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Habitat use and movement patterns of tiger sharks (Galeocerdo cuvier) in eastern Australian waters

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Abstract

Habitat use and movement patterns of tiger sharks (Galeocerdo cuvier) in eastern Australian waters. Understanding the movement of marine predators is vital for effective conservation and management. Despite being targeted by shark control programs, the tiger shark, Galeocerdo cuvier, is poorly studied off eastern Australia. To investigate the horizontal movement and habitat use in this region, 16 sharks (157-384 cm total length) were tagged with MiniPAT pop-up satellite archival tags in 2018 and 2019. Eleven of these individuals were also fitted with satellite-linked radio transmitting tags. After release, most sharks moved off the continental shelf and headed north, associating with seamounts as they moved towards Queensland. During their time at liberty they transited through temperate, subtropical and tropical waters and spent the majority of time in the upper 50 m of the water column and at temperatures between 22 and 25˚C. Horizontal movement was focused in waters off the continental shelf. Increased movement over shelf waters occurred during the aus-tral spring and summer when the East Australian Current is at its strongest and warm waters encroach the continental shelf. Broad latitudinal movement along the east coast of Australia was evident and highlights the connectivity between tropical and warm-temperate regions.
Habitat use and movement patterns of tiger sharks (Galeocerdo
cuvier) in eastern Australian waters
Rebecca S. Lipscombe
1
, Julia L. Y. Spaet
2
, Anna Scott
1
, Chi Hin Lam
3
, Craig P. Brand
4
, and
Paul A. Butcher
1,4
*
1
National Marine Science Centre, Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, PO
Box 4321, Coffs Harbour, NSW 2450, Australia
2
Evolutionary Ecology Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
3
Large Pelagics Research Center, School for the Environment, University of Massachusetts Boston, Gloucester, MA, USA
4
NSW Department of Primary Industries, National Marine Science Centre, Coffs Harbour, NSW 2450, Australia
*Corresponding author: tel: þ61 438 650 129; e-mail: paul.butcher@dpi.nsw.gov.au.
Lipscombe, R. S., Spaet, J. L. Y., Scott, A., Lam, C. H., Brand, C. P., and Butcher, P. A. Habitat use and movement patterns of tiger sharks
(Galeocerdo cuvier) in eastern Australian waters. ICES Journal of Marine Science, doi:10.1093/icesjms/fsaa212.
Received 14 July 2020; revised 14 October 2020; accepted 15 October 2020.
Understanding the movement of marine predators is vital for effective conservation and management. Despite being targeted by shark con-
trol programs, the tiger shark, Galeocerdo cuvier, is poorly studied off eastern Australia. To investigate the horizontal movement and habitat
use in this region, 16 sharks (157–384 cm total length) were tagged with MiniPAT pop-up satellite archival tags in 2018 and 2019. Eleven of
these individuals were also fitted with satellite-linked radio transmitting tags. After release, most sharks moved off the continental shelf and
headed north, associating with seamounts as they moved towards Queensland. During their time at liberty they transited through temperate,
sub-tropical and tropical waters and spent the majority of time in the upper 50 m of the water column and at temperatures between 22 and
25˚C. Horizontal movement was focused in waters off the continental shelf. Increased movement over shelf waters occurred during the aus-
tral spring and summer when the East Australian Current is at its strongest and warm waters encroach the continental shelf. Broad latitudinal
movement along the east coast of Australia was evident and highlights the connectivity between tropical and warm-temperate regions.
Keywords: archival tag, bather protection, carcharhinid, geolocation, satellite tag, shark management
Introduction
Analysis of the spatial dynamics and patterns in shark movement
has become detailed and accurate, revealing complex horizontal
and vertical habitat use and behavioural patterns (Barnes et al.,
2016;Spaet et al., 2017). While previously limited to mark-
recapture studies, over the last two decades, satellite and acoustic
technology has increased our ability to document broad-scale
movement, migration, residency, and philopatric behaviour
(Holmes et al., 2014;Werry et al., 2014). In addition, evaluation
of depth and temperature preference and diving behaviour can be
performed through archived data and biologging (Andrzejaczek
et al., 2019a) and has revealed dynamic vertical movements and
plasticity of habitat use (Holmes et al., 2014;Afonso and Hazin,
2015).
The tiger shark, Galeocerdo cuvier (Carcharhinidae), is a large
predatory shark that is currently listed as ‘Near Threatened’ on
the International Union for Conservation of Nature Red List of
threatened species (Ferreira and Simpfendorfer, 2019). Galeocerdo
cuvier have a global distribution and utilize both nearshore and
offshore habitats (Fitzpatrick et al., 2012;Holmes et al., 2014) in
tropical and warm-temperate regions (Last and Stevens, 2009).
On the east coast of Australia, G. cuvier shows a high level of in-
teraction with commercial, recreational and illegal fisheries due
to its broad-scale movements (Field et al., 2009;Macbeth, 2009;
Butcher et al., 2015). Yet, movement along the east coast of
Australia is relatively undocumented, with only one previous
study encompassing New South Wales (NSW) and Queensland
(QLD) waters (Holmes et al., 2014).
V
CInternational Council for the Exploration of the Sea 2020. All rights reserved.
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Interactions with fisheries are not the only management issue
for G. cuvier on the east coast of Australia (Butcher et al., 2015),
as they are also one of the main species targeted in state shark
control programmes (Reid et al., 2011;Holmes et al., 2012).
To minimize the threat of some shark species to water users, gov-
ernment shark control programmes have been in place in both
NSW and QLD for over 50 years and use a combination of shark
nets and/or baited drumlines (Reid et al., 2011). Galeocerdo cuvier
was among three ‘target’ species of shark that were identified as a
threat to humans, being responsible for several deaths and severe
injuries on the east coast of Australia (McPhee, 2014). Although
shark bites are rare, they receive excessive media attention and
community concern (Fraser-Baxter and Medvecky, 2018).In
2015, the NSW Government implemented the ‘Shark
Management Strategy’ (Simmons and Mehmet, 2018). This strat-
egy uses a multidisciplinary approach and involves the trials of al-
ternative or new tools for bather protection including SMART
drumlines (Guyomard et al., 2019) and drones (Colefax et al.,
2019;Butcher et al., 2019).
One component of the shark management strategy in NSW is
to further quantify the movements and behaviour of target sharks
like G. cuvier to help minimize human–shark interactions. Here,
we aim to characterize broad-scale movement of G. cuvier and ex-
amine habitat use between coastal and pelagic environments
along the east coast of Australia using satellite-linked radio trans-
mission and pop-up archival tagging technologies.
Material and methods
Tagging
Targeted fishing for G. cuvier occurred between 13 February 2018
and 23 July 2019 using SMART drumlines (Guyomard et al.,
2019) and vertical droplines (Williams et al., 2016). Sharks were
caught at Lennox Head (28"480S, 153"360E), Ballina (28"500S,
153"330E), Evans Head (29"060S, 153"250E), and Coffs Harbour
(30"170S, 153"060E), NSW, Australia. SMART drumlines were
baited with 0.75–1.0 kg sea mullet, Mugil cephalus and deployed
500 m offshore on sandy substrate in 6–10 m water depth. Vessels
monitoring the SMART drumlines were immediately alerted of
the capture via email, text message, and phone call and attended
to the shark within 30 min. Up to four droplines were deployed
off Coffs Harbour only, between 1400 m and 4000 m from shor-
ein depths between 25 and 35 m over rocky reef substrate.
Mainlines were vertically orientated, consisting of 80 m of 8-mm
diameter polypropylene/polyethylene blend rope and weighted to
the seabed by a 4-kg Danforth anchor and 5 m of 10-mm galva-
nized chain. A large A3 polyform buoy (43.2 cm diameter #
58.4 cm length) was attached to the top of the mainline with two
smaller buoys (27.9 cm diameter #38.1 cm length) connected to
the main buoy. Three gangions were attached from the top of the
mainline at 8-m intervals. Each gangion comprised 2.8 m of 2-
mm diameter stainless steel wire attached to a stainless steel clip
and a 16/0 non-offset circle hook. Each hook was baited with
$0.5 kg of M. cephalus or Australian salmon, Arripis trutta. All
sharks were kept in the water during tagging operations. Full
details of the tagging procedure and shark capture and handling
are provided in Spaet et al. (2020a). Each shark was classified as
juvenile (<259 cm total length [TL]), sub-adult (259–329 cm TL)
or adult (>330 cm TL) according to the life history descriptions
by Werry et al. (2014) and Whitney and Crow (2007).
Tag details and programming
Archival tagging
Sharks were tagged with MiniPAT pop-up satellite archival tags
(MiniPAT), Wildlife Computers (Redmond, WA, USA), to re-
cord ambient light-level, depth (accuracy 61% of reading), and
water temperature (accuracy 60.1"C). MiniPATs were pressure
rated to 2000 m and had a pre-programmed deployment of 120 d
(n¼3) or 180 d (n¼13), after which they released from the ani-
mal and commenced transmission of archived data to the
ARGOS network. During deployment, tags were programmed to
record and archive a time series of temperature ("C), water depth
(m), and ambient light, with a sample interval of 5 min for data
transmission upon release. Recording of summary data occurred
over 24 h and consisted of depth-temperature profiles, light-level
curves, and percentage of time spent in the mixed layer. If tag-
defined mortality occurred, the tags inbuilt premature release de-
vice activated and the tag released from the shark. Activation of
this release ensued if one of the following three parameters was
met for 4 consecutive days: (i) the tag was recording a constant
depth of 0 m, (ii) the tag was recording a constant depth 62.5 m,
or (iii) the tag remained at, or below 1 400 m. MiniPATs (12 cm
length, volume 60 cm
3
) were tethered using a 15-cm filament of
1.3-mm diameter stainless steel wire covered with black heat
shrink and crimped at either end. Tags were secured to each shark
using either a stainless steel anchor plate (Speed et al., 2013) or
bolt attachment. Anchor attachment required implantation of a
5 cm titanium plate $5 cm into the basolateral dorsal muscula-
ture using a handheld tagging pole. Tags were inserted at an angle
of 45"towards the shark’s head, which ensured that the tag as-
sumed a trailing position on the body. Bolt attachment involved
passing a stainless steel bolt through the tether loop on the
MiniPAT and a pre-drilled 4-mm hole at the base of the dorsal
fin in conjunction with a satellite-linked radio transmitting tag
(Wildlife Computers ‘SPOT6’ tags) .
Satellite tagging
SPOT6 tags were used to support MiniPAT light-based position
estimates and were positioned so that the wet/dry sensor was ex-
posed to air when the dorsal fin broke the water’s surface, en-
abling ARGOS satellites to identify the shark’s location. These
positions are classified on a scale of decreasing accuracy using
seven location classes (LC) from 3, 2, 1, 0, A, B, and Z (CLS,
2011). LC3 is the most reliable and accurate with an error of
<250 m, LC2 has an error between 250 and 500 m, LC1 between
500 and 1500 m and LC0 to LCB >1500 m, and LCZ indicates no
position was recorded. SPOT6 tags (maximum battery life 280 d,
53 g, length 8.1 cm) were attached to the dorsal fin using 50-mm
stainless steel bolts.
In addition to MiniPAT and SPOT6 tags, all sharks were fitted
with acoustic transmitters (Supplementary Table S1); however,
acoustic data were disregarded from the analysis as only one fix
existed for each shark. The study was conducted under research
permits from NSW DPI Scientific Research (P01/0059[A]),
Marine Parks (P16/0145-1.1), and Animal Care and Ethics (07/
08) permits and Southern Cross University Animal Care and
Ethics Committee Animal Research Authority (19/036).
Track reconstruction and data analysis
Sharks were instrumented with satellite-linked radio transmission
and pop-up satellite tags that provide a myriad of positional
2R. S. Lipscombe et al.
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estimates and quality. Radio fixes were all opportunistic in na-
ture, as a shark’s fin needed to break the sea surface long enough
to communicate with an orbiting satellite. As a result, positional
information was often clustered in space and time, yet varied
over the course of a deployment. Positioned via light-based geo-
location estimation, MiniPATs offered the least accurate
positions (Galuardi and Lam, 2014), yet given a shark’s
occupancy in the epipelagic layer, good light-level readings were
usually available throughout the deployment period. After visual
inspection of all available positional data, the following geo-
positional strategy was adopted:
(1) Use only positions from ARGOS satellites (T11 and T16)—
these sharks provided regular fixes. Shark T11 had 19 of its
23 tracked days covered by ARGOS fixes, so the combined
approach produced an estimated track mostly driven by
ARGOS fixes. Light-based estimated positions, which have
lower accuracy than ARGOS fixes, were very much
“drowned” out by higher accuracy ARGOS fixes. In this case,
the combined approach achieved interpolation among
ARGOS fixes across days when there was no ARGOS fixes.
Rather than interpolation within the Hidden Markov Model
(HMM), we used a standardized method for interpolation of
all sharks with crawl (Johnson et al., 2008;Johnson and
London, 2018) after estimation of tracks for each shark.
Furthermore, shark T16 had 114 out of 180 or 63% tracked
days covered by ARGOS fixes. In order for the HMM to con-
verge and successfully produce an estimated track, multiple
ARGOS fixes had to be sacrificed. Since the model had to
reconcile the two data streams of different accuracies, a high
proportion of ARGOS fixes had made the HMM struggle.
Subsequently, we chose to rely only on ARGOS here because
it is unclear how the reconcilation was conducted within the
HMM—the detailed inner workings of the model remain
opaque with limited useful published studies documenting
them (https://wildlifecomputers.com/blog/using-gpe3-to-im
prove-geolocation-estimates). Most position fixes were
flagged with LC A and B, i.e. positions with unquantifiable
error. However, it is established that class A and B messages
can be as valuable as those of higher quality classes (1, 2, 3)
(e.g. Costa et al., 2010). We also evaluated the temporal evo-
lution of position fixes, which showed consistency over time
and presented few outliers that zig-zagged out of the overall
trajectory trend.
(2) Provide ARGOS positions to improve Wildlife Computers’
Hidden Markov Model (HMM) for light-based geolocation
(T10)—this shark had clustered but irregular ARGOS fixes.
A hybrid approach was adopted by running the MiniPAT’s
light-based information through the manufacturer’s proprie-
tary Hidden Markov Model (Wildlife Computers, 2020),
with ARGOS fixes, subsampled daily, provided as known
positions to help ground the model. We ran the HMM with
1, 2, 3, and 4 ms
&1
as the prior. Since solutions were similar
throughout, we set the speed filter at 4 ms
&1
. This was the
highest speed that did not over-limit the model performance.
As the HMM continued failing to converge, known positions
were thinned 1 d at a time, to reduce clustering, until model
convergence was achieved. HMM especially struggled near
the East Coast where there were abundant small-scale sea-
surface temperature (SST) gradients that could have
swamped the model with conflicting signals.
(3) Light-based only geolocation via a Kalman-filter model,
trackit (T1, T2, T6, T14)—these sharks had few or no
ARGOS fixes, and therefore light-based geolocation was re-
quired. Previous problems by HMM with SST-matching
prompted the use of a light-only method, Trackit (Nielsen
and Sibert, 2007) for sharks that followed the coast through-
out the deployment. An extension to the same model, light,
and sea-surface temperature matching (Lam et al., 2010) was
applied for sharks that spent most of their time away from
the shelf break.
(4) No movement estimation for sharks at liberty for <2 weeks
(T4, T8, T12).
Results from the above cases were down-sampled to daily posi-
tions by using the first position of the day, for any day with mul-
tiple positions. Gap filling was then done on any missing days
(mostly for case 1) with the R package, crawl (Johnson et al.,
2008;Johnson and London, 2018). Priors for uncertainty for both
latitude and longitude were arbitrarily set at 0.5"for ARGOS
positions, since classes A and B were of unknown accuracy.
Similarly, priors were set at 0.25"for HMM estimates, which are
the search grid cell size. Lastly, error estimates supplied from the
Trackit model were directly input as uncertainty priors.
Due to errors associated with light-based geolocation data, all
distances calculated for sharks using light-based methods only are
estimates and may not be accurate. Track length was estimated in
Google Earth after plotting daily positions derived from track re-
construction analysis. An estimate of the average movement rate
per day was then calculated by dividing track length by days at
liberty. Patterns of habitat use and diel differences of G. cuvier
were examined after depth and temperature data archived by
MiniPATs were divided into periods of day and night. Due to
varying day lengths along the east coast of Australia, calculations
were based on sunrise and sunset times at Brisbane, QLD.
Utilization of depth and preference for environmental variables
were examined through histogram analysis and depth behaviour
of individual sharks plotted in a time series over the length of de-
ployment. Paired t-tests were performed to test for significant
diel differences in the average depth and temperatures occupied
by G. cuvier (Royer et al., 2020).
Results
Release condition and tag performance
Sixteen G. cuvier (13 females and 3 males) ranging from 157 to
384 cm TL (mean 6SD of 252.5 661.5 cm, Table 1) were tagged
with MiniPATs . At the time of tagging, 75% were juveniles (157–
251 cm TL; 10 females and 2 males), 6% were sub-adults (284 cm
TL; 1 male), and 19% were adults (330–384 cm TL; 3 females).
Eleven of these sharks (9 females and 2 males) were also tagged
with SPOT6 satellite tags (Table 1). Despite being released in a
healthy condition, T8 died 10 d after release, with the depth pro-
file recording a constant depth of 55 m for 4 consecutive days,
which initiated the premature release of the MiniPAT. At the
time of death, T8 was $34 km east from the southern tip of
Fraser Island, QLD (25˚470S, 153˚250E), and had travelled
$335 km (straight line distance).
Ten of the 16 MiniPATs deployed provided depth and temper-
ature data, while positional information was analysed for seven of
Habitat use and movement patterns of tiger sharks 3
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those sharks (Supplementary Table S1). Track estimation was not
conducted for four sharks: one shark did not report (T3) and
three sharks had short deployments of under 11 d (T4, T8, and
T12).MiniPATs deployed on T10, T14, and T16 were the only
MiniPATs that remained attached to the shark for their entire
pre-programmed deployment of 180 d, with the remaining
MiniPAT deployments shorter than programmed (120–180 d),
ranging from 7 to 140 d. Archived depth and temperature data
for T10 and T16 was less than the actual deployment of 180 d,
with only 166 and 144 d available for analysis. After 9 d at liberty,
the MiniPAT from T4 detached and was recovered, providing the
entire archived data for analysis. The MiniPAT on T3 released
from the shark after 8 d, and although the tag was retrieved, no
data were recorded due to unknown reasons. MiniPATs from T5,
T7, T9, and T15 either failed to release from the shark on the pro-
grammed dates or failed to reach the surface or connect to the
ARGOS system after reaching the surface. The MiniPAT from
T13 released after 180 d and washed ashore at Kirra Beach in
southern QLD (28˚090S, 153˚310E), $277 km (straight line dis-
tance) from the release site, where it was found by a member of
the public who discarded it, preventing data recovery.
Horizontal movements
Geolocation maps were constructed for seven sharks (Figure 1
and Supplementary Figure S2), with tracking periods between 20
and 180 d. In the first 24 h post-release, six of the seven sharks
moved offshore, across the continental shelf edge (>30 km) in a
general easterly direction. The only shark to remain inshore over
the continental shelf (<30 km) for >48 h post-tagging was T6.
Regardless of the season they were tagged, most sharks moved in
a northerly direction from NSW to QLD waters within 15 d of re-
lease. Two sharks, T10 and T16, both with deployments ¼180 d,
returned to NSW waters during the austral spring (March, April,
and May) and summer (December, January, and February) after
160 and 179 d post-release, respectively.
One individual (T2) remained in NSW waters for the entire
145 d at liberty from February to July 2018 and spent 85% of its
deployment in warmer (19–23˚C) offshore waters. T2 reached its
most southern location $356 km east of Pambula, NSW
(36"190S, 153"540E), in April (austral autumn), but by July (aus-
tral winter) returned to inshore waters off NSW, $90 km north
of its tagging location. Shark T1, tagged on the same day at the
same location, remained on the continental shelf for 86% of its
deployment, with 4 d spent off the continental shelf edge within
the first week after tagging (Figure 1). Of the six sharks that trans-
ited north into QLD waters, all swam either along the shelf edge
or in offshore waters.
The greatest distance travelled was by T10, covering $6937 km
(straight line distance) in 180 d ($39 km d
&1
), between Newcastle,
NSW (32"55’S, 151"47’E) (Figure 1), and the Whitsunday Islands
off the coast of Mackay, QLD. Shark T6 made the most substantial
move north eastward ($1030km straight line distance) from Coffs
Harbour, NSW, into the waters of New Caledonia $650 km west of
the mainland (Figure 1). Post-tagging, T16 moved north and off-
shore to the Great Barrier Reef in <1 month and then spent
3 months and 43% of its deployment on and surrounding Lihou
Reef (17"240S, 151"400E) adjacent to Cairns, QLD. During its 20 d
at liberty, T11 displayed the highest movement rate at 61.5km d
&1
,
travelling $1 235 km north from Coffs Harbour, NSW, maintaining
a path throughout the Tasman Basin, over the Queensland,
Brisbane, and Recorder Seamounts until it reached the southern
Coral Sea (23"440S, 154"40E). A similar northerly offshore route was
also undertaken by T14 and T16 after tagging in the austral winter
(June, July and August), with both sharks traversing near the
Brisbane, Moreton, and Recorder Seamounts. T16 continued on
this north east path over the Fraser Seamount before entering the
Cato Trough in the Coral Sea (154"540S, 23"200E). Overall, broad-
scale horizontal movement differed greatly among conspecifics.
Depth, temperature, and vertical habitat use
Tagged G. cuvier spent considerable time at, or within 1 m of the
surface (depths recorded above 0 m were omitted from analysis).
Overall, 71% of time was spent within 50 m of the surface and
96% of time in the upper 100 m of the water column (Figure 2a).
Table 1. Summary of biological details, tag deployment, and tracking data for 16 Galeocerdo cuvier tagged off the mid-north coast of eastern
Australia.
ID Sex
Total
length
(cm) Tagging date
Tagging location,
Lat. (˚S), Long. (˚E) Tag type
Date
detached
Days
tracked
Track
length
(km)
Average
movement
(km d
&1
)
T1 F 188 13 February 2018 30.279, 153.203 SPOT, MiniPAT, acoustic 19 March 2018 34 926 26.5
T2 F 242 13 February 2018 30.290, 153.169 SPOT, MiniPAT, acoustic 07 August 2018 145 3726 25.7
T3 F 229 23 January 2019 28.855, 153.612 MiniPAT, acoustic 30 January 2019
T4 F 234 23 January 2019 29.589, 153.264 MiniPAT, acoustic 02 February 2019 9
T5 M 248 25 January 2019 30.323,153.179 MiniPAT, acoustic
T6 F 251 29 March 2019 30.323, 153.180 SPOT, MiniPAT, acoustic 19 May 2019 51 2563 50.3
T7 F 216 29 March 2019 30.323, 153.180 SPOT, MiniPAT, acoustic
T8 F 223 24 April 2019 28.813, 153.613 SPOT, MiniPAT, acoustic 09 May 2019 10
T9 M 157 08 May 2019 30.228, 153.179 SPOT, MiniPAT, acoustic 20 October 2019
T10 F 236 08 May 2019 30.237, 153.184 SPOT, MiniPAT, acoustic 04 November 2019 180 6937 38.5
T11 M 284 09 May 2019 30.328, 153.151 SPOT, MiniPAT, acoustic 01 June 2019 21 1235 61.5
T12 F 228 14 May 2019 30.317, 153.147 SPOT, MiniPAT, acoustic 26 May 2019 8
T13
a
F 212 14 May 2019 30.322, 153.180 MiniPAT, acoustic
T14 F 354 11 June 2019 28.827, 153.595 MiniPAT, acoustic 13 December 2019 180 3342 18.5
T15 F 384 09 July 2019 28.837, 153.615 SPOT, MiniPAT, acoustic
T16 F 330 23 July 2019 28.872, 153.604 SPOT, MiniPAT, acoustic 23 January 2020 180 6257 33.9
a
T13 detached after 180 d but the tag was found by a member of the public who subsequently discarded the tag, preventing data recovery.
4R. S. Lipscombe et al.
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Time spent in the mixed layer varied among individuals and
ranged from 46.1% to 93.1% (Table 2). Maximum depths varied
among individual sharks and ranged from 92 to 904 m (Table 2).
Only three sharks (T1, T4, and T8) did not dive below 200 m
(Figure 3), yet depths used by T1 were unlikely constrained by ba-
thymetry as time was spent in waters beyond the continental shelf
edge. Shark T4, at liberty for 9 d, recorded the lowest mean
(6SE) depth (10.2 60.03 m) and used the upper 30 m of the wa-
ter column more than any other of the tagged sharks (Table 2).
Mean (6SE) depth (37.19 60.38 m) and temperature (23.42
60.02 ˚C) varied among sharks (Table 2). No significant differ-
ence was evident in use of depth and temperatures, irrespective of
day or night (paired t-test, depth: t¼0.44, n¼10, p>0.05, tem-
perature: t¼0.35, n¼10, p>0.05). Cumulative time-at-
temperature data indicated tagged G. cuvier spent most of the
daylight hours in water temperatures between 24 and 25˚C, with
little variation between day and night (Figure 2b). Water temper-
atures occupied by tagged G. cuvier ranged from 6.7 to 28.9"C,
with 70.2% of the time between 22 and 25"C. Temperatures
<16"C were infrequently occupied, with the minimum tempera-
ture of 6.7"C during the deepest dive by T2 at 784.5 m.
Diving behaviour differed among conspecifics. For example,
depth profiles revealed oscillatory diving behaviour in T2
(Figure 4a), which was characterized by $1 brief dive per hour to
100–300 m, with consistent returns to the surface. This pattern
was also evident in T6, yet occurred more frequently at $3 dives
per hour (Figure 4b). Sharks T2, T6, T11, and T14 made
infrequent dives into the mesopelagic zone to depths of 400–
800 m (Figure 3). Each of these deep dives lasted between 40 and
60 min, concluding with the shark returning to the top 20 m of
the water column. Shark T11 completed the deepest dive to
904 m, with the descent from, and ascent to the surface taking
$40 min (Figure 4c). Other deep dives performed by T2, T6, T11,
and T14 occurred throughout the day and night, with no diel pat-
tern evident.
Discussion
This study describes the habitat use of G. cuvier throughout tem-
perate, sub-tropical, and tropical waters of eastern Australia. It
confirms previous work demonstrating that, although typically
found in warm tropical waters, this species also inhabits warmer
temperate regions (Holmes et al., 2014;Ferreira et al., 2015).
Individual movements varied regardless of season, with all but
one shark moving north into warmer QLD waters after tagging.
Similarities among sharks in broad-scale movement patterns were
evident, with a preference for offshore oceanic habitats. The use
of offshore waters beyond the continental shelf edge was often
characterized by long-distance directional movement >100 km,
with frequent travel over seamounts within the Tasman Basin in
the western Pacific Ocean. These broad-scale movements using
predominantly offshore waters are consistent with earlier studies
(Holmes et al., 2014;Werry et al., 2014) and highlight the con-
nectivity between tropical and warm-temperate regions in eastern
Australia.
Figure 1. Most probable tracks of seven tagged Galeocerdo cuvier reconstructed using positions from ARGOS and light-based geolocation to
provide daily positions. All maps were generated using the marmap package in R (Pante and Simon-Bouhe, 2013).
Habitat use and movement patterns of tiger sharks 5
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Increased movement over the continental shelf by sharks was
observed during the austral spring and summer. Ocean currents,
water temperature and prey distribution are known drivers of
shark movement (Heupel et al., 2015;Andrzejaczek et al., 2018)
and likely influenced this move from offshore waters. On the east
coast of Australia, the East Australian Current brings warmer wa-
ter nearer the coastline during summer months and encroaches
onto the continental shelf (Ridgway and Godfrey, 1997). Areas
where the continental shelf narrows generally see upwelling of
nutrient-rich water (south of 28.5"S and 31"S), thus increasing
primary productivity (Everett et al., 2014). This change in move-
ment is observed by Papastamatiou et al. (2013), who found
higher chlorophyll aconcentrations and warm ocean
temperatures (23–26˚C) were also attributed to change in the
movement of G. cuvier around the Hawaiian Islands. Together,
warmer water during summer, in combination with higher pro-
ductivity, could serve as an indicator for increased presence of G.
cuvier in inshore and coastal waters.
Tagged sharks displayed a preference for water between 22 and
25"C, similar to previous studies (Holmes et al., 2014;Ferreira
et al., 2015). Decreased temperatures were occupied infrequently
during deep dive excursions, confirming that G. cuvier are capable
of withstanding a much broader temperature range (4–31.2˚C).
However, it is unlikely that these temperatures could be sustained
for long periods. Cold tolerance has been documented by the oc-
casional catch of conspecifics in Icelandic waters (Matsumoto
Figure 2. Time spent (6SE) during day (white bars) and night (black bars) at (a) depth (50 m bins) and (b) temperature (2"C bins) for
10 MiniPAT-tagged Galeocerdo cuvier.
6R. S. Lipscombe et al.
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et al., 2005) and in the Tasman Sea (Holmes et al., 2014) and sup-
ports the movement south into higher latitudes observed in this
study.
Sharks in this study often moved near seamounts in the
Tasman Basin on their transit north during the austral autumn
and winter, indicating that the oceanographic conditions and
prey availability at these locations might play a role in driving
seasonal offshore movement (Holmes et al., 2014;Werry et al.,
2014). Indeed, the seasonal variability in physical conditions at
seamounts influences their use by many other species of shark
(Oliver et al., 2011;Barnett et al., 2012), pelagic fish (Morato
et al., 2010), and turtles (Santos et al., 2007). These unique areas
of high biodiversity, and therefore food resource availability, may
influence the movement of G. cuvier to off-shelf habitats as ob-
served in this study. The findings of Ajemian et al. (2020) support
this use of deeper offshore waters, although their observations
were for sub-adult and adult G. cuvier, a higher use of off-shelf
waters during autumn and winter months was documented.
The distances travelled between northern QLD to southern
NSW demonstrate the broad latitudinal range that G. cuvier cov-
ered along the east coast of Australia. Large-scale movements are
not uncommon for G. cuvier and distances travelled by T10
($6937) and T16 ($6257 km) are consistent with previous stud-
ies (Heithaus et al., 2007;Holmes et al., 2014). Other shark spe-
cies, e.g. bull sharks, Carcharhinus leucas ($1770 km), and white
sharks, Carcharodon carcharias ($40 000 km)have also exhibited
similar dispersal behaviour along the east coast and into interna-
tional waters (Heupel et al., 2015;Spaet et al., 2020ab). The ex-
tensive movements performed by G. cuvier highlight the need for
multi-jurisdictional management among Australian states in re-
gard to shark mitigation measures, conservation strategies, and
fisheries management.
Highly directional swimming was observed in most sharks and
was often interspersed with localized movements over a smaller
spatial scale (<35 km). This behaviour was particularly evident in
individuals with longer deployments when they reached the Great
Barrier Reef, QLD, throughout the austral winter and spring.
Specifically, localized movements occurred for 71d (over $30 km)
for T16 as it moved around Lihou Reef (17˚240S, 151˚400E)
(Figure 1). This behaviour could be attributed to resource avail-
ability and foraging and could potentially be linked to the nesting
of green turtles, Chelonia mydas during October to April
(Department of Environment and Energy, 2011). Seasonal
availability of a particular resource, e.g. sea turtles, has been
exploited by tiger sharks (Fitzpatrick et al., 2012;Acu~
na-Marrero
et al., 2017). Extended tag deployments and a larger sample size of
tagged animals could illuminate on potential residency patterns.
The infrequent utilization of deeper oceanic waters, >600 m,
by three G. cuvier was associated with their horizontal movement
off the Australian continental shelf and consisted of a brief but
fast, near-vertical descent with an immediate, but slower return
to the surface. Previous studies suggest that deep-diving behav-
iour in G. cuvier is a means for navigation, with the use of topog-
raphy and bathymetric features providing orientation between
locations during broad-scale movement (Holland et al., 1999;
Holmes et al., 2014). Although difficult to quantify, the use of
brief, deep dives for orientation has been recorded in both C.
carcharias and shortfin mako sharks, Isurus oxyrinchus, during
migrations (Francis et al., 2019;Rogers et al., 2015). A similar
dive profile describing powered descents was described by
Nakamura et al. (2011) in G. cuvier tagged in Hawaii and has also
been observed in I. oxyrinchus (Sepulveda et al., 2004). Originally,
Weihs (1973) predicted that negatively buoyant fish would per-
form a gliding motion with a shallow angle upon descent, to con-
serve energy. This theory proved correct for some predatory
fishes (Andrzejaczek et al., 2019b),yet, was inconsistent with the
characteristic rapid vertical descent observed in previous studies
of G. cuvier, with navigation and foraging more likely explana-
tions for the deep-diving behaviour (Nakamura et al., 2011;
Holmes et al., 2014).
Oscillatory diving behaviour was observed in two sharks within
this study, a pattern of diving also documented in other shark
species (Sepulveda et al., 2004:Spaet et al., 2017). Patterns dif-
fered slightly between the two sharks, yet were characterized by
distinct repetitive near-vertical descents and a short time spent at
depth, followed by a near-vertical ascent to the upper water col-
umn. These dive patterns are consistent with movement often at-
tributed to foraging and prey detection in G. cuvier of benthic
and surface prey (Nakamura et al., 2011;Heithaus et al., 2002). A
similar observation was made by Carey et al. (1990) and
Campana et al. (2011) in P. glauca, with oscillatory dives repeated
every few hours. Campana et al. (2011) speculated that these di-
ves are likely a strategy employed to increase foraging potential
and efficiency and reduce metabolic losses that occur when
remaining in warm surface waters during the day. Furthermore,
the most recent study by Andrzejaczek et al. (2020) surmises that
G. cuvier oscillatory diving patterns reduce energy output when
compared to horizontal swimming and can provide a cost-
efficient foraging strategy. Although thermoregulation has been
previously reported as the purpose for oscillatory diving in several
shark species (Carey et al., 1990;Thums et al., 2013), conspecifics
in this study did not display the same oscillatory diving behav-
iour. Therefore, it is likely that the vertical patterns observed in
this study indicate energy conservation and prey searching by
individuals.
This study examines the highly dynamic and complex habitat
use by G. cuvier off eastern Australia through the combined use
of satellite and archival technology. Variation among conspecifics
existed in both broad-scale horizontal movement and vertical
habitat use and was likely associated with navigation, resource
availability, foraging, energy conservation, and fluctuations in
water temperature. Movement to lower latitudes coincided with
decreasing water temperature at mid-latitudes and, while thermal
conditions may influence horizontal movement, a tolerance for
Table 2. Galeocerdo cuvier depth, water temperature, and time
spent in mixed layer (0–193 m) archived by MiniPAT pop-up
satellite tags (n¼10).
Shark
ID
Depth (m) Temperature (˚C)
Mixed
layer (%)Min–max Mean 6SE Min–max Mean 6SE
T1 1.0–173.0 36.9 60.36 15.6–27.5 23.4 60.05 46.1
T2 0.0–784.5 29.3 60.22 6.7–28.0 22.7 60.01 78.5
T4 0.0–92.0 10.2 60.03 17.8–27.2 23.2 60.01 93.1
T6 0.5–755.0 46.8 60.56 7.0–27.2 23.9 60.02 78.6
T8 0.5–150.0 44.9 60.41 15.6–25.8 24.1 60.03 75.6
T10 1.0–255.0 20.7 60.22 15.3–24.8 22.0 60.01 85.4
T11 1.0–904.0 36.6 60.71 6.8–25.6 24.3 60.01 92.7
T12 0.5–276.0 27.9 60.40 15.4–24.5 22.6 60.02 74.0
T14 0.5–560.0 39.9 60.31 9.9–28.9 23.4 60.01 90.1
T16 0.5–576.0 48.8 610.2 8.9–28.1 24.6 60.03 74.3
Habitat use and movement patterns of tiger sharks 7
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Figure 3. Depth profiles for ten MiniPAT-tagged Galeocerdo cuvier produced using summary data sampled at 5-min intervals (summary data
were used for all sharks except T4 where data were sampled at 3-s intervals providing the entire archive).
8R. S. Lipscombe et al.
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cooler water was evident through intermittent use of water below
the thermal preference of 22–25˚C. Increased cross-shelf activity was
observed during the austral spring and summer, which coincided
with a strengthening EAC, warmer water temperature, and changes
in prey distribution. Yet, it was evident that, despite being associated
with coastal waters and captured and released relatively close to
shore, G. cuvier spent majority of time in waters off the continental
shelf. To better understand the varied and extensive movements
documented in this study, long-term movement patterns and the in-
fluence of environmental drivers should be investigated.
Supplementary data
Supplementary material is available at the ICESJMS online ver-
sion of the manuscript.
Figure 4. Examples of diving behaviour from three MiniPAT-tagged Galeocerdo cuvier depicting (a) oscillatory diving of T2 on day 67, (b)
oscillatory diving of T6 on day 7, and (c) a deep dive of T11 on day 1. Profiles were produced using summary data sampled at 5-min intervals.
Habitat use and movement patterns of tiger sharks 9
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Funding
Funding was provided by the NSW Department of Primary
Industries, (NSW DPI Grant Number: SMS2015-20). Southern
Cross University provided funding towards an honours project to
R Lipscombe.
Acknowledgements
Primary support was provided by the New South Wales
Department of Primary Industries, Australia, through the Shark
Management Strategy. We would like to thank contracted
SMART drumline fishers from Ballina and Evans Head for their
assistance with this project.
Author contributions
RSL conducted fieldwork and wrote the manuscript, JLYS and
CHL analysed horizontal data, reconstructed tracks, and edited
the manuscript, AS edited the manuscript, CPB conducted field-
work, and PAB designed the study, conducted fieldwork, and
edited the manuscript.
Data availability statement
The data underlying this article will be shared on reasonable re-
quest to the corresponding author.
References
Acu~
na-Marrero, D., Smith, A. N. H., Hammerschlag, N., Hearn, A.,
Anderson, M. J., Calich, H., Pawley, M. D. M., et al. 2017.
Residency and movement patterns of an apex predatory shark
(Galeocerdo cuvier) at the Galapagos Marine Reserve. PLoS One,
12: e0183669.
Afonso, A. S., and Hazin, F. H. 2015. Vertical movement patterns and
ontogenetic niche expansion in the tiger shark, Galeocerdo cuvier.
PLoS One, 10: e0116720.
Ajemian, M. J., Drymon, J. M., Hammerschlag, N., Wells, R. D.,
Street, G., Falterman, B., McKinney, J. A., et al. 2020. Movement
patterns and habitat use of tiger sharks (Galeocerdo cuvier) across
ontogeny in the Gulf of Mexico. PLoS One, 15: e0234868.
Andrzejaczek, S., Gleiss, A. C., Jordan, L. K., Pattiaratchi, C. B.,
Howey, L. A., Brooks, E. J., and Meekan, M. G. 2018.
Temperature and the vertical movements of oceanic whitetip
sharks, Carcharhinus longimanus. Scientific Reports, 8: 1–12.
Andrzejaczek, S., Gleiss, A. C., Lear, K. O., Pattiaratchi, C. B.,
Chapple, T. K., and Meekan, M. G. 2019a. Biologging tags reveal
links between fine-scale horizontal and vertical movement behav-
iors in tiger sharks (Galeocerdo cuvier). Frontiers in Marine
Science, 6: 1–13.
Andrzejaczek, S., Gleiss, A. C., Lear, K. O., Pattiaratchi, C., Chapple,
T. K., and Meekan, M. G. 2020. Depth-dependent dive kinematics
suggest cost-efficient foraging strategies by tiger sharks. Royal
Society Open Science, 7: 200789–200714.
Andrzejaczek, S., Gleiss, A. C., Pattiaratchi, C. B., and Meekan, M. G.
2019b. Patterns and drivers of vertical movements of the large
fishes of the epipelagic. Reviews in Fish Biology and Fisheries, 29:
335–354.
Barnes, C. J., Butcher, P. A., Macbeth, W. G., Mandelman, J. W.,
Smith, S. D., and Peddemors, V. M. 2016. Movements and mor-
tality of two commercially exploited carcharhinid sharks following
longline capture and release off eastern Australia. Endangered
Species Research, 30: 193–208.
Barnett, A., Abrantes, K. G., Seymour, J., and Fitzpatrick, R. 2012.
Residency and spatial use by reef sharks of an isolated seamount
and its implications for conservation. PLoS One, 7: e36574.
Butcher, P. A., Peddemors, V. M., Mandelman, J. W., McGrath, S. P.,
and Cullis, B. R. 2015. At-vessel mortality and blood biochemical
status of elasmobranchs caught in an Australian commercial long-
line fishery. Global Ecology and Conservation, 3: 878–889.
Butcher, P. A., Piddocke, T. P., Colefax, A. P., Hoade, B., Peddemors,
V. M., Borg, L., and Cullis, B. R. 2019. Beach safety: can drones
provide a platform for sighting sharks? Wildlife Research, 46:
701–712.
Campana, S. E., Dorey, A., Fowler, M., Joyce, W., Wang, Z., Wright,
D., and Yashayaev, I. 2011. Migration pathways, behavioural ther-
moregulation and overwintering grounds of blue sharks in the
Northwest Atlantic. PLoS One, 6: e16854.
Carey, F. G., Scharold, J. V., and Kalmijn, A. J. 1990. Movements of
blue sharks (Prionace glauca) in depth and course. Marine
Biology, 106: 329–342.
CLS. 2011. Argos User’s Manual. http://www.argos-system.org (last
accessed 10 Apr 2019).
Colefax, A. P., Butcher, P. A., Pagendam, D. E., and Kelaher, B. P.
2019. Reliability of marine faunal detections in drone-based mon-
itoring. Ocean & Coastal Management, 174: 108–115.
Costa, D. P., Robinson, P. W., Arnould, J. P. Y., Harrison, A.-L.,
Simmons, S. E., Hassrick, J. L., Hoskins, A. J., et al. 2010.
Accuracy of ARGOS locations of pinnipeds at-sea estimated using
Fastloc GPS. PLoS One, 5: e8677.
Department of Environment and Energy. 2011. Recovery Plan for
Marine Turtles in Australia, Commonwealth of Australia 2017.
Retrieved from https://www.environment.gov.au/system/files/
resources/46eedcfc-204b-43de-99c5-4d6f6e72704f/files/recovery-
plan-marine-turtles-2017.pdf (last accessed 12 May 2020).
Everett, J. D., Baird, M. E., Roughan, M., Suthers, I. M., and Doblin,
M. A. 2014. Relative impact of seasonal and oceanographic drivers
on surface chlorophyll a along a Western Boundary Current.
Progress in Oceanography, 120: 340–351.
Ferreira, L. C., and Simpfendorfer, C. 2019. Galeocerdo cuvier. The
IUCN Red List of Threatened Species, 2019: e.T39378A2913541.
Ferreira, L. C., Thums, M., Meeuwig, J. J., Vianna, G. M., Stevens, J.,
McAuley, R., and Meekan, M. G. 2015. Crossing latitudes—long-
distance tracking of an apex predator. PLoS One, 10: e0116916.
Field, I. C., Meekan, M. G., Buckworth, R. C., and Bradshaw, C. J.
2009. Protein mining the world’s oceans: Australasia as an exam-
ple of illegal expansion-and-displacement fishing. Fish and
Fisheries, 10: 323–328.
Fitzpatrick, R., Thums, M., Bell, I., Meekan, M. G., Stevens, J. D., and
Barnett, A. 2012. A comparison of the seasonal movements of ti-
ger sharks and green turtles provides insight into their
predator-prey relationship. PLoS One, 7: e51927.
Francis, M. P., Shivji, M. S., Duffy, C. A., Rogers, P. J., Byrne, M. E.,
Wetherbee, B. M., Tindale, S. C., et al. 2019. Oceanic nomad or
coastal resident? Behavioural switching in the shortfin mako shark
(Isurus oxyrinchus). Marine Biology, 166: 1–16.
Fraser-Baxter, S., and Medvecky, F. 2018. Evaluating the media’s
reporting of public and political responses to human–shark inter-
actions in NSW, Australia. Marine Policy, 97: 109–118.
Galuardi, B., and Lam, C. H. 2014. Telemetry analysis of highly mi-
gratory species. In Stock Identification Methods, pp. 447–476. Ed.
by S. X. Cadrin, L. A. Kerr and S. Mariani. Academic Press,
London. UK.
Guyomard, D., Perry, C., Tournoux, P. U., Cliff, G., Peddemors, V.,
and Jaquemet, S. 2019. An innovative fishing gear to enhance the
release of non-target species in coastal shark-control programs:
the SMART (shark management alert in real-time) drumline.
Fisheries Research, 216: 6–17.
Heithaus, M., Dill, L., Marshall, G., and Buhleier, B. 2002. Habitat
use and foraging behavior of tiger sharks (Galeocerdo cuvier) in a
seagrass ecosystem. Marine Biology, 140: 237–248.
Heithaus, M. R., Wirsing, A. J., Dill, L. M., and Heithaus, L. I. 2007.
Long-term movements of tiger sharks satellite-tagged in Shark
Bay, Western Australia. Marine Biology, 151: 1455–1461.
10 R. S. Lipscombe et al.
Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa212/6000674 by James Cook University user on 25 November 2020
Heupel, M. R., Simpfendorfer, C. A., Espinoza, M., Smoothey, A. F.,
Tobin, A., and Peddemors, V. 2015. Conservation challenges of
sharks with continental scale migrations. Frontiers in Marine
Science, 2: 1–7.
Holland, K. N., Wetherbee, B. M., Lowe, C. G., and Meyer, C. G.
1999. Movements of tiger sharks (Galeocerdo cuvier) in coastal
Hawaiian waters. Marine Biology, 134: 665–673.
Holmes, B. J., Pepperell, J. G., Griffiths, S. P., Jaine, F. R., Tibbetts, I.
R., and Bennett, M. B. 2014. Tiger shark (Galeocerdo cuvier)
movement patterns and habitat use determined by satellite tag-
ging in eastern Australian waters. Marine Biology, 161:
2645–2658.
Holmes, B. J., Sumpton, W. D., Mayer, D. G., Tibbetts, I. R., Neil, D.
T., and Bennett, M. B. 2012. Declining trends in annual catch
rates of the tiger shark (Galeocerdo cuvier) in Queensland,
Australia. Fisheries Research, 129: 38–45.
Johnson, D., and London, J. 2018. Crawl: an R Package for Fitting
Continuous-Time Correlated Random Walk Models to Animal
Movement Data. Zenodo.
Johnson, D. S., London, J. M., Lea, M. A., and Durban, J. W. 2008.
Continuous-time correlated random walk model for animal te-
lemetry data. Ecology, 89: 1208–1215.
Lam, C. H., Nielsen, A., and Sibert, J. R. 2010. Incorporating
sea-surface temperature to the light-based geolocation model
TrackIt. Marine Ecology Progress Series, 419: 71–84.
Last, P. R., and Stevens, J. D. 2009. Sharks and Rays of Australia, 2nd
edn, p. 513. CSIRO Publishing, Collingwood, Australia.
Macbeth, W. G., Geraghty, P. T., Peddemors, V. M., and Gray, C. A.
2009. Observer-Based Study of Targeted Commercial Fishing for
Large Shark Species in Waters off Northern New South Wales.
Cronulla Fisheries Research Centre of Excellence, Industry and
Investment NSW, Cronulla, Australia.
Matsumoto, T., Saito, H., and Miyabe, N. 2005. Report of observer
program for Japanese tuna longline fishery in the Atlantic Ocean
from August 2003 to January 2004. Collective Volumes of
Scientific Papers, ICCAT, 58: 1694–1714.
McPhee, D. 2014. Unprovoked shark bites: are they becoming more
prevalent? Coastal Management, 42: 478–492.
Morato, T., Hoyle, S. D., Allain, V., and Nicol, S. J. 2010. Seamounts
are hotspots of pelagic biodiversity in the open ocean.
Proceedings of the National Academy of Sciences of the United
States of America, 107: 9707–9711.
Nakamura, I., Watanabe, Y. Y., Papastamatiou, Y. P., Sato, K., and
Meyer, C. G. 2011. Yo-yo vertical movements suggest a foraging
strategy for tiger sharks Galeocerdo cuvier. Marine Ecology
Progress Series, 424: 237–246.
Nielsen, A., and Sibert, J. R. 2007. State-space model for light-based
tracking of marine animals. Canadian Journal of Fisheries and
Aquatic Science, 64: 1055–1068.
Oliver, S. P., Hussey, N. E., Turner, J. R., and Beckett, A. J. 2011.
Oceanic sharks clean at coastal seamount. PLoS One, 6: e14755.
Pante, E., and Simon-Bouhe, B. 2013. Marmap: a package for import-
ing, plotting and analyzing bathymetric and topographic data in
R. PLoS One, 8: e73051.
Papastamatiou, Y. P., Meyer, C. G., Carvalho, F., Dale, J. J.,
Hutchinson, M. R., and Holland, K. N. 2013. Telemetry and ran-
dom-walk models reveal complex patterns of partial migration in
a large marine predator. Ecology, 94: 2595–2606.
Reid, D., Robbins, W., and Peddemors, V. 2011. Decadal trends in
shark catches and effort from the New South Wales, Australia,
Shark Meshing Program 1950–2010. Marine and Freshwater
Research, 62: 676–693.
Ridgway, K., and Godfrey, J. 1997. Seasonal cycle of the East
Australian current. Journal of Geophysical Research: Oceans, 102:
22921–22936.
Rogers, P. J., Huveneers, C., Page, B., Goldsworthy, S. D., Coyne, M.,
Lowther, A. D., Mitchell, J. G., et al. 2015. Living on the continen-
tal shelf edge: habitat use of juvenile shortfin makos Isurus oxyrin-
chus in the Great Australian Bight. Southern Australia. Fisheries
Oceanography, 24: 205–218.
Royer, M., Maloney, K., Meyer, C., Cardona, E., Payne, N.,
Whittingham, K., Silva, G., et al. 2020. Scalloped hammerhead
sharks swim on their side with diel shifts in roll magnitude and
periodicity. Animal Biotelemetry, 8: 1–12.
Santos, M. A., Bolten, A. B., Martins, H. R., Riewald, B., and
Bjorndal, K. A. 2007. Air-breathing visitors to seamounts: sea tur-
tles. In Seamounts: Ecology, Fisheries and Conservation. Fisheries
and Aquatic Resource Series, pp. 239–244. Ed. by T. J. Pitcher, T.
Morato, P. J. B. Hart, M. R. Clark, N. Haggan and R. S. Santos.
Blackwell Scientific, Oxford, UK.
Sepulveda, C. A., Kohin, S., Chan, C., Vetter, R., and Graham, J. B.
2004. Movement patterns, depth preferences, and stomach tem-
peratures of free-swimming juvenile mako sharks, Isurus oxyrin-
chus, in the Southern California Bight. Marine Biology, 145:
191–199.
Simmons, P., and Mehmet, M. I. 2018. Shark management strategy
policy considerations: community preferences, reasoning and
speculations. Marine Policy, 96: 111–119.
Spaet, J. L. Y., Lam, C. H., Braun, C. D., and Berumen, M. L. 2017.
Extensive use of mesopelagic waters by a Scalloped hammerhead
shark (Sphyrna lewini) in the Red Sea. Animal Biotelemetry, 5:
1–12.
Spaet, J. L. Y., Manica, A., Brand, C. P., Gallen, C., and Butcher, P. A.
2020b. Environmental conditions are poor predictors of imma-
ture white shark Carcharodon carcharias occurrences on coastal
beaches of eastern Australia. Marine Ecology Progress Series, 653:
167–179.
Spaet, J. L. Y., Patterson, T. A., Bradford, R. W., and Butcher, P. A.
2020a. Spatiotemporal distribution patterns of immature
Australasian white sharks (Carcharodon carcharias). Scientific
Reports, 10: 1–13.
Speed, C. W., O’Shea, O. R., and Meekan, M. G. 2013. Transmitter
attachment and release methods for short-term shark and stingray
tracking on coral reefs. Marine Biology, 160: 1041–1050.
Thums, M., Meekan, M., Stevens, J., Wilson, S., and Polovina, J.
2013. Evidence for behavioural thermoregulation by the world’s
largest fish. Journal of the Royal Society Interface, 10:
20120477–20120475.
Weihs, D. 1973. Mechanically efficient swimming techniques for fish
with negative buoyancy. Journal of Marine Research, 31: 194–209.
Werry, J. M., Planes, S., Berumen, M. L., Lee, K. A., Braun, C. D., and
Clua, E. 2014. Reef-fidelity and migration of tiger sharks,
Galeocerdo cuvier, across the Coral Sea. PLoS One, 9: e83249.
Whitney, N. M., and Crow, G. L. 2007. Reproductive biology of the
tiger shark (Galeocerdo cuvier) in Hawaii. Marine Biology, 151:
63–70.
Wildlife Computers. 2020. Location Processing (GPE3 & Fastloc
V
R
)
in the Wildlife Computers Data Portal User Guide.
static.wildlifecomputers.com/Location-Processing-UserGuide.pdf
(last accessed 1 February 2020).
Williams, A., Upston, J., Green, M., and Graham, K. 2016. Selective
commercial line fishing and biodiversity conservation co-exist on
seamounts in a deepwater marine reserve. Fisheries Research, 183:
617–624.
Handling editor: Howard Browman
Habitat use and movement patterns of tiger sharks 11
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... Post release, all three sharks moved east from the coast (>20 km), before heading in a northerly or southerly direction following the Australian coastline. This offshore movement post capture 'fright and flight' response after tagging has been recorded in white sharks caught from the same area [34] and other species that occupy eastern Australian waters, including Tiger shark, Galeocerdo curvier [35], Sandbar, Carcharhinus plumbeus and Dusky, C. obscurus, sharks [36]. Tagged white sharks have also been documented to have an inshore phase immediately after release off the coast of central California [37]. ...
... The East Australian Current (EAC) produces nutrient rich upwellings that encroach on continental shelf and slope waters increasing primary productivity and foraging opportunities to these areas [41][42][43]. Similar longitudinal movement has been documented for tiger sharks in eastern Australia and were attributed to foraging and resource availability [35]. ...
... Thus, temperature may be considered a driver for habitat preference outside of primary productivity and prey items. Indeed, water temperature has been identified as a driver for movement in other shark species including: Rhincodon typus [51], G. cuvier [35] and Isurus oxyrinchus [52]. Furthermore, the preferred temperature range experienced by W2 may also serve as an indicator for presence of white sharks in eastern Australian waters. ...
Article
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In eastern Australia, white sharks (Carcharodon carcharias) are targeted in shark control programs, yet the movement of subadults and adults of the eastern Australasian population is poorly understood. To investigate horizontal and vertical movement and habitat use in this region, MiniPAT pop-up satellite archival tags were deployed on three larger white sharks (340–388 cm total length) between May 2021 and January 2022. All sharks moved away from the coast after re- lease and displayed a preference for offshore habitats. The upper < 50 m of the water column and temperatures between 14–19 °C were favoured, with a diel pattern of vertical habitat use evident as deeper depths were occupied during the day and shallower depths at night. Horizontal movement consisted of north–south seasonality interspersed with periods of residency. Very little information is available for adult white sharks in eastern Australia and studies like this provide key baseline information for their life history. Importantly, the latitudinal range achieved by white sharks illu- minate the necessity for multijurisdictional management to effectively mitigate human-shark inter- actions whilst supporting conservation efforts of the species.
... There is increasing support for non-lethal mitigation measures (Gibbs et al., 2020;Martin et al., 2022;McPhee et al., 2021) that align with conservation values and minimise marine biodiversity impacts. New technologies have been developed to achieve this and involve drone surveillance Colefax et al., 2020), tagging and tracking of target species Lipscombe et al., 2020;Spaet et al., 2020a), detecting shark presence through eDNA (van Rooyen et al., 2021) and using SMART (Shark-Management-Alert-in-Real-Time) drumlines (Tate et al., 2021a). Such mitigation approaches and their limitations have been discussed in detail by McPhee et al. (2021). ...
... Catch rates can also be influenced by environmental variables (Lee et al., 2018;Tate et al., 2021a). On the east coast of Australia, target shark presence is highly correlated with seasonal fluctuations in water temperatures (Espinoza et al., 2021;Holmes et al., 2014;Lee et al., 2021;Lipscombe et al., 2020;Niella et al., 2020Niella et al., , 2022Smoothey et al., 2019;Spaet et al., 2020b). Increases in water temperature associated with a strengthening East Australian Current will likely influence the movements of bull and tiger sharks. ...
... Conversely, tiger shark catches occurred throughout the year at Ballina, increasing in autumn and again in spring when water temperatures increased above 20 ºC. The variation in catches reported here is supported by the seasonal movement patterns recorded for white and tiger sharks on the east coast of Australia (e.g., Lipscombe et al., 2020;Spaet et al., 2020a). These movements are associated with water temperature fluctuations and the distribution of potential prey species (Holmes et al., 2014;Lee et al., 2018;Niella et al., 2020Niella et al., , 2022Tate et al., 2021a) driven by the East Australian Current. ...
Article
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There is increasing support for shark bite mitigation measures, such as SMART (Shark-Management-Alert-in-Real-Time) drumlines that minimise impacts on target sharks and other marine animals. On the east coast of Australia, SMART drumlines are used in a shark management program to catch and relocate white (Carcharodon carcharias), tiger (Galeocerdo cuvier), and bull sharks (Carcharhinus leucas; herein referred to as target sharks). This study examines the effect of bait position relative to the seabed on SMART drumline catches in eastern Australian waters, aiming to optimise catches of target sharks while reducing bycatch. Over 17 months, SMART drumlines were deployed at Ballina and Evans Head, New South Wales. Trace extensions were attached to 3.2 m standard traces to test the effect of bait height above the seabed on shark catch in an experimental design that alternated bait position every fortnight. White and tiger shark catches accounted for 67% of the total catch, whereas bull sharks were infrequently caught (3%). Bait position above the seabed did not significantly influence catch probability of white and tiger sharks. However, catches of Critically Endangered grey nurse sharks (Carcharias taurus) and false alarm events significantly increased when baits were closer to the seabed. Catches of white and tiger sharks varied throughout the year and were linked to seasonal water temperature changes. The standard traces effectively caught target shark species whilst reducing catches of grey nurse sharks and false alarm events, highlighting that the trace length currently used for NSW SMART drumline deployments is optimal.
... This result is similar to, albeit slightly higher than, findings from the NSW drone trial, where only 1.9% of flights recorded bull, white and/or whaler sharks [11]. Importantly, there were only nine sightings of bull or white sharks during the current trial, with only four beach evacuations, highlighting that occurrence of these shark species close to beaches are rare, even though they migrate through the study region [26][27][28]. No tiger sharks were sighted during the trial, despite them occurring in this area [26,29] and being caught on drumlines at North Stradbroke Island Ocean Beach during the drone trial period (Queensland Shark Control Program, unpublished data). ...
... Importantly, there were only nine sightings of bull or white sharks during the current trial, with only four beach evacuations, highlighting that occurrence of these shark species close to beaches are rare, even though they migrate through the study region [26][27][28]. No tiger sharks were sighted during the trial, despite them occurring in this area [26,29] and being caught on drumlines at North Stradbroke Island Ocean Beach during the drone trial period (Queensland Shark Control Program, unpublished data). The lack of tiger shark sightings on drones may have occurred because they typically occur further offshore and thus may be less likely to come in close to beaches and also because they were more active at night, as shown by higher catch data in La Réunion Island [30]. ...
Article
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Drones enable the monitoring for sharks in real-time, enhancing the safety of ocean users with minimal impact on marine life. Yet, the effectiveness of drones for detecting sharks (especially potentially dangerous sharks; i.e., white shark, tiger shark, bull shark) has not yet been tested at Queensland beaches. To determine effectiveness, it is necessary to understand how environmental and operational factors affect the ability of drones to detect sharks. To assess this, we utilised data from the Queensland SharkSmart drone trial, which operated at five southeast Queensland beaches for 12 months in 2020–2021. The trial conducted 3369 flights, covering 1348 km and sighting 174 sharks (48 of which were >2 m in length). Of these, eight bull sharks and one white shark were detected, leading to four beach evacuations. The shark sighting rate was 3% when averaged across all beaches, with North Stradbroke Island (NSI) having the highest sighting rate (17.9%) and Coolum North the lowest (0%). Drone pilots were able to differentiate between key shark species, including white, bull and whaler sharks, and estimate total length of the sharks. Statistical analysis indicated that location, the sighting of other fauna, season and flight number (proxy for time of day) influenced the probability of sighting sharks.
... However, other methods are available, including arbitrary selection (e.g. [91]) or based on published rates of movement of the study animal (e.g., 1.2 m s −1 for reef manta rays, [92]) or using algorithms as part of the model [93]. Our case study shows (in addition to [89,90]) that selecting movement speed through an iterative approach, considering a range of speeds and comparing the resultant estimated movements, can help to inform and improve interpretation. ...
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Background The use of biologging tags to answer questions in animal movement ecology has increased in recent decades. Pop-up satellite archival tags (PSATs) are often used for migratory studies on large fish taxa. For PSATs, movements are normally reconstructed from variable amounts of transmitted data (unless tags are recovered, and full data archives accessed) by coupling geolocation methods with a state-space modelling (SSM) approach. Between 2018 and 2019, we deployed Wildlife Computers PSATs (MiniPATs) from which data recovery varied considerably. This led us to examine the effect of PSAT data volume on SSM performance (i.e., variation in reconstructed locations and their uncertainty). We did this by comparing movements reconstructed using partial (< 100%) and complete (100%) geolocation data sets from PSATs and investigated the variation in Global Position Estimator 3 (GPE3; Wildlife Computers’ proprietary light-based geolocation SSM) reconstructed locations and their certainty in relation to data volume and movement type (maximum dispersal distance). Results In this analysis, PSATs (n = 29) deployed on Atlantic bluefin tuna (Thunnusthynnus) transmitted data after detaching from study animals for between 0.3 and 10.8 days (mean 4.2 ± 3 days), yielding between 2 and 82% (mean 27% ± 22%) of total geolocation data. The volume of geolocation data received was positively related to the amount of time a tag transmitted for and showed a weak negative relationship to the length of the tag deployment. For 12 recovered PSATs (i.e., 100% of geolocation data; mean ± 1 S.D. = 301 ± 90 days of data per fish), (i) if ABT travelled short-distances (< 1000 km), movements reconstructed from partial data sets were more similar to their complete data set counterpart than fish that travelled over longer distances (> 1000 km); (ii) for fish that travelled long distances, mean distance of locations from corresponding complete data set locations were inversely correlated with the volume of data received; (iii) if only 5% of data was used for geolocation, reconstructed locations for long-distance fish differed by 2213 ± 647 km from the locations derived from complete data sets; and, (iv) track reconstructions omitted migrations into the Mediterranean Sea if less than 30% of data was used for geolocation. Conclusions For Wildlife Computers MiniPATs in our specific application, movements reconstructed with as little as 30% of the total geolocation data results in plausible outputs from the GPE3. Below this data volume, however, significant differences of more than 2000 km can occur. Whilst for a single species and manufacturer, this highlights the importance of careful study planning and the value of conducting study-specific sensitivity analysis prior to inclusion of modelled locations in research outputs. Based on our findings, we suggest general steps and refinements to maximise the value of light geolocation data from PSATs deployed on aquatic animals and highlight the importance of conducting data sensitivity analyses.
... Shark mitigation programs have historically focused on catch-and-kill mitigation measures targeted at bull (Carcharhinus leucas), tiger (Galeocerdo cuvier), and white (Carcharodon carcharias) sharks [8][9][10], the species responsible for the majority of serious and fatal interactions. However, concerns associated with the impacts of lethal mitigation measures on marine wildlife has recently led to shifts in priorities towards non-lethal mitigation strategies (drones [11,12]; SMART drumlines [13,14]; personal shark deterrent devices [15][16][17]) and increasing our understanding of the ecology of bull, tiger and white sharks [18][19][20][21][22][23][24][25][26][27]. Knowledge of their occurrence and movement behaviour could enhance the predictability of shark encounters and thereby potentially reduce the risk of shark bites through advising beach authorities and the public to modify human behaviour in areas, times and conditions of increased risk, coupled with deploying site-specific mitigation measures to reduce the risks of negative human-shark interactions. ...
Article
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Unprovoked shark bites have increased over the last three decades, yet they are still relatively rare. Bull sharks are globally distributed throughout rivers, estuaries, nearshore areas and continental shelf waters, and are capable of making long distance movements between tropical and temperate regions. As this species is implicated in shark bites throughout their range, knowledge of the environmental drivers of bull shark movements are important for better predicting the likelihood of their occurrence at ocean beaches and potentially assist in reducing shark bites. Using the largest dataset of acoustically tagged bull sharks in the world, we examined the spatial ecology of 233 juvenile and large (including sub-adult and adult) bull sharks acoustically tagged and monitored over a 5.5-year period (2017–2023) using an array of real-time acoustic listening stations off 21 beaches along the coast of New South Wales, Australia. Bull sharks were detected more in coastal areas of northern NSW (<32° S) but they travelled southwards during the austral summer and autumn. Juveniles were not detected on shark listening stations until they reached 157 cm and stayed north of 31.98° S (Old Bar). Intra-specific diel patterns of occurrence were observed, with juveniles exhibiting higher nearshore presence between 20:00 and 03:00, whilst the presence of large sharks was greatest from midday through to 04:00. The results of generalised additive models revealed that large sharks were more often found when water temperatures were higher than 20 °C, after >45 mm of rain and when swell heights were between 1.8 and 2.8 m. Understanding the influence that environmental variables have on the occurrence of bull sharks in the coastal areas of NSW will facilitate better education and could drive shark smart behaviour amongst coastal water users.
... Geolocation protocols are detailed in Lipscombe et al. [32], and are briefly described here. Argos location estimates provided by SPOT tags were all opportunistic in nature, as a shark's fin needed to break the sea surface long enough to communicate with an orbiting satellite. ...
Article
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Knowledge of the 3-dimensional space use of large marine predators is central to our understanding of ecosystem dynamics and for the development of management recommendations. Horizontal movements of white sharks, Carcharodon carcharias, in eastern Australian and New Zealand waters have been relatively well studied, yet vertical habitat use is less well understood. We dual-tagged 27 immature white sharks with Pop-Up Satellite Archival Transmitting (PSAT) and acoustic tags in New South Wales coastal shelf waters. In addition, 19 of these individuals were also fitted with Smart Position or Temperature Transmitting (SPOT) tags. PSATs of 12 sharks provided useable data; four tags were recovered, providing highly detailed archival data recorded at 3-s intervals. Horizontal movements ranged from southern Queensland to southern Tasmania and New Zealand. Sharks made extensive use of the water column (0–632 m) and experienced a broad range of temperatures (7.8–28.9 °C). Archival records revealed pronounced diel-patterns in distinct fine-scale oscillatory behaviour, with sharks occupying relatively constant depths during the day and exhibiting pronounced yo-yo diving behaviour (vertical zig-zag swimming through the water column) during the night. Our findings provide valuable new insights into the 3-dimensional space use of Eastern Australasian (EA) white sharks and contribute to the growing body on the general ecology of immature white sharks.
... Tiger shark bites dominate open ocean and islands which may be explained by Australia's geography. There are more islands in northern Australia due to the presence of coral reefs 28 and tiger sharks are more commonly found in warmer, northern, waters 29,30 , although they also inhabit cooler temperature conditions 31 . In contrast, white sharks are commonly found in colder waters in the mid to high latitudes 32,33 . ...
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The perceived and real threat of shark bites have significant direct health and indirect economic impacts. Here we assess the changing odds of surviving an unprovoked shark bite using 200 years of Australian records. Bite survivability rates for bull (Carcharhinus leucas), tiger (Galeocerdo cuvier) and white (Carcharodon carcharias) sharks were assessed relative to environmental and anthropogenic factors. Survivability of unprovoked bull, tiger and white shark bites were 62, 75 and 53% respectively. Bull shark survivability increased over time between 1807 and 2018. Survivability decreased for both tiger and white sharks when the person was doing an in water activity, such as swimming or diving. Not unsurprisingly, a watercraft for protection/floatation increased survivability to 92% from 30%, and 88% from 45%, for tiger and white sharks respectively. We speculate that survival may be related to time between injury and treatment, indicating the importance of rapid and appropriate medical care. Understanding the predictors of unprovoked bites, as well as survivability (year and water activity), may be useful for developing strategies that reduce the number of serious or fatal human-shark interactions without impacting sharks and other marine wildlife.
... Alternative non-lethal shark control approaches have been trialled in a number of locations around the world. These include: physical barriers (O'Connell et al., 2018); electrical shark deterrents (Huveneers et al., 2018); shark spotter programs (Engelbrecht et al., 2017); tagging research (Lipscombe et al., 2020;; and plane, helicopter and drone based aerial monitoring . These methods offer a means of protecting water users whilst significantly reducing the impact on non-target marine fauna (McPhee et al., 2021). ...
Technical Report
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This report presents the results of the Queensland SharkSmart Drone Trial, which ran from Sept 2020 - Oct 2021. The results include the number of shark sightings, the influence of environmental and operational factors on the ability of drones to detect sharks and a comparison of shark sightings vs catch in shark control nets and drumlines. The report includes recommendations for the future use of drones for shark monitoring at Queensland beaches.
... The optimal temperature niche at which Tiger sharks (Galeocerdo cuvier) exhibit highest activity levels and abundance has been found to be 22-24°C(Payne et al., 2018). Along Australia's East Coast this species demonstrates differences in movement strategies based on latitudinal distribution(Holmes et al., 2014;Lipscombe et al., 2020). Animals tagged at the ...
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Despite its consequences for ecological processes and population dynamics, intra-specific variability is frequently overlooked in animal movement studies. Consequently, the necessary resolution to reveal drivers of individual movement decisions is often lost as animal movement data are aggregated to infer average or population patterns. Thus, an empirical understanding of why a given movement pattern occurs remains patchy for many taxa, especially in marine systems. Nonetheless, movement is often rationalized as being driven by basic life history requirements, such as acquiring energy (feeding), reproduction, predator-avoidance, and remaining in suitable environmental conditions. However, these life history requirements are central to every individual within a species and thus do not sufficiently account for the high intra-specific variability in movement behavior and hence fail to fully explain the occurrence of multiple movement strategies within a species. Animal movement appears highly context dependent as, for example, within the same location, the behavior of both resident and migratory individuals is driven by life history requirements, such as feeding or reproduction , however different movement strategies are utilized to fulfill them. A systematic taxa-wide approach that, instead of averaging population patterns, incorporates and utilizes intra-specific variability to enable predictions as to which movement patterns can be expected under a certain context, is needed. Here, we use intra-specific variability in elasmobranchs as a case study to introduce a stepwise approach for studying animal movement drivers that is based on a context-dependence framework. We examine relevant literature to illustrate how this context-focused approach can aid in reliably identifying drivers of a specific movement pattern. Ultimately, incorporating behavioral variability in the study of movement drivers can assist in making predictions about behavioral responses to environmental change, overcoming tagging biases, and establishing more efficient conservation measures.
... Basking sharks were shown to seasonally inhabit epipelagic zones of coastal areas while otherwise undertaking large-scale offshore migrations and spending extended periods in the mesopelagic realm 22 . A similar connectivity of coastal and oceanic habitats was also shown for white sharks and tiger sharks 23,24 . ...
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The identification of movement and behaviour patterns, as well as inter- and intra-population connectivity is crucial in order to implement effective and functional management and conservation measures for threatened migratory species such as tope (Galeorhinus galeus). Yet, previous studies struggled to elucidate clear and consistent movement and depth usage patterns of adult tope in the Northeast Atlantic, suggesting a high plasticity in the migration and behaviour. We deployed pop-up satellite archival tags on adult tope during their seasonal summer aggregations in the inner German Bight of the south-eastern North Sea and near a presumed mating site in southwest Scotland. Depth distribution and migration pathways were derived from time series data with location processing. Four individuals followed migration trajectories leaving coastal areas and crossed the European shelf slope into oceanic areas of the Northeast Atlantic, remaining fully pelagic for the rest of the deployment duration. These sharks showed far-ranging migration trajectories and undertook regular and frequent diel vertical migrations, reaching daytime depths of over 700 m. Vertical migration patterns closely overlapped with biological mesopelagic habitat structures and closely tracked the diel migration of organisms from deep scattering layers derived from hydroacoustic recordings. It is hypothesized that adult tope regularly utilize oceanic habitats, foraging on mesopelagic layers in an environment generally considered of low prey density.
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Understanding and predicting the distribution of organisms in heterogeneous envi- ronments is a fundamental ecological question and a requirement for sound management. To implement effective conservation strategies for white shark Carcharodon carcharias populations, it is imperative to define drivers of their movement and occurrence patterns and to protect critical habitats. Here, we acoustically tagged 444 immature white sharks and monitored their presence in relation to environmental factors over a 3 yr period (2016−2019) using an array of 21 iridium satellite-linked (VR4G) receivers spread along the coast of New South Wales, Australia. Results of generalized additive models showed that all tested predictors (month, time of day, water temper- ature, tidal height, swell height, lunar phase) had a significant effect on shark occurrence. How- ever, collectively, these predictors only explained 1.8% of deviance, suggesting that statistical sig- nificance may be rooted in the large sample size rather than biological importance. On the other hand, receiver location, which captures geographic fidelity and local conditions not captured by the aforementioned environmental variables, explained a sizeable 17.3% of deviance. Sharks tracked in this study hence appear to be tolerant to episodic changes in environmental conditions, and movement patterns are likely related to currently undetermined, location-specific habitat characteristics or biological components, such as local currents, prey availability or competition. Importantly, we show that performance of VR4G receivers can be strongly affected by local envi- ronmental conditions, and provide an example of how a lack of range test controls can lead to mis- interpretation and erroneous conclusions of acoustic detection data.
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Tiger sharks, Galeocerdo cuvier , are a keystone, top-order predator that are assumed to engage in cost-efficient movement and foraging patterns. To investigate the extent to which oscillatory diving by tiger sharks conform to these patterns, we used a biologging approach to model their cost of transport. High-resolution biologging tags with tri-axial sensors were deployed on 21 tiger sharks at Ningaloo Reef for durations of 5–48 h. Using overall dynamic body acceleration as a proxy for energy expenditure, we modelled the cost of transport of oscillatory movements of varying geometries in both horizontal and vertical planes for tiger sharks. The cost of horizontal transport was minimized by descending at the smallest possible angle and ascending at an angle of 5–14°, meaning that vertical oscillations conserved energy compared to swimming at a level depth. The reduction of vertical travel costs occurred at steeper angles. The absolute dive angles of tiger sharks increased between inshore and offshore zones, presumably to reduce the cost of transport while continuously hunting for prey in both benthic and surface habitats. Oscillatory movements of tiger sharks conform to strategies of cost-efficient foraging, and shallow inshore habitats appear to be an important habitat for both hunting prey and conserving energy while travelling.
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The tiger shark (Galeocerdo cuvier) is globally distributed with established coastal and open-ocean movement patterns in many portions of its range. While all life stages of tiger sharks are known to occur in the Gulf of Mexico (GoM), variability in habitat use and movement patterns over ontogeny have never been quantified in this large marine ecosystem. To address this data gap we fitted 56 tiger sharks with Smart Position and Temperature transmitting tags between 2010 and 2018 and examined seasonal and spatial distribution patterns across the GoM. Additionally, we analyzed overlap of core habitats (i.e., 50% kernel density estimates) among individuals relative to large benthic features (oil and gas platforms, natural banks, bathymetric breaks). Our analyses revealed significant ontogenetic and seasonal differences in distribution patterns as well as across-shelf (i.e., regional) and sex-linked variability in movement rates. Presumably sub-adult and adult sharks achieved significantly higher movement rates and used off-shelf deeper habitats at greater proportions than juvenile sharks, particularly during the fall and winter seasons. Further, female maximum rate of movement was higher than males when accounting for size. Additionally, we found evidence of core regions encompassing the National Oceanographic and Atmospheric Administration designated Habitat Areas of Particular Concern (i.e., shelf-edge banks) during cooler months, particularly by females, as well as 2,504 oil and gas platforms. These data provide a baseline for future assessments of environmental impacts, such as climate variability or oil spills, on tiger shark movements and distribution in the region. Future research may benefit from combining alternative tracking tools, such as acoustic telemetry and genetic approaches, which can facilitate long-term assessment of the species’ movement dynamics and better elucidate the ecological significance of the core habitats identified here.
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In Australian and New Zealand waters, current knowledge on white shark (Carcharodon carcharias) movement ecology is based on individual tracking studies using relatively small numbers of tags. These studies describe a species that occupies highly variable and complex habitats. However, uncertainty remains as to whether the proposed movement patterns are representative of the wider population. Here, we tagged 103 immature Australasian white sharks (147–350 cm fork length) with both acoustic and satellite transmitters to expand our current knowledge of population linkages, spatiotemporal dynamics and coastal habitats. Eighty-three sharks provided useable data. Based on individual tracking periods of up to 5 years and a total of 2,865 days of tracking data, we were able to characterise complex movement patterns over ~45° of latitude and ~72° of longitude and distinguish regular/recurrent patterns from occasional/exceptional migration events. Shark movements ranged from Papua New Guinea to sub-Antarctic waters and to Western Australia, highlighting connectivity across their entire Australasian range. Results over the 12-year study period yielded a comprehensive characterisation of the movement ecology of immature Australasian white sharks across multiple spatial scales and substantially expanded the body of knowledge available for population assessment and management.
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Background Great hammerhead sharks (Sphyrna mokarran) routinely swim on their sides and periodically roll from side to side. A previous study used wind tunnel tests with a rigid model hammerhead shark to demonstrate that the rolling behavior could improve swimming efficiency using the tall first dorsal fin as a lift-generating surface. Scalloped hammerhead sharks (Sphyrna lewini) also have proportionally taller dorsal fins compared to pectoral fins than most shark species and similar to that of great hammerhead sharks, and thus might exhibit similar rolling behavior. This was assessed by deploying multi-sensor accelerometer instrument packages on free-swimming adult scalloped hammerhead sharks to directly measure swimming depth, body orientation and swimming performance. Specific objectives were to (1) determine whether scalloped hammerhead sharks exhibit side swimming and rolling behavior, (2) characterize the patterns of these behaviors, and (3) evaluate the purpose of these behaviors. Results We obtained 196.7 total days (4720 h) of data from 9 free-swimming adult scalloped hammerhead sharks equipped with multi-instrument biologgers with deployment durations ranging from 7 to 29 days. All sharks exhibited rolling behavior throughout the entire period of observation. The roll angle magnitude and periodicity of rolling showed a clear diel pattern. During daytime, the sharks spent an average of 48% of the time swimming at a roll angle > 30°, with an average roll angle of 41° and rolling periodicity of around 4 min. At night, the sharks spent an average 82% of their time at an angle > 30°, with an average roll angle of 60° and rolling periodicity of around 13 min. In addition to an increase in degree of roll and roll duration, overall dynamic body acceleration (ODBA) also increased at night, and tailbeat frequency was more regular and consistent than during daytime. Conclusion We observed rolling behavior in scalloped hammerhead sharks similar to that observed in great hammerhead sharks. The diel changes in roll angle and periodicity were accompanied by other changes in swimming behavior. These changes are possibly due to interplay between reducing cost of transport and social interactions with conspecifics.
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ContextA series of unprovoked shark attacks on New South Wales (Australia) beaches between 2013 and 2015 triggered an investigation of new and emerging technologies for protecting bathers. Traditionally, bather protection has included several methods for shark capture, detection and/or deterrence but has often relied on environmentally damaging techniques. Heightened environmental awareness, including the important role of sharks in the marine ecosystem, demands new techniques for protection from shark attack. Recent advances in drone-related technologies have enabled the possibility of real-time shark detection and alerting. AimTo determine the reliability of drones to detect shark analogues in the water across a range of environmental conditions experienced on New South Wales beaches. MethodsA standard multirotor drone (DJI Inspire 1) was used to detect shark analogues as a proxy during flights at 0900, 1200 and 1500 hours over a 3-week period. The 27 flights encompassed a range of environmental conditions, including wind speed (2–30.0kmh−1), turbidity (0.4–6.4m), cloud cover (0–100%), glare (0–100%), seas (0.4–1.4m), swells (1.4–2.5m) and sea state (Beaufort Scale 1–5 Bf). Key resultsDetection rates of the shark analogues over the 27 flights were significantly higher for the independent observer conducting post-flight video analysis (50%) than for the drone pilot (38%) (Wald P=0.04). Water depth and turbidity significantly impaired detection of analogues (Wald P=0.04). Specifically, at a set depth of 2m below the water surface, very few analogues were seen by the observer or pilot when water turbidity reduced visibility to less than 1.5m. Similarly, when water visibility was greater than 1.5m, the detection rate was negatively related to water depth. Conclusions The present study demonstrates that drones can fly under most environmental conditions and would be a cost-effective bather protection tool for a range of user groups. ImplicationsThe most effective use of drones would occur during light winds and in shallow clear water. Although poor water visibility may restrict detection, sharks spend large amounts of time near the surface, therefore providing a practical tool for detection in most conditions.
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Background Despite being frequently landed in fish markets along the Saudi Arabian Red Sea coast, information regarding fundamental biology of the Scalloped hammerhead shark (Sphyrna lewini) in this region is scarce. Satellite telemetry studies can generate important data on life history, describe critical habitats, and ultimately redefine management strategies for sharks. To better understand the horizontal and vertical habitat use of S. lewini in the Red Sea and to aid with potential future development of zoning and management plans for key habitats, we deployed a pop-up satellite archival transmitting tag to track a single female specimen (240 cm total length) for a tracking period of 182 days. Results The tag was physically recovered after a deployment period of 6 months, thus providing the complete archived dataset of more than one million depth and temperature records. Based on a reconstructed, most probable track, the shark travelled a circular distance of approximately 1000 km from the central Saudi Arabian Red Sea southeastward into Sudanese waters, returning to the tagging location toward the end of the tracking period. Mesopelagic excursions to depths between 650 and 971 m occurred on 174 of the 182 days of the tracking period. Intervals between such excursions were characterized by constant oscillatory diving in the upper 100 m of the water column. Conclusions This study provides evidence that mesopelagic habitats might be more commonly used by S. lewini than previously suggested. We identified deep diving behavior throughout the 24-h cycle over the entire 6-month tracking period. In addition to expected nightly vertical habitat use, the shark exhibited frequent mesopelagic excursions during daytime. Deep diving throughout the diel cycle has not been reported before and, while dive functionality remains unconfirmed, our study suggests that mesopelagic excursions may represent foraging events within and below deep scattering layers. Additional research aimed at resolving potential ecological, physiological and behavioral mechanisms underpinning vertical movement patterns of S. lewini will help to determine if the single individual reported here is representative of S. lewini populations in the Red Sea.
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Background: Despite being frequently landed in fish markets along the Saudi Arabian Red Sea coast, information regarding fundamental biology of the Scalloped hammerhead shark (Sphyrna lewini) in this region is scarce. Satellite telemetry studies can generate important data on life history, describe critical habitats, and ultimately redefine management strategies for sharks. To better understand the horizontal and vertical habitat use of S. lewini in the Red Sea and to aid with potential future development of zoning and management plans for key habitats, we deployed a pop-up satellite archival transmitting tag to track a single female specimen (240 cm total length) for a tracking period of 182 days. Results: The tag was physically recovered after a deployment period of 6 months, thus providing the complete archived dataset of more than one million depth and temperature records. Based on a reconstructed, most probable track, the shark travelled a circular distance of approximately 1000 km from the central Saudi Arabian Red Sea southeastward into Sudanese waters, returning to the tagging location toward the end of the tracking period. Mesopelagic excursions to depths between 650 and 971 m occurred on 174 of the 182 days of the tracking period. Intervals between such excursions were characterized by constant oscillatory diving in the upper 100 m of the water column. Conclusions: This study provides evidence that mesopelagic habitats might be more commonly used by S. lewini than previously suggested. We identified deep diving behavior throughout the 24-h cycle over the entire 6-month tracking period. In addition to expected nightly vertical habitat use, the shark exhibited frequent mesopelagic excursions during daytime. Deep diving throughout the diel cycle has not been reported before and, while dive functionality remains unconfirmed, our study suggests that mesopelagic excursions may represent foraging events within and below deep scattering layers. Additional research aimed at resolving potential ecological, physiological and behavioral mechanisms underpinning vertical movement patterns of S. lewini will help to determine if the single individual reported here is representative of S. lewini populations in the Red Sea.
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Large epipelagic fishes (> 30 kg maximum size) are known to display a variety of patterns of vertical movement. Although advances in the affordability and sophistication of electronic tags now allows researchers to routinely document these patterns, there is no standardised approach to classify these behaviours and investigate their physical and biological drivers. This paper reviews the existing knowledge of the vertical movements of large, epipelagic fishes and the evidence for the underlying factors that structure this behaviour. The review focuses on behaviours occurring at a range of temporal scales, from seconds to years. We propose that patterns of vertical movement in gill-breathing animals of the epipelagic are best characterised by the need to move continuously in a three-dimensional environment while optimising food encounter and energy expenditure, avoiding predators, searching for mates and remaining within the limits imposed by the physical environment on their physiology (notably water temperature and oxygen). Modern biologging technologies that record both the internal (body temperature, heart rate) and external physical environment coupled with direct recording of behaviour from tri-axial sensors and animal-borne cameras offer a new approach to the analysis of drivers of vertical movement. Ultimately, this can provide insights into the evolution of the behaviour and morphology of these animals.