The Vocal Repertoire of Feathertail Gliders (Acrobates Pygmaeus) and How Macrocomparisons Can Shape Future Research on Acoustic Communication in Mammals

Home , Acrobatidae, Feathertail glider, Greater glider, Sugar glider

The vocal repertoire of feathertail gliders (Acrobates pygmaeus) and how macrocomparisons can shape future research on acoustic communication in mammals

Kobé Martin

A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy

Evolution and Ecology Research Centre School of Biological, Earth and Environmental Sciences Faculty of Science University of New South Wales

March 2019

Thesis / Dissertation Sheet

Surname/Family Name : Martin Given Name/s : Kobe Abbreviation for degree as given in the University : PhD calendar Faculty : Science School : Biological, Earth and Environmental Sciences Thesis Title : The vocal repertoire of feathertail gliders (Acrobates pygmaeus) and how macrocomparisons can shape future research on acoustic communication in mammals

Abstract 350 words maximum: (PLEASE TYPE)

Australia is home to the largest diversity of marsupial mammals in the world. It also has one of the highest extinction rates of animals in the world. The use of acoustics in environmental monitoring is growing in popularity, and it is important to have a solid knowledge of the acoustic repertoires of those species at risk from urbanisation and deforestation in order to be able to monitor them in the wild. The aim of this thesis was to add to our knowledge of the gliding marsupials by describing the vocal repertoire of the smallest gliding marsupial, the feathertail glider, and using macrocomparative techniques to determine where they fit in the broad scale of mammalian acoustic communication.

A macrocomparison of the vocalisation frequencies of 193 species, and the hearing limits of 126 species of mammals found that body size and environment had a large influence on the limits a species communicates at. Aquatic species utilise higher frequency vocalisations and hearing than terrestrial species of similar body mass, demonstrating that the divergence of signal frequencies in mammals has arisen from the need to adapt to their environment. The results of these macrocomparison studies have extended our knowledge of the influence of life history traits on the acoustic communication of mammals, a topic of increasing interest and importance in an increasingly noisy world.

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Publications can be used in their thesis in lieu of a Chapter if: • The student contributed greater than 50% of the content in the publication and is the “primary author”, ie. the student was responsible primarily for the planning, execution and preparation of the work for publication • The student has approval to include the publication in their thesis in lieu of a Chapter from their supervisor and Postgraduate Coordinator. • The publication is not subject to any obligations or contractual agreements with a third party that would constrain its inclusion in the thesis

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This thesis contains no publications, either published or submitted for publication ☐ (if this box is checked, you may delete all the material on page 2) Some of the work described in this thesis has been published and it has been documented in the relevant Chapters with acknowledgement (if this box is ☒ checked, you may delete all the material on page 2)

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CANDIDATE’S DECLARATION I declare that: • I have complied with the Thesis Examination Procedure • where I have used a publication in lieu of a Chapter, the listed publication(s) below meet(s) the requirements to be included in the thesis. Name Signature Date (dd/mm/yy) Kobe Martin 18/03/19

‘And hearing is special among senses. Sound can travel a long way. It will propagate through anything – the ground, water. It works at night, and goes around corners.’

- Seth Horowitz

For my parents, who encouraged me to follow my dreams, always.

Acknowledgements

First of all, I would like to thank my supervisor Tracey for all her help and guidance throughout my thesis journey. You believed in my dream project, and taught me how to embrace the change when it didn’t go exactly as planned; and turned into something even better. I have you to thank for so many of the amazing opportunities I experienced throughout my candidature. To my co-supervisor Lisa, thank you for stepping in in my times of need and offering words of encouragement and advice.

I owe massive thanks to the members of the Mammal Lab both past and present for providing feedback on draft manuscripts and presentations, for all our lunch dates and the occasional lab field trip. Marlee, Alicia, Naysa, Ricardo, Gary, Cat, Dani, Sarah, Kate, Nahal, Adelaide and Annie, thank you.

Even though a lot of the data never made it into the thesis, I was lucky to work with some amazing people at zoos and aquariums, bush care groups and national parks; Rob, Wendy, Vanessa, Ryan, Tony, Greg, Nat and Jennifer from Taronga Zoo Sydney and Taronga Western Plains Zoo; Marissa and Justin from Zoos Victoria and Wild Seas Zoos Victoria; Aaron and Mariana from Dolphin Marine Magic; Mark from Underwater World Mooloolaba; the Captive Research and Advisory Group (CRAG) and Save the Tasmanian Devil Program; Jodie, Sam and David from DPIPWE Tasmania; Channing from Carnivore Conservancy; and last but by no means least, Lesley and the lovely residents from the Rocky Point Bush Care Group. I would also like to thank my wonderful volunteers Cat, Sarah and Dani for their help in the field.

The biggest thanks of all goes to my amazing family, for keeping me accountable and pushing me toward the finish line; to Sam for talking me out of more than one meltdown, and attempting to teach me not to sweat the small stuff; and to my mum for being my biggest cheerleader and critic, celebrating my achievements with the greatest enthusiasm, and always believing I could achieve great things no matter what anyone says.

Abstract

Australia is home to the largest diversity of marsupial mammals in the world. It is also the country with the highest extinction rate of animals in the world. The use of acoustics in environmental monitoring is growing in popularity, and it is important to have a solid knowledge of the acoustic repertoires of those species at risk from urbanisation and deforestation in order to be able to monitor them in the wild. The aim of this thesis was to add to our knowledge of the gliding marsupials by describing the vocal repertoire of the smallest gliding marsupial, the feathertail glider, and using macrocomparative techniques to determine where they fit in the broad scale of mammalian acoustic communication.

This was the first acoustic description of the feathertail glider’s vocal repertoire, and the first description of ultrasonic vocalisation for gliding marsupials. I found that feathertail gliders produce a diverse and complex repertoire of vocalisations which is representative of their social lifestyle. Using the patterns found in the macrocomparison studies I found that feathertail gliders produce frequencies slightly above what is expected for a terrestrial mammal of their size. The frequencies of their vocalisations are also well matched to their hearing sensitivity. I found that a subset of their ultrasonic call types were produced purely in the ultrasonic frequency range, whereas two of the broadband call types detected in the ultrasonic range were higher frequency components of their audible call types in their vocal repertoire. The calls produced in the ultrasonic range are highly stereotyped, making them ideal for potential use in passive acoustic monitoring of the species. No contextual evidence was able to be gathered for the vocalisations, however based on the call characteristics it is possible that one of the call types, the pulse, could be used in a rudimentary form of echolocation for navigation.

Using macrocomparative approaches I compared the vocalisation frequencies of 193 species and the hearing limits of 126 species of mammals and found that body size and environment had a large influence on the frequency limits a species communicates at. Aquatic species utilise higher frequency vocalisations and hearing than terrestrial species of similar body mass,

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demonstrating that the divergence of signal frequencies in mammals has arisen from the need to adapt to their environment. The results of these macrocomparison studies have extended our knowledge of the influence of life history traits on the acoustic communication of mammals, a topic of increasing interest and importance in an increasingly noisy world.

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Contents

Acknowledgements ...... vi

Abstract ...... vii

Contents ...... ix

List of Tables ...... xi

List of Figures ...... xiii

Chapter 1 General Introduction ...... 1 1.1 Acoustic Communication ...... 1 1.2 Australian Gliding Marsupials ...... 5 1.3 Australian Mammal Extinctions ...... 7 1.4 Passive Acoustic Monitoring ...... 10 1.5 Macrocomparison Studies ...... 12 1.6 Drivers of Acoustic Communication ...... 15 1.7 Tying Two Worlds Together ...... 16 1.8 Thesis Outline ...... 17

Chapter 2 The audible vocal repertoire of adult Acrobates pygmaeus in captivity ...... 19 2.1 Introduction ...... 19 2.2 Methods ...... 21 2.3 Results ...... 24 2.4 Discussion ...... 48

Chapter 3 Does size matter? Examining the drivers of mammalian vocalisations ...... 52 3.1 Introduction ...... 52 3.2 Methods ...... 56 3.3 Results ...... 60 3.4 Discussion ...... 65 3.5 Conclusion ...... 74

Chapter 4 Do you hear what I hear? A comparison of the drivers of hearing limits in mammals ...... 75

4.1 Introduction ...... 75 4.2 Methods ...... 81 4.3 Results ...... 84 4.4 Discussion ...... 89 4.5 Conclusion ...... 96

Chapter 5 Marsupial gliders, Acrobates pygmaeus, produce ultrasonic vocalisations ...... 97 5.1 Introduction ...... 97 5.2 Methods ...... 99 5.3 Results ...... 101 5.4 Discussion ...... 107

Chapter 6 General Discussion ...... 110 6.1 Macrocomparison ...... 110 6.2 A. pygmaeus Audible Range Vocal Repertoire ...... 114 6.3 A. pygmaeus Ultrasonic Vocal Communication ...... 115 6.4 General Conclusion ...... 118

References ...... 119

Appendix 1 Supplementary Material for Chapter 3 ...... 140

Appendix 2 Supplementary Results for Chapter 3 ...... 160

Appendix 3 Supplementary Material for Chapter 4 ...... 161

List of Tables

Table 1.1 Number of extinct animals per country for the top four countries in the world according to the IUCN Red List. These numbers include species listed as both Extinct and Extinct in the Wild...... 7

Table 1.2 A breakdown of Australia’s extinct animal species by group according to the Australian Government’s EPBC Act list...... 8

Table 2.1 Summary of the call characteristics for A. pygmaeus' audible call types (n = number of calls)...... 24

Table 3.1 Calculation of parameter contributions from r2 values for the minimum and maximum frequency limit...... 58

Table 3.2 A comparison of the level of support for possible explanatory models that describe the evolution of the minimum frequency in vocalisations of mammals. The results are produced from phylogenetic generalised least squares (PGLS) analysis...... 60

Table 3.3 A comparison of the level of support for possible explanatory models that describe the evolution of maximum frequency limits of vocalisations. Results are produced from phylogenetic generalised least squares (PGLS) analysis...... 62

Table 3.4 Results of pairs analysis for minimum frequency limit slopes based on the Body Size Rule, environment adjusted body size rule by Fletcher (2004), and the empirical data from this study. The values presented are the p-values calculated by the pairs analysis. A p-value of <0.05 indicates a significant difference between the slopes. Bold values indicate those pairs that were NOT significantly different...... 64

Table 3.5 Results of pairs analysis for maximum frequency limit slopes based on the Body Size Rule, environment adjusted body size rule by Fletcher (2004), and the empirical data from this study. The values presented are the p-values calculated by the pairs analysis. A p-value of <0.05 indicates a significant difference between the slopes. Bold values indicate those pairs that were NOT significantly different...... 64

Table 4.1 Comparison of level of support for explanatory models that describe the evolution of the minimum hearing frequency in mammals for 123 species...... 84

Table 4.2 Comparison of level of support for explanatory models that describe the evolution of maximum hearing frequencies in mammals for 125 species tested using behavioural and neurological methods...... 86

Table 5.1 Summary of call characteristics for A. pygmaeus' ultrasonic call types...... 101

Table 5.2 Summary of call characteristics for A. pygmaeus' ultrasonic components of 2 call types...... 101

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List of Figures

Fig. 1.1 Percentage of hours per month of blue whale calls across sites. From Balcazar et al. (2017)...... 4

Fig. 1.2 (A) Yellow-bellied glider (Photo credit: David Cook, source ABC), (B) squirrel glider (Photo source Queensland Museum), (C) sugar glider (Photo credit: Anke Meyring, source Wikipedia), (D) mahogany glider (Photo credit: Daryl Dickson, source Wildlife Preservation Society of Queensland), E) greater glider (Photo source The Guardian), (F) feathertail glider (Photo credit: Rick Stevens, source Taronga Zoo)...... 6

Fig. 1.3 (A) The relationship between generation time and body length across species (Adapted from McMahon and Bonner (1983)). (B) Home range size as a function of body mass compared for terrestrial carnivorous (black circles), herbivorous (white circles) and omnivorous (grey circles) mammal species (From Tucker et al. (2014))...... 13

Fig. 2.1 Map showing occurrence records (yellow dots) for Acrobates pygmaeus within their distribution range. Map source: Atlas of Living Australia www.ala.org.au...... 20

Fig. 2.2 Zoom H4n handy recorder on top of a wall-mounted nest box inside the enclosure at Taronga Zoo...... 21

Fig. 2.3 Location of the Spromise S108 wildlife camera mounted on foliage inside the A. pygmaeus enclosure...... 22

Fig. 2.4 Boxplot of center frequency, measured in kilohertz, by call type with a horizontal dashed line at 8 kHz. The green boxes are tonal call types, and the orange boxes are broadband call types...... 25

Fig. 2.5 Boxplot of call duration, measured in milliseconds, by call type. The green boxes are tonal call types, and the orange boxes are broadband call types...... 25

Fig. 2.6 Stylised representation of A. pygmaeus' audible vocalisation repertoire. (A) LF whistle, (B) long whistle, (C) short whistle, (D) whistle, (E) HF whistle, (F) LF chirp, (G) chirp, (H) LF pip, (I) pip, (J) hiss, (K) pulse train, (L) clicks, (M) toot, (N) LF click train...... 26

Fig. 2.7 Stacked bar graph showing the number of each call type detected over the time of the recording. The black bar represents the 'night' part of the light cycle in the A. pygmaeus enclosure...... 26

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Fig. 2.8 Scatterplot demonstrating the gradation in the durations of the temporal whistle types...... 27

Fig. 2.9 Scatterplot demonstrating the gradation in the frequencies of the spectral whistle types...... 28

Fig. 2.10 A. pygmaeus’ whistle call type (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 29

Fig. 2.11 Three short whistles indicated by the red boxes (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming wndow, DFT size = 512 points). (C) A zoomed in view of the frequency modulation of the short whistle call. There are two lower frequency calls occuring simultaneously...... 30

Fig. 2.12 A. pygmaeus' long whistle (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 31

Fig. 2.13 Three LF whistles (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 32

Fig. 2.14 Two HF whistles (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 33

Fig. 2.15 The pip call of A. pygmaeus (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the pip indicated by the red box on (B)...... 34

Fig. 2.16 A series of A. pygmaeus' LF pips with a LF chirp at the end (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 35

Fig. 2.17 Histogram demonstrating the binomial distribution of the center frequencies of the 'LF pip' and 'pip' call types...... 36

Fig. 2.18 A series of A. pygmaeus' LF chirp (A) waveform and (B) spectrograms (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) and (D) are zoomed in views of the LF chirps indicated by the red boxes on (B)...... 38

Fig. 2.19 Series of A. pygmaeus' chirp (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the chirp indicated by the red box in (B)...... 39

Fig. 2.20 Histogram demonstrating the binomial distribution of the center frequencies of the 'LF chirp' and 'chirp' call types...... 40

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Fig. 2.21 A. pygmaeus' hiss (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 41

Fig. 2.22 A series of pulses and pulse packets (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 42

Fig. 2.23 A series of A. pygmaeus' clicks (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 43

Fig. 2.24 A toot call (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the toot shown in (B)...... 44

Fig. 2.25 A. pygmaeus’ LF click train (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points)...... 45

Fig. 2.26 A toot followed by a toot-whistle of the frequency modulated whistle form (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the toot-whistle pictured in (B)...... 46

Fig. 2.27 A series of three-part chirp-whistles (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the three-part chirp-whistle indicated by the red box in (B)...... 47

Fig. 2.28 Histogram showing the binomial split in center frequencies of A. pygmaeus' calls. Green bars represent tonal call types, and orange bars represent broadband call types...... 50

Fig. 2.29 Auditory Brainstem Response (ABR) audiogram of A. pygmaeus (Adapted from Aitkin (2012)...... 50

Fig. 3.1 Illustration of vocalisation strategies of mammals in-air and underwater. (A) Pinnipeds in-air vocalisation through the larynx; (B) Pinnipeds underwater vocalisation potentially via expansion of the tracheal membrane; (C) Terrestrial species’ in-air production via perpendicular vocal folds in the larynx; (D) Mysticete underwater production of sound using parallel vocal folds in the larynx; (E) Odontocete underwater production via phonic lips and the melon. Silhouettes by Tracy Heath, Steven Traver and Chris Huh were downloaded from http://phylopic.org...... 54

Fig. 3.2 Vocalisation minimum frequency as a function of species body mass for terrestrial (n =

105), semi-aquatic (n = 23) and aquatic (n = 42) environments on a log10 scale. The dotted line represents the phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (log10(Y) = -0.41log10(X)-0.21) (CI -0.50, -0.32), the dash-dot line for semi-aquatic mammals (log10(Y) = -0.41log10(X)+0.06) (CI -0.50, -0.32) and the solid line for aquatic mammals

(log10(Y) = -0.41log10(X)+0.93) (CI -0.50, -0.32). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org...... 61

Fig. 3.3 Vocalisation maximum frequency as a function of species body mass for terrestrial (n =

125), semi-aquatic (n = 23) and aquatic (n = 41) environments on a log10 scale. The dotted line represents the phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (log10(Y) = -0.38log10(X)+0.98) (CI -0.34, -0.06), the dash-dot line for semi-aquatic mammals (log10(Y) = -0.18log10(X)+1.13) (CI -0.28, 0.45) and the solid line for aquatic mammals

(log10(Y) = -0.18log10(X)+1.63) (CI -0.31, -0.06). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org...... 63

Fig. 3.4 Comparison of regressions based on the Body Size Rule, theoretical equations based on those by Fletcher (2004), and empirical data for (A) terrestrial minimum frequency, (B) aquatic minimum frequency, (C) terrestrial maximum frequency and (D) aquatic maximum frequency limits of vocalisations...... 66

Fig. 3.5 Illustration of the 2-D and 3-D propagation properties of the terrestrial and aquatic environments respectively. Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org...... 69

Fig. 3.6 (A) A graphical summary of vocalisation minimum frequency showing general trends for functional groups of acoustic interest. (B) A graphical summary of vocalisation maximum frequency showing general trends for functional groups of acoustic interest...... 70

Fig. 4.1 Graphical representation of the model with highest level of support. Minimum hearing frequency as a function of species body mass for all mammals, on a log10 scale, as a function of both environment and method of hearing testing. The dotted line represents the phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (n = 87), the dot-dash line for semi-aquatic (n = 15), and the solid line for aquatic mammals (n = 21). Terrestrial mammals (log10(Y) = -0.37log10(X)-0.52), semi-aquatic mammals (log10(Y) = -0.33log10(X)+0.49) and aquatic mammals (log10(Y) = 0.18log10(X)-0.09). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org...... 85

Fig. 4.2 Graphical representation of the model with highest level of support. Maximum hearing frequency as a function of species body mass, on a log10 scale. The dotted line represents the phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (n = 90), the dot-dash line for semi-aquatic (n = 14), and the solid line for aquatic mammals (n = 21). Terrestrial mammals (log10(Y) = -0.12log10(X)+1.62), semi-aquatic mammals

(log10(Y) = -0.12log10(X)+1.85) and aquatic mammals (log10(Y) = -0.12log10(X)+2.29). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org...... 87

Fig. 4.3 Hearing frequency limits for (A) minimum frequency as a function of pinna width, (B) minimum frequency as a function of pinna height, (C) maximum frequency as a function of pinna width, (D) maximum frequency as a function of pinna height. Green circles are Terrestrial species, purple triangles are Semi-aquatic, and blue squares are Aquatic. Pinna

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width and height were measured as percentages of body length. Results were obtained from linear regressions...... 88

Fig. 5.1 Recorder setup of SM4BAT FS song meter and SMM-U1 microphone within the enclosure at Taronga Zoo, Sydney...... 99

Fig. 5.2 A. pygmaeus’ ultrasonic whistle call type (A) waveform and (B) spectrogram (sampling rate 192 kHz, hamming window, DFT size = 1024 points). (C) A zoomed in view of the frequency modulation of the call. (D) A zoomed in view of the narrowband waveform indicated by the red box in (A)...... 102

Fig. 5.3 A. pygmaeus ultrasonic chirp vocal type, (A) waveform and (B) spectrogram (sampling rate 192 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the ultrasonic chirp indicated by the red box on (B) (sampling rate 192 kHz, hamming window, DFT size = 1024 points)...... 103

Fig. 5.4 Diagram of the gradation in complexity of the waveforms of the ultrasonic chirp call type...... 104

Fig. 5.5 A. pygmaeus’ hiss (A) waveform and (B) spectrogram (sampling rate 192 kHz, hamming window, DFT size = 1024 points). (C) A zoomed in view of the waveform indicated by the red box on (A) to showcase the waveform of the hiss...... 105

Fig. 5.6 (A) Waveform and (B) spectrogram (sampling rate 192 kHz, hamming window, DFT size = 1024 points) of a series of A. pygmaeus’ pulse trains. (C) A zoomed in view of the waveform indicated by the red box on (A) to showcase the waveform of one pulse train...... 106

Fig. 5.7 Maximum recorded frequency of A. pygmaeus (red star) in comparison to chiropterans (purple circles), rodentia (orange circles) and other mammals (black circles). ... 107

Fig. 6.1 Stylised representation of A. pygmaeus' full repertoire...... 116

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Chapter 1 General Introduction

1.1 Acoustic Communication

Acoustic communication is critical to the survival success of many species, particularly when it comes to breeding, migration and location of prey (Wilkins et al. 2013). As a form of communication it has evolved in a number of taxa: fish, anurans, insects, birds and mammals. These taxa have developed different forms of acoustic communication using various methods, techniques or structures. For example, in fishes sound may be produced by stridulation of teeth, bones or fin spines, swim bladder sound production, or hydrodynamic sound produced by movements made in the water (Tavolga 1971). While in birds sounds may be produced by using ‘instruments’. For example: palm cockatoos (Probosciger aterrimus) have been shown to produce individualised drumming sequences using sticks or seedpods (Heinsohn et al. 2017); woodpeckers (Aves: Picidae) drum on branches to attract females (For a summary see: Miles et al. (2018)); owl species clap their mandibles, also known as bill-clapping, as a threat display (Beruldsen 2016; Zdenek 2017) and various bird species produce sounds by clapping their wings or specially modified feathers together in courtship and territorial displays (Coward 1928; Mengel et al. 1972; Norberg 1991; Bostwick and Prum 2003; Clark and Prum 2015). Further to such ‘instrumental’ acoustic communication, some vertebrate groups have developed vocal forms of acoustic communication. It is believed that vocal communication, or vocalisations, evolved as a secondary trait of respiratory actions (Owings and Morton 1998; Okanoya 2017). Teleost fish produce sonic vocalisations through high frequency contractions of skeletal, sonic muscles that are attached to the walls of the swim bladder (Myrberg 1981; Bass et al. 1994; Bass and McKibben 2003; Bass and Ladich 2008); anurans and mammals produce their vocalisations by passing oxygen through the larynx and vocal tract (Kelemen 1949; Papousek et al. 1992; McClelland et al. 1996; Mergell et al. 1999; Fitch et al. 2002; Fitch 2006; Preininger et al. 2016); while birds produce syringeal (a specialised organ unique to birds) vocalisations (Warner 1972; Brackenbury 1980; Hartley and Suthers 1990; Kumar 2003).

Although these groups use different morphological structures to produce their vocal signals, they share similarities in their traits and characteristics. For example, teleost fish produce both simple broadband vocalisations, as well as harmonic long duration vocalisations similar to those produced by members of the vocalising tetrapod groups (Bass and Ladich 2008). All vocalising groups are known to produce species and sex-specific vocalisations (Bass et al. 1994), though they utilise different musculature to do so.

As a social behaviour, acoustic behaviour is important for social contact between conspecifics, for aggressive displays (territorial, over food) and mating (Janik and Slater 2000). Acoustic communication is particularly useful in situations where other modes of communication, such as vision, are less effective. For example, in situations or habitats with low light, such as for nocturnal, subterranean or aquatic species, visual signalling would be ineffective. Under these conditions sound can be an important and effective mode of information transfer. For example, species communicating underwater face limited light conditions as light attenuates faster than sound in water (Urick 1982; Wartzok and Ketten 1999; Ketten 2004) and they commonly utilise acoustic communication. Other communication signals are also rendered ineffectual over long distances, due to obstruction of the signal by objects in its path, whereas sound is less limited in this way and is easily manipulated to travel great distances (Waser and Waser 1977).

However, acoustic communication costs the signalling individual energy; as is the case with most forms of communication. Why then would so many species employ this method of communication? Owings and Morton (1998) explain that it is because such communication is less costly than other actions such as attacking another individual or developing large weapons and ornaments. For example numerous species employ vocal signalling to win mates in the form of male-male sexual competition or vocal territorial defence by males (For example (Arak 1983; Ten Cate et al. 2002; Tobias et al. 2004)) whereas other species might butt heads, literally and figuratively, to determine dominance. The costs associated with the acoustic signals are often said to be less than those incurred from a physical battle with other individuals or the energy needed to grow and maintain large ornaments and weapons (Owings and Morton 1998).

In situations where species use vocal communication as a means of territorial or aggressive displays, some species manipulate this particular form of competition and implement ‘dishonest signalling’ strategies in order to increase their chances of successfully outcompeting

opponents (Dawkins and Guildford 1991). It was originally thought that conspecific signalling evolved for the transfer of “reliable information” alone (Rowell et al. 2006) and that such ‘honest’ systems were able to succeed because the difficulty of a signal was related to its meaning (Zahavi 1977; Burk 1988). For example, the formant frequency of a call is often related to the size of the animal producing the signal (this concept is covered in detail in Section 1.6) such that large individuals are able to produce lower frequency calls than smaller individuals due to the coupling of the size of the larynx with body size. This was believed to be an ‘honest’ signal as body size could not be ‘faked’. However some species have developed adaptations in order to produce calls that infer to the receiver of the call that they are of larger body size, in what is known as dishonest signalling. For example, male red deer (Cervus elaphus) have descended larynges which they are able to lower even further during vocal rutting events throughout the mating season (Fitch and Reby 2001; Reby et al. 2005). This gives the calling individual the appearance of being larger than their actual size. This holds multiple purposes as it can influence male receivers by exaggerating the size of the caller (Fitch and Reby 2001) and possibly dissuading physical combat, but also because females are attracted to larger males, it could thus increase the chance of successfully attaining mating opportunities (Reby et al. 2005; Charlton et al. 2007). A number of bird species are known to possess elongated trachea which allows them to produce calls of a lower frequency than is expected for their body size (Fitch 1999). While the evolutionary reason behind this elongation is not definitively known, some hypotheses have been suggested in regards to attracting mates and dissuading raids by competing males into the caller’s territory. Male green frogs (Rana clamitans) have been shown to implement dishonest signalling in the context of male-male competition (Bee et al. 1999; Bee et al. 2000). Smaller males were shown to lower the frequency of their calls in response to calls that had been simulated to represent larger males, whereas large males did not alter their calls as they were of equal or greater quality to the simulated frog in terms of size and fighting ability and it would be of no benefit to lower the frequency of their call. The concept of dishonest signalling demonstrates the plasticity of vocal communication and how studies of vocal behaviour can inform about important ecological processes.

Acoustic communication by animals has been used to study many aspects of species’ life histories and behaviours. A few examples are listed here but the full extent of the studies’ applicability is much broader. Presence/absence acoustic studies are able to inform about the migration patterns of a species (for example monitoring of blue whale (Balaenoptera musculus) temporal and spatial population structure using presence/absence acoustic data

(Fig. 1.1) (Stafford et al. 2001; Balcazar et al. 2015; Balcazar et al. 2017), or spatial and temporal variations in habitat use by a species (for example bird species richness and abundance (Hobson et al. 2002), and influence of bat’s microhabitat and landscape features with species diversity and abundance (Ford et al. 2006; Gonsalves et al. 2012)). Acoustic studies also reveal relationships between individuals and group dynamics, or even help us understand how species are able to navigate in visually limited conditions (for examples of acoustic surveying applications see Blumstein et al. (2011), Marques et al. (2013) and Rogers et al. (2013).

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Fig. 1.1 Percentage of hours per month of blue whale calls across sites. From Balcazar et al. (2017).

The levels of anthropogenic noise being introduced into the environment have been steadily increasing over the past few decades. From urbanisation and construction in the terrestrial environment, to sonar, transportation and dredging in the aquatic environment (Richardson et al. 1995; Slabbekoorn and Peet 2003; Nowacek et al. 2007), growing human populations and technological advances are largely responsible for the increase in these noise levels. Noise impedes an individual’s ability to perceive sounds, called masking (Klump 1996; Barber et al. 2010), and can result in the shifting of vocalisations’ frequencies, amplitude and patterns to mitigate the effects of the masking and improve the reception of the sound (Brumm and Slabbekoorn 2005). Of particular interest in the past decade, has been the impact of such anthropogenic noise on aquatic mammals (E.g. Ketten 1995; Nowacek et al. 2007; Tyack 2008; Ellison et al. 2012; Moore et al. 2012; Williams et al. 2015) due to an increased incidence of ship strikes (for databases of worldwide ship strikes of large whales up to 2002 see Laist et al.

(2001) and Jensen et al. (2004)), the endangered status of many species, their cryptic life histories, and the importance of acoustic signalling for their survival (Ketten 2012). The impact of anthropogenic noise has also been studied in terrestrial mammals (Brown et al. 2012; Byrnes et al. 2012; Shannon et al. 2014) with mixed results. While some species appear to become habituated to the presence of excessive noise such as vehicle traffic (Byrnes et al. 2012) other species have been found to respond with increased levels of vigilance, resulting in reduced foraging (Shannon et al. 2014), and anti-predator behaviours (Brown et al. 2012).

1.2 Australian Gliding Marsupials

Australian gliding marsupials currently represent a gap in our knowledge of the acoustic communication of mammals. While we have a detailed description of the vocal repertoire of the yellow-bellied glider (Petaurus australis) (Fig. 1.2A) and the squirrel glider (Petaurus norfolcensis) (Fig. 1.2B), we know less about the other four smaller species. Six vocalisation types have been identified as being produced by the yellow-bellied glider (Goldingay 1994). However despite describing the frequency (0.7-6.4 kHz) and duration (0.5-4.0 s) range of their vocalisations as a whole, along with onomatopoeic description of the calls, no spectrograms are provided, making it difficult to confirm call categorisation across studies. The squirrel glider has had six call types of its repertoire described (Sharpe and Goldingay 2009). However these call types were categorised phonetically by the observers and their frequency (kHz) and duration (of individual calls) were not described (Sharpe and Goldingay 2009). The ‘yap’ call type and alarm call of the sugar glider (Petaurus breviceps) (Fig. 1.2C) have been anecdotally described by those in glider research and captive management but have not been acoustically described (Lindenmayer 2002; Sharpe and Goldingay 2009). The mahogany glider (Petaurus gracilis) (Fig. 1.2D) has had its vocal behaviours mentioned briefly (Jackson 2000; Jackson and Johnson 2002); the observers named only one call type, the ‘na-when’, and alluded to ‘several calls’ (Jackson 2000) but no acoustic data has been provided for the species. The minimal vocal activity of the species was suggested to be linked to the lack of social interaction observed in the mahogany gliders (Jackson and Johnson 2002). No studies acoustically describing the greater glider’s (Petauroides volans) (Fig. 1.2E) vocalisations have been published but a list of the call types attributed to the species has been published in Lindenmayer’s (2002) summary of Australian glider species ecology, along with those for the other glider species.

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The feathertail glider (Acrobates pygmaeus) (Fig. 1.2F) is a small nocturnal marsupial glider, the smallest of the gliding marsupials, with an average body mass of 14g (Jones et al. 2009). They possess a patagium (gliding membrane) that spans from the wrist to the ankle (Ward 2000a) that forms a parachute for gliding between surfaces and a flattened, feather-like tail that acts a rudder to control direction and angle whilst gliding (Jackson 2012; Pridmore and Hoffmann 2014). They inhabit the sclerophyll and woodland forests along the east of Australia (Smith 1980), forming ball shaped nests constructed from eucalyptus leaves in natural tree hollows, artificial nest boxes and telecommunications boxes (Fanning 1980). Studies of natural populations have found that feathertail gliders feed on the nectar, sap, and small invertebrates that are available in the habitat in which they reside (Turner 1984). They are a social species, occasionally occurring as solitary animals as well as occurring in groups of up to 14 individuals in the wild (Fleming and Frey 1984; Frey and Fleming 1984). The larger groups are often made up of parents and offspring, as well as groups consisting of multiple breeding adults (Fleming and Frey 1984; Frey and Fleming 1984). However captive populations reside successfully in much larger groupings. Though it is believed to be one of the most abundant arboreal mammals in eastern Australia and one of the most common Australian glider species (Johnston and Shaw 2000; Ward 2000a) the species is very difficult to survey in the wild. Surveying is conducted using nest box attendance (Ward 2000a; Goldingay and Sharpe 2004), spotlighting

(Ward 2000a; Goldingay and Sharpe 2004) and camera traps (Goldingay et al. 2013; Harley et al. 2014; personal observation) however as they are fast moving, particularly when gliding, very small, and use their suction-like grip to manoeuvre quickly around tree trunks (Ward 1990, 2000a), population numbers are generally said to be underestimated. Much of our knowledge of their life history has come from studies of captive populations (Ward 1990) but the application of such knowledge in surveys of wild populations is limited. New technological developments and techniques have meant that researchers are still discovering new things about the species. The vocal repertoire of this smallest member of the marsupial glider group has not been described in detail. Their vocalisations have been characterised by researchers studying their ecology and life history as being very soft (Lindenmayer 2002), suggesting that they are low intensity, making them difficult for the human ear to hear, unlike some of the louder calls of the larger gliding species. They are therefore an interesting species to study to continue to fill the gap in our current knowledge of the acoustic communication of mammals.

1.3 Australian Mammal Extinctions

According to the International Union for Conservation of Nature’s (IUCN) Red List, in the last 200 years Australia has had the fourth highest number of animal extinctions globally (Table 1.1). It also has one of the highest numbers of species listed as data deficient (602 species). The Australian Government’s Environment Protection and Biodiversity Conservation (EPBC) Act shows that of Australia’s extinct fauna 27 of those species are mammals, and of those, 17 are marsupials (Table 1.2).

Table 1.1 Number of extinct animals per country for the top four countries in the world according to the IUCN Red List. These numbers include species listed as both Extinct and Extinct in the Wild.

Country Number of Extinct Species

United States of America 241 French Polynesia 69 Mauritius 44 Australia 40

Table 1.2 A breakdown of Australia’s extinct animal species by group according to the Australian Government’s EPBC Act list.

Group Number of Extinct Species*

Mammals 27 Birds 22 Frogs 4 Other (Earthworm) 1

* The total number of species listed here differs from the IUCN Red List due to differing legislations.

It is this high level of mammal extinction, particularly of marsupials, and the even higher levels of data deficient species that has led to a call for further research to be carried out on Australian marsupials.

Australia has a large number of unique marsupials, some endemic to the island continent and some not. It is also home to all but two of the world’s gliding marsupials (also referred to as gliding possums; (Lindenmayer 2002)). Australia’s gliding marsupials occupy a special niche in their environment; they are all nocturnal, arboreal species. It has even been suggested that arboreal gliding marsupials acted as a precursor to nocturnal mammals capable of powered flight, making this group even more intriguing (Bishop 2007). However despite the novelty this group presents to researchers they remain largely a mystery, especially in their natural habitat, in terms of their behaviour due to the limited success of detecting glider species in monitoring surveys (Goldingay and Sharpe 2004).

There are posited to be three main forces associated with high rates of Australian mammal extinction; habitat loss, impacts by invasive species and human exploitation (Loehle and Eschenbach 2012). Populations of Australia’s arboreal marsupial gliders are of interest to researchers as they are at risk from a number of threats, largely habitat loss and invasive species. Despite not spending a lot of time on the forest floor marsupial gliders face the threat of predation by medium-sized ground-based predators, particularly introduced predators such as cats (Felis catus) and foxes (Vulpes vulpes) (Jones and Coman 1981; Suckling and Macfarlane 1983; Körtner and Geiser 2000). It has been hypothesised that the risk of extinction of smaller mammal species has increased due to predation by increased medium-sized predator populations as large-sized predator populations have been suppressed by humans, causing a top-down effect (Johnson et al. 2007). For example, dingoes (Canis lupus dingo), a large-sized

mammalian top predator in Australia, were hunted to extremely low population sizes by European settlers and continue to be persecuted to this day, particularly in areas where livestock were being farmed (Glen et al. 2007; Johnson et al. 2007), and have been excluded from large areas using fences. The dingo is believed to have a limiting impact on populations of medium-sized predators such as foxes and cats through predation, exclusion (through scent- marking and intimidation), and resource competition (Glen et al. 2007; Johnson et al. 2007). Evidence of both of these medium-sized predators, cat and fox, have been found in the faeces of dingoes, and foxes have been anecdotally suggested to avoid areas where dingoes are present (Glen et al. 2007). The effect of dingo populations on limiting the impacts of the medium-sized predators has a knock-on effect for small-mammal populations, such as marsupials, that would otherwise be negatively impacted by these medium-sized predators (Glen et al. 2007; Johnson et al. 2007).

Carnivorous arboreal mammals such as antechinus are also a risk to the smaller glider species (Geiser and Ferguson 2001; Lunney et al. 2001). Studies focusing on the ecology of feathertail gliders found evidence that suggested agile antechinus (Antechinus agilis), which were inhabitants of the same area of the study, had predated upon the smaller gliding marsupial (Ward 2000a, b). The two species are similar in size, the antechinus being slightly larger, and so the antechinus are able to use the nest boxes that are designed to allow feathertails, but not larger marsupials, to enter the box (Ward 2000b). With the continued habitat loss due to human deforestation and urban expansion, there is an increase in the competition for suitable nesting hollows between the two species and more of these instances may be observed into the future. Two feathertail glider carcasses were found within nest boxes during the study by Ward (2000b). Examination of the feathertail remains showed that the soft tissues had been removed with intricate detail through a small incision behind the ear, with the bones having been left intact, suggesting the predator responsible was also quite small, ruling out most other arboreal mammals in the area (Ward 2000b). The study was undertaken in winter, when the antechinus’ usual diet of insects is reduced, and it was suggested that they had shifted to prey on smaller mammal species such as the gliders (Ward 2000a, b). Evidence of antechinus predating on feathertails has also been observed in faecal analysis studies (Lunney et al. 2001). There have even been observations of feathertail gliders being caught and consumed by the larger sugar glider, though the reason behind the sugar glider cases are unknown (Ward 2000b).

Gliding marsupials are also at risk of predation by many bird species (Suckling and Macfarlane 1983; Kavanagh 1988; Körtner and Geiser 2000; Ward 2000b; Geiser and Ferguson 2001). Kavanagh (1988) found that the population of greater gliders in their study site in south- eastern New South Wales decreased after the first twelve months of the study. This corresponded with an increase in sightings of powerful owls (Ninox strenua) with captured greater gliders in their possession and bodily remains of the gliders on the forest floor. The powerful owl had not been detected in the area in the first twelve months, and it was suggested that they focus on a pocket of their home range until the population of their prey diminishes to levels where it cannot sustain the powerful owl population and the owls move to a new patch (Kavanagh 1988). It has also been suggested that gliding marsupials are not only at risk of predation by bird species at night, but also at dusk when their hours of activity overlap with predatory diurnal species (Körtner and Geiser 2000). Deforestation in the areas where these species co-exist means that there is less suitable hunting habitat for the powerful owls to move between so their hunting efforts are focused on fewer local prey populations. It also means there is less suitable habitat for the prey species to take refuge in so that prey populations may diminish faster than they have historically, causing the predator species to either diminish or move to a new prey species causing an ecosystem shift.

Australia’s arboreal gliders are also at risk directly from habitat loss (Tyndale-Biscoe and Smith 1969); not only due to deforestation but also from urban expansion and alteration. Fisher et al. (2003) explain that marsupial species with more specialised habitat requirements that are forced from their preferred habitats, due to degradation or destruction, are less likely to survive. Deforestation leads to the loss of food resources as well as nesting hollows (Suckling and Macfarlane 1983; Beyer and Goldingay 2006). Even with reforestation efforts, it can take hundreds of years to form suitable hollows for gliders to occupy (Beyer and Goldingay 2006).

1.4 Passive Acoustic Monitoring

Passive acoustic monitoring (PAM), also referred to as remote bioacoustics monitoring uses acoustic recording devices to garner information on the biology and ecology of environments and acoustically active species without the researcher needing to be physically present (Acevedo and Villanueva-Rivera 2006; Blumstein et al. 2011). The acoustic makeup of an environment is commonly known as the soundscape (Schafer 1969). The term soundscape is used to refer to the encompassing sound produced by: natural sounds, for example

vocalisations produced by animals, termed biophony (Krause 1987); the sounds of wind, water and other natural elements known as geophony (Krause 1987); and the sounds produced by human activities and development, coined anthropophony (Krause 2002). Further, soundscape ecology is the study of the relationship between these three sources as a ‘landscape’ of sound (Pijanowski et al. 2011). Recording devices that are able to be left in the field unmanned, for long periods of time (Acevedo and Villanueva-Rivera 2006), and the development of large data storage solutions developed for PAM have allowed the continual progression of this particular field of acoustic study (Pijanowski et al. 2011).

PAM methodologies are particularly useful for studying animals that are hard to study by other traditional surveying methods (sighting and capture-recapture) (Marques et al. 2013). Many of the species that are of particular interest in monitoring surveys (for example, rare or endangered species) are visually cryptic; for example they may be small, nocturnal, underground, underwater, or live in dense forest habitat (Marques et al. 2013). In such situations where species are visually cryptic PAM can be a useful alternative survey tool. PAM methodologies are also useful for gathering large quantities of continuous or intermittent data over long timescales that would otherwise be logistically impossible to carry out using human observers (Acevedo and Villanueva-Rivera 2006; Marques et al. 2013).

PAM has been used in both the terrestrial and aquatic environments to study birds, anurans, fish and mammals as these groups are highly vocally active (for summaries see Acevedo and Villanueva-Rivera 2006; Marques et al. 2013). It is particularly useful for bird studies as it increases detection range significantly further than visual surveys, and in studies of marine mammals, particularly large cetaceans, as these species spend large amounts of time underwater out of sight (Mellinger et al. 2007; Rogers et al. 2013; Sousa-Lima et al. 2013) making visual surveys and capture-recapture methods less effective for monitoring. PAM has been developed for use in monitoring species diversity, acoustic behaviour of species and how they use acoustics for navigation, hunting and localization, presence of rare species in a habitat of interest, and is being developed for estimating abundances of species in a habitat (Blumstein et al. 2011; Marques et al. 2013; Rogers et al. 2013). Recently it has proven to be a valuable tool for measuring variability in the acoustic habitats of animals and how changing levels of anthropogenic noise may impact the behaviours and survival of the species (Blumstein et al. 2011).

1.5 Macrocomparison Studies

Darwin was the first to propose that the evolutionary origin of signals could be shown by comparing a signal of interest with signals of other species (Owings & Morton, 1998). Macrocomparison approaches or comparative analyses allow researchers to view large-scale patterns in traits across a broad range of taxa. They are used to reveal relationships between traits and explore the evolution and ecology of those traits (Fairbairn et al. 2007). They allow the testing of hypotheses that are not able to be tested using experimental manipulation beyond what is able to be achieved in simple correlational studies (Harvey and Pagel 1991; Fairbairn et al. 2007). For example, in a relationship where there appears to be a general linear pattern, such as in McMahon and Bonner’s (1983) study of body size and generation time, or the home-range size of mammals (Tucker et al. 2014), comparative analyses test for traits that could be attributing to differences, such as the differences seen in similar-sized species that show variation in home-range size or generation time (Harvey and Pagel 1991) (Fig. 1.3). However comparative analyses, as well as regular correlation analyses, of a group of species from the same taxonomic group face the issue of non-independence of the data points, particularly in groups of closely related species (Felsenstein 1985; Harvey and Pagel 1991). Correlational and comparative studies require independence of the data, otherwise facing over exaggeration of the significance of the results. To account for this non-independence phylogeny (both where the species sit on the tree and the length of the branches within the tree) needs to be taken into account (Felsenstein 1985). This proves to be challenging in a lot of studies as it requires a detailed phylogeny of the study species or group. This area has seen significant advances for the group of mammals in the past fifty years. A number of researchers have developed phylogenetic trees for taxonomic groups (primates (Purvis 1995; Purvis and Webster 1999), carnivora (Bininda-Emonds et al. 1999), artiodactyla (Mahon 2004), chiroptera (Jones et al. 2002), lagomorphs (Stoner et al. 2003), marsupials (Cardillo et al. 2004)), supertrees for smaller groupings of mammals (eutherians (Liu et al. 2001), cetartiodactyla (cetaceans and even-toed hoofed mammals) (Price et al. 2005)), as well as for mammals overall. Issues arise when developing such supertrees due to the high levels of variability between individual taxonomic groups’ trees and the debate as to whether morphological or molecular based trees provide the most accuracy of grouping of species (Novacek 2001; Springer and de Jong 2001). Polytomies (branches with more than two species deviating at the one node) are also problematic for the construction of these larger trees, but the more recent supertrees account for this by including multiple trees with different iterations of solutions to the polytomous nodes (For example; Faurby and Svenning 2015). Perhaps most importantly

comparative analyses and macrocomparisons allow us to reconsider and revisit long-held theories (Harvey and Pagel 1991).

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A few examples of studies which have successfully employed macrocomparative analyses to find patterns and explanatory traits in animals are described here. Fairbairn et al. (2007) studied the occurrence of sexual size dimorphism across the animal kingdom. Packer et al. (1992) used comparative analyses to look at the occurrence of non-offspring nursing in mammals and found that it was increased in captive animals but also in species that have larger litters in the wild. In their study of coloniality in birds Rolland et al. (1998) found that

coloniality was correlated with the absence of a feeding territory, but not with body mass or clutch size. Interestingly, their comparative analysis revealed that the move to a marine life was dependent on coloniality. That is, coloniality evolved before bird species moved to the marine habitat.

Studies featuring macrocomparisons of acoustic features of various animal groups have steadily increased over the past few decades. For example a number of studies have looked at the acoustic allometry of mammals, showing relationships between body size (and proxies of size) and various acoustic features; Matthews et al. (1999) carried out studies of vocalisation frequency and duration characteristics of cetaceans, showing that a relationship exists with body size in odontocetes (toothed whales) but less so in mysticetes (baleen whales). Mason (2001) compared the middle ear features of fossorial (underground) and non-fossorial mammals, and concluded that the low anatomical ratios of the middle ear in fossorial species may be responsible for their low sensitivity to airborne and high frequency sounds. Bowling et al. (2017) highlighted the negative relationship between body size and dominant frequency of vocalisations in one of the first empirical studies focusing on primates and carnivores. Macrocomparative studies have also been used to demonstrate correlations between social and vocal complexity in specific groups of mammals; Blumstein and Armitage (1997b) showed that in some groups of sciuridae (squirrels) there was a positive relationship between the social complexity of the species and the number of alarm calls in their vocal repertoire. Ramsier et al. (2012a) found that social complexity had a positive correlation with high- frequency hearing limits and a negative correlation with overall hearing sensitivity in the primate suborder of Strepsirrhini. Dunn et al. (2015) were the first to look at the evolutionary trade-off between pre-copulatory and post-copulatory traits in the howler monkey and how this trade-off can explain their unusually low frequency calls for a species of its size. Their study showed that species with fewer males in the group invested more in hyoid volume and thus had lower frequency vocalisations to mimic large body size, whilst simultaneously investing less in testes size, therefore decreasing post-copulatory sperm competition. Macrocomparisons have also shown the influence of a species’ environment of their call characteristics; Ey and Fischer (2009) found that the influence of habitat on vocal characteristics varied with species, environmental factors within a habitat and function of the call such that influences of environment are tailored closely to specific situations and are difficult to apply generally to groups of taxa. Energetic costs have also been linked to calling characteristics across taxa by Gillooly and Ophir (2010). They developed models of patterns in call duration and signal power and how they are driven by energetic costs and individual

metabolisms across all animals. Studies such as these have paved the way for future research into even more aspects of acoustic communication in a wide range of taxa.

Perhaps the most compelling reason for using macrocomparison analyses to study new patterns or revisit past theories is that new data is constantly accumulating that can be added to new analyses (Harvey and Pagel 1991). Larger datasets allow for more powerful interpretation and offer greater insight into such large-scale patterns. For example in the mammal kingdom new data is continuously being gathered for aquatic species that were unavailable when key ecological and evolutionary theories were being developed, but new technologies have made them more accessible for study. This is particularly true of acoustic communication data for mammals. When previous comparative studies were carried out on mammal vocalisation and hearing traits, the species for which data was available were largely terrestrial. However thanks to new technologies and techniques such as PAM more aquatic species can be represented in these analyses. To that end the databases that are created as a result of these comparative analyses also highlight where there are still gaps in the data that has been collected thus far and therefore where future research efforts could potentially focus.

1.6 Drivers of Acoustic Communication

The study of vocal communication in animals has shown that signals may remain the same, change, or disappear over time (Moynihan, 1970). Researchers have long been interested in what aspects of a species’ life history and biology have shaped its characteristics and behaviours of acoustic communication, and there have been a number of hypotheses as to what drivers are responsible for the variation in characteristics of acoustic communication between taxa. Of particular interest in this thesis are the drivers of vocalisation and hearing frequency limits. These potential drivers and their related hypotheses are briefly described here, and are covered in greater detail in the respective macrocomparison chapters (Chapter 3 and Chapter 4). Body size, and its proxies, have been shown to be correlated with both vocalisation and hearing frequencies in birds and mammals (Ryan and Brenowitz 1985; Hemilä et al. 1995; Bradbury and Vehrencamp 1998a; Fitch 2000; Huang et al. 2000; Laiolo and Rolando 2003; Reby and McComb 2003; Fletcher 2004; Heffner 2004; Heffner and Heffner 2008). Both have a negative relationship with body size such that larger species tend to send and receive signals of a lower frequency. Social group size and social complexity have also

been found to be correlated with vocal and hearing limits in select cetacean and primate groups, with species in larger groups producing and receiving higher frequencies than species in smaller groups (McComb and Semple 2005; May-Collado et al. 2007a; Ramsier et al. 2012a). Higher frequency vocalisations hold and transfer more complex information to the receiver such that in larger social groups, with larger networks of social connections where more complex information transfer is an advantage, higher frequency signals would be beneficial. Environment has also been investigated as a potential driver of acoustic communication in what is known as the Acoustic Adaptation Hypothesis (Morton 1975; Wiley and Richards 1978; Kime et al. 2000; Saunders and Slotow 2004); however, this has only been investigated on the vocalisation end of the spectrum. In particular, studies have shown that the composition of the environment or habitat, and therefore the propagation of sound through that environment, has an influence on the frequencies that species produce for successful transfer of information. Species in subterranean habitats should benefit from using lower frequencies which propagate more efficiently through their underground tunnels (Lange et al. 2007; Bednářová et al. 2013); species in dense forest habitats should produce lower frequencies as they are less easily attenuated by the interference of the leaves and trunks of the trees (Morton 1975). Arguably the largest difference in habitat or environment is the difference in transmission properties of air and water, with sound in water travelling approximately 5 times faster in water than in air (Madsen and Surlykke 2013), such that we would expect to see a difference in the frequencies used by species producing sound in these two different environments. Diet has also been loosely suggested as having had an influence on the hearing of a species because of the need to detect predators (prey trophic level), prey (top-predator trophic level) or both (meso-predator trophic level) (Michelsen 1992). This particular driver would be somewhat influenced by the size of the species that the receiver is detecting. This last driver has not been empirically tested across taxa as the others have, such that its inclusion in this thesis is a preliminary test of this novel driver.

1.7 Tying Two Worlds Together

Though they may seem to be at dichotomous ends of the study of acoustic communication, species-based studies and macrocomparative studies often rely on each other whilst also furthering the progress of the other through a feedback system. For example, macrocomparative studies can show broad scale patterns in traits (such as vocalisation or hearing frequency limits) across a large range of taxa. However, such comparative studies are

reliant on the availability of data from various species which can further be compiled into large-scale informative databases. These macrocomparative studies also assist in highlighting gaps of missing species or taxonomic groups from our current database of knowledge of the trait of interest. As a result, new data continually becomes available for additional species. In turn, this new data feeds back in to future comprehensive macrocomparisons allowing researchers to have greater power in drawing conclusions from such studies and garner future directions of investigation for the field.

At the onset of this thesis, I was aware that the Australian marsupial gliders presented a taxonomic gap in our current database of mammalian acoustic communications. I set out to partially fill this gap by describing the vocal repertoire of the feathertail glider, and feed this information back into a comprehensive macrocomparison of the vocal communication frequency limits of mammals. As a result of the macrocomparison study of vocalisation frequency limits, it was highlighted that the maximum vocalisation frequency of the feathertail glider should in fact be higher than the upper limit of the recording equipment that had been initially used. This prompted a secondary study in which equipment specialised for the range indicated by the estimation from the macrocomparison study was utilised in the hopes of extending our knowledge of the species’ vocal communication even further.

1.8 Thesis Outline

The majority of acoustic comparative studies and theories were developed in birds and have subsequently been applied to mammals. Mammalian comparative studies have focused on primates (Hauser 1993; Heffner 2004; Dunn et al. 2015; Bowling et al. 2017), carnivores (Bowling et al. 2017), felids (Huang et al. 2000; Peters and Peters 2010), rodents (Burda et al. 1988; Blumstein and Armitage 1997b) and odontocetes (May-Collado et al. 2007a; May‐ Collado et al. 2007b) but there are still gaps in our knowledge of acoustic communication in mammals broadly, particularly for hearing thresholds, and comparisons of taxa across different environments. This is an interesting field of research that is continuously growing with the increasing availability of acoustic data on diverse mammalian taxa, yet there are still interesting groups, such as the marsupial gliders, left unexplored. The aim of this thesis is to add to our knowledge of this group’s acoustic communication by focusing on one species, A. pygmaeus. This thesis re-examines the drivers of both aspects of the acoustic communication

of mammals. It also illustrates the unique features of A. pygmaeus’ vocal repertoire and how it fits into the broad scale of mammalian acoustic communication.

In Chapter 2 I describe the vocal repertoire in the human audible range of the smallest gliding marsupial, A. pygmaeus. This is the first study to extensively describe their full repertoire. It is also the first to use acoustic characteristics to describe the calls of A. pygmaeus’ rather than using onomatopoeic descriptors. With this I show that A. pygmaeus has an extensive vocal repertoire representative of their highly social lifestyle.

Chapter 3 re-examines the drivers of vocalisation frequency limits of mammals. To achieve this I compare the performance of multiple models containing drivers that had previously been proposed and investigated individually. The drivers, body size, environment and sociality had not been compared with each other. This comparison contains a more extensive database of mammal species, and compares species from the terrestrial, semi-aquatic and aquatic environments. I then determine which drivers have the most influence on vocalisation frequency limits and what evolutionary changes might have made these changes possible. Chapter 3 has been published in Evolution.

I also re-examine the drivers of hearing frequency thresholds of mammals (Chapter 4) across all three environments (i.e. terrestrial, semi-aquatic and aquatic). Previous comparisons had focused solely on terrestrial or aquatic species, but had not compared the two. With this I show which drivers influence the patterns that are observed in the hearing thresholds of mammals.

In Chapter 5 I extend the investigation of A. pygmaeus’ repertoire into the ultrasonic range. In previous studies of another gliding marsupial it had been suggested that it was probable that some gliding marsupial species would produce ultrasonic components of their calls. However this had not been investigated. In this Chapter I describe which of A. pygmaeus’ call types are produced solely in the ultrasonic range and which call types are ultrasonic components extending from the audible range.

Chapter 2 The audible vocal repertoire of adult Acrobates pygmaeus in captivity

2.1 Introduction

Acoustic communication, in particular vocalisation, may be driven by a number of factors such as a species’ social structure, the environment they live in, or body size. The influence of social structure or social complexity on the vocal repertoire of a species has been studied in numerous species of birds and mammals (Blumstein and Armitage 1997b; McComb and Semple 2005; Bergey and Patel 2008; Freeberg et al. 2012; Krams et al. 2012; Pollard and Blumstein 2012; Bouchet et al. 2013) in what is known as the ‘social complexity hypothesis’. In most cases researchers have found that as the social complexity (in terms of social group size or complexity of individual relationships) of a species increased vocal complexity, either as the complexity of the call types or the size of the vocal repertoire, increased in parallel.

A. pygmaeus are nocturnal, omnivorous diprotodont marsupials that are found in forests and woodlands on the east of Australia, from northern Queensland to South Australia (Fig. 2.1) (Smith 1980). They are a social species, with nests typically hosting two to five individuals, though nests have been found to accommodate up to 14 individuals (Fleming and Frey 1984). Despite being one of the most common glider species in Australia (Johnston and Shaw 2000; Ward 2000a) very little is known about their vocal repertoire. However, given the large social group size of A. pygmaeus I hypothesised that they would have a large and complex vocal repertoire. Their vocalisations are briefly mentioned in summaries of glider natural history as having four call types, described as ‘ticking’, ‘popping’, ‘psss- psss- psss-‘ and ‘hissing’ (Biggins 1984; Lindenmayer 2002), though these descriptions were qualitative rather than quantitative.

Fig. 2.1 Map showing occurrence records (yellow dots) for Acrobates pygmaeus within their distribution range. Map source: Atlas of Living Australia www.ala.org.au.

Body size has an overall negative relationship with vocalisation frequency in birds and mammals in what is known as body size allometry (Ryan and Brenowitz 1985; Fitch 2000; Laiolo and Rolando 2003; Reby and McComb 2003; Fletcher 2004; Martin et al. 2017; Torres et al. 2017). According to the size allometry rule larger animals tend to produce vocalisations with lower frequencies and smaller species produce higher frequency vocalisations. This concept is covered in greater detail in Chapter 3. A. pygmaeus are small, weighing approximately 14g, in fact they are the smallest of the gliding marsupials. Given the relationship between body mass and vocalisation frequencies predicted by body size allometry, and considering the vocal repertoires of species of similar body mass I hypothesised that A. pygmaeus should possess a vocal repertoire within the audible range of 3-15 kHz.

The aim of this study was to qualitatively describe the vocal repertoire of A. pygmaeus and then reflect in later chapters on how the acoustic features of their vocalisations perform in regards to both the body size allometry hypothesis and the social complexity hypothesis.

2.2 Methods

2.2.1 Experimental Design

A captive population of A. pygmaeus were studied at Taronga Zoo (Sydney, Australia) under the Opportunistic Sample Policy LSRC5.4 and Animal Observation Specimen Licence Agreement LSRC5.4.1 of Taronga Conservation Society Australia (Sample request number: R15/L197). The animals were housed in their usual display enclosure, a 1.8 m x 1.2 m x 1.8 m glass fronted space, with the majority of the available space containing branches and foliage. The animals are housed with conspecifics, with no other species present in the enclosure, and there is limited disturbance by human keepers, only for feeding and enclosure cleaning. The animals are kept on an artificial reverse light cycle. At the time of recording the population consisted of 6 males and 24 females, all of which were considered to be mature adults. Three pre-existing wooden nest boxes were attached to the wall at approximately 160cm and 180cm above the ground, and the acoustic recorders were placed on top of the nest boxes (Fig. 2.2). A Zoom H4n handheld recorder was placed on top of the nest box to carry out recordings over approximately 16 hours from 23 to 25 November 2015. The recorder was configured with a sampling rate of 44.1 kHz (frequency response up to 22 kHz) and 16 bit recording format. A Spromis S108 wildlife camera was positioned on a tree (Fig. 2.3) opposite an enrichment food station to record behavioural contexts.

This Content Has Been Removed To Comply With Copyright

Fig. 2.2 Zoom H4n handy recorder on top of a wall-mounted nest box inside the enclosure at Taronga Zoo.

This Content Has Been Removed To Comply With Copyright

Fig. 2.3 Location of the Spromise S108 wildlife camera mounted on foliage inside the A. pygmaeus enclosure.

2.2.2 Acoustic Analysis

Vocalisations were extracted from the recordings using the cluster function of the Kaleidoscope Pro software (Version 4.0.0; Wildlife Acoustics) and then was manually cross- checked by observing spectrograms of all recordings produced in Raven Pro (Version 1.4; Cornell Lab of Ornithology). A total of 2210 vocalisations were detected from the 16 hours of recordings. The cluster function was useful for detecting calls but not for clustering the calls accurately into types, such that call types needed to be determined manually. All detections were categorised manually into call types based on similarities in spectral and temporal characteristics as well as their corresponding features and structural appearance on the spectrograms. The recordings were slowed to half speed in order to hear the calls clearly. Call types were categorised as either tonal or broadband based on the spectral structure of the call of the spectrogram as well as the temporal structure of the waveform. Tonal calls have energy at only on frequency at a single time point, although its intensity may vary, they have a simple sine shaped waveform. Although there is only on dominant frequency at any given time of the call, harmonic frequencies may be present, seen as higher intervals, at regular intervals, on the spectrogram. These calls may be constant in frequency, frequency modulated or amplitude modulated. A common type of tonal call among animals is the whistle. Conversely broadband calls have energy that spans a range of frequencies at any given time point, such that the

waveform is complex as it shows the sum of the energy of all the frequency bands. Call characteristics were measured using the Raven Pro software; duration (ms), minimum frequency (kHz), maximum frequency (kHz), range (kHz) and peak frequency (kHz). Spectrograms of the vocalisations were made in Raven Pro. As the recordings were made within the animals’ enclosure there was unavoidable environmental noise; from the keepers’ preparation area, and from movement within the enclosure as well as in the back area of the nocturnal house. This, coupled with the low intensity of the animals’ calls resulted in poor signal-to-noise recordings. To account for this, noise reduction of 11 dB was applied three times in the program Audacity to produce the spectrograms for figures (any further noise reduction resulted in the loss of the animal’s signal, not only noise).

2.3 Results

The 2210 calls detected in the audible range were categorised into 10 call types based on their acoustic features (Table 2.1, Fig. 2.4, Fig. 2.5, Fig. 2.6).

Table 2.1 Summary of the call characteristics for A. pygmaeus' audible call types (n = number of calls).

Peak Frequency (kHz) Duration (ms) Call Type Structure n Mean (S.D.) Min Max Mean (S.D.) Min Max

Whistles Tonal 1323 12.2 (2.0) 2.2 21.4 30 (20) 7 194 Whistle 1001 12.3 (1.4) 7.4 15.8 38 (18.3) 8 139 Short 231 11.6 (1.8) 6.5 17.9 19 (6.3) 7 41 Whistle Long 14 11.2 (1.1) 9.3 12.7 95 (50.3) 38 194 Whistle LF Whistle 26 4.5 (1.2) 2.2 6.7 29 (16.9) 8 68 HF Whistle 51 16.2 (1.5) 14.5 21.4 25 (11.4) 8 61 LF Pip Tonal 7 3.4 (0.2) 3.1 3.6 12 (4.1) 8 20 Pip Tonal 16 12.3 (1.9) 7.8 15.0 12 (2.8) 8 18 LF Chirp Broadband 163 4.3 (1.7) 0.9 8.4 30 (9.4) 8 53 Chirp Broadband 164 11.4 (2.1) 7.8 17.4 31 (8.9) 9 54 Hiss Broadband 131 13.3 (1.6) 9.6 18.9 130 (59.6) 24 367 Pulse Broadband 40 8.0 (3.9) 1.0 16.7 2 (0.8) 1 4 Click Broadband 199 8.5 (3.0) 2.4 16.0 3 (2.1) 0 28 Toot Broadband 79 4.8 (3.1) 0.9 12.6 7 (3.0) 1 17 LF Click Broadband 21 3.8 (2.2) 0.9 7.6 2 (1.4) 0 5

Fig. 2.4 Boxplot of center frequency, measured in kilohertz, by call type with a horizontal dashed line at 8 kHz. The green boxes are tonal call types, and the orange boxes are broadband call types.

Fig. 2.5 Boxplot of call duration, measured in milliseconds, by call type. The green boxes are tonal call types, and the orange boxes are broadband call types.

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G I C D

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Fig. 2.7 Stacked bar graph showing the number of each call type detected over the time of the recording. The black bar represents the 'night' part of the light cycle in the A. pygmaeus enclosure.

2.3.1 Tonal Vocalisations

2.3.1.1 Whistles

A. pygmaeus produced calls categorised as whistles, frequency modulated tonal calls, detected over a broad frequency range (2 – 21 kHz). They were the most commonly detected (1323/2210) call type produced during the recording period, making up over half the detections. A high degree of variation was observed among the whistles detected, with sub- types identified. These sub-types formed a gradation of the whistle call, both temporally (Fig. 2.8) and spectrally (Fig. 2.9). While the term whistle is used to categorise these calls and their sub-types, I do not intend to imply a production mechanism, but merely a description based on their spectral characteristics. Temporal whistle sub-types had similar mean peak frequencies (Table 2.1) and though their mean durations were different to each other, there was overlap in their range (Table 2.1, Fig. 2.8). Spectral sub-types had similar mean durations (Table 2.1) but they had differences in their mean peak frequencies though there was some overlap (Fig. 2.9). Detailed descriptions of the sub-types follow.

Fig. 2.8 Scatterplot demonstrating the gradation in the durations of the temporal whistle types.

Fig. 2.9 Scatterplot demonstrating the gradation in the frequencies of the spectral whistle types.

Whistle The ‘whistle’ is a tonal call produced in the 6.2 – 17.7 kHz frequency range with a gradual onset and decay (Fig. 2.10). They varied in structure from up swept, down swept, and frequency modulated forms. They are often produced in a bout of multiple whistles but also with other call types, and were also detected singularly. They were the most commonly detected call type (45%) and were the only call detected in every hour of the recordings (Fig. 2.7).

Fig. 2.10 A. pygmaeus’ whistle call type (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

Short Whistle The ‘short whistle’ was a common call type (10 %) with a frequency range of 6.1 to 18.2 kHz (Fig. 2.11). They have a similar structure to the ‘whistle’ though they had a much shorter duration (19.1 ms). They were often detected in bouts of multiple (up to 14) short whistles, but were also produced in conjunction with other call types.

Long Whistle The ‘long whistle’ is a whistle with high frequency modulation (Fig. 2.12B) and an average duration of 95.3 ms, three times that of the ‘whistle’ type. As well as frequency modulation, the pulsed waveforms of these calls show amplitude modulation with multiple cycles of similar intensity within the call (Fig. 2.12A). They had a similar frequency range (7.7 – 15.1 kHz) to the regular whistle but were a less commonly detected call type (0.6 %).

Fig. 2.12 A. pygmaeus' long whistle (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

LF Whistle The ‘low frequency (LF) whistle’ although similar in structure to the ‘whistle’ type, most of the energy of the call occurs below 7 kHz (2.0 – 7.2 kHz) (Fig. 2.13).These calls varied in form from down swept, and up swept, to frequency modulated. They were more readily audible to the human ear with the recording played at full speed. They were a less commonly detected call type (1%).

Fig. 2.13 Three LF whistles (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

HF Whistle The ‘high frequency (HF) whistle’ was a less commonly detected call (2%) that initiated in the upper frequencies of the audible range and were distinctly higher in frequency than the whistle type (12.5 – 21.6 kHz) (Fig. 2.14). The majority of detections were down swept however up swept and frequency modulated variations were also common.

Fig. 2.14 Two HF whistles (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

2.3.1.2 Pip

The ‘pip’ call type is a short (average duration of 12 ms) call that occurs in the mid frequency audible range (7.0 – 15.5 kHz). It initially increases and then decreases frequency rapidly over its short duration (Fig. 2.15). The waveform of the pips shows a gradual onset and decay with amplitude decaying cycles. These were a rare call (0.7%) that were detected in bouts with other call types or singularly.

B C C

Fig. 2.15 The pip call of A. pygmaeus (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the pip indicated by the red box on (B).

2.3.1.3 LF Pip

The ‘LF pip’ call is similar in structure to the ‘pip’ except that it occurs in the lower frequency audible range (2.7 – 3.7 kHz) (Fig. 2.16). The pips and LF pips were categorised based on their center and fundamental frequency. The spectral differences between the two call types can be seen in the binomial segregation of their center frequencies (Fig. 2.17). LF pips were the least common call type (0.3%), often heard in a bout of similar calls (up to 6 calls) resulting in a sound that could be described as a low chatter or squabble.

Fig. 2.16 A series of A. pygmaeus' LF pips with a LF chirp at the end (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

Fig. 2.17 Histogram demonstrating the binomial distribution of the center frequencies of the 'LF pip' and 'pip' call types.

2.3.2 Broadband Vocalisations

2.3.2.1 LF Chirp

The ‘LF chirp’ is similar in structure to the chirp, a broadband call with slight frequency modulation and visible sidebands (shown in Fig. 2.18C and D), with the difference being that the fundamental frequency of the call was in the lower frequency audible range (0.6 – 8.7 kHz) (Fig. 2.18). They were a relatively commonly detected call type (7%).

2.3.2.2 Chirp

The ‘chirp’ call type is a broadband call with slight frequency modulation (Fig. 2.19) and visible sidebands (shown in Fig. 2.19C). The chirps and LF chirps were categorised based on their fundamental and center frequency. The spectral differences between the two call types can be seen in the binomial segregation of their center frequencies (Fig. 2.20). Chirps were a frequently detected call type (7%) in the higher frequency audible range (7.6 – 18.2 kHz). They were often observed in bouts of multiple chirps (up to 12) as well as with other call types, but were also observed to occur singularly.

C D

Fig. 2.20 Histogram demonstrating the binomial distribution of the center frequencies of the 'LF chirp' and 'chirp' call types.

2.3.2.3 Hiss

‘Hisses’ were a regularly detected vocalisation (6%). These broadband calls spanned the audible range from 6.6 to 22.1 kHz (Fig. 2.21B) with an average duration of 130 ms. Some calls were short whereas others were quite long and drawn out, with a gradual onset and decay of the call (Fig. 2.21A). Hisses were produced singularly as wells as in bouts of multiple hisses (up to five).

Fig. 2.21 A. pygmaeus' hiss (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

2.3.2.4 Pulse

The ‘pulse’ call type is a very short (average duration of 1.6 ms) broadband call that spans the audible frequency range (0.9 – 21.5 kHz) (Fig. 2.22). Their waveforms are diamond shaped, with a rapid onset and equally rapid offset. They were a less commonly detected call type (1.8%) that were often vocalised in bouts of multiple pulses and as pulse packets.

Fig. 2.22 A series of pulses and pulse packets (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

2.3.2.5 Click

The ‘click’ is a short (average duration of 2.7 ms) broadband call that spans the audible frequency range (0.2 – 22 kHz) (Fig. 2.23). The defining difference between the pulse and the click call types was their duration and their amplitude, with pulses being shorter in duration and higher in amplitude. Clicks had amplitude of up to 60 u, which was one tenth of the amplitude of the pulse calls, which extended up to 400 u. Clicks were a commonly detected call type (9%) and were the most common of the broadband calls. They have a rapid onset and offset, with a waveform consisting of two diamond shaped pulses. They were often detected in bouts with other clicks (up to 7 calls) as well as with other call types.

Fig. 2.23 A series of A. pygmaeus' clicks (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

2.3.2.6 Toot

The ‘toot’ was a less commonly detected call type (3.5%) that spanned the entire audible frequency range (0.5 – 21.0 kHz) (Fig. 2.24B). They have a rapid onset and offset with a pulsed waveform (Fig. 2.24A). They are the longest of the broadband call types with an average duration of 6.9 ms. They were often detected in bouts with other toots (up to 4 calls) as well as with other call types. Of note were the sequences containing multiple toot calls that ended in a toot-whistle, a combination call that grew in intensity over the sequence.

A C

Fig. 2.24 A toot call (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points). (C) A zoomed in view of the toot shown in (B).

2.3.2.7 LF Click Train

The ‘LF click train’ is a broadband call that spans the lower half of the audible range with a frequency of 0.7 to 14.3 kHz (Fig. 2.25B). They are a very short call (1.8 ms) that were detected in packets of up to seven clicks with a rapid onset and offset (Fig. 2.25A). When the recording was played at full speed these packets sounded like a drawn out croaking. Only four of these click packets were detected in the 16 hour recording time, making them a rare call type (1%).

Fig. 2.25 A. pygmaeus’ LF click train (A) waveform and (B) spectrogram (sampling rate 44 kHz, hamming window, DFT size = 512 points).

2.3.3 Combination Vocalisations

2.3.3.1 Toot-Whistle

The toot-whistle was a combination call containing a broadband toot call immediately followed by a whistle (Fig. 2.26B). Some calls contained a HF down swept whistle, while others contained a frequency modulated whistle with a narrow frequency range. These calls had a rapid onset and a gradual decay (Fig. 2.26A). The toot-whistle combination call was a moderately common call type (1.8%) most often produced at the end of a bout of toot calls, though it was sometimes observed singularly. These calls were quite powerful, up to 52 dB average power, and were likely produced by individuals closest to the recorder, on or in the nest box.

2.3.3.2 Chirp-Whistle

The chirp-whistle was a combination call containing a LF chirp as well as a whistle of at least 9 kHz minimum frequency, and occasionally a middle chirp component (Fig. 2.27). The components were produced simultaneously as two- or three-part calls. While these were a less commonly detected call type (1.2%) they were most often produced in bouts of multiple calls of the same and different types.

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2.4 Discussion

I found that A. pygmaeus produces 10 call types within the audible frequency range. They had been described previously to produce only four different types of calls (Lindenmayer 2002), but the repertoire with a larger number of call types that I found in this study is anticipated for a social species like A. pygmaeus. Past reports of the vocalisations of A. pygmaeus were described using onomatopoeic descriptors without accompanying call characteristic data so it is difficult to make direct comparisons with the call types I have identified in this study. However, the clicks and pulses I describe here could be the ‘ticking’ and ‘popping’ and the whistles I have described could be the ‘pss- pss- pss-’ referred to by Biggins (1984) and Lindenmayer (2002). The calls produced by A. pygmaeus are complex; 3 call types were purely tonal, 5 were purely broadband, and 2 of the call types were combination calls consisting of both tonal and broadband spectral features within the same call. The greater repertoire size with highly complex call types of A. pygmaeus suggests that sociality in marsupials, along with primates (McComb and Semple 2005; Bouchet et al. 2013), rodents (Pollard and Blumstein 2012; Vanden Hole et al. 2014),bats (Wilkinson 2003), sciurids (Blumstein and Armitage 1997a), mongoose (Manser et al. 2014), parids (Freeberg 2006) and cetaceans (May-Collado et al. 2007a), may be an important driver of the vocal repertoire size as well as vocal complexity.

Combinatory calls allow an animal to increase the amount of information decoded in the vocalisation compared to that coded by the call types singularly (Crockford and Boesch 2005). The proposed functions of these combinatory calls include emotional state emphasis, syntactic modification, individual’s quality, or act as an intermediary between two alarm expressions (Crockford and Boesch 2005). However, combination or multi-part calls are problematic in acoustic repertoire studies as it is difficult to determine whether they are indeed a single call with multiple parts, or individual calls produced in quick succession or by multiple animals simultaneously. In the case of the ‘chirp-whistle’ there is the potential that multiple animals were vocalising at the same time given that there was more than one animal being recorded. However, with 27 detections of this call type, and the consistently simultaneous onset of the parts of the call, it is unlikely that these calls were being produced by multiple animals, but rather were produced by a single animal in a multi-part call. The ‘toot-whistle’ is slightly more problematic to categorise since the parts of the call are not produced simultaneously, but immediately following each other. It could be argued that this is a result of multiple animals vocalising in a call-response situation, or even a single animal producing two calls in quick succession. However, if that were the case there would be some interval between the two

components of the call, however slight, and in the instances observed in this study no interval was observed. The onset of the second component coincided with the offset of the first component. There were 40 detections of this call type and this lack of interval between call types was consistent among them. This call type often came at the end of a toot sequence. In their study of wolf vocal communication Schassburger (1993) demonstrated that combination calls acted as intensifiers of the context in which the individual components usually occur. I hypothesise that the ‘toot-whistle’ is used as the conclusion of toot calling bouts as a means of emphasis, though this is only speculation. I therefore conclude that this is indeed a combinatory call type produced by an individual animal, rather than multiple calls by an individual or multiple animals in quick succession.

When the center frequency of the calls was plotted by call type an interesting pattern emerged. With the exception of some of the broadband calls, the calls produced by A. pygmaeus rarely had center frequencies in the 6-8 kHz range (Fig. 2.4, Fig. 2.28). The hearing sensitivity of A. pygmaeus drops to approximately 50 dB at 8 kHz (Aitkin 2012) (Fig. 2.29) although based on the 60 dB criteria used by mammal hearing biologists, they are probably capable of hearing at that frequency. This drop in hearing sensitivity could explain why they avoid producing calls at this frequency. Their hearing sensitivity increases again at frequencies beyond 8 kHz up until the mid-20 kHz range at which point their sensitivity to higher frequencies decreases (Fig. 2.29). This corresponds with most of the audible tonal calls produced having center frequencies between 1 to 5 kHz and 8 to 16 kHz (Fig. 2.4).

A rise in vocal activity by A. pygmaeus at 9 am and 7 pm during this recording session (Fig. 2.7) corresponds with the simulated dusk and dawn within the enclosure. In their study radio- tracking wild A. pygmaeus Johnston and Shaw (2000) observed individuals leaving the nest shortly after dusk and returning as dawn approached. As the recording apparatus was positioned on top of the nest box, it was the opportune position for capturing vocal activity in and around the box. This increase in activity provides preliminary evidence that A. pygmaeus are most vocal when leaving and arriving at the nest. However as I only have 16 hours of recording data additional recordings need to be carried in order to make conclusive inferences about the diel vocal activity of the species.

Fig. 2.28 Histogram showing the binomial split in center frequencies of A. pygmaeus' calls. Green bars represent tonal call types, and orange bars represent broadband call types.

Fig. 2.29 Auditory Brainstem Response (ABR) audiogram of A. pygmaeus (Adapted from Aitkin (2012).

I show that A. pygmaeus produces both tonal and broadband call types in the audible frequency range of 0.6 to 22.1 kHz. The maximum audible range frequency limit of 22 kHz is slightly higher than you would expect based on the frequency limits of similarly sized mammals such as Reithrodontomys mexicanus (13.3 kHz) and Reithrodontomys fulvescens (13.5 kHz) (Miller and Engstrom 2010). However, the frequency limit I describe also coincides with the upper limit of the recording apparatus. This is one of the reasons I examined whether A. pygmaeus produces ultrasonic vocalisations in Chapter 5 in a separate study.

I have demonstrated here that A. pygmaeus produces a diverse repertoire of call types typical of a highly social species. It should be noted that this repertoire was developed from sixteen (16) hours of recordings, and it is possible that this is not the full repertoire of the species, and that additional call types have yet to be described. A number of A. pygmaeus’ call types are highly complex in form, a trait also associated with social species. Further to that, I show that they match the frequencies at which they produce vocalisations with their hearing sensitivities. The high maximum frequency I have observed for the audible vocal repertoire of A. pygmaeus appears to be representative of their very small body size. However I will explore the concept of body size allometry in mammals in greater detail in Chapter 3 in order to determine whether A. pygmaeus does in fact produce vocalisations at the frequencies to be expected for a mammal of their size.

Chapter 3 Does size matter? Examining the drivers of mammalian vocalisations

This chapter has been published in the journal Evolution as: Martin, K., Tucker, M.A., Rogers, T.L., 2017. Does size matter? Examining the drivers of mammalian vocalisations. Evolution 71(2), 249-260.

K.M. and T.L.R. conceived the study. K.M. collected and compiled the data. K.M. conducted the analyses with assistance and guidance from M.A.T. K.M. wrote up the study with editing and advice from T.L.R. and M.A.T.

3.1 Introduction

The movement of mammals back into the water over 45 million years ago has prompted many questions from evolutionary biologists. Perhaps the most important of which is, what changes occurred with this drastic change in environment? Most comparative studies of mammalian acoustic evolution focus on either terrestrial or aquatic species. Thus far there has been no comparison of mammals as a whole to determine if this monumental change has driven acoustic communication in as yet undefined ways.

Acoustic signals are critical to the survival success of many species, particularly when it comes to communication, breeding, migration and location of prey. There is long standing debate over the evolutionary drivers of acoustic signals in mammals, with two dominant theories; the physical constraint of body size, or the environment that an animal lives within, that has driven the divergence of vocalisation frequencies.

One theory that has been extensively explored is that body size, or body mass, is a driver of the evolution of acoustic signalling in mammals (Fitch 2000; Reby and McComb 2003; Fletcher 2004), as the larger an animal’s size, the lower the vocalisation frequency it can produce. The effect of body mass on vocalisations has been studied in the form of body mass-frequency allometry, and has long been argued to be the main evolutionary driver behind acoustic signals (Ryan and Brenowitz 1985; Laiolo and Rolando 2003). This is denoted by the body scaling rule originally proposed by Bradbury and Vehrencamp (1998a):

푓 ∝ 푀−0.33 (1) (Bradbury and Vehrencamp 1998a)

Where f is frequency and M is body mass. This relationship arises because body size limits the size of an animal’s sound producing organs such as the length of the vocal tract, affecting vocalisation frequencies and the formants (Ryan and Brenowitz 1985; Peters and Peters 2010). A second hypothesis, which rivals the body size theory, argues that the environment is the dominant evolutionary driver of vocalisation abilities in mammals (Morton 1975; Wiley and Richards 1978; Kime et al. 2000; Saunders and Slotow 2004; Peters and Peters 2010). The Acoustic Adaptation Hypothesis (AAH) explains that within an environment there are many objects that obstruct the path of signals between communicating animals (Morton 1975). These differences within a species’ environment, such as different densities of vegetation, can cause a species to adopt a different vocalisation frequency to optimise its success (Peters and Peters 2010). It has been proposed that in order to maximise communication distance, mammals would use an optimal frequency that accounts for absorption in air (Fletcher 2004) derived as:

푓 ∝ 푀−0.4 (2) (Fletcher 2004)

The movement of mammals back to the water from the terrestrial environment is of great interest in terms of evolutionary change. The issue of acoustic coupling in the water, and the increase in pressure has resulted in aquatic and semi-aquatic species evolving a number of strategies, residual modifications from diving adaptations to cope with the change in pressure and retention of oxygen (Reidenberg and Laitman 2010), to effectively produce sound underwater (Fig. 3.1). Sound absorption in water is less than that in air, and it is therefore

likely that the optimal frequency needed to maximise communication distance would be altered. This shift in frequency can be predicted by:

푓 ∝ 푀−0.6 (3) (Fletcher 2004)

A B

C D E

In addition aquatic mammals have some of the greatest body masses, up to 150,000 kg, allowing us to see how the transition and subsequent increase in body mass has influenced vocalisations.

While environment and body mass as potential drivers are well established and highly successful hypotheses, there are a number of less developed hypotheses that have arisen recently. As vocalisation has been said to function in the maintenance of group cohesion, sociality has been explored in a number of mammalian species, largely from the aquatic environment (May-Collado et al. 2007a) but also in non-human primates (Ramsier et al.

2012a). It is suggested that species that live in larger groups have higher minimum frequencies of vocalisations, whereas non-social species have lower minimum frequencies due to the increased distances over which they communicate (May-Collado et al. 2007a). Low frequency sound waves are characterised by longer wavelengths than those of high frequency, and can travel greater distances (Richardson et al. 1995) with less absorption or scattering by particles as they travel through the environment, and thus would be more suitable for use by non-social species and species that are widely distributed. In addition, high frequencies are typical of alarm calls, allowing them to be heard over low-frequency ambient noise. Since larger social group size is often used to deter predators, larger groups would find cause to utilise these predator-specific alarm calls more frequently, resulting in a relationship between the increase in social group size and high-frequency vocalisations (Ramsier et al. 2012a). The Dolphin Hypothesis (Herman and Tavolga 1980) also suggests that high-frequency whistles evolved in concert with sociality in the delphinids, however high-frequency whistles have also been found in other groups of non-social aquatic mammalian species.

I re-examined the possible drivers of vocalisations in mammals. Recent technological advancement has seen an increase in the amount of data collected on aquatic mammals’ vocalisations, allowing for a more robust and comprehensive comparison of terrestrial and aquatic species. Using minimum and maximum vocalisation frequencies obtained from the literature, I investigated the strength of potential drivers in the evolution of mammalian vocalisation. Using species from across a broad group of over 190 mammalian species I re- examined the influence of environment and body mass and also consider the more novel driver of sociality. As body size is a known dominant driver of terrestrial vocalisation, body size was considered to be a fixed component in all the models. I propose three hypotheses based on the drivers proposed: 1) mass: a negative relationship between body mass and frequency, such that larger species produce lower frequencies; 2) environment: aquatic mammals will produce lower frequencies than terrestrial species to account for the larger distances over which they communicate, and semi-aquatic species will be intermediate between the two; 3) and sociality: social species will produce higher frequencies due to their close proximity to conspecifics and solitary species will use lower frequencies as they degrade less quickly and are able to propagate further.

3.2 Methods

3.2.1 Database

A database was collated of vocalisation data (minimum and maximum frequency), measured in kilohertz (kHz), for all available mammalian species from the literature (Appendix 1). Searches were carried out using the Web of Science, Scopus and Google Scholar databases. Searches included variations of the terms: acoustic, acoustic repertoire, mammal vocalisation/vocalization, vocal, vocal communication, vocal repertoire. The majority of results were repertoire studies; however some were studies of a single call type (21 studies of single call type and these were all contact calls). The full data set is available on the Dryad data repository (https://doi.org/10.5061/dryad.289kh; See Appendix 1 for full dataset).

The literature was examined to obtain the complete vocal repertoire for each species from both sonograms and tabulated data. From the vocal repertoire I identified the signal with the lowest, and the signal with the highest peak frequency. Using peak frequency resulted in the exclusion of any lower or higher energy components (formants, higher harmonics etc.), reducing the error associated with selective frequency shift. Frequency shift can result from: (1) variable distance to the calling animal from the receiving system. A product of this is the loss of high frequency components due to propagation loss, such as in dense vegetation, distortion of the signal etc.; (2) recording quality due to the frequency response limitations of the receiving system e.g. clipping low or high frequency components; and (3) the settings used for the analysis in the production of sonograms. The peak frequency remains unchanged. By minimising these sources of error it is unlikely that they will add bias to the results. For many species there was insufficient data to identify the behavioural context in which the signals are produced, particularly in forest dwelling primate species and aquatic species. Only vocalisations produced by adults were included as juveniles are known to produce higher frequencies. Both males and females were included. The majority of studies reported on vocalisations were from both genders; however of those that reported the gender of the animals, only 10 studies focused on a single gender, usually male. Vocalisation minimum frequency consisted of 170 species and maximum frequency 189 species.

The traits of body mass, environment (physical habitat), and sociality were collected for all mammalian species in the database. Body mass data was obtained from the PanTHERIA database (Jones et al. 2009), and from the published literature. Environment was categorised

as one of three categorical variables (terrestrial, semi-aquatic, and aquatic), grouping all habitat types into one of these broad inclusive categories. Aquatic mammals were defined as a species that relies upon an aquatic environment for any combination of breeding, locomotion and/or feeding, and those species that rely on both the water and the land were categorised as ‘semi-aquatic’. Sociality of species was defined to be either solitary or social, where social species lived in groups of three or more, such that solitary mother-offspring and mating pairs which were not part of a larger colony or group were not considered social. Sociality was determined from social group size and population density data from the PanTHERIA database, and also from the literature.

3.2.2 Phylogeny Construction

The species for each of the two frequency limits were compiled into phylogenetic trees to be used in a phylogenetic generalised least squares (PGLS) analysis (Freckleton et al. 2002), to account for phylogenetic relatedness that could confound variation in vocalisation frequencies (Laiolo and Rolando 2003). The mammalian supertree (Faurby and Svenning 2015) was pruned in R ver. 3.0.1 to include only the species for which data was available for each frequency limit. This tree included one thousand iterations to resolve any polytomies that may have been present. A supplementary analysis was carried out using a further pruned tree which excluded species from the original supertree that had had their time of divergence interpolated. This resulted in a tree containing 159 and 174 species for minimum and maximum frequency respectively.

3.2.3 Analysis

A model selection approach was applied to test for the suitability of models to explain the evolution of the two vocalisation frequency limits. The body mass and vocalisation frequency data was log10 transformed in order to conform to the assumptions of normality of the analysis. Stepwise regression was carried out using backward elimination in the MASS package (Venables and Ripley 2002) in R (ver 3.0.1) starting from an additive model of all variables, to determine which variable to drop in successive models. The models tested compared vocalisation limits (minimum frequency and maximum frequency) with the following models: a

Null model (훽0), a Body Mass model to show the effect of body mass on frequency (훽0 +

훽푚푎푠푠), two Environment models, one to test for different slopes of environments (훽0 +

훽푚푎푠푠 ∗ 훽푒푛푣𝑖푟표푛푚푒푛푡), and one to test for a uniform slope with different intercepts, used to determine the contribution of environment (훽0 + 훽푚푎푠푠 + 훽푒푛푣𝑖푟표푛푚푒푛푡), and an additive model with all three variables (훽0 + 훽푚푎푠푠 + 훽푒푛푣𝑖푟표푛푚푒푛푡 + 훽푠표푐𝑖푎푙𝑖푡푦).The caper package (Orme et al. 2013) in R ver. 3.0.1 was used to carry out PGLS analyses and calculate Akaike’s Information Criterion (with a correction for sample size; AICc) for each model.

The percent of variation explained by each model was calculated from the model’s r2 value. r2 values were also used to calculate the contribution of the individual parameters. Starting with the mass model, its r2 indicates the contribution of mass, and then the contributions of the other variables were calculated by subtracting the r2 value of the next additive model from the one before. For example, the contribution of sociality was calculated by subtracting the r2 of

2 the 훽0 + 훽푚푎푠푠 + 훽푒푛푣𝑖푟표푛푚푒푛푡 model from the r of the 훽0 + 훽푚푎푠푠 + 훽푒푛푣𝑖푟표푛푚푒푛푡 +

훽푠표푐𝑖푎푙𝑖푡푦 model (Table 3.1).

Table 3.1 Calculation of parameter contributions from r2 values for the minimum and maximum frequency limit.

Model r2 Parameter Contribution

Minimum Frequency

0+mass 0.18 Mass = 18%

0+mass+environment 0.33 Environment = 33-18 = 15%

0+mass +environment +sociality 0.33 Sociality = 33-33 = <1% Maximum Frequency

0+mass 0.08 Mass = 8%

0+mass+environment 0.33 Environment = 33-8 = 25%

0+mass +environment +sociality 0.33 Sociality = 33-33 = <1%

The model with the lowest AICc is representative of the model with the highest support, though models within two units (∆AICci < 2) of the lowest model are also considered to have substantial support (Mazerolle 2004). Akaike weights were calculated using the formula:

1 푒푥푝{− ∆ (퐴퐼퐶푐)} 2 푖 푤𝑖(퐴퐼퐶푐) = 1 (4) ∑푘 푒푥푝{− ∆ (퐴퐼퐶푐)} 푘=1 2 푘 (Akaike 1978; Wagenmakers and Farrell 2004)

Where i is the candidate model and k represents all the plausible candidate models. The conditional probability that the model with the lowest AICc is more likely to be the best model was also calculated using the formula:

푤 (퐴퐼퐶푐) 푖 (5) 푤푗(퐴퐼퐶푐) (Wagenmakers and Farrell 2004)

Where i is the best fitting model and j is the candidate model being compared. In this case this was the second best fitting model.

The PGLS analysis also produced the parameter lambda (), representing the influence of phylogeny on the accumulation of changes along branches over time.  approaching 0 indicates variation in the trait being studied is less similar between species than would be expected from their relatedness and therefore independent of phylogeny, whereas  approaching 1 assumes these accumulated changes are linked to phylogenetic relatedness, known as a Brownian Motion model of evolution (Pagel 1999; Freckleton et al. 2002).

The slopes produced by the PGLS analysis for the environmental groups “terrestrial” and “aquatic” were compared to the slope predicted by the Body Size Rule and the environment- propagation-adjusted slope proposed by (Fletcher 2004) using a pairs analysis in the lsmeans (Lenth 2015) package of R.

3.3 Results

3.3.1 Minimum Frequency

The Mass + Environment model had the lowest AICc value (Table 3.2), making it the best supported model for describing the driver of the minimum frequency of vocalisations. The model accounted for 33% of variance (r = 0.57). From Akaike weights the Mass + Environment model was shown to be 2.9 times more likely to be the best model than the next ranked model. An Akaike weight of 0.66 suggested little model selection uncertainty. In addition, the remaining models had ∆AICc values > 2, thus proving less likely to be the driver of minimum vocalisation frequency. Contributions were determined using r2 values from the additive models. Mass had the highest contribution at 18% (r = 0.42), with a 15% contribution by environment (r = 0.57), and <1% by sociality (r = 0.57). approached the upper limit for the Mass + Environment model, which suggests a Brownian Motion model of evolution.

95% CI of slope CI Weighted Effect Model ∆AICc parameter PGLS  (Lower, AICc size (r) (Lower, Upper) Upper)

0+mass+environment 0.00 0.6644 -0.50, -0.32 0.53 0.53, 0.54 0.57  + + 0 mass environment 2.13 0.2290 -0.50, -0.32 0.53 0.53, 0.54 0.57 +sociality -0.18, 0.25 (T)

0+mass*environment 4.09 0.0860 -0.34, 0.51 (S) 0.51 0.50, 0.51 0.59 -0.63, -0.26 (A)

0+mass 7.56 0.0152 -0.42, -0.21 0.79 0.79, 0.79 0.42

0 33.98 0.0000 - 0.89 0.89, 0.90 -

T is terrestrial, S is semi-aquatic and A is aquatic.

Terrestrial, semi-aquatic and aquatic mammals have the same trend (slope = -0.41 ± 0.05) of minimum frequency in vocalisations by body mass (Fig. 3.2). Mammals in the aquatic environment have on average higher minimum frequencies (intercept = 0.93) in their

vocalisations than semi-aquatic species (intercept = 0.06), which in turn are higher than those of terrestrial mammals (intercept = -0.21).

Fig. 3.2 Vocalisation minimum frequency as a function of species body mass for terrestrial (n = 105),

semi-aquatic (n = 23) and aquatic (n = 42) environments on a log10 scale. The dotted line represents the

phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (log10(Y) = -

0.41log10(X)-0.21) (CI -0.50, -0.32), the dash-dot line for semi-aquatic mammals (log10(Y) = -

0.41log10(X)+0.06) (CI -0.50, -0.32) and the solid line for aquatic mammals (log10(Y) = -0.41log10(X)+0.93) (CI -0.50, -0.32). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org.

3.3.2 Maximum Frequency

Mass * Environment was the best supported model, calculated from Akaike weights to be 10.5 times more likely to be the driver of maximum frequency in the vocalisations of mammals (Table 3.3) than the next ranking model, and accounting for 53% of variance (r = 0.73). An Akaike weight of 0.88 suggested substantial model selection certainty. All other candidate models possessed ∆AICc values > 2 and Akaike weights <0.1, suggesting these models are unlikely to drive the differences in high vocalisation frequency thresholds among mammals, as was supported by their Akaike’s weights (Table 3.3). Environment contributed 25% (r = 0.58), mass contributed 8% (r = 0.29), and sociality <1% (r = 0.57). The  value of the

Mass * Environment model was on the lower bound of zero suggesting the observed values evolved independently of phylogeny.

95% CI of slope 95% CI Weighted Effect Model ∆AICc parameter PGLS  (Lower, AICc size (r) (Lower, Upper) Upper) -0.34, -0.06 (T)

0+mass*environment 0.00 0.8846 -0.28, 0.45 (S) 0.00 0.00, 0.00 0.73 -0.31, -0.06 (A)

0+mass+environment 4.70 0.0844 -0.36, -0.24 0.23 0.23, 0.23 0.58  + + 0 mass environment 6.70 0.0310 -0.36, -0.24 0.24 0.24, 0.24 0.57  sociality

0+mass 26.05 0.0000 -0.24, -0.08 0.66 0.66, 0.67 0.29

0 37.89 0.0000 - 0.77 0.77, 0.77 -

T is terrestrial, S is semi-aquatic and A is aquatic.

Terrestrial, semi-aquatic and aquatic mammals have very different trends of maximum frequencies in their vocalisations as a function of body mass (Fig. 3.3). Terrestrial species have a stronger relationship (slope = -0.38 ± 0.07, intercept = 0.98) between frequency and mass than semi-aquatic (slope = -0.18 ± 0.19, intercept = 1.13) and aquatic species (slope = -0.18 ± 0.06, intercept = 1.63).

Fig. 3.3 Vocalisation maximum frequency as a function of species body mass for terrestrial (n = 125),

semi-aquatic (n = 23) and aquatic (n = 41) environments on a log10 scale. The dotted line represents the

phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (log10(Y) = -

0.38log10(X)+0.98) (CI -0.34, -0.06), the dash-dot line for semi-aquatic mammals (log10(Y) = -

0.18log10(X)+1.13) (CI -0.28, 0.45) and the solid line for aquatic mammals (log10(Y) = -0.18log10(X)+1.63) (CI -0.31, -0.06). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org.

3.3.3 Removal of Interpolated Species

The removal of interpolated species from the composite tree resulted in similar lambda values for all models for minimum frequency (Appendix 2). For maximum frequency, the removal of these species resulted in a larger lambda value for the top three models, while the others remained similar (Appendix 2).

3.3.4 Theoretical versus Empirical Slopes

For the minimum frequency limit the terrestrial empirical slope was the only one to show similarities with the environment-adjusted body size theoretical slope, but it was not significantly similar to the body size rule slope (Table 3.4). The empirical slope for the aquatic species was significantly different to both the body size rule and the environment-adjusted

body size theoretical slope (Table 3.4). A similar result was found for the maximum frequency limit slopes. The only significantly similar slopes were those of the empirical terrestrial data with the environment-adjusted body size theoretical slope (Table 3.5). All other empirical data slopes were significantly different to the theoretical slopes (Table 3.5).

Terrestrial Aquatic

Empirical Theoretical Empirical Theoretical Body Size Rule <0.001 <0.001 <0.001 <0.001 Empirical 0.686 Terrestrial Theoretical Empirical <0.001 Aquatic Theoretical

Terrestrial Aquatic

Empirical Theoretical Empirical Theoretical Body Size Rule <0.001 <0.001 <0.001 <0.001 Empirical 0.122 Terrestrial Theoretical Empirical 0.007 Aquatic Theoretical

3.4 Discussion

This is the first study to examine and compare the evolutionary drivers of the vocalisations of mammals of both terrestrial and aquatic species. For both minimum and maximum frequency limits of vocalisation, body mass and environment together consistently best described the evolution of vocalisation in mammals, performing better than either body mass or environment alone. These results demonstrate that both body mass and environment contributed in varying degrees to the evolution of vocalisations.

Mammals returned to the water from the land in five extant groups belonging to three orders (Carnivora, Cetartiodactyla, and Sirenia). In-air vocalisers face different challenges than those communicating underwater. Terrestrial species must overcome interference caused by vegetation density, weather, temperature barriers etc., while aquatic species compete with inhomogeneities such as salinity and temperature discontinuities, underwater landscapes and bathymetry (Urick 1982). On top of these factors, sound travels differently through air and water, and these differences are taken into account when adjusting to the challenges faced in the communication medium, to maximise the success of signal transmission. Aquatic mammals have shifted to use higher frequency vocalisations compared to terrestrial mammals of similar body mass. This is most likely due to the beneficial propagation properties of water.

3.4.1 Body Mass

I found that the minimum frequency of vocalisations of mammals in all three environments retained the negative relationship with body mass; with the minimum frequency limits of vocalizations of aquatic mammals being higher in frequency than those of terrestrial mammals of a similar body mass. The resulting slope (-0.41) is more similar to the -0.4 slope proposed by Fletcher (2004) which takes into account environmental factors, than the slope predicted by the body scaling rule (-0.33) proposed by Bradbury and Vehrencamp (1998a) (Fig. 3.4A).

As was the case for the minimum frequency limits of vocalisations, mammals of the aquatic environment demonstrated higher maximum frequency limits. The results confirmed the negative relationship between body mass and maximum frequencies in vocalisation for terrestrial and aquatic species, though this was less pronounced in the aquatic species. However, linear regression analysis found this relationship was not significant for the semi- aquatic species. Therefore, while terrestrial species still retain the influence of body mass, semi-aquatic and aquatic species are not as restricted in their maximum frequency limits of vocalisation. Terrestrial species had a slope (-0.38) similar (Table 3.4; Table 3.5) to that predicted by Fletcher (2004) (-0.4), while aquatic species presented a slope (-0.18) different from that proposed by both Fletcher (2004) (-0.6) and the body scaling rule (Fig. 3.4B, D; Table 3.4; Table 3.5). The results show that the incorporation of allowances for a species’ environment into the pre-existing body mass models is an appropriate approach for minimum and maximum frequency limits for terrestrial mammal species. In the case of aquatic and semi- aquatic species environment has an even greater influence on the frequencies produced than

previously predicted. It is also evident that body mass does not have as powerful an influence on mammals from these environments as was previously believed.

The retention of the relationship between body mass and minimum frequency limit was expected (Fletcher 2004; Peters and Peters 2010). To produce low minimum frequency vocalisations animals require a sound-production system proportional in size in order to produce the signal. Correlations have been found between body size (mass) and the length of the vocal tract trachea as it influences the formant frequencies of the vocalisations (Reby and McComb 2003), as well as the skull morphology and palate length (Fitch 2000). Therefore large mammals have large vocalising organs and can produce lower frequency vocalisations than smaller animals (Huang et al. 2000). This relationship holds true for the minimum frequencies of mammals from all three environments and supports the findings of previous studies (Hauser 1993; May-Collado et al. 2007a). However, for the maximum frequency limits of vocalisations body mass is a weaker predictor, attributing just 8% of variance, down from 18% for minimum frequency. This is not unexpected. While body size is a limiting factor for minimum frequencies, the same size relationships do not apply for producing maximum frequencies and mammals have developed other strategies for producing higher frequency sounds, which can be implemented by a relatively small area or apparatus in relation to body size. For example, the superfast-moving laryngeal muscles of echolocating bats (Elemans et al. 2011), and the production of high-frequencies through a system involving phonic lips and the melon in some odontocetes (toothed whales) (Au and Hastings 2008).

3.4.2 Environment

The results newly highlight the importance of environment in driving the frequency limits of vocalisations in mammals. The concurrence of my empirical slopes with the theoretical slopes provided by Fletcher (2004) for terrestrial species offers support to my conclusion that it is an important combination of both body mass and environment that drive these frequency limits (Fig. 3.4A, C). However, it is the complete deviation of aquatic species from either the body mass rule (Bradbury and Vehrencamp 1998a) or the environment corrected slope (Fletcher 2004) that accentuates the power of a species’ environment in driving their vocalisation frequency limits (Fig. 3.4B, D).

As a general pattern aquatic and semi-aquatic mammals use higher frequency vocalisations than terrestrial mammals both at the minimum and maximum limits of their repertoires. This resulted in species of similar body mass producing frequencies some 10 kHz apart. For example, in the terrestrial environment Panthera tigris weighs in at 162 kg and has a frequency range of 0.1 – 10 kHz, whereas the aquatic Stenella coeruleoalba has a mean mass of 142 kg and produces frequencies of 6 – 24 kHz. Sound travels faster through water than through air (approximately five times faster), although propagation speed and distance are affected by local characteristics such as bathymetry, substrate and the presence of boundary layers (Urick 1982; Au and Hastings 2008). This means that wavelengths of sound travelling through water are shorter than expected (Madsen and Surlykke 2013). The result is that higher frequency sounds can travel further through water in the aquatic environment with less loss of acoustic energy than they would be able to in the terrestrial environment (Forrest 1994). This phenomenon is accentuated in the Arctic and Antarctic regions, where the water column below the surface is comprised of an isothermal layer which normally occurs at deep abyssal depths, and sound velocity is increased (Urick 1982). This means higher frequency sounds can travel faster and therefor further in the waters near the surface where many pinniped and cetacean species spend much of their time. The loss of acoustic energy due to absorption increases with the frequency of the call and this loss occurs at a lower level in water than in air. Thus, the aquatic species are able to utilise higher frequencies than the terrestrial species of similar body mass are.

In terrestrial landscapes vocalisations behave in a 3-dimensional pattern of propagation (Fig. 3.5). The troposphere extends to approximately 10 km above ground level, while a terrestrial mammal call generally only travels up to 1 km from the source, with elephant calls travelling as far as 2.5 km (McComb et al. 2003). With the depth of the environment extending further than the distance of a call, the sound propagates in a spherical manner. Sound propagation in the aquatic environment however, takes on a 2-dimensional cylindrical property (Fletcher 2004) (Fig. 3.5). Ocean depth is 4 km on average, with most aquatic mammal species occupying areas of 1 km depth or less. The vocalisations of aquatic species therefore travel greater distances, up to 1000 km (blue whale (Balaenoptera musculus)), than the depth of their environment, and hence propagate in a cylindrical manner (Richardson et al. 1995). Most vocalisations aren’t able to propagate all the way to the ocean floor due to temperature and salinity profiles within the water column. A surface duct is often present in the layer just below the ocean surface where sound is ‘trapped’, bound by the ocean surface and the lower boundary of the duct in a cylindrical propagation path (Urick 1982). Sound

attenuates more rapidly with distance with spherical (terrestrial environments) rather than cylindrical (aquatic environments) spreading (Richardson et al. 1995; Fletcher 2004), hence the increased propagation of sounds and higher frequencies produced by species vocalising in the aquatic environment compared with the terrestrial environment.

It is possible that the demarcation resulting from the shift in semi-aquatic species is responsible for the boost in the contribution of the environment model. It may also be responsible for the decrease in influence from phylogeny. For minimum frequency, pinnipeds were situated with their terrestrial cousins in the Carnivora, whereas they produce more similar maximum frequencies to the true aquatic species, such that the influence of environment is stronger than that of phylogeny. Pinnipeds, as an amphibious group, require a communication system that is effective both in air and underwater. The pinnipeds were situated within the terrestrial data points and for the majority, fit to the terrestrial trend line (Fig. 3.6A). The three aquatic otter species are similarly nested within the terrestrial data points. These species vocalise in air in a similar manner to terrestrial species. This fits with the amphibious lifestyle of these two groups, inhabiting both the land and water. For the pinnipeds, there was no discernible difference between the frequencies produced by phocids (true seals), of which most species communicate underwater, and otariids (eared seals),

generally in-air communicators. These groups appear to act as a functional intermediate between the terrestrial and true aquatic species at low frequencies.

Where the pinnipeds and aquatic mustelids were positioned more with terrestrial mammals for their minimum frequencies, their maximum frequencies told a very different story. The otariids remained close to the terrestrial species, while the phocids showed much higher frequencies, clearly breaking away from the terrestrial species and displaying frequencies similar to the odontocetes (Fig. 3.6B). This begs the question, why do these two groups behave so similarly in their minimum frequencies and yet produce vastly different maximum frequencies of vocalisations? While phocids spend more time underwater than otariids, their amphibious lifestyle means that even they must return to land (Schusterman et al. 2004). Studies have shown that while pinniped ears were indeed an intermediate between terrestrial and fully aquatic ears, otariid ears were primarily air-adapted, while phocids demonstrated an affinity to underwater hearing (Kastak and Schusterman 1998). Now, examining vocalisations, a similar pattern is emerging. While it may be energetically easier to produce higher frequencies, those higher frequencies are less useful in a terrestrial environment as they attenuate and lose information more rapidly. The terrestrial-like minimum frequencies produced by both pinniped groups are therefore likely to be a consequence of needing to transfer information over long distances and in noisy conditions whilst on land (colonial breeding, social aggregations, locating mates/offspring). Otariids, spending the majority of their time on land, are more constrained by the acoustic propagation properties of the terrestrial environment for their maximum frequency limits of vocalisations as well. However, the majority of phocids vocalise underwater. It is probable that they have adapted to utilise the propagation efficiency of the aquatic environment in a similar fashion to the fully aquatic cetacean species. Some Arctic (Halichoerus grypus (Asselin et al. 1993), Phoca vitulina (Renouf et al. 1980), Pagophilus groenlandicus (Richardson et al. 1995), Pusa hispida (Cummings et al. 1984)), and Antarctic (Hydrurga leptonyx (Thomas et al. 1983a), Leptonychotes weddellii (Thomas and Stirling 1983)) phocid species have been shown to produce maximum frequencies in the same range as echolocating odontocetes (for example, the leopard seal is capable of producing frequencies up to 60 kHz (Thomas et al. 1983b)). While the debate as to whether pinnipeds do or do not echolocate has been mostly closed, these results suggest it may be appropriate to re-examine the acoustic behaviours of this group, particularly with an ultrasonic focus.

3.4.3 Sociality

The results of the stepwise regression showed that sociality became less important as a driver of the maximum frequencies produced by mammals. It has been suggested that the vocalisation abilities of a species are in large part focused on optimising maximum long- distance communication (Wiley and Richards 1978; Kime et al. 2000; Fletcher 2004; Saunders and Slotow 2004; Peters and Peters 2010). Since low frequencies are characteristic of long- distance calls, and solitary species are more likely to require their vocalisations to travel greater distances than a social species, it may be expected that there would be some difference in the degree to which species of different levels of sociality may need to exploit the long-ranging characteristics of these frequencies.

However, not all animals use their vocalisations over the maximum distances possible. Species may employ different strategic uses of frequencies depending on the contextual demands (motivation for the use of a particular sound (Morton 1977)) and the influences of their life history. For example, social or contact calls are often used by both social and solitary species. These calls do not need to travel far and are therefore characteristically of high frequency. It would therefore not be expected that there would be a great amount of difference between the two groups’ maximum frequencies, as is portrayed in the results by the sociality variable being the first to be dropped from the additive models. It has thus been suggested that a focus should perhaps be put on features correlated with spacing between signaller and receiver (Wiley and Richards 1978; Peters and Peters 2010).

For both minimum and maximum frequency vocalisation limits environment and body mass only accounted for 33% and 53% of variance respectively, leaving a large amount of variance as yet unexplained by any of the drivers that were examined, with the potential for further studies of other contributors. Previous studies (Saunders and Slotow 2004) have suggested that long- and short-range communication is determined by differing ecologies (mating systems and territory size). It is possible that such ecologies are in part responsible for the remaining 50-70% of variance.

Background noise in both the terrestrial and aquatic environments has been increasing over the past few decades, largely due to anthropogenic activities. Background noise is a major contributor to degradation of sounds in the environment, which in turn can lead to inability of a receiver to recognise or receive a signal (Kime et al. 2000). Echolocating species that rely on

sound for feeding and navigation are one example of a group of species highly susceptible to impacts from the increase in anthropogenic noise due to their heavy reliance on acoustic communication. As a result of a receiver’s inability to distinguish calls from conspecifics amongst the background noise, the broadcaster will need to alter their vocalisations or behaviours so as to optimise transmission of the signals in their environment (Saunders and Slotow 2004).

There are some caveats involved with this type of study, for example the wide variation in the types of calls recorded (Fletcher 2004), with no dataset of a particular call type large enough for any significant comparison and analysis. In order to compare acoustic signals of vocalisations across mammals it was sometimes required to incorporate calls where the context of the calls was sometimes unknown. This is particularly the case for aquatic mammals where the intention of the signaller cannot be determined.

3.5 Conclusion

This study has revealed a number of novel patterns in the vocalisation abilities of mammals that have not previously been explored. Notably, my results highlight the importance of environment in combination with body mass in driving the acoustic communication of mammals. The evolution of vocalisation frequencies in aquatic mammals has clearly diverged from terrestrial mammals as they returned to the water. The addition of aquatic species to comparative studies allows us to examine the effect of an extreme shift in environment and size, such as that of moving from the land to the water. The comparison of the amphibious pinnipeds to fully terrestrial and aquatic mammals has accentuated their role as an intermediary group. In addition, comparing the two groups of pinnipeds and their use of vocalisations has highlighted the difference in propagation properties of the two environments and how mammals have evolved to utilise these properties to their advantage.

Acoustic signals are complex traits that have been shaped by a variety of intrinsic (e.g. morphology or physiology) and extrinsic factors (e.g. environment). Understanding what has shaped the acoustic features of animal vocalisation is important for understanding how it may change into the future in response to changing niches and roles, and shifts occurring in their environments. Studies such as this one, which highlight the difference in vocal behaviours of mammals, are important for identifying potential impacts from these changing niches and roles, and aiding in the development of measures to minimise any negative impacts.

Chapter 4 Do you hear what I hear? A comparison of the drivers of hearing limits in mammals

4.1 Introduction

Hearing is an important ability of vertebrates with many purposes and uses; intra- and interspecies communication, gaining information about the environment, auditory scene analysis, and sound source localisation. Mammals are an ideal species grouping for comparative analysis of acoustic communication due to the large range of variation in multiple factors of their life history; their body size spans a 9-fold range from tiny microbats (0.002kg; Surlykke et al. (1993)) to gigantic blue whales (150,000kg; Lockyer (1976)), and inhabit both land and water, making them an interesting taxonomic group for comparative investigations. However, comparative studies of mammalian acoustic communication have largely focused on the vocalising abilities of species, and hearing has remained a less studied subject. To date, comparative studies of hearing have focused on comparing hearing frequency limits and the importance of hearing for functions such as localisation in terrestrial, semi-aquatic and aquatic mammals (Heffner 2004; Heffner and Heffner 2008; Nummela 2008; Reichmuth et al. 2013). However, few comparative analyses have been carried out calculating the effects of various correlates on the reception end of the spectrum of acoustic communication. It is therefore unclear whether the different groups of mammals are affected by various evolutionary drivers in the same way or very differently. With the growing data availability for both terrestrial and aquatic species it is a timely opportunity to explore further this side of acoustic communication and what life history traits are correlated with its variance.

Despite an increase in available data, there remains a noticeable limitation in the number of hearing studies for non-human mammals, particularly for aquatic species, large species and free-ranging species. Unlike vocalisation studies which can be carried out by passive recording,

hearing studies require the training of animals (behavioural audiograms) or, at the very least, immobilisation of the animals to carry out testing (psychoacoustic measures and brainstem analyses). There are large gaps in the available data for free-ranging mammals, both aquatic and terrestrial. With the exception of a number of bat species and a limited number of studies on seal and dolphin species (For example: Cook et al. 2004; Lucke et al. 2016), there is an under-representation of studies of wild mammals, particularly the larger and more cryptic species. The underrepresentation of hearing frequency data for aquatic mammals is due primarily to the inaccessibility of aquatic species, particularly the larger odontocete (toothed whales) and mysticete (baleen whales) species. These animals tend to have cryptic, pelagic lifestyles and are generally not kept in captivity therefore, the currently practised methods for hearing studies are rarely practicable (Ketten and Mountain 2009b). Attempts have been made to predict the audiograms of large aquatic species through modelling, combining finite element analysis (a computerised method that predicts how a structure will react to various stressors and physical effects such as sound moving through the inner ear structure) and anatomical measurements of the cochlea structure (Ketten and Wartzok 1990; Parks et al. 2007; Ketten and Mountain 2009b; Tubelli et al. 2012a). This method has been shown to produce reliable estimates of hearing capabilities when compared to frequency limits obtained by behavioural and electrophysiological methods (Fay 1992; Tubelli et al. 2012a; Tubelli et al. 2012b) and provides a viable alternative for identifying the probable minimum hearing frequency limits of these large cryptic species. However these modelling methods are less accurate when predicting upper frequencies of hearing.

While the evolutionary drivers of mammalian vocalisation frequency limits have been extensively discussed and debated (Morton 1975; Ryan and Brenowitz 1985; May‐Collado et al. 2007b; Martin et al. 2017), there has been no comparable investigation into the drivers behind the hearing frequency limits of mammals. Body size or body mass, interaural distance, and sociality are the only drivers that have been investigated in detail for mammalian hearing (Hemilä et al. 1995; Heffner 2004; Heffner and Heffner 2008; Ramsier et al. 2012a). Previous studies have shown that the larger a mammal’s body mass, the lower the vocalisation frequency that can be produced (Fitch 2000; Reby and McComb 2003; Fletcher 2004; Bowling et al. 2017; Martin et al. 2017). For hearing, the middle ear and cochlea are responsible for minimum and maximum hearing (Nummela 1995; Tubelli et al. 2012a), and the relationship between the sizes of these organs and the body size of the animal has been studied for mammals in a comparative context. Previous comparative studies of terrestrial mammals have found negative correlations between skull size and hearing frequency (Huang et al. 2000), as

well as interaural distance (the time taken for a sound to travel from one ear to another, representing functional head size) and high-frequency hearing (Heffner and Heffner 2008). These studies have shown that species with large skulls or interaural distances have hearing frequency limits lower than species of smaller proportions. Specifically, Heffner and Heffner (2008) demonstrate a negative slope for terrestrial mammals in their comparison of interaural distance and high-frequency hearing and a similar trend was expected to occur in my dataset. Nummela (1995) found that the relationship between skull mass and ossicular mass for very small mammals was closer to isometry, whereas medium sized mammals with skull mass between 0.5 and 200g maintained a clear negative allometric relationship, and species >200g fell somewhere between those relationships. The exception to the allometric pattern of medium sized mammals was the phocids and odobenidae (Nummela 1995). Indeed Repenning (1972) makes note that both the basal whorl of the cochlea and the ossicles of phocids are enlarged compared to terrestrial carnivores. In contrast the ossicles of otariids are not comparatively larger than similar sized terrestrial carnivores (Repenning 1972). Based on these differences in the middle and inner ear, it might be expected to see a difference in the hearing frequencies of some pinniped species to terrestrial species.

Despite the conserved anatomy of the inner ear among mammals, there is a noticeable amount of variance in the external appendages, the pinnae (the external ear), to the point where they are greatly reduced in otariid seals and absent in subterranean rodents, phocid seals and cetaceans. Given the role of pinnae in capturing sound and aiding in the localising of high-frequency airborne sounds (Heffner 2004), I wanted to examine whether the variations and loss of pinnae impact the hearing capabilities of mammals from various environments.

Sociality, a potential explanatory variable for vocalisation design that has only recently been investigated (McComb and Semple 2005; May-Collado et al. 2007a), has also been suggested to drive hearing frequencies in mammals. Sociality in primates (McComb and Semple 2005) and cetaceans (May-Collado et al. 2007a) influences vocalisation limits because of the number of social interactions and the level of complexity needed to communicate information in such groups, as well as because of the distances over which individuals must communicate and consequently the degradation properties of sound over distances. Lower frequencies degrade less quickly and are not absorbed as well as higher frequencies; therefore, they are able to transfer information further. Sociality has been found to be a driver of hearing sensitivity and upper hearing frequency limit in primates (Ramsier et al. 2012a), such that as the social complexity of primate species increases so too does their hearing sensitivity and maximum

hearing frequency limit. Higher frequency sounds provide more information to the receiver, a valuable trait in a large social group with many individuals and complex social interactions. We wished to test the influence of sociality more broadly across mammals, to find if solitary species tended to possess lower frequency hearing limits due to longer inter-individual distances and less complex social networks, than social species.

Michelsen (1992) proposed that “the strategies used for sound production have consequences for the coding of social signals and the reception of sound.” Because of the lack of comparative analyses of hearing for factors other than body size and sociality, I propose two potential factors that are novel to the comparative analysis study of hearing, one of which has been shown to play a role in shaping the vocalisation frequencies of mammals, i.e., environment (Morton 1975), and another which has not been studied in terms of either vocalisation or hearing, i.e., diet, as a foundation for further hearing comparative analyses. For example, in accordance with the acoustic adaptation hypothesis (Morton 1975), animals will alter the characteristics of their vocalisations over evolutionary time to maximise the transmission of information to conspecifics through their environment. An extreme example of needing to adapt to a change in environment over evolutionary time is the movement of mammals from the land into the water. It is of particular interest how this change due to environment (in-air to underwater hearing) rather than changes due to habitat differences within an environment, and further in the changes which resulted from an amphibious lifestyle in mammals such as the pinnipeds must have undergone, have influenced the frequencies at which the different groups of mammals hear.

An animal’s environment might also influence its hearing capabilities indirectly. For example, in environments where light or visibility is limited and investing in vision capabilities would be unnecessarily costly, animals become increasingly reliant on their acoustic senses (Murayama and Somiya 1998). This is particularly important for terrestrial nocturnal species, which hunt and navigate in the dark and aquatic species for which the sense of vision is limited by their water environment. These two groups from different environments face similar issues of limited vision and have developed their acoustic senses to be able to ‘see’ acoustically in these vision-limiting environments. Thus, though differences may be seen between species from different environments it may be expected to find groups faced with similar limitations to have developed similar solutions acoustically. An important part of ‘seeing’ acoustically is the ability to localise sounds, and it is high frequencies that are used in the process of localising a sound (Heffner 2004). An animal’s ability to localise sounds has also been shown to be negatively

correlated with the width of the field of best vision (the distance and degree to which an animal can see reliably) (Heffner 2004). That is, species with narrower fields of best vision are better at localising sounds, and given the dependency of the relationship between hearing frequency and localisation on the width of the field of best vision (Heffner et al. 2007) it may be assumed they would have higher-frequency maximum hearing limits.

I propose that an animal’s diet may influence its hearing sensitivity, as listening is important for identifying prey, predators or both. Predatory carnivores and omnivores often listen for acoustic signs of their prey in what has been described as the passive listening hypothesis (Morisaka and Connor 2007). The counterpart to this strategy is an anti-predator behaviour employed by prey species, in which an animal uses the acoustic cues of other animals to detect predators. This has been described in insects, which have been said to have evolved hearing for two purposes, one of which is the detection of predators (Michelsen 1992). A mammalian example of this is the kangaroo-rat (Dipodomys) which has been proposed to have developed enlarged bulla in order to hear the lower frequency sounds of their predators to instigate appropriate avoidance measures (Webster and Webster 1972; Mason 2016). The kangaroo rat weighs in at approximately 40g and would be expected to have quite high frequency hearing. Their predators are generally medium-sized mesopredators, such as bobcats, foxes and coyotes. The enlarged bulla presumably allows the kangaroo rat to hear the lower frequencies (minimum frequency hearing limit = 0.05 kHz (Heffner and Masterton 1980)) that are produced by their larger predators. Therefore, one might expect to see a distinction between the hearing frequencies of the different diet groups based on the animals they are listening out for. For example, as in the case of the kangaroo rat, it may be that herbivores have adapted lower frequency hearing to hear their predators, which are generally larger than themselves. Carnivores might be expected to hear higher frequencies if they prey on smaller species, whereas those that prey on species of a similar or larger body size as themselves might have reduced hearing in the upper frequencies. Omnivores, which are both prey and predator, have the potential for a broader hearing frequency range, potentially acting as an intermediate between carnivores and herbivores.

To date, comparative studies of hearing frequency limits do not appear to have quantified or compared the various contributions of potential evolutionary drivers. The accumulation of hearing data available from both terrestrial and aquatic species has provided the data required for comparative analysis to examine the relative influence of body mass, environment, sociality and diet on acoustic signal reception in mammals.

Using minimum and maximum frequency limits of hearing obtained from the literature, I investigated the strength of potential factors on the frequency limits of mammalian acoustic signal reception. Using 126 mammalian species from a wide range of families (n = 51), I compared the influence of each potential explanatory variable on the evolution of hearing frequency limits in mammals. I found that environment was an important factor for hearing limits, evidenced by the separation of the three environmental groups. Body size also influenced hearing, though to a lesser degree than environment.

4.2 Methods

4.2.1 Database

A database of acoustic reception (minimum and maximum frequency hearing limits) data, measured in kilohertz (kHz), was collated from the literature for all available mammalian species (Appendix 3). Papers were sourced from the online databases Scopus, Web of Science and Google Scholar using various combinations of the following search terms: mammal, hearing, audiogram, behavioural audiogram and auditory brainstem response. I recorded the minimum and maximum hearing frequency limit as reported by the authors of the studies. For in-air hearing studies, minimum and maximum limits are calculated from the audiogram at the 60 dB SPL (or re20µPa) level (Heffner 2004). I measured frequency limits for underwater hearing from published audiograms using 120 dB re1µPa underwater as this approximately equates to 60 dB SPL in air. This value has been calculated using equations developed by physicists based on the difference in intensity and sound pressure in different mediums (Richardson et al. 1995; Au and Hastings 2008). The 120 dB re1µPa value accounts for the sound pressure level difference between air and water, where the sound pressure level in air is referenced as 20 µPa, and in water as 1 µPa:

푝 20휇푃푎 푑퐵 = 20 푙표푔 ( 푤푎푡푒푟 ) = 20 푙표푔 ( ) = 20 푙표푔(20) = 26 푑퐵 (1) 푝푟푒푓−푤푎푡푒푟 1휇푃푎 and for the sound intensity level of water, which has an approximate impedance 3600 times that of air:

10 푙표푔(3600) = 36 푑퐵 (2)

Such that the equivalent underwater threshold level of the in air 60 dB SPL threshold was:

60(푑퐵 푆푃퐿) + 26 + 36 = ~120 푑퐵 푟푒1휇푃푎 (3)

Where 60 is the dB level used for determining hearing thresholds in air. Hearing frequency limits are determined in the literature from behavioural audiograms from trained animals, psychoacoustic measures such as auditory brainstem response (ABR) and auditory evoked potentials (AEP). The complete dataset contained data on minimum hearing frequencies for n = 123 species and maximum hearing frequencies had n = 125 species. 81

The traits of body mass, environment, sociality and diet were collected for all mammalian species in the database. Body mass (measured in grams) data were obtained from the PanTHERIA database where available (Jones et al. 2009). Environment was categorised as terrestrial, semi-aquatic or aquatic. Aquatic mammals were defined as species that live in the water for the entire duration of their life. Semi-aquatic mammals were defined as species that rely on both the aquatic and terrestrial environments for any combination of breeding, locomotion and/or feeding. By this definition, species such as polar bears, sea otters and pinnipeds (seals) were classified as semi-aquatic mammals. The sociality of species was classified as either solitary or social using the definition of Martin et al. (2017). Sociality was determined from social group size and population density data when available in the PanTHERIA database. Diet was assigned one of three categories: herbivore, omnivore or carnivore. The diet of a species was defined from the literature for carnivores and herbivores as the food type that made up 90% or more of foods ingested (animal matter and plant matter respectively) (Kelt and Van Vuren 2001). Insectivores (invertebrate eaters) and piscivores (fish eaters) were included as carnivores. Omnivores were classified as species whose diet consisted of between 10 and 90% animal matter (Harestad and Bunnel 1979).

4.2.2 Phylogenetic Construction

A phylogenetic generalised least squares (PGLS) analysis (Freckleton et al. 2002) was used to account for phylogenetic relatedness that could confound variation in hearing frequency limits (Laiolo and Rolando 2003). The most recent mammalian supertree (Faurby and Svenning 2015) was pruned in R (ver. 3.0.1) to include only the species present in the dataset. The supertree contained 1000 randomly resolved tree iterations to account for polytomies.

4.2.3 Analysis

A model selection approach similar to that used in Chapter 3 was carried out to test the suitability of alternate models to explain the variation in minimum and maximum hearing limits. Stepwise regression using backward elimination in the R (ver. 3.0.1) package MASS (Venables and Ripley 2002) advised a series of additive models dropping one driver at a time. These models compared hearing limits (minimum and maximum frequency) with the following

models: an additive model containing all four variables (훽0 + 훽푚푎푠푠 + 훽푒푛푣𝑖푟표푛푚푒푛푡 +

훽푚푒푡ℎ표푑 + 훽푠표푐𝑖푎푙𝑖푡푦 + 훽푑𝑖푒푡); an additive model containing three variables (훽0 + 훽푚푎푠푠 +

훽푒푛푣𝑖푟표푛푚푒푛푡 + 훽푠표푐𝑖푎푙𝑖푡푦); two Environment models, one to test for a uniform slope with different intercepts (훽0 + 훽푚푎푠푠 + 훽푒푛푣𝑖푟표푛푚푒푛푡) and one to test for different slopes of terrestrial and aquatic environments (훽0 + 훽푚푎푠푠 ∗ 훽푒푛푣𝑖푟표푛푚푒푛푡); a Body Mass model

(훽0 + 훽푚푎푠푠); and a Null model (훽0). Phylogenetic generalised least squares (PGLS) analysis was carried out using the caper package (Orme et al. 2013) in R (ver. 3.0.1).

I used the same methods and definitions as detailed in Chapter 3 to produce Akaike’s information criteria (AICc) and Akaike weights. The PGLS also produced a lambda () parameter representing the likelihood that variation in the data is due to phylogenetic relatedness. For a detailed description of this parameter, see Martin et al. (2017) or the Caper package documentation (Orme et al. 2013).

4.2.4 Pinna Ratios

The pinna is the visible outer part of the ear. Given the variance in the distribution of hearing frequencies among terrestrial mammals, I examined how relative pinna size is related to hearing. Given the diverse range in the sizes of the taxa being studied, I controlled for this by collecting ratios of pinna width and pinna height to body size for a subset (n=51) of the terrestrial species in the dataset using ImageJ (ver. 1.48). Three photos were obtained from open access websites for each of the 51 species from the hearing dataset. The animal was in a side-on position so that the entire length of the body could be observed and the pinna was positioned such that one whole flat surface was visible. The body length was measured from the top of the head to the end of the tail bone and was set as 1 unit, and the pinna height and width were measured as a proportion of the body length. The pinna height was measured from the base of the pinna, where it joins with the head, to the tip. The pinna width was measured perpendicularly to the pinna height at the widest part of the pinna. A phylogenetic generalised least squares analysis was performed in R (ver. 3.0.1) to account for the relatedness of the species, and to determine whether either of these relative pinna size measurements influenced the hearing frequency limits of terrestrial mammals. The minimum frequency limit dataset was log transformed to conform to statistical assumptions of normal distribution.

4.3 Results

4.3.1 Minimum Hearing Frequency

The interaction model that included body mass and environment (mass x environment) received the most support (36% of variance) (Table 4.1). The Akaike weights showed that the interaction model was 25 times more likely to be the driver of minimum frequency limits than the second most supported model. An Akaike weight of 0.95 suggested little model uncertainty. The environment contributed 25% (r = 0.54), body mass contributed 4% (r = 0.21), diet contributed 2% (r = 0.56) and sociality contributed 1% (r = 0.57). There was a moderate influence by phylogeny with a  value of 0.54 suggesting a midway between independent trait evolution and a Brownian model of evolution.

Table 4.1 Comparison of level of support for explanatory models that describe the evolution of the minimum hearing frequency in mammals for 123 species.

95% CI of slope 95% CI Weighted Effect Model ∆AICc parameter PGLS  (Lower, AICc size (r) (Lower, Upper) Upper) -0.88, -0.22 (T)

0+mass*environment 0.00 0.95 -0.97, -0.04 (S) 0.54 0.28, 0.74 0.60 -0.13, 0.49 (A)

0+mass+environment 6.40 0.04 -0.41, -0.19 0.56 0.29, 0.76 0.54  + + 0 mass environment 8.99 0.01 -0.40, -0.17 0.54 0.25, 0.75 0.56 diet  + + 0 mass environment 10.38 0.01 -0.40, -0.17 0.52 0.24, 0.75 0.57 +socialitydiet

0+mass 33.00 0.00 -0.26, -0.02 0.76 0.61, 0.87 0.21

0 36.35 0.00 - 0.78 0.62, 0.88 -

T is terrestrial, S is semi-aquatic and A is aquatic.

A significant negative interaction (terrestrial CI -0.88, -0.22, semi-aquatic CI -0.97, -0.04) was observed between body mass and frequency for the terrestrial and semi-aquatic environments but not for the aquatic environment (CI -0.13, 0.49). The terrestrial and semi-aquatic mammals showed similar trends (Fig. 4.1), and displayed a negative relationship between minimum hearing frequency and body mass (terrestrial slope = -0.37, semi-aquatic slope = -0.33), and

the larger species were able to detect lower frequency signals than smaller species. Conversely, the aquatic species displayed a positive relationship between body mass and minimum hearing frequency (slope = 0.18) such that the larger species detected on average higher frequencies than smaller species. Overall, the aquatic mammals presented higher minimum frequencies than terrestrial mammals of equivalent body mass, displaying more similarities with terrestrial mammals of much smaller body mass. There was no obvious gap separating the three groups, with the semi-aquatic species being intermediate in hearing frequencies between the terrestrial and fully aquatic species and overlapping with both.

Fig. 4.1 Graphical representation of the model with highest level of support. Minimum hearing

frequency as a function of species body mass for all mammals, on a log10 scale, as a function of both environment and method of hearing testing. The dotted line represents the phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (n = 87), the dot-dash line for semi-aquatic

(n = 15), and the solid line for aquatic mammals (n = 21). Terrestrial mammals (log10(Y) = -0.37log10(X)-

0.52), semi-aquatic mammals (log10(Y) = -0.33log10(X)+0.49) and aquatic mammals (log10(Y) =

0.18log10(X)-0.09). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org.

4.3.2 Maximum Hearing Frequency

The additive model with environment and body mass (mass + environment) was the most likely model to explain the variation in maximum hearing frequencies (Table 4.2), explaining 13% of variance. The Akaike weights indicated that the additive model of environment and body mass was 4.7 times more likely to be the driver of maximum frequency limits than the next ranked model. Akaike weight of 0.62 suggests a small amount of model uncertainty compared with the other tested models. Environment contributed 7% (r = 0.33), body mass 4% (r = 0.19), diet 1% (r = 0.35), and sociality 1% (r = 0.36). There was a significant negative relationship (CI -0.20, -0.02) between body mass and frequency across all environments (slope = -0.12).

95% CI of slope 95% CI Weighted Effect Model ∆AICc parameter PGLS  (Lower, AICc size (r) (Lower, Upper) Upper)

0+mass+environment 0.00 0.62 -0.20, -0.04 0.42 0.11, 0.17 0.33  + + 0 mass environment 3.09 0.13 -0.18, -0.02 0.35 NA, 0.74 0.35 + diet  + + 0 mass environment 3.52 0.11 -0.19, -0.02 0.41 0.04, 0.77 0.36 + diet+sociality -0.30,0.24 (T)

0+mass*environment 4.23 0.07 -0.45,0.31 (S) 0.42 0.12, 0.78 0.33 -0.34,0.16 (A)

0+mass 4.98 0.05 -0.17, 0.01 0.64 0.34, 0.98 0.19

0 7.28 0.02 - 0.54 0.26, 0.85 -

T is terrestrial, S is semi-aquatic and A is aquatic.

Body mass had a negative relationship with frequency, and aquatic species once again displayed higher hearing frequencies than semi-aquatic, which in turn had higher frequencies than terrestrial species (Fig. 4.2). Phylogeny had a moderate influence and presented a  value of 0.42. This value suggests that phylogeny was not as strong of an influence as expected, but that the species underwent a degree of independent trait evolution.

Fig. 4.2 Graphical representation of the model with highest level of support. Maximum hearing

frequency as a function of species body mass, on a log10 scale. The dotted line represents the phylogenetic generalised least squares (PGLS) regression line for terrestrial mammals (n = 90), the dot- dash line for semi-aquatic (n = 14), and the solid line for aquatic mammals (n = 21). Terrestrial mammals

(log10(Y) = -0.12log10(X)+1.62), semi-aquatic mammals (log10(Y) = -0.12log10(X)+1.85) and aquatic

mammals (log10(Y) = -0.12log10(X)+2.29). Silhouettes by Oscar Sanisidro and Chris Huh were downloaded from http://phylopic.org.

4.3.3 Pinna Ratios

The linear regression models illustrated that only relative pinna height had a significant positive interaction (n=51, p=0.009, slope=1.5) with the maximum frequency limits in terrestrial mammal species. That is species with taller pinnae as a ratio of their body size had higher maximum frequencies of hearing. Although the relative pinna width in terrestrial species did not show a significant relationship with the maximum frequency limits (n=51, p=0.108), it did show a positive trend (slope=1.7) that was similar to that of relative pinna height. A non-significant relationship was observed between relative pinna width (n=51, p=0.066, slope=0.8) and height (n=51, p=0.109, slope=0.6) and the minimum frequency hearing limits (Fig. 4.3A; B). All comparisons had a lambda value greater than 0.8, representing strong phylogenetic influence.

4.4 Discussion

By comparing the influence of various explanatory variables on hearing frequency limits I found that environment and body mass were identified, of those considered, to be the factors most likely influencing the evolution of hearing in mammals. Environment appears to heavily influence hearing (minimum and maximum) frequency limits, contributing 25-7% respectively of the variance, along with body mass, which contributed 4%.

4.4.1 Environment

Semi-aquatic species displayed higher hearing limits than terrestrial species, and aquatic species in turn had higher limits than semi-aquatic species, a pattern that has also been described for mammalian vocalisation frequencies (Chapter 3). This offset of higher hearing limits in semi-aquatic and aquatic species is most likely due to the propagation properties of their environment (Tyack 2008). With sound travelling approximately five times faster in water than in air (Madsen and Surlykke 2013), larger species are not required to produce and receive the same low frequency sounds that terrestrial species of the same body mass are. However, hearing under water brings the issue of acoustic impedance mismatching. The movement of sound across different mediums, in the case of an aerial-adapted ear in water, from water to the air of the outer ear and further to the middle ear and fluid filled inner ear, results in a loss of acoustic energy and essential information being communicated. Aquatic and semi-aquatic mammals are faced with a similar challenge when vocalising underwater and are having to overcome impedance issues for both hearing and vocalisation. To minimise the loss of acoustic energy when vocalising, these groups developed specialised mechanisms, passing the sound through denser tissue and out to the water (Fig. 3.1). The tissue is more similar in density to water than to air, and so less energy is lost during the transmission of the vocalisation. These two groups also appear to have developed adaptations to receive transmitted sounds.

In air, pinnipeds use the same hearing mechanism via the outer and middle ear as terrestrial species. Of the pinniped groups, the otariids communicate more often than phocids in air, while the phocids are the more frequent underwater communicators. Phocid ears are more adapted to underwater hearing, whereas otariids are more adapted to in air hearing, though both are intermediate between terrestrial and fully aquatic (Kastak and Schusterman 1998; Nummela 2009). This is reflected in their position between the terrestrial and aquatic groups

(Fig. 4.1 & Fig. 4.2). While under water, rather than hearing through air-filled channels like their terrestrial relatives, it has been suggested that pinnipeds receive sound through bone conduction in the lower jaw (Repenning 1972; Au and Hastings 2008). Muscular closure of the meatal opening results in the blockage of the outer canal during dives (Kastak and Schusterman 1998).It has been hypothesised that during the dive the meatus and middle ear are engorged with blood which raises the impedance within the middle ear space to more closely match that of the surrounding water (Møhl 1968; Kastak and Schusterman 1998). Bone conduction and the transport of the sounds through tissue and fluid filled chambers negate the impedance mismatch of sound travelling from water to air. Wider bandwidths can be perceived through bone conduction in water than in air with less potential for distortion. In humans, the upper hearing frequency increases from approximately 28 kHz via air conduction to approximately 108 kHz using bone conduction (Lenhardt et al. 1991). Given that there is less distortion of higher frequency sound when conducted through bone, potentially aquatic mammals that use bone conduction would have improved hearing at higher frequencies.

The semi-aquatic and terrestrial species have surprisingly similar negative relationships for both upper and lower frequency hearing limits. While it would appear upon inspection of the data that the semi-aquatic environment group should have only a minor, if not absent, interaction with body mass, it is the values of the semi-aquatic American mink (Neovison vison) that emphasises the negative relationship observed in Fig. 4.1 and Fig. 4.2. This species, as well as a number of the pinniped species, produces and perceives sounds in air, therefor resulting in a similar negative relationship as that shown for terrestrial mammals.

It should be noted that cetaceans did not, for the most part, evolve hearing frequencies indicative of their body size. Rather, it is illustrated in this study that odontocetes evolved higher frequency limits than expected for their body mass. Due to the unavailability of hearing measurements using behavioural or psychoacoustic methods, mysticetes were not represented in this analysis. However, morphological methods estimating the hearing frequencies of mysticete species indeed indicate that these large species possess hearing limits representative of their large body mass (Houser et al. 2001; Parks et al. 2007; Ketten and Mountain 2009a; Tubelli et al. 2012b; Ketten et al. 2013; Cranford and Krysl 2015). As mentioned previously, sound travels faster in water than in air (Madsen and Surlykke 2013), and I believe it is this physical property that facilitated the upward shift in frequency of the odontocetes. Cetaceans do not use bone conduction, but the preferential transmission of sound along “acoustic fat” channels (Ketten 1997). It is believed that sound enters through an

area of the head known as the acoustic window, anterior to the pan bone, and passes through the pan bone window, to the mandibular fat body and through to the bulla (Varanasi and Malins 1971, 1972; Aroyan 1996; Cranford et al. 2008). These various fat tissues through which sound passes are similar in density to water; overcoming the issue of impedance mismatching that is faced by mammals in the aquatic environment. There is some discussion as to whether odontocetes and mysticetes utilise the same hearing pathways due to different cranial anatomy but it would appear that both odontocetes and mysticetes utilise acoustic fat channels (Yamato et al. 2012).

4.4.2 Body Mass

Body mass had the expected negative relationship with both the minimum and maximum frequency hearing limits of terrestrial, semi-aquatic and aquatic mammal species; with species of larger body mass perceiving lower frequencies than smaller species, however an upward shift was observed in semi-aquatic species, and a further upward shift for aquatic species, particularly odontocetes.

The terrestrial species showed similar patterns to those previously described by Heffner and Heffner (2008), with body mass strongly correlated with hearing frequencies, subterranean species lacking high-frequency hearing, and echolocating bats displaying particularly high frequencies for receiving their echolocation signals. Semi-aquatic and aquatic species also exhibited the negative trend of body mass with frequency limits of hearing, though the acoustic properties of their environment meant that they were able to perceive higher frequencies (through bone, tissue and fat conduction) compared to the terrestrial species of similar body mass.

The influence of body mass was surprisingly low at 4% for both frequency limits. Similar to the constraint of low vocalisation frequencies by the requirement of larger vocalising organs which are in turn constrained by body size (Ryan and Brenowitz 1985; Peters and Peters 2010), low frequency hearing is limited by the size and coil of the cochlea (Manoussaki et al. 2008). However, just as mammals developed specialised vibrating organs for producing high frequency vocalisations, I believe that they also possess specialised organs for hearing high frequencies which are not constrained by body mass. Pinnae have been suggested to influence terrestrial mammals’ hearing in various ways, particularly in species which utilise echolocation,

and more broadly for passive sound localisation (for example large pinnae are said to amplify the faint low-frequency sounds produced by moving prey (Guppy and Coles 1988; Obrist et al. 1993). The linear regressions of pinnae measurements show that pinna height has a positive relationship with maximum frequency limits, such that species with taller pinna (in comparison to their body size) are capable of hearing higher frequencies. This positive relationship is surprising given that previous studies have found the opposite, or no relationship between frequency and pinna size. Coleman and Colbert (2010) found no correlation between pinna shape or area and audiometric data in non-human primates, whereas Obrist et al. (1993) found a negative relationship between pinna height and width and echolocation call frequency in only one microchiropteran family (Hipposideridae), while the other families studied did not show a significant relationship. Conversely, Huihua et al. (2003) found a significant negative correlation between call frequencies and pinna length in Rhinolophids but not in Hipposiderids. Both of these groups use high-duty-cycle (narrowband) echolocation to detect the fluttering movement of their prey, and to distinguish their prey from the background (Lazure and Fenton 2011). High-duty-cycle species are tuned to very narrow ranges of frequencies and hence their pinnae would be an important aspect of echolocating and assisting in receiving sounds that would otherwise not have been shadowed by the head for transmission to the inner ear. Further, Huihua et al. (2003) also found that forearm length was correlated with ear length in the Rhinolophid species. However, these differing results on small groups of related species, as well as my results, showing a strong phylogenetic influence, highlight that such relationships are variable among the different groups, but that there is a general trend among terrestrial mammals as a whole.

Because pinnae are external to the body, they are not constrained by body size and may be as large or small as required. For example very small mammals may have large pinnae, such as Darwin’s leaf-eared mouse (Phyllotis darwini) and large mammals may have small pinnae relative to their body size, such as the Japanese macaque (Macaca fuscata). The pinnae act as a funnel that directs the sound through to the middle and inner ear. The pinna selectively funnels high frequencies for sound localisation, whereas low frequencies are not as affected by the pinna (Heffner and Heffner 1992). However, aquatic and most semi-aquatic species do not possess external pinnae, and yet they are also capable of detecting a diverse range of very high frequencies. It is believed that in cetaceans the equivalent of pinnae lies within their head in the form of fat channels that provide a pathway for sound transmission. The anterior fat channel has a similar function in capturing and transmitting high frequency signals, whereas lower frequencies are captured by the funnel-shaped lateral fat channel (Ketten 1994; Ketten

1997). The pathways of capturing sound appear to be less constrained by body size than those associated with producing sound such that higher-frequency sound reception by cetaceans, beyond that predicted for the body size of the species, appears common. For terrestrial mammals, the larger external pinnae enhance reception of higher-frequency sound as do the fat-channel pathways of the odontocetes within the aquatic species.

4.4.3 Sociality

Sociality was found to have a small contribution (1%) to hearing frequency. It is possible that there was more of an influence of sociality than was found but it was masked by how sociality was measured in the current study. In this study I used a binary categorisation of sociality rather than a measure of social complexity. While hearing range has been indirectly linked to sociality of birds and reptiles through morphological measurements (Walsh et al. 2009), those studies defined sociality in the form of pair bonding, <20 in a group, and >20 in a group. Walsh et al. (2009) found that the length of the endosseous cochlear duct (i.e., the component contained within the bone) was positively related to the mean hearing frequency and that species with longer ducts were more likely to come from larger social aggregations. Their more detailed definition of sociality may have provided more strength to this explanatory variable; however, this was not an option in this study as such data were not available for all species in the dataset.

4.4.4 Diet

Diet was consistently the first explanatory variable to be dropped from the additive models. It is possible that my hypothesis may have been too simplistic for such a broad comparative study. While it is probable that prey are indeed listening out for their predators and vice versa, the sheer diversity in the size of prey and predator species has proven to confound its use here as an explanatory variable. Predators alone range from the largest baleen whales, which feed on tiny krill, to lions, which sometimes prey upon species larger than themselves (Tucker and Rogers 2014). In addition, while prey are likely listening out for predators which are larger than themselves, and top predators are not confined to listen out for larger predators but for their prey, lower trophic level predators are required to listen for both smaller prey and larger predators much the same way an omnivore is. It is possible that using predator and prey body mass (e.g. large predators and small predators, or perhaps top and lower predators, in

separate groups), rather than the broad categories employed in our study, would return more comprehensive results. Such a detailed investigation, however, is not within the scope of this study.

4.4.5 Method of Measuring Hearing Limits

There tends to be a sampling bias in the method that hearing frequency limits have been collected for different taxa. In the odontocetes, it is common for species’ hearing to be measured using AEP methods and these species tend to have higher minimum frequency hearing limits, whereas the majority of the audiograms of terrestrial and semi-aquatic species’ audiograms have been measured using behavioural methods with a few measured as AEPs. One could suggest the inclusion of the method of collection as a variable in the model selection analyses in order to account for this bias. However, as the method of measuring the hearing of an animal has no evolutionary influence on the hearing limit, and it is the evolutionary drivers that are being investigated, this is not an appropriate action in this case. Any apparent link between phyletic order and sampling method closely resembles the differences correlated with environment group, and so, was considered to be a possible error variance in the results of the analysis. Data points for species whose hearing limits were collected using FEM and cochlea data were excluded from the analysis due to their appearance as outliers. These data points were grouped closely together and added to the weight of the method on the trends observed, particularly in the aquatic group. As these data points represented the mysticete species, a group for which hearing measurements from other methods are not available and therefore not able to have their accuracy tested, I removed them from the dataset to improve the power of the analysis.

A caveat of this comparative approach of examining hearing frequency limits lies in the method of measurement used by the various investigators of the hearing of each species. For species that are more logistically difficult to work with, typically rare, very large, semi or fully aquatic mammals, hearing sensitivity trials need to be conducted on a single animal or a small number of animals. This is unavoidable, but the smaller number of replicates means that the results are exposed to potential errors due to equipment faults or a test subject having compromised hearing. This may explain the apparent erratic spread of minimum frequency limits among the semi-aquatic and aquatic species.

The dataset shows that it is of great importance that we continue to gather additional hearing data, particularly for semi-aquatic and aquatic species as well as large terrestrial species. In particular, otariids and mysticetes are underrepresented in this area.

4.5 Conclusion

Studies such as this one are useful for informing strategies with knowledge of hearing abilities and highlighting gaps in our current knowledge. They provide a comparative framework for the hearing capabilities of mammalian groups. These results highlight that aquatic mammal species have developed adaptations to cope with an aquatic lifestyle that led to further adaptations for efficient underwater hearing, just as they did for their vocalisations. The move back to the water by mammals therefore appears to have had a profound impact on the evolution of their acoustic communication.

Chapter 5 Marsupial gliders, Acrobates pygmaeus, produce ultrasonic vocalisations

5.1 Introduction

Ultrasonic communication (acoustic signals above 20 kHz) is used in a number of invertebrate and vertebrate species for intraspecific, interspecific and autocommunication (navigation) purposes (Sales 2012). Among vertebrates the use of ultrasonic communication has been described in select amphibians (for example Amolops tormotus; (Feng et al. 2006)), microchiropteran bats (and the Rousettus genus of the megachiropterans), cetaceans (whales and dolphins), primates (for example tarsiers; (Ramsier et al. 2012b)) and rodents (for example Mus musculus produces frequencies up to 110 kHz (Holy and Guo 2005), voles of the families Microtus and Myodes (Kapusta and Sales 2009), and ground squirrels (Wilson and Hare 2004)).

The vocal repertoire of A. pygmaeus was previously described (Chapter 2) to fill a gap in our knowledge of the vocal capabilities of mammals; that of the small gliding marsupials. Although Lindenmayer (2002) comments in his review of Australian gliders that the squirrel glider (Petaurus norfolcensis) produces vocalisations with an ultrasonic component, and that it was possible that other glider species were capable of producing ultrasonic frequencies, spectrographic evidence nor acoustic data were not provided . Recent information obtained on the vocal repertoire of A. pygmaeus raised the possibility of their producing ultrasound (Chapter 2). While the minimum frequency limit of their calls fell within the expected range for their body mass, the maximum frequency of their calls was lower than expected, even when bats are excluded from the equation. A terrestrial mammal of A. pygmaeus’ body mass (14 grams) should produce a minimum frequency of 3.6 kHz and a maximum frequency of 48.4 kHz (from equations in Chapter 3). Many species of chiropterans employ echolocation signals in their vocal repertoire which could potentially skew the upper frequency limit for non-

echolocating species, so an upper limit was also calculated for a subset of terrestrial mammalian species with chiropterans excluded, which resulted in an expected maximum frequency limit of 31.2 kHz.

I showed that the minimum frequency of the calls produced by A. pygmaeus of 0.5 kHz (Chapter 2) is in line with the predicted minimum frequency of 3.6 kHz (Chapter 3); however, the upper frequency limit of the recording equipment I used for Chapter 2 constrained the recording to frequencies below 22 kHz, which is below the predicted 31 and 48 kHz limits. I re- examined the acoustic behaviour of the captive A. pygmaeus population using an ultrasonic recorder, but while a full-spectrum ultrasonic recorder can record from the audible range (20 Hz) into the ultrasonic range (in this case 96kHz), the quality and clarity of the vocalisations recorded in the lower frequency range (from 20 Hz to 20 kHz) is diminished. It was therefore necessary to explore these two aspects of A. pygmaeus’ vocal repertoire separately.

Further support for investigating the possibility of vocalisations produced in the ultrasonic frequency range was found in the hearing capabilities of A. pygmaeus. Aitkin (2012) reported an upper frequency hearing limit for A. pygmaeus of 40 kHz at 60 dB from auditory brainstem response (ABR) testing, which matches closely to the predicted upper vocalisation limit of 48.35 kHz. In addition, a study of the anatomy of the outer and inner ear of A. pygmaeus suggests that certain ultrasonic frequencies, namely in the range of 27-29 kHz, are selectively resonated by a bony disc while others are attenuated (Aitkin and Nelson 1989). While this extension of hearing frequencies above those reported for the vocalisations could be the result of the fact that the range of hearing can extend beyond vocal capabilities for the purposes of hearing prey/predators (Fay 1988), it is also possible that the frequencies of hearing and vocalisation are somewhat correlated in a source-receiver relationship. With the new knowledge of the predicted maximum frequency (Chapter 3), and recording equipment with the capability to record ultrasonic vocalisations, additional investigation into the vocal repertoire was warranted.

The aim of this study was to determine if A. pygmaeus produce ultrasonic vocalisations and if so were these unique call types or ultrasonic versions of their audible call types.

5.2 Methods

5.2.1 Experimental Design

Five male and 16 female adult A. pygmaeus were recorded in the same enclosure under the same conditions as described in the repertoire study (Chapter 2). An SM4BAT FS song meter with attached SMM-U1 ultrasonic microphone (Fig. 5.1) was used to carry out ultrasonic recordings for two days in June 2017 for a total of 48 hours of recording. The recorder was configured with a sampling rate of 192 kHz (frequency response from 5 kHz to 96 kHz). While video recording equipment was also deployed I was unsuccessful in obtaining corresponding behavioural context for the vocalisations recorded.

This Content Has Been Removed To Comply With Copyright

Fig. 5.1 Recorder setup of SM4BAT FS song meter and SMM-U1 microphone within the enclosure at Taronga Zoo, Sydney.

5.2.2 Acoustic Analysis

Acoustic files were analysed using the cluster function of the Kaleidoscope Pro software (Version 4.0.0; Wildlife Acoustics). A total of 109 confirmed ultrasonic calls were detected from the 48 hours of recordings. As in Chapter 2 these detections were categorised into call types

based on similar frequency and temporal characteristics from the spectrograms and waveforms produced in Raven Pro software (Version 1.4; Cornell Lab of Ornithology) and confirmed by observing the structural spectral differences in the spectrograms and temporal differences in the waveforms. In order to hear the calls, recordings were slowed down to half speed. Calls were measured for their call characteristics using the Raven Pro software (Version 1.4; Cornell Lab of Ornithology); Duration (ms), Minimum frequency (kHz), Maximum frequency (kHz), Range (kHz) and Peak frequency (kHz). Spectrograms were produced with a sampling rate of 192 kHz, hamming window and DFT size = 512 points. Noise reduction of 11 dB was applied three times to produce the spectrograms for figures.

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5.3 Results

The 109 ultrasonic calls detected were categorized into 4 call types based on their acoustic features (Table 5.1, Table 5.2).

Table 5.1 Summary of call characteristics for A. pygmaeus' ultrasonic call types.

Peak Frequency (kHz) Duration (ms) Call Type Structure Mean (S.D.) Min Max Mean (S.D.) Min Max

Ultrasonic Tonal 29.2 (2.9) 24.8 39.0 12 (4.5) 7 22 Whistle

Ultrasonic Chirp Broadband 28.9 (1.9) 24.8 33.0 14 (4.1) 6 22

Table 5.2 Summary of call characteristics for A. pygmaeus' ultrasonic components of 2 call types.

Peak Frequency (kHz) Duration (ms) Interval (ms) Call Structure Type Mean Mean Mean Min Max Min Max Min Max (S.D.) (S.D.) (S.D.) 20.0 Hiss Broadband 12.8 37.5 104 (28.6) 49 158 - - - (7.5) 25.4 Pulse Broadband 12.0 37.5 0.5 (1.4) 0 6 2 (2.6) 1 12 (9.0)

5.3.1 Tonal Vocalisations

5.3.1.1 Ultrasonic Whistle

The ‘ultrasonic whistles’ are tonal calls with energy within the ultrasonic range only (22.7-40.2 kHz though most of the calls were at 30 kHz; Fig. 5.2). They have a short duration (12 ms) high amplitude (Fig. 5.2A and D) tonal component which was frequency modulated, increasing, and then decreasing in frequency (Fig. 5.2B and C). Ultrasonic whistles were amplitude modulated with a gradual onset and decay (Fig. 5.2A and D). They were typically stereotyped; there was little variation in the structure between calls. They are often produced in a succession of 2 to 3; however, they were sometimes produced singularly. They were a less commonly heard call type (17%: 19 of 109).

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A D

B C

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5.3.2 Broadband Vocalisations

5.3.2.1 Ultrasonic Chirps

The ‘ultrasonic chirp’ calls are mostly flat in structure with slight frequency modulation (Fig. 5.3 B). They are relatively short (average duration = 14 ms) ultrasonic calls in a similar frequency range (24.4-34.4 kHz) to the ultrasonic whistle call type. Ultrasonic chirps are a common call type (31%: 34 of 109).

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The ultrasonic chirp is composed of a series of pulses (Fig. 5.3 A). These calls are not stereotyped but rather there is a gradation in structure of amplitude modulation (Fig. 5.4). In some ultrasonic chirps the pulse series starts with a gradual onset and terminates with a gradual decay (Fig. 5.4A). Other ultrasonic chirps show amplitude modulation of the pulse series so that there are multiple cycles of similar intensity (Fig. 5.4B and C) or of diminishing intensity (Fig. 5.4D and E) across the call. For some ultrasonic chirps the multiple cycle calls are preceded by a single lower intensity cycle prior to a series that diminished in intensity through time (Fig. 5.4E). Ultrasonic chirps were most often produced singularly, but were observed in bouts of up to three chirps within 1 second on at least two occasions.

Fig. 5.4 Diagram of the gradation in complexity of the waveforms of the ultrasonic chirp call type.

5.3.2.2 Hiss

The ‘hiss’ is a broadband structured call (Fig. 5.5). Although energy extends over a broad band (7.1 to 77.4 kHz) it is divided into higher and lower broadband components (Fig. 5.5B) with a gap where there is no energy, from approximately 20-25 kHz. The structure of the hiss was fairly consistent between calls. Overall the energy in the hiss spans from the audible range (7.1 kHz) into the ultrasonic frequencies (77.4 kHz) although most energy extends to 62.3 kHz. Because the lower energy band of the hiss (7.14 to 20 kHz) is predominantly within the range of traditional recording equipment the lower-frequency component of this call has been described previously (Chapter 2). The higher-frequency ultrasonic component ranges from 25 to 77.4 kHz (Fig. 5.5B). When noise reduction is applied the ultrasonic component can be lost in low signal-to-noise recordings.

Hisses are the longest of A. pygmaeus’ call types (average duration = 100 ms). They had a gradual onset and decay (Fig. 5.5C), resulting in a slow build-up of intensity in the call and a gradual tapering off with little amplitude modulation. They were a common (24%: 26 of 109)

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call type that was rarely observed singularly but more often in bouts of up to 6 utterances (Fig. 5.5B).

A C

5.3.2.3 Pulse Trains

‘Pulse trains’ are a series of broadband short duration pulses (average duration of 1 ms) (Fig. 5.6). Energy spanned from the audible into the ultrasonic range (6.8-58.4 kHz) but energy is divided into higher and lower broadband components (Fig. 5.6B). The gap between the two bands ranges from approximately 20-25 kHz. The lower energy band of the pulse trains spanned from 6.8 to ~20 kHz. The higher-frequency ultrasonic component ranges from ~25 to 58.4 kHz (Fig. 5.6). The pulse trains were most often produced in trains of up to 11 pulses (average inter-pulse duration = 2 ms). Multiple trains were observed in one instance, with 4

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trains uttered within half a second (Fig. 5.6B). The pulse calls have a rapid onset and equally rapid offset. Individual pulses consistently had a diamond shaped waveform. Conversely to the hiss call type, in low signal-to-noise ratio pulse trains it was the audible part of the call that was lost first when noise reduction was applied to the recordings (See last two pulse trains in Fig. 5.6B). They were a commonly heard call type (28%: 31 of 109).

A C

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5.4 Discussion

I found that A. pygmaeus did in fact produce four (4) vocalisation types in the ultrasonic frequency range (frequencies above 20 kHz); the whistle, chirp, pulse train and hiss. The maximum frequency of the ultrasonic vocalisations were observed in the broadband call types, hisses with predominant energy up to 77.4 kHz, and pulse trains with energy up to 58 kHz (Fig. 5.7). These new records for maximum frequency limits of A. pygmaeus place the upper frequency of their vocalisations slightly above that predicted for a mammal of their body size. For example, the predicted frequency based on correlational calculations when excluding echolocating chiropteran species would be 31.2 kHz, and even when chiropterans are included it is 48.4 kHz (Fig. 5.7). The broadband calls were not the only ones to exceed the predicted maximum, as the two tonal vocalisations also had a maximum fundamental frequency up to 40 kHz.

Fig. 5.7 Maximum recorded frequency of A. pygmaeus (red star) in comparison to chiropterans (purple circles), rodentia (orange circles) and other mammals (black circles).

Interestingly, both of the broadband call types, the hiss and pulse trains, had a frequency band gap at 20-25 kHz which corresponded with the 22 kHz frequency that Aitkin and Nelson (1989) predicted was attenuated by a bony disk in the ear structure. It would be interesting to discover what the purpose of this bony disk is, and why those frequencies are attenuated specifically. In addition the tonal calls were all produced at frequencies above 24 kHz. It is

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therefore evident that A. pygmaeus predominantly produces ultrasonic vocalisations in the frequencies at which their hearing has been shown to be most sensitive, and avoid producing frequencies to which they are less sensitive (Aitkin and Nelson 1989; Aitkin 2012).

All of the ultrasonic call types were a subset of their audible call type repertoire. Unlike the call types previously described for A. pygmaeus (Chapter 2) which had high levels of variation, there was a high level of stereotypy observed in the ultrasonic call types. Miller and Engstrom (2007) suggest that stereotypy is among the features which aid in localising a signal. In a nocturnal species any assistance in localising calls would be very useful.

As behaviour was not recorded while animals were producing the ultrasonic vocalisations I can only speculate as to their functions. The frequencies of the pulse trains were similar to the predicted value that included chiropteran bats. Their duration (minimum of 1 ms) and rapid delivery in packets suggest similarities to the echolocation calls of Pteropodid chiropterans. For example Rousettus aegyptiacus produces very short (1-2 ms) echolocation signals using tongue clicks (Herbert 1985) which look very similar to the pulse packets I found A. pygmaeus produces. This begs the question of the purpose for which A. pygmaeus uses these pulses. It is possible they are used for social communication, but more intriguingly they could be used for navigation. If these pulse calls are indeed used for echolocation purposes it is unlikely that they are being used for prey location as the frequency range A. pygmaeus produces is too low to reflect off small insects etc.(Jones 1999). A. pygmaeus feeds on pollen, nectar, sap and arthropods (Goldingay and Kavanagh 1995), but there have been no observations of them landing directly on prey, but instead they forage for them in the substrate (Goldingay and Kavanagh 1995). It is unlikely then that they are using pulses as an echolocatory sense to find their prey whilst in flight. The low-frequency ultrasonic pulse trains they produce are more suited to detecting large stationary objects such as for navigation. A. pygmaeus are known to glide from one substrate to another within their habitat and most often glide to tree trunks and foliage than branches or to the ground (Goldingay and Kavanagh 1995). A nocturnal arboreal rodent, the Vietnamese pygmy dormouse (Typhlomys chapensis) is suggested to use echolocation to navigate their habitat whilst positioned on the arboreal substrates (Panyutina et al. 2017). A. pygmaeus may also potentially be using their pulse vocalisations for navigational purposes.

This leads to the question of energy costs. Producing sound pulses requires substantial energy, particularly when the animal is at rest (Speakman et al. 1989). Bats synchronise their

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echolocation calls with their wingbeats during flight to compensate for this energy expenditure (Lancaster et al. 1995). I hypothesise that if A. pygmaeus were producing navigational echolocation calls perhaps it utilises the force from its own ‘flight’ mechanism, the parachute formed by its patagium during a glide, to offset the energy costs involved in producing these high energy calls. Without contextual evidence it is purely speculation, but there is the possibility that they are using these pulses in a context other than conspecific communication.

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Chapter 6 General Discussion

Gliding marsupials, of which the feathertail glider (A. pygmaeus) is the smallest member, previously represented a gap in our understanding of mammal acoustic communication. The aim of this thesis was to partially fill this gap by describing A. pygmaeus’ vocal repertoire for the first time and compare how they perform vocally compared with other mammals. Knowledge of their vocal repertoire has the potential to improve the monitoring of this species in its natural habitat which is constantly under threat from deforestation and urbanisation.

I have demonstrated here that A. pygmaeus produces a diverse range of vocalisations unique to the species which extend well into the ultrasonic range. With this description of their vocal behaviour it is possible that Passive Acoustic Monitoring (PAM) paired with cameras might become a suitable method for monitoring this species. I have also shown the benefits of using macrocomparison approaches to help guide our investigation of the vocal behaviours of species which prove to be difficult to study; by providing the means to estimate the frequency range in which they communicate and therefore providing a target area in which to focus such research.

6.1 Macrocomparison

Macrocomparison studies allow us to find patterns in traits and how they are influenced by a range of variables. Additional information on the vocal communication of mammal species, particularly semi-aquatic and aquatic species, is continuously becoming available for inclusion in macrocomparative studies, such that the theories and patterns that were originally discovered in birds and later applied to mammals are able to be expanded upon and revisited. Further, we can then use the patterns discovered in these comparisons to help guide our investigation of the vocal behaviour of new species which are still missing from the database.

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In Chapters 3 and 4 I collated vocalisation data for 193 species and hearing data for 126 species of mammals to carry out model selection and find which traits had the greatest influence in driving the patterns observed. For both vocalisation and hearing I found that environment had a surprisingly large influence on the frequencies produced and received, along with body size. While body size has been well established as a driving trait of these communication modes, environment presented an exciting and novel driver to be explored. Mammals that communicate in the water are faced with overcoming the impedance mismatch between water and air and making use of the superior propagation of sound through water. It became obvious that semi-aquatic and aquatic mammals had evolved strategies for dealing with the challenge of producing and receiving sound in the water. These involved the sound travelling through more dense mediums such as fatty tissue and bone, though these strategies involved different pathways for the two aspects of acoustic communication.

There are currently three broad groups of methods used to study what frequencies mammals hear; behavioural, AEP, and morphological models. There has been some suggestion among studies that the method used has some influence on the hearing range detected. By compiling the data for this study and comparing across groups it became evident that certain methods are more appropriate for collecting data from particular groups of mammals, usually for logistical reasons. For example it is near impossible to obtain behavioural hearing data from the large mysticetes and so researchers have developed morphological methods to estimate their hearing limits from scans of their middle ear organs. Similarly for small wild odontocetes, AEP methods have been developed to quickly measure their hearing in the field. This is a timely opportunity for further research to explore the comparability of the different methods of data collection to each other, to continue developing the accuracy of morphological models and applicability of AEP methods to behavioural contexts.

It should be noted that when comparing the vocal limits of mammals, echolocation vocalisations were avoided where possible (Section 3.2.1). This was due to the concern that vocalisations produced in the context of autocommunication (also referred to as echolocation, or where the same individual acts as both the signaller and receiver (Bradbury and Vehrencamp 1998b; Jones 2008; Dechmann et al. 2013)) and heterocommunication may be driven by different traits or selective pressures. A similar concern may be extended to the reception of auditory signals. Unlike vocalisation, it is not possible to distinguish which aspects of the hearing audiogram are purely for the purposes of echolocation, and in fact those that are, are often nested within the overall audiogram for the species. Researchers have

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previously aimed to answer the question of what it is that differentiates the auditory systems of echolocating and non-echolocating species with little success (Neuweiler 1990). Bats generally have two regions of increased auditory sensitivity, one in the ultrasonic range and the other in the audible range (Bohn et al. 2006). However as Neuweiler (1990) demonstrates, echolocation signals are not purely produced in the ultrasound, nor are all ultrasonic vocalisations produced by echolocating species used for autocommunication. And so this distinction alone is not a viable option for investigating the potentially different drivers of these two forms of communication.

Some species of bats have been shown to eavesdrop on the echolocation vocalisations of both conspecifics and heterospecifics in order to find prey or navigate their surroundings successfully (Barclay 1982; McGregor 2005; Gillam 2007; Jones 2008; Dechmann et al. 2009; Arnold and Wilkinson 2011). Such eavesdropping behaviour means that echolocation may also be an important part of social communication as well, rather than just for use as a biosonar system (Arch and Narins 2008; Jones 2008; Knörnschild et al. 2012). For example, the characteristics of the echolocation call of the little brown bat (Myotis lucifugus) may only be effective up to 5 m, but they are able to eavesdrop on the echolocation calls of other species in their vicinity up to 50 m in order to find food (Barclay 1982; Jones 2008). A similar eavesdropping hypothesis has been proposed for echolocating dolphin species (Dawson 1991; Xitco and Roitblat 1996; Götz et al. 2006; Gregg et al. 2007). For example, it has been suggested that the simple repertoire of Hector’s dolphins (Cephalorhynchus hectori), consisting largely of ultrasonic clicks, is possible because the dolphins are able to use the echolocation calls of conspecific individuals to gather location and activity information (Dawson 1991). Therefore, rather than extending the vocal repertoire to include specialised social calls, social information is available within the echolocation calls themselves. Götz et al. (2006) found a similar result to that observed in bats in that wild rough-toothed dolphins (Steno bredanensis) swimming in synchronised groups usually had only one echolocating animal, and they hypothesised that the other members of the groups were receiving and utilising information regarding prey from the echolocation calls of the signalling individuals.

While echolocation calls are said to be under selection by the size or type of prey being hunted, by the environmental clutter in the signalling animal’s surrounds and phylogenetic history (Dechmann et al. 2013), it is unclear whether these same selective pressures are acting on the echolocation-specific hearing of these species. Dechmann et al. (2009) proposed that it was the evolution of sociality that facilitated the development of sophisticated group foraging

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behaviours such as eavesdropping on the autocommunication signals that have been shown in bats and dolphins. It is therefore possible that sociality has greater importance as an evolutionary driver of hearing in autocommunication systems than in heterocommunication systems or overall mammalian hearing. Researchers have also noted that, similarly to the influence of environment on the overall hearing limits of mammals, the hearing specialisation of echolocating species of bat do show adaptations to the environmental clutter of the habitat in which that species hunts and feeds (Neuweiler 1990; Ey and Fischer 2009). For example, Neuweiler (1990) shows that species that glean their prey from the ground and foliage have lower frequency hearing (~10 – 26 kHz best frequency); species that forage flying insects above vegetation have low frequency hearing (15 – 30 kHz best frequency) in order to echolocate over long distance; and rhinolophid species that hunt close to or within vegetation have higher frequency hearing (>120 kHz best frequency) for hunting prey using their fluttering movements for detection. The theory with the most support for what it is that likely differentiates the hearing capabilities of echolocators and non-echolocators is in the neural processes that allow echolocating species to analyse the time delays and differences between the signal they produce, and the signal that returns to them (Neuweiler 1990; Kanwal and Rauschecker 2007).

While diet did not feature as a strong driver of hearing in this study I feel that it has interesting potential for future research in this area. Perhaps with a modified definition of diet I would have seen a higher impact from this trait. Future study exploring the diet of the species in terms of predator status rather than the category of food type they consume might unveil additional patterns. For example, a categorisation of ‘top-predators, meso-predators, and prey’ might provide more insight into how species are using their acoustic communication pathways in terms of diet. Unfortunately there were only four species in the current hearing dataset that met the criteria of ‘top-predator’. This, in addition with the necessity to merge the remaining carnivores and the omnivores into a ‘meso-predator’ category unfortunately resulted in a heavy skewing of data in the categories, making statistical results ecologically irrelevant. Alternatively, the size of prey might be important if the hearing of the consumer is tuned to eavesdrop on the prey. Such a definition might also mean that diet could be included as a covariate for studying vocalisation frequencies which I had previously considered but eventually removed due to diet under the definition I have used in this study not having an influence on the sounds mammals make.

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Having collated an extensive database of mammalian vocalisation and hearing frequencies I was able to use the equations developed in Chapter 3 to determine where A. pygmaeus should fit in the overarching scheme of mammalian acoustic signalling and signal reception. The results suggested that while A. pygmaeus certainly produces vocalisations in the audible range (20 Hz to 20 kHz) it was also likely that their vocal communication extended well into the ultrasonic range (above 20 kHz).

6.2 A. pygmaeus Audible Range Vocal Repertoire

In Chapter 2 I used an acoustic recorder to record and describe the vocal repertoire of a captive population of A. pygmaeus in the audible range. Australian marsupial gliders, of which A. pygmaeus is the smallest member, are currently an underrepresented group in the database of mammalian vocalisation studies.

The captive population of A. pygmaeus in this study was vocally active over the entire 16 hour recording period (Fig. 2.7), from which I described 10 previously undescribed call types for A. pygmaeus’ audible vocal repertoire. Their diverse repertoire of complex call types and highly vocal behaviour is likely a result of their social lifestyle; living in large groups in a nocturnal environment where vocal communication is a valuable tool.

The hearing sensitivity of A. pygmaeus drops at 8 kHz, followed by an increase in sensitivity to higher frequencies (Aitkin 2012). This drop in hearing sensitivity coincides with the gap I found in the center frequencies at which they produce tonal calls. It would therefore appear that A. pygmaeus produces vocalisations to which it is most sensitive. Aitkin (2012) suggested that the sensitivity to higher frequencies of this nocturnal omnivorous marsupial could be for detecting the sounds of predators. However, as I have shown from both their audible (Chapter 2) and ultrasonic (Chapter 5) vocal repertoire they themselves produce calls in the upper frequency range of their hearing. So whilst they may in part be listening for the sounds of their predators, it is evident that they have also optimised their vocalisations to match their hearing in an excitingly precise way.

Research on the behavioural context in which they produce these calls would be an intriguing follow up to this study. Unfortunately the field of view of the video equipment was unable to cover the entire area of the enclosure and was therefore unable to capture all the instances of

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vocal production. Many of the vocalisations may have been produced out of the field of vision of the camera, or even within the nest boxes. In addition, the speed with which A. pygmaeus travels, both along the vegetation substrate and whilst gliding (personal observation), was often too fast for the motion sensor technology of the camera, such that an animal that triggered the camera was often out of the frame before the camera was able to start recording. Studies dedicated to the behavioural context of A. pygmaeus’ vocalisations might unveil some interesting results about how this mysterious species behave vocally as individuals, in pairs and in groups.

To date acoustic surveying techniques have not been used to monitor this species. Their vocal behaviour was largely unknown, and it is likely this was because many of their calls are low amplitude and produced in the high frequency range, above the human audible hearing range. However, a commercial acoustic recorder can easily detect the frequencies of their calls. At present there is no evidence as to what distance their vocalisations can be detected and further investigation is needed on this subject, however current monitoring studies of the species utilise nest boxes in areas of suitable habitat for counting individuals, and a setup similar to that used in this study, with a recorder on top of or below the nest box would easily detect their vocalisations. With this knowledge of their vocal repertoire, there is enormous potential for this method of monitoring to be implemented in future PAM surveying efforts of this species in areas that are under threat from urbanisation and deforestation.

Having described the audible repertoire for A. pygmaeus I aimed to determine how the frequencies they produce compared to other mammals and whether they perform as would be expected for their minute size, or whether they exhibit exceptional vocal behaviours.

6.3 A. pygmaeus Ultrasonic Vocal Communication

Following on from my description of A. pygmaeus’ audible range vocal repertoire in Chapter 2 my calculations using the results from Chapter 3 suggested their upper frequency limit should have been 48 kHz, a frequency beyond the recording capability of the acoustic recording equipment used in Chapter 2. Using an acoustic recorder specialised to the ultrasonic range I recorded A. pygmaeus’ ultrasonic vocalisations and found that they produce vocalisations up to a surprising 77.4 kHz.

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I found that A. pygmaeus produce ultrasonic versions of two of their audible range call types, as well as ultrasonic components of two more call types. The ultrasonic versions of whistles and chirps were produced entirely in the ultrasonic range (Fig. 6.1). The duration of the ultrasonic chirp and whistle were approximately half that of the audible chirp and whistle and so I conclude that they are not ultrasonic components of the same call. Based on the call characteristics from the audible range (Chapter 2) and ultrasonic data (Chapter 5) the hisses and pulses described in both chapters were the same call types (Fig. 6.1); they extended from the audible range into the ultrasonic range. The click, toot, and LF click trains were exclusively produced in the audible range.

Fig. 6.1 Stylised representation of A. pygmaeus' full repertoire.

Hiss calls are often used in both interspecific and intraspecific agonistic encounters. As the signal to noise ratio decreased it was the ultrasonic component of the call which was lost, this could suggest that the audible part of the call is most important or it may be due to high frequency sound degrading faster than low frequencies. If the former is the case, it is likely because the lower frequency component is important for interactions with members of other species, or alternately it is a warning to members of their own species that may need to be communicated over some distance. In contrast, when signal to noise ratio of pulse calls

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decreased it was the audible part of the calls that was lost. This is likely the reason that fewer pulse trains were detected in the audible range, instead pulses were often observed singularly with longer intervals. The audible component of the pulses was potentially degraded from these calls so that they appeared singularly instead of in trains.

In Chapter 5 I proposed that the pulse call is used by A. pygmaeus for echolocation purposes. The echolocation-first theory (Fenton et al. 1995; Panyutina et al. 2017) proposes that the early ancestors of modern bats possessed the ability to echolocate, if only in a rudimentary sense and that this ability was secondarily lost in the megabats and retained by the microbats. The origin of echolocation is said to have been from small quadrupedal mammals adapted to fast locomotion in complex but poorly-lit environments (Teeling et al. 2012) similar to the nocturnal forest habitat in which A. pygmaeus navigates today. Further, Heffner et al. (2006) propose that the high-frequency hearing of small mammals would have supported initial echolocation in the early evolution of bats.

It is widely believed that bats, the only mammals capable of powered flight, evolved from small arboreal gliders similar to extant gliders, like A. pygmaeus, such that gliding is seen as a rudimentary form of flight (Bishop 2007). It would have been of great advantage to these early gliders to possess a rudimentary form of echolocation when leaping and gliding as a means to navigate and find the best surface to land on in dimly lit habitats.

The initial function of echolocation was believed to have been for orientation rather than prey detection (Panyutina et al. 2017). Even the most primitive use of echoes is enough to aid navigation/orientation. For example humans with vision impairment have been able to develop echolocation skills using mouth clicks (Thaler and Goodale 2016). The reflected echoes can be used to determine aspects of the environment such as location, size, and even the material objects are made of.

At present I am only able to speculate that A. pygmaeus uses pulse trains as a primitive form of echolocation to navigate. Future research on the vocal behaviour of A. pygmaeus focusing on behavioural studies to gain situational context for the call types I have described would be of great interest. It would be particularly interesting to carry out gliding experiments under controlled vision limited situations to test if pulses are used during flight and further testing their specific use in echolocation as a form of navigation.

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6.4 General Conclusion

This thesis has re-examined old concepts and filled gaps in our current knowledge of acoustic communication in mammals. I have described a diverse and complex acoustic repertoire for the worlds’ smallest marsupial glider, A. pygmaeus, which suggests that sociality is also important in driving vocal repertoire size and complexity in marsupials. Using macrocomparison techniques I showed how the vocal signals of A. pygmaeus scale for mammals broadly, while simultaneously re-evaluating which life history traits contribute most to the patterns observed in the frequency limits of both vocalisation and hearing of mammals. From my macrocomparison studies I was able to obtain the theoretical frequency range of A. pygmaeus’ calls and I used those results to identify that further acoustic study was necessary, in the ultrasonic range, to correctly describe their vocal repertoire. Given that gliding in small mammals is believed to be a precursor to the powered flight of bats, and that echolocation is hypothesised to have originated in small arboreal mammals in visually limited environments, it does not seem that much of a stretch to suggest that A. pygmaeus may possess a primitive form of echolocation, just as it exhibits a primitive form of flight in its gliding abilities. I propose that the pulse call may be involved.

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Appendix 1 Supplementary Material for Chapter 3

Minimum and maximum frequencies of vocalisation for 193 mammalian species. Body mass data was obtained from the PanTHERIA database by Jones et al. (2009) and data sources for vocalisation frequency are provided.

140

Low log High log log 10 10 Body Mass 10 Taxon Sociality Environment Freq Low Freq High Body Reference (g) (kHz) Freq (kHz) Freq Mass Acinonyx_jubatus Solitary Terrestrial 0.2 -0.6990 8 0.9031 50578 4.7040 Volodina (2000) Ailuropoda_melanoleuca Solitary Terrestrial 0.2 -0.6990 0.5 -0.3010 118000 5.0719 Charlton et al. (2009) Aonyx_cinerea Social Aquatic 0.28 -0.5528 10.71 1.0298 3528 3.5475 Lemasson et al. (2014) Aotus_trivirgatus Social Terrestrial 0.19 -0.7212 1.95 0.2900 912 2.9602 Moynihan (1964), Hauser (1993) Stirling & Warneke (1971), Page et al. Arctocephalus_forsteri Social Aquatic 0.1 -1.0000 8 0.9031 101250 5.0054 (2002) Stirling & Warneke (1971), Hill et al. Arctocephalus_gazella Social Aquatic 0.09 -1.0458 8.96 0.9523 96600 5.2516 (2001), Page et al. (2002) Norris & Watkins (1971), Richardson et Arctocephalus_philippii Social Aquatic 0.1 -1.0000 0.2 -0.6990 95000 4.9777 al. (1995) Stirling & Warneke (1971), Tripovich et al. (2005), Tripovich et al. (2006), Arctocephalus_pusillus Social Aquatic 0.09 -1.0458 5.5 0.7404 178500 5.2516 141 Tripovich et al. (2008a), Tripovich et al. (2008b)

Arctocephalus_tropicalis Social Aquatic 0.01 -2.0000 7.8 0.8921 92223 4.9648 Hill et al. (2001), Page et al. (2002) Ateles_belzebuth Social Terrestrial 0.4 -0.3979 2.85 0.4548 6692 3.8256 Eisenberg (1976), Hauser (1993) Ateles_fusciceps Social Terrestrial 0.2 -0.6990 3.8 0.5798 9068 3.9575 Eisenberg (1976), Hauser (1993) Ljungblad et al. (1982), Clark & Johnson Balaena_mysticetus Social Aquatic 0.02 -1.6990 3.5 0.5441 79691179 7.9014 (1984), Richardson et al. (1995) Balaenoptera_acutorostrata Solitary Aquatic 0.06 -1.2218 20 1.3010 5587094 6.7472 Richardson et al. (1995) Knowlton et al. (1991), Richardson et Balaenoptera_borealis Social Aquatic 1.5 0.1761 3.5 0.5441 22106252 7.3445 al. (1995) Edds et al. (1993), Richardson et al. Balaenoptera_edeni Social Aquatic 0.07 -1.1549 0.95 -0.0223 20000000 7.3010 (1995) Beamish & Mitchell (1971), Richardson Balaenoptera_musculus Solitary Aquatic 0.01 -2.0000 8 0.9031 154321305 8.1884 et al. (1995), McDonald et al. (2006), Oleson et al. (2007) Balaenoptera_physalus Social Aquatic 0.01 -2.0000 28 1.4472 47506008 7.6767 Richardson et al. (1995) Berardius_arnuxii Social Aquatic 1 0.0000 10.91 1.0378 7000000 4.9648 Rogers & Brown (1999)

Brachyphylla_nana Social Terrestrial 34 1.5315 103 2.0128 37.3 1.5711 Macias et al. (2006) Cacajao_melanocephalus Social Terrestrial 0.57 -0.2441 11.55 1.0626 3122 3.4944 Bezerra et al. (2010) Callicebus_moloch Social Terrestrial 0.2 -0.6990 5.3 0.7243 958 2.9814 Moynihan (1966), Robinson (1979) Callithrix_jacchus Social Terrestrial 0.5 -0.3010 14.8 1.1703 290 2.4627 Epple (1968; 2008), Hauser (1993) Canis_lupus Social Terrestrial 0.09 -1.0458 1.1 0.0414 31757 4.5018 Palacios et al. (2007) Canis_lupus_dingo Social Terrestrial 0.1 -1.0000 10.5 1.0212 31000 1.4914 Deaux & Clarke (2013) Dawbin & Cato (1992), Richardson et al. Caperea_marginata Solitary Aquatic 0.14 -0.8539 0.3 -0.5229 32000000 7.5051 (1995) Cavia_porcellus Social Terrestrial 0.2 -0.6990 30 1.4771 728 2.8621 Berryman (1976) Cebuella_pygmaea Social Terrestrial 0.8 -0.0969 14 1.1461 140 2.1461 Pola & Snowdon (1975), Hauser (1993) Cebus_olivaceus Social Terrestrial 0.8 -0.0969 6.5 0.8129 2788 3.4452 Robinson (1984), Hauser (1993) Ceratotherium_simum Solitary Terrestrial 0.1 -1.0000 - - 2285939 6.3591 Policht et al. (2008) Cercopithecus_cephus Social Terrestrial 0.2 -0.6990 3.51 0.5453 3445 3.5372 Hauser (1993) Cerdocyon_thous Solitary Terrestrial 0.05 -1.3010 2.1 0.3222 5742 3.7590 Brady (1981) 142 Cervus_elaphus Social Terrestrial 0.1 -1.0000 0.2 -0.6990 240867 5.3818 Long et al. (1998) Minami & Kawamichi (1992), Long et al. Cervus_nippon Social Terrestrial 0.13 -0.8861 2.7 0.4314 53000 4.7243 (1998) Chlorocebus_aethiops Social Terrestrial 0.19 -0.7212 5.6 0.7482 3696 3.5677 Hauser (1993) Chrysocyon_brachyurus Solitary Terrestrial 0.05 -1.3010 2.5 0.3979 23325 4.3678 Brady (1981) Crocuta_crocuta Social Terrestrial 0.2 -0.6990 4.02 0.6042 63370 4.8019 East & Hofer (1991), Theis et al. (2007) Cryptomys_anselli Social Terrestrial 0.5 -0.3010 16 1.2041 85.6 1.9324 Credner et al. (1997) Cynictis_penicillata Social Terrestrial 0.3 -0.5229 - - 694 2.8416 Le Roux et al. (2009) Terhune & Ronald (1973), Richardson Cystophora_cristata Solitary Aquatic 0.2 -0.6990 6 0.7782 278897 5.4454 et al. (1995) Dasyurus_hallucatus Solitary Terrestrial 0.4 -0.3979 19 1.2788 471 2.6729 Aitkin et al. (1994), Dempster (1994) Sjare & Smith (1986), Richardson et al. Delphinapterus_leucas Social Aquatic 0.26 -0.5850 20 1.3010 1381641 6.1404 (1995) Diceros_bicornis Solitary Terrestrial 0.02 -1.6990 - - 995941 5.9982 Budde & Klump (2003) Nair & Lalmohan (1975), Richardson et Dugong_dugon Social Aquatic 1 0.0000 8 0.9031 295000 5.4698 al. (1995)

Elephas_maximus Social Terrestrial 0.014 -1.8539 0.02 -1.6990 3269794 6.5145 Payne et al. (1986) Enhydra_lutris Social Aquatic 0.3 -0.5229 13.1 1.1173 27411 4.4379 McShane et al. (1995) Eptesicus_hottentotus Social Terrestrial - - 31.8 1.5024 30.3 1.4819 Schoeman & Jacobs (2003) Equus_africanus Social Terrestrial 0.08 -1.0969 8 0.9031 250000 5.3979 Moehlman (1998) Stirling et al. (1983), Richardson et al. Erignathus_barbatus Solitary Aquatic 0.02 -1.6990 6 0.7782 280000 5.4472 (1995) Fish et al. (1974), Richardson et al. Eschrichtius_robustus Social Aquatic 0.02 -1.6990 2 0.3010 27324024 7.4365 (1995) Eubalaena_australis Social Aquatic 0.03 -1.5229 2.2 0.3424 23000000 7.3617 Clark (1982), Richardson et al. (1995) Eulemur_coronatus Social Terrestrial 0.04 -1.3979 5.35 0.7284 1700 3.2304 Gamba & Giacoma (2007) Felis_silvestris Solitary Terrestrial 0.15 -0.8239 2 0.3010 4573 3.6602 Peters & Tonkin-Leyhausen (1999) Galago_senegalensis Social Terrestrial 0.28 -0.5528 5.44 0.7356 215 2.3328 Hauser (1993) Glaucomys_volans Social Terrestrial 18.5 1.2672 65 1.8129 71.9 1.8567 Murrant et al. (2013) Globicephala_melas Social Aquatic 0.32 -0.4949 21.2 1.3263 800000 5.9031 May-Collado et al. (2007)

143 Gorilla_beringei Social Terrestrial 0.06 -1.2218 8 0.9031 149325 5.1741 Fossey (1972), Hauser (1993)

Watkins (1968), Richardson et al. Grampus_griseus Social Aquatic 0.1 -1.0000 8 0.9031 387500 5.5883 (1995) Asselin et al. (1993), Richardson et al. Halichoerus_grypus Solitary Aquatic 0.1 -1.0000 40 1.6021 197570 5.2957 (1995) Histriophoca_fasciata Solitary Aquatic 0.1 -1.0000 7.1 0.8513 90000 4.9542 Richardson et al. (1995) Stirling & Siniff (1979), Thomas et al. (1983), Richardson et al. (1995), Rogers Hydrurga_leptonyx Solitary Aquatic 0.04 -1.3979 164 2.2148 352675 5.5474 et al. (1995), Rogers et al. (1996), Rogers (2007), Rogers & Cato (2002), Kreiss et al. (2013) Hylobates_agilis Social Terrestrial 0.4 -0.3979 2.4 0.3802 5829 3.7656 Hauser (1993) Hyperoodon_ampullatus Social Aquatic 3 0.4771 26 1.4150 3393361 6.5306 Richardson et al. (1995) Ictonyx_striatus Solitary Terrestrial 0.3 -0.5229 8 0.9031 811 2.9090 Channing & Rowe-Rowe (1977) Inia_geoffrensis Solitary Aquatic 0.22 -0.6576 48.1 1.6821 121431 5.0843 May-Collado et al. (2007) Lagenodelphis_hosei Social Aquatic 7.6 0.8808 13.4 1.1271 164000 5.2148 Richardson et al. (1995)

Lagenorhynchus_acutus Social Aquatic 6 0.7782 15 1.1761 186518 5.2707 Steiner (1981), Richardson et al. (1995) Watkins & Schevill (1972), Richardson Lagenorhynchus_albirostris Social Aquatic 3 0.4771 35 1.5441 186635 5.2710 et al. (1995), Ramussen & Miller (2002) Lagenorhynchus_obliquidens Social Aquatic 2 0.3010 20 1.3010 109694 5.0402 Caldwell & Caldwell (1970) Lagenorhynchus_obscurus Social Aquatic 1 0.0000 27.3 1.4362 127500 5.1055 Richardson et al. (1995) Lemur_catta Social Terrestrial 0.24 -0.6198 2.35 0.3711 2626 3.4194 Macedonia (1986), Hauser (1993) Thomas & Stirling (1983), Richardson et Leptonychotes_weddellii Solitary Aquatic 0.1 -1.0000 12.8 1.1072 400000 5.6021 al. (1995), Collins et al. (2005) Lipotes_vexillifer Solitary Aquatic 3.8 0.5798 4.6 0.6628 112138 5.0498 May-Collado et al. (2007) Stirling & Siniff (1979), Richardson et al. Lobodon_carcinophaga Solitary Aquatic 0.1 -1.0000 8 0.9031 225000 5.3522 (1995), Klinck et al. (2010) Lophocebus_albigena Social Terrestrial 0.2 -0.6990 1.23 0.0899 7690 3.8859 Hauser (1993) Loxodonta_africana Social Terrestrial 0.01 -2.0000 8 0.9031 3824540 6.5826 Berg (1983) Lycaon_pictus Social Terrestrial 0.2 -0.6990 14 1.1461 22000 4.3424 Robbins (2000)

144 Macaca_arctoides Social Terrestrial 0.25 -0.6021 7.4 0.8692 9358 3.9712 Hauser (1993) Macaca_fascicularis Social Terrestrial 0.4 -0.3979 4.9 0.6902 4569 3.6599 Palombit (1992), Hauser (1993)

Macaca_fuscata Social Terrestrial 0.17 -0.7696 4.9 0.6902 10115 4.0050 Hauser (1993) Macaca_mulatta Social Terrestrial 0.2 -0.6990 4.32 0.6355 6455 3.8099 Hauser (1991; 1993) Gouzoules & Gouzoules (1989), Hauser Macaca_nemestrina Social Terrestrial 0.2 -0.6990 3.41 0.5328 7821 3.8933 (1993) Macaca_radiata Social Terrestrial 0.15 -0.8239 10.36 1.0154 5000 3.6990 Hohmann (1989), Hauser (1993) Macaca_silenus Social Terrestrial 0.15 -0.8239 0.48 -0.3188 5995 3.7778 Hauser (1993) Mandrillus_sphinx Social Terrestrial 0.2 -0.6990 3 0.4771 16685 4.2223 Kudo (1987), Hauser (1993) Marmota_monax Solitary Terrestrial 2.7 0.4314 4.8 0.6812 3881 3.5889 Lloyd (1972) Martes_americana Solitary Terrestrial 0.25 -0.6021 8 0.9031 874 2.9414 Belan et al. (1978) Thompson et al. (1986), Richardson et Megaptera_novaeangliae Social Aquatic 0.02 -1.6990 8 0.9031 30000000 7.4771 al. (1995) Meles_meles Solitary Terrestrial 0.02 -1.6990 0.5 -0.3010 11884 4.0750 Wong et al. (1999) Mesoplodon_carlhubbsi Solitary Aquatic 3 0.4771 80 1.9031 3400000 6.5315 Richardson et al. (1995) Mesoplodon_densirostris Solitary Aquatic 1 0.0000 6 0.7782 2300000 6.3617 Richardson et al. (1995)

Miniopterus_schreibersii Social Terrestrial - - 53.6 1.7292 11.5 1.0592 Schoeman & Jacobs (2003) Miopithecus_talapoin Social Terrestrial 0.15 -0.8239 20 1.3010 1250 3.0969 Gautier (1974) Mirounga_leonina Solitary Aquatic 0.015 -1.8239 0.9 -0.0458 1112391 6.0463 Sanvito & Galimberti (2000) Ford & Fisher (1978), Richardson et al. Monodon_monoceros Social Aquatic 0.3 -0.5229 18 1.2553 938126 5.9723 (1995) Mus_musculus Social Terrestrial 30 1.4771 110 2.0414 19.3 1.2856 Holy & Guo (2005) Mustela_eversmanii Solitary Terrestrial 1.6 0.2041 6.3 0.7993 1684 3.2264 Farley et al. (1987) Mustela_frenata Solitary Terrestrial 0.5 -0.3010 6 0.7782 190 2.2788 Svensden (1976) Mustela_nivalis Solitary Terrestrial 1.5 0.1761 8 0.9031 78.5 1.8946 Huff & Price (1968) Myotis_tricolor Social Terrestrial - - 50 1.6990 13.7 1.1358 Schoeman & Jacobs (2003) Nasua_narica Social Terrestrial - - 17.9 1.2529 4578 3.6607 Compton et al. (2001) Neofelis_nebulosa Solitary Terrestrial 0.1 -1.0000 6.6 0.8195 14945 4.1745 Peters & Tonkin-Leyhausen (1999) Charrier & Harcourt (2006), Gwilliam et Neophoca_cinerea Social Aquatic 0.4 -0.3979 3.4 0.5315 189275 5.2771 al. (2008), Attard et al. (2010)

145 Neophocaena_phocaenoides Social Aquatic 1.6 0.2041 2.2 0.3424 32500 4.5119 Richardson et al. (1995)

Notomys_alexis Social Terrestrial 1.5 0.1761 55 1.7404 32.3 1.5097 Watts (1975) Notomys_cervinus Social Terrestrial 1.5 0.1761 40 1.6021 34.8 1.5415 Watts (1975) Notomys_fuscus Social Terrestrial 1.5 0.1761 70 1.8451 39.3 1.5942 Watts (1975) Notomys_mitchellii Social Terrestrial 1.5 0.1761 40 1.6021 42.8 1.6314 Watts (1975) Nycticebus_coucang Solitary Terrestrial 0.13 -0.8861 5.95 0.7745 925 2.9659 Zimmermann (1985) Stirling et al. (1983), Richardson et al. Odobenus_rosmarus Social Aquatic 0.1 -1.0000 10 1.0000 1042996 6.0183 (1995) Odocoileus_virginianus Solitary Terrestrial 0.16 -0.7959 0.67 -0.1739 75901 4.8802 Richardson et al. (1983) Ommatophoca_rossii Solitary Aquatic 0.25 -0.6021 4 0.6021 208252 5.3186 Watkins & Ray (1985) Onychomys_leucogaster Solitary Terrestrial - - 64 1.8062 27.9 1.4459 Hafner & Hafner (1979) Orcinus_orca Social Aquatic 1.5 0.1761 18 1.2553 5628759 6.7504 May-Collado et al. (2007) Ovibos_moschatus Social Terrestrial 0.09 -1.0458 3.5 0.5441 312500 5.4949 Frey et al. (2006) Pagophilus_groenlandicus Social Aquatic 0.1 -1.0000 16 1.2041 132250 5.1214 Richardson et al. (1995) Pan_paniscus Social Terrestrial 0.2 -0.6990 2.9 0.4624 35120 4.5456 de Waal (1988), Hauser (1993)

Pan_troglodytes Social Terrestrial 0.2 -0.6990 2.23 0.3483 45000 4.6532 Hauser (1993) Panthera_onca Solitary Terrestrial 0.1 -1.0000 5 0.6990 83943 4.9240 Peters & Tonkin-Leyhausen (1999) Panthera_tigris Solitary Terrestrial 0.1 -1.0000 10 1.0000 161915 5.2093 Peters & Tonkin-Leyhausen (1999) Pardofelis_marmorata Solitary Terrestrial 0.15 -0.8239 5 0.6990 2827 3.4513 Peters & Tonkin-Leyhausen (1999) Petaurus_australis Social Terrestrial 0.7 -0.1549 6.4 0.8062 567 2.7537 Goldingay (1994) Phascolarctos_cinereus Solitary Terrestrial 1 0.0000 5 0.6990 6529 3.8148 Smith (1980), Charlton et al. (2011) Renouf et al. (1980), Hanggi & Phoca_vitulina Solitary Aquatic 0.1 -1.0000 40 1.6021 87317 4.9411 Schusterman (1994), Richardson et al. (1995) Phocoena_phocoena Social Aquatic 2 0.3010 - - 52731 4.7221 Richardson et al. (1995) Phocoenoides_dalli Social Aquatic 0.04 -1.3979 12 1.0792 106043 5.0255 Richardson et al. (1995) Levenson (1974), Richardson et al. Physeter_catodon Social Aquatic 0.1 -1.0000 30 1.4771 14540960 7.1626 (1995), Madsen et al. (2002) Piliocolobus_badius Social Terrestrial 0.75 -0.1249 4.6 0.6628 8240 3.9159 Hauser (1993)

146 Pithecia_pithecia Social Terrestrial 0.95 -0.0223 6.75 0.8293 1667 3.2220 Henline (2007)

Poecilogale_albinucha Solitary Terrestrial 0.35 -0.4559 8 0.9031 308 2.4888 Channing & Rowe-Rowe (1977) Pongo_pygmaeus Solitary Terrestrial 0.2 -0.6990 0.5 -0.3010 53408 4.7276 Mackinnon (1974), Hauser (1993) Procapra_gutturosa Social Terrestrial 0.4 -0.3979 2.4 0.3802 28274 4.4514 Frey & Gebler (2003) Procavia_capensis Social Terrestrial 0.1 -1.0000 15 1.1761 2952 3.4702 Fourie (1977) Procyon_lotor Solitary Terrestrial 0.16 -0.7959 5.4 0.7324 6374 3.8044 Sieber (1984) Pseudomys_australis Social Terrestrial 0.2 -0.6990 40 1.6021 53.0 1.7240 Watts (1976) Pseudorca_crassidens Social Aquatic 1.87 0.2718 18.1 1.2577 1360000 6.1335 May-Collado et al. (2007) Pteronura_brasiliensis Social Aquatic 0.31 -0.5086 9.53 0.9791 2600 3.4150 Bezerra et al. (2011) Pteropus_poliocephalus Social Terrestrial 0.35 -0.4559 16 1.2041 703 2.8468 Nelson (1964) Puma_concolor Solitary Terrestrial 0.15 -0.8239 5 0.6990 53954 4.7320 Peters & Tonkin-Leyhausen (1999) Stirling (1973), Cummings et al. (1984), Pusa_hispida Solitary Aquatic 0.4 -0.3979 16 1.2041 70964 4.8510 Richardson et al. (1995) Rattus_rattus Social Terrestrial 1 0.0000 70 1.8451 143 2.1544 Kaltwasser (1990) Reithrodontomys_fulvescens Solitary Terrestrial 8.1 0.9085 13.5 1.1303 11.6 1.0626 Miller & Engstrom (2010) Reithrodontomys_mexicanus Social Terrestrial 5.6 0.7482 13.3 1.1239 15.7 1.1962 Miller & Engstrom (2010)

Rhinolophus_affinis Social Terrestrial - - 76 1.8808 13.7 1.1367 Heller & Helversen (1989) Rhinolophus_alcyone Social Terrestrial - - 80 1.9031 18.6 1.2700 Roberts (1972) Rhinolophus_blasii Social Terrestrial - - 86.6 1.9375 10.3 1.0124 Jacobs et al. (2007) Rhinolophus_capensis Social Terrestrial - - 83.9 1.9238 12.9 1.1096 Jacobs et al. (2007) Rhinolophus_darlingi Social Terrestrial - - 88.1 1.9450 8.9 0.9513 Jacobs et al. (2007) Rhinolophus_denti Social Terrestrial - - 110.9 2.0449 6.3 0.7993 Jacobs et al. (2007) Rhinolophus_euryale Social Terrestrial - - 104 2.0170 9.3 0.9661 Heller & Helversen (1989) Rhinolophus_ferrumequinum Social Terrestrial - - 81 1.9085 22.6 1.3539 Heller & Helversen (1989) Rhinolophus_fumigatus Social Terrestrial - - 53.8 1.7308 13.1 1.1169 Jacobs et al. (2007) Rhinolophus_hildebrandtii Social Terrestrial - - 33 1.5185 26.0 1.4148 Jacobs et al. (2007) Rhinolophus_hipposideros Solitary Terrestrial - - 110 2.0414 4.6 0.6599 Heller & Helversen (1989) Rhinolophus_landeri Social Terrestrial 91.7 1.9624 104 2.0170 9.4 0.9727 Fenton & Fullard (1979) Rhinolophus_luctus Solitary Terrestrial - - 35 1.5441 34 1.5324 Roberts (1972) Rhinolophus_macrotis Social Terrestrial - - 48 1.6812 6.2 0.7910 Heller & Helversen (1989)

147 Rhinolophus_megaphyllus Social Terrestrial 62.4 1.7952 71 1.8513 10.2 1.0073 Fenton (1982)

Rhinolophus_sedulus Solitary Terrestrial - - 64 1.8062 8.7 0.9415 Heller & Helversen (1989) Rhinolophus_simulator Social Terrestrial - - 80.6 1.9063 8.1 0.9101 Jacobs et al. (2007) Rhinolophus_stheno Social Terrestrial - - 86 1.9345 7.9 0.8987 Heller & Helversen (1989) Rhinolophus_swinnyi Social Terrestrial - - 107 2.0294 7.1 0.8494 Jacobs et al. (2007) Rhinolophus_trifoliatus Solitary Terrestrial - - 51 1.7076 15.2 1.1807 Heller & Helversen (1989) Rhinopoma_hardwickii Social Terrestrial 18 1.2553 80 1.9031 13.1 1.1173 Simmons et al. (1984) Saguinus_fuscicollis Social Terrestrial 3 0.4771 8.25 0.9165 394 2.5955 Hauser (1993) Cleveland & Snowdon (1982), Hauser Saguinus_oedipus Social Terrestrial 1.15 0.0607 10.4 1.0170 462 2.6647 (1993) Saimiri_sciureus Social Terrestrial 0.1 -1.0000 16 1.2041 749 2.8748 Newman (1985), Hauser (1993) Sarcophilus_harrisii Solitary Terrestrial 0.12 -0.9208 4 0.6021 8202 3.9139 Eisenberg et al. (1975) Semnopithecus_entellus Social Terrestrial 0.15 -0.8239 4.74 0.6758 1715 3.2343 Hauser (1993) Sotalia_fluviatilis Social Aquatic 3.6 0.5563 23.9 1.3784 42833 4.6318 Richardson et al. (1995)

Schultz & Corkeron (1994), Richardson Sousa_chinensis Social Aquatic 1.2 0.0792 16 1.2041 280000 5.4472 et al. (1995) Spalacopus_cyanus Social Terrestrial 0.17 -0.7696 20.33 1.3081 101 2.0037 Veitl et al. (2000) Capranica et al. (1974), Credner et al. Spalax_ehrenbergi Solitary Terrestrial 0.5 -0.3010 8 0.9031 164 2.2158 (1997) Speothos_venaticus Social Terrestrial 0.05 -1.3010 1.6 0.2041 6325 3.8010 Brady (1981) Spermophilus_beldingi Social Terrestrial 4.2 0.6232 5.6 0.7482 273 2.4354 Leger et al. (1984) Stenella_attenuata Social Aquatic 3.1 0.4914 21.4 1.3304 65734 4.8178 Richardson et al. (1995) Stenella_coeruleoalba Social Aquatic 6 0.7782 24 1.3802 142103 5.1526 Richardson et al. (1995) Stenella_frontalis Social Aquatic 5 0.6990 19.8 1.2967 110000 5.0414 Richardson et al. (1995) Stenella_longirostris Social Aquatic 1 0.0000 22.5 1.3522 50500 4.7033 Richardson et al. (1995) Richardson et al. (1995), May-Collado Steno_bredanensis Social Aquatic 4 0.6021 7 0.8451 130000 5.1139 et al. (2007) Suricata_suricatta Social Terrestrial 0.6 -0.2218 11 1.0414 730 2.8633 Moran (1984)

148 Tamias_striatus Solitary Terrestrial 0.7 -0.1549 8 0.9031 89.9 1.9536 Brenner et al. (1978) Tapirus_terrestris Solitary Terrestrial 0.1 -1.0000 8 0.9031 169497 5.2292 Hunsaker & Hahn (1965)

Theropithecus_gelada Social Terrestrial 0.22 -0.6576 2.47 0.3927 15964 4.2031 Hauser (1993) Trachypithecus_johnii Social Terrestrial 0.15 -0.8239 5.12 0.7093 1150 3.0607 Hauser (1993) Evans & Gerald (1970), Richardson et Trichechus_inunguis Solitary Aquatic 6 0.7782 16 1.2041 418001 5.6212 al. (1995) Trichechus_manatus Solitary Aquatic 0.6 -0.2218 16 1.2041 467325 5.6696 Richardson et al. (1995) Trichosurus_vulpecula Solitary Terrestrial 1 0.0000 12 1.0792 2685 3.4290 Aitken et al. (1978) Tupaia_belangeri Solitary Terrestrial 0.4 -0.3979 15 1.1761 200 2.3010 Binz & Zimmerman (1989) Tursiops_truncatus Social Aquatic 0.94 -0.0269 41 1.6128 281041 5.4488 May-Collado et al. (2007) Uncia_uncia Solitary Terrestrial 0.1 -1.0000 5.8 0.7634 32500 4.5119 Peters & Tonkin-Leyhausen (1999) Varecia_variegata Social Terrestrial 0.2 -0.6990 0.85 -0.0706 3850 3.5855 Hauser (1993) Vulpes_lagopus Social Terrestrial 0.85 -0.0706 3 0.4771 3584 3.5544 Frommolt et al. (2003) Schusterman et al. (1967), Richardson Zalophus_californianus Social Aquatic 0.5 -0.3010 8 0.9031 137195 5.1373 et al. (1995)

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159

Appendix 2 Supplementary Results for Chapter 3

A comparison of the level of support for possible explanatory models that describe the evolution of minimum (top) and maximum (bottom) frequency in vocalisations of mammals. Excludes species for which interpolated data was used in the making of the phylogeny.

95% CI of slope 95% CI Weighted Effect Model ∆AICc parameter PGLS  (Lower, AICc size (r) (Lower, Upper) Upper)

0+mass+environment 0.00 0.6424 -0.50, -0.30 0.55 0.55, 0.56 0.54  + + 0 mass environment 2.11 0.2237 -0.50, -0.29 0.55 0.55, 0.56 0.54 sociality -0.21, 0.26 T

0+mass*environment 4.25 0.0767 -0.44, 0.49 S 0.53 0.53, 0.54 0.54 -0.62, -0.22 A

0+mass 4.84 0.0571 -0.41, -0.19 0.79 0.79, 0.79 0.40

0 26.35 0.0000 - 0.89 0.88, 0.89 -

95% CI of slope 95% CI Weighted Effect Model ∆AICc parameter PGLS (Lower, AICc size (r) (Lower, Upper) Upper) -0.38, -0.06 T

0+mass*environment 0.00 0.7781 -0.46, 0.30 S 0.32 0.32, 0.32 0.56 -0.25, 0.03 A

0+mass+environment 3.11 0.1643 -0.33, -0.19 0.43 0.43, 0.43 0.49  + + 0 mass environment 5.21 0.0575 -0.33, -0.19 0.43 0.43, 0.44 0.49 sociality

0+mass 19.01 0.0001 -0.22, -0.06 0.71 0.70, 0.71 0.26

0 27.96 0.0000 - 0.78 0.78, 0.78 -

T is terrestrial, S is semi-aquatic and A is aquatic.

160

Appendix 3 Supplementary Material for Chapter 4

Minimum and maximum frequencies of hearing for 126 mammalian species. Body mass data was obtained from the PanTHERIA database by Jones et al. (2009) and data sources for hearing frequency are provided.

Bold references indicate the papers the selected values were taken from.

For semi-aquatic species the medium in which the value was measured is indicated by * for in- air and ** for underwater.

161

Low log High log log 10 10 Body Mass 10 Taxon Sociality Environment Diet Freq Low Freq High Body Method Reference (g) (kHz) Freq (kHz) Freq Mass Heffner et al. Acomys_cahirinus Social Terrestrial Omnivore 2.3 0.3617 71 1.8513 41.2 1.6145 Behavioural (2001) Stansbury et al. Alopex_lagopus Social Terrestrial Carnivore 0.125 -0.9031 16 1.2041 3584 3.5544 Behavioural (2014) Beecher (1974); Aotus_trivirgatus Social Terrestrial Omnivore - - 49.5 1.6946 912 2.9602 Behavioural Heffner (2004) Heffner et al. Artibeus_jamaicensis Social Terrestrial Herbivore 2.8 0.4472 131 2.1173 43.6 1.6398 Behavioural (2003) Ateles_paniscus Social Terrestrial Omnivore 0.064 -1.1938 16.4 1.2144 8697.3 3.9394 Behavioural Wendt (1934) Heffner and Bos_primigenius Social Terrestrial Herbivore 0.025 -1.6021 35 1.5441 618642 5.7914 Behavioural Heffner (1990a) Seiden (1958); Callithrix_jacchus Social Terrestrial Omnivore - - 30 1.4771 290 2.4627 Behavioural Heffner (2004) 162 Moore &

Schusterman Callorhinus_ursinus Solitary Semi-Aquatic Carnivore 0.5* -0.3010 42** 1.6232 55465 4.7440 Behavioural (1987); Richardson et al. (1995) Heffner and Capra_aegagrus Social Terrestrial Herbivore 0.078 -1.1079 37 1.5682 47386 4.6757 Behavioural Heffner (1990a) Koay et al. Carollia_perspicillata Social Terrestrial Herbivore 5.2 0.7160 150 2.1761 19.2 1.2840 Behavioural (2003) Heffner, Heffner Cavia_aperea Social Terrestrial Herbivore 0.054 -1.2676 50 1.6990 728 2.8621 Behavioural and Masterton (1971) Cercocebus_torquatus Social Terrestrial Omnivore 0.064 -1.1938 16.4 1.2144 1209.6 3.0826 Behavioural Wendt (1934) Brown and Cercopithecus_mitis Social Terrestrial Omnivore 0.046 -1.3372 48 1.6812 5041 3.7025 Behavioural Waser (1984); Heffner (2004)

Owren et al. Cercopithecus_neglectus Social Terrestrial Omnivore 0.063 -1.2007 43 1.6335 5325 3.7263 Behavioural (1988); Heffner (2004) Heffner and Chinchilla_lanigera Social Terrestrial Herbivore 0.05 -1.3010 33 1.5185 480 2.6815 Behavioural Heffner (1991) Owren et al. Chlorocebus_aethiops Social Terrestrial Omnivore 0.069 -1.1612 45 1.6532 3696 3.5677 Behavioural (1988); Heffner (2004) Bruckman and Cryptomys_anselli Social Terrestrial Herbivore 0.25 -0.6021 18 1.2553 85.6 1.9324 Behavioural Burda (1997) Gerhardt et al. Cryptomys_mechowi Social Terrestrial Herbivore 0.125 -0.9031 4 0.6021 271.5 2.4338 AEP (2017) Gerhardt et al. Cryptomys_micklemi Social Terrestrial Herbivore 0.125 -0.9031 4 0.6021 108.6 2.0358 AEP (2017) Heffner et al. Cynomys_leucurus Social Terrestrial Herbivore 0.044 -1.3565 26 1.4150 964 2.9840 Behavioural (1994a) 163 Heffner et al. Cynomys_ludovicianus Social Terrestrial Herbivore 0.029 -1.5376 26 1.4150 797 2.9015 Behavioural

(1994a) Heffner et al. Cynopterus_brachyotis Social Terrestrial Herbivore 2.63 0.4200 70 1.8451 33.9 1.5298 Behavioural (2006) Aitkin et al. Dasyurus_hallucatus Solitary Terrestrial Carnivore 0.5 -0.3010 40 1.6021 471 2.6729 ABR (1994) Klishin et al. Delphinapterus_leucas Social Aquatic Carnivore 8 0.9031 128 2.1072 1381641 6.1404 AEP (2000) Heffner et al. Desmodus_rotundus Social Terrestrial Carnivore 0.716 -0.1451 113 2.0531 33.2 1.5206 Behavioural (2013) Ravizza et al. Didelphis_virginiana Solitary Terrestrial Omnivore 0.5 -0.3010 60 1.7782 2442 3.3878 Behavioural (1969a) Heffner and Dipodomys_merriami Solitary Terrestrial Herbivore 0.05 -1.3010 62 1.7924 37.9 1.5788 Behavioural Masterton (1980) Heffner et al. Eidolon_helvum Social Terrestrial Herbivore 1.38 0.1399 41 1.6128 255 2.4059 Behavioural (2006)

Heffner and Elephas_maximus Social Terrestrial Herbivore 0.017 -1.7696 10.5 1.0212 3269794 6.5145 Behavioural Heffner (1980) Ghoul and Reichmuth (2012); Ghoul Enhydra_lutris Social Semi-Aquatic Carnivore 0.125** -0.9031 38.1* 1.5809 27411 4.4379 Behavioural and Reichmuth (2014a); Ghoul and Reichmuth (2014b) Koay et al. Eptesicus_fuscus Social Terrestrial Carnivore 4 0.6021 100 2.0000 17.5 1.2428 Behavioural (1997) Heffner and Equus_ferus Social Terrestrial Herbivore 0.055 -1.2596 33.5 1.5250 403599 5.6059 Behavioural Heffner (1990a) Smith et al. Erythrocebus_patas Social Terrestrial Omnivore 0.245 -0.6108 30.5 1.4843 7966 3.9013 Behavioural (1987); Heffner (2004) 164 Eulemur_fulvus Social Terrestrial Herbivore 0.072 -1.1427 43 1.6335 2377 3.3760 Behavioural Heffner (2004)

Kastelein et al. (2005); Muslow Eumetopias_jubatus Social Semi-Aquatic Carnivore 0.25* -0.6021 32** 1.5051 382467 5.5826 Behavioural and Reichmuth (2010); Muslow et al. (2011) Heffner and Felis_catus Solitary Terrestrial Carnivore 0.048 -1.3188 85 1.9294 2885 3.4601 Behavioural Heffner (1985b) Montie et al. Feresa_attenuata Social Aquatic Carnivore 5 0.6990 110 2.0414 170000 5.2304 AEP (2011) Heffner et al. Galago_senegalensis Social Terrestrial Omnivore 0.092 -1.0362 65 1.8129 215 2.3328 Behavioural (1969b); Heffner (2004) Heffner and Geomys_bursarius Solitary Terrestrial Herbivore 0.35 -0.4559 8.7 0.9395 204 2.3091 Behavioural Heffner (1990b) Greenhow et al. Globicephala_macrorhynchus Social Aquatic Carnivore 5 0.6990 80 1.9031 726000 5.8609 AEP (2013)

Pacini et al. Globicephala_melas Social Aquatic Carnivore 4 0.6021 100 2.0000 800000 5.9031 AEP (2010) Nachtigall et al. (2008); Grampus_griseus Social Aquatic Carnivore 1.6 0.2041 150 2.1761 387500 5.5883 AEP Richardson et al. (1995) Nachtigall et al. (1995); Grampus_griseus Social Aquatic Carnivore 1.6 0.2041 100 2.0000 387500 5.5883 Behavioural Richardson et al. (1995) Ruser et al. Halichoerus_grypus Solitary Semi-Aquatic Carnivore 2* 0.3010 20* 1.3010 197570 5.2957 ABR (2014) Ravizza et al. Hemiechinus_auritus Solitary Terrestrial Carnivore 0.25 -0.6021 45 1.6532 322 2.5079 Behavioural (1969b) Heffner and Heterocephalus_glaber Social Terrestrial Herbivore 0.065 -1.1871 11.5 1.0607 39.4 1.5951 Behavioural Heffner (1993) 165 Homo_sapiens Social Terrestrial Omnivore 0.031 -1.5086 17.6 1.2455 58541 4.7675 Behavioural Heffner (2004)

Jacobs and Hall Inia_geoffrensis Solitary Aquatic Carnivore 1 0.0000 105 2.0212 121431 5.0843 Behavioural (1972) Nachtigall et al. Lagenorhynchus_albirostris Social Aquatic Carnivore 16 1.2041 181 2.2577 186635 5.2710 AEP (2008) Tremel et al. Lagenorhynchus_obliquidens Social Aquatic Carnivore 0.5 -0.3010 140 2.1461 109694 5.0402 Behavioural (1998) Gillette et al. Lemur_catta Social Terrestrial Herbivore - - 58 1.7634 2626 3.4194 Behavioural (1973); Heffner (2004) Ding Wang et al. (1992); Lipotes_vexillifer Solitary Aquatic Carnivore 1 0.0000 200 2.3010 112138 5.0498 Behavioural Richardson et al. (1995) Lophocebus_albigena Social Terrestrial Herbivore 0.063 -1.2007 32 1.5051 7419 3.8703 Behavioural Brown (1986)

Stebbins et al. Macaca_fascicularis Social Terrestrial Omnivore - - 42 1.6232 4569 3.6599 Behavioural (1966); Heffner (2004) Jackson et al. Macaca_fuscata Social Terrestrial Omnivore 0.028 -1.5528 34.5 1.5378 10115 4.0050 Behavioural (1999); Heffner (2004) Pfingst et al. (1975, 1978); Lonsbury-Martin Macaca_mulatta Social Terrestrial Omnivore - - 42 1.6232 6455 3.8099 Behavioural and Martin (1981); Bennett et al. (1983); Heffner (2004) Stebbins et al. Macaca_nemestrina Social Terrestrial Omnivore - - 34.5 1.5378 7821 3.8933 Behavioural (1966); Heffner (2004)

166 Heffner et al. Marmota_monax Solitary Terrestrial Herbivore 0.04 -1.3979 27.5 1.4393 3881 3.5889 Behavioural

(2001) Schmidt et al. Megaderma_lyra Social Terrestrial Carnivore 1 0.0000 130 2.1140 39.3 1.5941 Behavioural (1983) Meriones_unguiculatus Social Terrestrial Herbivore 0.1 -1.0000 60 1.7782 57.8 1.7616 Behavioural Ryan (1976) Heffner et al. Mesocricetus_auratus Solitary Terrestrial Omnivore 0.096 -1.0177 46.5 1.6675 98.6 1.9939 Behavioural (2001) Pacini et al. Mesoplodon_densirostris Solitary Aquatic Carnivore 5 0.6990 160 2.2041 2300000 6.3617 AEP (2011) Finneran et al. Mesoplodon_europaeus Solitary Aquatic Carnivore 20 1.3010 90 1.9542 5600000 6.7482 AEP (2009) Schopf et al. Microcebus_murinus Social Terrestrial Omnivore 0.75 -0.1249 44.9 1.6522 69 1.8388 BERA (2014) Kastak and Schustermann Mirounga_angustirostris Solitary Semi-Aquatic Carnivore 0.075** -1.1249 55** 1.7404 1112391 6.0463 Behavioural (1999); Reichmuth et al. (2013)

Thomas et al. (1990); Monachus_schauinslandi Solitary Semi-Aquatic Carnivore 2** 0.3010 40** 1.6021 223000 5.3483 Behavioural Richardson et al. (1995) Frost and Monodelphis_domestica Solitary Terrestrial Omnivore 3.6 0.5563 77 1.8865 93.5 1.9706 Behavioural Masterton (1994) Heffner and Mus_musculus Social Terrestrial Omnivore 2.3 0.3617 92 1.9638 19.3 1.2856 Behavioural Masterton (1980) Heffner and Mustela_nivalis Solitary Terrestrial Carnivore 0.051 -1.2924 60.5 1.7818 78.5 1.8946 Behavioural Heffner (1985a) Kelly et al. Mustela_putorius Solitary Terrestrial Carnivore 0.036 -1.4437 44 1.6435 976 2.9892 Behavioural (1986) Ayrapet'yants and Myotis_blythii Social Terrestrial Carnivore 0.5 -0.3010 160 2.2041 21.23 1.3270 Behavioural 167 Konstantinov

(1974) Myotis_lucifugus Social Terrestrial Carnivore 10 1 100 2 7.8 0.8921 Behavioural Dalland (1965) Lucke et al. Neophoca_cinerea Social Semi-Aquatic Carnivore 2* 0.3010 - - 189275 5.2771 ABR (2016) Popov et al. Neophocaena_phocaenoides Social Aquatic Carnivore 8 0.9031 152 2.1818 32500 4.5119 AEP (2005) Heffner and Neotoma_floridana Solitary Terrestrial Herbivore 0.94 -0.0269 56 1.7482 249 2.3965 Behavioural Heffner (1985) Brandt et al. Neovison_vison Solitary Semi-Aquatic Carnivore 1* 0.0000 70* 1.8451 1045 3.0193 ABR (2013) Wenstrup Noctilio_leporinus Social Terrestrial Carnivore 8 0.9031 100 2 29.93 1.4761 Behavioural (1984) Heffner and Masterton Nycticebus_coucang Solitary Terrestrial Herbivore 0.083 -1.0809 44 1.6435 925 2.9659 Behavioural (1970); Heffner (2004)

Kastelein et al. (1996); Odobenus_rosmarus Social Semi-Aquatic Carnivore 0.125** -0.9031 15** 1.1761 1042996 6.0183 Behavioural Kastelein et al. (2002b) Heffner and Odocoileus_virginianus Solitary Terrestrial Herbivore 0.115 -0.9393 54 1.7324 75901 4.8802 Behavioural Heffner (2010) Heffner and Onychomys_leucogaster Solitary Terrestrial Carnivore 1.85 0.2672 69 1.8388 27.9 1.4459 Behavioural Heffner (1985) Szymanski et al. Orcinus_orca Social Aquatic Carnivore 1 0.0000 100 2.0000 5628759 6.7504 Behavioural (1999) Heffner and Oryctolagus_cuniculus Social Terrestrial Herbivore 0.096 -1.0177 49 1.6902 1591 3.2016 Behavioural Masterton (1980) Heffner and Ovis_aries Social Terrestrial Herbivore 0.125 -0.9031 42 1.6232 39098 4.5922 Behavioural Heffner (1990a) Terhune 168 &Ronald (1972); Pagophilus_groenlandicus Social Semi-Aquatic Carnivore 1** 0.0000 100** 2.0000 132250 5.1214 Behavioural

Richardson et al. (1995) Elder (1934); Pan_troglodytes Social Terrestrial Omnivore - - 28.5 1.4548 45000 4.6532 Behavioural Kojima (1990); Heffner (2004) Papio_anubis Social Terrestrial Omnivore 0.064 -1.1938 16.4 1.2144 17728.6 4.2487 Behavioural Wendt (1934) Hienz et al. Papio_cynocephalus Social Terrestrial Omnivore 0.045 -1.3468 40 1.6021 15822 4.1993 Behavioural (1982); Heffner (2004) Heffner and Masterton Perodicticus_potto Solitary Terrestrial Omnivore 0.125 -0.9031 42 1.6232 1082 3.0342 Behavioural (1970); Heffner (2004) Niaussat and Phaner_furcifer Solitary Terrestrial Herbivore 0.15 -0.8239 70 1.8451 410 2.6126 Behavioural Molin (1978); Coleman (2009)

Phoca_largha Solitary Semi-Aquatic Carnivore 0.075* -1.1249 72.4** 1.8597 98879 4.9951 Behavioural Sills et al. (2014) Wolski et al. (2003); Kastelein Phoca_vitulina Solitary Semi-Aquatic Carnivore 0.1** -1.0000 72** 1.8573 87317 4.9411 Behavioural et al. (2009); Reichmuth et al. (2013) Kastelein et al. Phocoena_phocoena Social Aquatic Carnivore 0.25 -0.6021 180 2.2553 52731 4.7221 Behavioural (2002a) Koay et al. Phyllostomus_hastatus Social Terrestrial Omnivore 1.8 0.2553 105 2.0212 91.4 1.9611 Behavioural (2002) Heffner et al. Phyllotis_darwini Solitary Terrestrial Omnivore 1.55 0.1903 73.5 1.8663 50.8 1.7060 Behavioural (2001) Boku and Pipistrellus_abramus Social Terrestrial Carnivore 4 0.6021 80 1.9031 5.9 0.7686 ABR Riquimaroux (2015) Procyon_lotor Solitary Terrestrial Omnivore 0.1 -1.0000 40 1.6021 6374 3.8044 Behavioural Wollack (1965)

169 Thomas et al. (1988); Pseudorca_crassidens Social Aquatic Carnivore 2 0.3010 115 2.0607 1360000 6.1335 Behavioural Richardson et al. (1995); Yuen et al. (2005) Terhune & Ronald (1975); Pusa_hispida Solitary Semi-Aquatic Carnivore 1** 0.0000 90** 1.9542 70964 4.8510 Behavioural Richardson et al. (1995) Flydal et al. Rangifer_tarandus Social Terrestrial Herbivore 0.07 -1.1549 38 1.5798 109089 5.0378 Behavioural (2001) Heffner et al. Rattus_norvegicus Social Terrestrial Omnivore 0.53 -0.2757 68 1.8325 283 2.4516 Behavioural (1994b) Long and Rhinolophus_ferrumequinum Social Terrestrial Carnivore 5 0.6990 100 2.0000 22.6 1.3539 Behavioural Schnitzler (1975)

Simmons et al. Rhinopoma_hardwickii Social Terrestrial Carnivore 10 1.0000 100 2.0000 13.1 1.1173 AEP (1984) Koay et al. Rousettus_aegyptiacus Social Terrestrial Herbivore 2.25 0.3522 64 1.8062 160 2.2041 Behavioural (1998) Beecher (1974); Saimiri_sciureus Social Terrestrial Omnivore 0.125 -0.9031 46 1.6628 749 2.8748 Behavioural Green (1975); Heffner (2004) Jackson et al. Sciurus_niger Solitary Terrestrial Omnivore 0.113 -0.9469 49 1.6902 800 2.9031 Behavioural (1997) Heffner and Sigmodon_hispidus Solitary Terrestrial Herbivore 1 0.0000 72 1.8573 111 2.0440 Behavioural Masterton (1980) Sauerland and Sotalia_fluviatilis Social Aquatic Carnivore 4 0.6021 135 2.1303 42833 4.6318 Behavioural Dehnhardt (1998) Wang et al.

170 Sousa_chinensis Social Aquatic Carnivore 5.6 0.7482 152 2.1818 280000 5.4472 AEP (2012)

Begall et al. Spalacopus_cyanus Social Terrestrial Herbivore 0.25 -0.6021 20 1.3010 101 2.0037 Behavioural (2004) Heffner and Spalax_ehrenbergi Solitary Terrestrial Herbivore 0.054 -1.2676 5.9 0.7709 164 2.2158 Behavioural Heffner (1992) Kastelein and Stenella_coeruleoalba Social Aquatic Carnivore 0.5 -0.3010 160 2.2041 142103 5.1526 Behavioural Hagedoom (2003) Mann et al. Steno_bredanensis Social Aquatic Carnivore 10 1.0000 120 2.0792 130000 5.1139 AEP (2010) Heffner and Sus_scrofa Social Terrestrial Omnivore 0.042 -1.3768 40.5 1.6075 84472 4.9267 Behavioural Heffner (1990a) Heffner et al. Tamias_striatus Solitary Terrestrial Omnivore 0.039 -1.4089 52 1.7160 89.9 1.9536 Behavioural (2001) Ramsier et al. Tarsius_syrichta Solitary Terrestrial Carnivore 1 0.0000 64 1.8062 116 2.0641 ABR (2012)

Frost and Thylamys_elegans Solitary Terrestrial Carnivore 3.8 0.5798 80 1.9031 28.9 1.4609 Behavioural Masterton (1994) Ryan et al. Trachops_cirrhosus Social Terrestrial Carnivore 0.2 -0.6990 150 2.1761 36.9 1.5670 Behavioural (1983) Gerstein et al. Trichechus_manatus Solitary Aquatic Herbivore 0.4 -0.3979 46 1.6628 467325 5.6696 Behavioural (1999); Gerstein (2002) Gates and Trichosurus_vulpecula Solitary Terrestrial Herbivore 0.33 -0.4815 39 1.5911 2685 3.4290 AEP Aitkin (1982) Heffner et al. Tupaia_glis Social Terrestrial Omnivore 0.25 -0.6021 60 1.7782 132 2.1220 Behavioural (1969) Brill and Moore Tursiops_truncatus Social Aquatic Carnivore 10 1.0000 150 2.1761 281041 5.4488 Behavioural (2001) Nachtigall et al. Ursus_maritimus Solitary Semi-Aquatic Carnivore 0.125* -0.9031 25* 1.3979 371704 5.5702 AEP (2007); Owen & 171 Bowles (2011)

Heffner et al. Vicugna_pacos Social Terrestrial Herbivore 0.04 -1.3979 32.8 1.5159 50000 4.6990 Behavioural (2014) Bowles and Vulpes_macrotis Social Terrestrial Carnivore 1 0.0000 20 1.3010 4500 3.6532 Behavioural Francine (1993) Malkemper et Vulpes_vulpes Solitary Terrestrial Carnivore 0.051 -1.2924 48 1.6812 4820 3.6831 Behavioural al. (2015) Schusterman et al. (1972); Richardson et al. Zalophus_californianus Social Semi-Aquatic Carnivore 0.1** -1.0000 43** 1.6335 137195 5.1373 Behavioural (1995); Reichmuth et al. (2013)

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