SONAR JAMMING IN THE BAT-MOTH ARMS RACE
BY
AARON J. CORCORAN
A Dissertation Submitted to the Graduate Faculty of
WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES
in Partial Fulfillment of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
Biology
May, 2013
Winston-Salem, North Carolina
Approved By:
William E. Conner, Ph.D., Advisor
Nickolay Hristov, Ph.D., Chair
Miriam Ashley-Ross, Ph.D.
Robert Browne, Ph.D.
Susan Fahrbach, Ph.D. ii
DEDICATION
To the late James Fullard, the primary proponent of the jamming hypothesis. You meant
so much to me, despite our brief acquaintance. iii
ACKNOWLEDGEMENTS
To say thank you is not nearly enough for the numerous people who have inspired me, taught me, challenged my thinking repeatedly in the kindest of ways, and worked by my side night after night until the sun rose. But here I attempt to do just that.
First, I thank Dr. William (“Bill”) Conner, my academic advisor and mentor over the last five years. You taught me what it means to be a scientist in the most complete sense of the word. Somehow you always knew when to give me a push and when to let me find my own way (usually the latter). I know few people with your breadth of knowledge from genes to neurons to behaviors to ecosystems and more. And you have an uncanny ability to relate that knowledge to your students whether in the classroom or one-on-one. You taught me how to simplify a problem into its constituent parts and dissect it experimentally with the skill of a surgeon. Your door was always wide open, literally and figuratively and you treated me as a friend and colleague from day one.
I thank my academic committee – Drs. Miram Ashley-Ross, Robert Browne, Susan Fahrbach, and Nickolay Hristov – for pushing my boundaries as a scientist. I must also thank my master’s advisor, Dr. Joe Szewczak, for sharing his love of bats with me; my brothers Dan and Tim Corcoran and Tom Brown Jr. for inspiring in me a love of all things wild; and my mom and dad for teaching me to always follow my heart and for unending faith that I would find my path in life. I thank my wife, Lauren, for keeping me grounded and humble through this journey.
Many others contributed directly to my research. Dr. Jesse Barber and Dr. Nickolay Hristov, my academic elder brothers, set the stage for this work with their ground-breaking studies on bats and moths. They also selflessly helped me at every turn, be that training me how to care for and raise baby bats, how to use high-speed cameras or develop 3-D models, or whatever the moment demanded. I had five extraordinary Wake Forest undergraduate researchers – Megan Cullen, Wesley Johnson, Ryan Wagner, Zachary Walker, and Kaitlyn Roman – work with me over the summers at Wake and in southern Arizona. I could not have asked for a harder working or more insightful and intelligent group to work with than you. I am especially thankful to Ryan Wagner, whose exceptional devotion to the project led to chapter five of this dissertation. My good friends Jean-Paul Kennedy and Nick Dowdy also worked with me in the field in Arizona and your companionship and dedication made all the difference, especially on those nights when everything seemed to go wrong and all there was left to do was crack open a beer and watch the sun rise.
I am also especially grateful to the staff, interns and fellow researchers at the Southwestern Research Station where I spent four summers studying bats and moths. There are too many of you to name here, but your help and comradery brought me joy as iv
much as did the charismatic bats and moths I got to know so well. Several people also helped with the many technological challenges of this work. Most notably, Jeff Muday was always at hand to fix a computer glitch, or even build a new computer. Mark Jenson built a custom microphone to specification for chapter five; Manfred Berger provided assistance with his Maxtraq software and Brad Chadwell graciously provided computer code and gave me lessons on 3D Geometry. Finally, I must thank Mindy Conner, who graciously edited many of the pages of this work. Of course any errors left here are mine alone.
Chapter-specific acknowledgements
Chapter II -- I thank F. Insana and the staff of the Southwestern Research Station for assistance collecting B. trigona ; M. Cullen for providing bat care and training; N. Hristov and J. Muday for technical assistance; and B. Chadwell for software. M. Conner, J.-N. Jasmin, T. Eisner, and the Wake Forest University ecology group provided editorial comments. Funding for research was provided by National Science Foundation Grant IOB-0615164 to W.E.C. and a W.F.U. Dean’s Fellowship to A.J.C.
CHAPTER III – I am most grateful to Frank Insana, Jeff Paull, and the staff of the Southwestern Research Station for assistance collecting B. trigona ; Megan Cullen for bat care; Jeff Muday for technical support; and Brad Chadwell for software development. Two anonymous reviewers provided several insightful and constructive comments. Funding was provided by grant #IOB-0615164 to W. E. C. by the National Science Foundation.
CHAPTER IV -- I 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 the manuscript, and two anonymous reviewers provided numerous valuable comments.
CHAPTER V -- I thank the staff and interns of the Southwestern Research Station of the American Museum of Natural History for logistical support. Dr. Bradley Chadwell provided software for 3-D analysis. Jeffrey Muday provided technical computing support. Funding was provided by National Science Foundation grant number IOS-0615164 and IOS-0951160 to W. E. C. v
TABLE OF CONTENTS
Page
DEDICATION ...... ii
ACKNOWLEDGEMENTS ...... iii
LIST OF FIGURES ...... viii
LIST OF TABLES ...... xi
ABSTRACT ...... xii
CHAPTER I
Bats and tiger moths: an introduction ...... 1
CHAPTER II
Tiger moth jams bat sonar
Published in Science (2009)
ABSTRACT ...... 17
INTRODUCTION ...... 18
METHODS ...... 20
RESULTS/DISCUSSION...... 21
REFERENCES AND NOTES ...... 24
SUPPORTING MATERIAL ...... 29
CHAPTER III
How do tiger moths jam bat sonar?
Published in Journal of Experimental Biology (2011)
ABSTRACT ...... 42
INTRODUCTION ...... 43 vi
MATERIALS AND METHODS ...... 46
RESULTS ...... 53
DISCUSSION ...... 58
CONCLUSION ...... 64
REFERENCES ...... 66
CHAPTER IV
Sonar jamming in the field: Effectiveness and behavior of a unique prey defense
Published in Journal of Experimental Biology (2012)
ABSTRACT ...... 83
INTRODUCTION ...... 84
MATERIALS AND METHODS ...... 87
RESULTS ...... 93
DISCUSSION ...... 96
CONCLUSION ...... 105
REFERENCES ...... 108
CHAPTER V
Optimal predator risk assessment by the sonar-jamming moth Bertholdia trigona
ABSTRACT ...... 125
INTRODUCTION ...... 126
MATERIALS AND METHODS ...... 131
RESULTS ...... 139
DISCUSSION ...... 143
REFERENCES ...... 149 vii
CONCLUSIONS AND FUTURE DIRECTIONS...... 165
CURRICULUM VITAE ...... 169 viii
LIST OF FIGURES
CHAPTER I
Figure 1.1 Spectrogram of an echolocation attack sequence of a Myotis bat ...... 15
CHAPTER II
Figure 2.1 Jamming acoustic complex ...... 26
Figure 2.2 Predicted and observed success rates for Eptesicus fuscus attacking moths using various defensive strategies...... 27
Figure 2.3 Example echolocation attack sequence of a bat being jammed by Bertholdia trigona ...... 28
Figure 2.S1 Attack persistence of Eptesicus fuscus on clicking moths through successive foraging nights ...... 36
Figure 2.S2 Oscillogram of a Bertholdia trigona click modulation cycle...... 37
CHAPTER III
Figure 3.1 Diagram illustrating bat flight parameters...... 72
Figure 3.2 Example pulse interval graph of a big brown bat unsuccessfully attacking a sonar-jamming moth...... 73
Figure 3.3 Oscillograms, spectrograms and power spectra of an approach phase echolocation call of a big brown bat ( Eptesicus fuscus ) and two overlapping click modulation cycles of Bertholdia trigona ...... 74
Figure 3.4 Three categories of bat behavioral responses to moth clicks, as illustrated by bat flight and echolocation behavior...... 75
Figure 3.5 The distribution of bat responses to moth jamming changed after the first two nights of the experiment...... 76 ix
Figure 3.6 Spectrograms of three attacks on silent control or clicking experimental moths...... 77
Figure 3.7 Simulations of competing hypotheses of sonar-jamming mechanisms .....79
CHAPTER IV
Figure 4.1 Diagram of field recording setup ...... 115
Figure 4.2 Ethogram showing results of bats attacking clicking and silenced Bertholdia trigona ...... 116
Figure 4.3 Three-dimensional flight trajectories of bats attacking clicking, and silenced Bertholdia trigona exhibiting defensive maneuvers...... 117
Figure 4.4 Percentage of bat attacks in which Bertholdia trigona exhibited defensive behaviors, and bat capture rates on moths exhibiting different combinations of behaviors ...... 118
Figure 4.5 Percentage of moths diving, and captured by bats relative to moth distance from a light source...... 119
Figure 4.6 Box plots of bat-moth distances and bat echolocation pulse intervals at the initiation of Bertholdia trigona defenses and bat approaching behavior .120
Figure 4.7 Directionality of Bertholdia trigona diving behavior in response to bat attack ...... 121
Figure 4.8 Cross-species comparison of the effectiveness of insect defenses at preventing capture by bats...... 122
CHAPTER V
Figure 5.1 Hypothesized predator risk level as it relates to predation stage ...... 156
Figure 5.2 Three-dimensional simulation of the sonar beam of a bat attacking a moth, and a spectrogram of the bat echolocation sequence with two-dimensional plots of the bat’s echolocation beam shape and direction relative to the target ...... 157 x
Figure 5.3 Three-dimensional schematic of field setup for recording bat attacks on tethered and free-flying moths ...... 158
Figure 5.4 Acoustic and kinematic characterization of “real” and “false” threats ....159
Figure 5.5 Predicted and actual clicking thresholds for different bat echolocation pulse-intervals in Bertholdia trigona ...... 161
Figure 5.6 Discrimination of bat passes that did or did not elicit clicking by tethered Bertholdia trigona in the field...... 162
Figure 5.7 Cost-benefit models of escape behavior with varying prey knowledge of predator attack stage ...... 164 xi
LIST OF TABLES
CHAPTER II
Table 2.S1 Contact rates for repeated big brown bat attacks on individual Bertholdia trigona ...... 38
Table 2.S2 Click parameters for the tiger moth Bertholdia trigona...... 39
CHAPTER III
Table 3.1 Multiple linear regressions of bat flight and echolocation responses to moth jamming...... 80
Table 3.2 Bat echolocation responses to moth jamming ...... 81
CHAPTER IV
Table 4.1 Capture rates of Bertholdia trigona by bat species ...... 123 xii
ABSTRACT
Aaron J. Corcoran
SONAR-JAMMING IN THE BAT-MOTH ARMS RACE
Dissertation under the direction of William E. Conner, Ph.D. Professor of Biology
Bats and moths are an example of a diffuse evolutionary arms race – a situation where groups of prey and predator species evolve increasingly sophisticated means to out-compete the other. Chapter one provides an overview of this arms race. Bats capture night-flying insects using highly sophisticated echolocation and flight. Most macrolepidoptera are eared and bat echolocation calls stimulate evasive flight reactions.
Some tiger moths (Lepidoptera: Erebidae, subfamily Arctiinae) also respond to bats with trains of ultrasonic clicks which 1) truthfully or deceitfully warn bats of a distasteful prey,
2) startle bats, or 3) interfere with or jam bat sonar. Evidence is strong for the warning and startle hypotheses, although the startle effect is ephemeral. The jamming hypothesis has not been confirmed to occur in nature.
In chapter two I tested the sonar jamming hypothesis using a moth ( Bertholdia trigona ) that clicks at over 1000% of the rate of other tiger moths that have been studied behaviorally. I pitted tethered B. trigona and silent control moths against naïve, lab- reared big brown bats ( Eptesicus fuscus ) in a flight room over seven nights, followed by two nights against B. trigona with ablated tymbal organs. The bats captured control moths 400% more often than clicking B. trigona . Bats ate 78% of silenced B. trigona ,
refuting the acoustic aposematism hypothesis. Bats failed to habituate to the clicking
moths, rendering startle highly unlikely. The pattern of bat capture success matched the
predictions of the jamming hypothesis. Additionally, the bat echolocation’s pattern xiii during attack was disrupted by moth clicking. We conclude that moths that click at a high rate jam bat sonar, a first in the natural world.
In chapter three I analyzed 3-D flight trajectories and echolocation behavior of bats attacking jamming moths to determine how moth clicks interfere with bat sonar. I simulated attacks based on three hypotheses from the literature. Predictions based on the ranging interference hypothesis were largely met. This hypothesis holds that moth clicks disrupt the bat’s ability to determine the time of arrival of returning echoes, and therefore the distance of the prey. It was previously shown that moth clicks degrade bat ranging ability from 5 mm to 20 cm; I found that bats missed prey by 15.7 ± 7.9 cm, a margin very close to what was previously predicted. Moth clicks occurred in the narrow 1-ms window required for ranging interference 78% of the time.
Chapter four reports the results of the first field study on bats and insects to capture interactions in 3-D. Sonar jamming is highly effective in nature, as B. trigona with ablated tymbals were caught ten times more often than clicking B. trigona . Jamming is about three times as effective as evasive flight alone. B. trigona exhibited evasive flight including directional fly-aways and dives. Diving and jamming was particularly effective as all moths exhibiting both behaviors escaped bats.
In chapter five I address why moths click when they do using the perspective of optimal escape theory. Employing a combination of field and laboratory studies, I demonstrate that moth clicking thresholds closely match predicted optimal thresholds for discriminating legitimate attacks and “false attacks” where bats attack nearby moths.
Moths clicked as soon they could reliably determine they were under attack. 1
CHAPTER I
BATS AND TIGER MOTHS: AN INTRODUCTION
Arms races – situations where competing organisms evolve reciprocally repeatedly over time – have fascinated biologists because of the intricate and sophisticated adaptations that often result from them (Dawkins and Krebs, 1979). Prey and predator are one system where arms races sometimes occur. Selection is thought to act more strongly on prey because their consequences for failure (death) are greater than that for predator (a lost meal). This is called the life-dinner principle (Dawkins and Krebs,
1979). Indisputable examples of reciprocal evolution between individual prey and predator species (coevolution) are rare at least in part because prey often have many predators and predators usually have many prey (Brodie and Brodie, 1999). Groups of predators and prey evolving reciprocally is termed diffuse coevolution (Janzen, 1980), however concrete examples of this phenomenon are also rare (Goerlitz, 2010; Vermeij,
1994). Bats and insects provide one of the best-studied examples of a diffuse coevolutionary arms race, with over 800 bat species predating uncountable species of insects (Jones and Rydell, 2003). This diversity has allowed for the evolution of many novel defensive strategies and predator counter-tactics (Conner and Corcoran, 2012).
This dissertation is an exploration of one defensive strategy that has not previously been confirmed to occur in the natural world – sonar jamming. In this introduction I discuss some strategies used by bats and insects in this coevolutionary battle, the evidence for and against sonar jamming and a brief overview of the chapters that follow. 2
A. Insectivorous bats
There are over 1200 extant bat species that evolved from a common ancestor at least
52.5 mya (Jepsen, 1966; Schipper et al. 2008; Simmons et al., 2008; Simmons and
Conway, 2003). Bats evolved flight first, followed by laryngeal echolocation (Simmons et al., 2008). These features allow bats to be the dominant predator of night skies, with about 70% of species eating insects in almost all inhabitable locations worldwide (Jones and Rydell, 2003). Bats capture insects in the air (hawking), off the ground or vegetation
(gleaning), and from the surface of water (trawling) primarily using echolocation
(Schnitzler and Kalko, 2001; Fenton, 1990).
Bats sense insect prey and other objects in their environment by comparing the timing and time-frequency characteristics of emitted signals with those of returning echoes; this is termed echolocation, biosonar, or sonar (Griffin et al., 1960; Moss and Surlykke, 2001).
While capturing insects out of the air, bats modify their echolocation in a stereotyped pattern where pulse repetition rates increase and durations, call intensities and frequencies decrease (Figure 1.1; Surlykke and Moss, 2000). These changes help maximize temporal resolution of prey location while avoiding overlap between outgoing and returning sounds (Wilson and Moss, 2004). In the final phase of echolocation before insect capture – the terminal buzz – bats emit sounds at over 160 pulses per second using the fastest known mammalian muscle (Elemens et al., 2011).
B. Insect flight and hearing
Insects evolved flight at least 350 mya, some 90 million years before the first winged vertebrates (Grimaldi and Engel, 2005). Flying nocturnal insects were likely an 3
underexploited food resource until the evolution of bats (Jones and Rydell, 2003). Insects
from 7 orders have evolved bat-detecting ears at least 18 times in locations all over the
body (Hoy, 1998; Yack and Dawson, 2008). These ears alert ground-dwelling insects like
katydids to be still and flying insects like moths to begin evasive flight when an
echolocating bat is near (Belwood and Morris, 1987; Roeder, 1967). Insect hearing
usually matches the range of frequencies used by conspecific bat communities (Fullard,
1998). In many cases bat-detection is the only known function for insect ears; however
this defensive ability appears to have preadapted some insects for acoustic courtship
displays (Conner, 1999; Nakano et al. 2009).
C. Anti-bat insect defenses
Insect defensive responses to bats – and particularly those of moths – have been the subject of numerous studies over the last five decades (reviewed by Miller and Surlykke,
2001; Yager et al. 2012; Conner and Corcoran, 2012). Kenneth Roeder is credited with many discoveries that made moth defensive responses to bats a textbook example of a behavior whose neurological basis is known (Roeder, 1967). This discovery was aided by the simple two-neuron auditory nerve of most Noctuid moths (Roeder, 1964; Roeder,
1966). These two neurons are the more sensitive A1 cell and the less sensitive A2 cell.
When the A1 cell fires in response to the faint calls of distant bats, moths fly away from the sound source to avoid being detected (Roeder, 1962; Roeder, 1974). When the A2 cell fires (or possibly when the A1 and A2 cells fire above a set rate), this indicates a nearby bat in pursuit, and the moths initiate powered or passive dives, loops, or spirals to avoid capture (Roeder, 1974; Fullard et al. 2003; Ratcliffe et al. 2009). Many tiger moths
(Lepidoptera: Erebidae, Subfamily Arctiinae), also produce trains of ultrasonic clicks in 4
response attack echolocation of bats (Fullard and Fenton, 1977; Barber and Conner, 2006;
Corcoran et al. 2010). The function of these clicks has been the matter of some debate.
Fifty years of research has produced three hypotheses on why moths click to bats.
First, the warning, or aposematism hypothesis states that clicks serve as an acoustic
message to bats of an unpalatable prey. Many tiger moth caterpillars sequester toxins
from host plants and carry them into adulthood (Nishida, 2002); some adult tiger moths
also seek toxins from plants that make them distasteful to bats (Conner and Weller, 2004;
Hristov and Conner, 2005a). Mounting evidence shows that warning of this toxicity is the
primary function for tiger moth clicking in most species (Dunning, 1968; Hristov and
Conner, 2005b; Ratcliffe and Fullard, 2005). Bats must learn the association between
clicking and toxic prey; it is not innate. However, this can happen very quickly over a
small number of trials (Bates and Fenton, 1990; Hristov and Conner, 2005b). This
observation was used to tease apart the relative contributions of clicking and toxins in
deterring bats using a novel learning approach where naïve, lab-reared bats were pitted
against prey that varied in their abilities to click and in the presence or absence of toxins
(Hristov and Conner, 2005b). Bats only failed to capture clicking prey when they were chemically defended – a clear demonstration of the aposematic function of the clicks.
This learning approach was also used to demonstrate Batesian and Mullerian mimicry in tiger moths (Barber and Conner, 2007). After bats learn the aposematic message of moth clicks, they will generalize that message to other species, regardless of whether the novel species is chemically defended. Interestingly, some bats correctly uncovered the deceitful mimics. This demonstrates the tight selective pressure that forces mimics to acoustically resemble their models (Barber and Conner, 2007; Barber et al. 2009). 5
The second hypothesized function for moth clicks is that they startle bats at a critical moment of the attack, affording the moth a moment to escape. Startle has been demonstrated in several studies (Miller, 1991; Stoneman and Fenton, 1988; Bates and
Fenton, 1990), however the effect is ephemeral. Bats usually habituate to moth clicks within a few trials. Therefore, a startle defense can only be relied upon when clicking moths are rare. Startle has similarities with the third hypothesis – sonar jamming – in that both rely upon disorienting the predator. Sonar jamming is different from startle in that the moth clicks must interfere with the bat’s echolocation. This hypothesis is the subject of this dissertation and the evidence for and against it will now be reviewed.
D. The sonar jamming debate
The term “sonar jamming” was first used in the literature in 1979 by James Fullard and colleagues (Fullard et al. 1979). However, some have interpreted earlier work by
Dunning and Roeder (1965) to be suggestive of the jamming hypothesis. Dunning and
Roeder played moth clicks to bats through a loudspeaker as the bats pursued mealworms that were catapulted through the air. The moth clicks deterred the bats; however the control of broadcasted echolocation calls had no effect. They suggested that since the bats did not learn to ignore the clicks over time and capture the palatable mealworms that the clicks themselves deterred the bats. However, it was later acknowledged that the results of the experiment were invalid, as the moth clicks were broadcast at an unnaturally high intensity (Dunning, 1968). Dunning later conducted experiments demonstrating the aposematic function of moth clicks (Dunning, 1968), and Roeder also favored this explanation (Roeder, 1967). 6
Fullard and colleagues have been the strongest proponents of the jamming hypothesis. It was originally argued that moth clicks bear strong acoustic similarities to bat calls, and bats would not be able to differentiate the two (Fullard et al. 1979). Bats would then mistake moth clicks as echoes from objects that don’t exist (“phantom objects”) and bats would either try to avoid flying into phantom objects or try to capture phantom targets. Since this hypothesis was proposed much has been learned about bats’ abilities for discriminating sounds. This work suggests that bats would have little difficulty differentiating moth clicks from echoes (Masters and Raver, 1996; Yovel et al.
2009; Corcoran et al. 2010).
Fullard also suggested that the timing of moth clicking is suggestive of a jamming function (Fullard, 1994). Using playbacks of bat attack sequences to moths in a sound chamber, Fullard found that the moths click most during the terminal phase. He argued that this would be too late for the clicks to serve an aposematic function, and that clicking late is suggestive of jamming. However, this prediction was not held, as clicks presented late in the attack are effective aposematic messages (Hristov and Conner, 2005b;
Ratcliffe and Fullard, 2005; Barber and Conner, 2007).
The most compelling evidence for sonar jamming prior to this dissertation began with the work of Lee Miller (Miller, 1991). Miller used a psychophysical experiment to test the effect of moth clicks on a bat’s ability to determine the distance of a target (this is called ranging). In his experiment a bat was placed on a Y-shaped platform in an
“echolocation simulator”. When the bat echolocated, its calls were recorded by a nearby microphone and then time-delayed and broadcast back to the bat to simulate targets at different distances. If a target was presented at a pre-determined reference distance, the 7 bat was rewarded for moving to one side of the Y-shaped platform; if the target was presented at a different distance the bat was rewarded for moving to the other side of the platform. This was repeated with and without moth clicks being broadcast with the echoes and the timing of the moth clicks relative to the returning echoes was carefully controlled and adjusted in fine increments. Miller found that without moth clicks, bats could determine target distance to within 5 mm, but when moth clicks were presented within a narrow 1-ms window before the echoes, ranging errors increased by up to
4000%. The moth clicks appeared to interfere with the bats ability to accurately determine the timing of the returning echo, and therefore the distance to the target.
This hypothesis – termed the ranging interference hypothesis – was given further support by a neurophysiological study where bat short pure tones and moth clicks were broadcast to bats while electrophysiological recordings were made of neurons in the bat inferior colliculus (IC; Tougaard et al. 1998). The IC has been implicated as a region where bats encode information about target distance (Covey and Casseday, 1991).
Tougaard and colleagues demonstrated that when moth clicks occurred in the 1-ms window prior to a tone (representing an echo), they caused errors in the timing of IC neuron firing in response to the tone. If these errors propagated through the bat auditory system they could explain the ranging interference observed by Miller (1991). Because moths have no way of knowing when a bat will be calling, they cannot predict when to click to match the critical 1-ms window. The only remaining strategy is to click at a very high rate to ensure some clicks will fall in this window by chance (Miller 1991; Tougaard et al. 1998). In a study documenting the diversity of sounds made by tiger moths (Barber and Conner, 2006), several species were identified that produce clicks at an unusually 8 high rate. These moths make over ten times the number of clicks of any of the tiger moth species whose clicking function has been studied in detail.
I hypothesized that the high clicking rate of some tiger moth species allows them to jam bat sonar. In Chapter II, I report the results of an experiment pitting a moth with extraordinary clicking ability ( Bertholdia trigona ) against naïve, lab-reared bats using a previously established behavioral learning paradigm (Hristov and Conner, 2005b; Barber and Conner, 2007). In Chapter III, I analyze the results of this experiment using 3-D analysis of bat flight trajectories and detailed bioacoustic analysis of bat echolocation patterns to address the question of how tiger moths jam bat sonar. In Chapter IV I report the results of the first study to capture bat-insect interactions in 3-D in the natural environment. This study was conducted using the same focal moth species – Bertholdia trigona —and I examine the relative contributions of clicking and evasive flight to bat deterrence. Finally, in Chapter V I report the results of an experiment aimed at answering the question of why moths click when they do. I use the perspective of optimal escape theory to demonstrate that moth clicking thresholds closely match predicted optimal thresholds for discriminated predator risk levels. 9
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Figure 1.1. Spectrogram of an echolocation attack sequence of a Myotis bat. The bat increases the repetition rate of calls as it approaches its prey. The rapid trill at approximately 300 ms is termed the terminal buzz, and it is followed by contact with the insect. 16
CHAPTER II
Tiger Moth Jams Bat Sonar
Aaron J. Corcoran, Jesse R. Barber, and William E. Conner
The following manuscript was published in Science (vol. 325, pgs. 325-327, 2009) and is
reprinted with permission. Stylistic variations are due to requirements of the journal.
A.J.C. contributed to experiment design, executed the experiments, conducted data
analysis, and wrote the manuscript. J.R.B. collected moths in the field and assisted with
experimental design. W.E.C. designed the experiments, assisted with animal care and
provided oversight and editorial assistance on the project. 17
ABSTRACT
In response to sonar-guided attacking bats, some tiger moths make ultrasonic clicks of
their own. The lepidopteran sounds have previously been shown to alert bats of some
moths’ toxic chemistry and also to startle bats unaccustomed to sonic prey. The moth
sounds could also interfere with, or “jam,” bat sonar, but evidence for such jamming has
been inconclusive. Using ultrasonic recording and high-speed infrared videography of
bat-moth interactions, we show that the palatable tiger moth Bertholdia trigona defends against attacking big brown bats ( Eptesicus fuscus ) using ultrasonic clicks that jam bat sonar. Sonar jamming extends the defensive repertoire available to prey in the long- standing evolutionary arms race between bats and insects. 18
INTRODUCTION
The ability to pinpoint airborne insects in darkness, by echolocation ( 1), allowed bats to
master nocturnal insectivory ( 2) and set the stage for the evolution of defensive
countermeasures by insect prey ( 3). Some insects gained ears ( 4) and evasive
maneuvering ( 5). Tiger moths (Lepidoptera: Erebidae, subfamily Arctiinae) developed the ability to click ultrasonically in response to attacking bats ( 6, 7 ). Decades of research
on moth click defenses have led to three non-mutually exclusive hypotheses regarding
their function: startle (8), acoustic aposematism (“warning”) ( 8-14 ), and sonar
interference (“jamming”) ( 15-17 ). When ultrasonic clicks are paired with unpalatable
prey, bats learn to perceive clicks as a warning of unprofitability ( 8, 10-14 ). Moth clicks
also startle inexperienced bats or bats that have not heard clicks for multiple days ( 8).
Because bats habituate to startle quickly, its effectiveness as a defense requires clicking
moths to be rare. This situation does not appear typical in nature ( 12 ).
Finally, moth clicks may disrupt the sonar of an attacking bat ( 15-17 ). Clicks might
diminish a bat’s acuity in determining target distance ( 17-19 ), or feign echoes from
objects that do not exist ( 15 ). However, evidence that moth clicks can disrupt bat attacks
by jamming sonar is lacking. One recent study found that moth clicks had no discernible
effect on attacking bats unless clicks were paired with defensive chemistry ( 13 ). All
previous studies, however, have tested moths with relatively low duty cycles, or sound
production per unit time (20). High-duty-cycle moth clicks, such as those of Bertholdia
trigona (Fig. 2.1; Table 2.S2), are more likely to jam bats ( 17, 18 ), including the big
brown bat ( Eptesicus fuscus ), which co-occurs with B. trigona in much of Central
America and northward to Colorado, USA. 19
To determine whether the clicks of B. trigona startle, warn, or jam bats, we pitted E. fuscus against B. trigona in a flight room equipped with high-speed infrared video cameras and an ultrasonic microphone ( 21 ). The pattern of a naive bat’s success in catching clicking moths, when measured over several days, should differ for each of the three proposed moth click functions (Fig. 2.2A) ( 13 ). For a warning sound, bats initially capture and drop distasteful moths, then learn to abort attacks on hearing clicks ( 8, 13,
22 ). Alternatively, a bat could be initially startled by the clicks but would quickly habituate to the sounds ( 8, 23 ). A jamming defense, however, would immediately deter attacking bats and the effect of the defense would persist with time (Fig.2. 2A). 20
METHODS
We trained three naive juvenile E. fuscus (“J1”, “J2”, and “J3”) and one adult E. fuscus
(“A1”) to capture tethered moths in a sound-attenuated flight room. Naive bats allow us to control prior experience, while an adult bat’s prior experience catching insects may allow it to better overcome moth defense. On each of nine consecutive nights, 16 tethered moths – four B. trigona ; four silent, palatable, size-matched noctuid novelty controls; and eight silent, palatable wax moths (female Galleria mellonella ; used in pre-experiment bat training) – were presented to each bat individually in a random order. Bats were allowed up to one minute or five approaches for each moth. We presented unaltered B. trigona to the bats on nights one through seven, and B. trigona lacking the ability to click (from tymbal ablation) on nights eight and nine. Seventy-eight percent of the B. trigona (87 of
112) responded acoustically to bat sonar. The few intact B. trigona that did not produce sound in response to bat attack were excluded from the analysis. We used contact with the moths as our measure of attack success. Palatability of B. trigona to each bat was taken as the proportion of captured moths eaten rather than dropped. We conducted feeding trials with silenced moths and four additional stationary bats to further assess the palatability of B. trigona . Finally, we analyzed echolocation recordings to measure bat behavioral response to moth clicks ( 21 ). 21
RESULTS/DISCUSSION
In stationary feeding trials with silenced moths, three bats regularly ate B. trigona (J4,
92%; N = 12; J5, 92%; N = 12; J6, 83%; N = 6), and one did not (J7, 0%; N = 8).
Similarly, of the bats capturing moths in flight, three regularly ate B. trigona (J1, 94%, N
= 16; J2, 50%, N = 14; A1, 89%, N = 18), and one did not (J3, 0%, N =12). The observed
differences between individuals may reflect a greater reluctance of some individuals to
incorporate new items into their diet, as has been reported for some birds ( 24 ). B. trigona appears palatable; the six bats ate the moths with no detectable ill effects. Because warning and jamming are not mutually exclusive defense functions ( 25 ), our ability to detect a jamming effect is diminished for the bat that rejected B. trigona – any moth avoidance could be caused by jamming or warning or both Thus, to unambiguously test the hypothesis of sonar jamming we focus our analysis on the three bats that ate B. trigona in flight (J1, J2 and A1).
Each of the three bats contacted control moths over 400% more often than
clicking B. trigona (Table 1; Movie S1). In contrast, bats contacted 100% of B. trigona
when the moths’ tymbals were ablated (Fig. 2.2C; Movie S2). This confirms that the
bats’ poor performance on B. trigona was caused by the moth clicks. Rate of contact with
sound-producing moths did not change during the experiment (Fig. 2.2B; Friedman
nonparametric ANOVA, X 2 = 2.63, df = 6, P = 0.85). Thus, prior learning was not required for the defense to be effective; nor did the bats habituate to the clicks. Bat contact rates did not improve on succeeding attacks on the same moth – attacks that often occurred within seconds of each other (Table 2.S1; Friedman nonparametric ANOVA, X 2
= 2.55, df = 4, P = 0.64). This renders startle unlikely because nearly all mammals – 22
including bats – habituate to startle ( 8, 23 ). Echolocation recordings (e.g. Fig. 2.3)
showed bats persisting in attack attempts after onset of moth clicks to varying degrees
throughout the experiment (Fig. 2.S1A). In contrast, bats in a previous study quickly
learned to abort attacks on the noxious Cycnia tenera (Fig. 2.S1B) ( 22 ). This further
suggests that the bats in our study did not learn to take the moth clicks as a warning – the
bats continued attacks despite hearing the sounds. It also indicates that moth clicks are
not generally aversive to bats (SOM text). Together, we take the above results as strong
evidence for a jamming function of the moth sound. The defense was effective
immediately and persistently on juvenile and adult E. fuscus (Fig. 2.2B) that frequently tried to capture the clicking moths (Fig. 2.S1A) but had much difficulty doing so.
We also observed atypical echolocation behavior in response to B. trigona clicks. Each bat attacked all control moths by progressing through the approach, track, and terminal phases (e.g., 26, 27 ). However, in about a third (Bat J1, 21%, N = 19; Bat J2 40%, N =
15, Bat A1 28%, N = 25) of attacks on B. trigona , the bat reversed attack phase from track to approach or terminal to track, and then continued the attack (Fig. 2.3). This behavior did not diminish with experience (Friedman nonparametric ANOVA, X 2 = 4.5,
df = 6, P = 0.61), as would be expected with startle. It appears to be a direct response of
the bats to the sonar interference caused by the moth clicks.
The ultrasonic clicks of B. trigona appear well suited for jamming echolocation (SOM
text). Under all proposed jamming mechanisms, high-duty-cycle sounds made by highly
developed tymbals (e.g., Fig. 2.1B) should better jam echolocation ( 15, 17-19 ). Empirical
evidence now supports this prediction – the high-duty-cycle sounds of B. trigona jammed
bats, while the low-duty-cycle sounds of Euchaetes egle were ineffective jamming 23
signals under nearly identical conditions ( 13 ). The use of tiger moth sound for aposematic
signaling requires only simple tymbal structure and appears widespread in the Arctiidae
(7, 10-13 ). Thus, acoustic aposematism may have been a stepping-stone in the evolutionary development of a jamming signal, a unique acoustic countermeasure in the long-standing arms race between bats and their insect prey. 24
REFERENCES AND NOTES
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4. K. D. Roeder, J. Insect Physiol. 10, 529-546 (1964).
5. K. D. Roeder, Anim. Behav. 10, 300-304 (1962).
6. A. D. Blest, Zoologica 49, 161-181 (1964).
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8. D. L. Bates, M. B. Fenton, Can. J. Zool. 68, 49-52 (1990).
9. D. C. Dunning, K. D. Roeder, Science 147, 173-174 (1965).
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2223 (1992).
13. N. I. Hristov, W. E. Conner, Naturwissenschaften 92, 164-169 (2005).
14. A. Surlykke, L. A. Miller, J. Comp. Physiol. A 156, 831-843 (1985).
15. J. H. Fullard, M. B. Fenton, J. A. Simmons, Can. J. Zool. 57, 647-649 (1979).
16. J. H. Fullard, J. A. Simmons, P. A. Saillant, J. Exp. Biol. 194, 285-298 (1994).
17. L. A. Miller, J. Comp. Physiol. A 168, 571-579 (1991).
18. J. Tougaard, J. H. Casseday, E. Covey, J. Comp. Physiol. A 182, 203-215 (1998).
19. J. Tougaard, L. A. Miller, J. A. Simmons, in Echolocation in Bats and Dolphins, J.
A. Thomas, C. F. Moss, M. Vater, eds. (Univ. of Chicago Press, Chicago, 2004)
pp. 365-380. 25
20. J. R. Barber, W. E. Conner, J. Exp. Biol. 209, 2637-2650 (2006).
21. Materials and methods are available as supporting material on Science Online.
22. J. R. Barber, W. E. Conner, Proc. Natl. Acad. Sci. U.S.A. 104, 9331-9334 (2007).
23. M. Davis, in Neural Mechanisms of Startle Behavior , R. C. Eaton, Ed. (Plenum Press,
New York, 1984), pp. 287-351.
24. N. M. Marples, T. J. Roper, D. G. Harper, Oikos 83, 161-165 (1998).
25. J. M. Ratcliffe, J. H. Fullard, J. Exp. Biol. 208, 4689-4698 (2005).
26. S. A. Kick, J. A. Simmons, Neuroscience 4, 2725-2737 (1984).
27. W. W. Wilson, C. F. Moss, in Echolocation in Bats and Dolphins, J. A. Thomas, C.
F. Moss, M. Vater, eds. (Univ. of Chicago Press, Chicago, 2004) pp. 365-380. 26
FIGURE 2.1
Jamming acoustic complex. (A) Scanning electron micrograph of B. trigona sound- producing organ (tymbal). Some scales were removed for clarity. (B) Example oscillogram (time x voltage) and (C) spectrogram showing a simulated E. fuscus echo, reflected off of B. trigona at 1 m (left), and two overlapping B. trigona click modulation cycles (right). Echo and clicks are presented at average estimated intensities at the bat’s location 1.0 m from the moth ( 21 ). 27
FIGURE 2.2
Predicted and observed success rates for E. fuscus attacking moths using various defensive strategies. (A) Predicted bat attack success over successive nights for three proposed moth click functions – startle, aposematism, and jamming. (B) Observed bat attack success rates for three E. fuscus (bat J1: circle symbols, bat J2: triangle symbols, bat A1 square symbols) on B. trigona (closed symbols) and noctuid controls (open symbols). The solid line shows mean values for attacks on B. trigona . (C) Success rates of bats attacking intact (sound-producing) and silenced B. trigona . Values are mean ± s.e.m. Some values were offset slightly for display. 28
FIGURE 2.3
Example echolocation attack sequence (A) Spectrogram showing a big brown bat attacking a clicking B. trigona . (B) Pulse interval graph of echolocation attack. Each circle connected by solid lines indicates the time of a sonar pulse and the duration of the following pulse interval. Open triangles indicate moth click half modulation cycles. The dotted line shows an attack on a control moth for reference. 29
SUPPORTING MATERIAL
MATERIALS and METHODS
Animals. All vertebrate care was conducted in accordance with Wake Forest University’s
Animal Care and Use Committee guidelines (ACUC # A04-188). One adult female and
several pre-volant juvenile big brown bats were taken from a roost in Winston-Salem,
NC, and brought into the lab at Wake Forest University. Bat care and training methods
replicated that of ref. S1 for E. fuscus . Noctuid and geometrid moths used for training and in experiments were captured at outdoor lights on the Wake Forest University campus in
Winston-Salem, NC. B. trigona were captured at UV lights at the Southwestern Research
Station near Portal, Arizona, and shipped overnight to our facility at Wake Forest
University.
Equipment. Experiments were conducted in an anechoic indoor flight room (5.8 x 4.0 x
3.0 m). Bat-moth interactions were recorded at 250 frames per second by two digital high-speed video cameras (Photron FastCam PCI © 500, Tokyo, Japan) using Photron
FastCam Viewer v.2.4 © installed on a desktop computer. Nine Wildlife Engineering ©
(Tucson, AZ, USA) LED arrays provided infrared illumination and a low-intensity deep
red light allowed for behavioral observation by the experimenter. We recorded
ultrasound, synced to the video, using a Petterson Elektronik © D940 bat detector
connected via a National Instruments ® (Austin, TX, USA) 6062 PCMCIA A/D to a
laptop computer running Bat Sound Pro © v.3.3.We used a 250 kHz sampling rate. The
microphone was directed downward from the ceiling 1.2 m from each of two walls.
Moths were tethered directly below the microphone on a thin, 60 cm monofilament line. 30
Video recordings were used to confirm the results of each bat-moth interaction and to
reconstruct the positions of animals and the microphone (see below).
3D Reconstruction. The locations of the bat, moth, and microphone and the flight
direction of the bat were calculated using a 3D reconstruction. The interaction space was
calibrated using 192 points from four calibration frames over a volume of 140 x 150 x
210 cm. Each point was digitized from each camera view in a custom Matlab ® (Natick,
Massachusetts, USA) program ( S2 ) that fit a set of Direct Linear Transformation (DLT)
coefficients to the data. Video files were subsequently calibrated using the DLT
coefficients in a second Matlab ® program ( S2 ). The “center-of-object” for the bat and
moth and the tip of the microphone were digitized from video files. Another custom
Matlab ® program (BATracker.m; coded by B. Chadwell) fit a quintic smoothing spline
(Matlab ® spaps routine) to the 3D coordinates. Distances between bat, moth, and
microphone were measured using the smoothed data from each video frame. The time
derivative of the bat’s spline function provided the animal’s flight direction.
Bioacoustic Analysis. Locations of echolocation calls within recordings were extracted
using a custom version of SonoBAT ® v. 2.8 (DnDesign, Arcata, CA). For each recording we measured the sequence of echolocation phases (approach, track, terminal buzz) employed by the bat. We required two or more successive pulse intervals of the same phase to mark a transition, as single aberrant pulse intervals normally occur in E. fuscus
attack sequences ( S3 ). We determined echolocation phase for E. fuscus based on the
following pulse interval ranges: approach, >30 ms; track, 12-30 ms; terminal 5-12 ms
(S4 ). To ensure that the bat had time to react to the moth clicks, we used the presence of 31
five or more track phase or terminal phase calls made after the onset of moth clicks to
indicate a continuing attack. Moth click definitions and measurement methods match
those of ref. S5 . The inward and outward buckling of a moth tymbal (Fig. 2.1A) generates
a train of microclicks called a modulation cycle (Fig. 2. S2 ). This cycle is divided into the
active half-cycle (the inward buckling), the passive half-cycle (the outward buckling),
and the intra-cycle silent interval. Each half-cycle comprises a series of microclicks
produced by the buckling of microtymbals (the fine ridges in Fig. 2.1A). Duty cycle is
sound production per unit time. Maximum duty cycle was estimated by counting the
number of microclicks within a 100 ms interval where click production peaked. This
number was then multiplied by the average microclick duration and divided by 100 ms.
Temporal parameters of clicks were measured from oscillograms using BatSound © Pro v.
3.3. Spectral features were measured from a power spectrum of a modulation cycle.
Measurements were taken from one modulation cycle per bat-moth recording unless otherwise indicated. Microclick duration was averaged over five microclicks per modulation cycle.
We used a standardized distance of 1 m for comparing intensities of echoes and moth clicks at the bat’s location (Fig. 2.1B). This ratio should be relatively constant at the bat’s location as it flies within 1 m of the moth. For each halving of distance, moth click intensity increases by 6 dB. Echoes would normally increase by 12 dB per halving of distance, but bats decrease the intensity of their emissions by about 6 dB per halving of distance within 1-2 m of a target ( S6, S7 ). Therefore, the increases in moth click and echo intensities would be roughly equivalent as the bat closes within 1-2 m of the moth. At 1 m, the bat directs its relatively wide sonar beam (-3 dB beamwidth of 70º) in the direction 32
of the target ( S8 ), which was nearly directly below the microphone (7.6º ± 6.7 s.d. angular deviation from the bat’s position). Using the 3D reconstruction for each interaction, we found the time when the bat closed within 1 m of the moth and selected the echolocation call produced nearest to this time. We then calculated the RMS intensities of this echolocation call and the first moth click modulation cycle uninterrupted by a bat call. Several adjustment factors were necessary to estimate the call:click ratio at the bat’s location. At 1 m from the moth, bats were 59º (± 10º s.d.) off- axis from the microphone. Our microphone showed an 18 dB decrease in sensitivity for sound sources recorded from this position relative to an on-axis sound source. The difference in vertical angle between the vectors from bat to moth and bat to microphone was 23º (±6.7 s.d.). The shape of the vertical component of the bat’s sonar beam in flight is not fully known ( S9 ), but the observed angle approximates the edge of the -6 dB beamwidth measured from an electrically stimulated, anesthetized E. fuscus ( S10 ). We assumed an attenuation of 45 dB between the emitted bat call and returning echo from a moth the size of B. trigona (34 mm wingspan, 12 mm body length) at 1 m ( S11 ). The horizontal and vertical position of the moth was assumed to change randomly as it fluttered on the tether. We therefore assumed equal moth click intensity directed toward the microphone and bat. Finally, we adjusted for attenuation of sound due to spherical spreading, accounting for the distances between bat, moth, and microphone.
Statistical Analysis. Much of our data were heteroscedastic and taken repeatedly on individual bats across nights and on successive attacks on individual moths. We therefore used paired, nonparametric statistics. For investigating learning behavior between nights or between successive attacks on the same moth we used a Friedman’s ANOVA. For 33 comparing contact rates between successive attacks we used only those bat-moth sessions with five approaches; for all other comparisons we used only the first encounter between each bat and moth. Analysis was conducted in SPSS ® v. 16.0 (SPSS, Chicago, IL), and all alpha levels were set to 0.05. 34
SUPPORTING TEXT
While it is beyond the scope of this study to determine the mechanism of jamming employed by B. trigona , the moth’s clicks appear well suited for that task. (Fig. 2.1,
Table 2.S2). The moths can produce up to 456 microclicks in 0.1 s and can approach a duty cycle of 100% (Table 2.S2), virtually guaranteeing that clicks occur within the crucial time window necessary for diminishing the bat’s target-ranging acuity (Table
2.S2, refs. S12, S13 ) or masking echoes ( S14 ). The frequency modulation of B. trigona clicks (Fig. 2.1B) may reproduce the sonic signature of the bat’s own call sufficiently for the bat to mistake clicks as echoes from objects that do not exist ( S15 ). And finally, the high duty cycle and spectral complexity of B. trigona clicks could overload the bat’s acoustic processing networks, causing further degradation in echo processing ability
(S16 ).
A reviewer suggested that these same qualities could make an alternative hypothesis to jamming more likely – the clicks may be unpleasantly loud but not interfere with bat sonar. However, neither the intensity of moth clicks nor bat behavior support this hypothesis. We estimated that within 1 m of the moth, click intensity was about 5 dB greater than echo intensity, or about as loud as an echo from a large moth; this level is unlikely to be aversive to bats. Previous studies using stationary E. fuscus in a target simulator found that extraneous echoes, noise, and moth clicks played at up to 21 dB greater than echoes, but temporally isolated from echoes, had no effect on bat echolocation ability ( S14 , S17 ). On the contrary, bats were often attracted to the extraneous sounds as they learned to associate them with a food reward ( S17 ). When 35
similar sounds were played within about 1 ms of the start of an echo, the bat’s ability to
discriminate distances of targets was degraded up to 400% ( S12, S13 ). In our study the
bats also had the opportunity to associate extraneous sound with a food reward – each of
the bats ate B. trigona in the few cases when capture occurred. The bats frequently flew close to sonic prey rather than aborting attacks and turning away (Movie S1). It therefore appears unlikely that the clicks were unpleasantly loud for the bats, which would quickly learn to avoid an acoustically noxious moth just as they would a chemically noxious one
(Fig. 2.S2). A more likely explanation is that the high-duty-cycle moth clicks, which frequently preceded and co-occurred with echoes, caused ranging errors and, or masking sufficient to prevent the bat from accurately tracking and capturing its prey. 36
FIGURE 2.S1
Attack persistence of E. fuscus on clicking moths through successive foraging nights. (A)
Three bats that found B. trigona palatable. (B) Four E. fuscus that found Cycnia tenera unpalatable. Data on C. tenera attacks were extracted from a previous experiment (S1). 37
FIGURE 2.S2
Oscillogram of a B. trigona click modulation cycle. A modulation cycle is composed of two half-cycles, the active and passive, separated by the intracycle silent interval. For further details see ref. S5 . 38
TABLE 2.S1 Contact rates for repeated big brown bat attacks on individual Bertholdia trigona
Bat N Approach 1 Approach 2 Approach 3 Approach 4 Approach 5
(%) (%) (%) (%) (%) J1 11 27.3 0.0 0.0 0.0 9.1
J2 9 11.1 22.2 44.4 44.4 33.3
A1 15 6.7 6.7 20.0 13.3 0.0
Total 46 17.4 6.5 17.8 15.2 10.9 39
Table 2.S2. Click parameters for the tiger moth Bertholdia trigona. Abbreviations: cdur, microclick pulse duration; mhc, modulation half cycle duration; mc, modulation cycle duration; isi, intercycle silent interval; clicks, microclicks per active modulation half cycle; d kHz, dominant frequency in kHz; -15 dB kHz, frequency -15 dB below d kHz;
+15 dB kHz, frequency -15 dB above d kHz. Values are mean ± s.d.
TABLE 2.S2
-15 dB +15 dB
N cdur mhc mc isi clicks d kHz kHz kHz maxDC
59 0.24±0.04 6.5±1.6 14.8±3.0 2.6±1.4 19.9±4.8 51.2±10.9 24.9±7.1 89.3±11.9 57.1±21.7 40
SUPPORTING REFERENCES
S1. J. R. Barber, W. E. Conner, Proc. Natl. Acad. Sci. U.S.A. 104, 9331-9334 (2007).
S2. T. L. Hedrick, Bioinsp. Biomim. 3, 034001 (2008).
S3. W. W. Wilson, C. F. Moss, in Echolocation in Bats and Dolphins, J. A. Thomas,
C. F. Moss, M. Vater, eds. (Univ. of Chicago Press, Chicago, 2004) pp. 365-380.
S4. S. A. Kick, J. A. Simmons, Neuroscience 4, 2725-2737 (1984).
S5. J. R. Barber, W. E. Conner, J. Exp. Biol. 209, 2637-2650 (2006).
S6. D. J. Hartley, J. Acoust. Soc. Am. 91, 1133-1149 (1992).
S7. S. Hiryu, T. Hagino, H. Riquimaroux, Y. Watanabe, J. Acoust. Soc. Am. 121, 1749-
1757 (2007).
S8. K. Ghose, C. F. Moss J. Acoust. Soc. Am. 114, 1120-1131 (2003).
S9. K. Ghose, C. F. Moss, J. Acoust. Soc. Am. 122, 3717-3724 (2007).
S10. D. J. Hartley, R. A. Suthers, J. Acoust. Soc. Am. 85, 1348-1351 (1989).
S11. D. A. Waters, J. Rydell, G. Jones, Behav. Ecol. Sociobiol. 37, 321-328 (1995).
S12. L. A. Miller, J. Comp. Physiol. A 168, 571-579 (1991).
S13. W. M. Masters, K. A. S. Raver, J. Comp. Physiol. A 179, 703-713 (1996).
S14. B. Mohl, A. Surlykke, J. Comp. Physiol. A 165, 119-124 (1989).
S15. J. H. Fullard, M. B. Fenton, J. A. Simmons, Can. J. Zool. 57, 647-649 (1979).
S16. J. R. Barber, K. A. Razak, M. Fuzessery, J. Comp. Physiol. A 189, 843-855.
S17. A. Surlykke, L. A. Miller, J. Comp. Physiol. A 156, 831-843 (1985). 41
CHAPTER III
How Do Tiger Moths Jam Bat Sonar?
Aaron J. Corcoran, Jesse R. Barber, Nickolay I. Hristov, and William E. Conner
The following manuscript was published in Journal of Experimental Biology (vol. 214, pgs. 2416-2425, 20011) and is reprinted with permission. Stylistic variations are due to requirements of the journal. A.J.C. designed and executed the experiments, conducted data analysis, and wrote the manuscript. J.R.B. assisted with field research. N.I.H. developed the three-dimensional video simulations. W.E.C. provided oversight and editorial assistance on the project. 42
ABSTRACT
The tiger moth Bertholdia trigona is the only animal in nature known to defend itself by
jamming the sonar of its predators -- bats. In this study we analyzed the 3-D flight paths
and echolocation behavior of big brown bats ( Eptesicus fuscus ) attacking B. trigona in a flight room over seven consecutive nights to determine the acoustic mechanism of the sonar-jamming defense. Three mechanisms have been proposed: (1) the phantom echo hypothesis, which states that bats misinterpret moth clicks as echoes; (2) the ranging interference hypothesis, which states that moth clicks degrade the bats’ precision in determining target distance; and (3) the masking hypothesis, which states that moth clicks mask the moth echoes entirely, making the moth temporarily invisible. On nights one and two of the experiment the bats appeared startled by the clicks; however on nights three through seven, the bats frequently missed their prey by a distance predicted by the ranging interference hypothesis (approx. 15-20 cm). 3-D simulations show that bats did not avoid phantom targets, and the bats’ ability to track clicking prey contradicts the predictions of the masking hypothesis. The moth clicks also forced the bats to reverse their stereotyped pattern of echolocation emissions during attack, even while bats continued pursuit of the moths. This likely further hinders the bats’ ability to track prey.
These results have implications for the evolution of sonar-jamming in tiger moths, and we suggest evolutionary pathways by which sonar-jamming may have evolved from other tiger moth defenses. 43
INTRODUCTION
Bats and moths are intertwined in a predator-prey relationship that has spanned at least 50 million years (Miller and Surlykke, 2001). In response to the heavy predation pressure of echolocating bats, many moths have evolved simple ears that alert them to the danger of attack and initiate unpredictable evasive maneuvers (Roeder, 1967 ) Tiger moths
(superfamily Noctuoidea, subfamily: Arctiinae) also produce ultrasonic clicks whose defensive functions vary depending on the acoustic and chemical properties of the moth species. Most species produce short bursts of clicks that maximally occupy 1-12 % of time with sound (Corcoran et al., 2010). These low-duty cycle clicks are well suited for advertising the toxic chemistry many tiger moths acquire as caterpillars (Hristov and
Conner, 2005a; Hristov and Conner, 2005b; Nishida, 2002; Ratcliffe and Fullard, 2005).
After bats learn this aposematic association, they can be misled by palatable tiger moths that mimic their chemically defended relatives (Barber and Conner, 2007; Barber et al.,
2009). Moth clicks can also startle bats unaccustomed to sonic prey; however, bats typically habituate to moth clicks after only a few exposures (Bates and Fenton, 1990;
Miller, 1991). A minority of tiger moth species produce copious bursts of clicks capable of filling 25-52% of time with broadband noise (Corcoran et al., 2010). We recently demonstrated that one such species – Bertholdia trigona Grote—defends itself by jamming the sonar of bats (Corcoran et al., 2009). This is the only known example of such a defense in nature. It was demonstrated using a behavioral learning paradigm
(Barber and Conner, 2007; Hristov and Conner, 2005a), whereby naïve bats (i.e. never having experienced moth clicks) were pitted against clicking moths over several consecutive nights. Against aposematic moths, bats first catch the distasteful moths 44 before learning to avoid them (Barber and Conner, 2007; Hristov and Conner, 2005a).
The reverse pattern can be observed for a startle defense – the bats are startled at first, but habituate after only a few exposures to clicking moths (Bates and Fenton 1990). Only for jamming should the defense be effective throughout the experiment, and this was found to be the case for bats attacking the abundantly clicking and palatable B. trigona
(Corcoran et al., 2009).
Three mechanisms have been proposed for how moths jam bat sonar. First, the phantom echo hypothesis states that moth clicks that are sufficiently similar to bat calls could be misperceived by bats as echoes from objects that do not exist (Fullard et al.,
1979; Fullard et al., 1994). If perceived as clutter, bats should veer away from phantom objects that occur on the flight path leading to the moth; or, if perceived as another prey item, bats should be observed trying to capture “phantom targets”. Second, the ranging interference hypothesis holds that clicks that overlap or closely precede echoes may diminish a bat’s precision in determining target distance and therefore prevent the bats from properly coordinating their final capture maneuvers (Miller, 1991). Ranging interference has been demonstrated for bats conducting standardized tasks in psychophysical experiments, but only when clicks occur in a 2 ms window preceding returning echoes (Miller, 1991; Masters and Raver, 1996). Finally, the masking hypothesis suggests that if clicks are sufficiently numerous and intense, they may prevent the bat from detecting the moth’s echo entirely (Møhl and Surlykke, 1989; Troest and
Møhl, 1986). This would make the moth temporarily invisible to the bat. While other mechanisms of sonar-jamming could be proposed, we aimed to test these three hypotheses, which have been the focus of the literature on the subject to date. 45
The three jamming hypotheses can be differentiated by what the bat perceives: multiple objects surrounding the moth for the phantom echo hypothesis, a blurred target for the ranging interference hypothesis, and no target for the masking hypothesis.
Therefore, the bats’ flight and echolocation behavior should vary according to each hypothesis. Here we expand upon our previous work demonstrating the existence of a sonar jamming defense (Corcoran et al., 2009) by analyzing details of bat 3-D flight and echolocation behavior to address the question of how moths jam bat sonar. 46
MATERIALS AND METHODS
Animals and equipment
B. trigona were collected using Bioquip (Rancho Dominguez, CA, USA) black lights set over riparian corridors at the Southwestern Research Station near Portal, AZ in
July 2008. Moths were shipped overnight to our lab at Wake Forest University where they were held at 12.5º C until experimentation. Noctuid control moths were collected at lights on the campus of Wake Forest University. Three naïve pre-volant juvenile big brown bats ( Eptesicus fuscus Beauvois; J1, J2, and J3) and one adult big brown bat (A1) were taken from a roost in Forsyth County, North Carolina and held in captivity for the duration of the experiment. Three of the four bats were found to readily eat B. trigona , while one did not (Corcoran et al., 2009). To exclude the possibility that bats were responding to clicks as aposematic signals, only bats that ate B. trigona were used in our analysis. Animal care has been described elsewhere (Corcoran et al., 2009) and was conducted in accordance with Wake Forest University’s Animal Care and Use
Committee (ACUC # A04-188). After each bat could reliably capture silent control moths (female Galleria mellonella Linnaeus ) off a monofilament line in our anechoic indoor flight room (5.8m x 4.0m x 3.0m), they were put through a seven-night experiment. Each night, 16 moths – four B. trigona , four similarly sized noctuid novelty controls, and eight G. mellonella —were tethered to a monofilament line, one at a time, and presented in random order to an individual bat in the flight room. The line was 60 cm long and attached to the ceiling at a point 1.2 m from each of two walls. Moths flew freely on the tether, eliminating the possibility that bats could memorize the moth’s exact location from previous trials. Bats were allowed one minute or five attack attempts on 47 each moth, however here we only analyze the first attack attempt on each moth (N = 47).
Attacks on moths that did not click (N =19) and attacks that were not fully captured on video (N =16) were excluded from our analysis.
All interactions were recorded with two digital high-speed video cameras
(Photron FastCam PCI 500, Tokyo, Japan) sampling 250 frames per second directly to the hard drive of a desktop computer. Nine Wildlife Engineering (Tucson, AZ, USA) infrared LED arrays illuminated the interaction space, as well as a low intensity deep red light for behavioral observation by the experimenter. An ultrasonic microphone
(Pettersson D940, Pettersson Elektronik, Uppsala, Sweden; ±8 dB frequency response from 20 – 80 kHz) was placed on the ceiling, facing downwards directly above the tethered moth to record audio of all interactions. The microphone was connected to a laptop computer via a National Instruments (Austin, TX, USA) 6062 PCMCIA A/D card.
Audio was sampled at 250 kHz using BatSound Pro v. 3.3 and externally triggered to record synchronously with the high-speed video.
3-D flight-track analysis
Four calibration frames constituting 192 points and a volume of 1.4 x 1.5 x 2.1 m were placed in view of both cameras to calibrate the interaction space. All points were digitized from frames taken from each camera using a custom Matlab (Natick,
Massachusetts, USA) program (Hedrick, 2008), which fit a set of Direct Linear
Transform (DLT) coordinates to the data. These DLT coordinates were then used in a second Matlab program (Hedrick, 2008) to calculate the 3-D coordinates of objects 48
occurring in view of both cameras. For each interaction, the “center-of-object” of bat and
moth were determined for each frame while both animals were visible in each camera.
A third custom Matlab program (BATracker.m; coded by B. Chadwell) fit a
quintic smoothing spline (Matlab SPAPS routine) to the 3-D coordinates. The bat and
moth spline functions were then used to determine flight vectors, distances between bat
and moth, and vectors from bat to moth for each point of each interaction. For all time
parameters, time zero was taken as the time of bat- moth contact or when the distance
between bat and moth was minimized. φ is defined as the angular deviation between the
bat’s flight vector and the vector from bat to moth (Fig. 3.1A). To quantify the
trajectories of bats completing successful attacks, φ values were measured from 20 successful bat attacks on noctuid control moths. For each frame of the attack (occurring at 4 ms intervals) from t = -600 ms to t = 0 ms, the mean, upper and lower 95% confidence intervals of φ were calculated. For each interaction between a bat and a clicking moth, the time when φ first exceeded the upper 95% confidence limit for control attacks was taken as an indication of when an attack was aborted (Time of flight abort; T-
Fabort; Fig. 3.1).
Bioacoustic Analysis
We used a custom version of Sonobat v. 2.9 (DnDesign, Arcata, CA, USA) to semi-automatically detect and measure parameters from echolocation calls from bat attacks. The automated processing routines of Sonobat were overseen by an observer to manually confirm the accuracy of all measurements. The time of each echolocation call and the time interval between successive echolocation calls, or pulse interval, were 49 graphed for each attack. Two response variables were measured from each graph – the minimum pulse interval, and the time when attack echolocation was aborted (T-Eabort;
Fig. 3.2). T-Eabort was taken as the time of the last echolocation pulse interval of less than 35 ms. If no pulse interval was less than 35 ms, the time of minimum pulse interval was taken instead. Two moth click parameters were measured to test if they predict bat response: number of click modulation cycles (Fig. 3.3) and time of first clicks (T-clicks).
To determine whether and how bats modify their echolocation in response to jamming, parameters were measured from four echolocation calls from each interaction: one call preceding the first moth click and three calls following the first moth click. Two parameters were measured from each echolocation call: duration and peak frequency. To calculate these measurements Sonobat generates a spectrogram of each recording using a
196-point window with a Hanning function. It then creates a time-frequency trace of the first harmonic (or fundamental) of the bat call. The start and end points are defined as the time points when the call intensity decreases below -20 dB relative to the maximum intensity measured in the bat call. All intensity values are measured from points on the spectrogram. Call duration equals the time between the start and end points. Peak frequency was measured from the point of maximum intensity in the spectrogram. This method was used in favor of measuring the peak of a power spectrum generated from the entire call in order to isolate the peak frequency of the fundamental. In previous tests, the power-spectrum method sometimes led to measuring a peak associated with the frequency where the upper portion of the fundamental and the lower portion of the second harmonic cumulatively produced a greater spectral level than that of the lower portion of the fundamental (Fig. 3.3A). This added unnecessary variation to the analysis. 50
To account for the directionality of bat emissions the vertical and horizontal angular deviations between bat and moth and bat and microphone were calculated for all attacks at the time when bat calls were measured. The horizontal deviation in angle was 7.6±6.7º and the vertical deviation was 23±6.7º. These values are within the -6 dB bandwidth of E. fuscus (Ghose and Moss 2003; Hartley and Suthers 1989).
Statistical Analysis
To quantify what factors affected bat echolocation and flight responses to moth clicks, we conducted a series of multivariate linear regressions on each of four response variables: minimum bat-moth distance, T-Fabort, minimum pulse interval, and T-Eabort.
Five predictor variables were used for each regression: bat experience (in nights), moth click modulation cycle number, T-clicks, J1 dummy, and J2 dummy. Each dummy variable codes the attacks of J1 and J2 separately, allowing us to test and account for individual differences. Predictor variables had low correlations (R 2 < 0.12). For each response variable we created a single regression model that added all five predictor variables simultaneously. We also analyzed correlation values between all predictor and response variables and compared these results to our multiple regressions to look for interaction effects among predictor variables. This approach avoids the numerous problems associated with stepwise multiple linear regression (Tabachnick and Fidell
2007).
To determine how bats responded acoustically to moth clicks, we conducted a series of repeated-measures Analysis of Variance (ANOVA) tests on echolocation call parameters measured from one echolocation call preceding moth clicks and three calls 51
following clicks. In cases where sphericity could not be assumed the Greenhouse-Geisser correction was applied. Post-hoc pairwise comparisons were made between all groups with Bonferroni correction applied to adjust P-values for multiple comparisons. Due to heterogeneity of variance, we used a non-parametric Friedman’s ANOVA to test for differences in pulse interval values before and after moth clicks. Non-parametric post-hoc tests with adjusted P-values for multiple comparisons were used to test for differences between mean values.
For all tests, a separate statistical analysis was conducted for each category of bat attack (for categories, see Results ) and on 20 randomly selected attacks on silent control moths. We selected four calls (one before clicks and three after clicks) from each control attack so that they matched the timing of calls selected from attacks on clicking moths.
Measured T-click values were selected at random from attacks on clicking moths and applied to control attacks. For example, if a T-click value of -450 ms was selected, the control attack was processed as though clicks occurred 450 ms before the point of contact.
Statistical analysis was conducted in SPSS 16.0 (SPSS, Inc., Chicago, IL, USA). α was set at 0.05 and data are reported as mean ± s.d.
3-D Simulations
To test the plausibility of the three jamming hypotheses (phantom echo, ranging interference, and masking), we simulated attacks according to assumptions that follow from each hypothesis. According to the phantom echo hypothesis, bats perceive click bursts not as extrinsic sounds, but as echoes from objects. We assume that the bats would perceive these sounds in the same directional vector as that of the sound source – the 52 moth. Because the interval (325 µs) between moth clicks (Corcoran et al., 2009) is less than the bat’s 400 µs temporal resolution (Simmons et al. 1989), clicks within a burst would blur into one continuous sound, and are therefore modeled as one object. Due to the mechanism of sound production, moth click bursts come in pairs. The first click burst
(termed the active half-modulation cycle) decreases in frequency over time, as does the sweep of an echolocation call (Fig. 3.3). The second burst (the passive half-modulation cycle) changes frequency in the opposite direction. We therefore modeled only the active half-modulation cycles as phantom echoes. To calculate the distance from the bat to each phantom object, the time interval from the beginning of the echolocation call preceding a click burst to the beginning of the click burst was divided by the speed of sound (approx.
343 m/s) and again divided by two for the two-way travel distance. Measurements were made on click bursts from each of five randomly selected attacks from each of the three bats used in the experiment.
Ranging interference occurs only when clicks are present within a 2 ms interval preceding the return of echoes (Masters and Raver, 1996; Miller, 1991). For each attack, we determined how often clicks occurred within this crucial time window. To do this, we first used the 3-D reconstruction of the attack to determine the distance between bat and moth at the time each call was produced. We then calculated the two-way travel time of sound covering this distance and marked whether or not moth clicks occurred within the
2 ms time interval surrounding the estimated time of the returning echo. Masking requires sound to overlap the short time window of returning echoes (Møhl and Surlykke, 1989).
While the masking window is slightly offset from the ranging interference window they 53 are of similar durations, and therefore our measurements on ranging interference should roughly apply to the hypothesis of masking.
RESULTS
3-D flight and echolocation responses to jamming
Bat flight response to clicking moths took three forms: (1) in direct attacks bats completed flight paths typical of successful attacks on control moths (N = 11; Fig. 3.4 A,
D) and either captured the moths or made contact after making at least a partial capture attempt; (2) in close-range attacks bats continued their attacks after clicks and narrowly missed the moth without making a capture attempt (N = 24; Fig 3.4 B, E); and (3) in avoidances bats aborted attacks soon after hearing clicks and also did not make capture attempts (N = 14; Fig. 3.4 C, F). The three attack types were distinguished by their φ vs. time plots (Fig. 3.4D- F). In direct attacks, φ values (the angle between the bat’s flight vector and the bat-moth vector) did not exceed the upper 95% confidence limit of control attacks on non-clicking moths; close-range plots showed decreasing or constant φ values
(indicating continued moth pursuit) after the moth clicked, followed by a breach of the upper 95% confidence level of control attacks; avoidances showed immediate increases in φ values towards the upper 95% confidence level of control att acks after the moths clicked.
Bat echolocation responses (Fig. 3.4G-I) mirrored the differences in flight responses (Fig. 3.4D-F). Bats in direct attacks typically completed echolocation sequences with a buzz, or trill of calls with low (approx. 6 ms) pulse intervals. Buzzes were always present in attacks on control moths (Fig. 3.6A). Close-range attacks were 54
dominated by short pulse intervals (10-40 ms) that are typically used for bats
investigating near-by objects. Buzzes in these attacks were often highly abbreviated (Fig.
3.4H, 3.6B). In avoidances, bats began elongating pulse intervals early in the attack
(approx. 250 ms before minimum bat-moth distance; Fig. 3.4I, 3.6C), and did not buzz.
Bat experience was a consistent predictor of bat flight and echolocation response
to moth clicks (Table 3.1); bats continued attack flight and attack echolocation longer,
and flew closer to their prey, as they gained experience. This trend appears to be driven
by the higher proportion of avoidances and lower proportion of direct and close-range
attacks on the first two nights of the experiment (Fisher’s exact test; Χ2 =6.66; P = 0.04;
Fig. 3.5). This suggests that bats were initially startled by the clicks and habituated after about two nights. After the habituation period, close-range attacks were the most frequent result (Fig. 3.5).
The number of click modulation cycles was also important in predicting bat response (Table 3.1); bats flew closer, used attack echolocation later into the attack and achieved shorter minimum pulse intervals against moths that clicked less. This result appears to be driven by the difference in number of modulation cycles between direct attacks and other attack categories (18.7±16.7 modulation cycles in direct attacks vs.
38.0±23.7 modulation cycles for all other attacks; Student’s t-test; t=-2.52; d.f. = 47; P =
0.02). Moths that clicked less were less successful at deterring their predators. The timing of clicks was also found to be important in when bats aborted attack echolocation, but not any of the other three response variables. 55
Finally, J1 dummy was significant in the T-Fabort model; that is, one juvenile bat aborted flight earlier than the adult bat. J2 dummy was not significant in any model. The relatively small impact of individual dummy variables in our models indicates that the three bats tended to respond similarly to jamming. Together, these models account for about one third of the variation in bat responses to clicking moths. In summary, bat flight and echolocation responses to moth clicks demonstrated a high level of agreement on three results: (1) bats aborted attacks more frequently early in the experiment; (2) bats completed successful attacks more often against moths that clicked less; and (3) after the initial habituation period the most frequent result was for bats to narrowly miss their prey.
Immediate echolocation reaction to jamming
We looked for differences in parameters from echolocation calls made prior to and just after moth clicks to determine the bats’ acoustic reaction to jamming (Table 3.2).
As expected, in control attacks the bats decreased their call durations, and while not statistically significant, a trend towards decreasing pulse intervals and decreasing peak frequencies was observed. In contrast, in close-range attacks and avoidances bats increased call durations and pulse intervals in response to moth clicks (Table 3.2; Fig.
3.6). These results show a reversal of the changes in echolocation emissions typical of attack sequences. No changes in peak frequency were found. Interestingly, the changes of echolocation observed in close-range attacks were short-lived – the bats frequently advanced echolocation towards a buzz a second time as they continued pursuing their prey (Fig. 3.6B).
3-D simulations 56
To determine the plausibility of each sonar-jamming mechanism, the frequency of clicks occurring at critical time periods were calculated according to assumptions made for each hypothesis. Moths began clicking 379±190 ms before minimum bat-moth distance and produced 33.7±23.6 modulation cycles per attack. Moth clicks occurred in
13.5±5.8 of 16.1±6.8 pulse intervals (83.4%) that occurred after moths began clicking.
The locations of phantom echoes relative to actual targets were simulated for 15 randomly selected attacks that occurred after the habituation period. Of the 385 simulated phantom echoes, 307 (79.9%) occurred 25 -2624 cm beyond the target; 39 (1.0%), or 2.6 phantom echoes per attack, occurred within 25 cm of the target, and 38 (1.0%), or 2.5 phantom echoes per attack, occurred 25 – 134 cm in front of the target. These figures are roughly what would be expected by chance, as the time intervals that would place phantom objects between bat and moth or within 25 cm of the moth are much shorter than the time interval that would place phantom objects distant to the moth. Fig. 3.7A, C and Movie 1 show the modeled locations of phantom objects, as well as the measured locations of the bat and moth of an example attack. The locations of phantom objects are randomly distributed spatially and lack spatial continuity over time. If perceived in such a way, phantom objects would flash briefly in disparate locations. While a small number of simulated phantom objects do occur near the bat’s flight path, the bat’s flight trajectory does not appear to be influenced by them.
The frequency of clicks occurring in the critical ranging interference window was measured for all attacks. Moth clicks occurred in 11.6±5.5 (85.3% of calls with clicks in preceding call intervals) critical call windows per attack. Fig. 3.7B, C and Movie 2 show an individual attack with overlaid ranging errors based on previous interference estimates 57
(Miller, 1991). In this incidence, and in close-range attacks overall, the bat missed its prey by a distance similar to the ranging error predicted by the ranging interference hypothesis (approx. 15-20 cm) and no capture attempt was made. 58
DISCUSSION
Bats habituate slowly to jamming signals.
Substantial variation was observed in the flight and echolocation responses of bats
to moth clicks (Fig. 3.4). A primary predictor of this variation was bat experience (Table
3.1), with three of four response variables demonstrating that bats came closer to
completing attacks as they gained experience. This pattern suggests that bats were
startled by the moth clicks early in the experiments. The bats appeared to habituate to the
clicks after about two nights (Fig. 3.5). Bats typically habituate to moth clicks in one to
three trials (Bates and Fenton 1990; Miller, 1991). However, naïve bats attacking tethered,
low-duty cycle moths under nearly identical conditions to that of this study showed little to no startle response (Barber and Conner, 2007; Barber et al., 2009; Hristov and Conner,
2005a).
In the first two nights of our experiments, each bat had up to 40 exposures (four moths per night and up to five attack attempts per trial) to B. trigona moth clicks prior to habituation. Therefore, bats appeared to take much longer to habituate to the high-duty- cycle clicks of B. trigona than the low-duty-cycle clicks of other tiger moths. B. trigona and other low-duty-cycle moths tested in our experiments have similar peSPL values
(approx. 80-85 dB at 5 cm; Corcoran et al. 2010). The densely produced B. trigona clicks would have a 6 dB increase of power as perceived by the bat due to the number of clicks present within the bats integration window of 2.4 ms (click measurements based on average values; Corcoran et al. 2010; Surlykke and Bojeson, 1996). However this does not appear sufficient to explain the observed differences in habituation rates. We suggest 59 that the bats require longer habituating to B. trigona clicks due to their jamming effect.
This is akin to the situation for Catocala moths flashing their colorful hind wings to Blue
Jays; predators take longer to habituate to more conspicuous startle signals (Ingalls 1993).
Bat echolocation and flight responses to moth clicks support the ranging interference hypothesis.
After bats habituated to the clicks, they were still largely unsuccessful in capturing moths (30% attack success on nights 3-7; Fig. 3.5). The few successful attacks were typically on moths that produced few clicks. This supports the notion that only clicks produced in sufficient number are effective at jamming (Corcoran et al., 2009;
Corcoran et al., 2010; Hristov and Conner, 2005a ; Miller, 1991; Tougaard, 1999). The most frequent bat response to jamming after the initial habituation period was a close- range attack (Fig. 3.5). In these attacks, bats missed their prey by 15.7±7.9 cm and did not make capture attempts. These results are consistent with predictions of the ranging interference hypothesis. The absence of capture behavior suggests that bats were not trying to capture phantom objects, while the low percentage of avoidances on nights after the habituation period suggests that bats were not trying to avoid flying into phantom objects. Finally, the proximity of the bats to their prey and the continued use of attack echolocation at relevant times demonstrate that the moth’s presence was not completely masked by the moth clicks. We take these results as strong evidence in support of the ranging interference hypothesis.
The 3-D simulations demonstrate how ranging interference may affect an attacking bat (Fig. 3.7; Movie 2). At a distance the bat is able to gather enough positional 60 information on its prey to direct its flight trajectory appropriately. However, the bat is insufficiently aware of the prey’s precise location to coordinate its final capture maneuver.
Bats attacking tethered prey exhibit a stereotyped wing beat pattern for the last 400 ms of the attack, demonstrating that bats begin coordinating their capture attempts well in advance of contact (Wilson and Moss, 2004). This time period closely matches the time when moths jam bats. The coordination required to successfully capture prey would likely be obstructed by an imprecise knowledge of the prey’s location.
B. trigona clicks frequently occurred in the time window required for ranging interference to occur. In a two-alternative forced choice experiment involving bats performing tasks from a platform, Miller (Miller, 1991) demonstrated that clicks occurring in as few as 10% of critical time windows were sufficient to degrade ranging ability of bats. We found that during clicking bouts, clicks occurred in 85% of critical windows. When the moths click at full capacity there is rarely a millisecond absent of sound (Fig. 3.3). This capacity appears well adapted for ensuring that interfering sound occurs in the narrow time intervals when echoes are returning to the bat. Based on spectrogram correlation analysis, B. trigona clicks have been found to be highly disruptive of the acoustic properties of echoes (Corcoran et al., 2010). Also, clicks occurring in such critical windows have been demonstrated to cause latency ambiguity and suppression of neural responses to auditory stimuli in the nuclei of the lateral lemniscus of E. fuscus (Tougaard et al., 1998, Tougaard et al., 2004). These cells are specialized for coding the precise timing of echoes, and therefore target distance (Covey and Casseday, 1991). In summary, we now have behavioral evidence from bats attacking their natural prey and bats performing standardizing laboratory tasks, acoustic signal 61 processing evidence, and a neurophysiological mechanism, all supporting the ranging interference hypothesis.
Previous research has provided little evidence in support of the phantom echo and masking hypotheses. Behavioral and neurophysiological experiments have generally rejected the phantom echo hypothesis (Miller 1991; Møhl and Surlykke, 1989; Surlykke and Miller, 1985). And from an acoustic signal processing perspective, moth clicks are far from similar enough to bat calls to be mistaken by bats as echoes (Corcoran et al.,
2010; Surlykke and Miller, 1985). Recent work has found that bats can distinguish even the subtle differences of calls made by different individual bats (Yovel et al., 2009).
Therefore it is difficult to imagine bats mistaking moth clicks, which only have superficial similarities to bat calls, for echoes (Fig. 3.3).
Masking has previously been rejected as a function for low-duty cycle moth clicks based on the low probability of a sufficient number of clicks arriving simultaneously with returning echoes (Møhl and Surlykke, 1989). While B. trigona clicks met this requirement (overlapping 12 echoes per attack on average), the behavioral response of bats in our study demonstrates that complete masking was not achieved. However, we cannot eliminate the possibility that partial masking of echoes has an effect on bat perception. For example, the broadband moth clicks may disrupt spectral notches in echoes that may be indicative of multiple-wave-front echoes which provide information about the shape of targets (Simmons et al. 1989). These effects would be in addition to the ranging interference that appears to occur. 62
The echolocation reaction to moth clicks interferes with the stereotyped echolocation attack sequence
Bats attacking prey use a stereotyped pattern of changes in echolocation whereby pulse intervals, call durations, call intensities, and call frequencies decrease as bats near their prey (Griffin et al., 1960, Kick and Simmons 1984, Wilson and Moss, 2004). These acoustic alterations are highly adaptive for bats pursuing prey. The rapid emission of pulses late in the attack allows the bats to quickly update their “sonar screens” as to the position of their prey. The decreasing call durations prevent overlap between calls and echoes, which occur closer together as bats near their prey (Wilson and Moss, 2004). The decreased intensity of call emissions keeps returning echoes at a relatively stable level, which may help bats avoid ranging errors caused by time-intensity trading neurons, and allow bats to isolate amplitude changes caused by prey movements (Hartley 1992; Hiryu et al. 2008). And finally, the lower frequency of terminal buzz calls has the effect of changing the directionality of the emissions from a narrow beam to a wide angle in order to keep the prey in the ensonified volume (Jakobsen and Surlykke, 2010).
The bats in our study reversed this stereotyped pattern by elongating call durations and pulse intervals after hearing moth clicks, resulting in highly abbreviated or absent buzzes (Table 3.2; Fig. 3.6). In some cases (avoidances) these changes in echolocation reflected a decision to abandon pursuit of the prey. In others (close-range attacks) the changes in call structure were temporary and the bats continued pursuing the moths with a highly atypical echolocation sequence (Fig. 3.6B). Others have reported bats elongating pulse intervals in response to moth clicks, albeit for much shorter periods of time than what is reported here (Barber and Conner, 2007; Ratcliffe and Fullard, 2005). 63
These changes do not appear to be in response to the ranging interference caused by the clicks, as bats elongate pulse intervals even when clicks do not overlap echoes (Ratcliffe and Fullard, 2005) and ranging interference alone does not cause bats to change their echolocation (Miller, 1991). Instead, the bats may be compensating for the processing demands of two information streams (Barber et al., 2003).
Mexican free-tailed bats ( Tadarida brasiliensis ) flying in the laboratory increase the duration, bandwidth and amplitude of sonar emissions in response to broadband noise, an acoustic response typical of many vertebrates (Tressler and Smotherman, 2009). This response is presumably aimed at improving the detection of the signal in noise. The lengthening of call durations that we observed may be an attempt to do the same. Bats also shift call frequency in order to avoid jamming by conspecifics (Gillam et al., 2007;
Ratcliffe et al., 2004; Ulanovsky et al., 2004), chorusing insects (Gillam and McCracken,
2007), and synthetic narrowband noise (Bates et al., 2008; Tressler and Smotherman,
2009), but not broadband noise (Bates et al., 2008; Tressler and Smotherman, 2009). We did not observe bats changing the frequency of their emissions in response to moth clicks, only the durations and pulse intervals. The changes in echolocation we observed in close- range attacks appear to be an effort to avoid jamming; however, this comes at the cost of losing the advantages conferred by the specialized attack echolocation that bats more commonly employ. This has the end result of the bats being unable to capture their clicking prey. 64
CONCLUSION
The primary objective of this study was to determine the mechanism of the sonar jamming defense used by high-duty-cycle tiger moths. Our results, combined with evidence from the literature, provide strong support for the ranging interference hypothesis. In addition, the clicks disrupt the bats’ stereotyped echolocation attack sequence. The primary requirement for this defense is the ability to produce numerous, densely packed clicks continuously for several hundred milliseconds. By comparing the sounds produced by low-duty-cycle and high-duty-cycle moths (Barber and Conner, 2006;
Corcoran et al., 2010), we see that this is accomplished in three ways: (1) the morphological development of many striations on the bilateral tymbal organs in order to produce numerous clicks per modulation cycle; (2) the rapid and repeated behavioral activation of the tymbal organs; and (3) the rhythmic, alternating activation of the two tymbal organs to maximally occupy time with clicks. These observations provide a hypothetical blueprint for the evolution of a sonar-jamming signal. As has been suggested previously (Barber and Conner, 2006; Corcoran et al., 2010), it appears likely that tiger moths originally developed the ability to click in order to advertise their toxic chemistry
(Hristov and Conner, 2005a; Hristov and Conner 2005b), and later other tiger moths took advantage of this association through Batesian and Mullerian mimicry (Barber and
Conner, 2007; Barber et al., 2009). A rudimentary form of jamming may have made for a more salient warning cue for acoustic aposematic moths (Ratcliffe and Fullard, 2005), or provided some modest benefit to mimetic animals avoiding capture by potentially discerning predators (Barber et al., 2009). At this point a clear path was open to the evolution of a jamming signal. Currently, the arctiine phylogeny remains unresolved 65
(Weller et al. 2009). Without this evolutionary framework, we are unable to understand the order in which their various chemical and acoustic defenses were acquired. However, by understanding the mechanism of the sonar-jamming defense, we have demonstrated that in all probability, few barriers existed for tiger moths to adapt their pre-existing clicking defenses for sonar-jamming. 66
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Figure 3.1. Diagram illustrating bat flight parameters. (A) Overhead view of a bat
attacking a moth. At each point in time φ is measured as the angle between the bat’s
flight vector and the bat-moth vector. The minimum bat-moth distance is one of two
flight response parameters measured from each attack. (B) Time vs . φ plots of the mean and upper 95 % confidence interval of 20 successful attacks on control moths, as well as that of the attack in A. T-Fabort, the second flight parameter, is the time in an attack when φ exceeds the upper 95% confidence interval of cont rol attacks. The point of minimum bat-moth distance is used as the zero time point for each attack. 73
Figure 3.2. Example pulse interval graph of a big brown bat unsuccessfully attacking a sonar-jamming moth. Two echolocation response variables were measured from each attack: the minimum pulse interval (MinPI), and the time of aborted attack echolocation
(T-Eabort). T-Eabort is the time of the last pulse interval of less than 35 ms (dashed line); such pulse intervals are only used by bats examining near-by objects. For pulse interval graphs from successful bat attacks see Wilson and Moss (2004). 74
Figure 3.3. Oscillograms (top), spectrograms (bottom left) and power spectra (bottom
right) of (A) an approach phase echolocation call of a big brown bat ( Eptesicus fuscus ) and (B) two overlapping click modulation cycles of Bertholdia trigona . Each modulation cycle includes two series of clicks – the active and passive half-modulation cycles-- that change in frequency downward and upward, respectively. Each modulation cycle shown is made by one of the two thoracic tymbal organs. This figure was adapted from Corcoran et al. (2009) with permission from Science . 75
Figure 3.4. Three categories of bat behavioral responses to moth clicks, as illustrated by
bat flight and echolocation behavior. (A-C) These overhead views of example attacks
illustrate each of three bat flight responses to moth clicks. Time in seconds until
minimum bat-moth distance is denoted along the bat flight path. (D- F) φ vs. time graphs were used to distinguish between the three attack types. Note the differences in shape between the plots of each category, and when each plot intersects the upper 95% confidence limit line, which was measured from 20 successful attacks on control moths.
(G-F) Pulse interval graphs demonstrate differences in echolocation behavior that correlate with observed differences in flight behavior. The thick dashed lines indicate median values. 76
Figure 3.5. The distribution of bat responses to moth jamming changed after the first two nights of the experiment (Fisher’s exact test; Χ2 =6.66; P = 0.04; Fig. 3.5). For examples of responses see Fig. 3.3. For days one and two, N = 11 and for days three through five,
N = 36. 77
Figure 3.6. Spectrograms of three attacks on silent control or clicking experimental moths.
Arrows indicate locations of echolocation calls in attacks where Bertholdia trigona moth clicks are present. (A) A big brown bat ( Eptesicus fuscus ) successfully attacks a noctuid
control moth using a stereotyped pattern of echolocation emissions whereby the interval
between pulses decreases as the bat nears its prey. The attack ends with a buzz, or a series
of rapidly emitted calls. (B) In a close-range attack on a clicking B. trigona , the moth’s clicks disrupt the bat’s normal pattern of echolocation emissions. The bat’s ensuing use of intermediate pulse intervals and an abbreviated buzz just before 0 ms indicates a continued, but unsuccessful, pursuit of the moth. (C) In an avoidance attack on a clicking
B. trigona , the bat immediately lengthens pulse intervals to a rate that indicates the bat is no longer pursuing the moth. No buzz is present. Time zero represents the point of contact in A and the point of minimum bat-moth distance in B, C. This figure was adapted from Corcoran et al. (2009) with permission from Science . 78
Figure 3.6 79
Figure 3.7. Simulations of competing hypotheses of sonar-jamming mechanisms. (A) The modeled distances to phantom objects show poor consistency in location over time in comparison to the distances to the actual target. (B) The modeled ranging errors are proportionately greater compared to the bat-moth distance as the bat nears its prey. (C) In an overhead view of a simulation of the phantom echo hypothesis, the bat’s flight trajectory does not appear influenced by the modeled locations of phantom objects. (D) A simulation of the ranging interference hypothesis shows a bat missing its prey by a distance similar to predicted ranging errors. 80
Table 3.1. Multiple linear regressions of bat flight and echolocation responses to moth jamming.
Response Variable
BM -Dist T-Fabort MinPI T-Eabort
Predictor variables B P B P B P B P
Bat experience (nights) -0.32 0.03 0.34 0.009 -0.14 0.30 0.39 0.003
Click modulation cycles 0.38 0.03 -0.27 0.09 0.50 0.006 -0.34 0.04
T-clicks 0.13 0.81 0.07 0.65 0.16 0.38 -0.32 0.05
J1 dummy 0.26 0.10 -0.45 0.002 -0.09 0.55 0.07 0.60
J2 dummy 0.15 0.33 -0.20 0.22 0.07 0.65 0.22 0.11
Model R=0.58 0.01 R=0.61 0.001 R=0.47 0.05 R=0.61 0.001
BM-Dist ; minimum bat-moth distance; T-Fabort , time of flight abort (see Figure 3.1);
MinPI , minimum pulse interval; T-Eabort , time of aborted echolocation (see Figure 3.2);
T-clicks , time of moth clicks; J1 and J2 dummy, juvenile one and juvenile two dummy variables. B, standardized beta regression coefficient indicating the direction and strength of relationship between variables. P, P-value. Statistically significant comparisons are in bold type. 81 19.0±7.1 -17.9±10.3 38.5±10.6 -20.5±10.1(c) 23.2±11.5 Call/PI3 34.6±7.7 43.8±4.8 37.7±7.9 - 2.7±1.0(b) 42.6±6.9 38.2±8.8(a) 40.3±9.6 48.6±33.0(b) 63.1±33.6(b) 37.6±6.5 1.8±0.4 3.0±1.0(a) 2.1±0.8(b) -16.0±9.9 29.7±27.1 19.7±7.2 -17.0±11.0 39.1±10.0 20.9±10.1 Call/PI2 35.2±9.3 37.2±8.8 42.4±6.4 - 2.2±0.8(a) 44.1±8.4 40.8±8.3(a) 39.5±11.8 39.8±30.0(a,b) 46.3±23.5(b) -14.7±8.1(b) 36.9±9.9 -15.4±9.9 1.9±0.5 2.4±0.9(a) 2.3±0.9(a,b) 27.1±20.6 ±14.9 20.1±6.9 -17.7±10.3 40.4±9.0 43.8±11.0 41.5±8.0(a) 38.8±12.1 - 2.1±0.6(a) 26.3±14.1 1.8±0.4 Call/PI1 36.8±10.6 34.5±8.5 44.3±4.9 32.2±24.5(a,b) 27.0±14.9(a) -11.8±6.7(a) 35.7±10.7 -14.9±10.3 2.3±0.7(a) 2.5±0.9(a) 22.3 ±13.5 ±11.1 ±15.4 ±0.6 20.4±7.0 -15.9±9.2 39.7±9.1 45.6±10.8 41.5±7.6(a) 40.8±10.4 - 2.4±0.6(a,b) 26.4 2.1 Call/PI0 35.3 35.9±9.2 45.3±5.6 27.8±12.7(a) 27.0±15.2(a) -11.5±5.7(a) 36.8±10.5 -15.5±9.0 2.6±0.8(a) 2.5±0.9(a) 22.2 (Q) 0.39 0.43 0.06 Adj. P 0.68 0.50 0.43 0.57 0.06 0.47 0.57 0.003 0.03 0.003 0.11 0.003 0.59 0.48 0.04 0.003 0.65 -value; -value; Call/PI, echolocation call or pulse interval. P 0.28 0.35 0.03 0.99 0.54 0.37 0.01 0.001 0.07 0.001 0.70 0.037 0.44 0.66 0.001 0.77 0.49 P- value 0.016 0.001 0.90 3 3 3 3 2.2* 3 3 3 3 3 1.5* 3 3 3 3 3 3 d.f. 3 3 3 3 3 3 3 =11.23 =19.63 =0.60 =7.09 2 2 2 2 1.34 1.12 3.38 0.42 0.65 1.08 7.95 Χ Χ Χ 0.48 4.12 0.921 0.54 5.88 F 0.377 0.81 7.14 Χ 3.89 11 20 20 11 24 11 24 14 14 24 14 20 24 14 20 N 11 24 20 11 14 (Q) discovery ratefalse adjusted P range † range - - Direct Control Avoid Control Close-range Direct Direct Close-range Avoid Avoid Close Avoid Control Close Avoid Control Direct Close-range Attack typeAttack Control Direct This response is not a shift in spectral features made by the bat, but more likely due to bats directing their sonar away from the moths and the Harmonic intensity d.f., degrees of freedom; Adj. (ms) (ms) re.fundamental Duration *degrees of freedom adjusted due to lack of sphericity. For examples of attack types see Figure 3.3. † microphone. See text for further explanation. (kHz) Bandwidth (kHz) Table 3.2. Bat echolocation responses to moth jamming 3.2. Bat responses echolocation Table Parameter Peak Frequency PulseInterval (dB) 82
CHAPTER IV
Sonar Jamming in the Field: Effectiveness and Behavior of a Unique Prey Defense
A.J. Corcoran, and W. E. Conner
The following manuscript was published in Journal of Experimental Biology (vol. 215, pgs. 4278-87, 2012) and is reprinted with permission. Stylistic variations are due to requirements of the journal. A.J.C. designed and executed the experiments, conducted data analysis, and wrote the manuscript. W.E.C. provided oversight and editorial assistance on the project. 83
ABSTRACT
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 Grote’s tiger moth, Bertholdia trigona, with their sound-producing organs either intact or ablated. Jamming was highly effective, with bats capturing more than ten 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 rates 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. 84
INTRODUCTION
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 which 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, while 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 85
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, 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 ( Eptesticus fuscus ) in captivity (Corcoran et al ., 2009). To disrupt
echolocation, moth clicks must occur in a narrow 1-2 ms 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 which are involved in encoding
target distance (Touggard et al. 1998). The number of clicks that fall within this window
determine 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 86 predicted by psychophysical studies (Corcoran et al. 2011). Field studies of sonar- jamming moths have not previously been attempted.
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).
While 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 encounters.
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 moth 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 87
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 ( B. trigona ) known to jam the
sonar of its predator. 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), 8 km
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 25 m away from the observation area.
We recorded interactions using three Basler Scout cameras (model scA640-120gc;
Ahrensburg, Germany) recording at 60 frames-per-second at 640x480 resolution (Figure
4.1). 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 88
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 146 cm apart) using the
MaxTraq3D software. Over 2500 frames, the points had a mean error of 3.8 mm, or
0.26%. Our calibration volume was approximately 6 m x 6 m x 5 m, or 180 m 3.
Audio equipment
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 1.5 m off the
ground, in a square-like arrangement around the calibration volume (Figure 4.1). 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 89
and video to be <1 ms. The four microphones allowed us to record the relatively quiet
clicks of B. trigona and 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 15-watt ultraviolet lights (Leptraps LLC, Georgetown, KY, USA) placed on 4-m poles, with the two poles placed approximately 5 m apart (Figure 4.1). 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. Early in the evening (between approximately sunset and midnight), the many moths around the lights interfered with our ability to isolate individual bat-moth interactions. As the evening progressed (between 0000 and 0400), moth activity decreased, but the foraging bats remained. This is when we conducted our experiments. Individual B. trigona had previously been collected at black-lights within 100 m of the recording area. Moths were held individually in 30-ml plastic cups for up to 24 hours 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 90 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 1-m 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 one to three minutes. Several methods were used to ensure that the moths 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). 91
Based on 19 years of mist-netting capture data, 16 insectivorous bat species are
known to regularly occur at our field site (J. Tyburec, Bat Conservation International,
personal communication). Of these, 12 were included in our acoustic species classifier;
Idionycteris phylottis , Myotis auriculus , M.velifer , and Nyctinomops macrotus lacked a
sufficient number of known recordings to be included. I. phylottis and N. macrotus
produce characteristically low-frequency calls (12-17 kHz) that were not recorded during
the experiments. M. 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.
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” 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 β ( the angular deviation between the moth’s flight vector and the bat-moth vector) combined with a rapid rise in 92
tangential acceleration. Alternatively, a fly-away could also be designated by the moth
rapidly increasing translational acceleration while β remai ned 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 (100-300 ms) after clicking began. The duration of each attack was taken as the total time when φ (the angular deviation between the bat’s instantaneous flight vector and the vector from the bat to the moth) was less than 45 degrees.
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 1000 frames- per-second. In order to restrain the moths’ flight to an area within the camera’s view, two ultraviolet LEDs were suspended 1.5 m from the ceiling of an outdoor flight cage (6m x
4m x 3m) and approximately one meter 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
Statistical analysis was conducted in IBM SPSS version 19 (Armonk, NY, USA) and
R version 2.12.1 (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 93
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.
RESULTS
Effectiveness of the sonar-jamming defense
The presence of moth clicks had a strong effect on the result of bat-moth encounters (Figure 4.2; G-test; G-adjusted =56.1; d.f. = 3; P < 0.0001). Nearly all of the clicking moths avoided capture from attacking bats (Movie 1, Movie 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 (Figure 4.2). In order to later compare the effectiveness of different defenses, we will use a measure we call the “defense ratio,” which equals the capture rate when a defense is absent divided by the capture rate when the defense is present. In this case the defense ratio for sonar jamming is 10.4 (71% divided by 6.8%).
B. trigona evasive maneuvers
Moths employed two distinct evasive maneuvers – fly-aways and dives (Figure
4.3). 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 4 m·s -1 . The resolution of our
cameras did not allow us to determine visually whether these dives were passive or active. 94
However, the rate of maximum vertical acceleration during dives (18.8 ± 7.0 m∙s -2 ) well
exceeded the rate of gravitational acceleration (9.8 m∙s -2 ), indicating that the dives are active. A high-resolution video of the diving behavior obtained in captivity confirms this finding (Movie 2).
Diving occurred in more than half of all bat-moth encounters, and at similar rates for both clicking and silenced moths (G-test; G = 0.279; d.f. = 1; P = 0.60); fly-aways and no evasive maneuvering were less common (Figure 4.4A). Fly-aways occurred twice as often in silenced moths, while attacks where moths exhibited no evasive maneuvers were twice as common in clicking moths (Figure 4.4A); however, these differences were not statistically significant (Fly-aways; G = 3.09; d.f. = 1; P = 0.08; no maneuvers; G =
1.648; d.f. = 1; P = 0.20). Bats captured more silenced than clicking moths regardless of whether the moths were diving (G-test; G = 22.0; d.f. = 1; P < 0.0001), flying away (G- test; G = 24.7; d.f. = 1; P < 0.0001), or exhibiting no (G-test; G = 12.2; d.f. = 1; P < 0.001) evasive maneuvers (Figure 4.4B). Both clicking (G-adjusted = 4.78; d.f. = 1; P = 0.03) and silenced (G-adjusted = 5.37; d.f. = 1; P =0.02) moths that dove had lower capture rates than moths that either flew away or did no evasive maneuver (Figure 4.4B). Moths that flew away and moths that did no evasive maneuvers had similar capture rates
(clicking moths, P = 0.30; silenced moths P = 0.37).
Clicking caused bats to abort their attacks rapidly (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. trigona , a significantly different margin (G-test; G = 45.2; d.f. = 1; P <
0.0001). The duration of attacks on silenced moths (median 1.4 s; interquartile range 1.0- 95
1.5 s) was twice that of attacks on clicking moths (median 0.7 s; interquartile range 0.6 –
1.0 s), which was significantly different (Mann-Whitney U test; U = 1012.5; d.f. = 1; P <
0.0001). The distance silenced moths flew during attacks (median 2.2 m; interquartile range 1.7 - 3.4 m) was 47% greater than that of clicking moths (median 1.5 m; interquartile range 1.1 - 2.2 m); this difference was statistically significant (Mann-
Whitney U test; U = 882.0; d.f. = 1; P = 0.002), 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-adjusted =
13.8; d.f. = 1; P = 0.0002).
Effect of light on moth evasive maneuvers and bat capture success
The moth’s distance from a light did not affect the moth’s likelihood of diving
(Figure 4.5A; G-adjusted = 1.67; d.f. = 3; P = 0.64) or of being captured (Figure 4.5B; silenced moths; G-adjusted = 2.55; d.f. = 3; P = 0.46; clicking moths; G-adjusted = 3.34; d.f. = 3; P = 0.34). Silenced moths within 1 m of a light appeared to be captured less often than moths flying farther from a light (Figure 4.5B), although this may be a result of the small sample size of moths flying within 1 m of a light (N = 3).
Timing of moth defenses
Moth clicking, diving, and flying away were all initiated after the bat detected the moth and began its approach (Figure 4.6). 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 (Figure 4.6). This 96
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 (Figure 4.7). Prior to dives, the horizontal component of moth flight
was random with respect to the oncoming bat (Rayleigh test; z = 0.207; P =0.86; Figure
4.7A). However, during dives the horizontal component of the moths’ flight showed a
significant directional trend away from the oncoming bat (Rayleigh test; z = 4.77; P
=0.007 ; Figure 4.7B). Moths flew slightly upward (Rayleigh test; z = 5.03; P =0.006 ;
Figure 4.7C) prior to diving at a sharp angle away from the approaching bat (Rayleigh test; z = 27.4.03; P <0.0001; Figure 4.7D).
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 40-kHz Myotis group (Table 4.1). Three other bat species were identified in one to nine attacks. While sample sizes for species other than 40-kHz Myotis were small, capture rates for both clicking and silenced moths were fairly consistent between species (0-8.3% for clicking moths, 70-100% for silenced moths; Table 4.1).
DISCUSSION
Sonar-jamming is highly effective against a range of bat species 97
Bats captured over ten times more silenced moths than clicking moths, indicating that sonar-jamming is a highly effective defense (Figure 4.2). Sonar jamming’s defense ratio (10.4) compares favorably with that of insect evasive flight maneuvers (2.1-4.9;
Figure 4.8). 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. B. trigona clicking also appears to increase survival after capture, as none of the three clicking B. trigona that were captured were eaten, while two-thirds of silenced B. trigona that were captured were eaten (Figure 4.2). 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, 2005).
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-
89 kHz and with peaks of energy that sweep from 85 kHz down to 50 kHz 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 45 kHz (Table 4.1). These bats’ (which at our field site could include M. volans , M. auriculus , M. ciliolabrum , and
M. velifer ) most frequent prey are lepidopterans (Fenton and Bell, 1979; Fitch et al ., 1981;
Warner, 1985) and they only overcame the jamming defense in 8.3% of attacks. M. californicus (which uses echolocation above 50 kHz) (Gannon et al. 2001) and Lasiurus 98
blossevillii (a highly agile flyer) (Norberg and Rayner, 1987) were also each defeated by sonar-jamming in two of two encounters in the current study (Table 4.1). Previous laboratory work found that sonar-jamming is also effective against E. fuscus (Corcoran et al ., 2009; Corcoran et al ., 2010), whose peak echolocation frequency is near 30 kHz.
Therefore, the current data suggests that sonar-jamming by B. trigona is effective against bats that use a range of echolocation frequencies (30-50 kHz) 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, 1992).
B. trigona combine sonar-jamming with evasive maneuvering
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 other insects’ diving behaviors (Figure 4.8). Diving and clicking proved the most effective defense, with none of the 24 moths exhibiting both behaviors being captured (Figure 4.4B). 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). Since 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 99
flight maneuvers in B. trigona are not vestigial traits, such as are 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 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 to clicking moths. Attacks on silenced moths lasted longer and silenced moths flew longer distances evading capture. Silenced moths also flew to the ground nearly ten 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 100 differently when attacking 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.
B. trigona exhibited the same two categories of defenses – flying away and diving
– as was first described in the pioneering experiments of Kenneth Roeder (Roeder, 1962;
Roeder, 1967a). However, two key differences exist between our results and the paradigm established by Roeder some 50 years 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 101
echolocation, while 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). 102
B. trigona begins clicking earlier in attacks in the field compared to in captivity.
Having data on the defensive responses of B. trigona in the field (current study) and in captivity (Corcoran et al. 2009; Corcoran et al. 2011) allows 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 ms vs. 25 ms) and at a greater distance (170 cm vs. 76 cm). This cannot be explained by the two 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
Helverson, 2003). Bats in the field echolocate about 20 dB louder than bats in captivity
(Surlykke and Kalko, 2008; Holderied and von Helverson, 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., 2007). 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 neuroethology.
Moth proximity to ultraviolet lights does not inhibit defenses or affect capture rates. 103
Manmade 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 4 m of a 125-watt mercury vapor lamp exhibit fewer evasive flight maneuvers in response to pulsed ultrasound compared to moths flying in a dark woodland
(Svennssen 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 current study (where 15-watt ultraviolet lights were used) we found no such effect, as the percentage of moths diving and being captured were independent of moth distance to ultraviolet lights (Figure 4.5). The one difference that appeared to stand out (albeit not statistically significant and with the small sample size of N=3) was a decrease in capture success of silenced moths within 1 m 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, 2004). We conclude that low wattage ultraviolet lights appear 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 critical 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. 104
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 (Figure
4.8), 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 (Corcoran and Conner, unpublished data) Batesian mimicry would be predicted to be ineffective (Harper and Pfennig, 2007; Pfennig et al. 2001). B. trigona clicks may sometimes also serve as a legitimate aposematic signal, as 33% of silenced B. trigona that were captured were rejected (Figure 4.2B), 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 about two-thirds of attacks.
B. 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 105 activities when echolocating bats are present but not yet attacking (Ratcliffe et al ., 2008).
The question thus arises, why isn’t jamming 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.
CONCLUSION
We have demonstrated a reliable method for studying 3-D flight trajectories and acoustic behavior of bats attacking insects in their natural environment. This approach allows for determining the effectiveness of defense and attack strategies, the documentation and description of flight behaviors, and measurement of the effect of the environment (such as proximity to lights or background vegetation) on bat-insect interactions. The primary limitation of the current 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; Whitaker et al., 2009), bioacoustic and physiological studies of sound production in bats and sound reception in moths (e.g., Goerlitz, 2010), and laboratory experiments using captive animals (Corcoran et al., 2009; Ghose et al, 2006; Ghose et al., 2009). Field studies also provide an 106 opportunity to generate and test hypotheses in neuroethology, such as the relationship between insect hearing sensitivity and initiation of defensive behaviors.
The current 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. 107
List of Symbols and Abbreviations
3-D Three-dimensional
β The angular difference between the bat-moth vector and the moth flight vector
φ The angular difference between the bat-moth vector and the bat flight vector
FUNDING
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
ForestUniversity. 108
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Figure 4.1. 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. 116
Figure 4.2. 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. 117
Figure 4.3. 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, C, circles indicate the positions of bats and moths when the moth began clicking. Gridlines are each 1m apart. 118
Figure 4.4. (A) Percentage of bat attacks in which Bertholdia trigona exhibited defensive behaviors, and (B) bat capture rates on 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). *** indicates significance of P< 0.001. 119
Figure 4.5. 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. Diving and capture rates did not significantly differ between moths at different distances from a light (see results for statistics). 120
Figure 4.6. 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. * indicates significantly different distributions with P < 0.05. 121
Figure 4.7. Directionality of Bertholdia trigona diving behavior in response to bat attack.
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 plot’s origin. Dashed black arrows indicate the median distance and direction of moth flight when the direction of flight was not randomly distributed. 122
Figure 4.8. 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, while black bars indicate moth clicking defenses of acoustic aposematism and sonar-jamming. All insects are in order Lepidoptera, except C. carnea
(Neuroptera) and P. agrioninea (Mantodea). Data taken from 1, Agee, 1969; 2, Miller and Oleson, 1979; 3, Rydell, 1992; 4, Acharya and Fenton, 1999; 5, Triblehorn et al .,
2008; 6, current study; and 7, Acharya and Fenton, 1992. 123
Table 4.1. Capture rates of clicking and silenced Bertholdia trigona by bat species
Clicking Bertholdia trigona Silenced Bertholdia trigona % % Bat species N # Caught Caught N # Caught Caught 40 kHz Myotis 1 36 3 8.3% 30 21 70.0% M. californicus 2 0 0.0% 7 5 71.4% Lasiurus blossevilli 2 0 0.0% - - - M. auriculus - - - 1 1 100.0% Total 40 3 7.5% 38 27 71.1% 1 Includes M. auriculus , M. ciliolabrum , M. velifer , and M. volans 124
CHAPTER V
Optimal predator risk assessment by the sonar-jamming Arctiine moth Bertholdia trigona
Aaron J. Corcoran, Ryan D. Wagner, and W. E. Conner
The following manuscript has been submitted for publication to PLoS ONE . Stylistic variations are due to requirements of the journal. A.J.C. designed and executed the experiment experiments, conducted data analysis, and wrote the manuscript. R.D.W. also designed and executed the experiments, assisted collecting and analyzing data and provided editorial assistance. W.E.C. provided oversight and editorial assistance on the project. 125
ABSTRACT
Nearly all animals face a tradeoff between seeking food and mates and avoiding predation. Optimal escape theory holds that an animal confronted with a predator should only flee when benefits of flight (increased survival) outweigh the costs (energetic, lost foraging time, etc.). We propose a model for prey risk assessment based on the predator’s stage of attack. Risk level should increase rapidly after the predator detects the prey and commits to the attack. We tested this hypothesis using a predator – bats – whose active sensory system of echolocation reveals its stage of attack. We used a prey defense – clicking used for sonar jamming by the tiger moth Bertholdia trigona – that can be readily studied in the field and laboratory and is enacted simultaneously with evasive flight. We predicted that prey employ defenses soon after being detected, and that prey defensive thresholds discriminate between legitimate and false predatory threats. Laboratory and field experiments using playbacks of simulated bat calls and naturally behaving bats, respectively, confirmed our predictions. Moths clicked soon after bats detected and targeted them. Also, B. trigona clicking thresholds closely matched predicted optimal thresholds for discriminating legitimate and false predator threats for bats using search and approach phase echolocation – the period when bats are looking for and assessing prey. This is the first study to correlate quantitatively the sensory stimuli that trigger defensive behaviors with measurements of cues provided by predators during natural attacks. We propose theoretical models for explaining prey risk assessment depending on the availability of cues that reveal a predator’s stage of attack. 126
INTRODUCTION
Nearly all animals face a tradeoff between avoiding predation and seeking food and mates. This problem is most acute when an animal detects a predator. What actions will lead to maximum fitness? What defenses should be employed and when? These questions are the subject of optimal escape theory, which holds that an animal must balance the benefits of initiating a defense (i.e., the likelihood of increasing survival) with the costs (e.g., expended time and energy) (Ydenberg and Dill, 1986; Cooper and
Frederick, 2007).
Studies on optimal escape theory typically examine the factors that influence how close predators are allowed to approach before prey flee (flight initiation distance, FID).
A greater threat to survival should lead to a longer FID, while higher costs of fleeing should decrease FID (Ydenberg and Dill, 1986; Stankowich and Blumstein, 2005;
Cooper and Frederick, 2007). The predictions of optimal escape theory have largely been supported by empirical evidence (Lima and Dill, 1990; Stankowich and Blumstein, 2005), with aspects from the prey (e.g. morphological and behavioral defenses, amount of experience), predator (size, speed, approach trajectory) and environment (distance to refuge) consistently modulating FID.
A critical component of escape theory is the prey’s ability to recognize the threat level a predator presents, and to respond accordingly. Defensive responses are frequently graded in intensity depending on the threat level that is perceived. This behavioral response is termed “threat sensitivity” (Helfman, 1989). For example, prey frequently assign higher risk to predators that approach faster and use a more direct approach
(Cooper, 1997; Stankowich and Coss, 2006). A few studies have tested whether prey use 127 other cues from the predator that could establish “intent”. At least two studies have found that prey use predator gaze to indicate higher risk (Bateman and Fleming, 2011; Cooper et al. 2012); however, others found no effect of gaze on FID (Helfman and Winkelman,
1997; Stankowich and Coss 2006).
With the amounting body of literature on optimal escape theory, efforts have recently been made to find broad rules to explain the diversity of patterns examined
(Blumstein, 2010; Stankowich and Blumstein, 2005). Blumstein (2010) noted a trend common to birds, mammals, lizards, and an invertebrate where FID is dependent on the distance a predator begins its approach (start distance). These results suggest that the predator’s approaching movement causes the prey to flee, a finding that was not anticipated by early escape theory models (Blumstein, 2003). Blumstein suggested a general rule that prey flee as soon as they detect a predator and identify it as a threat. He argues that this minimizes the time prey spend devoting attention to a predator. However, it was not specified how prey should determine whether a predator is a threat, and if this rule holds, how it can be reconciled with our current understanding of optimal escape theory (Ydenberg and Dill, 1986; Cooper and Frederick, 2007).
A successful predation sequence can be catalogued into a series of stages to aid in description and analysis (Endler, 1986). A modified version of Endler’s (1986) predation sequence is illustrated in Figure 5.1A. The sequence begins with a predator searching for prey (search stage). After detection the predator will identify the prey and assess whether it is worth pursuing (assessment stage). If the predator immediately recognizes a profitable target, this stage will be rapid; however the predator may need to approach the prey to gather more information. After the predator decides the prey is profitable the 128 pursuit stage begins. We hypothesize that predator threat markedly escalates during the assessment stage until the predator commits to the attack (Figure 5.1B). At this time prey defenses of crypsis have failed and unless an alternate defensive strategy is available (e.g. aposematic signaling, startle), escape is the primary remaining option. We hypothesize that prey risk assessment is driven by determining whether the predator has reached the assessment stage and how close the predator is to committing to pursuit. If evidence suggests the predator is near or at the pursuit stage, then the prey should flee, as there is little benefit to waiting. At this point, the prey’s cost of attending to the predator is increasing (Blumstein, 2010) and the likelihood of escape is decreasing. While this hypothesis has been stated previously in various forms (Broom and Ruxton, 2005;
Cooper, 2005; Endler, 1986; Blumstein, 2010), it has not been tested using prey responding to their naturally-behaving predators in a situation where the predator’s stage of attack can be easily determined. Bats attacking insects provide just such an opportunity, as the bat echolocation attack sequence reliably indicates their stage of attack (Figure
5.1C).
Bats search for insect prey by emitting echolocation (or sonar) pulses at a relatively low rate of 7 – 12 calls per second (Holderied and von Helverson, 2003); this is termed search phase echolocation. Upon detecting a prey echo, the bat gradually increases the rate of emissions, beginning approach phase echolocation (figure 5.1C). The increased emission rate allows rapid updating of the bat’s “acoustic image” of the environment (Moss and Surlykke, 2001). During approach phase the bat localizes the target, eventually locking its directional sonar- beam to within 3˚ of the prey item (Figure
5.2; Ghose and Moss, 2003). Late in the approach phase (a period sometimes referred to 129 as the tracking phase; Kick and Simmons, 1984), the bat gradually decreases the intensity of its emissions as part of a system that keeps the intensity of returning echoes at a constant level despite decreasing target distance (automatic gain control; Figure 5.2)
(Hartley 1992a, 1992b; Boonman and Jones, 2002; Hiryu et al. 2007). This presumably allows the bat to extract maximum information from target echoes (Kick and Simmons,
1984). During this period, bats identify the target and decide whether to continue pursuit or abandon the attack (Kick and Simmons, 1984). Therefore, the approach phase of bat echolocation (Figure 5.1C) can be likened to the assessment stage of our generalized predation model (Figure 5.1A). In the final echolocation phase, the terminal buzz, the bat emits echolocation signals at a maximal rate (over 160 calls/s) as it attempts to intercept the prey and coordinate its final capture maneuver (Elemens et al. 2011). The terminal buzz phase can consequently be likened to the pursuit stage of our predation model
(Figure 5.1).
Insects from at least seven orders detect bats by hearing their high-frequency echolocation calls (Hoy, 1998). Moths have been the focus of much research on bat- insect interactions (Conner and Corcoran, 2012); and they have a two-part defensive response that is consistent with the threat sensitivity hypothesis (Roeder, 1967).
Relatively quiet echolocation calls indicate a distant bat that has not detected the moth
(Goerlitz et al. 2010; Roeder, 1967); they alert moths to fly away from the sound source to decrease the probability of being detected. Higher-intensity calls indicate a bat that has approached more closely, and they elicit passive or active power dives or spirals to the ground (Roeder, 1967). Tiger moths (Lepidoptera: Erebidae, Arctiinae; formerly
Lepidoptera: Arctiidae) also respond to high-intensity bat calls with ultrasonic clicks. 130
Depending on the chemistry and amount of sound produced by a tiger moth species, these clicks warn bats that the prey is toxic (acoustic aposematism; Hristov and Conner, 2005;
Barber and Conner, 2007) or disrupt the bat’s processing of prey echoes (sonar jamming;
Corcoran et al, 2009; Corcoran et al, 2011; Conner and Corcoran, 2012). The tiger moth clicking response provides a valuable model for studying prey defenses, as it can be recorded in the field under natural conditions (Corcoran and Conner, 2012) and in the laboratory in tethered animals being broadcast bat-like sounds (Barber and Conner, 2006;
Corcoran et al. 2010; Fullard et al., 2007; Ratcliffe et al., 2009). Because clicking is paired with diving in the sonar-jamming moth Bertholdia trigona (Corcoran and Conner,
2012), the initiation of clicking can be used as an indicator of when the moth begins its escape from attacking bats.
The dogbane tiger moth Cycnia tenera has been a model for the study of moth clicking for many decades (e.g. Fullard, 1979; Fullard, 2003; Fullard et al. 2007). It detects attacking bats using the acoustic cues of call intensity and call repetition rate (or its inverse, call pulse interval) (Fullard, 1984; Fullard et al. 2007). C. tenera is most sensitive to calling rates that bats use in the approach phase of attack. This is also when most tiger moths click in response to bats (Barber and Conner, 2006). Why tiger moths click when they do is a matter of debate. Some have argued that clicks presented late in the attack are optimized for jamming (Fullard et al. 1994) or enhancing their aposematic defense (Barber and Conner, 2006; Fullard et al. 2007; Ratcliffe and Fullard, 2005;
Ratcliffe and Nydam, 2008). Clicking too early could alert nearby predators to the moth’s presence, or alternatively, bats may not be able to hear moths clicking at a distance
(Barber and Conner, 2006). We believe that optimal escape theory provides a useful 131 framework for testing hypotheses regarding the timing of moth clicking, as this timing is under many of the same influences as the initiation of prey escape behaviors. If clicking does not conform to the predictions of escape theory, then the results could be taken as evidence that other factors (e.g. maximizing defensive effectiveness) predominate.
We studied the cues that stimulate B. trigona to click to test our hypothesis that prey assess predator risk by determining the stage of the predator’s attack. We predicted that B. trigona click in response to predator cues that indicate a bat has detected them and was committing to the attack. We tested this prediction by 1) determining the pulse interval and intensity thresholds that elicit clicking by broadcasting simulated bat calls to tethered moths in a sound chamber; 2) characterizing the sounds moths hear in the field when being attacked by bats (“true threat”) and when bats are attacking a nearby moth
(“false threat”); and 3) comparing the acoustic properties and flight trajectories of bat passes that did and did not elicit a clicking response from B. trigona in the field.
MATERIALS AND METHODS
Research location and animals
Field and laboratory experiments were conducted during July 2010 at the Southwestern
Research Station (run by the American Museum of Natural History), 10 km southwest of
Portal, AZ. Moths, including noctuid controls and experimental B. trigona (Grote, 1879), were collected and visually identified at black lights and mercury vapor lights set near riparian areas on the station grounds. Moths were held in 30 ml plastic vials for up to 48 hours prior to experimentation. Bats used in experiments were observed in their natural habitat. 132
Field experiment setup
We recorded infrared video and ultrasound of free-flying bats attacking tethered and free- flying moths in their natural habitat. Figure 5.3 illustrates the components of the field setup. Individual moths and a miniature ultrasonic microphone (Knowles Acoustics FG-
3329) were suspended from the end of a 10-meter telescopic pole that was anchored into the ground at a 45-degree angle. This arrangement, combined with three-dimensional reconstruction of bat flight trajectories, moth positions, and microphone positions, allowed us to calculate the bat call sound pressure levels at the point of emission (using an arbitrary distance of 10 cm from the bat) and at the moth’s position. These calculations are described below. A 3-mm diameter shielded microphone cable ran from an AR100 ultrasound receiver (Binary Acoustics Technology, Tucson, AZ, USA) along the length of the pole and down from the pole’s tip (Figure 5.3). Ultrasound was recorded on a laptop computer running Spect’r software (Binary Acoustics Technology) sampling at
250 kHz. Individual moths were suspended by a 25 cm monofilament line from the end of the microphone cable (Figure 5.3).
In preparation for tethering, we removed scales from the moth’s mesoscutum and affixed to it a small (5mm) loop of monofilament line with gel superglue (Loctite brand). At the time of experimentation, the moth was tied to the monofilament line on the telescoping pole by the affixed loop and then hoisted into the air. The pole was shaken periodically by the experimenter to add motion to the tethered moth and to keep the moth flying. A
15-watt ultraviolet light (Leptraps LLC, Georgetown, KY, USA) attached to a 1.5 m pole near the hoisted moth was used to attract insects, and therefore foraging bats, to the recording area. 133
Field experiment overview
We conducted two field experiments where we characterized the bat sounds moths hear in their natural environment. In experiment 1, using soundless noctuid control moths, the bat sounds correlating to “real” threats were compared to the bat sounds correlating to
“false” threats. A real threat was characterized as an attack on the tethered noctuid moth; a false threat was an attack on a neighboring, free-flying moth. In experiment 2, we recorded bats flying near and sometimes approaching tethered B. trigona . We then compared acoustic and flight parameters of bat passes that elicited a clicking response with those that did not elicit a response.
Videography
Three infrared-sensitive Basler Scout cameras (model scA640-120gc; Ahrensburg,
Germany) recording at 60 frames-per-second at 640x480 resolution were placed around the experimental area and focused on the tethered moth. Cameras were hard-wired to start simultaneously by a signal sent from a custom Innovision Systems (Columbiaville, MI) synchronization box, which was controlled by MaxTraq software (Innovision Sysetms) running on a desktop computer. Infrared illumination was provided by 12 Wildlife
Engineering IR-Lamp6 lights (Tucson, AZ, USA), and two Bosch UFLED20-8BD illuminators (Farmington Hills, MI, USA).
3-D calibration and reconstruction
Using the relative orientation method (Svoboda et al ., 2005) in MaxTraq3D (“Dynamic
Wand Method”; Innovision Systems) and the video recordings by the three cameras, the three-dimensional positions of bats, moths, and the microphone were reconstructed. This 134 approach has been used recently for studying bat-moth interactions in the field and is explained in more detail elsewhere (Corcoran and Conner, 2012). The accuracy of our reconstructions was tested by moving a “wand” (two spherical infrared markers fastened to a rod at fixed distance from each other) throughout our calibrated volume and comparing the known distance between the two markers to that measured using our 3-D reconstructions. Over 1500 frames, we found a mean error of 0.4 cm for the two markers set 145 cm apart (0.28 % error). Our calibration volume was approximately 6 m (width) by 6 m (depth) by 4 m (height) or 144 m 3.
Two infrared tape markers were placed at known positions on the microphone cable in order to determine the microphone’s position and directional axis. The “center- of-mass” of the bats, moths and two microphone markers were digitized from our video using MaxTraq2D (Innovision Systems). These 2-D data were transformed into 3-D coordinates using MaxTraq3D. The 3-D coordinates were then imported into Matlab
(Natick, MA, USA), where a custom program (BATracker.m, coded by B. Chadwell and
A. Corcoran) was used to fit a quintic smoothing spline to the 3-D data. This spline function was used to generate bat flight vectors, bat-moth vectors, bat approach angles, and other flight parameters (Barber et al. 2009; Corcoran et al. 2011; Corcoran et al.
2012).
Audio calibration
We used our audio recordings and 3-D reconstructions to determine the SPLs of bat calls at the tethered moth’s position and 10 cm from the bat (dB peSPL re. 20 µPa). Bats emit a highly directional call (Ghose and Moss, 2003). The estimated SPLs for bat emissions 135 represent that emitted in the moth’s direction. Early in the attack, until the bat has locked its sonar beam on the target, the moth is typically off-axis from the bat’s sonar beam emission (Figure 5.2; Ghose and Moss, 2003), and estimated emission SPLs are likely less than the maximum emitted by the bat.
Bat call intensities were first determined at the position of the microphone using several adjustment factors. The frequency response of the Knowles microphone was determined by playing pure tone signals from an Avisoft ScanSpeak Ultrasonic speaker
(Avisoft Bioacoustics, Berlin, Germany) to the exact microphone, cable, and receiving unit used in the experiments. Audio was recorded at the same position (50 cm directly in front of the speaker) by our Knowles microphone and by a Brüel & Kjær ¼” microphone
(grid off) connected to a Brüel & Kjær Type 2610 measuring amplifier. Playbacks were delivered at 5 kHz increments from 20 -100 kHz. These values provided a frequency- specific conversion from recorded voltage to SPL. The frequency response was fairly flat
(± 1.5 dB) from 20 – 40 kHz, followed by decreased sensitivity of approximately 5 dB per 10 kHz from 40-100 kHz. This response resulted partly from the long, thin microphone cable which was important for our experimental design. For 20 kHz, 30 kHz,
40 kHz, 50 kHz, and 60 kHz (the range of potential peak frequencies used by bats in this study) the directionality of the Knowles microphone was determined in 22.5˚ increments from the microphone pointing directly at the speaker to directly away from the speaker.
The microphone was fairly omnidirectional with adjustments ranging from ± 2-4 dB depending on frequency.
The estimated SPL of bat calls recorded by the microphone and the 3-D positions of the bat, moth, and microphone were used to determine the SPL at the moth’s position 136 and 10cm from the bat. Attenuation due to spherical spreading and excess atmospheric attenuation were accounted for using the temperature and humidity recorded at the beginning of each session (Bazley, 1976). There may have been a difference in the recorded SPL at the microphone’s position and the moth’s position due to the directionality of the bat call. This was minimized by having the microphone as close to the moth as possible while still allowing bats to interact naturally with the tethered prey.
To account for this we measured the angle between the bat-moth vector and the bat- microphone vector throughout each attack. Bat call directionality depends on the phase of attack, with search and approach phase calls having - 6dB horizontal beam width of 80˚
(40˚ off axis in either direction) a nd buzz phase calls having a -6dB horizontal beam width of 180˚ (90˚ off axis; Jakobsen and Surlykke, 2010). During search and approach phase the angle measured between bat-moth vector and bat-microphone vector was 23.5
± 9.7˚ and for buzz phase the angle was 44.4± 17.3˚. To compensate for this, we added 3 dB to our calculations of bat call SPLs; ± 1-3 dB of error in our calculations cannot be accounted for since the exact direction of bat calls was unknown.
In attack sequences in which the moths clicked, intensity was determined by averaging the intensity of the two pulses preceding the response. Pulse interval was measured as the distance from the start of one pulse to the start of the next. In attack sequences in which the moth did not click intensity was measured as the average of the most intense pulse, the pulse before, and the pulse after. Pulse interval was measured as the average of the distance between these three pulses.
Laboratory experimental setup 137
In our third experiment we determined the thresholds that elicited clicking in B. trigona for a range of pulse intervals (4-100 ms). Pulse interval and intensity have previously been shown to be the primary acoustic cues used by tiger moths to initiate their clicking response (Fullard, 1984; Fullard et al., 2007). We used a previously established experimental setup for broadcasting bat-like sounds to moths in a sound chamber (Barber and Conner, 2006; Corcoran et al. 2010). Briefly, moths were held with wings folded dorsally and clamped by a hemostat in a sound chamber (50 cm x 20 cm x
20 cm) lined with sound-absorbent foam. An Avisoft scanspeak ultrasonic speaker was placed 5 cm posterior to the moth’s thorax and was used to broadcast ultrasonic digital audio files to the moth. The playbacks and the moth’s clicking response were recorded by an Avisoft CM16/CMPA ultrasonic microphone connected to an Avisoft Ultrasound Gate
416H receiver. The microphone was placed 5 cm from the moth and directed perpendicular to its thorax. Ultrasound recordings were made on a laptop computer running Avisoft-Recorder software and sampling at 300 kHz. We calibrated the intensity of playbacks at the moth’s position (5 cm in front of the speaker) using a Brüel & Kjær
Type 2610 measuring amplifier with a Brüel & Kjær ¼” microphone (grid off).
Ultrasound playback files were generated in Matlab. They consisted of a series of
2 ms ultrasonic pulses (with 0.5 ms rise and fall times) separated by periods of silence, the duration of which varied depending on the playback file. Pulse interval is defined as the time from the beginning of one pulse to the beginning of the next pulse. Ten playback files were created with different pulse intervals: 4 ms, 7 ms, 12 ms, 20 ms, 30 ms, 45 ms,
60 ms, 80 ms, and 100 ms. Each digital file was 10s long, and pulse intensities increased at a rate of 5 dB/sec from 70 -120 dB peSPL. To prevent an extended clicking response, 138
playbacks were stopped as soon as the experimenter heard moth clicks. All playbacks
used pure tones at 40 kHz. This is a typical frequency used by bats that attack B. trigona
(Corcoran and Conner, 2012), and is near the peak auditory sensitivity of most moths
(Fullard et al., 2003).
For experimentation, moths were suspended in the dark sound chamber and trials were initiated two minutes after the moth ceased clicking. Playbacks were presented in a random order with two-minute silent periods between presentations. Only moths that regularly clicked to stimuli throughout the experiment were used in the final analysis.
The audio recordings of moth clicking and playbacks were analyzed to determine the intensity which elicited a clicking response. The time in the file of the first moth click was compared to the known intensity of the playback immediately preceding it, and this intensity was taken as the threshold for eliciting a clicking response.
Statistical analysis
A two-step regression analysis procedure was used to determine the relationship between continuous variables. First, a linear regression was employed; if found significant, a stepwise regression was conducted where the linear term was retained and a quadratic term was tested for improving the relationship using a P-to-enter of 0.05.
(Tabachnick and Fidell, 2007). Discriminant Function Analysis (DFA) was used to determine what acoustic and flight variables differ between bat passes that elicited and did not elicit clicking by B. trigona in the field. Stepwise forward variable selection was used with a P-to-enter of 0.05 (Tabachnick and Fidell, 2007). A Receiver Operating
Characteristic ROC curve was used to determine the degree of overlap in call intensities 139 between real and false threats for each echolocation phase using. The area under the ROC curve (AUR), which equals the likelihood a randomly chosen call from the real threat group has a higher intensity than a randomly chosen call from the false threat group, was used as our measure of the degree of overlap (Bradley, 1997).
Repeated-measures Analysis of Variance (ANOVA) was used to determine whether moths have different intensity thresholds for inducing clicking for pulse intervals representing different phases of echolocation attack. Group means were examined for normality using skewness and kurtosis statistics and for sphericity using Mauchley’s test.
Thresholds were averaged for representative pulse intervals for each phase (search: 80 and 100 ms; approach: 20, 30, and 45 ms; and buzz: 4 and 7 ms). Thresholds for pulse intervals of 60 and 12 ms were not used as they are transitional between phases. Planned
Bonferronni pairwise comparisons were used to determine whether means were significantly different (Sokal and Rohlf, 1995)
RESULTS
Characterization of real and false predator threats
We first characterized the bat sounds arriving at a moth when it is being attacked (real threat) and when a nearby moth is being attacked (false threat). All sound levels are indicators of the intensity being directed towards the moth, which is not always equal to the maximum intensity emitted by the bat. In the real threat scenario the source level of bat calls increased linearly to a peak (linear regression; B = 0.077 dB/ms; R 2 = 0.66; P <
0.0001) and then decreased linearly (linear regression; B = -0.037 dB/ms; R 2 = 0.71; P <
0.0001). This is seen clearly when the attacks are aligned by the bat call of maximum 140 intensity (Figure 5.4B). At the moth’s position bat call intensity increased linearly (linear regression; B = 0.086 dB/ms; R 2 = 0.83; P < 0.0001) until the bat call of maximum emitted intensity, and then call intensity nearly plateaued (linear regression; B = -0.007 dB/ms; R 2 = 0.11; P < 0.0001). This transition occurs about 100 ms into the approach phase (which starts with a decrease of pulse interval in Fig. 5.4B), and is followed by the bat turning towards the moth (indicated by decreasing approach angle in Fig. 5.4B; linear regression; R = 0.68; P < 0.0001). In contrast, in false threats bat call source levels
(quadratic regression; R = 0.80; P < 0.0001) and SPLs at the moth’s position (quadratic regression; R = 0.79; P < 0.0001) decreased as attacks progressed (Figure 5.4C). In false threats the bat’s flight direction relative to the moth was not related to the time of the attack (linear regression; R = 0.03; P = 0.74).
B. trigona clicking thresholds
B. trigona had the lowest clicking thresholds for pulse intervals in the late approach phase of bat echolocation (20-30 ms; green symbols in Figure 5.5). Intensity thresholds were significantly different between echolocation phases (Repeated measures
ANOVA; F = 19.2; d.f. = 2; P < 0.0001); thresholds for search (P = 0.015) and terminal phase (P = 0.001) pulse intervals were higher than those for approach phase pulse intervals. These results supported our hypothesis that moths are most likely to employ defenses when predators have detected them and committed to an attack.
Discriminating real and false threats
To determine whether it is possible for a moth to differentiate real and false threats, bat call pulse intervals and intensities (the cues used by tiger moths for 141 identifying an attack) for five attacks on a focal soundless moth (real threat) and five attacks on a nearby soundless moth (false threat) were plotted (Figure 5.5). We compared the degree of overlap in call intensities between real and false threats for each echolocation phase using the area under the ROC curve (AUR), which equals the likelihood a randomly chosen call from the real threat group has a higher intensity than a randomly chosen call from the false threat group. Search phase calls had a moderate degree of overlap between the two conditions (AUR = 0.72; P =0.009); however, approach (AUR = 0.97; P < 0.0001) and terminal phase calls (AUR = 0.997; P < 0.0001) had a large degree of separation. Using quadratic discriminant analysis we determined the optimal thresholds for discriminating real and false threats with 50% (p=0.5) and 75%
(p=0.75) likelihoods of the threat being real. Measured B. trigona clicking thresholds matched predicted p=0.75 thresholds for pulse intervals of 35-100 ms, but not for pulse intervals at or below 20 ms (Figure 5.5). Pulse intervals in the terminal buzz demonstrated the worst fit. These results support the hypothesis that B. trigona clicking thresholds are optimized for differentiating predator threat levels for bats in search phase and most of approach phase, but not for bats in buzz phase echolocation.
Clicking responses of tethered moths in the field
Using tethered B. trigona in the vicinity of free-flying bats, we determined how well two groups of characteristics – bat call properties and bat flight trajectories – could discriminate interactions where moths did or did not click. Bat call intensity at the moth’s position was significant in predicting whether or not moths clicked for all three moths tested (DFA; P < 0.0001); pulse interval was a significant discriminating variable for two of three moths tested (DFA; P < 0.0001), with shorter pulse intervals (higher repetition 142 rates) being more likely to elicit a clicking response (Figure 5.6). Moths typically clicked in response to bats in early to middle approach-phase (25-60 ms pulse intervals). Moth 3 also responded to moths in late search-phase (60-70 ms pulse intervals). Moth clicking was preceded by bat calls that increased in intensity and slightly decreased in pulse interval (bottom two rows of graphs in Figure 5.6), both indications that the moth had recently been detected and targeted by the bat.
For interactions where bats flew through the calibrated volume, bat distance (DFA;
P < 0.01), but not approach angle (P > 0.1), significantly discriminated between clicking and non-clicking cases for moths 1 and 2 (Figure 5.6). Neither variable was significant for moth 3 (DFA; P > 0.1). All bat passes outside of the calibrated volume (which had a minimum distance of 4 m from the moth) failed to elicit a clicking response. Because a threshold could not be determined for moth 3 using only passes occurring with the calibrated volume, we conducted an ROC analysis on bat distance using interactions in and out of the calibrated volume. This analysis found a statistically significant threshold
(Figure 5.6, moth 3; P < 0.001). Moths 1 and 2 had similar bat distance clicking thresholds, which were about 1.5 m less than the threshold for moth 3. Acoustic characteristics were slightly better at predicting whether moths clicked than bat flight characteristics. Acoustic characteristics correctly predicted an average of 98% of interactions where moths clicked (positive predictive value; PPV) and 93% of interactions where moths did not click (negative predictive value; NPV). Flight characteristics had an average PPV of 90% and an average NPV of 80%. This indicates that there were several cases where a bat’s flight trajectory could not explain the moth clicking response, but bat acoustic characteristics could. 143
DISCUSSION
Using a combination of field and laboratory experiments we tested the hypothesis that moths initiate defensive clicking in response to acoustic characteristics that reveal they have been detected and targeted by a bat. To determine if this is the case we first characterized the acoustic cues available to a moth under attack (Figure 5.4). Bats typically detect insects from their periphery, and then direct their beam towards the target as they decrease their pulse intervals (Figure 5.2; Ghose and Moss, 2003). To the moth this is perceived as a rapid increase in intensity and a small decrease in pulse interval
(time -200 ms to 0 ms in Figure 5.4B). These are the earliest cues available to a moth that it has been detected and targeted, and these cues appear to trigger moth clicking (Figure
5.4).
Another way to analyze the timing of moth clicking is by comparing the pulse intervals and intensities of real and false threats (Figure 5.5). Intensities of search phase calls are ill suited for discriminating predator threat levels, whereas approach and buzz phase calls of real and false threats are easily distinguished. Therefore, early approach phase (30-60 ms pulse intervals) is the first time moths could distinguish real and false threats, and this is when tethered (Figure 5.6) and free-flying moths (Corcoran and
Conner, 2012) click in response to naturally foraging bats.
Finally, we found that moth clicking thresholds closely matched predicted optimal thresholds for discriminating real and false threats for search and most of approach phase echolocation (Figure 5.5).This is when it is most important for a moth to determine whether it is under attack. After this point, in the terminal buzz, it may be too late for 144 clicking to be effective (Corcoran et al., 2010). Moths may also have difficulty accurately encoding buzz phase echolocation in nervous signals (Fullard et al. 2003), and moths may not be able to discriminate buzz phase echolocation from insect stridulations
(Fullard et al. 2007), however this has not been tested empirically. Together, these data support the hypothesis that moth clicking thresholds have been shaped by natural selection to discriminate between legitimate and false predatory threats. Moths appear to initiate clicking at the earliest time when they can determine that they have been detected and targeted by an echolocating bat. At least for sonar-jamming, it is not necessary to include other explanations for the timing of moth clicking, for example that moths click at a time to maximize the defense’s effectiveness (Fullard et al. 1994; Barber and Conner,
2006).
How do these results contribute to our understanding of optimal escape theory?
We hypothesized that prey assess predator risk based on where the predator is in its attack sequence, with risk increasing after detection and as the predator commits to an attack (Figure 5.1). Our results support this hypothesis, as moths reliably began their jamming defense in response to cues that indicated they had recently been detected and targeted. This happened when bats were at distances of 1-3 m (Figure 5.6), just inside the range where bats detect moths (2.2-4.5 m) (Corcoran and Conner, 2012). Moths did not click in response to several bats that flew within the distance expected to excite a response (Figure 5.6). Because acoustic features of bat calls explained whether a moth clicked in about 95% of interactions, it appears that these bats did not direct their sonar beams at the moths, and the moths correctly ignored a false threat. 145
Growing evidence suggests that prey dynamically respond to predator behaviors that indicate the predator’s stage of attack. Birds, mammals, lizards and invertebrates often appear to wait a fixed amount of time after a predator begins approaching to begin flight, regardless of the predator’s distance (Blumstein, 2003, 2010, Blumstein et al. 2005;
Cooper, 2005; Cooper, 2009; Stankowich and Coss, 2006). This response is more common when predators move rapidly, supporting the idea that prey perceive these movements as a directed attack (Cooper, 2005, 2009). Prey also flee from predators
(usually humans) at greater distances when they approach more directly (Cooper, 1997a;
Stankowich and Coss, 2006) turn towards prey (Cooper, 1997b, 1998), move faster
(Cooper, 2006; Stankowich and Coss, 2006 ), or approach while gazing at the prey
(Bateman and Fleming, 2011; Cooper, 2012). However, these finding are not universal.
For example, deer do not appear to respond to predator gaze or the presence of a gun
(Stankowich and Coss, 2006) and lizards do not flee from an approaching distant predator if it moves slowly (Cooper, 2005, 2009). Different prey, therefore, use different predator cues to assess risk. This may be a result of differing sensory capabilities, predators that provide different cues in their approach, and differing environmental conditions (Caro,
2005). Further comparative work is needed, especially with studies of naturally behaving predators and prey where predator cues, and prey responses to them, can be measured.
Bats and moths provide a useful system for testing the hypothesis that prey determine predator risk based on the predator’s stage of attack because bats inadvertently advertise when they have detected the prey and are committing to an attack. For many predator-prey systems this is unlikely to be the case, and there may be selective pressure for predators to conceal their stage of attack. For example, some bats use “stealth 146 echolocation” – that is, they purposefully echolocate quietly to prevent moths and other eared insects from knowing they are under attack (Goerlitz et al. 2010). Depending on the animals involved and environmental conditions, prey will have differing levels of information about a predator’s stage of attack.
We propose that a prey’s ability to perceive cues that reveal the predator’s stage of attack alters the shape of the predator risk assessment curve (Figure 5.7). Predator risk
(which equals to the benefit of fleeing) is typically presented as a linear (Cooper and
Frederick, 2007) or quadratic (Ydenberg and Dill, 1986) function of predator distance, with risk increasing as the predator approaches. The cost of prey fleeing is assumed to increase with predator distance (Figure 5.7A; Ydenberg and Dill, 1986; Stankowich and
Blumstein, 2005; Cooper and Frederick, 2007). The intercepts and slopes of these functions can change depending on factors relating to the prey, predator, and environment
(Stankowich and Blumstein, 2005).These factors shift the intersection of the two functions, and therefore the distance where it is optimal for prey to flee (D*). We argue that this model holds for prey that have little or no information about predator attack stage. For prey that have near complete information on predator attack stage (such as in the current study), we propose that the predator risk function is sigmoidal and risk increases rapidly in the predator’s assessment stage (Figure 5.1). The predator’s distance where it detects and assesses prey would differ between interactions, and therefore the predicted FID would vary depending on the predator’s detection/assessment distance.
The sigmoidal shape of the risk function reduces the effect that prey flight costs have on the optimal FID (Figure 5.7C). A prey’s cost of fleeing is rarely if ever greater than the threat posed by a lethal predator intent on capturing it. There may also be cases where 147 prey have partial information on predator attack stage. In this case we suggest that the predator risk function would be a mixture of the linear and sigmoidal functions relating to predator distance and attack stage, respectively (Figure 5.7B). This function may also relate to situations where prey have information on predator attack stage but are at a disadvantage if they are discovered at close range. This mixed model is similar to a model where risk shifts from a lower to a higher risk functions when prey recognize a change in predator behavior (Cooper, 2009).
The proposed model offers a theoretical explanation for the previous assertion that prey should flee as soon as they detect a predator and determine it is a threat
(Blumstein, 2010). When prey can easily determine a predator’s attack stage (such as with moths and bats), that factor overwhelms decision making. Prey costs and predator distance become less important. When it is unknown whether a predator is attacking, prey must use indicators like predator distance and approach speed to estimate the likelihood of attack. In this case risk level may rise more slowly as the predator approaches, and prey costs will have a greater effect on FID.
In summary, we have provided some of the first quantitative evidence linking prey defensive thresholds with cues measured from natural predatory attacks. B. trigona use relatively simple rules for determining when to initiate clicking based on two acoustic parameters: pulse interval and intensity. These rules, which are encoded in the moth’s nervous system (see discussion in Fullard et al. 2007), allow moths to respond rapidly soon after being targeted by echolocating bats. They also allow moths to discriminate between legitimate and false predatory threats with remarkable accuracy. This ability is facilitated by a predator who nearly continuously advertises its stage of attack through 148
echolocation. This finding demonstrates that economic considerations determine the time
at which B. trigona exhibits its combined jamming-and-diving defense, despite the possibility that other factors such as timing the defense for maximum effect (e.g. Fullard
et al. 1994), may be providing conflicting selective forces. 149
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Figure 5.1. Hypothesized predator risk level as it relates to predation stage. Predation can be categorized into a series of stages (A). That presented here is modified from Endler
(1986). Predator risk is hypothesized to relate to predator stage by a sigmoidal function
(B), with a rapid increase occurring after detection and as the predator increasingly commits to pursuing the prey. The bat echolocation attack sequence provides a model for studying risk relating to predator stage of attack, as the phases of bat echolocation reliably indicate the predator’s stage. See text for explanation of specific predation stages and echolocation phases. 157
Figure 5.2. Three-dimensional simulation of the sonar beam of a bat attacking a moth
(left panel), and a spectrogram of the bat echolocation sequence with two-dimensional plots of the bat’s echolocation beam shape and direction relative to the target (right panel).
The bat searches for prey with directional beams that are not aimed at the prey. After detection (beginning of approach phase) the bat localizes the prey and then locks its sonar beam on the target. The bat then decreases the intensity of emissions to keep echoes returning at a constant level (automatic gain control) through the tracking and buzz phases. Simulation was based on three-dimensional coordinates and bat call intensities of an actual attack. Beam shape and direction was not measured directly but was simulated based on previous literature (Ghose and Moss, 2003; Jakobsen and Surlykke, 2010).
Sonar beam shapes are depicted as the estimated volume ensonified by at least 90 dB
SPL. 158
Figure 5.3. Three-dimensional schematic of field setup for recording bat attacks on tethered and free-flying moths. Three infrared cameras recorded bat flight trajectories, positions of tethered and free-flying moths, and positions and directional axes of the ultrasound microphone. An ultraviolet (UV) light attracted free-flying moths and foraging bats to the observation area. In experiment 1 a silent Noctuid moth was tethered and suspended along with a miniature (3mm diameter) microphone from a 10 m telescoping pole. The microphone’s close proximity to the tethered moth allowed for estimating bat call intensities emitted in the moth’s direction (10 cm from the bat’s mouth) and arriving at the moth for attacks on the tethered moth (real attacks) and attacks on nearby free-flying moths (false threats). In experiment 2, the clicking moth Bertholdia trigona was tethered below the microphone and bat passes that elicited a clicking response were compared to passes that did not elicit clicking. 159
Figure 5.4. Acoustic and kinematic characterization of “real” and “false” threats. A real threat is a bat attacking the target moth (A, B); a false threat is a bat attacking a nearby moth (C). Real threats are shown (A) aligned by the end of the attack, and (B) aligned by the echolocation call of maximum intensity. The top panels show the bat echolocation intensities 10 cm from the bat (emitted in the moth’s direction) and at the moth’s position.
Middle and bottom panels show how bat pulse intervals and approach angles change over the course of an attack, respectively. The rise in bat call intensity early in attacks in A and
B reflects the bat localizing the moth, followed by an immediate decrease in emission intensity, which is the bat’s automatic gain control (see text and Figure 5.2). Bats engage gain control (time=0 s in B) in the middle of approach phase and then soon turn towards the prey (a decrease in approach angle). False threats are characterized by decreasing call intensities at the moth’s position and approach angles not correlated with time in the attack. 160
Figure 5.4 161
Figure 5.5. Predicted and actual clicking thresholds for different bat echolocation pulse- intervals in Grote’s tiger moth ( Bertholdia trigona ). Echolocation call intensity and pulse interval impinging upon a tethered focal moth are shown for five attacks on the focal moth (real threat) and five attacks on a nearby, free-flying moth (false threat; see figure
5.2). The optimal threshold for discriminating real and false threats was determined using quadratic discriminant analysis with 50% (p=0.5) and 75% (p = 0.75) likelihood of a call being assigned as a real threat. B. trigona clicking thresholds (mean +/- s.e.) measured from moths suspended in a sound chamber match predicted p=0.75 thresholds for pulse intervals of 35-100 ms. 162
Figure 5.6. Discrimination of bat passes that did or did not elicit clicking by tethered
Bertholdia trigona in the field. Acoustic characteristics (bat pulse interval vs. bat call intensity) and bat flight characteristics (distance from moth vs. flight trajectory relative to bat-moth vector, or phi) were used to differentiate bat passes that elicited a clicking response and those that did not (Top two rows of graphs). Dashed lines indicate thresholds for discriminating clicking from non-clicking interactions using discriminant function analysis. PPV, positive predictive value, or the percent of clicking interactions correctly classified; NPV, negative predictive value, or the percent of non-clicking interactions classified correctly. Some interactions were included where bats were outside the calibrated volume of the cameras and > 4m from the moth. Exact bat-moth distances and phi values were not available for these interactions. Bat call intensities increased and pulse intervals decreased prior to moth clicking (bottom two rows of graphs). 163
Figure 5.6 164
Figure 5.7. Cost-benefit models of escape behavior with varying prey knowledge of predator attack stage. Prey cost of fleeing C is dependent on predator distance and prey characteristics (C-low vs. C-high). In the classic model (A), benefit of fleeing B is equal to predator risk and increases as the predator gets closer (after Ydenberg and Dill, 1986).
The optimal escape distance D* occurs at the intersection of B and C. We propose that this model holds for prey that lack information about predator attack stage. For prey with information about predator attack stage (C), B is predicted to be a sigmoidal function with an increase in risk/benefit of fleeing when the predator detects a target and commits to an attack (see Figure 5.1). An intermediate level of information of predator stage has a risk function that is a mix of predator distance and attack stage (B). Increased availability of information on predator stage diminishes the effect of prey cost level (C-high vs. C- low) on optimal escape distance D*. 165
CONCLUSIONS AND FUTURE DIRECTIONS
This dissertation and the two that preceded it (Hristov, 2004; Barber 2007) have provided substantial evidence to settle a dispute over the defensive function of tiger moth clicks that is nearly 50 years old. All three hypotheses regarding click function are effective under certain conditions. Most tiger moths appear to click to warn bats of their toxic chemistry (Hristov, 2004), and this sets the stage for Batesian and Mullerian mimicry (Barber, 2007). Startle can be an effective defense against bats that have not habituated to clicking moths. This appears to be especially true for moths with high clicking rates (Chapter III). Finally, some minority of moth species with high clicking rates can jam bats sufficiently to prevent capture (Chapter II). It is unclear whether moths with low clicking rates gain advantage through a small degree of jamming, perhaps as a more potent message of toxicity (Ratcliffe and Fullard, 2005).
We have seen (Chapters IV and V) that the benefits of studying bat-insect
interactions in the field, and the methodology developed for these projects, makes
possible studies of many aspects of bat-moth biology. I predict in the coming years we
will see a sharp increase in the number of field studies on bats and insects.
Now that we have a more detailed grasp on the diversity of defensive strategies
employed by Arctiine moths, a next step is to place these discoveries in a phylogenetic
context, and such studies are planned. Of course it will not be possible to put each species
through the rigors of experimentation that have been the subject of this dissertation. New
methods will need to be developed to determine a moth’s primary defensive strategy
using relatively simple assays. The rate of moth clicking in response to bat echolocation 166 attacks can be determined using playbacks in a sound chamber, and this approach has yielded comparative acoustic recordings on dozens of species (Barber and Conner, 2006;
Corcoran et al. 2010). However, it is unknown if there is a specific threshold of clicking rate for jamming to effectively deter bats. Therefore a study is planned where bats can be trained to attack a prey and a miniature ultrasonic speaker where clicks can be broadcast at different rates and with different timing with respect to the bat’s attack. If a clicking threshold for jamming can be found, then a simple recording of a moth clicking would be all that is required for determining if it is a “jammer” species. Rapid assays are also required for determining moth palatability to bats. This could be accomplished using chemical or bio-assays including controlled feedings (Hristov and Conner, 2005). After these challenges have been overcome, it will be possible to understand the evolution of these fascinating defensive strategies in nature.
Another line of study would be to use a comparative ecological approach. Are acoustic Batesian mimics limited in their seasonal and nightly time of activity based on that of their models? Do jammers take advantage of their extraordinary defensive ability by being active at times of night other moths avoid? We know aposematic moths tend to be feeding specialists as caterpillars and are restricted to host plants with specific chemicals. Is this relaxed for Batesian mimics and jammers? Do they tend to be generalist foragers and benefit from this in some way? The list of questions is nearly endless.
Taking the broader view, the discovery that moths jam bat sonar demonstrates the powerful selective force that drives survival in a predator-prey system. It shows that, regardless of their sophistication, communication systems (including the 167 autocommunication of sonar) are subject to disruption and distortion. This is especially true when the organism taking advantage of the communication breakdown is relatively rare. The communicators are then under less selective pressure to overcome the disruption. In the case of sonar-jamming moths, these animals constitute a small percentage of the moths that are available as prey at a given time. It is unlikely that bats would be under sufficient selective pressure to specifically evolve a counter-measure to the jamming defense. It is more likely that bats would evolve strategies that give them advantage over all or most eared moths, which frequently make up a large percentage of the lepidopteran prey available (Goerlitz et al. 2010). Because of the diffuse co- evolutionary process, where many prey and predator species compete simultaneously, we can expect numerous attacking and defensive strategies to coexist. We should therefore expect sonar jamming to be only one entry on a long list of discoveries in the bat-moth arms race. 168
LITERATURE CITED
Barber, J. R. and Conner, W. E. (2006). Tiger moth responses to a simulated bat attack:
timing and duty cycle. Journal of Experimental Biology 209 , 2637-2650.
Barber, J.R. (2007). Diversity and Deception: Acoustic Aposematism and Mimicry in
the Bat-Moth Arms Race. Ph.D. Dissertation. Wake Forest University, Winston
Salem, NC.
Corcoran, A. J., Conner, W. E. and Barber, J. R. (2010). Anti-bat tiger moth sounds:
Form and function. Current Zoology 56 , 358-369.
Goerlitz, H. R., ter Hofstede, H. M., Zeale, M. R. K., Jones, G. and Holderied, M. W.
(2010). An aerial-hawking bat uses stealth echolocation to counter moth hearing.
Current Biology 20 , 1568-1572.
Hristov, N. I. (2004). A Multi-level Analysis of the Bat-Tiger Moth Arms Race. Ph.D.
Dissertation. Wake Forest University, Winston Salem, NC.
Hristov, N. I. and Conner, W. E. (2005a). Effectiveness of tiger moth (Lepidoptera,
Arctiidae) chemical defenses against an insectivorous bat ( Eptesicus fuscus ).
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Ratcliffe, J. M. and Fullard, J. H. (2005). The adaptive function of tiger moth clicks
against echolocating bats: an experimental and synthetic approach. Journal of
Experimental Biology 208 , 4689-4698. 169
Aaron J. Corcoran Department of Biology, Wake Forest University Winston Salem, NC 27106 336-251-2638 | [email protected]
EDUCATION
Ph.D. Biology , Wake Forest University, 2013 (projected) - Dissertation: Sonar Jamming in the Bat-Moth Arms Race
M.A. Biology , Humboldt State University, 2007 - Thesis: Automated Acoustic Identification of Twelve Bat Species of the Eastern United States
B.S. Wildlife , with Honors Purdue University, 2003
RESEARCH EXPERIENCE
2008 - 2012 NSF Graduate Research Assistantship , Wake Forest University 2007 - 2008 Research Analyst and Field Research Coordinator , Humboldt State University 2005 - 2007 SERDP Graduate Research Assistantship , Humboldt State University 2004 - 2005 Field Research Assistant , U.C. Davis, Oregon St. Univ., Utah St. Univ. (Separate appointments)
TEACHING EXPERIENCE
2012 Teaching Assistant , Animal Behavior and Comparative Physiology, Wake Forest University 2011 Teaching Assistant , Lepidoptera Field Course, Southwestern Research Station 2010 - 2012 Guest Lecturer , Animal Behavior, Sensory Biology, and Entomology, Wake Forest University 2008 Lecturer , General Zoology Lab, Humboldt State University 2005 - 2007 Teaching Associate , General Zoology Lab, Humboldt State University
PUBLICATIONS
Corcoran, A.J. , R. D. Wagner, and W.E. Conner 2012. Optimal predator risk assessment by the sonar-jamming moth, Bertholdia trigona . In preparation.
Corcoran, A. J. , and W. E. Conner 2012. Sonar jamming in the field: Effectiveness and behavior of a unique prey defense. Journal of Experimental Biology. 215: 4278-4287 170
Conner, W. E., and A. J. Corcoran 2012. Sound strategies: the 65-million-year-old battle between bats and insects. Annual Review of Entomology. 57: 21-39
Corcoran, A. J. , J. R. Barber, N. I. Hristov, and W. E. Conner 2011. How do tiger moths jam bat sonar? Journal of Experimental Biology. 214: 2416-2425
Corcoran, A. J. , W. E. Conner, and J. R. Barber 2010. Anti-bat tiger moth sounds: Form and function. Current Zoology. 56: 358-369
Corcoran, A. J. , Barber, J. R., and W. E. Conner 2009. Tiger moth jams bat sonar. Science. 325: 325-327
SCHOLARLY PRESENTATIONS
Corcoran, A.J. and W. E. Conner (2012) Sonar jamming in the field: Effectiveness and behavior of a unique prey defense. International Congress of Neuroethology, College Park, MD. Poster. Corcoran, A.J. (2012) Sonar-jamming in the bat-moth arms race. Invited speaker, Department of Biology seminar series, Clemson University. Corcoran, A.J. (2012) Sonar-jamming in the bat-moth arms race. Invited speaker, Department of Biology seminar series, Utah State University. Corcoran, A.J. (2012) Sonar-jamming in the bat-moth arms race. Invited speaker, Center for Evolutionary and Comparative Biology of Hearing, University of Maryland. Corcoran, A.J . , J.R. Barber, N.I. Hristov, and W.E. Conner (2010). How do tiger moths jam bat sonar? North American Symposium for Bat Research. Denver, CO. Corcoran, A.J. , and J.M. Szewczak (2010). Niche Breadth, Foraging Plasticity and Conservation of Myotis septentrionalis, M. lucifugus, and M. leibii. Southeastern Bat Diversity Network Annual Meeting, Asheville, NC Corcoran, A.J. , Barber, J.R., W.E. Conner (2009). Tiger moth jams bat sonar. North American Symposium for Bat Research, Portland, OR Corcoran, A.J. , Barber, J.R., W.E. Conner (2009). Limitations of biosonar: a tiger moth sonar jamming defense. Animal Sonar Symposium, Kyoto, Japan Corcoran, A.J. , Barber, J.R., Cullen, M.A. and W.E. Conner (2009). Sound strategies: acoustic aposematism, mimicry, and sonar jamming in the bat-moth arms race. Society for Integrative and Comparative Biology Annual Meeting, Boston, MA Corcoran, A.J. and J.M. Szewczak (2007). Automated acoustic identification of nine bat species of the Eastern United States. International Bat Research Conference, Merida, Mexico Corcoran, A.J. and J.M. Szewczak (2006). Automated acoustic identification of three bat species – Lasiurus borealis, Nycticeius humeralis, and Pipistrellus subflavus. North American Symposium on Bat Research, Wilmington, NC 171
GRANTS AND AWARDS
2012 Outstanding Graduate Student of the year, Wake Forest University 2012 Vecellio Award for Graduate Research, Wake Forest University 2011 Theodore Roosevelt Memorial Grant, American Museum Natural History 2010 Elton C. Cocke Scholarship, Wake Forest University 2010 Graduate School Alumni Student Travel Award, Wake Forest University 2010 National Science Foundation Grant w/ Dr. William Conner 2009 North American Symposium for Bat Research Outstanding Student Presentation 2009 Vecellio Award for Graduate Research, Wake Forest University 2008 Dean’s Fellowship, Wake Forest University 2008 Andrew’s Fellowship, Purdue University (declined) 2002 Agriculture Research Scholarship, Purdue University
UNDERGRADUATE MENTORING
2012 Kaitlyn Roman – Bats jamming bats? A playback experiment 2011 Zachary Walker – Defending food patches by jamming sonar 2010 Ryan Wagner – Acoustic cue recognition by sonar-jamming moths 2009 Wesley Johnson – Acoustic Müllerian mimicry in the field 2008 Megan Cullen – Sensory perception of prey by captive bats
OUTREACH AND MEDIA RELATIONS
2013 Featured in the nature documentary “Micro Monsters 3D” with David Attenborough 2012 Interviewed for feature article on field study of sonar jamming. Inside JEB Public presentation on sonar-jamming moths. Science Café , hosted by Sciworks and Reynolda village Youth presentation on bats and moths. King County Public Library Research on sonar-jamming moths featured in textbook. Animal Behavior by Shawn E. Nordell and Thomas J. Valone Provided materials and information for children’s article on sonar-jamming moths. Wild Magazine , Canadian Wildlife Federation Public presentation on sonar-jamming moths. Southwestern Research Station , Portal, Arizona 2011 Scientific consultant for National Geographic television mini-series Untamed Americas 172
Public presentation on sonar-jamming moths Southwestern Research Station, Portal, Arizona Presented on “Moth ears for bat defense and moth courtship”. Lepidoptera Field Course, Southwestern Research Station , Portal, Arizona Research highlighted in article “Masters of Disguise: Animal Mimics Fool Their Foes”. Scientific American Magazine Research on sonar-jamming moths featured in textbook. Animal Communication by Jack W. Bradbury and Sandra L. Vehrencamp 2010 Public presentation on sonar-jamming moths. Southwestern Research Station, Portal, Arizona Wrote “Featured Scientist of the Year” article on sonar-jamming moths. Southwestern Research Station Bulletin 2009 Live broadcast, nationwide radio interview on sonar-jamming moths. NPR’ s Science Friday . (Also featured for video pick-of-the-week) Research featured worldwide including by L.A. Times , National Geographic , New York Times , NewsTrack India,NPR, Scientific American , Sydney Morning Herald , Wired News, and Voice of America Interviewed on sonar-jamming research. Science Magazine podcast
Televised interview on sonar-jamming moths. Discovery Channel Canada .
PROFESSIONAL AFFILIATIONS
American Society of Mammalogists Animal Behavior Society North American Society for Bat Research Phi Beta Kappa Honor Society Society for Integrative and Comparative Biology
PROFESSIONAL SERVICE
Reviewer for PLOS ONE and ETHOLOGY