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Beach safety: can drones provide a platform for
sighting sharks?
Paul A. Butcher
A
,
E
,Toby P. Piddocke
A
,Andrew P. Colefax
A
,Brent Hoade
B
,
Victor M. Peddemors
C
,Lauren Borg
D
and Brian R. Cullis
D
A
NSW Department of Primary Industries, National Marine Science Centre, PO Box 4321, Coffs Harbour,
NSW 2450, Australia.
B
NSW Department of Primary Industries, Game licensing Unit, Port Macquarie, NSW 2444, Australia.
C
NSW Department of Primary Industries, Sydney Institute of Marine Science, Mosman, NSW 2088,
Australia.
D
National Institute of Applied Statistics Research Australia, Faculty of Engineering and Information
Sciences, University of Wollongong, Wollongong, NSW 2522, Australia.
E
Corresponding author. Email: paul.butcher@dpi.nsw.gov.au
Abstract
Context. A 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.
Aim. To determine the reliability of drones to detect shark analogues in the water across a range of environmental
conditions experienced on New South Wales beaches.
Methods. A 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 (2–30.0 km h
1
), turbidity (0.4–6.4 m), cloud cover (0–100%), glare (0–100%), seas (0.4–1.4 m),
swells (1.4–2.5 m) and sea state (Beaufort Scale 1–5 Bf).
Key results. Detection 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 P¼0.04). Water depth and
turbidity significantly impaired detection of analogues (Wald P¼0.04). Specifically, at a set depth of 2 m below the water
surface, very few analogues were seen by the observer or pilot when water turbidity reduced visibility to less than 1.5 m.
Similarly, when water visibility was greater than 1.5 m, 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.
Implications. The 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.
Additional keywords: aerial survey, bather protection, remotely piloted aircraft system, shark detection, unmanned
aerial vehicle (UAV).
Received 20 July 2018, accepted 17 August 2019, published online 4 December 2019
Introduction
Unprovoked shark attacks (or shark bites) are dramatic events
that command considerable public attention, and can increase
tension between policy mandates for the protection of public
safety and marine animal conservation (e.g. Neff 2012; Hazin
and Afonso 2014; Gibbs and Warren 2015). In this context,
unprovoked shark attacks are defined as those inflicted upon
humans who have not attempted to deliberately interact with a
shark (e.g. by feeding, touching, pursuing or capturing the
animal), or those who are not otherwise engaged in activities
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Wildlife Research
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likely to attract sharks (e.g. spearfishing or cleaning fish) (West
2011; McPhee 2014; Ricci et al. 2016). The word ‘attack’ may
not always accurately represent the motivations underpinning
these interactions (Neff and Heuter 2013), but is used here for
consistency. Both globally and in Australia, over half of all
unprovoked attacks are attributed to white (Carcharodon
carcharias), bull (Carcharhinus leucas) and tiger (Galeocerdo
cuvier) sharks (West 2011; McPhee 2014). These three species
are also implicated in almost all fatal attacks (West 2011;
McPhee 2014).
Although shark attacks remain rare, the total number of
global attacks increased 3-fold between 1982 and 2011
(McPhee 2014). Some of this increase reflects growing human
population and greater overall participation in aquatic recreation
(West 2011). The internet and widespread use of smartphones
have also facilitated public awareness and reporting of shark
attacks (West 2011). However, even after allowing for these
factors, 30 years of data extracted from the Global Shark Attack
File indicates that the proportional increase in attacks has out-
stripped population growth, suggesting a real, albeit small,
increase (McPhee 2014). The mechanisms underpinning this
rise are unclear, but may involve increased abundance of some
prey species (especially marine mammals) (McPhee 2014;
Chapman and McPhee 2016), changing human recreational
behaviours (West 2011) and sharks’ shifting habitat selection
in marine ecosystems undergoing anthropogenic change (Hazin
et al. 2008; Chapman and McPhee 2016). Greater shark abun-
dance has also been raised as a possible cause, and although
some local increases have been reported (e.g. Carlson et al.
2012; Froeschke et al. 2013), these appear insufficient to
account for the global rise in attacks (West 2011; McPhee
2014). Regardless of the underlying causes, spatially and tem-
porally clustered attacks, such as those along the coastlines of
Recife (Hazin et al. 2008,2013), Reunion Island (Blaison et al.
2015; Lemahieu et al. 2017), North and South Carolina (Amin
et al. 2015) and off the Australian states of New South Wales
(NSW; Neff 2012; Pepin-Neff and Wynter 2017) and Western
Australia (WA; Gibbs and Warren 2015), usually result in public
demands for risk mitigation (Hazin et al. 2008; Cliff and Dudley
2011; Hazin and Afonso 2014).
Shark attack clusters led to the establishment of the three
longest-standing shark control programs globally: the NSW
Shark Meshing Program (established 1937); the KwaZulu–
Natal shark control program in South Africa (established
1952); and the Queensland Shark Control Program
(established 1962) (Cliff and Dudley 2011). Initially, these
programs focused solely on localised reduction of shark num-
bers using mesh nets and a system of large baited hooks
suspended from surface floats, known as ‘drum lines’, to catch
and kill sharks (Cliff and Dudley 2011; Reid et al. 2011;
Sumpton et al. 2011). Gradually, increasing public concern over
the programs’ impacts upon both by-catch (typically non-target
elasmobranchs, marine mammals and reptiles) and target spe-
cies has seen governments searching for measures that improve
protection for swimmers while minimising the impacts on
sharks and other marine wildlife (Cliff and Dudley 2011; Hazin
and Afonso 2014; Engelbrecht et al. 2017).
Some of the most recent alternative measures are based on
modifications of existing technology. For example, the SMART
(Shark Management Alert in Real Time) drumlines developed in
Reunion Island incorporate satellite communications that auto-
matically alert operators to a hooked animal via email, phone
call and text message, enabling rapid release and/or relocation,
usually after acoustic or satellite tagging (McPhee and Blount
2015). Other bather protection measures include: (1) physical
barriers that aim to provide an impassable obstacle that separates
sharks from people; (2) visual barriers, such as bubble curtains,
that are not physically impassable but present aversive visual
stimuli; and (3) barriers that aim to exploit sharks’ aversion to
particular electrical and electromagnetic stimuli (O’Connell
et al. 2014a,2014b,2014c; McPhee and Blount 2015). Com-
bined deterrents are also under investigation, such as the
SharkSafe Barrier (Sharksafe Barrier
TM
, Stellenbosch, South
Africa; https://www.sharksafesolution.com/, accessed Novem-
ber 2019), which combines an electromagnetic deterrent with
PVC piping that mimics dense kelp beds, creating a threshold
that large sharks may be unwilling to cross (McPhee and Blount
2015; O’Connell et al. 2018). Personal deterrents, designed to be
worn or carried by an individual swimmer or surfer, are not
considered in detail here, but are reviewed by O’Connell et al.
(2014c)and Hart and Collin (2015). Most of the above measures
have reported some degree of success, but none represents a
complete solution at either the individual or whole-of-beach
scale. For example, physical barriers are susceptible to damage
during rough seas (Cliff and Dudley 2011), and the species-
specific nature of elasmobranch responses to electromagnetic
stimuli may preclude universal application of electrical and
electromagnetic deterrents (O’Connell et al. 2011,2014c; Hart
and Collin 2015).
The difficulty of identifying and implementing broadly
applicable and cost-effective shark deterrents has given rise to
measures aimed at shark detection (with subsequent warnings to
swimmers) rather than deterrence. Detection approaches include
trained observers working from elevated coastal vantage points
(Engelbrecht et al. 2017), automated sonar systems (McPhee
and Blount 2015; Parsons et al. 2015), acoustic tagging and
monitoring programs (McAuley et al. 2016) and manned aerial
patrols using fixed-wing aircraft or helicopters (McPhee and
Blount 2015; Robbins et al. 2014).
Methodologically, aerial shark patrols represent a specialisa-
tion of the aerial surveys used to estimate wildlife distribution
and abundance (e.g. Fleming and Tracey 2008; Rowat et al.
2009; O’Donoghue et al. 2010; Poole et al. 2013; Kleen and
Breland 2014; Fuentes et al. 2015). Although aerial shark patrols
aim simply to locate sharks and advise the public, rather than
attempting formal estimates of abundance or distribution, high
detection likelihoods and accuracy are important if the patrols
are to effectively protect bathers (Robbins et al. 2014). Unfortu-
nately, detecting sharks via aerial surveys can be difficult,
because unlike air-breathing aquatic animals that spend a
considerable portion of time at the water’s surface, and therefore
can be readily sighted from the air, white, bull and tiger sharks
spend most of their time completely submerged (Holland et al.
1999; Dewar et al. 2004; Bonfil et al. 2005; Carlson et al. 2010).
Aerial surveys have been used for locating sharks, particularly
for species inhabiting shallow, clear water (Kessel et al. 2013),
or when experimental designs allow flights to be undertaken
only during periods of optimal visibility (Dicken and Booth
BWildlife Research P. A. Butcher et al.
2013). However, these studies have not included formal esti-
mates of detection likelihood, instead assuming that favourable
conditions would facilitate detection of a high, but unspecified,
proportion of the sharks present. Furthermore, manned aerial
shark patrols are flown in a range of sea states and environmental
conditions that may reduce detection rates by an unknown
amount (Robbins et al. 2014). Studies quantifying detection
likelihood in aerial shark patrols are scarce, but indicate low
sighting rates. For example, Robbins et al. (2014) reported
sighting rates of 12.5% and 17.1% for analogue sharks seen by
observers from fixed-wing aircraft and helicopters, respectively.
These rates suggest that, although aerial shark patrols may
provide some psychological reassurance to bathers (Neff
2012), improved sighting rates and periods of surveillance
‘cover’ are required before they can be considered an effective
bather protection measure (Robbins et al. 2014).
One option to improve the efficacy of aerial shark detection is
to use drones, also known as remotely piloted aircraft systems
(RPAS) or unmanned aerial vehicles (UAV), which are avail-
able in a variety of fixed-wing and multi-rotor configurations
(Colefax et al. 2018). Drones increasingly present an alternative
to manned aircraft for a variety of wildlife survey and detection
tasks (Kudo et al. 2012; Martin et al. 2012; Linchant et al. 2015;
Evans et al. 2015; Kiszka et al. 2016; Colefax et al. 2019;
Kelaher et al. 2019). In the context of shark patrols, drones offer
several potential advantages over manned aircraft because they
can operate at individual beaches, and fly constantly at lower
airspeeds and altitudes than are physically or legally possible for
manned aircraft (Kudo et al.2012; Robbins et al.2014, but see
Linchant et al. (2015) for possible exceptions). Drones can also
carry polarising filters to facilitate viewing in real time, or
hyperspectral sensors for digitally enhancing images for post
processing so that we can ‘see’ further below the water’s surface
than human observers in manned aircraft (Stein et al. 1999;
Schoonmaker et al. 2011). Furthermore, drones are more cost-
effective to operate than manned aircraft for small-scale sur-
veys, produce fewer environmental impacts and pose a lower
risk to both operators and bystanders (Kudo et al. 2012; Martin
et al. 2012; Linchant et al. 2015).
Although recent technological advances have seen drones
used in many wildlife survey applications, their capacity to
detect predatory sharks remains untested. The primary aim of
the present study is to quantify the effectiveness of detecting
submerged shark analogues using drones across a range of
environmental conditions representative of those likely to be
encountered along the eastern Australian coastline. Beyond the
realm of bather protection, the research also aims to determine
the utility of drones as a shark survey method.
Materials and methods
Study site
The experiment was completed at Hills and Korora Beach
(30815.0930S, 15388.5930E), Coffs Harbour, New South Wales,
Australia during November and December 2015, using a drone
and two-dimensional shark analogues. The flight path covered a
1.9-km length (0.950 m either side of the control station) of
nearshore sand and rocky-reef habitat, creating a 3.8-km circuit
,35 m from the backline of the surf break on any given day.
Aircraft configuration and operation
All flights were completed using a DJI Inspire 1 (DJI, Shenzhen,
China) quadcopter drone with a DJI Zenmuse X5 camera (DJI
MFT 15 mm F/1.7 ASPH lens) and vibration absorbing board,
and a circular polarising filter (ProMaster Digital HGX CPL –
46 mm). The camera was set to record the entirety of each flight
in 4K video (3840 2160 pixels), and the pilot took still images
(16-megapixel resolution) when an animal was sighted. The
drone was flown at a height of 60 m to provide an uninterrupted
70-m wide swathe, with the camera pitched at 10 degrees
forward of nadir to optimise navigation and observation
(irrespective of sun position). The drone was controlled by a
multi-functional remote utilising 2.4 GHz wireless communi-
cation. Flights were maintained within line-of-sight by the
drone’s pilot, in accordance with Australia’s Civil Aviation
Safety Authority (CASA) regulations. The drone’s camera feed
was viewed in real time by the pilot, using a 24.6-cm Apple iPad
Air (Cupertino, CA, USA) screen with a protective hood on the
top and sides to minimise sun glare. All flights were completed
using one battery with up to 20% remaining.
Shark analogues
For the experiment, two-dimensional shark analogues were used
within the survey area (following the procedures of Robbins
et al. 2014). Although the use of live sharks would be preferred,
the use of analogue or decoy animals is well established in the
aerial survey literature as an effective means of rigorously
testing detection efficiency, because the number and positioning
of analogues is known to researchers, but not to the observers or
pilot (e.g. Jones et al. 2006; Koski et al. 2009; Robbins et al.
2014). Each analogue in the experiment measured 2430 mm
(total length) by 1190 mm (pectoral fin width), and was con-
structed of 11-mm marine plywood. Both sides of the analogues
were painted grey to replicate large oceanic sharks (Robbins
et al. 2014), with two shaped as white sharks and two as ham-
merhead sharks (Sphyrna sp.) (Fig. 1).
One to four analogues were deployed from a vessel at a given
location 2 m below the surface in 2.0–6.3 m water depth. This
depth was chosen because poor detection rates have previously
been reported for shark analogues positioned more than 2.6 m
deep (Robbins et al. 2014). The actual water depth is important
because the change in colour or background may influence the
ability to locate an analogue. To enable horizontal orientation in
the water, each analogue was attached at four points on the
underside using wire cables terminating at a single metal ring
attached to a rope and anchor. For retrieval purposes, a 50-mm
float (not visible to the pilot) was attached to the analogue’s
upper surface with clear monofilament line. All analogues were
positioned within 70 m of the back line of the surf break to ensure
they were visible within the strip width of the drone camera.
Environmental data
At each prescribed flight time, the pilot recorded 15 environ-
mental variables, including: average and maximum wind speed
(km h
1
) and direction (as a bearing); barometric pressure (hPa);
air temperature (8C); rainfall over the preceding 24 h (mm); sea
and swell height (average and maximum; m); and direction (as a
bearing) and humidity (%) from a meteorological station nearby
Beach safety: drones for sighting sharks Wildlife Research C
White shark (sand, 3.4 m water turbidity)
White shark (sand, 1.6 m water turbidity)
Hammerhead shark (sand, 3.3 m water turbidity)
White-spotted guitarfish (rock, 1.6 m water turbidity) White shark (rock, 3.0 m water turbidity)
Hammerhead shark (sand, 2.0 m water turbidity)
Hammerhead shark (sand, 1.9 m water turbidity)
Hammerhead shark (rock, 3.0 m water turbidity)
Fig. 1. Images of hammerhead shark and white shark analogues across different water turbidity (m) and
substrates (sand and rock) in comparison with a live white-spotted guitarfish (Rhynchobatus australiae) seen
during the trials. White square indicates the location of the analogue.
DWildlife Research P. A. Butcher et al.
(Manly Hydraulics Laboratory and Bureau of Meteorology).
Sea state (Beaufort scale 0–5 Bf) and cloud cover (scored as
a scale from 0 for no cloud to 8 for complete cloud cover)
were determined by personal observation, and water turbidity
(m) or water visibility at each shark analogue was measured
with a Secchi disk (m) by the boat crew before each flight.
When taking measurements, if the water visibility exceeded
the water depth (i.e. the seabed was visible), then the visibility
was instead measured at a nearby location to the deployed
analogue at a greater depth. At the end of the flight, the
percentage of battery remaining was recorded, as were survey
and flight end times.
All recorded environmental variables were included in the
analysis except for rainfall, swell height and direction (average
and maximum) and maximum wind speed. The rainfall variable
showed 90% of observations as 0 mm rainfall, and the remaining
10% were recorded as only 0.1 mm. Similarly, there was
insufficient variation in the swell average variable, ranging from
0.82 to 1, with 80% of observations taking a single value of 1.
Observations for the maximum swell variable ranged from 1.4 to
2.1, which was insufficient variation to detect any effects.
Average wind speed and maximum wind speed had a near
one-to-one relationship, so only average wind speed was
included in the model.
For statistical analysis, the wind speed variable (km h
1
) had
four categories: calm (0–5 km h
1
); light (6–10 km h
1
);
moderate (11–15 km h
1
); and strong (.16 km h
1
). The wind
direction variable, which originally had seven categories (east,
north, north east, north west, south, south east and south west),
was re-categorised to four (south–south west, east–south east,
north–north east and west–north west) as prevailing wind
directions. The air temperature (8C) and humidity (%) variables
were rounded to the nearest whole numbers, and the barometric
pressure variable (hPa) was rounded to one decimal place.
Flights
Twenty-seven flights were completed over 3 weeks and up to
3 days per week, with flights occurring at 0900, 1200 and
1500 hours on each flying day. The same commercial pilot
operated the drone throughout the experiment, with the pilot
deciding whether flights were to proceed based on environ-
mental conditions.
A GPS reference point was positioned at the northern, middle
and southern ends of the circuit so that all flights paths
were consistent with the northern and southern boundaries.
The ground control station (GCS) from which the drone would
take off and land was positioned on the beach adjacent to the
midpoint of the flight path. For each flight, the drone was flown
manually at 40 km h
1
parallel to the beach in a northerly
direction to the northern extremity of the flight path (0.95 km),
before turning 1808and travelling south (1.90 km) to the
southern extremity. The drone then turned around (1808) and
returned along the same flight path (0.95 km) to the flight
circuit’s midpoint, before landing at the GCS. The entirety of
each flight was recorded, with additional photographs of any
sighted shark analogues. Any analogue sightings made by the
pilot were also communicated verbally to an observer standing
next to the pilot, who recorded the time, location, analogue
shape and flight direction.
Drone footage review
In the week following the flights, all flight videos were reviewed
at real-time pace by a single observer in a laboratory. For
independence, this was done without the observer knowing the
date, day or flight number. The observer independently recorded
all analogues visible in the footage, and the drone’s direction of
travel at the time each analogue was detected. For each sighting,
the laboratory-based observer also recorded a value for glare
(0–4) based on the percentage of affected screen. A mean glare
value for each flight was calculated, and a per-flight glare cat-
egory (0, nil; 4, extreme) assigned. All footage was observed
using a PC (Dell Optiplex 9020) with a 58.4-cm (1920 1080
resolution) display. The main observer and a second observer
completed further assessments of random flights for compati-
bility, but these were not included in the overall analysis because
they were the same.
Statistical analysis
The analysis accounted for the structure of the experimental
design when examining the influence of environmental factors
on sighting rates. The model used in the analysis accommodated
for the dependence in the response variable through unmiti-
gated, shared covariates; this reduces the likelihood of false
associations caused by confounding environmental variables
with plot factors. For the formation of the model, we used the
approach of Smith and Cullis (2019), thereby defining the plot
and treatment factors, their associated structure and the design
function for the randomisation of treatments. This provided the
terms, which were included in the generalised linear mixed
model (GLMM), as well as determining the status (fixed or
random) of all terms in the model. Observations for analogue
sightings are not independent because their deployment
remained the same for both flight directions (north and south);
thus, we used a GLMM with random terms to allow for depen-
dence between observations. A logistic GLMM with a logit link
was fitted to the binary response of detectability, coded as 0 (for
non-detection) and 1 (for detection). The pilot and laboratory
observer data were analysed together, as is typical with double-
observer data in the field of ecology (e.g. Bernard et al. 2013;
Strobel and Butler 2014).
The experiment can be considered a multi-phase experiment,
where Phase I involved the construction of the shark analogues,
with two analogues for each species, and Phase II was the
deployment of analogues. The plot factor for Phase I was the
shark ‘analogue’ (four levels), and the treatment factor was
‘species’ (two levels). The treatment structure was 1 þspecies,
where 1 represents the overall mean, and the plot structure was
‘analogue’. For Phase II, the set of plot factors were ‘Flight’ (two
levels), ‘Week’ (three levels), ‘Day’ (three levels), ‘Time’ (three
levels), ‘Viewing’ (two levels) and ‘Location’ (four levels). We
further defined an additional plot factor ‘Set’, which was a factor
for levels that were the unique combinations of the three plot
factors ‘Week’, ‘Day’ and ‘Time’. For each level of ‘Set’,
analogues were deployed to each of the randomly chosen
locations within the pre-determined grid. The number of analo-
gues that were deployed varied from zero to four. This approach
was adopted to mask the true number of analogues deployed for
any given set from the pilot and the observer. There were three
Beach safety: drones for sighting sharks Wildlife Research E
sets that had no analogues deployed. These sets were discarded
from the analysis of detectability for analogues, but used in the
overall data. The factor ‘Viewing’, with two levels, corresponds
to the two viewings of the footage at each deployment by the
pilot and then subsequently by the laboratory observer. Finally,
‘Flight’ has two levels, north and south, corresponding to the
flight direction. The plot structure is given by:
Flight þWeek þWeek : Day þ
Week :Day :Time þWeek : Day :Time :Location þ
Week:Day :Time :Location :Viewing þ
Flight :Week þFlight :Week :Day þFlight :Week :Day :Time þ
Flight :Week :Day :Time :Location þ
Flight :Week :Day :Time :Location :Viewing
where Flight :Week :Day :Time :Location :Viewing indexes
the observational units.
These treatment and plot structures lead to what is termed the
working GLMM, where all model terms in the plot structure are
assumed to be random terms. Terms in the treatment structure
were then fitted as fixed terms, except for those terms in the plot
structure of Phase I (i.e. Analogue), which were fitted as random.
fixed ¼1þSpecies þObserver þSpecies :Observer
random ¼Analogue þFlight þWeek þWeek :Day þ
Week :Day :Time þWeek :Day :Time :Location þ
Week :Day :Time :Location :Viewing þ
Flight :Week þFlight :Week :Day þFlight :Week :Day :Time þ
Flight :Week :Day :Time :Location þ
Flight :Week :Day :Time :Location :Viewing
The working GLMM was then extended to the full set of
environmental factors and variates (Table 1). To assist with the
notation, we refer to ‘X’ as the matrix of covariates and dummy
factors associated with the environmental factors and variates
listed in Table 1. The GLMM was extended by including X,as
well as the interaction of species and observer with X. Terms were
then removed from this full GLMM using backward elimination,
with respect to marginality. That is, all interaction terms were
considered before examination of the main effects of X.
All analyses were conducted in the R (R Development Core
Team 2015) package ASReml-R, which fits GLMMs using the
approach of Breslow and Clayton (1993). Inference for fixed
effects was conducted using approximate Wald-type pivots
(Butler et al. 2009).
Results
Twenty-seven flights (mean s.d., 12:50 1:55 min : seconds)
across 9 days were completed over 3 weeks. The 54 shark ana-
logues (23 hammerhead and 31 white sharks) were set in water
depths between 2.0 and 6.3 m (mean s.d., 3.6 1.1 m), over
sand (39 deployments) and reef (15 deployments) habitats. Only
one flight at 0900 hours was delayed due to rain, while the rest
went as scheduled across the variety (mean s.d., range) of
environmental conditions: wind speed (14.6 7.5 km h
1
,
2.0–30.0 km h
1
); turbidity (2.1 1.1 m, 0.4–6.4 m); humidity
(67.1 6.8%, 52–80.0%); barometric pressure (1014 3.3 hPa,
1008–1021 hPa); cloud cover (3.7 3.1, 0–8); air temperatures
(surface: 25.5 1.28C, 23.5–28.48C); glare (1.2 0.7, 0.0–3.0);
seas (0.7 0.3 m, 0.4–1.4 m); swells (1.8 0.2 m, 1.4–2.5 m);
and sea state (2.3 1.2 Bf, 1.0–5.0 Bf).
Because a given analogue had two chances of being detected
(north- and south-bound flight segments), there was a total of 108
possible analogue detections, of which 67 were not sighted by
either the pilot or observer. In total, 31 analogues (29%) were
sighted by both the pilot and laboratory observer, and an addi-
tional 10 analogues (9%) were detectedby the laboratory observer
but not the pilot. No analogues were detected by the pilot and not
the observer. Fig. 2presentsa jittered scatter plot of the analogues
detected and missed by the pilot and laboratory observer against
water turbidity (m), which indicates that the detection probability
is very low in conditions where the water visibility is less than
1.5 m. Specifically, of the 31 analogue sightings detected by both
the pilot and observer, only two of the sightings for each group
occurred when the water visibility was less than 1.5 m (Fig. 2).
Deployments when the water visibility was less than 1.5 m
provideno insight into additional factorsthat may affect detection.
The remaining analyses were therefore conducted on those
deployments for which water visibility was greater than 1.5 m
(n¼78). Of these 78 shark analogue deployments in water
visibility .1.5 m, 29 (38%) were detected by the pilot and 39
(50%) from post analysis by the observer. There were no false
positives observed where either the pilot or laboratory observer
recorded a shark analogue when there were none deployed.
Detectability
For the GLMMs exploring the influence of environmental
variables relating to probability of analogue detection by the
pilot or laboratory-based observer, all treatment terms were non-
significant and dropped from the final model except for the main
effect of water depth (Table 2). The term ‘species’ was retained
because of the treatment structure, although there was no sig-
nificant difference (P¼0.50) in detection probability between
hammerhead (0.34 0.64) and white shark (0.47 0.64) ana-
logues (Table 2).
Table 1. Summary of environmental factors and variates considered in
the generalised linear mixed model for detecting shark analogues
Where cells in the Factor column are False, range is provided, where they are
True, levels are provided
Variable Factor Range (min–max) or levels
Air temperature False 23.5–28.48C
Barometric pressure False 1010–1021 hPa
Cloud cover True 0, 1, 2, 3, 6, 7, 8
Glare True 0, 1, 2, 3
Habitat True reef and sand
Humidity False 52–80%
Sea state True 1, 2, 3, 4
Water turbidity False 0.4–6.3 m
Water depth False 2.0–6.3 m
Wind direction True ESE, NNE, SSW, WNW
Wind speed True Calm, light, moderate, strong
FWildlife Research P. A. Butcher et al.
There was a significant difference (P¼0.04; Table 2)in
detection probability between the pilot (0.30 0.61) and the
laboratory observer (0.51 0.61), with a log odds ratio of 0.916.
The odds of detecting an analogue were 2.5 times greater for the
laboratory observer than for the pilot. Water depth was also
significant (P¼0.04), with a log odds ratio of 0.874.
Specifically, an increase in water depth by 1 m results in a
58% reduction in the odds of detection. The predicted detection
drops below 50% when the water depth exceeds 3.5 m (Fig. 3).
However, there were three detections by both the pilot and
observer in water depth .5 m and on both flight directions.
These detections involved the same white shark analogue, and
occurred over a sandy substrate on a windless day with smooth
sea conditions.
The estimates on the logit scale for the between-flight (Week :
Day : Time : Location) and within-flight variance (Flight : Week :
Day : Time : Location) were 0.65 and 3.38, respectively. An
approximate measure of dependence between flights (0.84) was
given by the within-flight variance divided by the sum of the
within- and between-flight variance. There was considerable
Water turbidity (m)
Pilot Laboratory observer
24682468
Yes
No
Analogue detection
Fig. 2. Scatter plot of jittered shark-sighting rates from the pilot and laboratory observer (hollow circle,
hammerhead shark analogue; black circle, white shark analogue) against water turbidity (m). Dashed line shows
the 1.5 m-turbidity level and analogues that were excluded from the formal analysis of detection probability.
Table 2. Summary of conditional Wald test statistical model for detection probability, and associated probabilities of
significance, based on an asymptotic Chi-squared reference distribution
d.f., degrees of freedom; F.con, F-statistic; pchisq, cumulative Chi-squared reference distribution
Pilot d.f. F.con pchisq
(Intercept) 1 2.263 0.133
Species 1 0.457 0.499
Observer 1 4.446 0.035
Water depth 1 4.184 0.040
Water depth (m)
Detection probability
0
0.4
0.8
1.2
23456
Fig. 3. The jittered binary outcomes (top, seen; bottom, not seen) with the
approximate 95% pointwise coverage interval for the predicted probability
for detection (shaded area) for shark sighting rates against water depth (m)
when water turbidity was .1.5 m depth.
Beach safety: drones for sighting sharks Wildlife Research G
correlation in detection probabilities between flight directions,
which was expected given that the location of analogue deploy-
ments remained the same for flights within a set. However, the
random component of the GLMM fitted accounted for this
dependence.
Discussion
The present study has quantified the diverse range of coastal
conditions under which drones can operate, demonstrating their
utility for detecting objects in the water (in this case shark
analogues), particularly in conditions of reasonable water visi-
bility and depth. Depending on the environmental conditions,
drones can potentially be used to spot sharks or other marine
fauna along coastal beaches, and could supplement existing
aerial survey methods (Colefax et al. 2019). Furthermore,
drones would provide a cheaper and more versatile platform
than manned aircraft for beach authorities and various user
groups for localised coastal areas (Colefax et al. 2018). The
potential benefits that drones can offer for bather protection, and
specifically shark attack mitigation, are discussed by consider-
ing the underlying mechanisms that may affect their use.
Water turbidity, which was high throughout the study, had a
strong negative relationship with detection probability. Specifi-
cally, when the depth of sightability (governed by turbidity) was
shallower (,1.5 m) than the depth of the shark analogues (2 m),
detection probability was very low, with neither the pilot nor
laboratory observer able to identify the shark analogues. Tur-
bidity is recognised as a major factor affecting data reliability in
aerial surveys because it decreases the depth in the water column
at which an animal can be sighted, thereby decreasing detection
probability proportionately to dive behaviour (Pollock et al.
2006; Hodgson et al. 2013; Hagihara et al. 2014; Fuentes et al.
2015). In clear water, the pilot and observer detected analogues,
but detection probability across the survey was skewed because
water clarity was poor for much of the study.
Water depth had a significant effect on detection probability,
particularly at depths .3.5 m. This is likely because light
attenuation changes with water depth, altering contrast between
the grey shark analogue and the substrate. Similarly, sighting
rates for dugong (Dugong dugon) decreased with water depth
and turbidity (Pollock et al. 2006; Hodgson et al. 2013). In
deeper water, wavelengths of light are either scattered or
absorbed before reflecting off the substrate, creating a darker
ocean colour and masking the grey shark-like colour of the
analogues (Bloom et al. 2019). Conversely, in shallower water,
where the depth of sightability is greater than the water depth,
light is reflected from the sand or reef substrate, giving the water
a lighter appearance and allowing better contrast with the
silhouette. Depth-related differences in light attenuation and
reflection suggest that multispectral and hyperspectral sensors
might allow better image contrasting through frequency band
selection (Colefax et al. 2018).
Although glare (as sea surface reflectance) did not signifi-
cantly affect sighting rates (through perception biasing; Pollock
et al. 2006), glare effects were detectable, particularly around
midday and at times when sea surface conditions produced
wavelets. The effects of sea surface reflectance can typically
be reduced in manned aerialsurveys by polarisation (Zhang et al.
2017), such as having observers wear polarised sunglasses
(Alves et al. 2013). In drone-based aerial surveys, the equivalent
is achieved by applying appropriate circular polarising filters for
the light intensity at a given location, as was done in this
experiment. Further measures can also be taken to potentially
reduce sea surface reflection, such as altering the drone’s
orientation and making minor adjustments to the gimbal angle
with regard to the sun’s position, particularly when near its
zenith (Zhang et al. 2017). However, these measures may have
consequences for the consistency of flight parameters (e.g.
transect width), and perhaps depth of sightability due to altered
sensor viewing angles.
The present study did not identify significant effects of wind
and sea state on sighting rates. This counterintuitive result may
be an artefact, reflecting the model’s inability to distinguish
wind and sea-state effects from the high turbidity levels present
throughout much of the study. Alternatively, strong winds and
rough seas may actually impair marine fauna sighting rates less
for drones than for manned aircraft, because drones are flying
substantially slower and lower. Although other drone-based
marine surveys have indicated likely declines in detection
probability associated with increasing sea states (Koski et al.
2009; Hodgson et al. 2013), it is likely that the comparatively
lower altitudes, smaller search area and slower speeds of drone-
based surveys would allow longer scanning time over an area for
the observer and pilot, and buffer some of the adverse effects on
detection rates in comparison to manned aircraft (Colefax et al.
2018). To conclusively determine if detection probability from
drone-based shark surveillance is less affected by environmental
variables, including wind and sea state, an empirical investiga-
tion directly comparing it with manned aircraft is required.
Flights throughout the study were exposed to a range of
environmental conditions, including fresh wind strengths of
30 km h
1
and sea states up to 5 Bf. Manned aircraft can also
conduct aerial shark surveillance during these conditions, but
generally avoid doing so because sighting probability is signifi-
cantly impaired (Rowat et al. 2009; Robbins et al. 2014).
Manned aerial surveys are costly (,AU$1200 per hour), and
the reduced sighting probabilities characteristic of high winds
and rough seas often result in decisions to restrict operations to
sea states less than 3 Bf and winds ,30 km h
1
) (Rowat et al.
2009; Kleen and Breland 2014; Fuentes et al. 2015). However,
the array of real-time tracking buoys that detect dangerous
sharks along 21 coastal beaches in NSW has shown that they
are present across all environmental conditions (Paul Butcher,
NSW DPI, unpubl. data).
The small but significant difference in sighting rates between
the pilot and the observer indicates a degree of perception
biasing in field-based analogue detections. Pilot perception error
was noticeable between water depths of 3.5 and 4.5 m, where the
observer detected more shark analogues than the pilot did. These
differences in sighting rates usually manifested in conditions of
low (but still .1.5 m) water clarity and over deeper water, so the
shark analogues had only very faint background contrasting for
the pilot to detect in real time. The results from the present study
suggest that in shallower or clearer conditions, there would
likely be negligible difference in detection probability between
the pilot and observer. In scenarios where detection becomes
increasingly difficult, perception biases in field detections may
HWildlife Research P. A. Butcher et al.
be explained by: (1) attention of the pilot spread across multiple
tasks; (2) sun glare on the telemetry screen; (3) the telemetry
screen size and/or decreased resolution compared with post-
processing; or (4) the observer’s ability to pause, slow down or
replay transect segments for sighting determinations.
Australian civil aviation regulations stipulate that drones
must remain within the pilot’s line-of-sight (unless under
special exemption), and that the drone’s condition and sur-
rounding airspace must be monitored for hazards and air traffic
throughout the flight. The multi-tasking nature of conducting
these tasks while observing the video feed may have reduced
the pilot’s detection performance (Adler and Benbunan-Fich
2012). One or two observers might significantly increase the
probability of detecting objects on the screen. Additionally,
although shading was used on the top and sides of the telemetry
screen to reduce sun glare and enable a clearer picture, viewing
drone footage on a larger, higher resolution screen in a
controlled environment would likely facilitate object detec-
tion. Measures including the use of high resolution ‘first-
person-view’ goggles (such as DJI goggles), a large protected
screen showing the live telemetry to an observer, or a brighter
screen (i.e. CrystalSky Ultra, DJI, China) could improve real-
time object detection by drone pilots in the field. Alternatively,
advancements in automatic software recognition may assist the
pilot in identifying otherwise missed detections. Computer-
based object recognition is a research focus in this field – and
will inevitably lead to increased post-processing efficiency of
drone-captured imagery – but is still largely in developmental
stages, particularly in heterogeneous environments (Chabot
and Francis 2016;Seymouret al. 2017;Guet al. 2018;Saqib
et al. 2018).
Despite all scheduled drone flights proceeding throughout
the 3-week survey period, the presence of rain did delay one
flight as the drone used in this study, like most drones, was not
rated to fly in wet conditions. Although water-resistant drones
exist, they generally have much shorter ranges and achieve
shorter flight durations than equivalent non-water-resistant
variants (Fiori et al. 2017). Marine fauna surveys from manned
aircraft technically can operate during rain, but avoid doing so
because it impairs the sightability of marine fauna (Pollock et al.
2006; Rowat et al. 2009; Kleen and Breland 2014). Similarly,
rainy conditions would likely impair faunal sightability even if
water-resistant drones were used. Precipitation is therefore
potentially the main limitation on drone-based shark surveil-
lance, because it can either force surveillance operations to cease
or impede faunal sightings (Colefax et al. 2019).
The use of shark analogues to determine sighting rates in the
present study provides an indication of performance, but, for a
given shark size and water depth, a moving shark would
undoubtedly be detected more easily than a still replica. The
difference in detection probability between a shark analogue and
live shark was assumed (but not tested) to be negligible from
manned aircraft (Robbins et al. 2014). However, due to their low
speed and narrow strip width, drones survey areas more inten-
sively, resulting in a shark being captured within the sensor for a
longer period (often .5 s). Movement may therefore be more
important as a distinguishing factor for faunal detection using
aerial surveys than previously assumed, particularly for surveys
using drones.
The applicability of drones for aerial shark surveillance
will depend partly on location- and jurisdiction-specific surveil-
lance requirements. Demand is growing for reliable shark
attack mitigation with minimal impact on marine animals
(Cliff and Dudley 2011; Hazin and Afonso 2014; Meeuwig
and Ferreira 2014).
Conclusions
Our results suggest that drones are a potential platform for non-
invasive shark surveillance, and that further research into refining
operational procedures and incorporating new technologies will
allow for improvements in detection reliability across a broader
range of operating conditions. In time, drones may offer better
detection reliability than manned aircraft, with developments in
real-time object recognition software likely to further improve
detection probabilities. Although adverse environmental condi-
tions impacted sighting rates, these conditions also likely corre-
spond with few-to-no water users, arguably reducingthe need for
shark surveillance (de Freitas 2015). An empirical investigation
to determine the effects of weather variables on the presence of
water users could, in future, better define the environmental
conditions under which reliable sighting rates are imperative.
Under the range of environmental conditions seen during the
present study, shark detection will be optimised: when the water
turbidity state allows vision to a depth of at least 1.5 m; in shallow
water depths (,3.5 m); and when a dedicated observer, rather
than the drone pilot, is responsible for shark detection. This study
has shown that drones can offer an alternative shark detection tool
that could meet increasing demands for bather protection while
minimising harm to marine life.
Conflicts of interest
The authors declare no conflicts of interest.
Acknowledgements
Project funding and support was provided by the New South Wales
Department of Primary Industries (NSW DPI) and associated NSW Shark
Management Strategy. NSW DPI provided scientific (Ref. P01/0059(A) and
Marine Parks (Ref. SA2015/21)) permits. This project would not have been
possible without the dedicated support of our research and compliance teams
that helped with field work. Our independent observer, Mel Buhler, did a
wonderful job, as did Dr Robert Brown, who applied expert opinions and
editing skills to early drafts. Thank you to the anonymous reviewers and
editor for your helpful comments.
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