statistically significant differentiation we
detected based on fixation indices between Southern Australia
and New Zealand in the mitochondrial data, and South
Africa and Southern Australia in the microsatellite data, are
artefactual (Waples, 1998; Waples et al., 2008; Gagnaire et al.,
2015). However, it is worth noting that many long-distance
movements by shortfin mako are return events and this could
potentially promote genetic differentiation at smaller geographic
scales than their mobility predicts. Additionally, the South
Frontiers in Ecology and Evolution | www.frontiersin.org 10 November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
Africa vs. Southern Australia comparison for the microsatellite
data was also statistically significant based on Jost’s DEST, a
complementary measure of population structure that quantifies
allelic differentiation rather than nearness to population fixation
and is less influenced by demographic variables (Jost et al.,
2017). This may indicate some potentially biologically relevant
partitioning of allelic diversity between these areas, although the
magnitude of this parameter was still low.
Methods for collecting genomic-scale data are becoming
readily available and the possibility to analyze data from
thousands of markers may allow better estimates of low
values of genetic differentiation in the future. However, this
will likely remain challenging for many marine species with
large population sizes (Waples et al., 2008; Gagnaire et al.,
2015; Waples, 2015). Quantifying adaptive divergence between
locations offers a solution for assessing differentiation even when
population sizes are large and gene flow is high. Unlike genetic
drift, selection counteracts the homogenizing effect of migration
more efficiently in large populations. Genome scans of large
marker datasets make it easier than ever to identify loci under
selection for this purpose. Applying such methods to studying
shortfin mako in the future may offer the possibility to delineate
locally adapted units that require independent management
even though they may be highly connected (Allendorf et al.,
2015; Gagnaire et al., 2015; Jost et al., 2017). Contrasting
patterns of neutral vs. adaptive variation may also be particularly
relevant to make future predictions regarding how populations
may respond to changing environmental conditions or fishing
pressure.
Sex-Biased Dispersal
Understanding biases in dispersal patterns can reveal ecologically
important areas such as feeding or breeding grounds. This
information can also guide fisheries management to avoid
selective overharvest of a more philopatric sex (Hueter et al.,
2005; Chapman et al., 2015). Male-biased dispersal has been
demonstrated in a number of elasmobranch species (Schultz
et al., 2008; Daly-Engel et al., 2012; Portnoy et al., 2015). This
includes other Lamniformes such as white sharks (Pardini et al.,
2001; Blower et al., 2012) in which both sexes are known to
undertake oceanic scale movements (Bonfil et al., 2005; Bruce
et al., 2006). Skewed sex ratios in shortfin mako catches indicate
regional and seasonal sexual segregation (Mucientes et al., 2009;
Francis, 2013) and Schrey and Heist (2003) propose male-biased
dispersal as a possible mechanism to explain differing patterns of
matrilineal vs. nuclear genetic structure in this species.
Under a scenario of sex-biased dispersal, allele frequencies
should be more similar across sampling sites among individuals
of the dispersing sex than those of the more philopatric sex.
Because of this, the dispersing sex will show greater variance
in assignment index and lower probability of local assignment,
while FST will be higher among the more philopatric sex
(Goudet et al., 2002). Observed values for these parameters were
consistent with these expectations, suggesting that dispersal may
be male-biased in shortfin mako. However, the difference in
these parameters between sexes was not statistically significant
(Figure S1).
There are several caveats to the interpretation of these results.
These tests lack power when dispersal occurs at intermediate
rates and sex-bias is subtle (<80:20; Goudet et al., 2002). Tracking
data, low pairwise fixation indices and the clustering analysis
based on genetic data suggest that both male and female shortfin
mako are highly mobile. Given their mobility and pelagic habit,
it seems more likely that any female philopatry will be weak,
perhaps at the scale of hemispheres given that we detected a
signature of trans-equatorial matrilineal substructure and warm
water at the equator appears to represent a physical barrier
to dispersal. These tests are also only applicable if sampling
occurs during the dispersed phase (Goudet et al., 2002). This
assumption is likely violated here given that we sampled multiple
cohorts of mostly juveniles and sub-adults. It is possible that
violation of these assumptions is masking any signal of sex-bias
in these particular analyses, although the trend indicates a male
bias.
We also did not detect any statistically supported differences
in spatial genetic structure across sexes based on spatial
autocorrelation analysis. Detecting a sex bias using this method
similarly requires large sample sizes and the development of
strong spatial genetic structure in the more philopatric sex
(Banks and Peakall, 2012). Banks and Peakall (2012) stress
the importance of sampling and concentrating pairwise data
points at the scale at which dispersal is restricted in the more
philopatric sex. This analysis and our inferences regarding sex-
biased dispersal in general, would thus benefit greatly from more
information regarding the movements and mating behavior of
adult individuals of both sexes, particularly identifying regions
that are used for mating and parturition. Satellite tracking
together with genetic analysis of a large sample of mature sharks
collected during the breeding season from both hemispheres will
be required.
Conservation and Management
Implications
Inferences of high connectivity based on analyses of our long-
term telemetry and molecular datasets spanning six key regions
support defining shortfin mako as a pelagic HMS in Australia
and neighboring regions of the Southern Hemisphere. Although
highly mobile, molecular data presented herein and elsewhere
(Michaud et al., 2011; Taguchi et al., 2011, 2015) indicate
separation between the Northern and Southern Hemispheres and
weaker evidence of separation within the Southern Hemisphere.
There appears to be distinct populations in the southeastern
and southwestern Pacific (Michaud et al., 2011; Taguchi et al.,
2015), and potentially southern Australia and the western Indian
Ocean, though connectivity across the Indian Ocean is somewhat
complicated to interpret. From a management perspective, it is
most important to determine whether inferred differentiation
is biologically meaningful such that units warrant management
as independent stocks. Only a small number of migrants are
required to homogenize allele frequencies across regions (Spieth,
1974; Mills and Allendorf, 1996). Significant spatial partitioning
may occur despite high genetic connectivity and the number of
migrants per generation required to allow stock rebuilding may
Frontiers in Ecology and Evolution | www.frontiersin.org 11 November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
be much higher than is required to produce genetic homogeneity
(Waples, 1998; Waples et al., 2008).
Tagging data support the separation of Northern and
Southern Hemisphere populations of shortfin mako, with only
one tagged shark known to have crossed the Equator (Sippel
et al., 2011; Rogers et al., 2015a,b; Holdsworth and Saul, 2017).
Most movements of tagged mako have occurred within half-
ocean basins (e.g., the southwest Pacific). Although Australasian
mako frequently make long-distance movements, they also often
return to near their tagging location, and importantly, show
fidelity to specific areas of continental shelf and slope over
several to many months (Rogers et al., 2015a,b; M. Francis
unpublished data; data presented herein). Hundreds of mako
tagged by gamefishers with standard tags in Australian and New
Zealand waters all remained within the southwest Pacific Ocean
(Sippel et al., 2011; Rogers et al., 2015a; Holdsworth and Saul,
2017). Fourteen mako tagged with electronic tags in New Zealand
and tracked for 34–588 days (mean 251 days, all but one longer
than 120 days) spent 42–100% (median 77%) of their time in
the New Zealand Exclusive Economic Zone (EEZ; M. Francis,
unpublished data). Those results, in combination with our own
telemetry data from southern Australia that also showed fidelity
to Australian waters, indicate that mako do not wander randomly
across the globe, but instead may be resident in comparatively
small areas for extended periods. Mako do cross international
boundaries and the high seas, however, such that management
at the scale of Regional Fisheries Management Organizations
is important. But the propensity for mako to spend extended
periods within national EEZs means that the homogenizing effect
of large-scale movements likely occurs at a rate that is too slow
to combat differing levels of fishing mortality across the entire
genetic stock. This means that effective fisheries management of
shortfin mako must occur at national as well as international
levels.
DATA AVAILABILITY STATEMENT
Mitochondrial DNA sequences are available via GenBank R

(www.ncbi.nlm.nih.gov/genbank/) Accession numbers
MH759795–MH760159. Microsatellite data are provided in
Supplementary Data Tables 1–3in the Supplementary Material.
AUTHOR CONTRIBUTIONS
SC, PJR, and SDG conceived and designed the study. SC and
PJR collected the data. ADL, LBB, BDB, GC, CAD, AF, MPF,
SDG, JRH, RWJ, DK, LM, GRM, GJPN, JGP, NQ, WTW, and
SPW contributed samples, laboratory infrastructure, and analysis
tools. SC, PJR, and ADL performed the analysis. SC, PJR, ADL,
LBB, GC, MPF, RWJ, WTW, and SPW wrote the paper.
ACKNOWLEDGMENTS
Funding for this research was provided by the Fisheries Research
and Development Corporation Tactical Research Fund (Shark
Futures: 2011-077) on behalf of the Australian Government.
Aspects of this research were reported in Rogers et al. (2015a)
and are reproduced with permission. Additional support was
provided by the SeaWorld Research and Rescue Foundation,
Nature Foundation SA Inc., Department for Environment and
Water (DEW), Australian Geographic Society, SARDI Aquatic
Sciences, the Victorian Department of Primary Industries
Recreational Fishing License Trust Account Large Grants
Program and Flinders University. LBB acknowledges financial
support from the Australian Research Council (FT130101068).
GRM was supported by the Isabel Barreto Human Resources
Plan of the Government of Galicia. Procedures were undertaken
under SARDI/PIRSA Ministerial exemptions (Section 115;
9902094, and S59; 9902064), DEW Permit U25570, Environment
Australia, EPBC Act 1999 Permit E20120068 and Flinders
University Animal Welfare Committee approval (Project 309).
We thank the international participants of the FRDC funded
workshop, Shark futures - a synthesis of available data on mako
and porbeagle sharks in Australasian waters: Current status
and future directions for constructive input and support of
this project. Andrew Oxley, Nicole Patten, an FRDC assigned
reviewer, Viorel Popescu and Melissa Millar provided valuable
comments that improved the final version of this manuscript.
We also thank the following people for their assistance during
satellite tag deployments or tissue sample collection: John
Collinson, Anton Blass, Callan Henley, Shane Gill (FV Rahi
Aroha), Dennis and Kerry Heineke, Adam Todd (FV Shaka-
Zura), Paul Irvine, Steve Toranto, Phil Stroker, Clinton Adlington
(FV Home Strait), Shane Sanders and Brodie Carter (FV
Baitwaster), Ashley and Neville Dance, Greg Barea, Charlie
Huveneers (Flinders University), Matt Heard, Mick Drew,
Crystal Beckmann (SARDI), Slavko Kolega, Chris Meletti (Sekol,
MV Lucky-S), Mark Lewis, Bruce Barker (CSIRO), staff of the
KwaZulu-Natal Sharks Board, The South African Institute for
Aquatic Biodiversity, Matias Braccini, and Rory McAuley.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fevo.
2018.00187/full#supplementary-material
REFERENCES
Abascal, F., Quintans, M., Ramos-Cartelle, A., and Mejuto, J. (2011).
Movements and environmental preferences of the shortfin mako, Isurus
oxyrinchus, in the southeastern Pacific Ocean. Mar. Biol. 158, 1175–1184.
doi: 10.1007/s00227-011-1639-1
Allendorf, F. W., Hohenloe, P. A., and Luikart, G. (2015). Genomics and the future
of conservation genetics. Nat. Rev. Genet. 11, 697–709. doi: 10.1038/nrg2844
Bandelt, H., Forster, P., and Rohl, A. (1999). Median-joining networks
for inferring intraspecific phylogenies. Mol. Biol. Evolu. 16, 37–48.
doi: 10.1093/oxfordjournals.molbev.a026036
Banks, S. C., and Peakall, R. (2012). Genetic spatial autocorrelation
can readily detect sex-biased dispersal. Mol. Ecol. 21, 2092–2105.
doi: 10.1111/j.1365-294X.2012.05485.x
Benavides, M. T., Feldheim, K. A., Duffy, C. A., Wintner, S. P., Braccini, J. M.,
Boomer, J., et al. (2011). Phylogeography of the copper shark (Carcharhinus
Frontiers in Ecology and Evolution | www.frontiersin.org 12 November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
brachyurus) in the Southern Hemisphere: implications for the conservation of a
coastal apex predator. Mar. Freshwater Res. 62, 861–869. doi: 10.1071/MF10236
Bester-Van Der Merwe, A. E., Bitalo, D., Cuevas, J. M., Ovenden, J. R.,
Hernández, S., Dasilva, C., et al. (2017). Population genetics of Southern
Hemisphere tope shark (Galeorhinus galeus): intercontinental divergence and
constrained gene flow at different geographical scales. PLoS ONE 12:e0184481.
doi: 10.1371/journal.pone.0184481
Block, B. A., Jonsen, I. D., Jorgensen, S. J., Winship, A. J., Shaffer, S. A., Bograd, S.
J., et al. (2011). Tracking apex marine predator movements in a dynamic ocean.
Nature 475, 86–90. doi: 10.1038/nature10082
Blower, D. C., Pandolfi, J. M., Bruce, B. D., Gomez-Cabrera, M., and Ovenden, J. R.
(2012). Population genetics of Australian white sharks reveals fine-scale spatial
structure, transoceanic dispersal events and low effective population sizes. Mar.
Ecol. Prog. Ser. 455, 229–244. doi: 10.3354/meps09659
Bonfil, R., Meyer, M., Scholl, M., Johnson, R., O’brien, S., Oosthuizen, H., et al.
(2005). Transoceanic migration, spatial dynamics, and population linkages of
white sharks. Science 310, 100–103. doi: 10.1126/science.1114898
Bruce, B. D., Stevens, J. D., and Malcolm, H. (2006). Movements and swimming
behaviour of white sharks (Carcharodon carcharias) in Australian waters. Mar.
Biol. 150, 161–172. doi: 10.1007/s00227-006-0325-1
Cailliet, G. M., Cavanagh, R. D., Kulka, D. W., Stevens, J.
D., Soldo, A., Clo, S., et al. (2009). Isurus oxyrinchus. The
IUCN Red List of Threatened Species 2009:e.T39341A10207466.
doi: 10.2305/IUCN.UK.2009-2.RLTS.T39341A10207466.en
Campana, S. E., Dorey, A., Fowler, M., Joyce, W., Wang, Z., Wright, D., et al.
(2011). Migration pathways, behavioural thermoregulation and overwintering
grounds of blue sharks in the northwest Atlantic. PLoS ONE 6:e16854.
doi: 10.1371/journal.pone.0016854
Carey, F. G. (1973). Fishes with warm bodies. Sci. Am. 228, 36–44.
doi: 10.1038/scientificamerican0273-36
Chabot, C. L., and Allen, L. G. (2009). Global population structure of the tope
(Galeorhinus galeus) inferred by mitochondrial control region sequence data.
Mol. Ecol. 18, 545–552. doi: 10.1111/j.1365-294X.2008.04047.x
Chang, J., and Liu, K. (2009). Stock assessment of the shortfin mako shark
(Isurus oxyrinchus) in the northwest Pacific Ocean using per recruit and virtual
population analyses. Fish. Res. 98, 92–101. doi: 10.1016/j.fishres.2009.04.005
Chapman, D. D., Feldheim, K. A., Papastamatiou, Y. P., and Hueter, R. E. (2015).
There and back again: a review of residency and return migrations in sharks,
with implications for population structure and management. Ann. Rev. Mar.
Sci. 7, 547–570. doi: 10.1146/annurev-marine-010814-015730
Cockerham, C. (1973). Analysis of gene frequencies. Genetics 74, 679–700.
Corrigan, S., Huveneers, C., Stow, A. J., and Beheregaray, L. B. (2016). A
multilocus comparative study of dispersal in three codistributed demersal
sharks from eastern Australia. Can. J. Fish. Aquat. Sci. 73, 406–506.
doi: 10.1139/cjfas-2015-0085
Daly-Engel, T. S., Seraphin, K. D., Holland, K. N., Coffey, J. P., Nance, H. A.,
Toonen, R. J., et al. (2012). Global phylogeography with mixed-marker analysis
reveals male-mediated dispersal in the endangered scalloped hammerhead
shark (Sphyrna lewini). PLoS ONE 7:e29986. doi: 10.1371/journal.pone.0029986
Dickson, K. A., and Graham, J. B. (2004). Evolution and consequences
of endothermy in fishes. Physiol. Biochem. Zool. 77, 998–1018.
doi: 10.1086/423743
Doherty, P. D., Baxter, J. M., Gell, F. R., Godley, B. J., Graham, R. T., Hall, G.,
et al. (2017). Long-term satellite tracking reveals variable seasonal migration
strategies of basking sharks in the north-east Atlantic. Sci. Rep. 7:e42837.
doi: 10.1038/srep42837
Dudgeon, C. L., Broderick, D., and Ovenden, J. R. (2008). IUCN classification
zones concord with, but underestimate, the population genetic structure of
the zebra shark Stegostoma fasciatum in the Indo-West Pacific. Mol. Ecol. 18,
248–261. doi: 10.1111/j.1365-294X.2008.04025.x
Dulvy, N. K., Baum, J. K., Clarke, S., Compagno, L. J. V., Cortes, E., Domingo,
A., et al. (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.
doi: 10.1002/aqc.975
Earl, D. A., and Vonholdt, B. M. (2012). Structure Harvester: a website and
program for visualizing STRUCTURE output and implementing the Evanno
method. Conserv. Genet. Resour. 4, 359–361. doi: 10.1007/s12686-011-9548-7
Evanno, G., Regnaut, S., and Goudet, J. (2005). Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14,
2611–2620. doi: 10.1111/j.1365-294X.2005.02553.x
Excoffier, L., and Lischer, H. E. L. (2010). Arlequin suite Ver 3.5: a new series of
programs to perform population genetics analyses under Linux and Windows.
Mol. Ecol. Resour. 10, 564–567. doi: 10.1111/j.1755-0998.2010.02847.x
Falush, D., Stephens, M., and Pritchard, J. K. (2003). Inference of population
structure using multilocus genotype data: linked loci and correlated allele
frequencies. Genetics 164, 1567–1587. doi: 10.3410/f.1015548.197423
Favre, L., Balloux, F., Goudet, J., and Perrin, N. (1997). Female-biased dispersal
in the monogamous mammal Crocidura russula: evidence from field data
and microsatellite patterns. Proc. R. Soc. Lond. Ser. B 264, 127–132.
doi: 10.1098/rspb.1997.0019
Feldheim, K. A., Gruber, S. H., Dibattista, J. D., Babcock, E. A., Kessel, S. T.,
Hendry, A. P., et al. (2014). Two decades of genetic profiling yields first evidence
of natal philopatry and long-term fidelity to parturition sites in sharks. Mol.
Ecol. 23, 110–117. doi: 10.1111/mec.12583
Francis, M. P. (2013). Commercial Catch Composition of Highly Migratory
Elasmobranchs. New Zealand Fisheries Assessment Report 2013/68.
Frankham, R., Ballou, J. D., and Briscoe, D. A. (2010). Introduction to Conservation
Genetics. Cambridge: Cambridge Universty Press.
Gagnaire, P. A., Broquet, T., Aurelle, D., Viard, F., Souissi, A., Bonhomme,
F., et al. (2015). Using neutral, selected, and hitchhiker loci to assess
connectivity of marine populations in the genomic era. Evol. Appl. 8, 769–786.
doi: 10.1111/eva.12288
Game, E. T., Grantham, H. S., Hobday, A. J., Pressey, R. L., Lombard,
A. T., Beckley, L. E., et al. (2009). Pelagic protected areas: the missing
dimension in ocean conservation. Trends Ecol. Evol. (Amst). 24, 360–369.
doi: 10.1016/j.tree.2009.01.011
Goudet, J. (2001). FSTAT, a Program to Estimate and Test Gene Diversities and
Fixation Indices (Version 2.9.3). Available online at: https://www2.unil.ch/
popgen/softwares/fstat.htm
Goudet, J., Perrin, N., and Waser, P. (2002). Tests for sex biased
dispersal using genetic markers. Mol. Ecol. 11, 1103–1114.
doi: 10.1046/j.1365-294X.2002.01496.x
Guttridge, T. L., Van Zinnicq Bergmann, M. P. M., Bolte, C., Howey, L. A., Finger,
J. S., Kessel, S. T., et al. (2017). Philopatry and regional connectivity of the great
hammerhead shark, Sphyrna mokarran in the U.S. and Bahamas. Front. Mar.
Sci. 4, 2296–7745. doi: 10.3389/fmars.2017.00003
Heist, E. J., Musick, J. A., and Graves, J. E. (1996). Genetic population structure
of the shortfin mako (Isurus oxyrinchus) inferred from restriction fragment
length polymorphism analysis of mitochondrial DNA. Can. J. Fish. Aquat. Sci.
53, 583–588. doi: 10.1139/f95-245
Heupel, M. R., Simpfendorfer, C. A., and Hueter, R. E. (2003). Running
before the storm: blacktip sharks respond to falling barometric pressure
associated with tropical storm Gabrielle. J. Fish Biol. 63, 1357–1363.
doi: 10.1046/j.1095-8649.2003.00250.x
Hoelzel, A. R., Shivji, M., Magnussen, J., and Francis, M. P. (2006). Low worldwide
genetic diversity in the basking shark (Cetorhinus maximus). Biol. Lett. 2,
639–642. doi: 10.1098/rsbl.2006.0513
Holdsworth, J. C., and Saul, P. J. (2017). New Zealand Billfish and Gamefish
Tagging, 2013–14 to 2015–16. New Zealand Fisheries Assessment Report
2017/14.
Holmes, B. J., Pepperell, J. G., Griffiths, S. P., Jaine, F. R. A., Tibbetts, I. R., and
Bennett, M. B. (2014). Tiger Shark (Galeocerdo cuvier) movement patterns and
habitat use determined by satellite tagging in eastern Australian waters. Mar.
Biol. 11, 2645–2658.doi: 10.1007/s00227-014-2536-1
Holts, D. B., and Bedford, D. W. (1993). Horizontal and vertical movements of the
shortfin mako shark, Isurus oxyrinchus, in the southern California Bight. Aust.
J. Mar. Freshwater Res. 44, 901–909.
Hubisz, M., Falush, D., Stephens, M., and Pritchard, J. K. (2009). Inferring weak
population structure with the assistance of sample group information. Mol.
Ecol. Resour. 9, 1322–1332. doi: 10.1111/j.1755-0998.2009.02591.x
Hueter, R. E., Heupel, M. R., Heist, E. J., and Keeney, D. B. (2005).
Evidence of philopatry in sharks and implications for the management
of shark fisheries. J. Northwest Atl. Fish. Sci. 35, 239–247. doi: 10.2960/J.
v35.m493
Frontiers in Ecology and Evolution | www.frontiersin.org 13 November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
Hueter, R. E., Tyminski, J. P., and De La Parra, R. (2013). Horizontal
movements, migration patterns and population structure of whale sharks in
the Gulf of Mexico and northwestern Caribbean Sea. PLoS ONE 8:e71883.
doi: 10.1371/journal.pone.0071883
Jakobsson, M., and Rosenberg, N. A. (2007). CLUMPP: a cluster matching and
permutation program for dealing with label switching and multimodality
in analysis of population structure. Bioinformatics 23, 1801–1806.
doi: 10.1093/bioinformatics/btm233
Johnson, D., London, J., Lea, M. A., and Durban, J. (2008). Continuous-
time correlated random walk model for animal telemetry data. Ecology 89,
1208–1215. doi: 10.1890/07-1032.1
Jost, L. (2008). GST and its relatives do not measure differentiation. Mol. Ecol. 17,
4015–4026. doi: 10.1111/j.1365-294X.2008.03887.x
Jost, L., Archer, F., Flanagan, S., Gaggiotti, O., Hoban, S., and Latch, E. (2017).
Differentiation measures for conservation genetics. Evol. Appl. 11, 1139–1148.
doi: 10.1111/eva.12590
Jukes, T. H., and Cantor, C. R. (1969). “Evolution of protein molecules,” in
Mammalian protein metabolism, ed H.M. Munro (New York, NY: Academic
Press), 21–132.
Kinney, M. J., and Simpfendorfer, C. A. (2009). Reassessing the value of
nursery areas to shark conservation and management. Conserv. Lett. 2, 53–60.
doi: 10.1111/j.1755-263X.2008.00046.x
Meirmans, P. G., and Hedrick, P. W. (2011). Assessing population
structure: FST and related measures. Mol. Ecol. Resour. 11, 5–18.
doi: 10.1111/j.1755-0998.2010.02927.x
Michaud, A., Hyde, J. R., Kohin, S., and Vetter, R. (2011). “Mitochondrial DNA
sequence data reveals barriers to dispersal in the highly migratory shortfin
mako shark (Isurus oxyrinchus),” in Report of the Shark Working Group
Workshop. Annex 4. International Scientific Committee for Tuna and Tuna-like
Species in the North Pacific Ocean. (La Jolla, CA), 38.
Mills, L. S., and Allendorf, F. W. (1996). The one-migrant-per-generation rule in
conservation and management. Conserv. Biol. 10, 1509–1518.
Mossman, C. A., and Waser, P. M. (1999). Genetic detection of sex-biased
dispersal. Mol. Ecol. Notes 8, 1063–1067.
Mucientes, G. R., Queiroz, N., Sousa, L. L., Tarroso, P., and Sims, D. W. (2009).
Sexual segregation of pelagic sharks and the potential threat from fisheries. Biol.
Lett. 5, 156–159. doi: 10.1098/rsbl.2008.0761
Musyl, M. K., Brill, R. W., Curran, D. S., Fragoso, N. M., Mcnaughton, L.
M., Nielsen, A., et al. (2011). Postrelease survival, vertical and horizontal
movements, and thermal habitats of five species of pelagic sharks in the central
Pacific Ocean. Fish. Bull. 109, 341–368.
Naylor, G. J. P., Caira, J. N., Jensen, K., Rosana, K. M., Straube, N., and Lakner,
C. (2012). “Elasmobranch phylogeny: A mitochondrial estimate based on 595
species,” in Biology of sharks and their relatives, 2nd Edn., eds J. C. Carrier, J. A.
Musick and M. R. Heithaus (Boca Raton, FL: CRC Press), 31–56.
Paetkau, D., Calvert, W., Stirling, I., and Strobeck, C. (1995). Microsatellite analysis
of population structure in Canadian polar bears. Mol. Ecol. 4, 347–354.
Palumbi, S. R. (2003). Population genetics, demographic connectivity, and
the design of marine reserves. Ecol. Appl. 13, 146–158. doi: 10.1890/1051-
0761(2003)013[0146:PGDCAT]2.0.CO;2
Pardini, A. T., Jones, C. S., Noble, L. R., Kreiser, B., Malcolm, H., Bruce, B. D.,
et al. (2001). Sex-biased dispersal of great white sharks. Nature 412, 139–140.
doi: 10.1038/35084125
Peakall, R., Ruibal, M., and Lindenmayer, D. B. (2003). Spatial autocorrelation
analysis offers new insights into gene flow in the Australian bush rat, Rattus
fuscipes.Evolution 57, 1182–1195. doi: 10.1111/j.0014-3820.2003.tb00327.x
Peakall, R., and Smouse, P. E. (2012). GenAlEx 6.5: genetic analysis in
Excel. Population genetic software for teaching and research – an update.
Bioinformatics 28, 2537–2539. doi: 10.1093/bioinformatics/bts460
Peakall, R., Smouse, P. E., and Huff, D. R. (1995). Evolutionary implications of
allozyme and RAPD variation in diploid populations of dioecious buffalo grass,
Buchloe dactyloides.Mol. Ecol. 4, 135–147.
Piry, S., Alapetite, A., Cornuet, J. M., Paetkau, D., Baudouin, L., and
Estoup, A. (2004). GeneClass2: a software for genetic assignment and first-
generation migrant detection. J. Hered. 95, 536–539. doi: 10.1093/jhered/
esh074
Portnoy, D. S., Mcdowell, J. R., Heist, E. J., Musick, J. A., and Graves,
J. E. (2010). World phylogeography and male-mediated gene flow in
the sandbar shark, Carcharhinus plumbeus.Mol. Ecol. 19, 1994–2010.
doi: 10.1111/j.1365-294X.2010.04626.x
Portnoy, D. S., Puritz, J. B., Hollenbeck, C. M., Gelsleichter, J., Chapman, D.,
and Gold, J. R. (2015). Selection and sex-biased dispersal in a coastal shark:
the influence of philopatry on adaptive variation. Mol. Ecol. 24, 5877–5885.
doi: 10.1111/mec.13441
Pritchard, J. K., Stephens, M., and Donnelly, P. (2000). Inference of population
structure using multilocus genotype data. Genetics 155, 945–959.
R Core Team (2013). R: A Language and Environment for Statistical Computing.
Vienna: RFoundation for Statistical Computing.
Raymond, M., and Rousset, F. (1995). Population genetics software for exact tests
and ecumenicism. J. Hered. 86, 248–249.
Rice, W. (1989). Analysing tables of statistical tests. Evolution 43, 223–225.
Rogers, P. J., Corrigan, S., and Lowther, A. D. (2015a). Using Satellite Tagging
and Molecular Techniques to Improve the Ecologically Sustainable Fisheries
Management of Shortfin Makos (Isurus oxyrinchus) in the Australasian region.
Final Report FRDC Tactical Research Fund Project No. 2011/077.
Rogers, P. J., Huveneers, C., Goldsworthy, S. D., Cheung, W. W. L., Jones,
G. K., Mitchell, J. G., et al. (2013a). Population metrics and movements of
two carcharhinids: a comparison of the vulnerability of pelagic sharks of
the southern Australian gulfs and shelves. Mar. Freshwater Res. 64, 20–30.
doi: 10.1071/MF11234
Rogers, P. J., Huveneers, C., Goldsworthy, S. D., Mitchell, J. G., and Seuront,
L. (2013b). Broad-scale movements and pelagic habitat of the dusky
shark Carcharhinus obscurus off Southern Australia determined using pop-
up satellite archival tags. Fish. Oceanogr. 22, 102–112. doi: 10.1111/fog.
12009
Rogers, P. J., Huveneers, C., Page, B., Goldsworthy, S. D., Coyne, M., Lowther, A.
D., et al. (2015b). Living on the continental shelf edge: habitat use of juvenile
shortfin makos Isurus oxyrinchus in the Great Australian Bight, southern
Australia. Fish. Oceanogr. 24, 205–218. doi: 10.1111/fog.12103
Rosenberg, N. A. (2004). Distruct: a program for the graphical
display of population structure. Mol. Ecol. Notes 4, 137–138.
doi: 10.1046/j.1471-8286.2003.00566.x
Ryman, N., and Palm, S. (2006). POWSIM: a computer program for assessing
statistical power when testing for genetic differentiation. Mol. Ecol. 6, 600–602.
doi: 10.1111/j.1471-8286.2006.01378.x
Sandoval-Castillo, J., and Beheregaray, L. B. (2015). Metapopulation structure
informs conservation management in a heavily exploited coastal shark
(Mustelus henlei). Mar. Ecol. Prog. Ser. 533, 191–203. doi: 10.3354/meps11395
Schrey, A. W., and Heist, E. J. (2002). Microsatellite markers for the shortfin mako
and cross-species amplification in Lamniformes. Conserv. Genet. 3, 459–461.
doi: 10.1023/A:1020583609967
Schrey, A. W., and Heist, E. J. (2003). Microsatellite analysis of population
structure in the shortfin mako (Isurus oxyrinchus). Can. J. Fish. Aquat. Sci. 60,
670–675. doi: 10.1139/f03-064
Schuelke, M. (2000). An economic method for the fluorescent labeling of PCR
fragments. Nat. Biotechnol. 18, 233–234. doi: 10.1038/72708
Schultz, J. K., Feldheim, K. A., Gruber, S. H., Ashley, M. V., Mcgovern,
T. M., and Bowen, B. W. (2008). Global phylogeography and seascape
genetics of the lemon sharks (genus Negaprion). Mol. Ecol. 17, 5336–5348.
doi: 10.1111/j.1365-294X.2008.04000.x
Sims, D. W., Mucientes, G. R., and Queiroz, N. (2018). Shortfin mako sharks
threatened by inaction. Science 359:1342. doi: 10.1126/science.aat0315
Sippel, T., Wraith, J., Kohin, S., Taylor, V., Holdsworth, J., Taguchi, M., et al.
(2011). A Summary Of Blue Shark (Prionace glauca) and Shortfin Mako Shark
(Isurus oxyrinchus) Tagging Data Available From the North and Southwest
Pacific Ocean. Working document submitted to the ISC Shark Working Group
Workshop ISC/11/SHARKWG-2/04 (La Jolla, CA).
Smouse, P. E., and Peakall, R. (1999). Spatial autocorrelation analysis of
individual multiallele and multilocus genetic structure. Heredity 82, 561–573.
doi: 10.1038/sj.hdy.6885180
Smouse, P. E., Peakall, R., and Gonzales, E. (2008). A heterogeneity
test for fine-scale genetic structure. Mol. Ecol. 17, 3389–3400.
doi: 10.1111/j.1365-294X.2008.03839.x
Spieth, P. T. (1974). Gene flow and genetic differentiation. Genetics 78, 961–965.
Sunnucks, P., and Hales, D. (1996). Numerous transposed sequences
of mitochondrial cytochrome oxidase in aphids of the genus
Frontiers in Ecology and Evolution | www.frontiersin.org 14 November 2018 | Volume 6 | Article 187

Corrigan et al. Shortfin Mako Population Connectivity
Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–523.
doi: 10.1093/oxfordjournals.molbev.a025612
Taguchi, M., Kitamura, T., and Yokawa, K. (2011). Genetic Population Structure of
Shortfin Mako (Isurus oxyrinchus) Inferred From Mitochondrial DNA on Inter-
Oceanic Scale. Working document submitted to the ISC Shark Working Group
Workshop ISC/11/SHARKWG-1/02" (Keelung).
Taguchi, M., Oshimo, S., and Yokawa, K. (2015). Genetic Stock Structure of Shortfin
Mako (Isurus oxyrinchus) in the Pacific Ocean. Working document submitted to
the ISC Shark Working Group Workshop ISC/15/SHARKWG-1/ (Shimizu-Ku:
National Research Institute of Far Seas Fisheries).
Teske, P. R., Sandoval-Castillo, J., Waters, J. M., and Beheregaray, L. B. (2017). An
overview of Australia’s temperate marine phylogeography, with new evidence
from high-dispersal gastropods. J. Biogeogr. 44, 217–229. doi: 10.1111/jbi.12783
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M., and Shipley,
P. (2004). Microchecker: software for identifying and correcting
genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538.
doi: 10.1111/j.1471-8286.2004.00684.x
Verity, P. G., Smetacek, V., and Smayda, T. J. (2002). Status, trends and
the future of the marine pelagic ecosystem. Environ. Conserv. 29, 207–237.
doi: 10.1017/S0376892902000139
Waples, R. S. (1998). Separating the wheat from the chaff – patterns of
genetic differentiation in high gene flow species. J. Hered. 89, 438–450.
doi: 10.1093/jhered/89.5.438
Waples, R. S. (2015). Testing for Hardy-Weinberg proportions: have we lost the
plot? J. Hered. 106, 1–19. doi: 10.1093/jhered/esu062
Waples, R. S., and Gaggiotti, O. (2006). What is a population? An empirical
evaluation of some genetic methods for identifying the number of
gene pools and their degree of connectivity. Mol. Ecol. 15, 1419–1439.
doi: 10.1111/j.1365-294X.2006.02890.x
Waples, R. S., Punt, A. E., and Cope, J. M. (2008). Integrating genetic
data into management of marine resources: how can we do it
better? Fish and Fisheries 9, 423–449. doi: 10.1111/j.1467-2979.2008.
00303.x
Wood, L. J. (2008). Assessing progress towards global marine protection
targets: shortfalls in information and action. Oryx 42, 340–351.
doi: 10.1017/S003060530800046X
Worm, B., and Tittensor, D. P. (2011). Range contraction in large pelagic predators.
Proc. Natl. Acad. Sci. U.S.A. 108, 11942–11947. doi: 10.1073/pnas.11023
53108
Worm, B., and Vanderzwaag, D. (2007). High-seas fisheries: troubled waters,
tangled governance, and recovery prospects. Behind Headlines 64, 2–32.
Available online at: https://ssrn.com/abstract=2101249
Conflict of Interest Statement: JGP was employed by Pepperell Research and
Consulting Pty Ltd. LM was employed by Stick Figure Fish Illustration.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2018 Corrigan, Lowther, Beheregaray, Bruce, Cliff, Duffy, Foulis,
Francis, Goldsworthy, Hyde, Jabado, Kacev, Marshall, Mucientes, Naylor, Pepperell,
Queiroz, White, Wintner and Rogers. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is permitted, provided the original
author(s) and the copyright owner(s) are credited and that the original publication
in this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these terms.
Frontiers in Ecology and Evolution | www.frontiersin.org 15 November 2018 | Volume 6 | Article 187