The study of bats and moths has provided fundamental biological
insights in areas ranging from the neurological basis of behavior
(Roeder, 1967a; Yager, 2012) to the co-evolution of predator and
prey (Fullard, 1998; Goerlitz et al., 2010). It has also revealed a
suite of defensive behaviors that either have not previously been
known to occur in the acoustic modality (aposematism and mimicry)
or, in the case of sonar jamming, are unique in the natural world
(reviewed by Conner and Corcoran, 2012).
Kenneth Roeder established the paradigm for moth defensive
behaviors (Roeder, 1967a). Eared moths employ one of two flight
behaviors depending on the intensity, and therefore proximity, of
an echolocating bat (Roeder, 1962; Roeder, 1964). Quiet
echolocation calls indicate a distant bat that has not yet detected the
moth; these calls elicit a controlled negative phonotactic response
(‘turning away’) aimed at avoiding detection (Roeder, 1962; Roeder,
1967b; Goerlitz et al., 2010). Louder echolocation calls indicate a
nearby bat in pursuit and elicit a non-directional evasive maneuver
such as a dive or spiral to the ground (Roeder, 1962).
Moths of the superfamily Noctuoidea have received most
attention in the study of anti-bat defenses. Most noctuoids have
paired metathoracic tympana that each have only two auditory
receptor neurons: A1, which is sensitive to lower intensity
ultrasound, and A2, which is sensitive to higher intensity ultrasound
(Fullard et al., 2003; Roeder, 1967a). A1 firing is thought to initiate
negative phonotactic behavior, whereas A2 firing has long been
associated with late-attack defenses such as diving (Roeder, 1974).
However, recent work disputes this hypothesis, and instead proposes
that late-attack defenses are stimulated by the summation of A1 and
A2 firing (Fullard et al., 2003; Ratcliffe et al., 2009).
Tiger moths (Lepidoptera: Erebidae, Arctiinae; formerly
Lepidoptera: Arctiidae) (Zahiri et al., 2011) respond to attacking
bats by producing ultrasonic clicks through the buckling of
metathoracic tymbal organs (Fullard and Heller, 1990; Barber and
Conner, 2006). Depending on whether a species is chemically
defended and the amount of sound it produces, moth clicks serve
as acoustic aposematic (Hristov and Conner, 2005a; Hristov and
Conner, 2005b; Ratcliffe and Fullard, 2005), mimetic (Barber and
Conner, 2007; Barber et al., 2009) or sonar-jamming signals
(Corcoran et al., 2009; Corcoran et al., 2010; Corcoran et al., 2011;
Conner and Corcoran, 2012). In some cases these defenses may be
combined; for example, a small degree of sonar jamming may
enhance bat learning of aposematic signals (Ratcliffe and Fullard,
2005). Sonar jamming has long existed as a hypothesis for the
function of moth clicks (Fullard et al., 1979; Fullard et al., 1994),
but only recently has this hypothesis been confirmed in a study
pitting a tiger moth with exceptional sound-producing abilities
[Bertholdia trigona(Grote 1879)] against big brown bats [Eptesicus
fuscus (Palisot de Beauvois 1796)] in captivity (Corcoran et al.,
2009). To disrupt echolocation, moth clicks must occur in a narrow
1–2ms window surrounding the time echoes return to the bat from
the ensonified moth (Miller, 1991). These clicks then disrupt the
timing of the firing of bat auditory neurons that are involved in
encoding target distance (Tougaard et al., 1998). The number of
Bats and insects provide a model system for integrating our understanding of predator–prey ecology, animal behavior and
neurophysiology. Previous field studies of bat–insect interactions have been limited by the technological challenges involved with
studying nocturnal, volant animals that use ultrasound and engage in battles that frequently last a fraction of a second. We
overcame these challenges using a robust field methodology that included multiple infrared cameras calibrated for three-
dimensional reconstruction of bat and moth flight trajectories and four ultrasonic microphones that provided a spatial component
to audio recordings. Our objectives were to document bat–moth interactions in a natural setting and to test the effectiveness of a
unique prey defense – sonar jamming. We tested the effect of sonar jamming by comparing the results of interactions between
bats and Groteʼs tiger moth, Bertholdia trigona, with their sound-producing organs either intact or ablated. Jamming was highly
effective, with bats capturing more than 10 times as many silenced moths as clicking moths. Moths frequently combined their
acoustic defense with two separate evasive maneuvers: flying away from the bat and diving. Diving decreased bat capture
success for both clicking and silenced moths, while flying away did not. The diving showed a strong directional component, a first
for insect defensive maneuvers. We discuss the timing of B. trigona defensive maneuvers – which differs from that of other moths
– in the context of moth auditory neuroethology. Studying bat–insect interactions in their natural environment provides valuable
information that complements work conducted in more controlled settings.
Supplementary material available online at http://jeb.biologists.org/cgi/content/full/215/24/4278/DC1
Key words: bioacoustics, bat, moth, predator–prey interaction, sensory ecology.
Received 3 July 2012; Accepted 5 September 2012
The Journal of Experimental Biology 215, 4278-4287
© 2012. Published by The Company of Biologists Ltd
Sonar jamming in the field: effectiveness and behavior of a unique prey defense
Aaron J. Corcoran* and William E. Conner
Wake Forest University, Department of Biology, Winston-Salem, NC 27106, USA
*Author for correspondence (firstname.lastname@example.org)
THE JOURNAL OF EXPERIMENTAL BIOLOGY
4279 Sonar jamming in the field
clicks that fall within this window determines the size of ranging
errors (Miller, 1991) and the likelihood of bat capture (Corcoran et
al., 2011). In captivity, jammed bats narrowly miss their prey by a
distance predicted by psychophysical studies (Corcoran et al.,
2011). Field studies of sonar-jamming moths have not previously
Research documenting the pursuit and escape flight of bats and
insects has largely been restricted to captive animals in controlled
environments (Corcoran et al., 2010; Dawson et al., 2004; Ghose
et al., 2006; Ghose et al., 2009; Triblehorn et al., 2008). Although
these laboratory studies have yielded many novel discoveries, the
limited space of a flight room prevents the animals from exhibiting
the full suite of behaviors that occur in nature. Bats in captivity alter
the temporal and spectral features of echolocation calls, and also
the directionality and intensity of their sonar beams (Surlykke and
Moss, 2000; Surlykke et al., 2009). This may substantially alter the
timing of insect defensive responses, and therefore the result of
Field studies of bats and insects suffer from their own limitations.
First, the species of bat and insect being observed are frequently
not known (Acharya and Fenton, 1999; Roeder, 1962; Rydell, 1992).
Second, echolocation calls of bats are rarely recorded (Acharya and
Fenton, 1999; Agee, 1969; Roeder, 1962; Rydell, 1992), and clicks
have never been recorded from free-flying tiger moths in the field
(Acharya and Fenton, 1992). Third, visual observation without video
documentation is the norm (e.g. Acharya and Fenton, 1999). This
is especially problematic because attack sequences typically last only
a fraction of a second. Fourth, observations, whether visual or
photographed, are frequently made only from a single location,
making assessment of directionality of movement prone to error.
And fifth, the effect of artificial lights, frequently used to attract
moth and bat activity to a focal observation area (Rydell, 1992;
Acharya and Fenton, 1999), has rarely been considered as a factor
affecting the animals’ behavior (Svensson and Rydell, 1998).
We aimed to overcome these limitations using a robust field
methodology in our study of natural bat attacks on the only animal
in nature known to jam the sonar of its predator (B. trigona). We
set out to answer three questions: (1) how effective is sonar
jamming compared with other moth defenses; (2) does B. trigona
use evasive maneuvers in addition to jamming, and if so, do these
behaviors aid in its survival; and (3) what are the acoustic stimuli
that cause B. trigona to use its various defensive behaviors?
MATERIALS AND METHODS
Field location and equipment
Research was conducted in early August 2010 and late July 2011
at the Southwestern Research Station (operated by the American
Museum of Natural History), 8km southwest of Portal, Arizona,
USA. Research was conducted in an open field surrounded primarily
by Arizona cottonwood (Populus fremontii) and alligator juniper
(Juniperus deppeana). Vegetation was a minimum of 25m away
from the observation area.
We recorded interactions using three Basler Scout cameras
(model scA640-120gc; Ahrensburg, Germany) recording at
60framess–1at 640×480 resolution (Fig.1). The cameras were
hardware-synchronized by a custom-built external trigger box
(Innovision Systems, Columbiaville, MI, USA). Video was acquired
with MaxTraq2D software (Innovision Systems) running on a
desktop computer. Infrared illumination was provided by 12 Wildlife
Engineering IR-Lamp6 lights (Tucson, AZ, USA), two Bosch
UFLED20-8BD illuminators (Farmington Hills, MI, USA) and one
Raytec Raymax 200 platinum illuminator (Ashington, UK).
Three-dimensional calibration of video recording
The cameras were calibrated for three-dimensional (3-D)
reconstruction of bat and moth flight paths using the relative
orientation method (Svoboda et al., 2005) as implemented in the
MaxTraq3D software (Dynamic Wand Method; Innovision Systems).
For this method, two spherical infrared markers are anchored at a
fixed distance from each other on a calibration object, or ‘wand’. This
wand is moved throughout the interaction volume, and the markers
are later digitized in the software. A second calibration object with
four calibration points along two perpendicular axes is used to set the
origin of the 3-D space and the x-, y- and z-axes. We tested the
accuracy of our calibration by moving the wand through the recorded
volume a second time and determining the distance between the two
points (which were set 146cm apart) using the MaxTraq3D software.
Over 2500 frames, the points had a mean error of 3.8mm, or 0.26%.
Our calibration volume was ~6×6×5m, or 180m3.
Audio was recorded using four Avisoft Bioacoustics CM16/CMPA
ultrasonic microphones (Berlin, Germany) connected to an Avisoft
Ultrasound Gate 416Hb recording interface. Each of the four
microphones was placed on a stand 1.5m off the ground, in a square-
like arrangement around the calibration volume (Fig.1). Audio was
triggered to begin simultaneously with the video by a signal sent
from the triggering box to the recording interface. Tests showed the
error of synchronization between audio and video to be <1ms. The
four microphones allowed us to record the relatively quiet clicks of
B. trigonaand the directional echolocation calls of bats over a larger
volume than would be possible with a single microphone. The
arrangement of microphones also provided a spatial component to
our audio recordings that was useful for assigning bat and moth
sounds to individual animals captured in our video recordings.
Bat–moth field observations
One of the challenges of studying bats and moths in the field and
capturing interactions on audio and video is finding a situation where
attacks occur reliably within a predefined volume. To overcome
this limitation we attracted moths to a focal area using two 15W
ultraviolet lights (Leptraps LLC, Georgetown, KY, USA) placed
on 4m poles, with the two poles placed ~5m apart (Fig.1). This
arrangement allowed for varied movement of the moths as they flew
between and around the lit areas.
The large numbers of moths around the lights attracted foraging
bats. Between approximately sunset and midnight, the many moths
around the lights interfered with our ability to isolate individual
bat–moth interactions. As the night progressed (between 00:00 and
04:00h), moth activity decreased, but the foraging bats remained.
This is when we conducted our experiments. Individual B. trigona
had previously been collected at blacklights within 100m of the
recording area. Moths were held individually in 30ml plastic cups
for up to 24h prior to experimentation. Individuals held over from
the previous night were fed a saturated sugar water solution. Moths
were either left intact or had both tymbal organs ablated by
puncturing the cuticle of the tymbal. No external bleeding was ever
observed from the ablation. Clicking and silenced moth flight
behavior was analyzed to assess whether the ablation procedure had
an effect on moth flight (see below).
During experimentation, B. trigona were placed on a release
platform consisting of a heating pad attached to a 1m pole anchored
in the ground. Individual moths flew from the heating pad to the
observation area of their own volition, and bat attacks ensued within
1 to 3min. Several methods were used to ensure that the moths
THE JOURNAL OF EXPERIMENTAL BIOLOGY
being attacked were indeed B. trigona. In many cases an observer
could track an individual moth from the release platform to an
encounter with a bat. In other cases moths could be identified based
on their distinctive clicks in the audio tracks. The relative timing
and intensity of the clicks on the four audio channels was used to
ensure that the clicks came from the moth being attacked in the
video. Only attacks in which the identity of the moth could be
unambiguously confirmed were used in the analysis.
Bat species identification
The species of bat in each encounter was determined using the
automated acoustic species recognition algorithms (Corcoran, 2007)
of Sonobat 3.0 (Arcata, CA, USA). Sonobat makes classifications
by measuring numerous acoustic properties of bat echolocation calls
and running them through a discriminant function analysis classifier
that was created using recordings of known species. A species
classification is accepted only if the discriminant probability (a
measure of statistical confidence) is >0.90. If a species cannot be
determined, an individual is assigned to a species group, which
consists of multiple species that make similar echolocation calls
(Parsons and Jones, 2000).
Based on 19years of mist-netting capture data, 18 insectivorous
bat species are known to occur at our field site (J. Tyburec, Bat
Conservation International, personal communication). Twelve of
these 18 species were used in our acoustic classifier. Two species
(Lasiurus xanthinusand N. femorrosaccus) were excluded from our
analysis because of their rarity (fewer than one bat per 387 net hours
each). Four species – Idionycteris phylottis, Myotis auriculus, M.
velifer and Nyctinomops macrotus – lacked a sufficient number of
known recordings to be included. Idionycteris phylottis and N.
macrotusproduce characteristically low-frequency calls (12–17kHz)
that were not recorded during the experiments. Myotis auriculus
and M. velifer both produce calls with peak frequency in the 40
kHz range (J. Szewczak, personal communication). Therefore, any
calls identified to the genus Myotis in the 40-kHz range are reported
only to the species-group level (referred to as 40kHz Myotis).
Analysis of bat and moth flight trajectories
The flight trajectories of bats and moths were manually digitized
using Maxtraq2D. A single point representing the ‘center of mass’
The Journal of Experimental Biology 215 (24)
for each animal was used. The digitized values were then imported
into MaxTraq3D, which generated 3-D coordinates for the bat and
moth for each frame of each attack. These values were then imported
into a custom MATLAB (Natick, MA, USA) program (BATracker;
coded by B. Chadwell). A smoothing spline function (MATLAB
spaps routine) was fit to each flight trajectory, and this function was
used to estimate various flight parameters.
A 3-D plot of the bat and moth for each encounter was analyzed.
The moth’s flight response was first described qualitatively, and
discrete behaviors were then confirmed using quantitative metrics.
Two categories of behaviors were observed: flying away and diving.
Fly-aways were designated by a rapid decrease in the angular
deviation between the moth’s flight vector and the bat–moth vector
(β) combined with a rapid rise in tangential acceleration.
Alternatively, a fly-away could also be designated by the moth
rapidly increasing translational acceleration while β remained low.
Diving involved a rapid vertical acceleration toward the ground.
The number of attacks where bats aborted pursuit in response to
moth clicks was determined using previously established criteria
(Corcoran et al., 2011); these include lengthening pulse intervals
and bats veering away from the moth soon (100–300ms) after
clicking began. The duration of each attack was taken as the total
time when the angular deviation between the bat’s instantaneous
flight vector and the vector from the bat to the moth (φ) was less
In order to visualize moth evasive behavior with greater detail
than that possible in the field, high-speed video of big brown bats
attacking free-flying B. trigona in captivity was taken using a
RedLake N3 camera (Tallahassee, FL, USA) shooting at
1000framess–1. In order to restrain the moths’ flight to an area within
the camera’s view, two ultraviolet LEDs were suspended 1.5m from
the ceiling of an outdoor flight cage (6×4×3m) and approximately
1m from each other. This effectively replicated our outdoor
recording setup, but on a much smaller scale. The moths were
attracted to the area around the suspended LEDs and bats released
into the flight cage attacked the freely flying moths.
Statistical analysis was conducted in SPSS version 19 (IBM,
Armonk, NY, USA) and R version 2.12.1 (R Foundation for
Statistical Computing, Vienna, Austria). The G-test was used to test
for independence between categorical variables (Sokal and Rohlf,
1995). The Williams correction was applied when expected cell
values in the G-test were less than five (Sokal and Rohlf, 1995). In
such cases, the G-statistic is labeled ‘G-adjusted’. For continuous
variables normality was assessed visually using normal quantile plots
(Sokal and Rohlf, 1995) and quantitatively using the Shapiro–Wilk
test of normality. Alpha was set at 0.05.
Effectiveness of the sonar-jamming defense
The presence of moth clicks had a strong effect on the result of
bat–moth encounters (G-adjusted56.1, d.f.3, P<0.0001; Fig.2).
Nearly all of the clicking moths avoided capture from attacking bats
(supplementary material Movies1, 2), and the few moths that were
captured were released unharmed. In contrast, bats captured a large
majority of the silenced moths, and ate two-thirds of those they
captured (Fig.2). In order to later compare the effectiveness of
different defenses, we will use a measure we call the ‘defense ratio’,
which equals the capture success when a defense is absent divided
by the capture success when the defense is present. In this case the
defense ratio for sonar jamming is 10.4 (71% divided by 6.8%).
Fig.1. Diagram of field recording setup. Two ultraviolet lights on poles were
used to attract insect and bat activity to a focal observation area. Three
infrared cameras with infrared lights were used to capture video, while four
ultrasonic microphones recorded audio of bat–moth interactions.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
4281 Sonar jamming in the field
Bertholdia trigona evasive maneuvers
Moths employed two distinct evasive maneuvers: fly-aways and
dives (Fig.3). Fly-aways involved a sharp turn or acceleration away
from an oncoming bat. We prefer this term over ‘turn-away’ as
turning is not necessarily required if the moth accelerates away from
a bat approaching from the rear. Dives involved a rapid acceleration
toward the ground with vertical speed of 1.5 to 4ms−1. The
resolution of our cameras did not allow us to determine visually
whether these dives were passive or active. However, the rate of
maximum vertical acceleration during dives (18.8±7.0ms−1) well
exceeded the rate of gravitational acceleration (9.8ms−1), indicating
that the dives were active. A high-resolution video of the diving
behavior obtained in captivity confirms this finding (supplementary
Diving occurred in more than half of all bat–moth encounters,
and at similar rates for both clicking and silenced moths (G0.279,
d.f.1, P0.60); fly-aways and no evasive maneuvering were less
common (Fig.4A). Fly-aways occurred twice as often in silenced
moths, whereas attacks where moths exhibited no evasive maneuvers
were twice as common in clicking moths (Fig.4A); however, these
differences were not statistically significant (fly-aways: G3.09,
d.f.1, P0.08; no maneuvers: G1.648, d.f.1, P0.20). Bats
captured more silenced than clicking moths regardless of whether
the moths were diving (G22.0, d.f.1, P<0.0001), flying away
(G24.7, d.f.1, P<0.0001) or exhibiting no evasive maneuvers
(G12.2, d.f.1, P<0.001; Fig.4B). Both clicking (G-adjusted4.78,
d.f.1, P0.03) and silenced (G-adjusted5.37, d.f.1, P0.02)
moths that dove were caught less often than moths that either flew
away or exhibited no evasive maneuver (Fig.4B). In both the
clicking (P0.30) and silenced treatments (P0.37), bats caught
moths that flew away and moths that exhibited no evasive maneuver
at similar rates (Fig.4B).
Clicking caused bats to rapidly abort their attacks (as evidenced
by increasing pulse intervals and bats veering away from the moth
soon after clicking began) in 61% (27 of 44) of bat–moth encounters.
This behavior was never observed in 38 attacks on silenced B.
No contact No capture
No contactNo capture
Fig.2. Ethogram showing results of bats attacking (A) clicking and
(B) silenced Bertholdia trigona. Line widths are proportional to the
percentage of encounters that include a particular transition.
C Clicking dive
A Clicking fly-away
D Silent dive
B Silent fly-away
Fig.3. Three-dimensional flight trajectories of bats (red)
attacking (A,C) clicking and (B,D) silenced Bertholdia
trigona (blue) exhibiting defensive maneuvers. Moths are
seen flying away (A,B) and diving (C,D). Both interactions
with silenced moths resulted in capture. Arrows show the
direction of flight. In A and C, circles indicate the positions
of bats and moths when the moth began clicking. Gridlines
are each 1m apart.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
trigona, a significantly different margin (G45.2, d.f.1, P<0.0001).
The duration of attacks on silenced moths (median 1.4s; interquartile
range 1.0–1.5s) was twice that of attacks on clicking moths (median
0.7s; interquartile range 0.6–1.0s), a significant difference
(Mann–Whitney U-test, U1012.5, d.f.1, P<0.0001). The distance
silenced moths flew during attacks (median 2.2m; interquartile range
1.7–3.4m) was 47% greater than that of clicking moths (median
1.5m; interquartile range 1.1–2.2m); this difference was statistically
significant (U882.0, d.f.1, P0.002), and may be an underestimate
as some silenced moths were chased out of the calibrated volume
during attacks. Finally, of moths that were not captured in attacks,
64% (7 of 11) of silenced moths, but only 7% (3 of 41) of clicking
moths, flew to the ground, a significantly significant margin (G-
adjusted13.8, d.f.1, P0.0002).
Effect of light on moth evasive maneuvers and bat capture
The moth’s distance from a light did not affect the moth’s likelihood
of diving (G-adjusted1.67, d.f.3, P0.64; Fig.5A) or of being
captured (silenced moths: G-adjusted2.55, d.f.3, P0.46; clicking
moths; G-adjusted3.34, d.f.3, P0.34; Fig.5B). Silenced moths
within 1m of a light appeared to be captured less often than moths
The Journal of Experimental Biology 215 (24)
flying farther from a light (Fig.5B), although this may be a result
of the small sample size of moths flying within 1m of a light (N3).
Timing of moth defenses
Moth clicking, diving and flying away were all initiated after the
bat detected the moth and began its approach (Fig.6). Moth
defenses occurred within a similar range of bat–moth distances
and bat pulse intervals. The one exception to this was that fly-
aways for silenced moths occurred when the bat was particularly
close (Fig.6). This late response may not have been observed for
clicking moths because bats frequently aborted their attacks earlier
in the sequence, not providing the moths an opportunity to evade
at such a close distance.
Directionality of moth diving
An additional analysis was conducted on the directionality of moth
flight prior to and during diving (Fig.7). Prior to dives, the
horizontal component of moth flight was random with respect to
the oncoming bat (Rayleigh test, z0.207, P0.86; Fig.7A).
However, during dives the horizontal component of the moths’ flight
showed a significant directional trend away from the oncoming bat
(z4.77, P0.007; Fig.7B). Moths flew slightly upward (z5.03,
Percent of moths captured
Percent of attacks
Moth distance from light (m)
Percent of moths diving
Percent of moths captured
Fig.4. (A)Percentage of bat attacks in which Bertholdia trigona exhibited
defensive behaviors, and (B) bat capture success of moths exhibiting
different combinations of behaviors. Numbers over bars show the
proportion of total attacks where a behavior was exhibited (A) or capture
was made (B). ***Significantly different capture success (P<0.001).
Fig.5. Percentage of moths (A) diving and (B) captured by bats relative to
moth distance from a light source. Numbers above bars indicate the
proportion of attacks resulting in a dive or capture. Frequency of moth
diving and bat capture success did not differ significantly with moth
distance from a light (see Results for statistics).
THE JOURNAL OF EXPERIMENTAL BIOLOGY
4283Sonar jamming in the field
P0.006; Fig.7C) prior to diving at a sharp angle away from the
approaching bat (z27.4.03, P<0.0001; Fig.7D).
Bat species identification and capture success rates
Ninety percent of bats attacking clicking B. trigona and 79% of
bats attacking silenced B. trigona were classified in the 40kHz
Myotis group (Table1). Three other bat species were identified in
one to nine attacks. Although sample sizes for species other than
40kHz Myotis were small, capture success for both clicking and
silenced moths was fairly consistent between species (0–8.3% for
clicking moths, 70–100% for silenced moths; Table1).
Sonar jamming is highly effective against a range of bat
Bats captured over 10 times more silenced moths than clicking
moths, indicating that sonar jamming is a highly effective defense
(Fig.2). Sonar jamming’s defense ratio (10.4) compares favorably
with that of insect evasive flight maneuvers (2.1–4.9; Fig.8).
Aposematic clicking in Hypoprepia fucosa has a defense ratio of
9.6. Clicking therefore appears to be more effective than evasive
flight maneuvers regardless of whether it is used to warn bats of
toxicity or jam their sonar. Bertholdia trigona clicking also appears
to increase survival after capture, as none of the three clicking B.
trigona that were captured were eaten, whereas two-thirds of
silenced B. trigona that were captured were eaten (Fig.2). Clicking
itself may therefore have an aversive effect on bats, either because
of the high intensity of the clicks at close range or possibly through
tactile stimulation (Masters, 1979). This effect could be important
against gleaning bats, whose relatively quiet echolocation calls do
not elicit a clicking response prior to capture (Ratcliffe and Fullard,
Can B. trigona clicks jam the sonar of a variety of bat species?
As the moth’s tymbal buckles, it produces a burst of broadband
microclicks covering frequencies of 25–89kHz and with peaks of
energy that sweep from 85kHz down to 50kHz and back again
(Corcoran et al., 2009). It is not clear what role, if any, these acoustic
properties play in enhancing the jamming function. The most
common predators in this study were Myotis bats that use peak
echolocation frequencies between 40 and 45kHz (Table1). The most
frequent prey of these bats (which at our field site could include M.
volans, M. auriculus, M. ciliolabrum and M. velifer) are
lepidopterans (Fenton and Bell, 1979; Fitch et al., 1981; Warner,
1985) and they only overcame the jamming defense in 8.3% of
attacks. Myotis californicus(which uses echolocation above 50kHz)
(Gannon et al., 2001) and Lasiurus blossevillii (a highly agile flyer)
(Norberg and Rayner, 1987) were also each defeated by sonar
jamming in two of two encounters in the present study (Table1).
Previous laboratory work found that sonar jamming is also effective
against E. fuscus (Corcoran et al., 2009; Corcoran et al., 2011),
whose peak echolocation frequency is near 30kHz. Therefore, the
current data suggest that sonar jamming by B. trigona is effective
against bats that use a range of echolocation frequencies (30–50kHz)
and flight behaviors. This is impressive considering that moths do
not dynamically alter their jamming defense to the bat predator, as
moths are tone deaf (Roeder, 1967a) and largely lack the ability to
alter the frequency and temporal composition of sounds they
produce (Barber and Conner, 2006; Corcoran et al., 2010; Fullard,
Bertholdia trigona combine sonar jamming with evasive
To our knowledge, this study represents the first 3-D analysis of
moth flight patterns in response to attacking bats in the natural
environment. We found that B. trigona use the same evasive
maneuvers employed by other moths: (1) flying away and (2) diving.
Diving behavior in silenced B. trigona had a defense ratio of 1.8,
a value only slightly less than that of other insects’ diving behaviors
(Fig.8). Diving and clicking proved the most effective defense, with
none of the 24 moths exhibiting both behaviors being captured
(Fig.4B). Diving may therefore serve two potential functions in B.
trigona: (1) enhancing the effectiveness of sonar jamming and (2)
serving as a ‘backup’ defense in cases where their tymbal organs
are not functioning. In captivity, B. trigona did not click in 22% of
attacks (Corcoran et al., 2009). Because these moths were tethered
it is not possible to know whether they would have exhibited diving
behavior, but if they had they would have increased their chances
of survival. These results demonstrate that evasive flight maneuvers
in B. trigona are not vestigial traits, such as the auditory systems
of certain noctuid moths that inhabit remote, bat-free islands
(Fullard et al., 2004; Fullard et al., 2007a), or have diurnal activity
periods (Fullard et al., 1997).
Jamming occurs by interfering with the bat’s neural processing
of prey echoes (Tougaard et al., 1998). This diminishes the bat’s
precision in determining target distance and prevents it from
coordinating its capture maneuver (Miller, 1991; Corcoran et al.,
2011). By simultaneously diving and clicking, B. trigona presents
a dual challenge to the attacking bat, which must simultaneously
overcome its errors in echo processing and adjust its flight path to
intercept the diving moth (Ghose et al., 2006). This proves to be
Pulse interval (ms)
Bat–moth distance (m)
Clicking Dive Fly-away
Fig.6. Box plots of (A) bat–moth distances and (B) bat echolocation pulse
intervals at the initiation of Bertholdia trigona defenses and bat approaching
behavior. Moth defenses were initiated at a similar range of bat–moth
distances and pulse intervals, with the exception of fly-aways for silenced
moths. Note that all moth defenses were initiated after bats began their
approach. Box plots show 5th, 25th, 50th, 75th and 95th percentiles of
distributions. *Significantly different distributions (P<0.05).
THE JOURNAL OF EXPERIMENTAL BIOLOGY
too great a challenge, and the bat is unable to capture the moth.
Based on visual observations, some researchers have reported that
tiger moths use diving or spiraling evasive maneuvers in addition
to clicking (Agee, 1969; Roeder, 1974; Fullard et al., 1979); others
have reported that evasive maneuvers were absent during bat attacks
(Dunning, 1968; Acharya and Fenton, 1992). It is not clear why
some tiger moths use evasive flight maneuvers while others do not.
A number of differences in flight were observed between silenced
and clicking moths. Silenced moths appeared to more frequently
exhibit fly-aways (and they did so with bats at a closer distance)
and less frequently exhibit no evasive maneuvers compared with
clicking moths. Attacks on silenced moths lasted longer and silenced
moths flew longer distances evading capture. Silenced moths also
flew to the ground nearly 10 times as often as clicking moths. It is
unlikely that these differences are due to the ablation procedure, as
manipulated animals would not be expected to exhibit evasive
maneuvers more frequently than unaltered animals. Instead, they
are likely a result of bats behaving differently when attacking
The Journal of Experimental Biology 215 (24)
clicking and silenced moths. Bats frequently (61%) aborted attacks
on clicking moths after hearing clicks, but never aborted attacks on
silenced moths prior to making a capture attempt.
These findings are the first evidence that moths dynamically alter
their evasive flight pattern depending on the behavior of an attacking
bat. They also demonstrate that moths are capable of processing bat
echolocation call information in the very late stages of attack. This
contradicts a hypothesis made by Fullard and colleagues (Fullard
et al., 2003), which stated that moth tympanic receptors are not
capable of encoding bat calls in the final stage of attack, as these
calls have reduced intensity, duration and time intervals between
calls. Finally, these results demonstrate that combining jamming
and diving provides maximum defensive advantage with minimum
costs in time spent avoiding predators.
Timing and directionality of B. trigona evasive maneuvers
Bertholdia trigona exhibited the same two categories of defenses
– flying away and diving – as was first described in the pioneering
Fig.7. Directionality of Bertholdia trigona
diving behavior in response to bat attack:
(A) overhead before dive, (B) overhead of
dive, (C) profile before dive and (D) profile
of dive. Prior to diving (A,C), moths flew
randomly with respect to the direction of
the oncoming bat. However, the diving
behavior (B,D) had a strong directional bias
away from the oncoming bat. Moths often
flew slightly upwards (C) prior to diving (D).
Arrowheads mark the starting (A,C) and
ending (B,D) positions of moth flight. Moth
flight trajectories were rotated and
translated such that the bat approaches
from the right (black bat symbols) and the
initiation of the moth dive is at the plotʼs
origin. Dashed black arrows indicate the
median distance and direction of moth
flight when the direction of flight was not
randomly distributed. P-values indicate
results of the Rayleigh test.
Table1. Capture success of clicking and silenced Bertholdia trigona by bat species
Clicking B. trigonaSilenced B. trigona
N No. caught % CaughtN No. caught% Caught
aIncludes M. auriculus, M. ciliolabrum, M. velifer and M. volans.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
4285Sonar jamming in the field
experiments of Kenneth Roeder (Roeder, 1962; Roeder, 1967a).
However, two key differences exist between our results and the
paradigm established by Roeder some 50years ago. First, Roeder
found that moths exhibited negative phonotaxis in response to
relatively quiet ultrasound pulses that simulated distant bats (Roeder,
1962). In contrast, B. trigona exhibited negative phonotaxis after
bats had already begun their approach and locked their intense sonar
beams on the moth (Ghose and Moss, 2003). This finding supports
prior evidence that clicking tiger moths do not alter their flight
activity in response to search-phase echolocation, whereas silent
noctuoid moths do (Ratcliffe et al., 2008). This finding is also
supported by evidence that some tiger moths have reduced sensitivity
of the A1 auditory neuron (Fullard et al., 2003), the sensory cell
implicated in the moth negative phonotactic response. However, we
cannot exclude the possibility that the lights used in our experiment
prevent the moths from exhibiting negative phonotactic behavior
earlier in attacks, or that the moths have habituated to the relatively
low-danger threat of quiet bat search calls (Ratcliffe et al., 2011).
Second, Roeder argued that the intense calls of bats late in an
attack saturate the moth’s tympanic receptors and eliminate its ability
to determine the directionality of the sound (Roeder, 1967b).
Diving behavior elicited late in the attack should therefore be non-
directional, as Roeder observed from unidentified moths using
stroboscopic flash photography (Roeder, 1962). In contrast, B.
trigona diving has a clear directional component, indicating that
their tympanic receptors are not saturated by approach-phase
echolocation. When a bat echolocation attack sequence was
broadcast to preparations of five species of noctuoid moths while
recording the auditory receptor cells, the dogbane tiger moth
(Cycnia tenera) demonstrated the highest thresholds for both the
A1 and A2 receptors. Tiger moths may be less susceptible to
saturation of auditory receptor cells, and directional diving responses
may be limited to this group. Alternatively, directional diving
responses may be more common than has previously been
documented. The only other insect that has been systematically
tested for this behavior is the praying mantis Parasphendale
agrionina, whose single functional ear precludes directional
behavioral responses (Ghose et al., 2009).
Bertholdia trigona begins clicking earlier in attacks in the field
compared with in captivity
Data on the defensive responses of B. trigona in the field (present
study) and in captivity (Corcoran et al., 2009; Corcoran et al., 2011)
allow us to compare the stimuli that provoke clicking under the two
environmental conditions. Clicking by tiger moths is triggered
primarily by the intensity and pulse intervals (the inverse of pulse
rate) of echolocation calls (Fullard et al., 2007b). In the field, bats
triggered B. trigona to click in response to longer pulse intervals
(47 versus 25ms) and at a greater distance (170 versus 76cm). This
cannot be explained by the studies using different bat species as the
larger-bodied E. fuscus used in captivity should have echolocated
more loudly and elicited clicking earlier than the smaller Myotis
used in the field (Holderied and von Helversen, 2003). Bats in the
field echolocate approximately 20dB louder than bats in captivity
(Surlykke and Kalko, 2008; Holderied and von Helversen, 2003;
Waters and Jones, 1995), because they are typically further from
background objects (Surlykke and Kalko, 2008). However, the
intensity of bat calls used during attack has not been measured in
the field (Fullard et al., 2007b). Studies that broadcast calls of
attacking bats while recording moth auditory nerve cell and
behavioral responses have employed echolocation attack sequences
recorded in captivity (Fullard et al., 1994; Fullard et al., 2003; Barber
and Conner, 2006; Corcoran et al., 2010). Results of such studies
may therefore differ from what occurs in open habitats in nature.
Further work is needed to characterize the acoustic field moths are
exposed to when being attacked by bats under natural conditions,
and to apply this information to our understanding of moth
Moth proximity to ultraviolet lights does not inhibit defenses
or affect capture success
Man-made lights provide a useful tool in the study of bat–insect
interactions by drawing the animal participants into a focal
observation area (Acharya and Fenton, 1992; Acharya and Fenton,
1999; Roeder, 1962; Rydell, 1992). These lights, however,
undoubtedly affect the behavior of moths, and likely bats, under
study. Geometrid winter moths flying within 4m of a 125W mercury
vapor lamp exhibit fewer evasive flight maneuvers in response to
pulsed ultrasound compared with moths flying in a dark woodland
(Svenssen and Rydell, 1998). Studies of bats attacking insects around
lights have typically not quantified the effects of lights on moth
behavior and on the outcome of interactions (Acharya and Fenton,
1992; Acharya and Fenton, 1999; Rydell, 1992). In the present study
(where 15W ultraviolet lights were used) we found no such effect,
as the percentages of moths diving and being captured were
independent of moth distance to ultraviolet lights (Fig.5). The one
difference that appeared to stand out (albeit not statistically
significant and with the small sample size of N3) was a decrease
in capture success of silenced moths within 1m of lights. This result
is the opposite of what would be expected if lights reduced the
effectiveness of moth defenses. Instead, this more likely
demonstrates the difficulty aerial hawking bats have when trying
to capture prey close to background objects (Siemers and Schnitzler,
Fig.8. Cross-species comparison of the effectiveness of insect defenses at
preventing capture by bats. The defense ratio equals the percent of insects
captured when the defense is absent divided by the percent captured when
the defense is present, and is used to control for varying environmental
conditions between studies. Gray bars indicate insect evasive flight
maneuvers, and black bars indicate moth clicking defenses of acoustic
aposematism and sonar jamming. All insects are in the order Lepidoptera,
except Chrysopa carnea (Neuroptera) and Parasphendale agrioninea
(Mantodea). Data were taken from: 1Agee (Agee, 1969); 2Miller and Oleson
(Miller and Oleson, 1979); 3Rydell (Rydell, 1992); 4Acharya and Fenton
(Acharya and Fenton, 1999); 5Triblehorn (Triblehorn et al., 2008); 6present
study; and 7Acharya and Fenton (Acharya and Fenton, 1992).
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2004). We conclude that the use of low-wattage ultraviolet lights
appears to be a valid method for attracting naturally behaving bats
and moths for study. This finding also demonstrates that the insect
species and the type of light may be crucial for understanding the
environmental impacts of light pollution. There may be effects of
lights on moth and bat behavior that we cannot observe, and further
work is required to confirm these findings using conditions that do
not require light.
Ecological benefits of sonar jamming
Sonar jamming provides many benefits to a nocturnal, volant insect.
It is more effective than evasive maneuvering, and as effective as
acoustic aposematism (Fig.8), without the costs of sequestering
toxins and having a limited number of available host plants (Nishida,
2002). In environments where toxic, clicking moths are present
(including our field site), palatable, jamming moths may gain
additional protection through acoustic Batesian mimicry (Barber and
Conner, 2007; Barber et al., 2009), whereby bats misperceive the
clicks as aposematic messages. However, the large differences in
the acoustic signals of aposematic moths and jamming moths
(Corcoran et al., 2010; Conner and Corcoran, 2012) would facilitate
predators’ ability to differentiate the two (Barber et al., 2009). Given
that jamming moths far outnumber aposematic moths at our field
site (A.J.C. and W.E.C., unpublished), Batesian mimicry would be
predicted to be ineffective (Harper and Pfennig, 2007; Pfennig et
al., 2001). Bertholdia trigona clicks may sometimes also serve as
a legitimate aposematic signal, as 33% of silenced B. trigona that
were captured were rejected (Fig.2B), a number similar to what
was found in palatability assays conducted using big brown bats in
captivity (Corcoran et al., 2009). Therefore, jamming would be
expected to be the predominant defense in approximately two-thirds
Bertholdia trigona clicks frequently turned away bat predators
without a chase, thus preventing lengthy and energetically expensive
escape sequences. Clicking moths also less frequently flew to the
ground during bat attacks, avoiding encounters with other predators
(Guignion and Fullard, 2004) and time lost foraging and looking
for mates. Having the security of a clicking defense may also allow
moths to continue their normal activities when echolocating bats
are present but not yet attacking (Ratcliffe et al., 2008). The question
thus arises, why is jamming not more common if it confers so many
advantages over other defenses? One possible answer is that
jamming defenses are more common than we realize, especially in
the tropics, where tiger moth diversity is high and most species
remain unstudied (Corcoran et al., 2010). Selection on anti-bat
defenses may be stronger in the tropics, where bat species richness
is high (Stevens and Willig, 2002). Costs of clicking for jamming
and other purposes – be they energetic, developmental or other –
have not been investigated.
We have demonstrated a reliable and robust method for studying
3-D flight trajectories and acoustic behavior of bats attacking insects
in their natural environment. This approach allows researchers to
determine the effectiveness of defense and attack strategies,
document and describe flight behaviors, and measure the effect of
the environment (such as proximity to lights or background
vegetation) on bat–insect interactions. The primary limitation of the
present method is the use of lights for confining moth activity within
a pre-defined observation area. Further field studies are needed to
complement the existing literature on bat–insect interactions, which
consists largely of dietary analyses of fecal pellets (Clare et al., 2009;
The Journal of Experimental Biology 215 (24)
Whitaker et al., 2009), bioacoustic and physiological studies of sound
production in bats and sound reception in moths (e.g. Goerlitz et
al., 2010), and laboratory experiments using captive animals
(Corcoran et al., 2009; Ghose et al., 2006; Ghose et al., 2009). Field
studies also provide an opportunity to generate and test hypotheses
in neuroethology, such as the relationship between insect hearing
sensitivity and initiation of defensive behaviors.
The present study focused on a few species of bat and one species
of moth with a unique defensive ability. We found that sonar
jamming is a highly effective defense against a variety of bat
predators, particularly when used in conjunction with diving
behaviors. Future work will highlight the similarities and differences
in behaviors of different predator and prey species. When combined
with our understanding of insect auditory systems, we will finally
understand the connections between physiology and behavior that
Kenneth Roeder began to unravel decades ago.
LIST OF SYMBOLS AND ABBREVIATIONS
angular difference between the bat–moth vector and the moth flight
angular difference between the bat–moth vector and the bat flight
We thank Wesley Johnson, Ryan Wagner, Zachary Walker, Jean-Paul Kennedy,
Nick Dowdy and several Southwestern Research Station interns for assistance in
the field. Brad Chadwell provided software for 3-D calculations. Nickolay Hristov
helped develop our field recording setup. Jeff Muday provided technical
assistance, and the staff of the Southwestern Research Station provided valuable
logistical support. Melinda Conner edited earlier versions of the manuscript, and
two anonymous reviewers provided numerous valuable comments.
Funding was provided by the National Science Foundation [grant number IOS-
0615164 and IOS-0951160 to W.E.C.], the American Museum of Natural History
and Wake Forest University.
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