i

Sensory and Cognitive Constraints and Opportunities in Bats

by

Jeneni Thiagavel

A thesis submitted in conformity with the requirements

for the Degree of Doctor of Philosophy

Department of Ecology and Evolutionary Biology

University of Toronto

© Copyright by Jeneni Thiagavel, 2019

ii

Sensory and Cognitive Constraints and Opportunities in Bats

Doctor of Philosophy, 2019

Jeneni Thiagavel, Department of Ecology and Evolutionary Biology, University of

Toronto

Here I report on three comparative studies and one review chapter addressing

the relationships between sensory reliance, neuroanatomy, skull morphology and body

size, as they relate to diet and foraging in bats.

In chapter two, I use morphological and echolocation data to test whether (i)

mass-signal frequency allometry or (ii) emitter-limited (maximum gape) signal

directionality better explain the negative relationship between size and peak frequency

in bats. The results suggest that body mass and forearm length were important

predictors of open space echolocation call peak frequency in ways that (i) reflect species-

specific size differences, and (ii) suggest the influence of preferred foraging habitat.

In chapter three, I test the predictions that the ancestral bat had (i) an auditory

brain design capable of supporting early laryngeal echolocation, but (ii) eyes of

insufficient absolute size to allow insect tracking at night. The results suggest that bats’

common ancestor had eyes too small to allow for successful aerial hawking of flying

insects at night, but an auditory brain design sufficient to afford echolocation. Further,

we find that those with less sophisticated biosonar have relatively larger eyes than do

sophisticated echolocators.

iii

In chapter four, I continue to explore apparent trade-offs between echolocation

call design and vision in predatory bats. I also explored the effects of foraging strategy,

roost preference, and migration on the brains and eyes of predatory bats. I found that

external roosters had large relative eyes, as did those with conserved calls which also

had larger visual regions than those with more derived calls. The results also suggest

that gleaners and sedentary bats have larger brains than aerial hawking and migrating

bats, respectively.

In chapter five, I provide an overview of sensory and cognitive ecology as they

relate to foraging ecology and diet in the Phyllostomidae. These bats have a wide

spectrum of feeding ecologies and sensory system specializations. Here, I use the

Phyllostomidae to illustrate the influences that foraging ecology and diet selection have

on the evolution of sensory systems and relative brain and brain region volumes.

iv

Acknowledgments

First, thank you Dr. John Ratcliffe for being the best mentor I could have ever asked for.

Thank you for sculpting me into the scientist I am today and for getting me this far.

Your advice and guidance has not only helped me get a doctorate degree but also

helped me lay out a path for my future. I can never thank you enough for putting your

faith in me when you took me on and for having brought me this far today. I am forever

grateful to you and I look forward to being friends for years to come.

Thank you to my committee members, Drs. Helene Wagner and Glenn Morris. You have

watched me grow from the first year of my Ph.D., fresh out of undergraduate studies, to

the graduate I am today. Thank you both for your insightful comments and advice for

each of my projects. Helene, thank you for being such a caring mentor who has taught

me how to effectively balance the various aspects of my life. Glenn, thank you for all the

laughs during our meetings and for showing me that science can be quite relaxing and

enjoyable to do.

Thank you to my father, Sivanandam Thiagavel, who has been the biggest pillar of

strength for my education. I would never have been able to get my Ph.D. without you.

Thank you for making my education your life’s number one priority. Thank you to my

mother, Nantha Thiagavel, for being one of my best well-wishers and for helping me

become the woman I am today. I am forever grateful to you both.

v

Thank you to my very best friend and fiancée, Dusan Namasivayam. You are the best

thing that has ever happened to me. Thank you for being my right hand and better half

during both my undergraduate and doctorate studies. I wouldn’t be where I am today

or who I am today, without you.

Thank you Dr. Aarthi Ashok for being my inspiration and for your constant support and

advice during both my undergraduate and graduate studies. Thank you to my co-author

Dr. Sharlene Santana for helping me use phylogenetic comparative methods in R, which

have now become the staple of the studies I do.

Thank you to my sisters, Jenodini Thiagavel and Jerubika Thiagavel, for all the laughs

and for being my personal cheerleaders in life. The support I have received from you

girls is a huge reason for my success. Thank you Blessing Goddey-Erikefe, for always

being there for me as my longest and closest girl friend. I appreciate everything you’ve

done for me. Thank you to my dear lab-mates and friends Livia Loureiro, Heather

Mayberry, Cylita Guy and A. Vikram Chochinov, for making this journey enjoyable for

me. Thank you as well to my co-authors for their contributions to our papers. Study-

specific acknowledgements to individuals and funding agencies can be found at the end

of each chapter.

My Ph. D. is dedicated to my father, Sivanandam Thiagavel.

vi

Table of Contents

Abstract ii

Acknowledgements iv

Table of Contents vi

List of Tables vii

List of Figures xi

Chapter 1 Synopsis 1

Chapter 2 Body Size Predicts Echolocation Call Peak Frequency 19

Better than Gape Height in Vespertilionid Bats

Chapter 3 Auditory Opportunity and Visual Constraint Enabled 44

the Evolution of Echolocation in Bats

Chapter 4 Sensory and Cognitive Correlates of Life-history 120

Traits in Predatory Bats

Chapter 5 Sensory and Cognitive Ecology of Phyllostomid Bats 159

Chapter 6 Concluding Remarks 212

vii

List of Tables

Chapter 2

Table 1. The fit of different evolutionary models (AICc values are shown).

Supplementary Table S1. Size and call parameters for the vespertilionid species used

in the study (N=86).

Chapter 3

Supplementary Table 1. Post hoc comparisons of phylogenetically adjusted data (log

transformed body mass, brain volume, eye mass, and brain regions) among foraging

strategies (t-values and in parentheses, Holm-Bonferroni corrected p-values).

Supplementary Table 2. Post hoc comparisons of phylogenetically adjusted data

(mass residuals of brain volume, eye mass, and brain regions) among foraging

strategies (t-values and in parentheses, Holm-Bonferroni corrected p-values).

Supplementary Table 3. Post hoc comparisons of phylogenetically adjusted data (log

transformed body mass, brain volume, eye mass, and brain regions) among

echolocating call type (t-values and in parentheses, Holm-Bonferroni corrected p-

values).

viii

Supplementary Table 4. Post hoc comparisons of phylogenetically adjusted data

(mass residuals of total brain volume, eye mass, and brain regions) among echolocating

call type (t-values and in parentheses, Holm-Bonferroni corrected p-values).

Supplementary Table 5. Post hoc comparisons of phylogenetically adjusted data (log

transformed body mass, brain volume, eye mass, and brain regions) among call type in

predatory bats (t-values and in parentheses, Holm-Bonferroni corrected p-values).

Supplementary Table 6. Post hoc comparisons of phylogenetically adjusted data

(mass residuals of total brain volume, eye mass, and brain regions) among call type in

predatory bats (t-values and in parentheses, Holm-Bonferroni corrected p-values).

Supplementary Table 7. Specimen numbers for the skulls from Royal Ontario Museum

(ROM) or Museum of Natural History in Denmark (MNHD).

Supplementary Table 8. Species categorized as having either a functional (YES) or

non-functional (NO) short-wave opsin (SWS) gene. Species from our dataset are shown

in bold.

Supplementary Table 9. Species, trait values, and categorizations by call type (MH:

multiharmonic, CF: constant frequency, DH: Fundamental harmonic frequency

modulated, NLE: non-laryngeal echolocator), diet (A: animal eating, P: phytophagous),

echolocation ability (LE: laryngeal echolocator, NLE: nonlaryngeal echolocator) and

roost type (I: roosts internally; E: roosts externally).

ix

Chapter 4

Table 1. Species, categorizations, trait values for predatory bats.

Table 2. Post hoc comparisons of phylogenetically adjusted residuals of eye mass

among call design and roost preference (t-values and in parentheses, Holm-Bonferroni

corrected p-values).

Table 3. Post hoc comparisons of phylogenetically adjusted residuals of hippocampus

mass among migratory status and foraging strategy (t- and in parentheses, Holm-

Bonferroni corrected p-values).

Table 4. Post hoc comparisons of phylogenetically adjusted residuals of superior

colliculus mass among migratory status and call design (t-values and in parentheses,

Holm-Bonferroni corrected p-values).

Table 5. Post hoc comparisons of phylogenetically adjusted residuals of neocortex

mass among foraging strategy and call design (t-values and in parentheses, Holm-

Bonferroni corrected p-values).

Table 6. Post hoc comparisons of phylogenetically adjusted residuals of neocortex

mass among migration status and call design (t-values and in parentheses, Holm-

Bonferroni corrected p-values).

Table 7. Post hoc comparisons of phylogenetically adjusted residuals of neocortex

mass among migratory status and foraging strategy (t-values and in parentheses, Holm-

Bonferroni corrected p-values).

x

Table 8. Post hoc comparisons of phylogenetically adjusted residuals of brain mass

among foraging strategy and call design (t-values and in parentheses, Holm-Bonferroni

corrected p-values).

Table 9. Post hoc comparisons of phylogenetically adjusted residuals of brain mass

among migration status and call design (t-values and in parentheses, Holm-Bonferroni

corrected p-values).

Table 10. Post hoc comparisons of phylogenetically adjusted residuals of brain mass

among migratory status and foraging strategy (t-values and in parentheses, Holm-

Bonferroni corrected p-values).

xi

List of Figures

Chapter 2

Figure 1. Body mass versus vocalization frequency for a range of vertebrates. Figure

made by Sara Vukson using data presented in Jones (1999) and Fletcher (2004).

Figure 2. Relationship between directionality, emitter size, and frequency in

vespertilionid bats. Directionality of the biosonar beam increases with emitter size and

frequency. Figure remade by Sara Vukson from Jakobsen et al. (2013).

Figure 3. Distances used to measure maximum gape height. Maximum gape height was

estimated using the distances between the posterior-most point of the

temporomandibular joint and the anterior-most point at the upper incisors (a), lower

incisors (b), and origin (A) and insertion (B) of the superficial masseter to estimate the

maximum gape for each individual. Figure made by Sara Vukson.

Figure 4. A phylogeny (Shi and Rabosky 2015) showing peak frequency (kHz) mapped

along the branches of the tree using Maximum Likelihood.

xii

Chapter 3

Figure 1. Two equally parsimonious hypotheses for the origination of laryngeal

echolocation in bats. The unshaded side depicts the two origins hypothesis and predicts

that laryngeal echolocation originated in the common ancestor to the Emballonuroidea,

Noctilionoidea, and Vespertilionoidea and again in the Rhinolophoidea. The shaded side

depicts the single origin hypothesis, which predicts laryngeal echolocation was present

in the common ancestor of all bats and lost in the Pteropodidae. Middle column displays

(top to bottom) five 30-35 gram species from each of these major groups. Cynopterus

brachyotis (non-echolocating, phytophagous), Rhinolophus hildebrandti (echolocating,

predatory), Taphozous melanopogon (echolocating, predatory), Tonatia evotis

(echolocating, predatory), Nyctalus noctula (echolocating, predatory). Please note that

bats with constant frequency (CF), multi-harmonic frequency-modulated calls (MH) and

fundamental harmonic frequency modulated calls (DH) (i.e. most energy in fundamental

harmonic) are found in both suborders of bats. Photographs by Brock Fenton and Signe

Brinkløv.

Figure 2. The ancestral state estimates of call types and foraging categories. (a) Bats

were also categorized as (i) predatory laryngeal echolocators (ALE), (ii) phytophagous

laryngeal echolocators (PLE) and (iii) phytophagous non-laryngeal echolocators (PNLE).

Models of evolution were compared using AICc scores and the character states for

ancestral call types were estimated under an equal rates model of evolution. These

marginal ancestral states have been overlain on the phylogeny. Our results suggest that

the ancestral bat was a predatory laryngeal echolocator (Bayesian posterior

xiii

probabilities. ALE. >0.999; PLE.

xiv

Figure 4. Phylogenetically informed linear regressions of eye mass and neocortex

on body mass by call type. Phylogenetic generalized least square models showing the

regressions of log-transformed (a) eye mass (N=162) and (b) neocortex (N=162) on body

mass while accounting for the phylogenetic non-independence between data points. The

calls of laryngeal echolocating bats were categorized as (i) constant frequency (CF), (ii)

multi-harmonic calls (MH), (iii) frequency modulated calls dominated by the

fundamental harmonic (DH) and are shown separately on the plots.

Figure 5. Phylogenetically informed linear regressions of brain and eye mass on

body mass by family. Phylogenetic generalized least square models showing the

regressions of log-transformed eye mass (N=183) on body mass accounting for

phylogenetic non-independence among data points. The 5 most speciose families of bats

are shown separately on the plots. The pteropodids have the largest absolute eye size

while the predatory emballonurids have the largest eyes among the laryngeal

echolocating bats. The smallest eyes are found in the more advanced echolocating

rhinolophids and vespertilionids, suggesting that there may be an echolocation-vision

trade-off even among predatory species.

Supplementary Figure 1. Phylogenetic reconstructions of absolute continuous traits.

Estimates of the maximum likelihood (ML) ancestral states for absolute (a) body, (b)

eye, (c) brain, and (d-i) brain regions. Masses were generated and mapped onto the tree

by estimating states at internal nodes using ML and interpolating the states along each

edge. The phylogeny and variation in the absolute trait values were combined to

visualize the increase and decrease of these traits in different lineages over

xv

evolutionary time. The color gradient indicates trait size, as mapped onto the phylogeny

(families indicated along right side).

Supplementary Figure 2. Phylogenetic reconstructions of mass-corrected brain

regions. Estimates of the maximum likelihood (ML) ancestral states for mass-corrected,

(a) eye, (b) brain, and (c-h) brain regions. Phylogenetically informed mass-residuals

were generated and mapped onto the tree by estimating states at internal nodes using

ML and interpolating the states along each edge. The phylogeny and variation in the

residuals were combined to visualize the increase and decrease of these traits in

different lineages over evolutionary time. The color gradient indicates trait size, as

mapped onto the phylogeny (families indicated along right side).

Supplementary Figure 3. The ancestral state estimates of short-wavelength sensitive

(SWS) opsin gene functionality. Bat species (N= 41) were categorized as having either

(i) functional or (ii) non-functional SWS opsin genes and the functionality of the

ancestral SWS opsin gene was estimated under an equal rates model of evolution. These

marginal ancestral states (i.e. Bayesian posterior probabilities), have been overlain on

the phylogeny. We found support for a functional SWS opsin in the ancestral bat

(Bayesian posterior probabilities. functional SWS opsin gene. 0.971; non-functional

SWS opsin gene. 0.029).

Appendix 1

Figure 1. Headshots of phyllostomid species representing four diet categories.

Clockwise from top left. Tonatia saurophila, a predatory species; Phyllostomus discolor,

xvi

an omnivorous species; Artibeus jamaicensis, a frugivorous species; Anoura geoffroyi, a

nectarivorous species. Note the relatively elongated snout of A. geoffroyi and relatively

large ears of T. saurophilia, species-specific morphological traits presumably associated

with taking nectar from flowers and listening to prey-generated cues, respectively.

Photographs by Marco Tschapka, used with permission. Figure adapted from Ratcliffe

2009.

Figure 2. Brain mass versus body mass in bats. Relative to body mass the

phyllostomids and pteropodids have larger brains than do the remaining bat families

(data from Baron et al. 1996).

Figure 3. Echolocation calls of four phyllostomid bats with different foraging

strategies. For each call we show an oscillogram (top), a spectrogram (bottom left) and

a power spectrum (fast Fourier transform (FFT), bottom right). All recordings have

been high-pass filtered (~15kHz). Note that the spectrogram axes differ between

species. Clockwise from top left. Artibeus jamaicensis, a frugivorous species;

Glossophaga soricina, a nectarivorous species; Micronycteris microtis, a gleaning species;

Macrophyllum macrophyllum, a trawling species.

Figure 4. Micronycteris microtis and its various prey. Clockwise from top left. adult

M. microtis with a lizard in its mouth; adult M. microtis with mantid in its mouth; adult

female M. microtis with caterpillar in its mouth (and its pup attached to its underside).

1

Chapter One

Synopsis

2

Introduction

Morphology evolves in response to the environment and can be indicative of a

species’ foraging ecology. These morphological characteristics and adaptations often

reflect specific sensory and nutritional requirements. Examples of physical traits being

well-suited for particular foraging strategies can be seen everywhere in the animal

kingdom. For instance, the cheetah, Acinonyx jubatus, is the fastest known land animal

and, not surprisingly, is an anatomical specialist with several morphological adaptations

for speed. Among the felids, it has the longest limbs and lumbar spine (Gonyea 1976),

and a lightweight skeleton and an aerodynamically efficient frame (Marker & Dickman

2003). The cheetah’s skull, jaw and teeth make clear that it is predator, one built to

handle large prey and its anatomy, physiology, and behavior reveal that it is an animal

built for speed.

Vertebrates make decisions based on external stimuli transduced by several

different sensory systems (Geva-Sagiv et al. 2015). Just like morphology, the sensory

systems an animal relies on and the specializations of these systems can reveal a lot about

a given species foraging ecology. For example, while the cheetah has several

morphological adaptations to quickly take down sprinting prey, it also exhibits

extraordinary sensory abilities. Cheetahs have very large eyes to maximize binocular

vision, are able to hold their gaze well, and have a specialized inner ear which allows for

the maintenance of visual and postural stability while running and capturing prey at high

speeds (Grohé et al 2018). Another sensory specialist is the star-nosed mole, Condylura

cristata, which specializes in somatosensory abilities. Despite foraging in dim lit

3

conditions and being functionally blind, these moles can detect and consume prey in 120

milliseconds, faster than any other mammal known to date (Catania 2011). They have 22

appendages around their nostrils, known as ‘its starnose’ which is covered with very

sensitive sensory organs which they use to navigate and find prey (Catania 2011).

The mammalian brain, and those of other animal groups, has long been under

selective pressure to improve sensory acquisition, decision-making and information

storage (Dukas & Ratcliffe 2009). With their morphological, physiological and

behavioural adaptations, as well as sensory system specializations, mammals are

constantly acquiring information from their surroundings and constructing neural

representations of their environments (Dusenbury 1992; Shettleworth 2010). Foraging

ecologies differ in their sensory requirements and their corresponding brain regions

often reflect these species-specific differences (Baron et al. 1996). For instance, the star

nosed mole has expanded areas of the somatosensory cortex that are dedicated to the

sensory surface of the star (Catania 2011). Sensory systems and associated brain regions

can become more (or less) specialized over evolutionary time, depending on the species’

foraging ecology and diet.

Bats (order Chiroptera), which may number more than 1300 species (Fenton &

Simmons 2015), are excellent models to investigate the relationships between species-

specific sensory and cognitive specializations. Among mammals, bats include the

smallest species, the bumblebee bat, Craseonycteris thonglongai, which weighs about 2g

(Nowak 1994). In addition to being small, most bats also have relatively small brains

(Striedter 2004). This because in bats both body and brain size are constrained by

4

powered flight (which is very energetically demanding). Thus, while relative brain size

tends to increase in vertebrate lineages over evolutionary time (Jerison 1973), brains

may have gotten smaller in bats, particularly in obligate aerial hawkers (Safi et al. 2005).

Different bat species and families also showcase extreme differences in diet and foraging

behaviour. That is, different bat lineages rely on different sensory systems, which in turn

tend to be more (or less) specialized to reflect these differences. Bats include frugivorous,

nectarivorous, omnivorous, sanguivorous species, as well as predatory species that feed

on invertebrates and vertebrates. While most species feed exclusively on insects and

other arthropods, frugivory and nectarivory (i.e. phytophagy) are commonly observed

among the phyllostomids and the pteropodids, two of 21 bat families. These

phytophagous bats rely on their sense of sight and smell much more than animal

predators (Eisenberg & Wilson 1978). These bats must use visual, olfactory, and spatial

cues to localize spatially distributed fruits and flowers, effectively handle these non-

animal foods, and assess their quality (e.g. ripeness, toxicity), before consumption

(Ratcliffe 2009). Unsurprisingly, their visual and olfactory brain regions are relatively

large (Baron et al. 1996). Most predatory bats take airborne prey (aerially hawking), but

some are also capable of gleaning (taking prey off solid surfaces) and trawling (taking

prey off water surfaces). Relative to the phytophagous species, these bats are thought to

have enhanced auditory regions (Baron et al. 1996; Thiagavel et al. 2018), as they rely

more heavily on echolocation to opportunistically catch their prey (Pirlot and Stephan

1970; Ratcliffe 2009).

Bats of the family Pteropodidae, of which there are some 200 species, are not

capable of laryngeal echolocation. While flight is generally agreed to have had a single

5

origin in bats, whether laryngeal echolocation evolved once and was subsequently lost in

the pteropodids or evolved multiple times after the pteropodids diverged, has long been

controversial among bat biologists (Jones & Teeling 2006). Different lines of evidence (i.e.

fossil, molecular, genetic, etc.) support both hypotheses (Teeling 2009; Veselka et al.

2010. Wang et al. 2017). Rather than echolocate, most pteropodids rely primarily on

vision and olfaction to find food and have enlarged visual and olfactory brain regions but

small auditory regions relative to laryngeally echolocating bats (which in turn have

reduced visual and olfactory areas) (Baron et al. 1996). This trade-off is less apparent in

the phyllostomids, which are characterized by the broadest range of dietary niches and

greatest inter-specific behavioral plasticity of any bat family (Jolicoeur & Baron; Kalko et

al 1996). Phytophagous phyllostomids are also capable of laryngeal echolocation (like all

other bats except the pteropodids) and have auditory regions similar in size to the

obligate predators (deWinter & Oxnard 2001).

Although absolute brain and brain region volumes were considered

uninformative, being replaced by more ‘informative’ relative measures, the importance

of absolute measures in brain research is once again appreciated today (see Deaner et al.

2007 for a thorough overview of the usefulness of absolute values in brain research). In

short, absolute size reflects sheer processing power, neural investment and information

use (Deaner et al. 2007). Thus, considering both absolute and relative measures is

sometimes appropriate. The importance of absolute parameters becomes especially

apparent when considering small vertebrates whose brains’ development and evolution

are physically constrained by the amount of available space. For instance, predatory bats

may be under pressure to maintain a reduced brain size, as smaller brains (and body

6

mass and skull size) may facilitate fast and long-distance flights (Safi et al. 2005; McGuire

& Ratcliffe 2011). Unlike most phytophagous bats, their need to react quickly to

opportunistically catch their airborne prey (Pirlot and Stephan 1970; Ratcliffe 2009)

may constrain maximum brain size in insectivorous bats. reAdditionally, the dimensions

of a bat’s skull may also impose constraints on maximum eye size (see Chapter 3) and

aspects of other sensory systems, including echolocation (see Chapter 2). The larger an

animal’s absolute eye size, the more light their eyes can collect. This becomes especially

important when operating in dim lit conditions. As alluded to above, laryngeally

echolocating predatory bats are constrained to being small. Thus, a visual solution in the

first bats (which most likely resembled these aerial hawking predators (see Chapter 3))

may have been precluded due to an upper limit on maximum eye size imposed by the

small skulls of the first bats. Further, gape and echolocation call peak frequency have

been suggested to be negatively correlated (see Chapter 2). Extant predators (which

remain small-bodied and small-skulled) may continue to be excluded from potential

vision-based solutions, developing enhanced echolocation abilities instead. A switch

from insect to plant diet (Freeman 2000) may have permitted the enlargement of skulls,

eyes, and brain regions associated with vision, olfaction and spatial memory in

phytophagous pteropodids (Sol et al. 2005).

Traditionally, phylogenetically informed comparative analyses allow us to account

for potential non-independence among species due to common ancestry (Felsenstein

1985). That is, the error structure of the statistical model incorporates the degree of non-

independence between species as estimated from the phylogeny (Felsenstein 1985).

Phylogenetic methods factor in the relationship between the species’ covariance and

7

their relatedness (Felsenstein 1985). In standard statistical models, on the other hand,

data points (and thus species) would be erroneously assumed to be independent.

Although sensory systems and brains in bats have been studied for years, early studies

did not use comparative methods (e.g., Eisenberg & Wilson 1978) to account for shared

ancestry. More recent studies that controlled for the effects of phylogeny, have used out-

dated or incorrect phylogenies (e.g., Hutcheon et al. 2002). With updated molecular

phylogenies and modern phylogenetic comparative methods, in this thesis I investigate

relationships between sensory system specializations and brain regions to better

understand vertebrate brain evolution within the ecologically diverse mammalian

lineage of bats.

However, modern phylogenetic methods allow us to go beyond simply achieving

a statistical control for phylogeny. Rather, we are able to treat phylogeny as an interesting

phenomenon on its own. We are now able to reconstruct evolutionary history with

greater confidence to test competing ideas about how that past gave rise to the present

(Paradis et al. 2004; Revell 2012). Therefore, in addition to treating common ancestry as

a confounding variable in my projects, I have used modern phylogenetic techniques to

reconstruct ancestral states and identify suites of traits that influence the sensory and

cognitive ecology in bats; particularly those that may drive or constrain the evolution of

vertebrate sensory systems and brains. Doing so has allowed to me to explore biological

diversity of bats in the light of phylogeny and to address fascinating questions, including

whether or not the ancestral bat was likely capable of laryngeal echolocation.

In chapter two of this thesis, I explore the influence of morphological measures

such as body size and skull morphology on echolocation call parameters under a

8

phylogenetic framework. In chapter three, I go on to use modern comparative methods

to explore differences in sensory system reliance and brain regions in 16 of the 21

families of bats. Chapter four is an extension of chapter three wherein I aim to

disentangle the effects of foraging strategy, migration status, roosting preference and call

design on the bodies, brains and eyes of predatory bats. Chapter five is a comprehensive

overview of sensory reliance and brain evolution in phyllostomid bats. Overall, I use

modern phylogenetic comparative methods to report on and improve our understanding

of the relationships between sensory reliance, neuroanatomy, skull morphology and

body size, as they relate to diet and foraging in mammals. Below I briefly describe and

summarize each chapter.

Chapter Two

Chapter 2 is a comparative analysis where I explored suites of body, skull, and

echolocation call characteristics that optimize foraging in the plain-faced bats

(Vespertilionidae). These bats are arguably the most sophisticated echolocators and rely

very little on vision and olfaction to hunt (Jakobsen & Surlykke 2010; Baron et al. 1996;

Thiagavel et al. 2018). They are also the largest group of bats, with more than 400 species

in the family (Nowak 1994). Within a given sound-producing vertebrate group, there are

often species-specific differences in signal frequencies. Specifically, smaller species tend

to produce higher frequency sounds than do larger ones (Bradbury & Vehrencamp

2011). This trend has been explained as the consequence of a universal scaling law,

where frequency is inversely proportional to size (Thompson 1917). That is, the

minimum frequency that can be produced decreases as the size of sound producing

9

structures increases (Thompson 1917). All else being equal, larger mammals with larger

sound producing structures (i.e. larynges) produce deeper sounds than smaller animals.

In vespertiliond bats, the negative relationship between size and call peak frequency has

been explained using body size (i.e. allometry) (Jones 1999) but also by echolocation call

directionality (Jakobsen et al. 2013a, 2013b). The allometry hypothesis explains this

negative relationship by suggesting that lighter bats with smaller larynges are

constrained to calls with higher frequencies (Pye 1979; Jones 1999). However,

vespertilionids vocalize at much higher frequencies than non-echolocating mammals of

similar body sizes, despite the latter having relatively smaller larynges (Suthers 2004).

Thus, for vesper bats - which emit their echolocation calls through their mouths - a

simple, allometric relationship between peak frequency and body size appears to be

insufficient to explain their signal diversity (Jakobsen et al. 2013a; 2013b). The

directionality hypothesis suggests that in open space, small bats use higher frequencies

to achieve more narrow sonar beams (Jakobsen et al. 2013b). The allometry hypothesis

is supported at the species-level but has not been explored under a comparative context

(Jones 1999). Conversely, while the directionality hypothesis has been supported within

a comparative context, it remains untested beyond a few species (Jakobsen et al. 2013).

Here, I use data from roughly one-quarter of all vespertilionid species to test these two

hypotheses by analyzing body mass, gape height and peak frequency, under a

phylogenetic framework. When controlling for body size and common ancestry, I found

no support for the directionality hypothesis. Instead, I found that body (rather than skull)

morphology best predicts the minimum call frequencies a species can produce. Both my

measures of body size (body mass and forearm length) were negatively related to call

10

peak frequency, reflecting species-specific size differences, and the influence of wing

design, and preferred foraging habitat, in vesper bat echolocation call design.

Chapter Three

Chapter 3 is also a comparative analysis, and this time includes specialized echolocators,

echolocators with more conserved and less flexible call designs, and bats that do not use

laryngeal echolocation and instead rely on other senses like vision and olfaction for

finding food. In this chapter, I explore the relationships between visual abilities and

echolocation behavior across 16 families of bats with respect to their diet and roosting

behaviors as well as the sensory systems they most heavily relied on. Specifically, I

explored whether brain volumes reflect sensory system specializations, estimated body,

eye, and brain (and brain region) size in the ancestral bat, and I looked for potential

trade-offs between visual and auditory specializations in extant bats, all under a

phylogenetic framework. I confirmed that brain regions do reflect the importance of their

corresponding sensory systems. Also, my results suggest that the ancestral bat was a

small, flying nocturnal predator with an auditory brain design capable of laryngeal

echolocation but with eyes that were too small to effectively track insects at night.

Whether laryngeal echolocation evolved once and was lost in the pteropodids, or evolved

more than once in bats, may never be known with utter certainty. However, my ancestral

state reconstructions support the single origin hypothesis of echolocation in bats and

that this complex sensory trait has regressed, but perhaps not disappeared entirely, in

the pteropodids. I also found support for a trade-off between vision and echolocation in

today’s bats.

11

Chapter Four

In Chapter 4 I again use comparative methods, this time to examine the effect that some

life history traits have on predatory bats. Specifically, I re-visit some of the traits in

Chapter 3 (i.e. call design and foraging strategy) as well as some novel characters that I

had not previously explored (i.e. migratory status). I had previously shown an apparent

trade off between vision and echolocation ability in bats (see Chapter 3), whereby bats

with more conserved echolocation calls (i.e. multi-harmonic calls) had relatively larger

eyes than those with more derived, echolocation calls (i.e. dominant or constant

frequency calls). Here, I explore this trade-off further by exploring the relationship

between call design (i.e. ancestral or derived) and vision (using mass-corrected eyes and

brain regions involved in vision like the neocortex and superior colliculus). As expected,

I found an apparent trade-off where bats with conserved calls had larger relative eyes,

brains, neocortices and superior colliculi than those with more derived calls. I also

explored the effects of foraging strategy on the brains and eyes of these predatory bats.

As expected, I found that gleaners had larger relative brains, neocortices and hippocampi

owing to their more demanding sensory needs. For instance, while hawking bats hunt

their flying prey opportunistically, gleaners need to relocate abundant patches of prey

and navigate in complex habitats (Safi & Dechmann 2005). I also found that bats that

roost externally had larger eyes than those that roost internally, as large eyes (and better

vision) may be useful for bats roosting on exposed surfaces to detect predators (Jones &

Rydell 2003). I also found that sedentary bats had larger brains, neocortices and superior

colliculi than migratory bats, as migrating bats may experience greater energetic

demands for efficient long-distance powered flight (McGuire & Ratcliffe 2011). To

12

disentangle the effects of these life history traits even further, I re-ran some of my

analyses after combining binary traits.

Chapter Five

In the fifth chapter of this thesis I provide a comprehensive overview of sensory and

cognitive ecology as they relate to foraging ecology and diet in the Phyllostomidae (New

World leaf-nosed bats). This work is ‘in press’ as a chapter in a book on phyllostomid bat

biology. While most bat families are characterized by a specific diet (i.e. strictly eat either

plants or animals), phyllostomids exploit a wide spectrum of diets, foraging niches, use

multiple sensory systems, and have diversified into one of the most species-rich families

among bats (Kalko et al. 1996; Jones et al. 2002). In addition to echolocation, many

phyllostomids also use visual and olfactory cues and thus have more balanced visual,

olfactory, and auditory regions than do other families (Jolicoeur & Baron 1980; Baron et

al. 1996). Within phyllostomid subfamilies, relative brain region volumes still reflect

feeding ecology and corresponding sensory specializations (Baron et al. 1996). For

instance, phytophagous phyllostomids have larger visual and olfactory regions relative

to the predatory species, which in turn have larger auditory centers (Baron et al. 1996;

Thiagavel et a. 2018). I use species-specific case studies to show how these differences

influence the evolution of their brains.

13

Chapter Six

Chapter 6 briefly summarizes the preceding chapters and suggests potential avenues for

future research.

14

References

Baron, G., Stephan, H. & Frahm, H.D. 1996. Comparative neurobiology in Chiroptera.

Vol 1. Birkhäuser Verlag, Basel, CH.

Bradbury J,W. & Vehrencamp, S.L. 2011. Principles of animal communication. Sinauer

Associates, Sunderland, MA.

Catania, K. 2011. The sense of touch in the star-nosed mole: from mechanoreceptors to

the brain. Proceedings of the Royal Society B: Biological Sciences. 366, 3016–3025.

Deaner, R. O., Isler, K., Burkart, J. & van Schaik, C. P. 2007. Overall brain size, and not

encephalization quotient, best predicts cognitive ability across non-human

primates. Brain, Behavior and Evolution, 70, 115–124.

deWinter, W. & Oxnard, C.E. 2001.Evolutionary radiations and convergences in the

structural organization of mammalian brains. Nature, 409, 710-714.

Dukas, R. & Ratcliffe, J. M. 2009. Prospects. In Cognitive Ecology II (Dukas, R. and J.M.

Ratcliffe Eds), pp. 298-300. University of Chicago Press, Chicago.

Dusenbery, D. B. 1992. Sensory ecology: how organisms acquire and respond to

information. (W.H. Freeman). USA Google Scholar, New York.

Eisenberg, J. F. & Wilson, D. E. 1978. Relative brain size and feeding strategies in the

Chiroptera. Evolution, 32, 740-751.

Felsenstein, J. 1985. Phylogenies and the comparative method. The American Naturalist,

125, 1-15.

Fenton, M.B. & Simmons, N. B. 2015. Bats: a world of science and mystery. Chicago:

University of Chicago Press.

15

Freeman, P. W. 2000. Macroevolution in Microchiroptera: recoupling morphology and

ecology with phylogeny. Evolutionary Ecology Research, 2, 317-335.

Geva-Sagiv, M., Las, L., Yovel, Y. & Ulanovsky. N. 2015. Spatial cognition in bats and

rats: from sensory acquisition to multiscale maps and navigation. Nature Reviews

Neuroscience, 16, 94-108.

Gonyea W, J. 1976. Adaptive differences in the body proportions of large felids. Cells

Tissues Organs, 96, 81-96.

Grohé, C., Lee, B., & Flynn, J. J. 2018. Recent inner ear specialization for high-speed

hunting in cheetahs. Scientific Reports, 8, 2301.

Hutcheon, J. M., Kirsch, J. W. & Garland Jr, T. 2002. A comparative analysis of brain size

in relation to foraging ecology and phylogeny in the Chiroptera. Brain, Behavior and

Evolution, 60, 165-180.

Jakobsen, L, Brinkløv, S.M. & Surlykke, A. 2013a. Intensity and directionality of bat

echolocation signals. Frontiers in Physiology, 4, 1-9.

Jakobsen, L., Ratcliffe, J.M. & Surlykke, A. 2013b. Convergent acoustic field of view in

echolocating bats. Nature, 493, 93-96.

Jakobsen, L. & Surlykke, A. 2010. Vespertilionid bats control the width of their biosonar

sound beam dynamically during prey pursuit. Proceedings of the National

Academy of Sciences of the United States of America,107, 13930-13935.

Jerison, H. J. 1973. Evolution of the brain and intelligence. Academic Press, New York.

Jolicoeur, P. & Baron, G. 1980. Brain center correlations among Chiroptera. Brain,

Behavior and Evolution, 17, 419-431.

16

Jones G. 1999. Scaling of echolocation call parameters in bats. Journal of Experimental

Biology, 202, 3359-3367.

Jones, K. E. & MacLarnon, A.M. 2004. Affording larger brains: testing hypotheses of

mammalian brain evolution on bats. The American Naturalist, 164, E20-E31.

Jones, K. E., Purvis, A. Maclarnon, A., Bininda-Emonds, O. R. P. & Simmons. N. B.

2002. A phylogenetic supertree of the bats (Mammalia: Chiroptera). Biological

Reviews, 77, 223-259.

Jones, G. & Rydell, J. 2003. Attack and defense: interactions between echolocating bats

and their insect prey. Pp 301-345 in: Bat ecology (Kunz, T.H. & Fenton, M.B.,

eds.). University of Chicago Press, Chicago.

Jones, G. & Teeling, E. C. 2006. The evolution of echolocation in bats. Trends in Ecology

and Evolution, 21, 149-156.

Kalko, E. K. V., Handley Jr, C. O. & Handley. D. 1996. Organization, diversity, and long-

term dynamics of a Neotropical bat community. In: Cody, M. and J. Smallwood (eds),

Long-term studies in vertebrate communities. Academic Press, 503-553.

Marker, L.L & Dickman, A.J. 2003. Morphology, Physical Condition, and Growth of the

Cheetah (Acinonyx jubatus jubatus). Journal of Mammalogy, 84, 840–850.

McGuire, L. P. & Ratcliffe, J. M. 2011. Light enough to travel: migratory bats have smaller

brains, but not larger hippocampi, than sedentary species. Biology Letters, 7, 233

236.

Medway, L. & Marshall, A. G. 1970. Roost-site selection among flat-headed bats

(Tylonycteris spp.) Journal of Zoology, 161, 237-245.

17

Nowak. R. M. 1994. Walker's bats of the world. Johns Hopkins University

Press, Baltimore, Maryland.

Paradis, E., Claude, J. & Strimmer, K. 2004. APE: Analyses of phylogenetics and

evolution in R language. Bioinformatics, 20, 289-290.

Pirlot, P. & Stephan, H. 1970. Encephalization in Chiroptera. Canadian Journal of

Zoology, 48, 433-444.

Pye J.D. 1979. Why ultrasound? Endeavour, 3, 57–62.

Ratcliffe, J. M. 2009. Neuroecology and diet selection in phyllostomid bats. Behavioral

Processes, 80, 247-251.

Revell, L. J. 2012. Phytools: an R package for phylogenetic comparative biology (and

other things). Methods in Ecology and Evolution, 3, 217-223.

Safi, K. & Dechmann D.K.N. 2005. Adaptation of brain regions to habitat complexity: a

comparative analysis in bats (Chiroptera). Proceedings of the Royal Society of

London B, 272, 179-186.

Safi, K., M.A. Seid, & Dechmann, D. K. N. 2005. Bigger is not always better: when brains

get smaller. Biology Letters, 1, 283-286.

Shettleworth, S. J. 2010. Cognition, Evolution and Behaviour. Oxford University Press,

Oxford.

Sol, D., Duncan, R. P. Blackburn, T. M., Cassey, P. & Lefebvre, L. 2005. Big brains,

enhanced cognition, and response of birds to novel environments. Proceedings of

the National Academy of Sciences of the United States of America, 102, 5460-5465.

Striedter, G. F. 2004. Principles of brain evolution.: Sinauer Associates, Sunderland, MA.

18

Suthers, R.A. 2004. Vocal mechanisms in birds and bats: a comparative view. Anais da

Academia Brasileira de Ciências, 76, 247-252.

Teeling, E. C. 2009. Hear, hear: the convergent evolution of echolocation in bats? Trends

in Ecology & Evolution, 24, 351-354.

Thiagavel, J., Céchetto, C., Santana, S., Jakobsen, L., Warrant, E. J. & Ratcliffe, J. M.

2018. Auditory opportunity and visual constraint enabled the evolution of

echolocation in bats. Nature Communications, 9, 98.

Thompson, D.W. 1917. On Growth and Form. Cambridge University Press.

considerations. The Journal of the Acoustical Society of America, 48, 1430-1432.

Veselka, N., McErlain, Holdsworth, D. W., Eger, J. L., Chhlem, R. K., Mason, M. J., Brain,

K. L., Faure, P. A. & Fenton, M. B. 2010. A bony connection signals laryngeal

echolocation in bats. Nature, 463, 939–942.

Wang, Z., Zhu, T., Xue, H., Fang, N., Zhang, J., Zhang, L., Pang, J., Teeling, E. C. &

Zhang, S. 2017. Prenatal development supports a single origin of laryngeal

echolocation in bats. Nature Ecology & Evolution, 1, 0021.

19

Chapter Two

Body Size Predicts Echolocation Call Peak Frequency Better than Gape

Height in Vespertilionid Bats

Thiagavel, J., Santana, S.E. & Ratcliffe, J. M. 2017. Body Size Predicts Echolocation Call

Peak Frequency Better than Gape Height in Vespertilionid Bats. Scientific Reports, 7,

828. DOI:10.1038/s41598-017-00959-2

20

Abstract

In most vocalizing vertebrates, lighter animals tend to produce acoustic signals of higher

frequency than heavier animals. Two hypotheses propose to explain this negative

relationship in vespertilionid bats: (i) mass-signal frequency allometry and (ii) emitter-

limited (maximum gape) signal directionality. The first hypothesis, that lighter bats with

smaller larynges are constrained to calls with high frequencies, is supported at the

species level. The second hypothesis proposes that in open space, small bats use higher

frequencies to achieve long, narrow beams, as beam directionality increases with both

emitter size (maximum gape) and signal frequency. This hypothesis is supported within

a comparative context but remains untested beyond a handful of species. Here, we also

consider forearm length as an additional proxy of body size and wing design. We

analyzed morphological and echolocation data under a phylogenetic comparative

framework to test the two hypotheses. Controlling for mass, we did not find support for

the directionality hypothesis. We did find that body mass and forearm length were

important predictors of open space echolocation call peak frequency in ways that (i)

reflect species-specific size differences, and (ii) suggest the influence of preferred

foraging habitat.

21

Introduction

Many bat species emit echolocation calls of extraordinarily high frequencies. The trident

bat, Cleotis percivalis (Hipposideridae), emits calls with a carrier frequency of 212 kHz,

the highest frequency pure tone documented from the natural world (Bell & Fenton

1984). However, this is the upper limit and most echolocating bats produce frequency

modulated calls with peak frequencies (PF, frequency of maximum energy) between 20

and 60 kHz, still well above the upper limit of adult human hearing. Across

vespertilionids (the most species rich bat family, ~420 species) echolocation call PFs

range from 10 to 150 kHz, and mass from 2 to 60 grams. Size-signal allometry has been

hypothesized to explain this intra-familial variation in echolocation call PFs (Jones 1999),

resembling the negative relationship observed between body size and acoustic signal

frequency in birds and anurans (Ryan 1985; Ryan & Brenowitz 1985). Across groups,

this trend has been explained as the consequence of a universal scaling law, where

frequency is inversely proportional to size (Thompson 1917). That is, the minimum

frequency that can be produced decreases as the size of the sound producing structure

increases (Thompson 1917). Thus, small vespertilionids are expected to be constrained

to high frequencies by their relatively smaller bodies and larynges (Pye 1979; Jones

1999) (Fig. 1).

An alternative hypothesis, sonar beam directionality, has recently gained

attention as an overlooked alternative explanation for (i) the production and use of high

frequency sounds and (ii) the negative relationship between size and call frequency in

vespertilionid bats (Jakobsen et al. 2013a; 2013b). Vesper bats emit echolocation calls

through their mouths, and emitter size (i.e., gape) and PF are both positively related to

22

call directionality (Strother & Mogus 1970) (Fig. 2). Directional (i.e., long, narrow) beams

have more energy focused along the acoustic axis, increasing signal intensity and thus

sonar range along the bat’s flight path while minimizing potentially distracting off-axis

echoes (Surlykke et al. 2009; Jakobsen & Surlykke 2010). Vesper species unable to

produce wide gapes (e.g., due to small mouths and/or short snouts), which would

otherwise produce low directionality calls, have been argued to use very high frequency

calls to achieve highly directional sonar beams while hunting in open space (Jakobsen et

al. 2013b). Thus, it has been suggested that maximum gape may be a better species-

specific predictor of PF than body mass (Jakobsen et al. 2013b).

None of the proposed morphological correlates of echolocation call frequency has

been assessed independently of the other, nor has either been considered within a

phylogenetic comparative framework. We therefore set out to test the explanatory

power of mass and gape on echolocation call PF in vesper bats under current

phylogenetic hypotheses. We also consider forearm length, a size measure perhaps more

precise than mass (which can vary greatly in bats during the night and over seasons)

(Wilson 1975; Altringham 1996). Forearm length is also a relative indicator of bat flight

style and habitat use (Norberg & Rayner 1987), and thus can provide insight into acoustic

specialization across preferred habitats. We predicted that the two proxies of body size

(i.e., mass and forearm length) would be strong, independent predictors of PF due to size-

signal allometry (as observed in non-echolocating vocalizing vertebrates) but that gape

height (when corrected for mass) would not influence PF as strongly.

23

Results

We found a significant phylogenetic signal in all variables (mass: λ=0.551, p=

24

that peak frequency decreases with size in vespertilionid bats (i.e. the mass-signal

frequency allometry hypothesis) and little to no support for the emitter-limited

directionality hypothesis.

However, that peak frequency decreases significantly with the two proxies of

body size, still does not account for the incongruence between body mass and signal

frequency relative to non-echolocating mammals. As mentioned earlier, vespertilionids

vocalize at much higher frequencies than do similarly sized non-echolocating mammals,

despite having much larger larynges (Suthers 2004) (Fig. 2). Thus, although body size

appears to be an important predictor of peak frequency, an allometric relationship alone

is insufficient to explain their signal diversity (Jakobsen et al 2013a; 2013b). In fact, the

reason behind (i) why the larynges of echolocating bats are larger than similar sized

mammals and (ii) why, despite this, they call at much higher frequencies than these

animals, remains unclear. Bats are louder than most mammals (Holdereid & von

Helversen 2003; Surlykke & Kalko 2008), and larger larynges may be required to

produce these loud sounds. Furthermore, the unusually high frequencies emitted by

laryngeal echolocating bats have been attributed to specialized vocal membranes found

atop the vocal folds, only the latter of which are typical of other non-echolocating

mammals (reviewed in Neuweiler 2000; Ratcliffe et al. 2013). More work on call

production mechanisms in vespertilionids may provide better insight into species-

specific echolocation call frequency composition.

After controlling for species’ size, we found no support for the emitter-limited

directionality hypothesis. Mass and absolute gape height appeared to be good predictors

25

of peak frequency (Jones 1999; Jakobsen et al. 2013b), but these trends are not

significant after size-correcting gape height within a phylogenetic context. While

directionality had been put forth as a potentially better predictor of open space PF than

size in a study that used non-size corrected gape height and only 6 vespertilionid species

(Jakobsen et al. 2013b), we do not find support for this hypothesis. When using 86

vespertilionid species and phylogenetically informed mass residuals (to account for both

common ancestry and size), the apparent relationship between PF and gape height

essentially disappeared. Thus, an open space convergent field of view may not be

applicable to all vespertilionids. Bats which preferentially hunt in cluttered habitat may

be constrained to relatively broad beams even when flying in open space as a result of

other, perhaps competing, demands on call design and may not have evolved to produce

call designs well suited for hunting in open spaces. Indeed, many factors, in addition to

echolocation call parameters, contribute to shape situation-specific optimal call design

for bats. For instance, facial features such as nose leaves and exaggerated lip and tongue

morphologies can affect sound emission patterns (Proffit 1978). Indeed, facial

musculature has recently been shown to alter beam formation in free-tailed bats (Trent

& Smotherman 2016). Taking these various features into account may provide further

insight into active sensing by echolocating vespertilionid bats.

Because small bats tend to call at higher frequencies than larger bats, it had once

been thought that such high frequencies were specialized to detect prey of a preferred

size class. This hypothesis has since been refuted. First, the majority of echolocating bats

call at frequencies 3 or more times higher than should be necessary to detect the smallest

prey found in their diet (Pye 1993; Jakobsen et al. 2013a). Second, while larger bats do

26

take larger prey than smaller bats, they can also detect and intercept small prey (Houston

& Boonman 2004). Instead, small bats appear to be limited in the prey sizes they can take

as a result of handling effort and perhaps interspecific competition, not sensory system

constraints (Houston & Boonman 2004). Still, consideration of diet is important. For

instance, some bats like the spotted bat, Euderma maculatum, use call designs that

circumvent insect defenses. E. maculatum calls at 10.5 kHz, a PF (much lower than

predicted by body size) and eats mostly eared moths that are mostly deaf to frequencies

below 15 kHz (Fullard & Dawson 1997). Further, diet relates to jaw morphology: bats

with long, gracile jaws are limited to soft bodied insects while the jaws of those species

that can consume harder shelled prey (e.g., beetles) are relatively shorter and stronger

(Freeman & Lemen 2010). More rigorous accounting of the relationships between diet,

gape, size, call parameters, and beam directionality across laryngeal echolocating bats

may provide further insight into active sensing and prey selection.

In keeping with the idea of ecological impacts, forearm length remains a

significant predictor of peak frequency, both before and after size corrections.

Interestingly, PF decreased more with absolute and mass-corrected forearm than it did

with mass (i.e., had a steeper negative slope), suggesting that, for a given mass, bats with

longer forearms use lower peak frequency echolocation calls in open space. Relative

forearm length is also used as a proxy for different wing morphologies, which are, in turn,

directly related to different foraging ecologies. Insectivorous bats with relatively short

forearms tend to have short, broad wings with low wing loadings (Norberg & Rayner

1987; Norberg & Fenton 1988; Dietz et al. 2006) and aspect ratios (Neuweiler 1984;

Norberg & Rayner 1987; Neuweiler 1989; Fenton 1990). It has been suggested that this

27

wing design is well suited for slow, maneuverable flight, in cluttered habitat, but

disadvantageous for successful prey competition in open space (Norberg & Rayner 1987;

Fenton 1990). Bats with relatively long forearms tend to have long, narrow wings of high

wing loading and aspect ratio. Wing designs that demand fast, agile flight and are thought

to be best suited for aerial capture in open spaces (Norberg & Rayner 1987). All else

being equal in open space, bats that have long narrow wings are expected to use

echolocation calls with lower PFs than those species with short, broad wings (Neuweiler

1989; Fenton 1990). Our results support this prediction. Such calls maximize an

echolocating bat’s detection range (Neuweiler 1990; Schnitzler & Kalko 2001). The

higher peak frequency calls of slower, more maneuverable fliers should translate into

more precise information for object ranging and resolution, and reduce the likelihood of

call-echo overlap in close quarters (Schnitzler & Kalko 2001; Jakobsen et al. 2013b).

28

Methods

Data assembly. We photographed, in lateral view, the rostrum and the mandible of

vespertilionid bat species at the Royal Ontario Museum (ROM) in Toronto, Canada using

a Nikon D40x digital SLR camera. We also photographed skins (≤4 individuals/species)

to obtain forearm measurements. Whenever possible, we selected skins and skulls from

the same specimen, and included the same number of males and females per species. We

imported all photos into Image J v. 1.49 (Schneider et al. 2012) to measure skull

characteristics and forearm length 9 times (3 hypothesis-blind assistants, 3 times each)

to obtain a single mean value for each measure for each species.

We measured the distances between the posterior-most point of the

temporomandibular joint and the anterior-most point of the upper incisors (a), lower

incisors (b), and origin (A) and insertion (B) of the superficial masseter to estimate the

maximum gape for each individual (Fig. 3). We used a reported gape angle of 90° and A:

B ratio of 2.1, as reported for Myotis lucifugus (Kallen & Gans 1972), to estimate

maximum gape height (GH) using equation (1).

𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝐻𝐻𝐺𝐺𝐻𝐻𝐻𝐻ℎ𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = �𝐺𝐺2 + 𝑏𝑏2 − 2𝐺𝐺𝑏𝑏 ∗ cos �90° ∗𝐴𝐴 𝐵𝐵𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠⁄𝐴𝐴 𝐵𝐵𝑙𝑙𝑙𝑙𝑠𝑠𝑠𝑠𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑠𝑠⁄

� (1)

as described by Herring and Herring (1974), and modified by Jakobsen and colleagues

(2013b) Fig. 3). This method of estimating gape height is robust for vespertilionids and

other primarily insectivorous bat families (Czarnecki & Kallen 1980). For a given

frequency, emitter size primarily determines signal directionality in vespertilionid bats

29

(Mogensen & Møhl 1979). This relationship among emitter size, maximum estimated

gape, actual call emission gape, and echolocation call frequency and directionality have

been confirmed in Myotis daubentonii (Surlykke et al. 2009; Jakobsen et al. 2013b).

We used mass and forearm length as proxies for body size. We measured

maximum forearm length as the length between the elbow and wrist (Dietz & von

Helversen 2004). Mass (g), species-specific open-space echolocation call peak frequency

(PF; frequency with the maximum energy in the fundamental call element in kHz) were

obtained from a single, comprehensive review of the literature (Collen 2012). All of the

continuous variables used in this study were not normally distributed and were thus log-

transformed in all subsequent analyses.

Statistical analyses. We found a significant phylogenetic signal in all variables (Pagel’s

λ not significantly different from 1; see Results). Thus, we conducted all subsequent

statistical analyses using a lambda evolutionary model and a pruned version of a recent,

time-calibrated, molecular phylogeny (Shi & Rabosky 2015) (Fig. 4). For these analyses,

we used all vespertilionid species (i) with call parameters in Collen (2012), (ii) included

in the Shi and Rabosky (2015) phylogeny, and (iii) for which the ROM had at least one

intact adult skull or taxidermied specimen. This resulted in 86 species (260 specimens)

with mass and gape data, and 69 species (200 specimens) with forearm data.

We used phylogenetic generalized least square (PGLS) regressions by restricted

maximum likelihood (REML) to test the relationship between peak frequency and (i)

body mass, (ii) forearm length, and (iii) maximum gape height, respectively. However,

because gape height is significantly and positively correlated with body size metrics

30

(b=0.263±0.02, t=12.09, p

31

References

Altringham, J. 1996. Bats: Biology and Behaviour. New York: Oxford University Press.

Bell, G. P. & Fenton, M. B. 1984. The use of Doppler-shifted echoes as a flutter detection

and clutter rejection system: the echolocation and feeding behavior of

Hipposideros ruber (Chiroptera: Hipposideridae). Behavioral Ecology and

Sociobiology, 15, 109-114.

Collen, A. 2012. The evolution of echolocation in bats: a comparative approach. University

College London: Doctoral dissertation.

Czarnecki, R.T. & Kallen, F.C. 1980. Craniofacial, occlusal, and masticatory anatomy in

bats. The Anatomical Record, 198, 87-105.

Dietz, C. & von Helversen, O. 2004. Illustrated identification key to the bats of Europe

(http://biocenosi.dipbsf.uninsubria.it/didattica/bat_key1.pdf).

Dietz, C., Ditz, I. & Siemers, B.M. 2006. Wing Measurement Variation in the Five

European Horseshoe Bat Species (Chiroptera: Rhinolophidae). Journal of

Mammalogy, 87, 1241-1251.

Fenton, M.B. 1990.The foraging behavior and ecology of animal-eating bats. Canadian

Journal of Zoology, 68, 411-421.

Fletcher N. 2004. A simple frequency-scaling rule for animal communication. Journal of

the Acoustical Society of America, 115, 2334-2338.

Freeman, P.W. & Lemen, C.A. 2010. Simple predictors of bite force in bats: The good,

the better, and the better still. Journal of Zoology, 282, 284-290.

32

Fullard, J.H. & Dawson, J.W. 1997. The echolocation calls of the spotted bat Euderma

maculatum are relatively inaudible to moths. The Journal of Experimental Biology,

200, 129-137.

Herring, S.W. & Herring, S.E. 1974. The superficial masseter and gape in mammals. The

American Naturalist, 108, 561–576.

Holderied, M.W. & von Helversen, O. 2003. Echolocation range and wingbeat period

match in aerial-hawking bats. Proceedings of the Royal Society of London B, 270,

2293–2299.

Houston, R.D., Boonman, A.M. & Jones, G. 2004. Echolocation in Bats and Dolphins.

Chicago: University of Chicago Press.

Jakobsen, L, Brinkløv, S.M. & Surlykke, A. 2013a. Intensity and directionality of bat

echolocation signals. Frontiers in Physiology, 4, 1-9.

Jakobsen, L., Ratcliffe, J.M. & Surlykke, A. 2013b. Convergent acoustic field of view in

echolocating bats. Nature, 493, 93-96.

Jakobsen, L. & Surlykke, A. 2010. Vespertilionid bats control the width of their biosonar

sound beam dynamically during prey pursuit. Proceedings of the National

Academy of Sciences of the United States of America,107, 13930-13935.

Jones G. 1999. Scaling of echolocation call parameters in bats. Journal of Experimental

Biology, 202, 3359-3367.

Kallen, F.C. & Gans, C. 1972. Mastication in little brown bat, Myotis lucifugus. Journal of

Morphology,136, 385-420.

33

Mogensen, F. & Møhl, B. 1979. Sound radiation patterns in the frequency domain of

cries from a vespertilionid bat. Journal of Comparative Physiology, 134: 165–171.

Neuweiler, G. 1984. Foraging, echolocation and audition in bats. Naturwissenschaften,

71, 446-455.

Neuweiler, G. 1989. Foraging ecology and audition in echolocating bats. Trends in

Ecology & Evolution, 4,160-166.

Neuweiler, G. 1990. Auditory adaptations for prey capture in echolocating bats.

Physiological Reviews, 70, 615-641.

Neuweiler, G. 2000. The Biology of Bats. New York: Oxford University Press.

Norberg, U.M. & Fenton, M.B. 1988. Carnivorous bats? Biological Journal of the Linnean

Society, 33, 383-394.

Norberg, U.M. & Rayner, J.M.V. 1987. Ecological morphology and flight in bats

(Mammalia; Chiroptera): wing adaptations, flight performance, foraging strategy

and echolocation. Philosophical Transactions of the Royal Society B, 316, 335–427.

Proffit, W. 1978. Equilibrium theory revisited: factors influencing position of the teeth.

The Angle Orthodontist, 48,175-186.

Pye J.D. 1979. Why ultrasound? Endeavour, 3, 57–62.

Pye J.D. 1993. Is fidelity futile? The ‘true’ signal is illusory, especially with ultrasound.

Bioacoustics, 4, 271–286.

Ratcliffe, J. M., Elemans, C. P. H., Jakobsen, L. & Surlykke, A. 2013. How the bat got

its buzz. Biology Letters, 9, 1-5.

Revell, L.J. 2009. Size-correction and Principal Components for Interspecific

Comparative Studies. Evolution, 63, 3258-3268.

34

Ryan, M.J. 1985. The Túngara Frog. Chicago: University of Chicago Press.

Ryan, M.J. & Brenowitz, E.A. 1985. The role of body size, phylogeny and ambient noise

in the evolution of bird song. The American Naturalist, 126, 87-100.

Shi, J.J. & Rabosky, D.L. 2015. Speciation dynamics during the global radiation of extant

bats. Evolution, 69, 1528-1545.

Schnitzler, H-U. & Kalko, E.K.V. 2001. Echolocation by insect-eating bats. BioScience,

51,557-568.

Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. 2012. NIH Image to ImageJ: 25 years of

image analysis. Nature Methods, 9, 671-675.

Siemers, B.M. & Schnitzler, H-U. 2004. Echolocation signals reflect niche differentiation

in five sympatric congeneric bat species. Nature, 29, 657-661.

Strother, G. & Mogus, M. 1970. Acoustical Beam Patterns for Bats: Some Theoretical

Considerations. The Journal of the Acoustical Society of America, 48, 1430.

Surlykke, A., and Kalko, E.K.V. 2008. Echolocating Bats Cry Out Loud to Detect Their

Prey. PLoS ONE, 3, e2036.

Surlykke, A., Pedersen, S.B. & Jakobsen, L. 2009. Echolocating bats emit a highly

directional sonar sound beam in the field. Proceedings of the Royal Society of

London B, 276, 853-860.

Suthers, R.A. 2004. Vocal mechanisms in birds and bats: a comparative view. Anais da

Academia Brasileira de Ciências, 76, 247-252.

Thompson, D.W. 1917. On Growth and Form. Cambridge University Press.

considerations. The Journal of the Acoustical Society of America, 48, 1430-1432.

35

Trent, S. & Smotherman, M. 2016. Specialized facial muscles support sonar beam-

forming by free-tailed bats. Journal of the Acoustical Society of America, 140, 2948-

2948.

Wilson, E.O. 1975. Sociobiology: The New Synthesis. Cambridge: Belknap Press of

Harvard University Press.

36

Table 1. The fit of different evolutionary models (AICc values are shown).

37

Supplementary Table 1. Size and call parameters for the vespertilionid species used in the study (N=86).

Species Peak Frequency

(kHz) Mass (g) Gape height (mm) Forearm Length

(mm) Antrozous pallidus 44.76 22.24 20.9 53.93 Barbastella barbastellus 35.33 8.31 14.02 37.37 Chalinolobus gouldii 29.39 14.24 16.44 Chalinolobus morio 52.92 8.91 11.5 40.75 Eptesicus brasiliensis 34.86 9.2 16.92 39.1 Eptesicus diminutus 40.85 5.99 14.59 Eptesicus furinalis 42.84 7.7 16.92 37.88 Eptesicus fuscus 32.55 17.49 21.97 45.86 Eptesicus hottentotus 30.88 30.33 21.72 47.57 Eptesicus nilssoni 28.55 10.72 16.61 39.07 Eptesicus serotinus 31.17 23.09 25.33 52.8 Euderma maculatum 10.5 16.17 14.08 50.29 Hesperoptenus tickelli 30.69 16.3 21.68 56.43 Histiotus macrotus 35.84 11 17.94 48.1 Idionycteris phyllotis 20.37 12.13 13.88 47.02 Laephotis wintoni 23.08 6.1 17.93 Lasionycteris noctivagans 30.19 11.02 17.93 40.79 Lasiurus borealis 40.95 12.33 13.63 37.02 Lasiurus cinereus 23.9 27.06 17.26 47.22 Lasiurus ega 32.2 12.2 15.45 Lasiurus intermedius 36.18 22.96 17.37 49.82 Lasiurus seminolus 40.13 9.88 12.18 43.15 Murina suilla 124.14 4 15.18 Myotis adversus 46 10.41 13.6 Myotis albescens 73.02 5.69 13.45 34.68 Myotis annectans 47.51 9.75 17.27 35.52 Myotis auriculus 49.6 38.04 17.93 34.54 Myotis austroriparius 48.23 7.35 15.82 39.36 Myotis bocagii 41.26 7.93 17 34.59 Myotis brandtii 49.27 5.3 16.56 34.3 Myotis californicus 51.44 4.39 13.47 Myotis chiloensis 43.4 8.41 14.12 Myotis dasycneme 39.43 15.16 18.31 Myotis daubentonii 46.98 7.63 14.64 37.69 Myotis elegans 66.28 4.21 14.06 33.5 Myotis evotis 52 6.91 16.07 36.77 Myotis goudoti 64.37 5.56 16.14 38.26 Myotis grisescens 44.52 10.84 17.98 43.68 Myotis horsfieldii 51.21 6.05 15.89 29.43 Myotis keaysi 62.93 5.45 14.79 36.81 Myotis keenii 59.67 6.51 14.74 35.47 Myotis leibii 55.78 5.22 14.31 31.08 Myotis levis 57.74 5.49 15.7 36.12 Myotis lucifugus 47.39 7.8 14.99 38.38 Myotis macrotarsus 42.82 12.64 21.75 43.08 Myotis muricola 48.69 4.8 13.52 Myotis myotis 35.45 25.59 27.35 Myotis mystacinus 52.66 7.61 15.39 38.86 Myotis nattereri 46.95 7.25 15.87 40.36 Myotis nigricans 56.2 5.53 13.58 34.44 Myotis ricketti 40.52 26.19 24.35 37.32 Myotis riparius 57.68 4.57 14.02 35.62 Myotis ruber 62.49 4.99 15.1 37.85 Myotis septentrionalis 49.93 5.4 16 35.91 Myotis simus 57.74 8.1 13.84 36.43 Myotis sodalis 51.42 7.15 14.8 38.13 Myotis thysanodes 36.52 8.49 16.53 42.29

38

Myotis tricolor 49.12 13.67 20.55 50.45 Myotis velifer 43.44 9.82 17.75 43.33 Myotis volans 46.39 8.71 14.77 40.25 Myotis welwitschii 32.66 15.88 22.6 Myotis yumanensis 51.47 5.15 14.11 31.91 Nyctalus leisleri 30.67 12.47 16.23 33.96 Nyctalus noctula 22.86 28.48 22.26 49.58 Nycticeius humeralis 42.82 9.12 14.07 33.57 Nyctophilus geoffroyi 49.77 8.2 17.5 Pipistrellus javanicus 47.51 4.92 12.97 Pipistrellus kuhlii 41.16 6.07 14.53 30.84 Pipistrellus nathusii 41.4 7.44 13.31 33.93 Pipistrellus pipistrellus 47.61 5.3 13.79 33.58 Pipistrellus subflavus 45.95 5.74 12.47 32.62 Pipistrellus tenuis 48 3.48 13.29 Plecotus auritus 36.39 8.19 13.5 39.99 Plecotus austriacus 29.66 6.75 13.23 40.83 Rhogeessa parvula 57.57 4.37 11.11 27.23 Rhogeessa tumida 48.65 4.58 12.62 28.83 Scotoecus hirundo 36.86 8.45 16.95 34.9 Scotophilus dinganii 36.38 25.12 19.44 53.04 Scotophilus heathii 34.81 36.13 21.99 Scotophilus kuhlii 38.94 20.31 17.53 51.32 Scotophilus leucogaster 46.27 20.24 18.01 49.46 Scotophilus nigrita 32.85 60 28.07 80.78 Scotophilus nux 36.09 30 21.55 53.99 Scotophilus viridis 40.1 19.81 17.01 46.17 Tylonycteris pachypus 48.95 4.1 14.2 Tylonycteris robustula 55 7.98 15.07 23.14

39

Figure Legends

Figure 1. Body mass versus peak frequency for a range of vertebrates. Figure made by

Sara Vukson using data presented in Jones (1999) and Fletcher (2004).

Figure 2. Relationship between directionality, emitter size, and frequency in

vespertilionid bats. Directionality of the biosonar beam increases with emitter size and

frequency. Figure remade by Sara Vukson from Jakobsen et al. (2013).

Figure 3. Distances used to measure maximum gape height. Maximum gape height was

estimated using the distances between the posterior-most point of the

temporomandibular joint and the anterior-most point at the upper incisors (a), lower

incisors (b), and origin (A) and insertion (B) of the superficial masseter to estimate the

maximum gape for each individual. Figure made by Sara Vukson.

Figure 4. A phylogeny (Shi and Rabosky 2015) showing peak frequency (kHz) mapped

along the branches of the tree using Maximum Likelihood.

40

Figure 1

41

Figure 2

42

Figure 3

43

Figure 4

44

Chapter Three

Auditory opportunity and visual constraint enabled the evolution of

echolocation in bats

Thiagavel, J., Céchetto, C., Santana, S., Jakobsen, L., Warrant, E.J. & Ratcliffe, J.M.

2018. Auditory opportunity and visual constraint enabled the evolution of echolocation

in bats. Nature Communications, 9 (98), 1-10. DOI: 10.1038/s41467-017-02532-x

45

Abstract

Substantial evidence now supports the hypothesis that the common ancestor of bats was

nocturnal and capable of both powered flight and laryngeal echolocation. This scenario

entails a parallel sensory and biomechanical transition from a nonvolant, vision-reliant

mammal to one capable of sonar and flight. Here we consider anatomical constraints and

opportunities that led to a sonar rather than vision-based solution. We show that bats’

common ancestor had eyes too small to allow for successful aerial hawking of flying

insects at night, but an auditory brain design sufficient to afford echolocation. Further,

we find that among extant predatory bats (all of which use laryngeal echolocation), those

with putatively less sophisticated biosonar have relatively larger eyes than do more

sophisticated echolocators. We contend that signs of ancient trade-offs between vision

and echolocation persist today, and that non-echolocating, phytophagous pteropodid

bats may retain some of the necessary foundations for biosonar.

Introduction

Bats (Chiroptera) are the second largest order of mammals, comprising >1300 species

and characterized by powered flight (Fenton & Simmons 2015). The vast majority of bats

are strictly nocturnal (Maor et al. 2017), with a few species also active around dawn and

dusk (Moore 1975; Russo et al. 1975). Long before the discovery of echolocation, bats

were once divided into “megabats” (members of the family Pteropodidae) and

“microbats” (the remaining ~20 chiropteran families) (Griffin 1958; Jones & Teeling

2006; Fenton & Ratcliffe 2010; Fenton & Simmons 2015). Today these are anachronistic

46

terms, and the pteropodids (~200 vision-dependent species, none using laryngeal

echolocation) are placed in Yinpterochiroptera (a.k.a. Pteropodiformes), which together

with Yangochiroptera (a.k.a. Vespertilioniformes) comprise the two chiropteran

suborders. Both suborders are otherwise comprised solely of laryngeal echolocators

(Jones & Teeling 2006; Fenton & Ratcliffe 2010). From a strict parsimony perspective,

laryngeal echolocation, if considered as a single trait, could therefore have evolved once

in bats, and subsequently been lost in the pteropodids. Alternatively, laryngeal

echolocation could have evolved at least twice independently, once or more in

Yangochiroptera, and once or more in Yinpterochiroptera, after the pteropodids

diverged (Jones & Teeling 2006; Wang et al. 2017) (Fig. 1). The sum of evidence, however,

indicates (i) that the bats’ common ancestor was a predatory laryngeal echolocator and

(ii) that the phytophagous pteropodids have lost most, but perhaps not all, hallmarks of

this complex active sensory system (Calford & McNally 1987; Teeling 2009; Veselka et al.

2010; Collen 2012; Wang et al. 2017).

Since Donald Griffin’s discovery of echolocation (Griffin 1944; Griffin 1958), the

prevailing view has been that the first bats were small, nocturnal, insectivorous

echolocators (Simmons & Stein 1980; Jones & Teeling 2006; ter Hofstede & Ratcliffe

2016; Fenton & Ratcliffe 2017). Specifically, bats are thought to have originated ≥64 mya

(Teeling et al. 2005; Bininda-Emonds et al. 2007) to exploit the then unrealized foraging

niche of small, night-flying insects, a resource most bats rely on today (Griffin 1958;

Collen 2012; Ratcliffe et al. 2013; ter Hofstede & Ratcliffe 2016). In darkness and dim

light, nascent echolocation would have allowed these bats to pursue these then

vulnerable insects (Griffin 1958; ter Hofstede & Ratcliffe 2016). Griffin (1958) too

47

divided bats into those that use laryngeal echolocation (hereafter, LE bats) and those that

do not (i.e. the pteropodids). Within LE bats, he identified three groups: (i) bats

producing multi-harmonic calls, with little frequency modulation, (ii) bats producing

downward-sweeping, frequency-modulated calls, with most energy in the fundamental

harmonic, and (iii) bats producing long, constant frequency calls, with energy typically

concentrated in the second harmonic (Griffin 1958).

These four sensory divisions -- non-laryngeal echolocators (hereafter,

pteropodids or NLE bats), multi-harmonic echolocators (MH bats), frequency

modulating, dominant harmonic echolocators (DH bats), and constant frequency

echolocators (CF bats) -- remain robust functional descriptors of biosonar diversity in

bats (Schnitzler & Kalko 2001; Jones & Teeling 2006;). Griffin (1958) and others since

(Simmons & Stein 1980; Neuweiler 2003; Schnitzler et al. 2004; Ratcliffe et al. 2005;

Jones & Teeling 2006; Ratcliffe et al. 2011; Collen 2012) have suggested that multi-

harmonic calls most closely reflect bats’ ancestral condition. If so, pteropodids, DH, and

CF bats would represent three derived sensory states (Jones & Teeling 2006; Teeling

2009; Collen 2012). While no pteropodid is thought to be predatory or to be capable of

laryngeal echolocation, members of the genus Rousettus use biosonar based on tongue

clicking for orientation (Yovel et al. 2011; Brinkløv et al. 2013). This form of echolocation,

while effective, falls short of the maximum detection distances and update rates observed

in LE bats (Yovel et al. 2011; Brinkløv et al. 2013; Ratcliffe et al. 2013). Among LE bats, it

has been previously argued that CF and DH bats possess more advanced abilities for

detecting and tracking flying prey than do MH bats (Griffin 1958; Jakobsen & Surlykke

2010; Brinkløv et al. 2011; Fenton et al. 2012; Jakobsen et al. 2015). Similarly, different

48

visual abilities exist within today’s bats, with pteropodids possessing the most advanced

visual systems, in some species including an optic chiasm (Pettigrew 1986), and the LE

vespertilionids and rhinolophids perhaps the least (Eklöf 2003). Interestingly, all

rhinolophids use CF calls, while most vespertilionids use DH calls (Schnitzler & Kalko

2001; Collen 2012; Fenton et al. 2012). It is among the MH species that we find the LE

bats capable of the greatest quantified visual resolution (Bell & Fenton 1986; Eklöf 2003)

and even ultraviolet light sensitivity (Winter & von Helversen 2003).

Here, we use phylogenetic comparative methods to further test the hypothesis

that the ancestral bat was a small, predatory echolocator, which produced multi-

harmonic biosonar signals (Griffin 1958; Simmons 1980; Collen 2012). Additionally, we

test three hypotheses about the relationships between visual abilities and echolocation

behaviour in bats across their four sensory divisions, and with respect to diet and

roosting behaviour, relative to ancestral states. These three hypotheses reflect

mechanistic explanations for the origination and evolution of laryngeal echolocation in

bats for pursuing flying insects and predict auditory opportunity and visual constraint

(Griffin 1958; Speakman 2008). Specifically, we test predictions that the ancestral bat

had (i) an auditory brain design capable of supporting early laryngeal echolocation, but

(ii) eyes of insufficient absolute size to allow insect tracking at night. We also test the

predictions that today not only would pteropodids possess relatively larger eyes than LE

bats but that among predatory bats (all of which use laryngeal echolocation), (i) MH bats

would have relatively larger eyes than DH and CF bats and (ii) short wavelength sensitive

(SWS) opsin genes would remain functional in MH and DH bats, but have lost

functionality in CF species (Zhao et al. 2009a).

49

Using modern phylogenetic comparative methods and a recent bat molecular

phylogeny, we find support for each of these four hypotheses. Specifically, our analyses

unambiguously support the idea that the common ancestor of modern bats was a small,

flying nocturnal predator capable of laryngeal echolocation and that this complex

sensory trait has regressed in the pteropodids. Further, our results suggest that this

vocal-auditory solution was favoured over vision due to pre-existing sensory

opportunities and constraints and that these sensory trade-offs persist to different

extents in bats today.

Results

Estimated states and traits of the ancestral bat

We estimated Bayesian posterior probabilities for both foraging strategy and call type

under an equal rates model of evolution, as this model produced the lowest AICc scores

for both categories (foraging category: equal rates (EQ) AICc = 45.43, symmetric

transition (SYM) AICc= 48.51, all-rates-different (ARD) AICc= 53.35; call design

categories: EQ (AICc)= 41.2, SYM= 44.17, ARD= 49.44). We found additional support for

the hypothesis that the ancestral bat was a predatory, echolocating bat (Bayesian

posterior probabilities: animal-eating laryngeal echolocator >0.999, phytophagous

laryngeal echolocator

50

ancestral bat produced multiharmonic calls (Bayesian posterior probabilities: multi-

harmonic >0.999, constant fr