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Article
Determining Stingray Movement Patterns in a
Wave-Swept Coastal Zone Using a Blimp for
Continuous Aerial Video Surveillance
David Ruiz-García1, 2,*, Kye Adams 2,3, Heidi Brown 2and Andrew R. Davis 2
1
Marine Zoology Unit, Cavanilles Institute of Biodiversity and Evolutionary Biology, Universitat de Val
è
ncia,
46980 València, Spain
2School of Earth, Atmospheric and Life Sciences, University of Wollongong, Northfields Avenue,
Wollongong NSW 2522, Australia; kye.adams@uwa.edu.au (K.A.); heidi@uow.edu.au (H.B.);
adavis@uow.edu.au (A.R.D.)
3Marine Ecology Group–Fisheries Research, University of Western Australia, Perth WA 6009, Australia
*Correspondence: daruiz7@alumni.uv.es
Received: 2 September 2020; Accepted: 24 September 2020; Published: 30 September 2020
Abstract:
Stingrays play a key role in the regulation of nearshore ecosystems. However, their movement
ecology in high-energy surf areas remains largely unknown due to the notorious difficulties in
conducting research in these environments. Using a blimp as an aerial platform for video surveillance,
we overcame some of the limitations of other tracking methods, such as the use of tags and drones.
This novel technology offered near-continuous coverage to characterise the fine-scale movements of
stingrays in a surf area in Kiama, Australia, without any invasive procedures. A total of 98 stingray
tracks were recorded, providing 6 h 27 min of movement paths. The tracking data suggest that
stingrays may use a depth gradient located in the sandflat area of the bay for orientating their
movements and transiting between locations within their home range. Our research also indicates
that stingray behaviour was influenced by diel periods and tidal states. We observed a higher stingray
occurrence during the afternoon, potentially related to foraging and anti-predatory strategies. We also
saw a reduced route fidelity during low tide, when the bathymetric reference was less accessible
due to stranding risk. Considering the increasing threat of anthropogenic development to nearshore
coastal environments, the identification of these patterns can better inform the management and
mitigation of threats.
Keywords:
aerostat; UAV; blimp; spatial ecology; behaviour; batoid; high-energy coastal zone;
Bathytoshia brevicaudata;Bathytoshia lata
1. Introduction
Stingrays (Dasyatidae) play a key role in the regulation of nearshore coastal ecosystems as bioturbators
and mesopredators [
1
,
2
]. They create physical disturbances by digging holes in unconsolidated sediments
to feed on infaunal invertebrates. In turn, they generate a mosaic of microhabitats with a distinct
invertebrate diversity and abundance, acting as ecosystem engineers [
3
,
4
]. Such digging behaviour also
enables the infiltration of oxygen and organic matter into sediments, supporting biogeochemical cycling [
3
].
These epibenthic mesopredators also play an important role by connecting trophic webs across habitats
and controlling prey populations through predation [
1
,
2
,
5
]. However, stingrays represent one of the most
threatened families of elasmobranchs [
6
]. They have low resilience against anthropogenic pressure because
of their life-history traits, including slow growth, late maturity, and low fecundity [
7
]. Habitat destruction
due to coastal development is an increasing threat affecting stingrays by compromising the viability of
Fishes 2020,5, 31; doi:10.3390/fishes5040031 www.mdpi.com/journal/fishes
Fishes 2020,5, 31 2 of 13
coastal ecosystems [
8
]. Therefore, identifying important habitats and habitat use by stingrays is essential
for their management and the mitigation of threats [9–11].
A common approach to identifying animal space usage and habitat requirements is to study
their patterns of movement [
12
,
13
]. Understanding their spatial ecology sheds light on animals’ life
history, behaviour, and the influence of environmental conditions over their use of space [
14
,
15
].
Tracking animal movements in aquatic habitats can be challenging [
16
]. Recent improvements in
acoustic and satellite telemetry have provided new insights into stingray behaviour and habitat use
in coastal areas, including the identification of a variety of environmental factors such as tides and
diel periods as drivers of their behaviour [
9
,
17
–
19
]. However, the fine-scale movement patterns of
stingrays in nearshore surf zones are still poorly understood. This lack of information is partly due
to technological difficulties when conducting research in these areas. Approaches such as acoustic
telemetry are complicated by the high-energy dynamics that characterise surf zones, hampering
acoustic signal detection [
20
,
21
]. In addition, the bottom-dwelling habits of stingrays hinder the
use of GPS loggers, requiring a towed-float GPS tag which is only useful if the studied animals
remain in shallow waters permanently [
22
]. Even when the use of tags is possible for studying
fine-scale movement patterns, they can be prohibitively expensive and require invasive procedures to
capture and tag the animals [
23
]. Furthermore, tagging technologies are unable to provide a complete
understanding of animal behaviour, deducing it from position records which are often affected by the
drift of dead reckoning data [
24
,
25
]. Thus, even though tracking marine animals via tags has been
the norm in the last few years, new techniques are required to gain insight into animal habitat use in
wave-swept coastal areas [26].
Aerial video surveillance is an emerging technique with great potential to provide behavioural
information in high-energy coastal zones [
23
,
27
]. Unlike tagging methods, this non-invasive
technique gathers continuous and direct information about animal locations and interactions [
28
,
29
].
Moreover, aerial video surveillance is relatively cost-effective given that, besides scientific staff, it only
requires a platform to keep a camera suspended in the air [
27
]. Despite the potential environmental
limitations related to light, wind, and water clarity conditions, this technique can provide access
to areas where other tracking methods are unsuitable, even when they are remote or dangerous to
access [
27
,
28
]. The use of this technique for gathering information on megafaunal habitat usage and
spatial ecology in coastal zones promises to contribute significantly to improving the understanding
and management of these ecosystems [29–31].
Recently, unmanned aerial vehicles (UAVs), also known as drones, have been used as a platform
for suspending cameras. The application of drones in marine research constitutes a new approach for
obtaining population estimates and study ecological interactions [
23
,
28
]. However, drones are noisy and
may influence animal behaviour when in close proximity [
32
,
33
]. Another major limitation of drones is
their battery life, which averages 30 min and constrains their observational capabilities, particularly if
continuous surveillance is required [
23
,
34
]. Furthermore, researchers need to simultaneously operate
the drone and monitor the field of view, which requires experience and can induce fatigue [
34
].
Aerostats (balloons or blimps) may constitute a more suitable aerial platform for particular cases by
overcoming some of the limitations of drones [
27
]. Aerostats differ from drones by using helium for
lift, offering a near-continuous coverage of study areas since they are only limited by the battery of the
camera, which can exceed 8 h. In addition, they are silent, reducing potential impacts on wildlife [
27
].
Aerostats have been used for monitoring the occurrence of marine wildlife, including whales [
35
];
dugongs [
36
]; sharks [
37
]; and, complimentarily to the present study, seals, sharks, and stingrays [
27
],
although no movement patterns have been analysed.
This case study aims to determine how stingrays use high-energy surf areas, while testing the
capability of a blimp as an aerial platform for the continuous surveillance of wildlife movement ecology.
Our focus was on determining the effect of diel periods and tidal stages on the occurrence of stingrays
and their patterns of movement in the nearshore zone. We also sought to explore how stingrays behave
when in close proximity to people.
Fishes 2020,5, 31 3 of 13
2. Results
2.1. Stingray Occurence
In total, 16 aerial surveys were completed with a mean flight time of 4 h 16 min
±
15 min and a total
elapsed time of 68 h 32 min. Stingrays, either smooth stingray, Bathytoshia brevicaudata, or black stingray,
Bathytoshia lata, were observed on 98 occasions, from which 6 h 27 min of movement paths were
retrieved (9.6% of the total recorded time). Low numbers of other marine megafauna were also recorded,
including grey nurse sharks, Carcharias taurus, and Australian fur seals,
Arctocephalus pusillus doriferus
,
and these have been reported by Adams et al. [27].
The occurrence of stingrays within the bay was significantly affected by the diel period (
χ2=10.78
;
df =1; p=0.001) but not by the tidal phase (
χ2
=0.03; df =1; p=0.87). The number of stingray
sightings was significantly higher than expected in the afternoon, whereas it was lower than expected
during the midday time period (Figure 1).
Fishes 2020, 5, x FOR PEER REVIEW 3 of 13
2. Results
2.1. Stingray Occurence
In total, 16 aerial surveys were completed with a mean flight time of 4 h 16 min ± 15 min and a
total elapsed time of 68 h 32 min. Stingrays, either smooth stingray, Bathytoshia brevicaudata, or black
stingray, Bathytoshia lata, were observed on 98 occasions, from which 6 h 27 min of movement paths
were retrieved (9.6% of the total recorded time). Low numbers of other marine megafauna were also
recorded, including grey nurse sharks, Carcharias taurus, and Australian fur seals, Arctocephalus
pusillus doriferus, and these have been reported by Adams et al. [27].
The occurrence of stingrays within the bay was significantly affected by the diel period (χ2 =
10.78; df = 1; p = 0.001) but not by the tidal phase (χ2 = 0.03; df = 1; p = 0.87). The number of stingray
sightings was significantly higher than expected in the afternoon, whereas it was lower than expected
during the midday time period (Figure 1).
Figure 1. Comparison between the observed (black bars) and expected (grey bars) number of stingray
sightings within the study area for each diel period and the tidal stage (nt = 98). Significant differences
(p < 0.05) in Chi-Square tests of independence are denoted by *.
2.2. Patterns of Movement
In 85 of the observations (87%), stingrays did not swim close to bathers (>5 m), but on 13
occasions stingrays swam in close proximity to them (<5 m). On five of these occasions (38%),
stingrays showed behaviours that appeared to be influenced by the presence of humans. In order to
classify such behaviour, the movement paths of these five sightings were analysed separately to the
other 93 paths.
2.2.1. Movement Metrics
The mean duration of the tracking period for the 93 movements paths was 3 min 42 s ± 16 s
(range 50 s–14 min 30 s), with an average distance of 144 ± 9 m (range 48–431 m) covered by each
stingray and a mean swimming speed of 0.70 ± 0.02 m/s (range 0.33–1.42 m/s). The average path
length and the duration of the tracking period for each stingray was significantly larger in the midday
time period than in the afternoon (Z = −2.98; p = 0.003 and Z = −3.47; p = 0.001, respectively; Figure 2).
In contrast, the tidal state did not have a significant effect on the path length (Z = −1.12; p = 0.264) or
duration (Z = −1.86; p = 0.062), although we note a trend toward a longer path duration during high
Figure 1.
Comparison between the observed (black bars) and expected (grey bars) number of stingray
sightings within the study area for each diel period and the tidal stage (n
t
=98). Significant differences
(p<0.05) in Chi-Square tests of independence are denoted by *.
2.2. Patterns of Movement
In 85 of the observations (87%), stingrays did not swim close to bathers (>5 m), but on 13 occasions
stingrays swam in close proximity to them (<5 m). On five of these occasions (38%), stingrays showed
behaviours that appeared to be influenced by the presence of humans. In order to classify such
behaviour, the movement paths of these five sightings were analysed separately to the other 93 paths.
2.2.1. Movement Metrics
The mean duration of the tracking period for the 93 movements paths was 3 min 42 s
±
16 s
(range 50 s–14 min 30 s), with an average distance of 144
±
9 m (range 48–431 m) covered by each
stingray and a mean swimming speed of 0.70
±
0.02 m/s (range 0.33–1.42 m/s). The average path length
and the duration of the tracking period for each stingray was significantly larger in the midday time
period than in the afternoon (Z =
−
2.98; p=0.003 and Z =
−
3.47; p=0.001, respectively; Figure 2).
In contrast, the tidal state did not have a significant effect on the path length (Z =
−
1.12; p=0.264) or
duration (Z =
−
1.86; p=0.062), although we note a trend toward a longer path duration during high tide
Fishes 2020,5, 31 4 of 13
(Figure 2). Stingrays also swam more rapidly in the afternoon and at low tide, although in neither case
was this trend statistically significant (Z =
−
1.75, p=0.081 and
−
1.71; p=0.086 respectively; Figure 2).
Fishes 2020, 5, x FOR PEER REVIEW 4 of 13
tide (Figure 2). Stingrays also swam more rapidly in the afternoon and at low tide, although in neither
case was this trend statistically significant (Z = −1.75, p = 0.081 and −1.71; p = 0.086 respectively; Figure 2).
Figure 2. Comparison between the mean (±SE) (a) path length, (b) duration, (c) speed, and (d)
sinuosity for each diel period and tidal state (nt = 93). Significant differences (p < 0.05) in the Mann–
Whitney–Wilcoxon tests are denoted by *.
On average, stingrays showed fairly low sinuosity movement paths across the bay (0.26 ± 0.03),
which were consistent through diel periods (Z = −1.18; p = 0.238) and tidal states (Z = −0.247; p = 0.805)
(Figure 2). However, there was variation in their path sinuosity with the structure of the habitat;
animals showed a significantly more sinuous path at the northern headland than when they swam
in the central area or southern headland (0.34 ± 0.04; 0.26 ± 0.03; 0.24 ± 0.04, respectively) (χ2 = 6.53; df
= 2; p = 0.038). Large quantities of drift algae were often associated with the northern headland.
2.2.2. Route Fidelity
Analysis of the 93 stingray movement paths revealed that these animals were using defined
routes when navigating across the bay. This route, as shown by the maximum stingray density
(Figure 3a), follows the edge of an abrupt change in bathymetry—a sandbar drop-off. A more detailed
examination of each movement path revealed that in 87% of the sightings (81/93) the stingrays
navigated unidirectionally either northwards or southwards following this defined route. Only in
13% of the occasions (12/93) the stingrays arrived at the bay through either of the rocky headlands,
circled around, and left through this same area, without entering the central, sandflat area. The
subdivision of path density by tidal state revealed that the stingray activity was more dispersed and
the route fidelity was lower at low tide relative to high tide (Figure 3b,c). In contrast, the route fidelity
appeared unaffected by the time of day (Figure 3d,e).
Figure 2.
Comparison between the mean (
±
SE) (
a
) path length, (
b
) duration, (
c
) speed,
and (
d
) sinuosity for each diel period and tidal state (n
t
=93). Significant differences (p<0.05)
in the Mann–Whitney–Wilcoxon tests are denoted by *.
On average, stingrays showed fairly low sinuosity movement paths across the bay (0.26
±
0.03),
which were consistent through diel periods (Z =
−
1.18; p=0.238) and tidal states (Z =
−
0.247;
p=0.805
)
(Figure 2). However, there was variation in their path sinuosity with the structure of the habitat;
animals showed a significantly more sinuous path at the northern headland than when they swam
in the central area or southern headland (0.34
±
0.04; 0.26
±
0.03; 0.24
±
0.04, respectively) (
χ2
=6.53;
df =2; p=0.038). Large quantities of drift algae were often associated with the northern headland.
2.2.2. Route Fidelity
Analysis of the 93 stingray movement paths revealed that these animals were using defined
routes when navigating across the bay. This route, as shown by the maximum stingray density
(Figure 3a), follows the edge of an abrupt change in bathymetry—a sandbar drop-off. A more detailed
examination of each movement path revealed that in 87% of the sightings (81/93) the stingrays navigated
unidirectionally either northwards or southwards following this defined route. Only in 13% of the
occasions (12/93) the stingrays arrived at the bay through either of the rocky headlands, circled around,
and left through this same area, without entering the central, sandflat area. The subdivision of path
density by tidal state revealed that the stingray activity was more dispersed and the route fidelity was
Fishes 2020,5, 31 5 of 13
lower at low tide relative to high tide (Figure 3b,c). In contrast, the route fidelity appeared unaffected
by the time of day (Figure 3d,e).
Fishes 2020, 5, x FOR PEER REVIEW 5 of 13
Figure 3. Density heat maps drawn from (a) the complete pool of stingray movement paths (n = 93);
(b) from those paths occurring during low tide (n = 34), (c) high tide (n = 59), (d) midday (n = 32), and
(e) afternoon (n = 61).
2.3. Human Influence on Stingray Movement Paths
The co-occurrence of stingrays and humans in close proximity (<5 m) was recorded a total of 13
times. On five of these occasions (38%), it occurred when stingrays swam into the shallower region
of the central area—the designated “bather area”, which is delineated by flags and patrolled by
lifeguards. On each occasion, these stingrays conducted highly sinuous and long movement paths at
a slow pace, a strikingly different pattern to their normal behaviour (Figure 4). On the remaining
eight occasions (62%) in which stingrays were in close proximity, the swimmers were in deeper
regions outside the bather area. Hence, the bathers were in lower abundance and the stingrays could
swim below them. On these occasions, we did not observe the same behavioural responses as were
observed in shallow water.
Figure 4. Comparison between the mean (±SE) (a) path length, (b) duration, (c) speed, and (d)
sinuosity for stingray movement paths non-influenced (n = 93) and those influenced by humans (n =
5). Significant differences (p < 0.05) in Mann–Whitney–Wilcoxon tests are denoted by *.
3. Discussion
This is the first time that an aerostat, particularly a blimp, has been used as an aerial platform to
study the fine-scale movement patterns of marine wildlife. Although there are some sampling
limitations (e.g., dependency on light, weather, and water conditions), this novel approach enabled
the continuous aerial video surveillance of a high-energy surf zone. The results of these prolonged
Figure 3.
Density heat maps drawn from (
a
) the complete pool of stingray movement paths (n=93);
(
b
) from those paths occurring during low tide (n=34), (
c
) high tide (n=59), (
d
) midday (n=32),
and (e) afternoon (n=61).
2.3. Human Influence on Stingray Movement Paths
The co-occurrence of stingrays and humans in close proximity (<5 m) was recorded a total of
13 times. On five of these occasions (38%), it occurred when stingrays swam into the shallower
region of the central area—the designated “bather area”, which is delineated by flags and patrolled by
lifeguards. On each occasion, these stingrays conducted highly sinuous and long movement paths
at a slow pace, a strikingly different pattern to their normal behaviour (Figure 4). On the remaining
eight occasions (62%) in which stingrays were in close proximity, the swimmers were in deeper regions
outside the bather area. Hence, the bathers were in lower abundance and the stingrays could swim
below them. On these occasions, we did not observe the same behavioural responses as were observed
in shallow water.
Fishes 2020, 5, x FOR PEER REVIEW 5 of 13
Figure 3. Density heat maps drawn from (a) the complete pool of stingray movement paths (n = 93);
(b) from those paths occurring during low tide (n = 34), (c) high tide (n = 59), (d) midday (n = 32), and
(e) afternoon (n= 61).
2.3. Human Influence on Stingray Movement Paths
The co-occurrence of stingrays and humans in close proximity (<5 m) was recorded a total of 13
times. On five of these occasions (38%), it occurred when stingrays swam into the shallower region
of the central area—the designated “bather area”, which is delineated by flags and patrolled by
lifeguards. On each occasion, these stingrays conducted highly sinuous and long movement paths at
a slow pace, a strikingly different pattern to their normal behaviour (Figure 4). On the remaining
eight occasions (62%) in which stingrays were in close proximity, the swimmers were in deeper
regions outside the bather area. Hence, the bathers were in lower abundance and the stingrays could
swim below them. On these occasions, we did not observe the same behavioural responses as were
observed in shallow water.
Figure 4. Comparison between the mean (±SE) (a) path length, (b) duration, (c) speed, and (d)
sinuosity for stingray movement paths non-influenced (n = 93) and those influenced by humans (n =
5). Significant differences (p < 0.05) in Mann–Whitney–Wilcoxon tests are denoted by *.
3. Discussion
This is the first time that an aerostat, particularly a blimp, has been used as an aerial platform to
study the fine-scale movement patterns of marine wildlife. Although there are some sampling
limitations (e.g., dependency on light, weather, and water conditions), this novel approach enabled
the continuous aerial video surveillance of a high-energy surf zone. The results of these prolonged
(a) (b) (c) (d)
Figure 4.
Comparison between the mean (
±
SE) (
a
) path length, (
b
) duration, (
c
) speed, and (
d
) sinuosity
for stingray movement paths non-influenced (n=93) and those influenced by humans (n=5).
Significant differences (p<0.05) in Mann–Whitney–Wilcoxon tests are denoted by *.
Fishes 2020,5, 31 6 of 13
3. Discussion
This is the first time that an aerostat, particularly a blimp, has been used as an aerial platform
to study the fine-scale movement patterns of marine wildlife. Although there are some sampling
limitations (e.g., dependency on light, weather, and water conditions), this novel approach enabled
the continuous aerial video surveillance of a high-energy surf zone. The results of these prolonged
surveys demonstrated that stingrays generally reached the studied bay through the lateral rocky
headlands and exhibited an oriented pattern of displacement parallel to shore, but never exhibited
stationary behaviours. Stingrays appeared to be following a 2 m depth contour when navigating
across the bay from either of the rocky headlands. Acoustic telemetry studies of the southern stingray,
Hypanus americanus, in the Mesoamerican Barrier Reef System suggested that these animals may also
use depth contours for orientation [
38
]. Therefore, the use of spatially structured depth gradients may
be a common strategy in stingrays to orientate their movements. Straight movement paths increase
efficiency [
39
] and likely reflect directed movement towards certain locations, such as sheltering or
feeding areas [38,40].
Oriented movements frequently involve experiential learning and memory, potentially increasing
foraging efficiency [
38
,
41
] and reducing predation risk [
42
,
43
]. The stingrays in our study generally
exhibited straight movements even at fine spatial scales, except when swimming in the northern
headland, where the sinuosity increased significantly. Sinuous movement has been related to searching
behaviours in other stingray species and demersal elasmobranchs—with animals seeking to locate
resource patches, including foraging opportunities, shelter, or potential mates [
19
,
44
–
46
]. Our findings
are consistent with stingrays using the central, sandflat area of the bay for transiting between locations
in their home range and the northern headland for exploring resource patches.
Despite the general patterns we observed, some variation in habitat usage was detected during
the diel periods and tidal states. The occurrence of stingrays within the bay was higher during
the afternoon, when the light intensity began to decrease. In addition, the paths undertaken by
stingrays during this period were faster and shorter, exhibiting a high route fidelity by following
the edge of the depth contour. Increases in activity with reduced light intensity, especially at night,
have been reported in other stingray species and have been attributed to the animals responding
to local environmental factors, such as predation risk and prey availability [
18
,
20
,
38
,
47
,
48
]. Indeed,
a higher use of shallower areas during the night has been observed for one of our target species,
Bathytoshia brevicaudata, using pop-up satellite archival tags [
17
]. The authors suggested that this
species may conduct diel vertical migrations from deep (>100 m deep) to shallow (<1 m deep) waters
as a strategy to increase their feeding opportunities, remarking that it is unlikely that the predation
risk had an influence on such movement patterns [
17
]. However, recent studies in southern Australia
suggested that the presence of potential predators such as white sharks, Carcharodon carcharias, triggers
a change in habitat use and the rate of movement of B. brevicaudata [
49
]. Clearly, the inability to
determine nocturnal patterns of movement is a limitation of aerial video surveillance, but our results
may indicate that the increased occurrence of stingrays and the shorter and faster movement paths
that they undertook may be related to an increase in activity with decreasing light intensity, using the
bathymetric gradient to quickly transit along their home range.
Variation in route fidelity was also observed in relation to tidal periods, with animals exhibiting
lower route fidelity and slower rates of transit during low tide. We contend that, during low tide,
the bathymetric reference was closer to shore and its use increased the risk of stranding for these large
stingray species, likely triggering the observed behavioural change. Prior studies on other stingray
species also documented that when water levels drop, individuals use deeper areas to transit and
avoid stranding [
9
,
18
,
19
]. Previous research also suggested that some stingray species modulate their
feeding and shelter behaviour in relation to tides, using the increased available area during high tide
to forage while seeking refugia during low tide [
18
,
20
,
50
]. However, our results do not indicate that
stingrays modulate their feeding or sheltering behaviour in the study area with tides.
Fishes 2020,5, 31 7 of 13
A marked change in behaviour was observed when stingrays entered the bathing area and swam
in close proximity to humans. This included long and highly sinuous paths in which the stingrays
swam at a slow speed. The animals frequently made loops around humans; swimming next to
them
(≤1 m distance)
for an extended period (
≥
1 min in all cases). This pattern was observed in five
independent sightings and occurred on four separate days; it was noteworthy that on one occasion two
stingrays interacted simultaneously with humans within the bather area. Prior research has reported
that these interactions are frequent in areas nearby fish-cleaning facilities [
51
,
52
]. Such facilities are built
at many Australian harbours and beaches to support recreational fishing, resulting in fish scraps being
thrown into the water on a daily basis, providing food to stingrays and other scavengers, potentially
altering their normal foraging behaviours [
52
,
53
]. There is evidence that both Bathytoshia brevicaudata
and Bathytoshia lata frequent fish-cleaning stations, exhibiting grouping behaviours and altered patterns
of movement, including a high dependence on fishers and human tolerance [
51
,
52
]. A boat ramp in
the nearby Kiama Harbour includes a fish-cleaning station approximately 1 km away from our study
location, and large stingrays have been observed scavenging on fish scraps there [
54
]. Considering
that Bathytoshia brevicaudata have home ranges of approximately 25 km
2
[
17
], the stingrays that inhabit
Kiama Harbour boat ramp may be the same stingrays that are seen in Kiama Surf Beach. It remains
unclear whether human supplementation of food or animals being simply inquisitive accounts for the
unusual behaviour of some animals in close proximity to humans.
The present study demonstrated that using a blimp as an aerial platform for continuous video
surveillance constitutes a powerful approach for the study of fine-scale patterns of movement.
Aerial surveillance is also an excellent platform to examine the behaviour of marine wildlife without
requiring invasive procedures and minimising sampling disturbance. Moreover, this novel technology
enabled easy access to high-energy environments where other techniques may be unsuitable, enhancing
the value of this emerging tool for the research discipline of marine spatial ecology. We also provided
new insights into stingray behaviour in surf areas, which likely apply broadly to other beaches.
We provide support for previous research that has documented the influence of diel periods and tidal
states in stingray behaviour [
9
,
18
,
19
,
48
]. Importantly, our research suggests that stingrays may use
near-shore depth contours to orientate their movements when transiting through their home range.
Given the increasing direct and indirect anthropogenic pressure upon coastal habitats [
8
], improving our
understanding of habitat usage for these megafaunal ecosystem engineers is an important outcome.
Looking to the future, the application of machine learning techniques to aerial surveillance promises to
improve our understanding of megafauna in wave-swept environments even further [55].
4. Materials and Methods
4.1. Study Area
Aerial surveys were conducted during December 2017 and January 2018 at Surf Beach in Kiama,
located in New South Wales, Australia (34
◦
40
0
S; 150
◦
51
0
E; Figure 5). This coastal embayment is
enclosed by two rocky headlands. The morphology of this beach accords with a “low tide terrace” [
56
],
characterised by a moderately steep drop-offjoined to a shallow terrace composed of fine and coarse
sand. At this site, the terrace stretches out, reaching a maximum depth of 1.5 m at approximately 30 m
offshore during low tide and 50 m during high tide, where a 2 m-deep “drop-off” occurs. The depth
continues to increase progressively until it reaches 7 m deep 300 m offshore. The study area experiences
a semidiurnal tide, with a maximum tidal range of 2 m and a wave height ranging from 0.5 to 1.5 m
during the surveys. Topographical rips occur adjacent to the lateral rocky headlands, fed by an inshore
current whose direction varies with the wind and swell direction. Both topographic rips scour sand
and generate a depressed channel approximately 2 m deep, extending offshore from the headlands.
Since summer winds are primarily from the northeast, drift algae tend to accumulate in the north end
of the bay. As a result of the hydrodynamic features of the embayment, it can be divided into three
sub-habitats: (1) northern headland, characterised by a rocky substratum, low wave action, and high
Fishes 2020,5, 31 8 of 13
rate of drift algal accumulation; (2) southern headland, also composed of rocky substratum, but with
the highest wave action of the bay and no accumulation of drift algae; and (3) central area, composed
of fine and coarse sediment (sandflat) and characterised by a high wave action and, as mentioned
previously, a sharply demarcated depth contour. Wave breaking occurred once the waves hit the
sandbar “drop-off”. The foam generated by waves occasionally reduced the visibility in the shallowest
areas (<1.5 m deep), especially near the southern headland, but rarely affected the visibility when
tracking stingrays, which occurred in deeper areas beyond the sharp depth contour.
Fishes 2020, 5, x FOR PEER REVIEW 8 of 13
Figure 5. Geographical location of Kiama Surf Beach in Australia and map of the study area showing
the three sub-habitats (NH: northern headland; CA: central area; SH: southern headland) and the
sharp depth contour located approximately 30 m offshore during low tide and 50 m during high tide
(black line).
4.2. Data Collection
Daily surveys were conducted between 11 am and 5 pm, except when winds exceeded 40 km/h.
A helium-filled blimp of 5 m long and 1.8 m in diameter (Figure 6a), tethered onshore 70 m above
sea-level, was used as an aerial platform for conducting aerial video surveillance. The camera
attached beneath the blimp was a Tarot Peeper with a 10× optical zoom, capable of streaming and
recording in 1080 p. The camera was equipped with a self-stabilising 3-axis gimbal with 360-degree
rotation, which automatically sustained the field of view of interest and compensated the movement
of the blimp driven by winds. However, the orientation and zoom could be manually controlled by
an operator, who constantly monitored the streaming footage to spot marine megafauna, covering a
total area of approximately 18,500 m2 (see Supplementary Videos S1–S3 to observe the conditions and
setup in action).
During the surveys, video footage from two large stingray species, each up to 2 m in disc width,
was taken: Bathytoshia brevicaudata and Bathytoshia lata (Figure 6b,c) [57]. Although it was not possible
to distinguish between these species from the video recordings, their large size and colour rendered
them a good target for aerial video surveillance. Visual analysis of the stingray footage allowed the
manual tracing of their movement paths. The oblique view from the camera was transformed into a
perpendicular perspective by using 16 equidistant reference points to precisely determine the
location of each stingray in the video frames of interest. The position of each stingray was recorded
every 10 s using ArcGIS Pro 2.0. (ESRI, Redlands, California, USA). The selection of 10 s intervals
aimed to optimise the resolution of the movement paths, while enabling the analysis of a large
number of videos. The complete path of each animal was recorded in the metric X and Y coordinates
using the WGS 1984 Web Mercator coordinate system in ArcGIS Pro.
4.3. Stingray Occurrence
The occurrence of stingrays was measured as the number of independent stingray sightings
within the bay. The influence of the tidal state and diel period on stingray occurrence was assessed
by conducting Chi-Square tests of independence. Analyses were performed in R 4.0.2 [58] using the
Figure 5.
Geographical location of Kiama Surf Beach in Australia and map of the study area showing
the three sub-habitats (NH: northern headland; CA: central area; SH: southern headland) and the
sharp depth contour located approximately 30 m offshore during low tide and 50 m during high tide
(black line).
4.2. Data Collection
Daily surveys were conducted between 11 am and 5 pm, except when winds exceeded 40 km/h.
A helium-filled blimp of 5 m long and 1.8 m in diameter (Figure 6a), tethered onshore 70 m above
sea-level, was used as an aerial platform for conducting aerial video surveillance. The camera attached
beneath the blimp was a Tarot Peeper with a 10
×
optical zoom, capable of streaming and recording
in 1080 p. The camera was equipped with a self-stabilising 3-axis gimbal with 360-degree rotation,
which automatically sustained the field of view of interest and compensated the movement of the
blimp driven by winds. However, the orientation and zoom could be manually controlled by an
operator, who constantly monitored the streaming footage to spot marine megafauna, covering a total
area of approximately 18,500 m
2
(see Supplementary Videos S1–S3 to observe the conditions and setup
in action).
Fishes 2020,5, 31 9 of 13
Fishes 2020, 5, x FOR PEER REVIEW 9 of 13
chisq.test() specified by Mangiafico [59]. The tidal cycle was divided into two categories, given the
maximum tidal range of 2 m: high tide (>1 m) and low tide (<1 m). Tidal information was obtained
from the Australian Bureau of Meteorology [60] (see Supplementary Figure S1 for information about
the tidal elevation over the entire study period).
The diel periods covered in this research were constrained between 11 a.m. and 5 p.m. due to
the sharing of airspace with other stakeholders (daily flights of a shark patrol helicopter) and the
work hours of lifeguards. Within this time frame, two diel periods were considered. We divided the
survey period into two equal bouts of 3 h: (i) midday, from 11 a.m. to 2 p.m., producing a range of
~1.5 h from the meridian position of the sun, which occurred on average at 12.11 p.m.; and (ii)
afternoon, from 2 p.m. to 5 p.m., ~1.5 h after the meridian position of the sun and 2 h before sunset,
which occurred on average at 07.03 p.m. during the surveyed period. The diel periods were set based
on the time of sun transit and the sunset times published by the Geoscience Department of the
Australian Government [61].
The interaction between the tidal stages and diel periods could not be tested due to constraints
in the surveying period. The number of sightings during the “low tide-afternoon” and “high tide-
midday” categories were too low to conduct meaningful analyses.
Figure 6. (a) Blimp used as aerial platform for conducting video surveillance; (b) Example of image
taken from the blimp in which it is possible to determine the position of a stingray (▼) in reference to
the broad view of the bay; (c) zoomed picture of a stingray (▼) swimming in close proximity to a
swimmer and a school of Australian salmon (Arripis trutta) (■).
4.4. Movement Metrics
The stingray trajectories were quantitively described using the total track duration, total distance
covered, mean speed, and sinuosity. Sinuosity was calculated as the ratio of the Euclidian distance
(length of a straight line between the end points of the curve) and the curvilinear length (actual path
length) minus one, ranging from 0 (straight line) to 1 (closed loop). The effect of the tide, diel period,
and presence of humans on these parameters was assessed by a non-parametric comparison of
Figure 6.
(
a
) Blimp used as aerial platform for conducting video surveillance; (
b
) Example of image
taken from the blimp in which it is possible to determine the position of a stingray (
H
) in reference
to the broad view of the bay; (
c
) zoomed picture of a stingray (
H
) swimming in close proximity to a
swimmer and a school of Australian salmon (Arripis trutta) ().
During the surveys, video footage from two large stingray species, each up to 2 m in disc width,
was taken: Bathytoshia brevicaudata and Bathytoshia lata (Figure 6b,c) [
57
]. Although it was not possible
to distinguish between these species from the video recordings, their large size and colour rendered
them a good target for aerial video surveillance. Visual analysis of the stingray footage allowed the
manual tracing of their movement paths. The oblique view from the camera was transformed into a
perpendicular perspective by using 16 equidistant reference points to precisely determine the location
of each stingray in the video frames of interest. The position of each stingray was recorded every
10 s using ArcGIS Pro 2.0. (ESRI, Redlands, California, USA). The selection of 10 s intervals aimed
to optimise the resolution of the movement paths, while enabling the analysis of a large number of
videos. The complete path of each animal was recorded in the metric X and Y coordinates using the
WGS 1984 Web Mercator coordinate system in ArcGIS Pro.
4.3. Stingray Occurrence
The occurrence of stingrays was measured as the number of independent stingray sightings
within the bay. The influence of the tidal state and diel period on stingray occurrence was assessed
by conducting Chi-Square tests of independence. Analyses were performed in R 4.0.2 [
58
] using the
chisq.test() specified by Mangiafico [
59
]. The tidal cycle was divided into two categories, given the
maximum tidal range of 2 m: high tide (>1 m) and low tide (<1 m). Tidal information was obtained
from the Australian Bureau of Meteorology [
60
] (see Supplementary Figure S1 for information about
the tidal elevation over the entire study period).
Fishes 2020,5, 31 10 of 13
The diel periods covered in this research were constrained between 11 a.m. and 5 p.m. due to the
sharing of airspace with other stakeholders (daily flights of a shark patrol helicopter) and the work
hours of lifeguards. Within this time frame, two diel periods were considered. We divided the survey
period into two equal bouts of 3 h: (i) midday, from 11 a.m. to 2 p.m., producing a range of ~1.5 h from
the meridian position of the sun, which occurred on average at 12.11 p.m.; and (ii) afternoon, from 2 p.m.
to 5 p.m., ~1.5 h after the meridian position of the sun and 2 h before sunset, which occurred on average
at 07.03 p.m. during the surveyed period. The diel periods were set based on the time of sun transit
and the sunset times published by the Geoscience Department of the Australian Government [61].
The interaction between the tidal stages and diel periods could not be tested due to constraints in
the surveying period. The number of sightings during the “low tide-afternoon” and “high tide-midday”
categories were too low to conduct meaningful analyses.
4.4. Movement Metrics
The stingray trajectories were quantitively described using the total track duration, total distance
covered, mean speed, and sinuosity. Sinuosity was calculated as the ratio of the Euclidian distance (length
of a straight line between the end points of the curve) and the curvilinear length (actual path length)
minus one, ranging from 0 (straight line) to 1 (closed loop). The effect of the tide, diel period, and presence
of humans on these parameters was assessed by a non-parametric comparison of means. Analyses
were performed in R using the wilcox.test() specified by Mangiafico [
59
]. In addition, each section of
the movement paths was categorised according to the region of the beach
(NH, SH, and CA)
in which
it occurred. The mean sinuosity of the stingray paths in each of the three regions was compared in
order to examine differences in stingray behaviour relating to the characteristics of the subhabitat above
which they are swimming. The comparison of means was conducted using the kruskal.test() following
Mangiafico [59].
4.5. Route Fidelity
Route fidelity was assessed through the examination of density maps created with the tool Line
Density from ArcGIS Pro. These maps differentiate between heavily used and less-frequented areas by
calculating a density index. This index is calculated as the linear distance that stingrays cover within
each square meter of the study area normalised to a range from 0 to 1. In order to examine differences
in habitat usage between diel periods and tidal states, a density map for each of these categories was
also created.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2410-3888/5/4/31/s1:
Video S1: taken on 24 January 2018, during high tide and in the midday period; example of a stingray reaching
Kiama Surf Beach through the southern rocky headland and exhibiting an oriented pattern of displacement
parallel to shore. Video S2: taken on 25 January 2018, during high tide and in the midday period; example of a
stingray reaching the bay through the northern headland and crossing the sandflat area exhibiting the described
general pattern. A school of Australian salmon (Arripis trutta) can be observed in the middle of the bay. Video S3:
Two stingrays swimming in close proximity to humans and exhibiting a strikingly different pattern to their normal
behaviour during low tide and in the afternoon on 12 January 2018. These movement paths were analysed
separately in the group “human-influenced movement paths”. Figure S1: Tidal elevation (m) obtained from the
Australian Bureau of Meteorology [
60
]. The timing of the lowest and highest peaks of tide are indicated in the
figure, together with the exact tidal elevation occurring on such peaks.
Author Contributions:
Conceptualisation, D.R.-G. and K.A.; Formal analysis, D.R.-G., K.A. and H.B.;
Funding acquisition, K.A. and A.R.D.; Methodology, D.R.-G., K.A. and H.B.; Project administration, A.R.D.;
Supervision, A.R.D.; Writing—original draft, D.R.-G.; Writing—review and editing, D.R.-G., K.A., H.B. and A.R.D.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Centre for Sustainable Ecosystem Solutions and the Global Challenges
Program at the University of Wollongong Australia, the NSW Department of Primary Industries (DPI Shark
Observation Tower Program and Shark Management Strategy Grants Program), Kiama Municipal Council,
the Save Our Seas Foundation, and the Australian Government Research Training Program Scholarship.
Acknowledgments:
We thank Allison Broad, Doug Reeves, and Lifeguard Supervisor Andrew Mole for their
support and assistance with the project and Pablo García Salinas for figure editing.
Fishes 2020,5, 31 11 of 13
Conflicts of Interest: The authors declare no conflict of interest.
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