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Patterns of connectivity and population structure
of the southern calamary Sepioteuthis australis in
southern Australia
Timothy M. Smith
A
,
C
, Corey P. Green
B
and Craig D. H. Sherman
A
A
Centre for Integrated Ecology, School of Life and Environmental Sciences, Deakin University,
96 Pigdon Road, Waurn Ponds, Vic 3216, Australia.
B
Department of Environment and Primary Industries, 2a Bellarine Highway, Queenscliff,
Vic 3225, Australia.
C
Corresponding author. Email: tim.smith@deakin.edu.au
Abstract. The southern calamary, Sepioteuthis australis, is a commercially and recreationally important inshore
cephalopod endemic to southern Australia and New Zealand. Typical of other cephalopods, S. australis has a short life
span, form nearshore spawning aggregations and undergo direct development. Such life history traits may restrict
connectivity between spawning grounds creating highly structured and genetically differentiated populations that are
susceptible to population crashes. Here we use seven polymorphic microsatellite markers to assess connectivity and
population structure of S. australis across a large part of its geographic range in Australia. Little genetic differentiation was
found between sampling locations. Overall, F
ST
was low (0.005, 95% CI ¼ ,0.001–0.011) and we detected no significant
genetic differentiation between any of the locations sampled. There was no strong relationship between genetic and
geographical distance, and our neighbour joining analysis did not show clustering of clades based on geographical
locations. Similarly, network analysis showed strong connectivity amongst most locations, in particular, Tasmania
appears to be well connected with several other locations and may act as an important source population. High levels of
gene flow and connectivity between S. australis sampling sites across Australia are important for this short-lived species,
ensuring resilience against spatial and temporal mortality fluctuations.
Additional keywords: invertebrate, microsatellites, null alleles, population resilience, squid.
Received 11 July 2014, accepted 4 December 2014, published online 19 March 2015
Introduction
Genetic connectivity can enhance species resilience to distur-
bance through the introduction of genes with greater adapt-
ability and by allowing the recolonisation of locally extinct
populations (Van Oppen and Gates 2006). Cephalopods (squid,
cuttlefish, octopus and nautilus) are one such group where
restricted connectivity may have detrimental effects on popu-
lations. Cephalopods are often short lived and mate in com-
munal spawning aggregations (Boyle and Rodhouse 2005). For
species that do not spawn all year round, or have reduced
spawning capacity throughout the year, such life history traits
can restrict connectivity and generational overlap, resulting in
population crashes if there is recruitment failure in any given
year (Basson and Beddington 1993). Increased fishing pressure
from both commercial and recreational sectors targeting
spawning aggregations, along with environmental stochasticity,
stressors such as climate change at a global level, and spawning
habitat loss at a local level, may result in failed recruitment and
consequent spawning biomass loss (Fogarty et al. 1991).
The southern calamary squid, Sepioteuthis australis is a
cephalopod endemic to nearshore waters around southern
Australia and New Zealand (Winstanley 1983)andisan
important commercial and recreational fishing species with
an estimated catch of 368 tonnes in Victoria, Tasmania and
South Australia in 2008–2009 (Department of Primary Indus-
tri es 2009; Fowler et al. 2013; Andre et al. 2014) and valued at
$487 000 (D epartment of Primary Industries 2009). S. australis
has a short life-span (less than 1 year) during which they
experience rapid growth (4–5% BW day
1
) until reaching
maturity and begin spawning (Pecl and Moltschaniwskyj
2006). Peak spawning of S. australis occurs in nearshore
habitats between spring and early summer along suitable
habitat each year (Moltschaniwskyj and Steer 2004). Spawning
can however occur throughout the year (Moltschaniwskyj and
Steer 2004) and there is also strong evidence that some
spawning grounds act as important sources of squid larvae that
pop ulate areas with poor spawning habitat (Pecl et al. 2011).
Such life history strategies may lead to greater mixing of genes
CSIRO PUBLISHING
Marine and Freshwater Research, 2015, 66, 942–947
http://dx.doi.org/10.1071/MF14328
Journal compilation Ó CSIRO 2015 www.publish.csiro.au/journals/mfr
Short Communication
across locations and generations enhancing the resilience of
S. australis to disturbances and population decline.
Previous studies on the population structure of S. australis in
Australia and New Zealand using allozyme markers suggested
the presence of two distinct genetic groups or subspecies
(Triantafillos and Adams 2001). Within Australia, one genetic
grouping comprises populations in Tasmania and South Aus-
tralia, whereas the second group is made up of populations in
Western Australia and New South Wales; with a ‘hybrid’ zone
found where the groups overlap (Triantafillos and Adams 2001).
However, how the same two genetic subgroups in Western
Australia and New South Wales have become established and
have maintained the same genetic signature despite intervening
hybrid zones (suggesting some gene flow), remains unexplained
(Triantafillos and Adams 2001). The genetic groupings were
based on data from two out of seven allozyme loci used in the
study, and were not apparent when the remaining five loci were
used in their analysis. An alternative and more parsimonious
interpretation of these data is that these patterns reflect similar
selection patterns acting on these coding loci, resulting in a
similar genetic signature in the two locations. These patterns
may therefore not reflect neutral genetic variation and thus
estimates of population structure and connectivity may be
confounded. The development of microsatellite genetic markers
provides the opportunity for assessing population structure and
connectivity of S. australis within Australia with much greater
resolution and power.
Understanding population structure and patterns of connec-
tivity are crucial components in assessing stock structure of
S. australis to ensure effective management. Restricted connec-
tivity may result in isolated populations that are more suscepti-
ble to recruitment failure and subsequent population crashes.
The aim of this study was to use microsatellite markers to assess
the population genetic structure and connectivity patterns of
S. australis across a large part of its geographic range in southern
Australia to assist in the understanding of the scale at which
populations should be potentially managed.
Methods
Sample collection
S. australis samples were collected from seven sites across
southern Australia; Port Phillip Bay, Corner Inlet and Western
Port in Victoria, Storm Bay in Tasmania, Spencer Gulf and Gulf
Saint Vincent in South Australia and Albany in Western
Australia (Fig. 1). At each site 40 S. australis samples were
collected by commercial fishermen using seine nets (Corner
Inlet, Spencer Gulf, Albany) and recreational fishermen using
jigs (Port Phillip Bay, Western Port, Gulf Saint Vincent, Storm
Bay). Once collected, the mantle length of each individual was
recorded and a tentacle removed and frozen. Samples were
collected between February and October 2013 with the excep-
tion of Western Port where four of the 40 samples were collected
in November 2012.
Genetic analysis
Genomic DNA was extracted from each sample using DNeasy
blood and tissue kits (QIAGEN, Valencia, CA, USA) following
the manufacturer’s instructions. Levels of connectivity between
populations was assessed using seven polymorphic microsatellite
markers; Sau03, Sau06, Sau11, Sau13, Sau14, Sau16 and Sau18
(Van Camp et al. 2003). Microsatellites were amplified using a
polymerase chain reaction (PCR) touchdown program under the
following conditions; initial hot start at 948C for 15 min; five
cycles of 948C for 45 s, 658C for 45 s, 728C for 45 s; five cycles of
948C for 45 s, 608C for 45 s, 728C for 45 s; 10 cycles of 948Cfor
45 s, 578C for 45 s, 728C for 45 s; 20 cycles of 948C for 45 s, 558C
for 45 s, 728C for 45 s; final elongation at 728C for 15 min. PCR
was conducted in 11-mL volumes containing; 10 ng of genomic
DNA; 5 mL PCR Master Mix (QIAGEN) 4-mLprimermultiplex
consisting of 0.26 mM of each forward primer with a fluorescent
dye associated tag (FAM-GCCTCCCTCGCGCCA; NED-GCC
TTGCCAGCCCGC; VIC-CAGGACCAGGCTACCGTG; PET-
CGGAGAGCCGAGAGGTG) and 0.13 mM of reverse primer.
PCR amplicons were electrophoresed using an ABI 3130xl
0 250 500 1000 1500 2000
N
Kilometres
Fig. 1. Sampling sites around southern Australia.
Calamari population genetics Marine and Freshwater Research 943
Genetic Analyzer, incorporating LIZ 500 (-250) size standard
(Applied Biosystems, Foster City, CA, USA). Alleles were scored
using GeneMapper, v3.7 (Applied Biosystems).
Data analysis
To determine if loci assorted independently, each pairwise
combination of loci were tested for linkage disequilibrium
within each population using the program GENEPOP v4.2.
Significant departures from Hardy–Weinberg Equilibrium
(HWE) were carried out using exact tests with significance
determined by a Markov chain method (GENEPOP v4.2) and
the presence of null alleles at each locus tested using MICRO-
CHECKER (Van Oosterhout et al. 2004). Out of the 60 pairwise
comparisons, no significant linkage was detected between any
loci, however, significant deviations from HWE were found at
four loci, Sau3, Sau11, Sau14 and Sau16 and MICRO-
CHECKER revealed the presence of null alleles at these four
loci. Null alleles are common in microsatellites for many
invertebrate species including molluscs (Astanei et al. 2005;
Lemer et al. 2011; Kang et al. 2012) and can cause deviations
from HWE (homozygote excess), overestimating genetic dif-
ferentiation, particularly in structured populations (Chapuis and
Estoup 2007). To mitigate this effect of null alleles in the
dataset, null allele frequencies were estimated using the
Expectation Maximisation algorithm (Dempster et al. 1977). To
determine population structure we calculated F
ST
estimates
across all sites and between all pairwise combinations of sites.
F
ST
varies between zero (no structure) and one (populations
fixed for different alleles) and estimates the level of genetic
variation within a site compared with the overall genetic vari-
ation and is considered the most reliable method of determining
genetic structure (Freeland 2005). Data adjusted for null alleles
was used to estimate global and pairwise F
ST
values and boot-
strapping over loci for 95% confidence intervals using the
Excluding Null Allele method (Chapuis and Estoup 2007) that
provides little bias from estimates without null alleles. Null
frequencies and F
ST
values were calculated in FreeNA (Chapuis
and Estoup 2007). Any patterns of isolation by distance were
assessed using the log values of F
ST
/(1-F
ST
) and the shortest
geographic distance between each site within the ocean using
Isolation By Distance on the web (Jensen et al. 2005). Patterns of
genetic diversity were measured as the mean number of alleles
across loci, observed (H
O
) and expected (H
E
) heterozygosity
and inbreeding coefficient (F
IS
) (GENEPOP v4.2). To asses any
regional grouping between S. australis sampling locations,
Neighbour Joining Analysis based on adjusted F
ST
values was
carried out in Mega 6 (Tamura et al. 2013). Patterns of con-
nectivity and gene flow were further explored using network
analysis using adjusted F
ST
values. Network analysis estimates
the number connections each site has to another site (degree) and
the number of connections that pass through a site (betweeness
centrality) which can be interpreted as the level of gene flow.
The threshold value for the network was set just above the
threshold where all sites were included in the network and
produced using EDENetworks 2.18 (Kivela¨ et al. 2015).
Results
A total of 280 individual S. australis were genotyped at seven
microsatellite loci across the seven sampled locations. All loci
were highly variable and the mean number of alleles across all
loci and sites was 11.20 0.84 s.e. (Table 1). Tasmania had the
highest mean number of alleles 12.00 2.67 s.e., whereas
Western Australia recorded the lowest number of alleles
10.71 2.21 s.e. (Table 1). Both observed and expected het-
erozygosities were similar across all sites. Observed heterozy-
gosity was highest in Tasmania (0.585 0.06 s.e.) and only
slightly different at Corner Inlet (0.522 0.08 s.e.) the lowest
site. Similarly, Tasmania had the highest expected heterozy-
gosity (0.777 0.06 s.e.) which was only slightly higher than
Western Australia (0.735 0.06) which had the lowest. The
inbreeding coefficient (F
IS
) was high across all sites. Corner
Inlet had the highest F
IS
value (0.317 0.09 s.e.), whereas Gulf
Saint Vincent had the lowest (0.220 0.09 s.e.). High F
IS
values
result from an excess of homozygous samples at a locus and may
result from inbreeding, assortive mating, or the presence of null
alleles. For individual loci, F
IS
was high for those that had null
alleles and low for those without null alleles indicating that our
high F
IS
values are mostly caused by null alleles and not
inbreeding. F
IS
for the loci Sau03, Sau11, Sau14 and Sau16 that
had null alleles were 0.482 0.02 s.e., 0.577 0.04 s.e.,
0.252 0.08 s.e. and 0.388 0.06 s.e. respectively, whereas
loci without null alleles, Sau06, Sau13 and Sau18 had F
IS
values
of 0.091 0.03 s.e., 0.037 0.03 s.e. and 0.032 0.05 s.e.
respectively.
The analysis of population structure showed low amounts of
genetic differentiation between all sampling sites with a global
F
ST
of 0.005 (95% CI ¼ ,0.001–0.011). Pairwise estimates of
genetic differentiation between populations showed strong
mixing between all sites with F
ST
estimates consistently being
low and not significantly different from zero (Table 2). Analysis
of isolation by distance showed a trend of increasing genetic
distance with geographic distance, however the trend was
marginally non-significant (R
2
¼ 0.448, P ¼ 0.079).
Neighbour joining analysis showed Corner Inlet and Port
Phillip Bay forming a clade closely related to a clade including
Western Australia and Gulf Saint Vincent (Fig. 2). Western Port
was isolated from the other sites, forming a single clade and was
most similar to Tasmanian samples. The network analysis
Table 1. Summary of number of alleles, allelic richness, observed
heterozygosity (H
O
), expected unbiased heterozygosity (H
E
), inbreeding
coefficient (F
IS
), degrees and betweeness centrality (BC) for each site
pooled across all loci
Corner Island (CI), Western Port (WP), Port Phillip Bay (PPB), Tasmania
(Tas.), Spencer Gulf (SG), Gulf Saint Vincent (GSV) and Western
Australia (WA)
CI WP PPB Tas. SG GSV WA Mean
Number
alleles
11.14 11.57 11.00 12.00 11.00 11.00 10.71 11.20
Allelic
Richness
6.909 7.308 6.850 7.554 6.606 6.649 5.752 6.804
H
O
0.522 0.564 0.566 0.585 0.519 0.562 0.542 0.551
H
E
0.773 0.775 0.758 0.777 0.749 0.747 0.735 0.759
F
IS
0.317 0.260 0.244 0.226 0.288 0.220 0.234 0.256
Degrees 4146141
BC 0009000
944 Marine and Freshwater Research T. M. Smith et al.
threshold was set at 0.042, slightly above the threshold where all
sites were included in the network. No distinct geographical
groups appeared in the network with Tasmania being connected
to all other sites whereas Spencer Gulf and Western Port were
only connected to Tasmania (Fig. 3). Tasmania was the most
important node in the network with six degrees (connections)
and the only site with any betweeness centrality (9, Table 2)
indicating that it has the highest level of gene flow.
Discussion
The genetic analysis of S. australis from seven locations across
Australia revealed little genetic structuring in this species. Global
F
ST
and pairwise F
ST
values were low and not significantly
different between any sites indicating strong connectivity among
sampling sites. The low level of genetic structuring across
southern Australia sites provides evidence that spawning grounds
consist of a mixture of individuals that may have originated
from distant locations. Thus S. australis does not appear to form
discrete breeding populations but are likely to consist of a mixture
of individuals hatched from different spawning locations or
alternatively from small ephemeral spawning aggregations that
occur throughout the year (Moltschaniwskyj and Steer 2004).
This result suggests that S. australis disperse across the coast and
enter large bays and inlets to spawn but do notnecessarily return to
their natal spawning ground. The pattern of isolation by distance,
although marginally non-significant, did show a trend for popu-
lations more closely situated to each other to be more genetically
similar and suggests that squid may regularly disperse between
populations tens to hundreds of kilometres away. Management of
S. australis populations should therefore focus on the protection
of spawning areas to ensure that reproductive success is main-
tained to allow dispersal and gene flow across sites.
Network analysis showed Tasmanian S. australis to be well
connected to all other populations and the most important site
for gene flow. Fishing pressure on Tasmanian S. australis has
increased over the past 20 years (Andre et al. 2014) and may
pose a threat to this important source population. However,
further study that incorporates temporal and seasonal sampling
would be valuable in determining the importance of the Tasma-
nia population as a primary source for other populations in this
species geographical range. Conversely, Western Port was
relatively poorly connected to other sites in the network analysis
and formed its own clade in the neighbour joining analysis
suggesting that there is some isolation from the other sites.
S. australis from South Australia have been reported to spend
the beginning of their life history over nearshore habitats before
moving out to deeper water as sub adults and returning to
nearshore spawning habitats as adults (Steer et al. 2007). In
contrast, Tasmanian S. australis are generally recruited from
specific spawning grounds on the islands east coast (Pecl et al.
2011). The results of this study support the suggestion that
S. australis move between spawning grounds throughout their
life cycle, either during the larval stage, as sub adults or mature
adults, thereby creating gene flow across sites.
It has previously been suggested there are at least two
breeding populations of S. australis in southern Australia
(Triantafillos and Adams 2001), however, the results from this
study found no such genetic subdivision and that locations
appear to be generally well connected via gene flow. There
are several factors that could be used to explain differences in
population structure between studies. Triantafillos and Adams
(2001) used allozyme markers to assess S. australis population
structure which show less diversity than microsatellite markers
and differences in genetic resolution between studies may have
caused a discrepancy in population structure. Allozyme markers
represent coding loci, and therefore are more likely to be under
selection which can confound estimates of genetic structure and
patterns of connectivity (Freeland 2005), especially when indi-
viduals are sampled from different environments. Triantafillos
and Adams (2001) also collected samples from a greater number
of sites, and, of the sites they allocated to a distinct second
population, only Albany in Western Australia was sampled in
CI
PPB
GSV
WA
SG
TAS
WP
0.001
Fig. 2. Neighbour joining analysis of Sepioteuthis australis sites across
southern Australia. Corner Island (CI), Western Port (WP), Port Phillip Bay
(PPB), Tasmania (Tas.), Spencer Gulf (SG), Gulf Saint Vincent (GSV) and
Western Australia (WA).
WA
SG
GSV
PPB
WP
TA S
Cl
Fig. 3. Network analysis of pairwise F
ST
values for S. australis sampled at
Corner Island (CI), Western Port (WP), Port Phillip Bay (PPB), Tasmania
(Tas.), Spencer Gulf (SG) and Western Australia (WA).
Table 2. Pairwise F
ST
values for S. australis sampled at Corner Island
(CI), Western Port (WP), Port Phillip Bay (PPB), Tasmania (Tas.),
Spencer Gulf (SG) and Western Australia (WA)
CI WP PPB Tas. SG GSV WA
CI 0
WP 0.011 0
PPB 0.002 0.010 0
Tas. 0.001 0.004 0.003 0
SG 0.005 0.012 0.004 0.001 0
GSV 0.002 0.011 0.002 0.004 0.007 0
WA 0.003 0.011 0.002 0.002 0.012 0.002 0
Calamari population genetics Marine and Freshwater Research 945
both studies. However, we still found a strong degree of
connectivity between Albany and the other sites even though
the nearest site was 1880 km away.
Although only seven microsatellite loci were used in this
study, each loci was highly polymorphic (5–21 alleles per loci)
providing a much higher level of resolution of the genetic
structure in this species compared than the previous study of
S. australis (Triantafillos and Adams 2001). The number of loci
used here are similar to studies on other squid species such as
Doryteuthis paeleii (5 loci, Buresch et al. 2006; Shaw et al.
2010), Loligo reynaudii (8 loci, (Shaw et al. 2010), L. vulgaris
(6 loci, Garoia et al. 2004) and L. opalscens (6 loci, Reichow and
Smith 2001). However, to gain a better understanding of
S. australis connectivity and identify vulnerable populations,
further research is required that includes both temporal (annual,
seasonal) and spatially explicit sampling regime and the devel-
opment of more loci.
S. australis show similar genetic patterns to other squid
species where populations tend to be panmictic over large
geographical scales. Studies on L. reynaudii (Shaw et al.
2010), L. vulgaris (Garoia et al. 2004), Dosidicus gigas (Iba´n
˜
ez
et al. 2011), L. opalscens (Reichow and Smith 2001) and
Doryteuthis paeleii (Shaw et al. 2010) all found no evidence
of genetic structuring across large geographic ranges
(.1000 km). Structuring has been found in L. forbesi and
D. gahi and can be attributed to large barriers to gene flow,
such as deep water and unfavourable currents (Shaw et al. 1999;
Iba´n
˜
ez et al. 2012). The lack of genetic structuring of many
squid species is generally attributed to widespread larval dis-
persal and adult migration (Shaw et al.
2010; Iba´n
˜
ez et al. 2011).
Large-scale genetic homogeneity signifying high gene flow
is important for the maintenance of genetic diversity within
populations, ensuring they are able to adapt to environmental
changes and ensure they are resilient to population crashes
(Gunderson 2000). The absence of structuring within the south-
ern Australian S. australis population indicates it has the
potential to maintain a level of resilience if large scale mixing
is maintained. Therefore, management strategies need to be
implemented at large geographical ranges and ensure successful
spawning occurs across large geographical scales to maintain
high levels of gene flow.
Acknowledgements
We thank Dr Justin Bell, Dr Mike Steer, Sean Brodie, Dr Peter Coulson and
Grant Leeworthy who collect squid for the project and all the recreational
fishermen who provided a sample of their catch. Annalise Stanley and Mark
Richardson assisted with the laboratory work and Brent Womersley for
generating a map of sampling sites. All work was done at Deakin University
(Victoria) and the Victorian Marine Science Consortium with funding from
the Paddy Pallin Foundation, Royal Zoological Society of New South Wales,
and the Western Australia Recreational Fishing Initiatives Fund.
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