Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2016), 27(1), 83–92. doi:10.1093/beheco/arv125
Original Article
The swaying behavior of Extatosoma tiaratum:
motion camouflage in a stick insect?
Xue Bian,a Mark A. Elgar,a and Richard A. Petersb
aDepartment of Zoology, The University of Melbourne, Parkville, Victoria 3000, Australia and bAnimal
Behaviour Group, Department of Ecology, Environment & Evolution, La Trobe University, Kingsbury
Drive, Bundoora, Victoria 3086, Australia
Received 15 December 2014; revised 12 June 2015; accepted 10 July 2015; Advance Access publication 5 August 2015.
Several species of stick insects sway from side to side when blown by wind. Although anecdotal evidence suggests this is a camouflage strategy to resemble wind-blown vegetation, this behavior has never been experimentally investigated. We evaluated the
responses of female Macleay’s Spectre (Extatosoma tiaratum) to wind cues and quantified the degree of conspicuousness of the
insect against a background of natural wind-blown plants. Wind alone is sufficient to initiate but not maintain swaying behavior. Sway
frequency declined over time in all trials, but the number of sways was significantly higher in variable wind conditions compared
with constant wind conditions. This suggests the insects pay attention to environmental cues and adjust their behavior accordingly.
Consistent with this view, in trials involving plants in which the insects did not sway, plant motion was significantly stronger than at
times when insects were observed to sway. The movement of insects at these times was indeed consistent in the frequency domain
with the movement of wind-blown plants. This study provides evidence that the swaying movement of E. tiaratum is quantitatively
similar to that of wind-blown plants. Further studies will be required to identify functional benefits of this behavior and whether it represents a form of motion crypsis, motion masquerade, or a combination of the two.
Key words: 3D reconstructions, camouflage, motion analysis, motion crypsis, motion masquerade, wind cues.
INTRODUCTION
Camouflage is a striking example of Darwin’s theory of evolution
by natural selection. Various forms of camouflage have evolved
in diverse species in response to selection pressure from predators
(Stevens and Merilaita 2008). The most familiar examples are of
visual camouflage, particularly those in which the animal’s appearance prevents detection by the visual system of a searching predator
(Stevens and Merilaita 2008). This is typically achieved by matching body coloration and/or patterns with the background against
which it is seen, allowing the organism to remain undetected (Cott
1957; Norris and Lowe 1964; Edmunds 1974; Endler 1978, 1984;
Edmunds 1990; Guilford 1992). In addition to morphological adaptations necessary to avoid detection, the success of background
matching inevitably depends on the selection of appropriate background by the animal (Endler 1981). Furthermore, incorrect alignment of the body can reduce effectiveness of camouflage patterns
(Webster et al. 2009). Therefore, crypsis must be mediated by behavior, and indeed, animals such as the cryptic moth species Jankowskia
fuscaria use visual cues concerning the tree trunks on which they rest
to select appropriate resting orientations (Kang et al. 2013).
Address correspondence to R.A. Peters. E-mail: [email protected].
© The Author 2015. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
permissions, please e-mail: [email protected]
An alternative camouflage strategy is to ensure predators misclassify them as nonprey. This is achieved with morphological adaptations to resemble inedible objects in the general environment
(Skelhorn, Rowland, et al. 2010). Prey species that benefit from masquerading as inedible objects do not seek to avoid detection; rather,
their objective is to hinder recognition mechanisms that cause the
predator to refrain from attacking the masquerader postdetection
(Edmunds 1974; Skelhorn and Ruxton 2010). Masquerade as an
adaptive strategy has been proposed in a diverse selection of species, from spiders that resemble bird droppings (Ornithoscatoides decipiens; Liu et al. 2014), seadragons that look like seaweed (Phycodurus
eques; Cott 1940) to caterpillars that resemble twigs and branches
(Opisthograptis luteolata and Selenia dentaria; Skelhorn, Rowland, Speed,
et al. 2010). As outlined by Skelhorn, Rowland, Speed, et al. (2010),
and in contrast to studies of crypsis, demonstrating masquerade
is a challenge, as it requires evidence that the predator has indeed
detected the potential prey but subsequently ignored it.
A common feature of the aforementioned examples of camouflage
is that efficacy relies on the absence of movement (Poulton 1890; Cott
1957; Heatwole 1968; Zhang and Richardson 2007). Lower levels of
activity of cryptic prey decrease the probability of detection (Endler
1978; Merilaita and Lind 2005; Ioannou and Krause 2009). “Freeze”
behavior in the presence of a predator is also observed in diverse prey
Behavioral Ecology
84
species, from hermit crabs to deer (Broom and Ruxton 2005; Eilam
2005). Cryptic individuals even keep still in the presence of dominant conspecifics, thereby avoiding detection (Takahashi et al. 2001).
Clearly, as motion enhances figure-ground separation for a potential
predator facilitating detection (Regan and Beverly 1984), remaining
immobile is an effective strategy to match a stationary background.
But what if the background is moving? Fleishman (1985) showed
that the peculiar movement of the vine snake Oxybelis aeneus, which
moves forward using “rhythmic pendulous movement” (Kennedy
1965, p. 136) when the wind is blowing (Gans 1978), is triggered by
visual cues of moving vegetation. Indeed, other animals that inhabit
dense vegetation, such as the lizard Polychrus gutturosus and lizards in
the genus Chamaeleo also superimpose a rocking movement on their
forward locomotion (Gans 1967). Although most of these animals use
visual detection of moving plants as a primary cue to stimulate rocking
or swaying movements, species of stick insects and leaf insects generate similar swaying behavior in response to wind or slight disturbance
(Robinson 1966, 1969). For example, Bässler and Pflüger (1979) noted
that Macleay’s Spectre Extatosoma tiaratum began to sway parallel to their
transverse axis after a slight disturbance. Unlike the other examples
described above, however, this behavior is performed in the absence of
forward locomotion. Although it is intriguing to consider the possibility
that such behavior represents a form of camouflage (Tinbergen 1963;
Bässler and Pflüger 1979), the rocking and swaying motion of animals
and plants have not been quantified and compared in detail.
If the swaying behavior of animals is a form of camouflage, then
it must necessarily generate movements consistent with those of surrounding vegetation. To our knowledge, only Fleishman (1985, 1986)
has quantified and compared animal and plant movements in this way,
and this was limited to a single animal. We investigate this behavior in
quantitative detail using the stick insect E. tiaratum. This phytophagous,
sexually dimorphic phasmatid is found in the rainforests of tropical
and subtropical Queensland and northern New South Wales, Australia
(Gurney 1947; Brock and Hasenpusch 2009). The adult female (~12 cm
long) is relatively thick, covered with cuticular spines, and typically hangs
inverted among the foliage with her procryptic abdomen curled over
her back (Figure 1a). The smaller (~8 cm long) male has large mottled
wings and relatively longer antennae (Key 1991). The eggs of E. tiaratum
are taken into the nests of Leptomyrmex ants (also distributed along the
eastern seaboard of Australia), and the coloration, shape, and behavior
of first-instar nymphs bear a close resemblance to these ants (Key 1991).
Females may live up to 18 months after maturation, whereas males live
3–6 months after maturation (Schneider and Elgar 2010). The primary
predators of adult phasmatids are birds, which search forest canopies
for insects hanging in the trees (Readshaw 1965).
Our first objective in the present study was to confirm that wind
is a sufficient cue to stimulate swaying behavior, after which we
examined how insect swaying behavior is influenced by variation
in wind. We subsequently explored the behavior in more detail
through quantitative measurements of both insect and plant swaying movements under the same natural conditions. In this study, we
combined simple observational measures of behavior with quantitative analyses of swaying from 3D reconstructions of movement.
MATERIALS AND METHODS
Study species
Subjects were drawn from a stock population of female E. tiaratum
maintained in large habitat screen cages (46 × 46 × 91 cm3) under
natural light and at a temperature of 20–25 °C. Individuals were
fed with fresh leaves from locally sourced trees (Eucalypt sp.) and
sprayed with water regularly.
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Figure 1
(a) Female Australian Macleay’s Spectre or spiny stick insect Extatosoma tiaratum hanging upside down from the branch of a Eucalyptus sp. plant. (b) Filming set
up to reconstruct signals in 3D, which allows for subsequent consideration of movements from different viewing positions (c).
Bian et al. • Motion camouflage in a stick insect
85
Experimental manipulations
selected from the stock population and gently placed on a length
of wooden doweling (9-mm diameter) suspended across a hollow
frame (20 × 30 × 26 cm3) and left to acclimate for 3 min. Using a
household pedestal fan (Sunbeam FA8900, Sunbeam Australia)
positioned at the side of the insect (Figure 1b), all insects were
Wind versus no wind
Our first experiment was designed to test the prediction that
E. tiaratum sway in response to wind. Thirteen adult (average
weight 14.25 g) and 8 subadult (average weight 6.36 g) females were
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Figure 2
Representative data from 1 insect during the wind-present and wind-absent trials. (a) Data showing the position of the tip of the abdomen in 3D space for
wind-present (black circles) and wind-absent (gray circles) trials, and (b) the corresponding 2D sweep areas from different viewing positions. (c) Displacement
profile of the abdomen tip over time showing the change in position relative to the position in frame 1. (d) Power spectral density of insect swaying movement.
Each displacement profile was split into 8 partially overlapping epochs (gray lines) and averaged (black line). (e) Log–log plot of frequency and amplitude for
a representative wind-present (black lines) and wind-absent trial (dark gray line).
Behavioral Ecology
86
in 3D space of the 60 points, are then used to calculate a series
of coefficients that specifies how points in space are projected onto
a 2D image plane. Footage of insect movement from each camera was subsequently read into Matlab, and the position of the tip
of the lower abdomen was then located in every frame from both
camera views. The position of the tip could then be reconstructed
in 3D using the x–y coordinate data from both cameras along with
the DLT calibration coefficients. The frame rate for both cameras
was 50 frames/s.
We first measured the volume of 3D space swept by the tip of
the abdomen of each insect (Figure 2a) by calculating the 3D convex hull of position data in Matlab. Furthermore, as perceived
movement depends on viewing position, we also calculated sweep
area (2D convex hull) as if viewed from side on, above, and front
on to the insect (Figure 1c). Calculation of sweep area from different viewing positions necessarily reduces the data to 2 coordinates
instead of 3 (Figure 2b). We compared the 3D sweep area of insect
movements in wind and no wind conditions with a linear mixed
effects model in the R statistical environment (R Development
exposed to a wind treatment (fan on with a wind speed measured
at the insect of 2 m/s) and no wind treatment (fan off) for 2 min
in a randomly allocated order, with a 3-min break in between the
2 treatments. The resting time before and between treatments
ensured that insects returned to hanging in their natural position
and to reduce the production of chemical secretions associated
with stress (Carlberg 1981).
All trials were filmed with 2 video cameras (Panasonic
HDC-SD80 Full HD Camcorder) positioned perpendicular to each
other with the insect in the center position (Figure 1b). The cameras
were calibrated using direct linear transformation (DLT) following
Hedrick (2008). Briefly, this involved placing a transparent plastic
container with 60 noncoplanar points distributed evenly throughout the volume of the object. The calibration object was placed
such that all points could be located in images from both cameras.
Still images of the object, one from each camera view, were loaded
into Matlab (MathWorks Inc.) and the location of each point digitized using the freely available Matlab application (Hedrick 2008).
These points, along with a text file containing the known position
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Figure 3
(a) Mean volume of 3D space swept by the tip of the abdomen of E. tiaratum during wind (black bars) and no wind (gray bars) trials. (b) Mean sweep area of
the tip of the abdomen by viewing position during wind (black bars) and no wind (gray bars) trials. (c) Signal power for movement of the tip of the abdomen
for wind (black bars) and no wind (gray bars) trials. (d) Signal power for insects (black bars) and plants (white bars) during trials in which the insect moved or
did not move. Error bars in all plots are + standard error.
Bian et al. • Motion camouflage in a stick insect
Constant versus variable wind
We investigated whether the nature of prevailing wind influenced
the swaying behavior in E. tiaratum. Wind speed is unlikely to stay
constant in nature and insects are likely to adjust their behavior
accordingly. Consequently, we predicted that the insects would
exhibit measurable differences in swaying behavior in variable wind
relative to constant wind. Thirty-two adult females were allocated
randomly into 2 treatment groups featuring either constant or variable wind using a household pedestal fan. In the constant wind
treatment, individuals were subjected to 5 min of continuous, constant wind (speed 2.0 m/s measured at the position of the insect),
whereas those in the variable wind treatment were subjected to five
20-s bursts of wind (speed 2.0 m/s), randomly initiated in each successive minute. In each treatment, individual E. tiaratum were gently placed in the same experimental frame described above and left
to acclimate for 3 min. All trials were recorded using a Panasonic
HDC-SD80 Full HD Camcorder camera and an observer, who was
done blind to the treatment, counted the number of swaying cycles
in each successive minute for both treatments. A swaying cycle
was defined as one contraction and one relaxation of the tibia and
femur joint, which cause the insect to swing its body from side to
side (Bässler 1988). We compared swaying frequency within each
of the five 1-min time periods in the R statistical environment (R
Development Core Team 2014). We constructed a linear mixed
effects model using the lmer function from the lme4 package, setting wind condition (constant or variable) and time (5 time bins) as
fixed factors and used insect identity as a random factor. Pairwise
comparisons were undertaken using the difflsmean function from the
lmerTest package (Kuznetsova et al. 2014).
Insects and plants
Our final experiment considered whether insect movement is consistent with the movement of wind-blown plants. Insects were placed
on the stem of a potted Eucalytpus gregsoniana tree that was surrounded by 2 potted E. radiata trees, located outside and subject to
natural, variable wind. The stem of the E. gregsoniana host tree was
held stable using a dowel rod so that movement of the insect was not
due to movement of the stem. We used the same approach to filming as described above, positioning 2 cameras perpendicular to each
other but with a clear line-of-sight to the insect. The insects were left
for 5 min to acclimate before filming commenced and filming ceased
after 10 min. Ten insects were used in this part of the study.
We observed 3 different scenarios within each trial: no insect
movement and no noticeable plant movement, plant movement
but no insect movement, and simultaneous insect and plant movement. We chose to focus on the latter 2 scenarios and randomly
selected an 8-s sample of both scenarios from each insect for further analysis. The sequences were analyzed using the approach
described above, which involved tracking the tip of the insect’s
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Core Team 2014). Treatment (wind or no wind) was set as a fixed
effect and insect identity as a random effect to account for repeat
observations of the same insect, using the lmer function from the
lme4 package (Bates et al. 2014). Two-dimensional sweep area was
considered in the same way, but included viewing position (front,
above, side) as a second fixed effect.
To quantify the swinging movement of the insect, we calculated
the Euclidean distance between the xyz coordinates of the tip of
the abdomen in each frame relative to its position at the start of
the sequence. In so doing, we defined displacement–time profiles
for each session (Figure 2c). A 1000-frame segment (20 s at 50
frames/s) of each profile was subjected to fast Fourier transformation (FFT) analysis that converts movement in space into component frequencies and their corresponding amplitudes. We used the
spectrogram function in Matlab that splits the signal into 8 partially
overlapping epochs and analyses them separately, after which we
calculated the average (Figure 2d). Displacement–time profiles in
2D were also determined for each viewing position and subjected
to FFT analysis as described above. Frequency spectra were visually
inspected, while swaying movements were more formally compared
by calculating signal power as the root mean square of displacement profiles and compared using linear mixed effects models. As
above, we used fixed factors of treatment (3D) and treatment ×
viewing position (2D) along with insect identity as a random effect.
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Figure 4
Power spectral density for insect swaying movements when viewed from
different viewing positions: viewed from front on and along the length of
the insect (solid line), from side on (dotted line), and from above (dashed
line). Representative plots from 4 insects are shown during wind trials of
experiment 1.
Behavioral Ecology
88
abdomen in footage from 2 DLT-calibrated cameras and reconstructed in 3D. We also tracked the movement of the host and
surrounding plants. Prior to the experiment, crosses were drawn
on the leaves of plants so that they could be reliably tracked on
video and to ensure that the same part of the plant was tracked in
successive frames. Four points on the host plant and one each on
the surrounding plants were randomly selected with the only constraint being that they were visible from both camera views. Our
approach enabled us to quantify plant motion that would likely
be seen by a predator scanning the scene and simultaneously with
a swaying insect. To compare insect and plant movement, we
examined log–log plots of insect and plant movement (Figure 2e)
and compared signal power using the linear mixed effects model
described above and setting fixed factors of object (insect, plant),
scenario (insect moved, insect stationary), and viewing position
(top, front, side), along with insect identity as a random effect.
Pairwise comparisons were undertaken using the difflsmean function from the lmerTest package.
RESULTS
Wind versus no wind
Our observations suggest that female E. tiaratum are stimulated
to sway in response to the tactile cue of wind. The majority of
insects swayed when stimulated by wind (17/21), whereas no insects
exhibited swaying movement in the wind-absent trials (0/21). The
volume of space covered by the tip of the abdomen was, not surprisingly, significantly greater during wind-present trials than during
wind-absent trials (Figure 3a; F1,20 = 60.67; P < 0.001). When we
considered sweep area from different perspectives (Figure 3b), we
again found a significant effect of wind treatment (F1,100 = 234.51;
P < 0.001). Although there is variation in sweep area as a function
of viewing position, the difference was not significant (F1,100 = 0.36;
P = 0.697), and neither was the interaction between viewing position and wind treatment (F1,100 = 0.09; P = 0.915). Nevertheless,
different viewing positions for the same motion do result in notable
differences in the “perceived” motion (Figure 4). The frequency
spectra shown in Figure 4 indicate similarities in frequency peaks
but clear differences in amplitudes. In each example, the view from
front on generated larger amplitudes than the other 2 viewpoints.
Our analyses of signal power revealed a significant effect of wind
treatment when considering signal power in 3D space (F1,20 = 22.79;
P = 0.0001); considering signal power in 2D, we found a significant effect of wind treatment (F1,100 = 98.89; P < 0.001), but no
effect of viewing position (F1,100 = 0.09; P = 0.915) or the interaction between wind treatment and viewing position (F1,100 = 0.02;
P = 0.982).
Constant versus variable wind
Although swaying is triggered by wind, the nature of the wind
stimulus exerts an important effect on swaying behavior (Figure 5).
The number of sways per minute declined significantly irrespective of wind treatment (F1,29 = 32.60; P < 0.001), and there was
significantly more sways in response to variable wind irrespective
of time (F4,116 = 26.06; P < 0.001). However, we found a significant interaction between the 2 factors (F4,116 = 13.69; P < 0.001)
that warranted further consideration. We explored the interaction
in more detail by comparing between wind treatments within each
block of time. There was no difference in the number of sways in
the first 1-min time block (t = 0.08, degrees of freedom [df] = 82.4,
P = 0.938), but a significant difference in each of the 4 subsequent
1-min blocks (T2: t = −1.12, df = 82.4, P = 0.004; T3: t = −4.48,
df = 82.4, P < 0.001; T4: t = −4.72, df = 82.4, P < 0.001; T5:
t = −5.29, df = 82.4, P < 0.001).
Insects and plants
Log–log plots of the trials in which insects and plants were both
moving show that the movement of swaying E. tiaratum is consistent with the movement of wind-blown plants (Figure 6). Not
surprisingly, the trials in which insects did not move revealed
how different the insect might appear when surrounded by
swaying plants (Figure 7). We explored the insect–plant trials
further by considering signal power from the different viewpoints. The linear mixed model revealed significant main effects
for object (F1,99 = 102.43; P < 0.001) and scenario (F1,99 = 8.59;
P < 0.004), but not for viewing position (F2,99 = 0.16; P = 0.850).
Interactions involving viewing position were also not significant (object × view: F2,99 = 1.16; P = 0.316; scenario × view:
F2,99 = 0.10; P = 0.903; object × scenario × view: F2,99 = 0.43;
P = 0.647). However, the interaction between object and scenario was significant (F1,99 = 74.31; P < 0.001; Figure 3d),
and we explored this further with pairwise comparisons. We
first confirmed the observation above that signal power of
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Figure 5
Mean (+ standard error) number of sways observed during constant (black bars) and variable (white bars) wind conditions for 5 consecutive 1-min time
periods.
Bian et al. • Motion camouflage in a stick insect
89
DISCUSSION
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swaying insects and moving plants are not significantly different (t = −1.06, df = 99, P = 0.291), but nonswaying insects
and moving surrounding plants exhibited significantly different
power (t = −13.25, df = 99, P < 0.001). We also confirmed
that moving insects exhibit significantly greater power than stationary insects (t = −4.02, df = 99, P < 0.001). Interestingly,
we also found that plant movement in trials in which insects
did not move exhibited significantly greater power than plant
movement during trials in which insects did move (t = −8.17,
df = 99, P < 0.001).
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Wind alone appears to be sufficient to trigger swaying motion in
E. tiaratum. However, wind per se does not maintain this behavior,
as the insects significantly decreased the number of sways under
constant wind relative to variable wind conditions. One explanation for the observed reduction in swaying behavior in the present
study is that insects might be habituated to the constant unchanging stimulus and a secondary stimulus or cue might be needed to
prolong this behavior. Also, if there were an energetic cost associated with generating sways then a mechanism that helps to limit the
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Log–log plots of frequency and amplitude for trials in which plants moved in response to wind and the insects swayed. Gray lines represent average spectra
of 4–6 parts of the plant, whereas the black line in each plot represents the insect’s movement.
Behavioral Ecology
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Log–log plots of frequency and amplitude for trials in which plants moved in response to wind, but the insects did not sway. Gray lines represent average
spectra of 4–6 parts of the plant, whereas the black line in each plot represents the insect’s movement.
activity seems likely. It is intriguing to consider the possibility that
insects pay close attention to environmental conditions and adjust
their behavior accordingly. The results of the final experiment
comparing insect and plant movements are consistent with this
view, where it is possible that the insects perceived the conditions
to be unsuitable and ceased swaying. Although this might be necessary to ensure they are not blown off the plant, it is also possible
that insects are unable to match the frequency of leaf movements
under strong wind conditions. This is particularly important from
a motion vision perspective, as movement that is atypical to plant
motion will be highly conspicuous (Fleishman 1988; Peters et al.
2007), and as such, staying still might indeed be a better strategy.
These non-mutually exclusive explanations require further investigations. Nevertheless, in circumstances when plant motion was
not too strong, the movement of insects in the frequency domain
is performed in a manner that is consistent with the movement of
wind-blown plants.
Variable wind conditions are likely to create highly fluctuating
plant motion patterns across different species of plant due to the
differences in shape and mechanical properties of the leaf and
Bian et al. • Motion camouflage in a stick insect
branches, leaf density, stem thickness, and height above ground
(Hannah et al. 1995). Therefore, the effectiveness of this motion
matching should not require that they precisely replicate the
motion of a given plant. E. tiaratum are generalists, feeding on a
variety of eucalyptus (Brock and Hasenpusch 2009), and may forage on different tree species during their life, so it is unlikely that
they would benefit from matching the movement of one type of
plant. Therefore, we would not expect a perfect motion overlap
between the insect and the background plants; the results from
one particular plant type may not necessarily emerge in others.
Motion matching is not the only defense against predators utilized by E. tiaratum. Other adaptations include body armor covered
with spikes, the production of alarm chemicals that deters predators, and the cryptic coloration of a senescing leaf (Carlberg 1981;
Dossey et al. 2008). Nonetheless, at times when the insect swayed
along with the plants, the frequency domain of the sway motion
laid within the frequency range of the plant motion, which might
suggest a relatively good motion match between the insect and its
surrounding plants (Figure 5).
Our methodological approach allowed us to reconstruct the
movement of insects and plants in 3D so that we can quantify how
the insect or plant moved in space. However, this approach does
not take into account that movements in 3D space are effectively
projected onto 2D during the early stages of vision, which means
that movements in and out of this virtual 2D plane will not be fully
represented. Our characterization of sweep area from different
viewpoints in experiment 1 quantified this variation (Figure 3b).
Frequency analysis showed that the same physical movements
could indeed generate different signals from different viewpoints
(Figure 4), although the variation between viewpoints was predominantly in terms of amplitudes, as frequency profiles corresponded reasonably well. The front on view consistently produced
the largest amplitudes, but interestingly, the relative amplitudes
of the other 2 viewpoints were not as consistent. Our analysis of
the effect of viewing position on this behavior is preliminary but
certainly warrants further consideration. Of particular interest
is whether this variability affects the effectiveness of the motion
matching behavior. Furthermore, if the swaying behavior does
indeed affect detection and/or recognition by would-be predators,
then it is exciting to consider the possibility that insects alter their
behavior accordingly.
Our investigation provides compelling evidence that the swaying behavior of E. tiaratum could indeed function as a camouflage
strategy. The next step is to determine whether this translates into
a survival advantage for these insects. If so, it would be then necessary to determine the mechanism that achieves this. Does the similarity in insect and plant motion prevent detection (motion crypsis)
or promote misclassification by the predator (motion masquerade)?
Insects such as E. tiaratum are mostly found in densely vegetated rainforests (Gurney 1947; Brock and Hasenpusch 2009).
Consequently, an insect swaying in a similar way to plants could
exploit the filtering mechanisms of animal visual systems that filter
out irrelevant environmental movement (Fleishman 1988; Pallus
et al. 2010). The complex motion segmentation task, and the likelihood that animals allocate more attention to ecologically relevant
motion, supports a motion crypsis explanation for this behavior.
This would be relatively straightforward to test by adapting the
approach used to explore predator identification of cryptic colors
and patterns (Merilaita and Lind 2005; Dimitrova et al. 2009; Stuart
et al. 2012). Our findings thus far, and the ecological circumstances
in which insect and plant motion is viewed, suggests masquerade
91
may not be applicable. Skelhorn and Ruxton (2010) showed that
simultaneous viewing of both the model and masquerader has
important consequences. Specifically, as insect swaying (masquerader) and plant motion (model) are viewed simultaneously, the
degree of motion matching would need to be much closer than if
the 2 are rarely viewed together. Plant motion is highly variable and
so it is difficult to see how insect swaying could be so well matched
in one instance, yet be still useful at other times. Given that the ecological circumstances of the insect largely rules out the possibility
of sequential assessment of masquerader and mimic, experimental
tests of masquerade in this context necessarily must examine simultaneous assessment by predators. Such an experiment requires a
predator motivated to find prey, a design that allows confirmation
that detection by the predator has occurred, then clear evidence that
the predator has subsequently discounted the prey item. However,
the notion of motion masquerade is quite different to other examples reported thus far that rely on physical appearance. Insect swaying behavior can be modified very quickly and so we cannot rule
out the possibility that they make moment-to-moment adjustments
when necessary. Our data show that there is variation in the way
insects sway; whether this has adaptive benefits remains to be tested.
We thank D. Deng and J. Endler for discussions about our analytical
approach and S. Watson for statistical advice.
Handling editor: Johanna Mappes
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