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.

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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

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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.

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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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15

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, X2 = 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, X2

= 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, X2 = 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

1. D. R. Griffin, Anim. Behav. 8, 141-154 (1960).

2. H. U. Schnitzler, E. K. V. Kalko, Bioscience 51, 557-569 (2001).

3. L. A. Miller, A. Surlykke, BioScience 51, 570-581 (2001).

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).

7. S. J. Weller, N. L. Jacobsen, W. E. Conner, Biol. J. Linn. Soc. 68, 557-578 (1999).

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).

10. D. C. Dunning, Z. Tierpsychol. 25, 129-138 (1968).

11. L. Acharya, M. B. Fenton, Can. J. Zool. 70, 1292-1298 (1992).

12. D. C. Dunning, L. Acharya, C. B. Merriman, L. Dal Ferro, Can. J. Zool. 70, 2218-

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

N cdur mhc mc isi clicks d kHz

-15 dB

kHz

+15 dB

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 (R2 < 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 attacks 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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72

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 control 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

Tabl

e 3.

2. B

at e

chol

ocat

ion

resp

onse

s to

mot

h ja

mm

ing

Para

met

er

Att

ack

type

N

F

d.f.

P- valu

e

Adj

.

P(Q

) C

all/

PI 0

C

all/

PI 1

C

all/

PI 2

C

all/

PI 3

Peak

Fre

quen

cy

Direc

t 11

0.

42

3 0.

99

0.68

35

.3±1

1.1

34.5

±8.

5 35

.2±

9.3

34.6

±7.

7

(kH

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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 m3.

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 remained low. Diving involved a

rapid vertical acceleration toward the ground. The number of attacks where bats aborted

pursuit in response to moth clicks was determined using previously established criteria

(Corcoran et al. 2011); these include lengthening pulse intervals and bats veering away

from the moth soon (100-300 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

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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

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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

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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

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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

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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%

Caught40 kHz Myotis1 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

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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.

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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.

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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

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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

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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

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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.

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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

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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.

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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.

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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

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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 m3.

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

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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

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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) and 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

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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,

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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

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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; R2 = 0.66; P <

0.0001) and then decreased linearly (linear regression; B = -0.037 dB/ms; R2 = 0.71; P <

0.0001). This is seen clearly when the attacks are aligned by the bat call of maximum

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intensity (Figure 5.4B). At the moth’s position bat call intensity increased linearly (linear

regression; B = 0.086 dB/ms; R2 = 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; R2 = 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

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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

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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.

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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.

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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

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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.

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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.

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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.

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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.

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Figure 5.4

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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.

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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).

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Figure 5.6

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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*.

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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

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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

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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.

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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).

Chemoecology 15, 105-113.

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.

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Aaron J. Corcoran Department of Biology, Wake Forest University

Winston Salem, NC 27106 336-251-2638 | corcaj8@wfu.edu

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

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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