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Fish Manag Ecol. 2021;00:1–11. wileyonlinelibrary.com/journal/fme
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1 | INTRODUCTION
Unprovoked shark bites on ocean users have increased glob-
ally over time (Florida Museum of Natural History, 2020; Shark
Research Institute, 2020). The species most involved in these in-
cidents are white (Carcharodon carcharias L.), bull (Carcharhinus
leucas, Müller & Henle) and tiger (Galeocerdo cuvier, Péron &
Lesueur) sharks, which are responsible for approximately eight
human deaths annually worldwide (Florida Museum of Natural
History, 2020; Shark Research Institute, 2020). These incidents
are largely concentrated in hotspots where ocean recreation is
popular (Chapman & McPhee, 2016) and environmental conditions
favour shark populations (Ryan et al., 2019a). Following traumatic
shark bite events, coast al communities adjacent to these incidents
often experience heightened negative attitudes towards sharks
(Pepin- Neff & Wynter, 2018a, 2018b, 2018c) and suf fer economic
loss (Hazin et al., 2008). Additionally, mainstream and social media
often sensationalise these rare events, further contributing to neg-
ative attitudes towards sharks (Fraser- Baxter & Medvecky, 2018;
Le Busque et al., 2019; McCagh et al., 2015; Simmons & Mehmet,
2018). Affected communities commonly call for government in-
tervention, pressuring authorities to implement strategies that
attempt to catch, deter, detect, track or kill those shark species
deemed responsible.
Received: 9 November 202 0
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Revised: 31 March 2021
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Accepted: 11 Apr il 2021
DOI : 10.1111/fme.1 2489
ARTICLE
The effectiveness of Shark- Management- Alert- in- Real-
Time (SMART) drumlines as a tool for catching white sharks,
Carcharodon carcharias, off coastal New South Wales, Australia
Rick D. Tate1 | Brendan P. Kelaher1 | Craig P. Brand2 | Brian R. Cullis3 |
Christopher R. Gallen2 | Stephen D.A. Smith1 | Paul A. Butcher1,2
1Nationa l Marine Science Centre,
Southern Cross University, Coffs Harbour,
NSW, Australia
2NSW Department of Primary In dustries,
Nationa l Marine Science Centre, Coffs
Harbour, NSW, Australia
3Centre for Bioinformatics and Biometrics,
National Institute for Applied Statistics
Research Australia, University of
Wollongong, Wollongong, NSW, Australia
Correspondence
Rick D. Tate, Southern Cross U niversit y,
Nationa l Marine Science Centre, PO
Box 4321, Coffs Harbo ur, NSW 2450,
Australia.
Email: r.tate.10@student.scu.edu.au
Funding information
NSW Department of Primary In dustries;
Southern Cross University
Abstract
White (Carcharodon carcharias L.), bull (Carcharhinus leucas, Müller & Henle) and tiger
(Galeocerdo cuvier, Péron & Lesueur) sharks are the primar y species responsible for
unprovoked shark bites. Historically, management practices were based on culling
“target” shark species (i.e. white, bull and tiger sharks), which resulted in high levels
of bycatch and mortality. Shark- Management- Alert- in- Real- Time (SMART) drumlines
were trialled in New South Wales, Australia, aiming to optimise the capture of target
shark species while minimising bycatch and mortality. Target shark species accounted
for 70% of the total catch, with white sharks contributing 298 of the 350 sharks that
were caught. Four animals died, and bycatch consisted of 13 species including two
threatened species. Generalised linear mixed models (GLMMs) revealed a signifi-
cant spatial, temporal, environmental and gear influence on white shark catch rates.
SMART drumlines are a useful tool for catching target shark species with low bycatch
and mortality relative to historical bather protection methods.
KEYWORDS
bather protection, bycatch, mort ality, protected species, sea surface temperature, shark bite
management
This is an op en access ar ticle under the terms of the Creative Commons Attribution-NonComme rcial-NoDerivs License, which permits use an d distribu tion in
any medium, provided t he original work is properly cited, the use is non-commercial an d no modific ations or adaptations a re made.
© 2021 The Authors. Fisheries Management and Ecology publishe d by John Wiley & S ons Ltd.
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TATE ET Al.
Management strategies aimed at reducing the number of shark
bites often involve fishing techniques used to catch and kill sharks.
For example, in 1937, mesh nets were installed at 18 coastal beaches
in Sydney, New South Wales (NSW), Australia, to cull local popula-
tions of dangerous sharks (Reid & Krogh, 1992). This initiative, which
is now used at 51 beaches in NSW, was adopted by the KwaZulu-
Natal Sharks Board (K ZN) in South Africa in 1952 (Cliff & Dudley,
1992, 2011) and the Que ensland (QLD) state g overnment in Austr alia
in 1962 (Dudley, 1997). Traditional drumlines, involving a large float
and baited hook, are another shark bite mitigation method, which
typically improves survival rates of captured animals compared with
mesh nets (Dudley et al., 1998; Sumpton et al., 2011). Other shark
bite mitigation strategies include physical exclusion barriers, protec-
tive materials and biophysical deterrents (Huveneers et al., 2018;
McPhee & Blount, 2015; O'Connell et al., 2018; Whitmarsh et al.,
2019). Various visual survey techniques, in combination with bather
alert s, have also been trialled, including coastal headland surveys
(Kock et al., 2012) and aerial sur veillance with fixed- wing planes, he-
licopters (Bryson & Williams, 2015; Robbins et al., 2012) and drones
(Butcher et al., 2019; Colefax et al., 2017, 2019; Kelaher et al., 2019).
Bycatch resulting from poor gear selec tivity and animal mortal-
ity due to capture are key concerns for bather protection policies
(Broadhurst & Cullis, 2020). Incidental mortality is particularly con-
cerning for non- target species, many of which are threatened (e.g.
turtles) or protected (e.g. dolphins). Further, many non- dangerous
elasmobranchs (sharks, skates and rays) exhibit vulnerable life-
history strategies, including long gestations, late sexual maturity and
low fecundity (Dapp et al., 2016; Dulvy et al., 2014; Pardo et al.,
2016). The vulnerability of sharks, in par ticular, is reflected in the
global decline of populations of large sharks, mostly because of over-
fishing (Dulvy et al., 2017; Ferretti et al., 2010; Worm et al., 2013).
Additionally, some elasmobranch species (e.g. hammerhead sharks,
Sphyrnidae spp.) may be more vulnerable and sensitive to capture
across a range of gears than others (Butcher et al., 2015; Gallagher
et al., 2014a, 2014b; Gulak et al., 2015; Morgan & Burgess, 2007).
To minimise injur y to captured sharks and other marine fauna,
while providing effective bather protection and potentially allowing
relocation and monitoring of target sharks, there is a need to de-
velop new and innovative strategies. In 2013, a catch- and- release
bottom longline and drumline programme in Recife, Brazil, suc-
cessfully reduced shark bites on people, as well as animal mortal-
ity (Hazin & Afonso, 2014). Similarly, between 2014 and 2017, the
MLi- S buoy (Marine Instruments™) was used in conjunction with
drumlines to target bull and tiger sharks, providing a solar- powered
GPS beacon that is triggered by the dislocation of a magnetic pin
linked to a baited fishing trace (Guyomard et al., 2019; Lemahieu
et al., 2017). This “Catch- A- Live”® system was successful in reduc-
ing bycatch of f the coast of Reunion Island in the Indian Ocean
(Guyomard et al., 2019). The same technology was trialled in 2016
in response to a spike in shark bites off the east coast of Australia
using drumlines integrated with MLi- S buoys and termed Shark-
Management- Alert- in- Real- Time (SMART) drumlines. While cap-
ture by this gear caused minimal stress on white sharks during the
catch- and- release process (Tate et al., 2019), further research on
gear configuration is needed to maximise the catch rates of target
shark species (Guyomard et al., 2019).
The overarching aim of the NSW Shark Management Strategy is
to increase protection for beachgoers by intercepting and catching
white, bull and tiger sharks (hereafter referred to as target species) off
coastal beaches, while minimising bycatch and mortality of all captured
animals. To maximise the efficiency of the programme to deliver its ob-
jectives, trials were conducted in an experimental framework allowing
tests of specific hypotheses (Gallagher et al., 2019; Tate et al., 2019). In
the current study, various combinations of gear configuration and bait
types, as well as the influence of environmental variables, were tested
on white shark catches over a two- year period.
2 | MATERIALS AND METHODS
SMART drumlines were deployed at six locations along the NSW
coast, Australia: Ballina (28.811 S 153.610 E), Evans Head (29.111
S 153.439 E), Coffs Harbour (30.310 S 153.156 E), Forster (32.175
S 152.518 E), Kiama (34.673 S, 150.844 E) and Ulladulla (35.357 S,
150.461 E). The SMART drumlines were deployed for 24 months
at Evans Head and Ballina, from December 2016 to November
2018, while 6- month trials were conducted at Forster and Coffs
Harbour (August 2017 to February 2018), and Kiama and Ulladulla
(November 2017 to May 2018). A total of 22,025 individual drumline
deployments, over 1,637 fishing days, were conducted throughout
the trials.
On each fishing day, SMART drumlines were deployed during
daylight hours approximately 500 m from shore in water depths of
4– 19 m. SMART drumlines were deployed by 08:00 and retrieved
at the end of the day at either 16:0 0 or two hours before sunset (to
allow enough time to handle a shark if it was caught). Earlier retriev-
als were made if weather conditions were poor, and it was deemed
unsafe to leave the gear at sea for a full day (i.e. due to navigational
concerns or handling sharks in rough seas). Ten sampling sites at
Evans Head and 15 at Ballina were established at the commence-
ment of the deployment period at each location . However, from 10
May 2017, the number of sites increased from 10 to 15 at Evans
Head and 15 to 20 at Ballina, due to a perceived need to increase
protection from shark bites (Table 1).
Once an alert was received from the SMART drumline (via email,
phone call or SMS), the line was attended within ~30 min to assess the
catch (Tate et al., 2019). If a shark was caught on the line, it was only
approached when it maintained a normal upright swimming position
without heavy thrashing. The shark was then secured to the side of
the vessel using a sling, with the vessel moving slowly to allow water to
pass through the animal's gills (Gallagher et al., 2019; Tate et al., 2019).
The sex and length (fork – FL to the nearest cm) of each animal were
recorded, along with time (h: m) and location of capture and release.
Environmental data were also collected on fishing days, including
the average and maximum wind speeds (km/h), wind direction (as a
bearing), barometric pressure (hPa), sea state (Beaufort scale 1−5),
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TATE ET Al.
water temperature (°C) and depth (m), cloud cover (scored as 0– 8,
with 0 being no cloud and 8 total cloud cover), turbidity/visibility
(scored as 0 turbid to 5 clear), average and maximum swell and sea
height (m), humidity (%), sunrise and sunset times, moonrise and set
times and the moon phase (% visible) (Table S1).
2.1 | Fishing gear and animal capture
Each SMART drumline comprised an anchor (either 3 m of
10- mm- diameter galvanised chain and/or with a 4.5 kg Danforth an-
chor), and 20 m of 8- mm- diameter polypropylene (PP) rope and an A1
polyform anchor buoy (279 × 381 mm). A second (surface) line (2.0 m
of 8- mm- diameter elasticised cord) was attached to a SMART buoy
(model MLi- S), and then a holding line of 0.5 m of 8- mm- diameter PP
rope and a larger A3 polyform drumline buoy (432 × 584 mm). A shock
sleeve (incorporating two 1.1 m lengths of elasticised cord (10 mm di-
ameter) encased in herringbone material) and trace were suspended
from the buoy with a circle hook (Mustad 39937NP- DT 20/0) at the
end. A 2.0 - m monofilament trigger line (2.0 mm diameter) was at-
tached between the elasticised cord and the SMART buoy. When the
baited hook was bitten, the trigger line separated the magnetic pin
from the SMART buoy, and a signal was transmitted.
The effect of various combinations of four gear components on
the catch rates of white sharks was tested. This consisted of: (1) trace
lengths (short - 1.6 m or long −3.2 m); (2) trace materials (W – plain wire
or P – wire coated in a poly- vinyl chloride plastic sleeve; (3) barb type
(B – barbed or SB semi- barbed); and (4) bait type (grey mullet, Mugil
cephalus L., or Australian salmon, Arripis trutta, Forster). In each case,
hooks were constructed of 9- mm- diameter, duratin- coated, carbon
steel wire and bait consisted of a single fish weighing 0.75– 1.00 kg.
2.2 | Experimental design
Sixteen gear treatments were considered in the design. The set of
gear treatments included the 24- factorial combinations of trace length,
trace material, barb type and bait. For each fishing day, within a loca-
tion, SMART drumlines were deployed in a manner that was practical
and simple to implement. As a result, it could not be assumed that each
of the factorial combinations could be deployed in a balanced design.
Consequently, there were insufficient numbers of deployments across
all eight combinations of gear factors to warrant consideration of a
saturated 24- factorial treatment structure in the statistical analysis.
The analytical approach was selected since the SMART drumline
programme was set up as a longitudinal experimental design (Brien &
Demétrio, 2009). Thus, successive observations were made on some
“unit” over time or space, and other factors in the experiment were
not randomised to them. The units in this study were the fishing
sets, nested within sampling sites and nested within locations. The
successive observations were the binary outcomes of white shark
capture (0 being no capture; 1 being a capture). Capture rates at
Ulladulla and Kiama were too low to include in a formal analysis but
are still reported in the results. The study was unbalanced, so a gen-
eralised linear mixed model (GLMM) was used as the basis for the
analysis. The terms included in the fixed and random model of the
GLMM addressed the aims of the experiment, but there were also
terms that accommodated the structure of the experimental design.
To ensure that the GLMM contained all appropriate model terms,
the design tableau of Smith and Cullis (2018) was used. This included
the steps outlined in Table S2.
For the design tableau (Table S3), U and 1 represent the uni-
versal factors (Bailey, 2008) and the overall intercept variate, re-
spectively, each with a single level and value. The latter was set
to 1. Time was a factor with 616 levels and each level correspond-
ing to a unique calendar date. The variable day was confounded
with time and date. Its values are the Julian days coded such that
1 ≡ 08/12/2016 (ddmmyyyy). The terms day,spl(day) and water-
temp,spl(watertemp) fitted cubic smoothing splines to the response
profiles for catch rate against day and water temperature, respec-
tively (Verbyla et al., 1999). Terms that included set (within time)
were not fit ted as there was insufficient information in the data to
estimate reliably the variation associated with (fishing) sets within
days and higher- order interactions (see Table S3). The term 1[U]
indicated that U was aliased with 1, and once the term 1 was fit ted
as a fixed effect, then U could not be included. This then implies
that inference on the overall intercept could not be conducted in
a strict sense [see Bailey (2008) for details]. Terms associated with
time, other than the linear effect of day, were fitted as random ef-
fects. These were then represented by time[date] as opposed to
shire[location], where the plot factor appears before the anatomical
factor if the model term is fitted as random, otherwise the reverse
if the term is fitted as a fixed effect (see Nelder, 1977). Shire was a
factor with four levels and was the factor associated with location.
Location
Deployment period
Days Sets
Mullet/Salmon used
Start End Start End
Ballina 08/1 2/16 29/11/18 478 8400 05/06/17 14/02/18
Coffs Harbour 17/08/17 13/02/18 130 13 04 27/08/17 13/02/18
Evans Head 08/12/16 30/11/18 547 7515 05/06/17 11/02/18
Forster 14/08/17 13/02/18 162 1626 14/08/17 13/02/18
Kiama 03/11/17 02/05/18 155 1550 03/11/17 02 /05/18
Ulladulla 03/11/17 0 2/05/18 165 1650 0 3/11/17 02/05/18
TABLE 1 Summary of the deployment
periods, total number of fishing days and
the period when Australian salmon and
sea mullet were used for bait at the same
time at each location
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TATE ET Al.
To reduce the possibility of false discovery rate, all environ-
mental variables were included as random effects. This meant that
only the key environmental variable and the terms associated with
first- order interactions between the treatment factors were exam-
ined during the backward elimination procedure, which determined
the final model. Preliminary examination of the data indicated that
capture rates were strongly seasonal, and that water temperature
was the most likely cause, given its strong association with time and
date. Terms that did not influence catch rate were automatically
dropped from the model as the penalised quasi- likelihood estimate
of the associated variance component was fixed at a very small pos-
itive number. The remaining candidate model terms were succes-
sively removed, one at a time, using a Bonferroni- based test in the
backward elimination procedure that commenced with the baseline
model, including all terms in the column labelled Term in Model in the
GLMM. All analyses were conducted in the R statistical package. All
models were fitted following Butler et al., (2018), which fits GLMMs
using penalised quasi- likelihood as described in Breslow and Clayton
(1993).
3 | RESULTS
3.1 | Catch results
A total of 22,025 individual SMART drumline deployments resulted
in the capture of 500 animals from 16 different species ( Table 2).
Thirteen species of shark were caught, including 298 white sharks,
48 dusky whalers Carcharhinus obscurus (Lesueur), 43 tigers, 26
grey nurse Carcharias taurus Rafinesque, 26 smooth hammerheads
Sphyrna zygaena L. and 20 common blacktips Carcharhinus limbatus
(Müller & Henle) (Table 2). Target sharks constituted 70.0% of the
total catch (Table 2). The bycatch comprised 144 non- target sharks
(10 species), four black rays Dasyatis thetidis Ogilby, one black mar-
lin Istiompax indica (Cuvier) and a loggerhead turtle Caretta caretta
(L.). Excluding white sharks, 5.4% of the total catch was protected
species, comprising 26 grey nurse sharks and one loggerhead turtle
(Table 2).
Only four animals (0.8%) died on the line during capture. This in-
cluded one white shark, one black marlin, one common blacktip and
one smooth hammerhead shark. The hammerhead shark (170 cm
total length – TL), which had hook- related puncture wounds on the
gills, failed to set off an alert to contractors and was found at the
end of the day when the gear was being retrieved. The remaining
three animals set off alerts, with the animals at tended bet ween 18
and 28 min after the alert. However, the white shark (320 cm TL)
bit the main buoy and anchor line off, before rolling in the remain-
ing rope and becoming entangled and washing up on the adjacent
beach. The black marlin (193 cm TL) was hooked in the dorsal fin and
the common blacktip (116 cm TL) had been bitten by another shark,
presumably after capture.
For the four locations that were sampled for six months, very
few white sharks were caught at Kiama (1) and Ulladulla (3), while 16
and 65 were caught at Coffs Harbour and Forster, respectively. Over
the 2- year period at Evans Head and Ballina, 95 and 118 white sharks
were caught, respectively (Table 2). More tiger sharks were caught
TABLE 2 Marine fauna caught on SMART drumlines at Ballina (B), Cof fs Harbour (CH), Evans Head (EH), Forster (F), Kiama (K) and
Ulladulla (U) during the trial
Name BCH EH FK U Tot al
White shark (Carcharodon carcharias)*95 16 118 65 13298
Dusky whaler (Carcharhinus obscurus)12 12 1 6 5 12 48
Tiger shark (Galeocerdo cuvier)10 18 2 2 9 2 43
Grey nurse shark (Carcharias taurus)*7316 - - - 26
Smooth hammerhead (Sphyrna zygaena)20 1 1 1 2 1 26
Common blacktip (Carcharhinus limbatus)14 - 6 - - - 20
Shortfin mako (Isurus oxyrinchus) 2 1 2 - 3210
Bull shar k (Carcharhinus leucas) 7 - 2 - - - 9
Bronze whaler (Carcharhinus brachyurus) - - - 4 - 1 5
Thresher shark (Alopias vulpinus) - - - 5 - - 5
Black ray (Dasyatis thetidis) - 1 1 - 2 - 4
Black marlin (Istiompax indica) 1 - - - - - 1
Loggerhead turtle (Caretta caretta)*1 - - - - - 1
Sandbar shark (Carcharhinus plumbeus) 1 - - - - - 1
Silky shark (Carcharhinus falciformis) - - - 1 - - 1
Spinner shark (Carcharhinus brevipinna) 1 - - - - - 1
Whaler unidentified (Carcharhinus spp.) 1 - - - - - 1
*Denotes protected species under NSW and Commonwealth legislation.
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TATE ET Al.
at Coffs Harbour (18) over six months, than Ballina (10) and Evans
Head (2) over 24 months. Bull sharks were rarely caught, and only at
Ballina (7) and Evans Head (2) (Table 2). Smooth hammerhead sharks
were predominantly caught at Ballina, thresher sharks, Alopias vulpi-
nus (Bonnaterre) were only caught at Forster, and common blacktip
sharks were only caught at Ballina and Evans Head (Table 2). Most
bycatch species were caught as a single catch ( Table 2).
The GLMM indicated that water temperature accounted for
94.6% of the variation in white shark catch rates ( p < 0.01) (Table 3,
Figure 1). Additional significant factors included location ( p < 0.01),
day (p < 0.05) and trace length (p < 0.01) (Table 4). More white sharks
per 100 set s (±SE) were caught at Forster (1.5 ± 0.2) than Ballina
(1.1 ± 0.2), Evans Head (1.0 ± 0.1) and Coffs Harbour (0.9 ± 0.1)
(Table 4).
For the significant main ef fect of “day” (GLMM, p < 0.05; Table 4),
white shark catches per 100 sets peaked during the austral winter
for Ballina, Evans Head and Coffs Harbour, and during spring at
Forster (Table 4; Figures 2 and 3). Water temperatures varied among
locations with ranges from 16.7 to 25.9℃ at Evans Head, 17.4 to
26.6℃ at Ballina, 17.2 to 24.1℃ at Forster and 18.1 to 25.0℃ at
Coffs Harbour (Figure 2). For water temperature, the highest pre-
dicted white shark catch rates occurred at Evans Head with 2.5
sharks caught per 100 hook sets at 18.7℃, followed by Forster (2.4
sharks at 19.1℃), Ballina (1.8 sharks at 19.1℃) and Coffs Harbour (1.7
sharks at 18.9℃) (GLMM, p < 0.01; Table 4, Figure 2).
There was no influence on the catch rates of white sharks from
bait type, hook type or trace material (Table 4). However, there was
a significant effect of trace length, such that the average (±SE) catch
per 100 hook sets on long traces (1.53 ± 0.28) was almost double
that of shor t traces (0.77 ± 0.14) (GLMM, <0.01; Table 4).
TABLE 3 PQL estimates of the variance components of the
random model terms. These are also expressed as a percentage of
the total variance on the underlying (logistic) scale. B = the PQL
estimate of the variance component is set to the boundar y of the
allowable parameter space - being 0
Ter m Variance % Variance
MoonPhase 0.0030 0.002
SeaState 0.0007 0.000
WaterViz B -
WindDirection B -
CloudCover 0.0359 0.024
WaterDepth B -
Barometer B -
spl(day) B -
spl(watertemp) 139.3973 94.594
Location:Site 0.0713 0.048
Location:spl(day) B -
Location:spl(watertemp) 4.0094 2.721
Time 0.0514 0.035
Location:Time 0.4160 0.282
Location:Site:Time 0.0895 0.061
residual 3.2899 2.232
FIGURE 1 Jittered scatter plot of
the catches of white shark against water
temperature for each location
Forster KiamaUlladulla
BallinaCoffs Harbour Evans Head
15 18 21 24 27 15 18 21 24 27 15 18 21 24
27
No
Yes
No
Yes
Water Temperature (
o
C)
White shark caught?
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TATE ET Al.
TABLE 4 Summary of probabilit y values for conditional Wald t ype pivots during the backward elimination process for the determination
of the final fixed model formula
M1 M2 M3 M4 M5 M6 M7
(Intercept) 0.0085 0.0085 0.0084 0.0083 0.0083 0.0084 0.0083
Shire (Location) 0.0003 0.0002 0.0002 0.0003 0.0003 0.0003 0.0002
Day 0.0135 0.0135 0.0136 0.0135 0.0135 0.0135 0.0141
Water temperature (watertemp) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Barb Type (hook) 0.8896 0.8801 0.8822 0.8810 0.8807 0.9106 0.9065
Bait 0.4850 0. 4855 0.4868 0 .4811 0 .4759 0.4936 0. 5078
Trace Length 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Trace Material 0.3366 0.3358 0.3366 0.3371 0.3528 0.3497 0.3294
Shire:day 0.0784 0.0750 0.0750 0.0769 0.0771 0.0731 0.0769
Shire:watertemp 0.6080 0.6080 0.6044 0.6070 0.6 010 0.6078 0.5983
Barb Type:Bait 0.2216 0.2247 0.2318 0.2419 0.2383 0. 2474
Barb Type:Trace Length 0.5896 0.6022 0.6137 0. 6163
Barb Type:Trace Material 0.6289 0 .6418 0.6480
Bait:Trace Length 0.7612 0. 7749
Bait:Trace Material 0.2289 0. 2291 0.2301 0 .2419 0 .2479
Trace Material:Trace Length 0.7895
Note: Conditional Wald pivots were derived according to marginality rules as described in the statistical met hods.
FIGURE 2 Predicted white shark capture rates per 100 sets against water temperature for each Shire[Location]. Summer, Autumn,
Winter, Spring are representative of the water temperatures found for each of these seasons during the current study at each location
Winter
Spring
Summer
Auturmn
Spring
Autumn
Summer
Winter
Spring
Summer
Winter
Spring
Summer
Evans Head Forster
BallinaCoffs Harbour
18 21 24 27 18 21 24 27
0.0
0.5
1.0
1.5
2.0
2.5
0.0
0.5
1.0
1.5
2.0
2.5
Water Te mperature (
o
C)
White sharks (per 100 sets)
Winter
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TATE ET Al.
4 | DISCUSSION
Marine fauna caught by SMART drumlines at six locations were
successfully identified, and the influences of various combinations
of gear type and environmental factors on the catch rates of white
sharks were quantified. Two of the three target species, namely
white and tiger sharks, accounted for the highest and third highest
propor tions of the catch, respectively, throughout the trial. High sur-
vival rates were recorded across all species and the low numbers of
bycatch consisted largely of other shark species. Water temperature
explained most of the variation in white shark catch rates, with more
being caught between 18.7 and 19.1°C. The highest catch rates oc-
curred during the austral winter and spring across all locations when
water temperatures were lowest. Overall catch rates were highest
at Forster. Tiger sharks accounted for the third highest number of
animals caught (8.3% of the total catch), but very few bull sharks
were caught. Most of the gear components assessed as part of the
experimental design had no influence on white shark catch rates,
although long traces caught significantly more white sharks. These
results support the utility of SMART drumlines in programmes
attempting to catch and relocate target sharks responsible for bites
on people, while minimising harm to those target sharks and to non-
target species.
Global populations of some shark species captured by SMART
drumlines are considered as least near- threatened (IUCN, 2020).
There is consequently a clear need and growing public support
for less invasive bather protection programmes that limit the po-
tential for marine fauna to perish because of their use (Simmons &
Mehmet, 2018). SMART drumlines have consistently resulted in few
mortalities across multiple capture and tracking studies (Gallagher
et al., 2019; Guyomard et al., 2019; Spaet et al., 2020a; Tate et al.,
2019). The current study results support this, with only four deaths
recorded, representing <1% of the total catch. These low mor tality
rates are likely the result of the rapid (~30 min) response times in
the SMART drumline programme following a catch aler t (Guyomard,
et al., 2019; Tate et al., 2019). The use of large circle hooks increases
the likelihood of capture of larger sharks and minimises instances of
foul and deep hooking (Read, 2007; Veldhuizen et al., 2018; Watson
et al., 2005), thereby contributing to the low mortality rate reported
in this study.
FIGURE 3 Predicted water temperature against dates (Julian days) for each location. Dots represent raw data, dotted lines represent
approximate upper and lower pointwise 95% confidence inter vals, and the seasons are coloured. The predicted “peak” white shark catches
per 100 hooks set at each location is shown in the black line and
Evans Head Forster
Ballina Coffs Harbour
1/12/16
28/2/17
31/5/17
31/8/17
30/11/17
28/2/18
31/5/18
31/8/18
30/11/18
1/12/16
28/2/17
31/5/17
31/8/17
30/11/17
28/2/18
31/5/18
31/8/18
30/11/18
17.5
20.0
22.5
25.0
17.5
20.0
22.5
25.0
Date (ddmmyy)
Water Te mperature (oC)
Season
Autumn
Spring
Summer
Winter
18.7 19.1
18.9
19.1
8
|
TATE ET Al.
Mortality rates are often high in marine fauna caught by set
fishing gear (Butcher et al., 2015; Marshall et al., 2012; Morgan &
Burgess, 2007). Although most of the species caught during this trial
have high sur vival rates after short capture durations (Butcher et al.,
2015; Guyomard et al., 2019; Tate et al., 2019), hammerhead sharks
and istiophorid billfish (e.g. marlin) are highly susceptible to capture-
related stressors and usually experience high mortality rates
(Butcher et al., 2015; Gallagher & Klimley, 2018). However, these
species can tolerate capture on fishing gear (Graves & Horodysky,
2015; Musyl et al., 2015; Pepperell & Davis, 1999), and any mortal-
ity is more likely attributed to handling and air exposure (Schlenker
et al., 2016). In the current SMART drumline programme, the short
response times after initial hooking, and limited, in- water handling
likely contributed to the high survival rates. However, further reduc-
tions in response times will help prevent mortalities associated with
entanglement in gear, or depredation of captured animals, which is
common in commercial line fisheries (Ryan et al., 2019b).
White sharks were the most abundant species caught by SMART
drumlines, accounting for almost 60% of all captured animals, with
target sharks collectively accounting for 70% of the catch. There are
several factors that may explain the predominance of white sharks
in captures. White shark population estimates on the east coast of
Australia suggest good survival rates of both juveniles (70%– 75%)
and adult s (>90%) (Hillary et al., 2018). Juveniles undergo coastal mi-
gration (Bruce et al., 2019; Spaet et al., 2020a, 2020b), and their size
and association with the coast place them in direct competition with
other shark species that also hunt pelagic fish (Grainger et al., 2020).
Evidence also suggests that white sharks are not only ac tive hunt-
ers, but also opportunistic scavengers that often feed at slow speeds
(Colefax et al., 2020; Tate et al., 2021). In addition, they have a wide
thermal tolerance of 10– 27°C, which indicates they can occupy
NSW coast al waters all year- round (Domeier & Nasby- Lucas, 2008;
Spaet et al., 2020a; Weng et al., 2007; White et al., 2019). These
factors may explain why white sharks are more commonly caught on
SMART drumlines than other species. Conversely, the lower catch
rate of tiger sharks may reflect its preference for offshore migration
around the continental slope waters (Holmes et al., 2014). Few bull
sharks were caught during the SMART drumline trials. By contrast, a
similar trial at Reunion Island caught many more tiger and bull sharks
than in the present study, although the bull sharks were primarily
caught overnight (Guyomard et al., 2019). The exclusive deployment
of SMART drumlines during daylight hours in the current programme
could potentially explain the relatively small catch of bull sharks as
they are more active and forage at night (Smoothey et al., 2016).
Additionally, bull and tiger sharks are typically caught in warmer wa-
ters (Payne et al., 2018; Smoothey et al., 2019) during the austral
summer and autumn.
bycatch of other marine wildlife was low, accounting for 30%
of the total catch (150 animals). The bycatch consisted of ten shark
species that are commonly caught along the east coast of Australia
(Braccini et al., 2012; Butcher et al., 2015; Kennelly et al., 1998) and
six other animals (1.2%), including four black rays (D. thetidis), a black
marlin and a loggerhead turtle (C. caretta) that was foul hooked in
the flipper. In addition to white sharks, only 5.4% of all animals cap-
tured by SMART drumlines are protected under jurisdictional and
Commonwealth fisher y laws in Australia through the Environment
Protection and Biodiversity Conservation Act 1999 (Department of
the Environment, 2020; EPBC, 1999). This included critically endan-
gered grey nurse sharks (C. taurus), which were always released alive
and in good condition. While SMART drumlines have successfully
limited bycatch, it is prudent to investigate potential improvements
to safeguard other susceptible species, such as smooth hammerhead
sharks.
Consistent with previous studies, water temperature explained
most of the variation in white shark catch rates (Bruce & Bradford,
2012; Dewar et al., 20 04; Lee et al., 2018; Weltz et al., 2013; Weng
et al., 2007; Wintner & Kerwath, 2018). Catch rates were highest
when water temperatures were in the optimal range reported for
juvenile white sharks on the east coast of Australia (18°C to 27°C
– Spaet et al., 2020a) and in the north- east Pacific (17.5°C to 26°C
– Domeier & Nasby- Lucas, 2008; Weng et al., 2007; White et al.,
2019). White sharks were mostly caught during the austral win-
ter to spring, which reflects seasonal patterns of migration (June–
November) along Australia's east coast (Bruce et al., 2019; Spaet
et al., 2020a, 2020b) and peak catch rates in bather protection nets
from September to November (Reid et al., 2011). High catch rates
also coincided with a seasonal increase in food supply associated
with prey migrations (Bruce et al., 2019; Duff y et al., 2012). It should
be emphasised that, while maximum catch rates occurred in cooler
months, white sharks were also caught during the austral summer
and autumn, and so consistent, year- round deployment of SMART
drumlines is strongly recommended adjacent to popular swimming
and surfing areas. Fur thermore, bull and tiger sharks are key species
observed within the study region and are likely to be more prevalent
during these periods of warmer water (Holmes et al., 2014; Lee et al.,
2019; Smoothey et al., 2019).
The higher catch rates at Evans Head, Forster and Ballina provide
managers with information to allow objective resource allocation
to maximise white shark catch rates and the overall effec tiveness
of SMART drumlines as par t of the ongoing management strategy.
The tracking of white sharks indicates predictable northward and
southward migrations along the temperate east coast of Australia
(Bruce et al., 2019; Spaet et al., 2020a, 2020b). Historically, two
nurser y grounds have been identified at Corner Inlet/Ninety Mile
Beach (38.854S, 146.583E), Victoria, and Port Stephens (32.675S,
152.202E), New South Wales (Harasti et al., 2016; Spaet et al.,
2020b). Forster was initially regarded as the northernmost exten-
sion of the Por t Stephens nursery region (Harasti et al., 2017), but
this has now been extended to a ~300 km stretch of coastline be-
tween Terrigal and South West Rocks on the mid- nor th coast of
NSW, Australia (Spaet et al., 2020b), possibly explaining the rela-
tively high catch rate of white sharks at Forster.
The effectiveness of SMART drumlines for catching white sharks
was enhanced by using long traces. Water depth at deployment had
no effec t on their catch rates, indicating that other factors may ex-
plain the significant ef fect of longer tr aces. Given mount ing evidence
|
9
TATE ET Al.
that white sharks are a cautious species (Colefax et al., 2020; Tricas,
1985; Tricas & McCosker, 1984), it is possible that greater distances
from the surface and the SMART drumline structure facilitated more
confident approaches to bait. However, an important management
consideration is the potential for higher risk of entanglements to
both the shark and contractors’ vessels when using long traces (Tate
et al., 2021).
In conclusion, catch on SMART drumlines was quantified over
24 months across six locations with multiple gear configurations in
a bather protection programme. White sharks were the most abun-
dant animals captured, and the catch of non- target animals was low.
Few mortalities were observed, and the use of long traces increased
white shark catch rates. The results of this study provide evidence
to suppor t improvement s to current SMART drumline practices.
Implementation of these improvements will enhance the capture of
target shark species, reduce their mortalit y and reduce the bycatch
of other marine wildlife.
ACKNOWLEDGEMENTS
Primary project funding and support of the SMART drumline trial
were provided by the New South Wales Department of Primary
Industries (NSW DPI), Australia, through the Shark Management
Strategy. NSW DPI and Southern Cross University provided funding
towards a co- funded PhD to Rick Tate. NSW DPI provided “scientific”
(Ref. P01/0059(A)), “Marine Parks” (Ref. P16/0145- 1.1) and “Animal
Care and Ethics” (ACEC Ref. 07/08) permits. This project would not
have been possible without the dedicated support of contracted fish-
ers, David Guyomard, Pierre Ugo Tournoux and Christophe Perry
from Reunion Island and the NSW DPI shark research team.
DATA AVAIL ABILI TY STATEMENT
Data available on request from the authors.
ORCID
Rick D. Tate https://orcid.org/0000-0003-4333-2833
Paul A. Butcher https://orcid.org/0000-0001-7338-6037
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Tate RD, Kelaher BP, Brand CP, et al.
The effectiveness of Shark- Management- Alert- in- Real- Time
(SMART) drumlines as a tool for catching white sharks,
Carcharodon carcharias, off coastal New South Wales,
Australia. Fish Manag Ecol. 2021;00:1–11.
https: //doi .org/10.1111/fme.12489
