Zonotrichia Leucophrys Pugetensis)

Transmission of Structurally Distinct Song Phrases in the White-Crowned Sparrow (Zonotrichia leucophrys pugetensis)

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Erica Marie Szeyller-Macolley, B.A.

Graduate Program in Evolution, Ecology, and Organismal Biology

The Ohio State University

2012

Master’s Examination Committee:

Dr. Douglas A. Nelson, Advisor

Dr. W. Mitch Masters

Dr. J. Andrew Roberts

Dr. Angelika Poesel

Erica Marie Szeyller-Macolley

2012

Abstract

Birdsong in many species has evolved to transmit over long distances to potential mates and competitors. Song can be structurally complex, often consisting of many different components. Variation in the frequency and duration of these components may influence attenuation and degradation of songs as they transmit through the environment.

Habitat structure will also affect attenuation and degradation of certain song traits and influence what information is available for receivers. To understand how variation in song and habitat structure may affect song degradation during transmission, we broadcast songs of the Puget Sound white-crowned sparrow, Zonotrichia leucophrys pugetensis, in two different habitats commonly occupied by the species. The song of this species contains four structurally and functionally distinct phrases (whistle, note complex, buzz, and trill), thirteen examples of which were broadcast over two biologically relevant distances (48 m and 96 m). Spectrogram cross-correlation between the song phrases recorded at 48 and 96 m, and undegraded model songs provided a measure of song degradation. Overall, sound attenuation and degradation increased from 48 to 96 meters.

Transmission fidelity differed among phrases; in particular, whistle phrases showed less degradation and attenuation than other phrases. Despite the high average transmission fidelity, whistle phrases were misclassified more often and were most similar among

ii phrase examples than any other phrase, indicating that simple whistles may be potentially difficult for receivers to discriminate amongst. Conversely, the acoustically diverse note complex phrases were the least likely to be misclassified and were most dissimilar among phrase examples. Transmission of song phrases differed between the two habitats.

Phrases were more attenuated and degraded as well as more similar after degradation in an open dune habitat (Bullard’s Beach) while there was more reverberation in a forest edge habitat (Cape Blanco). Lastly, phrases attenuated and degraded similarly within each site. Certain phrases propagate and are potentially perceived differently than others, thus supporting a correlation between signal structure and function. Simple tonal whistles resist attenuation and degradation and may alert listeners to the individual- and dialect- identifying information in acoustically complex note complexes and trills that follow.

While the note complex and trill phrase attenuate and degrade more than the whistle phrase over transmission, their structural complexity nevertheless may contain sufficient redundancy that allows them to be more easily discriminated.

iii

Acknowledgements

First and foremost, I am thankful for the guidance and support of my advisor,

Douglas A. Nelson as well as for the thoughtful advice from present and past committee members, Angelika Poesel, W. Mitch Masters, J. Andrew Roberts, and Amanda

Rodewald.

A number of graduate students helped immensely with my project. In particular, I would like to thank Jimmy Chiucchi, Brandon Sinn, and Jessica Hall for their statistical, writing, and presentation guidance. Also, thanks to Stephanie Wright, Dee Bolen, Desiree

Narango, and Laura Kearns for lively pizza-fueled discussions on theory and methods in animal behavior.

At Ohio State, I have been fortunate to work with a number of inspiring teachers that have helped me learn and grow as an instructor. In particular, I want to thank Cindy

Bronson, Jimmy Chiucchi, John Harder, Douglas A. Nelson, Angelika Poesel, Joe

Raczkowski, and Judy Ridgway.

Throughout my time here, I have been lucky enough to surround myself with friends & family who have provided me with unwavering emotional support, confidence, and lots of laughter through this process. Most special thanks, to my husband Brent for seeing me through every step of this process with patience, thought, understanding, and most importantly humor.

Vita

June 2004 ...... B. Reed Henderson High School

May 2008 ...... B.A. Psychology, West Chester University

August 2008 to present ...... Graduate Teaching Associate, Department of EEOB, The Ohio State University

Awards

2010-2011 Dept. of Evolution, Ecology, and Organismal Biology Graduate Teaching Award. Presented June 2011.

Publications

Published Abstracts of Presentations at Professional Meetings

Johnson, V. K., Gans, S. E., Kerr, S., LaValle, W., Bee, S., Szeyller, E., and Wonsock, G. (March, 2007). Emotion coping and adolescent behavior problems: Validating the TMMS. Poster presented at the Biennial Meeting of the Society for Research in Child Development, Boston, MA. Szeyller-Macolley, E., Poesel, A., and Nelson, D. A. (August, 2011). Transmission of structurally distinct song phrases in the white-crowned sparrow. Poster presented at the Joint Meeting of the Animal Behavior Society and the International Ethological Conference, Bloomington, IN. Szeyller-Macolley, E., Poesel, A., and Nelson, D. A. (April, 2012). Transmission and reception of structurally distinct song phrases in the white-crowned sparrow (Zonotrichia leucophrys pugetensis). Talk presented at the 19th Annual Animal Behavior Conference, Bloomington, IN.

Fields of Study

Major Field: Evolution, Ecology, and Organismal Biology

Table of Contents

Abstract ...... ii

Acknowledgements ...... iv

Vita ...... v

List of Tables ...... ix

List of Figures ...... x

Chapter 1: Acoustic Communication in Songbirds ...... 1 Introduction ...... 1 Signaling Modalities ...... 2 Properties of Sound ...... 5 Song Production ...... 6 Sound Transmission ...... 7 Sound Reception ...... 11 Signaling Ecology ...... 13 Song Structure Function and Evolution ...... 14 Conclusion ...... 17

Chapter 2: Transmission of Structurally Distinct Song Phrases in the White-Crowned Sparrow (Zonotrichia leucophrys pugetensis) ...... 19 Introduction ...... 19 Methods ...... 23 Sites ...... 23 Sound File Preparation ...... 23 Sound Transmission Trials ...... 24 Sound Analysis ...... 25 Results ...... 27 Interaction of Sites and Song Phrases...... 27 Song Phrases ...... 28 vii

Sites ...... 30 Discussion ...... 32 Interaction of Sites and Song Phrases...... 32 Phrase Degradation ...... 34 Phrase Dissimilarity ...... 36 Site Differences ...... 37 Conclusion ...... 38

References ...... 58

viii

List of Tables

Table Page

1 Structural description of frequency components (kHz) of Z. l. pugetensis song phrases based on descriptive statistics of model phrases recorded at 2 m (see methods) ...... 40

List of Figures

Figure Page

1 Spectrogram of Puget Sound white-crowned sparrow (Z. l. pugetensis) song with four phrases identified (whistle (WH), buzz (BZ), note complex (NC), and trill (TR)) ...... 41

2 Photo taken in April 2011 of typical habitat along one transect at Bullard’s Beach State Park, OR, U.S.A...... 42

3 Photo taken April 2010 along one transect at Cape Blanco State Park, OR, U.S.A ...... 43

4 Map depicting the 10 dialects of Z. l. pugetensis song and their approximate location along the Pacific North West coast. Dialects 4, 8, and 9 are not shown...... 44

5 Box plot of dissimilarity of 4 song phrases (whistle (WH), buzz (BZ), note complex (NC), and trill (TR)) with distances combined at Bullard’s Beach State Park, and Cape Blanco State Park. Statistical significance (P < 0.05) within each site is denoted by dissimilar letters above each box. Whiskers represent the lowest and highest value within 1.5 times the inter-quartile range. Outliers are excluded ...... 45

6 Box plot of minimum frequency (kHz) of the trill phrase at 2 m (model trills) versus the trills recorded at 48 m at Bullard’s Beach State Park, and Cape Blanco State Park. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols ... 46

7 Box plot of spectrogram cross-correlation coefficients of four song phrases at 48 and 96 m with both sites combined. See Figure 1 for descriptions of plot symbols ...... 47

8 Bar plot of percent mismatches of four song phrases at 96 m with both sites combined. See Figure 1 for descriptions of plot symbols ...... 48

9 Box plot of song phrase dissimilarity at 48 and 96 m with both sites combined. See Figure 1 for descriptions of plot symbols ...... 49

10 Box plot of maximum frequency (kHz) measures of four song phrases at 2 (white boxes) and 48 (gray boxes) m. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols ...... 50

11 Box plot of minimum frequency (kHz) measures of four song phrases at 2 (white boxes) and 48 (gray boxes) m. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols ...... 51

12 Box plot of dominant frequency (kHz) measures of four song phrases at 48 (white boxes) and 96 (gray boxes) m. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols ...... 52

13 Box plot of spectrogram cross-correlation coefficients at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park. The ‘*’ denotes statistical significance (P < 0.05) between the sites. See Figure 1 for descriptions of plot symbols ...... 53

14 Box plot of phrase dissimilarity at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park at 48 and 96 m. The ‘*’ denotes statistical significance (P < 0.05) between the sites. See Figure 1 for description of plot symbols ...... 54

15 Box plot of minimum frequency (kHz) measures at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park. The ‘*’ denotes statistical significance (P < 0.05) between the sites. See Figure 1 for descriptions of plot symbols ...... 55

16 Box plot of dominant frequency (kHz) measures at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park at 48 and 96 m. The ‘*’ denotes statistical significance (P<0.05) between the sites. See Figure 1 for description of plot symbols ...... 56

17 Box plot of reverberation measures at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park at 48 and 96 m. The ‘*’ denotes statistical significance (p<0.05) between the sites. See Figure 1 for descriptions of plot symbols ...... 57

Chapter 1: Acoustic Communication in Songbirds

Introduction

Communication occurs when a sender produces a signal that changes the behavior of the receiver (Wiley 1983). Signals have evolved to transmit information from one organism to another via traits or actions that benefit both the sender and receiver, on average (Bradbury & Vehrencamp 2011). Information content in signals can influence the survival and reproductive success of both the sender and receiver. In regards to survival, signals can provide information regarding the external environment. Alarm signals indicate the presence of a predator to conspecifics. In some species, these signals are highly specialized to denote the type and/or location of the predator (Evans et al.

1993; Zuberbühler 2000). Other signals are sexually selected and may not necessarily affect survival but can provide reproductive fitness benefits to an individual. Many animals use signals to mark territory boundaries, thereby decreasing conflicts associated with intruding conspecifics while defending a resource-rich area for reproduction (Jacobs

1955; Krebs et al. 1978; Gosling 1982) Signals can also be used directly for mate attraction and courtship by providing information on sender identity and potentially

information on the quality of a particular individual (McGregor et al. 1981; Reece-Engel

1988; Kotiaho et al. 1996).

Chapter 1 provides a background to signaling modalities as well as discusses acoustic communication specifically in songbirds. Songbirds, also known as oscines, are within the order Passeriformes and make up approximately half of all living bird species

(Beecher 1953; Catchpole & Slater 1995). Oscines communicate using vocalizations known as songs that are second in complexity only to human speech. In contrast to other vocalizations, songs are longer in duration, more complex, variable in structure, and learned (Catchpole & Slater 1995).

The first portion of this chapter gives an overview of costs and benefits of the primary signaling modalities found in the animal kingdom. The second portion of the chapter focuses primarily on the questions of how songbirds communicate using song, including information on song production, transmission, and reception. Finally, the last portion of the chapter discusses the evolution and function of song in relation to song transmission through the environment.

Signaling Modalities

There are multiple modalities by which signals can be broadcast, each with different costs and benefits associated with production, transmission, and reception. The most prominent signaling modalities include electrical, chemical, visual, and auditory

(Bradbury & Vehrencamp 2011).

Electrical signals are limited to two groups of fish, the gymnotids, a New World tropical group, and the mormyriids, an African group (Hopkins 1974). These species are

nocturnal or live in murky waters where visual signals are not an option (Hopkins 1974).

The environments inhabited by these two groups of fish selected for the independent evolution of a low voltage communicative signal produced primarily by an organ near the tail (Bennett 1971). Electric fields that build up around the fish rapidly attenuate therefore they are not influenced by reverberation (Hopkins 1974). Thus, the active space, or distance over which a signal can be detected (Marten & Marler 1977; Brenowitz 1982), is very limited, for example, a 10-20 cm fish may have a signal active space of only1 m

(Hopkins 1974). Electric signals are suited for rapid aquatic communication in visually occluded habitats over short distances.

Chemical signals are the oldest and most widespread modality (Wyatt 2003;

Bradbury & Vehrencamp 2011). Pheromones are chemical signals that function among conspecifics whereas allomones function among heterospecifics (Karlson & Lüscher

1959; Whittaker & Feeny 1971). Chemical signals are typically secretions from exocrine glands or body orifices and are transmitted via release into air or water, or by being deposited on substrates (Alberts 1992). Chemical signals are energy-efficient, can travel around large objects, can be used in visually occluded environments, and are typically persistent (Alberts 1992; Endler 1993). Even so, chemical signals lack inherent directionality and a strict temporal structure therefore they typically do not specify recipients over long distance communication (Endler 1993).

Light is used as a visual signal either when it is reflected off an animal or is self- generated (bioluminescence). Animals can have pigments that selectively absorb or reflect particular wavelengths, or they can have structural components that interfere with

or scatter light (Hill & McGraw 2006). Bioluminescent organisms produce light by using chemical energy to move electrons to higher orbitals (Nealson & Hastings 1979). Visual signals can vary in a number of attributes including: brightness, color, spatial characteristics, and/or temporal variation (Marler 1967; Hill & McGraw 2006).

Therefore, visual signals have a lot of potential channels to encode information (Endler

1993). Transmission of visual signals depends on the amount of available light as well as on atmospheric conditions and vegetation between sender and receiver (Endler 1990,

1993). Light requires straight-line transmission; therefore, signaling must take place during the day with sender and receiver in visual contact (Endler 1990, 1993). For diurnal species, visual signals are the most efficient way to transmit large amounts of information quickly over short distances (Marler 1967; Endler 1993).

Acoustic signals are widely used within the animal kingdom and are particularly prominent in arthropods, including decapod crustaceans and some insect taxa, such as cicadas and orthopterans (Bailey 1991). Within vertebrates, sound is frequently used in mammals, birds, and frogs (Sebeok 1977; Clutton-Brock & Albon 1979; Searcy &

Andersson 1986; Gerhardt & Huber 2002; Marler 2004). Acoustic signals rapidly transmit over long distances when visual signals would be occluded due to low light or vegetation (Endler 1993). Information can be encoded in sound by altering the amplitude, frequency, or time components of the signal. Acoustic signals are affected by background noise and weather and are subject to interception by unintended receivers like predators

(Endler 1993; Brumm & Slabbekoorn 2005; Peake 2005).

In summary, electrical signals are restricted to aquatic environments and propagate information over short distances in visually occluded habitats or in nocturnal species. Chemical signals are persistent, energy-efficient signals that can transmit large amounts of information around objects, although they may not be able to specify an individual signaler. Visual (light) signals can privately transmit large amounts of information over short distances when there is available light. Acoustic signals transmit a moderate amount of information over long distances within a short time frame but may be masked by other noises.

From this point forward, the chapter will focus on acoustic communication with a focus on songbirds.

Properties of Sound

Acoustic signals are made up of sound, a mechanical radiant energy that forms longitudinal pressure waves produced in a medium containing particles. At a sound’s source, particles in a medium are pushed against those next to them in a particular direction; those compressed particles then push against subsequent particles while the first return to their original position. This creates as oscillating pattern of particle compression and rarefaction in a pressure waveform (Fletcher 1992).

There are a number of terms used to describe this sound pressure waveform. One repeated action is known as a cycle, while the time to complete one cycle is known as the period. The inverse of the period is the number of cycles per second or frequency (Hz) which is perceived as pitch. Wavelength is the distance one cycle spans and is inversely related to frequency. Also, wavelength of a sound divided by the period equals the speed

of sound propagation. This relationship means that high frequencies have small wavelengths and low frequencies have large wavelengths. Also, wavelength will change depending on the speed of sound in a medium. Amplitude is a measure of the sound pressure or the magnitude of particle displacement. Lastly, phase is the value of the angle at a particular point on the sound wave (Fletcher 1992; Bradbury & Vehrencamp 2011).

Song Production

Birds produce vocalizations using a vocal organ known as the syrinx. The syrinx is contained within the interclavicular air sac and is located at the junction of two bronchi at the base of the trachea (Chamberlain et al. 1968; Ames 1971). The extrinsic musculature of the syrinx can change length or control tension of the tracheal and bronchial tubes (Gaunt & Gaunt 1985) while the intrinsic musculature controls membrane tension (Goller & Suthers 1996). In oscines, ventral muscles control tension of the medial tympaniform membrane (located on the medial-side of each bronchi) which manipulates sound frequency while the dorsal muscles influence sound amplitude (Goller

& Suthers 1996). Sound waves are produced as air is expired through the syrinx and interact with oscillating medial labia. Since the medial labia are positioned on each bronchus, birds are able to produce two notes simultaneously (Goller & Suthers 1995;

Suthers 1997). For example, catbirds (Dumetella carolinensis) can switch from using one side of the syrinx to the other or use both sides at the same time (Suthers 1990). Although either side of the syrinx can produce sound, the right side of the syrinx tends to be specialized for high frequencies due to its smaller size (Suthers & Goller 1997; Suthers

1997). During vocalization, many songbird species take “mini-breaths” between syllables

that allows them to sing for a long period of time continuously (Hartley 1990). As sound travels from the syrinx it may also be filtered in the vocal tract and altered by the beak gape (Suthers et al. 1999; Fletcher et al. 2006; Riede et al. 2006).

Sound Transmission

As sound leaves the bird’s mouth, it decreases in intensity (attenuates) as well as degrades in amplitude and frequency patterns, or time components as it transmits through the environment to the receiver. Song is attenuated by spherical spreading, absorption, and scattering due to reflection, refraction and diffraction as well as degraded due to irregular amplitude fluctuations and reverberation (Wiley & Richards 1978; Richards &

Wiley 1980).

Spherical spreading & absorption

Spherical spreading occurs as the volume of air occupied by a sound pressure wave increases and results in an expected attenuation at 6 dB per doubling of distance

(Wiley & Richards 1982). Spherical spreading often deviates from expectation due to channeling of the signal or absorption and/or scattering losses (Wiley & Richards 1978).

Sound energy decreases over transmission due to absorption when sound energy is dissipated as heat or by transmission through media of different acoustic impedances

(Wiley & Richards 1978). Acoustic impedance describes the ease at which a sound can be produced in a particular medium and is determined by the speed of sound and density of the medium. The higher the impedance, the less energy is needed to produce sound at a given pressure (Fletcher 1992; Bradbury & Vehrencamp 2011). Absorption increases

monotonically with frequency and is also increased in environments with higher temperatures and lower relative humidity (Wiley & Richards 1982).

Scattering & interference

Scattering losses result from reflection, refraction and diffraction of sound when it comes into contact with media of different acoustic impedances (Wiley & Richards

1978). The type of scattering that occurs depends on the wavelength of the sound and the dimensions of the object it comes into contact with; therefore, scattering is frequency dependent in natural environments (Wiley & Richards 1982). If the wavelength is larger than the object, the sound will pass around the object without much change (Bradbury &

Vehrencamp 2011). When sound reaches a barrier of differing acoustic impedance that is several wavelengths long, part of the wave is reflected while the other portion is refracted. The reflected wave leaves the object in a different direction although at the same angle as the incident wave relative to the boundary. The angle of the refracted wave depends on the properties of the new medium. When sound moves from a medium with higher sound velocity to lower velocity, the wave is bent further into the new medium. If the sound moves from a lower to higher velocity medium the sound is bent towards the first medium (Wiley & Richards 1978; Fletcher 1992; Bradbury & Vehrencamp 2011).

When sound interacts with an object that is similar in size to the wavelength, two waves are produced, a reflected wave and a diffracted wave that is bent around the object. After moving around the object, these two waves can come back in contact and interfere with one another (Fletcher 1992).

In general, higher frequencies are more likely to be scattered because small wavelengths have a higher probability of coming into contact with objects in the environment that are larger than they are (Wiley & Richards 1982). Habitats with lots of small reflecting surfaces like forests are especially detrimental to high frequencies

(Morton 1975; Marten & Marler 1977). Forests with broad leaves scatter more sounds than forests with smaller leaves, like coniferous forests (Marten & Marler 1977; Wiley &

Richards 1982). Edge habitats show intermediate amounts of frequency-dependent scattering between open, and forest habitats (Morton 1975). Sound may also be scattered when it comes into contact with air with different temperature, humidity, or wind gradients than the surrounding medium (Wiley & Richards 1978, 1982). Open habitats are more likely to have scattering due to atmospheric disturbance because they typically have higher wind velocities than forested habitats (Wiley & Richards 1982). Scattering due to atmospheric turbulence increases with increasing frequencies (Wiley & Richards

1982).

While atmospheric gradients and vegetation will most likely attenuate and/or degrade sound, they may also channel sound by acting as wave guides if there are parallel layers to the ground (Wiley & Richards 1978). There can also be shadow zones, where sound is reflected and/or refracted away from a particular location. For example, the speed of sound is higher in warm air, therefore on sunny days when air by the ground is warmer, sounds are reflected/refracted away from the ground (Wiley & Richards 1978).

Low-frequency signals within a meter of the ground are affected by ground attenuation. This occurs when part of the signal is reflected off of the ground and

destructively interferes with the direct signal wave (Morton 1975; Marten & Marler

1977). Ground attenuation is most prominent within 1 meter of the ground with frequencies below 1 kHz (Wiley & Richards 1982).

Degradation

Degradation refers to alteration of the amplitude and frequency patterning, or time components of a signal due to reverberations or amplitude fluctuations. Reverberation is caused by scattering when reflected waves arrive at the receiver slightly after the directly transmitted wave. This delayed reflected wave can change the frequency patterns and/or temporal properties of the signal (Marten & Marler 1977; Marten et al. 1977; Richards &

Wiley 1980) and may resemble a sound “tail” following the signal. Reverberations are more prominent in high frequency signals and in habitats with lots of reflecting surfaces

(Wiley & Richards 1978; Richards & Wiley 1980).

While reverberations are more prominent in forested habitats, open habitats are more prone to amplitude fluctuations (Richards & Wiley 1980; Wiley & Richards 1982).

As sound travels through atmospheric turbulence it acquires irregular amplitude fluctuations (Knudsen 1946; Kriebel 1972; Marten & Marler 1977; Waser & Waser 1977;

Marten et al. 1977; Richards & Wiley 1980). In general, amplitude fluctuations increase with signal frequency and distance traveled, and can be substantial even with small amounts of atmospheric turbulence, wind in particular (Richards & Wiley 1980; Wiley &

Richards 1982).

Sound Reception

Sound is received in many vertebrates by pressure-sensing organs. As sound enters the ear, the pressure vibrates a thin membrane known as the tympanum. In the middle ear, the columella transmits the pressure changes to a smaller oval window, which sets fluid within the cochlea in motion. Within the cochlea, there are hair cells attached to the basilar membrane which transduces mechanical to electrical energy. The basilar membrane varies in mass and flexibility such that different frequencies stimulate particular positions along the membrane. High frequencies stimulate the proximal portion of the basilar membrane (by the oval window) whereas lower frequencies stimulate the apex of the basilar membrane thus creating a tonotopic map. The electrical signals produced by the basilar membrane are then transmitted to the central nervous system for further processing (Manley et al. 2004).

Bird species are most sensitive to frequencies from 1-5 kHz although some species are sensitive beyond that range (Dooling 1982). This range normally covers frequencies used among conspecifics but may also be broader to include other relevant sounds, like heterospecific alarm calls (Brumm & Slabbekoorn 2005). In general, birds are sensitive to changes in the frequency, intensity, and time components of signals

(Dooling 1982).

Detection/discrimination of relevant acoustic signals from background noises becomes more difficult as the signal-to-noise ratio decreases due either to increasing distance from the sender or to background noises that mask the signal (Brumm &

Slabbekoorn 2005). Feature detectors are brain cells that are tuned to specific sound

characteristics, like duration or frequency/amplitude modulation, and are an adaptation to detect a signal in high background noise (Brumm & Slabbekoorn 2005). They are often species-specific which is particularly useful if a number of heterospecifics are signaling concurrently (Margoliash & Fortune 1992; Doupe & Solis 1997; Soha & Marler 2000).

Signaler localization

To determine the location of a signaler, a receiver needs to know the direction and distance of the signaler. Direction is determined by comparing differences in time, phase and/or amplitude between two pressure detectors (ears) that are situated on either side of the head (Bradbury & Vehrencamp 2011). In birds there is an interaural pathway that connects both ears with a relatively small interaural distance. These physiological traits indicate that birds likely use phase or pressure differences for sound localization (Klump

& Larsen 1992; Dooling & Popper 2000), although there is likely large variation in methods of sound localization among species (Klump 1996; Nelson & Stoddard 1998;

Dooling & Popper 2000).

Distance of a signaler can be determined through ranging by using structural properties in sound that attenuate and degrade predictably (Richards 1981a). Receivers in many avian species have been found to respond more aggressively to playback of undegraded than degraded song, as if undegraded song indicates a close territory threat

(McGregor & Falls 1984; McGregor & Krebs 1984; Shy & Morton 1986; Wiley &

Godard 1996). In order to range, the receiver can cue onto certain components of the signal. Receivers have been found to detect attenuation and/or degradation based on: reverberation (Wiley & Godard 1996; Naguib 1997b), relative intensity of high

frequencies (Naguib 1995, 1997b), and amplitude (Naguib 1997a; Nelson 2000). Even though receivers can determine signaler distance when only one cue is available, accuracy of ranging increases with cue integration (Naguib 1995, 1997a). Morton (1982) suggested that receivers detect attenuation/degradation in a signal by comparing it to a memorized copy. While receivers do not need to have the same song type as the sender to range, they do better at ranging when they posses similar song types (McGregor et al.

1983; McGregor & Falls 1984; McGregor & Krebs 1984; Shy & Morton 1986).

Signaling Ecology

Considering the constraints of sound production, transmission and reception, senders and receivers can alter their behavior in order to optimize communication.

In general, in noisy conditions, animals can vary their signal amplitude (Brumm 2004), signal length (Brumm et al. 2004), and/or redundancy (Potash 1972). Senders can also avoid signaling during noise. For example, tawny owls (Strix aluco) avoid calling on nights with consistent heavy rain (Lengagne & Slater 2002). Also, birds may alter the timing of their vocalization to avoid overlapping conspecific or heterospecific songs that may mask them (Ficken et al. 1974; Wasserman 1977). Even when noise levels are low, it is beneficial for the sender to situate itself at least 1 meter off the ground to avoid effects of ground attenuation of low frequency signals (Morton 1975; Marten & Marler

1977; Mathevon et al. 1996).

To increase the signal-to-noise ratio, receivers benefit from moving to a location closer to the sound source (Brumm & Slabbekoorn 2005), moving up in the vegetation

(Mathevon et al. 2005), and/or turning their head to attend to the signal (Obrist et al.

1993; Keller et al. 1998). In transmission experiments where both speaker and microphone height were varied, signal degradation was negatively correlated with microphone height (Dabelsteen et al. 1993; Mathevon et al. 2005). This negative correlation was more pronounced when modifying microphone height than speaker height, and therefore, it is inferred that taking a high perch may be more important for receiving than sending a signal (Mathevon et al. 2005).

Song Structure Function and Evolution

Songs are sexually-selected signals that function primarily to gain and defend territories in intra-sexual competition and as an inter-sexual signal for mate attraction

(Searcy & Andersson 1986). There is a wide range of structural variation in song signals within and among species (Catchpole & Slater 1995). The evolution of song structure is based on information content as well as efficacy or traits that make songs effective information carriers (Marler 1960; Endler 1993; Dawkins & Guilford 1997; Brumm &

Slabbekoorn 2005).

Information content

Song can signal information on identity (species, dialect, and/or individual), motivation, and/or quality that may influence signal evolution. In order to code identifying information, a signal must vary enough for the receiver to perceive differences among signal variants. However, if the signal varies too much, the receiver may not recognize the song as a relevant signal (Marler 1960). In order to code different types of information or multiple messages, song structure can become more complex or have multiple components (Marler 1960; MacDougall-Shackleton 1997). Individual

differences in measures of song that vary predictably based on age, experience or physiological state, like song rate and repertoire size, can be used as an honest indicator of an individual’s quality (Payne & Payne 1977; Catchpole 1980; Houtman 1992;

Hasselquist et al. 1996; Reid et al. 2004).

Signal efficacy

Signal efficacy can influence signal structure evolution by selecting for conspicuous or diverse signals that are effective at transmitting information to receivers

(Dawkins & Guilford 1997). Signal efficacy can be increased by incorporating signal traits that improve information reception. One example of this is structural redundancy.

By repeating song components receivers are more likely to detect and discriminate a signal (Brumm & Slabbekoorn 2005). Similarly, species may add an acoustic component to song that resists degradation to alerts receivers to the message components of a signal

(Gerhardt 1976; Richards 1981b; Brenowitz 1982; Mitchell et al. 2006). In bird species, these are typically pure tonal notes with narrow frequency ranges that resist degradation

(Richards 1981b; Wiley & Richards 1982). In both avian and non-avian species, alerting notes increase probability of signal detection (Richards 1981b; Peters et al. 2007; Ord &

Stamps 2008).

Signal efficacy can also be increased by having a song structure that maintains structural fidelity in a given habitat. Therefore, habitat differences may lead to consistent structural differences in song among species (Morton 1975; Wiley 1991). Forested habitats contain more reflecting surfaces than open habitats; therefore, there is more scattering and reverberation of sounds (Morton 1975; Marten & Marler 1977; Richards &

Wiley 1980). This is particularly true for high frequencies which are more likely to be scattered by small reflecting surfaces like leaves (Wiley & Richards 1978). Increased reverberations in forests degrade the temporal structure of signals (Richards & Wiley

1980). Therefore in forested habitats species should have signals that are tonal, contain lower frequencies, and have notes that are spaced out (Morton 1975; Marten & Marler

1977; Slabbekoorn et al. 2002). In contrast, open habitats do not have those same constraints therefore species are likely have songs with fast frequency modulation or harmonics (Morton 1975). Open habitats are more prone to ground attenuation, therefore for long-distance communication species should limit singing low frequencies close to the ground (Morton 1975; Marten & Marler 1977). Open habitats also have more atmospheric disturbances, which mask amplitude modulation in signals; therefore, songs should encode information in frequency patterns rather than amplitude (Wiley &

Richards 1982)

Research has found differentiation in song structure among species that appears to be related to habitat-type. In a study on tropical bird species, Morton (1975) found that forest dwelling species had more tone-like songs with a constant frequency, whereas open habitat species were more likely to have rapid frequency modulations. In temperate environments, forest species space out their notes to avoid temporal degradation due to reverberation while grassland species have fast frequency modulated songs (Wiley 1991).

Overall it appears that song structure can be associated with habitat features but there also may be other factors that influence song variation, including phylogeny and body size

(Ryan & Brenowitz 1985; Wiley 1991; Boncoraglio & Saino 2007).

Evidence suggests that there may be intra-specific variation in song due to habitat-related differences throughout a species’ range (Nottebohm 1975; Wasserman

1977; Hunter & Krebs 1979; Gish & Morton 1981; Doutrelant & Lambrechts 2001). For example, song in the rufous-collared sparrow (Zonotrichia capensis) varies throughout its range to potentially encode dialect information in trill speed (Nottebohm 1975). Sparrows in forested habitats had slower trills than those found in more open habitats, possibly to avoid reverberations (Nottebohm 1975). In fact, rapidly modulated trills are found to degrade more quickly than slowly modulated trills in forested habitats (Naguib 2003).

Great tits (Parus major) were found to vary in sound characteristics due to habitat differences (Hunter & Krebs 1979). Great tits in forested habitats had lower maximum frequencies, narrower frequency ranges, and fewer notes than birds in more open woodland habitats (Hunter & Krebs 1979).

Conclusion

Communication requires a sender to produce a signal that alters the behavior of a receiver. While there are multiple signaling modalities available, acoustic signals provide a way for songbirds to transmit a relatively large amount of information over long distances, quickly, in visually occluded environments. Acoustic signaling in birds is facilitated by adaptations in signal structure, production and reception to effectively communicate information from sender to receiver. Senders and receivers can also employ behavioral tactics to promote effective communication. Song structure is determined partially by the amount and type of information encoded as well as by signal efficacy.

Since attenuation and degradation vary with signal structure and habitat, certain song

structures are suited for optimal signal transmission in particular habitats. Therefore as habitats vary we see variation in song structure among and within species. Factors selecting for information content and efficacy are diverse among species and may account for some of the variation seen today in song structure. Given that song functions to gain access to reproductive opportunities, variation in song has implications for the reproductive success of the individual as well as reproductive isolation among species.

Chapter 2: Transmission of Structurally Distinct Song Phrases in the White-Crowned Sparrow (Zonotrichia leucophrys pugetensis)

Introduction

Birdsong that is used in advertising to potential mates and competitors has evolved to broadcast over long distances (Catchpole & Slater 1995). As song transmits through the environment from sender to receiver it is attenuated and degraded (Morton

1975; Marten & Marler 1977; Wiley & Richards 1982). Attenuation is caused by spherical spreading loss, absorption, and scattering from vegetation and atmospheric conditions, like temperature gradients and wind (Marten & Marler 1977; Richards &

Wiley 1980). Degradation of signal structure is caused by scattering and results in irregular amplitude fluctuations and reverberation (Wiley & Richards 1978). The amount of degradation and attenuation is influenced by song structure as well as by the habitat

(Morton 1975; Marten & Marler 1977; Wiley & Richards 1978). In general, high frequencies, notes with high frequency harmonics, and rapid frequency modulations are more easily attenuated and/or degraded than are low frequencies, tonal notes, and slow frequency modulations (Morton 1975; Marten & Marler 1977; Richards & Wiley 1980).

Attenuation and degradation are particularly high in habitats with many reflecting surfaces (e.g. forest), or large atmospheric fluctuations like high wind (Wiley & Richards

1978). Other noises in the environment may impact song reception. For instance, habitats that produce low frequency noise, such as ocean surf or roadways, may mask low frequencies in songs (Brumm & Slabbekoorn 2005).

The benefits of obtaining optimal long-distance signal transmission through a given habitat should seemingly select for small range of song structure, although in fact song structure varies enormously within and among bird species. Variation in song structure is partially related to receiver spacing (Endler 1993). Songs are structured for optimal transmission to receivers over a limited distance to avoid interception by eavesdroppers, such as predators (Peake 2005). Songs intended for long-distance receivers should have characteristics that resist attenuation and/or degradation, but songs functioning at short distances do not have that structural constraint (Wiley & Richards

1978). For example, male nightingales (Luscinia megarhynchos) sing whistle and trill song types (Naguib et al. 2002; Schmidt et al. 2008). The simple tonal structure of whistle vocalizations allows them to transmit over long distances to many potential mates, while trill vocalizations degrade and attenuate over shorter distances, and function in close male-male interactions (Naguib et al. 2002, 2008; Schmidt et al. 2008).

The information content of a song will also influence the complexity of song structure. Songs that encode large amounts of information, e.g., on motivation, quality, and/or identity, require a more complex structure or multiple components (Marler 1960;

Dawkins & Guilford 1997). Therefore within one song functioning in long-distance communication there may be substantial structural variation that, in turn, should influence transmission. To understand how these complex and/or multi-component songs

transmit information, we need to understand what structural characteristics are available for receiver perception. This is particularly true of song components that signal identifying information because the signal must vary enough among different individuals for accurate receiver discrimination (Marler 1960; Richards & Wiley 1980). The focus of this research is to investigate how structural differences within one long-distance signal influence the amount of attenuation and/or degradation of song components, as well as the potential for confusion among components over functionally relevant distances.

The songs of male Puget Sound white-crowned sparrows (Zonotrichia leucophrys pugetensis) contain four structurally and functionally distinct phrases: whistle, note complex, buzz, and trill (Table 1, Figure 1; Soha & Marler 2000; Nelson & Poesel 2007).

The whistle is a simple tonal phrase at the beginning of the song that remains at a constant frequency and may function as a species-specific sign-on (Richards 1981b; Soha

& Marler 2000). Conversely, the note complex and trill are more structurally complex.

The note complex consists of multiple tonal notes with rapid frequency modulation and is distinctive among individuals, thereby signaling individual identity (Nelson & Poesel

2007). The trill contains a series of repeated broadband notes and functions as a dialect identifier for geographic regions (Nelson & Soha 2004). Variation in trill length also encodes motivational information in close conspecific interactions (Nelson & Poesel

2011). Lastly, the buzz phrase has a constant bandwidth that varies in length and carrier frequency. The function of the buzz is unknown.

In order to investigate the amount of degradation of each phrase over transmission, thirteen different examples of each distinct phrase were broadcast and

recorded over two biologically relevant distances (48 m and 96 m) within the species’ natural habitat. Spectrogram cross-correlation analysis between model (undegraded) phrases and the phrases recorded at 48 and 96 m was performed to determine the amount of degradation as well as the potential ability to discriminate among phrase examples based on the number of mismatches between model and re-recorded phrase, and the degree of difference between alternative phrases. Acoustic measurements of the re- recorded phrases were made to investigate what song parameters were degraded.

We hypothesize that song phrases will transmit differently based on structure, and we expect the structurally simple whistles to be the least attenuated/degraded phrase because their sound energy is focused in a narrow band at lower maximum frequencies than other phrases. Buzzes, note complexes and trills all have energy at higher frequencies than whistles; therefore, it is expected that they will be more attenuated and/or degraded than whistles. We also used similarity measures to investigate the hypothesis that increased structural complexity and variation among song examples potentially promotes discrimination among examples. We predict that the species-specific whistle phrase will not show much variation among whistle examples; therefore, whistle examples will be similar and potentially difficult to discriminate amongst. Conversely, the structurally complex trill phrase varies between dialect types; therefore, they are expected to be less similar and potentially easier to discriminate amongst. This is expected to be especially true of the note complex phrase which varies between individuals. As explained above, habitat can influence signal transmission. Therefore, we also investigated how two habitat types inhabited by Z. l. pugetensis influence phrase

attenuation and degradation in order to determine whether differences in phrase attenuation, degradation, and similarity were consistent in different acoustic environments.

Methods

Sites

Sound transmission experiments took place at Bullard’s Beach State Park,

Bandon, OR, U.S.A. and Cape Blanco State Park, Port Orford, OR, U.S.A. The habitat at

Bullard’s Beach consists of coastal dunes with ground cover predominately of European beach grass, Ammophila arenaria, and widely-scattered small shore pine, Pinus contorta, scotch broom, Cytisus scoparius, and gorse, Ulex europaeus (Figure 2). Strong winds limit the height of vegetation in unprotected areas to under 2-3 m. Habitat at Cape Blanco consisted of forest edge and clearing (old pasture), with 1- to 10-m tall spruce, Picea, and

Douglas-fir, Pseudotsuga, trees, as well as small shrubs (salal, Gaultheria shallon)

(Figure 3). These habitats represent two of the three commonest habitat types (residential areas being the third) used by this subspecies (Baptista 1977).

Sound File Preparation

We compiled a sound file of 3 pure tones (2, 4, and 8 kHz) and 13 songs from different dialects of male Puget Sound white-crowned sparrow, Zonotrichia leucophrys pugetensis (Figure 4). Songs were recorded from males throughout the subspecies’ range

(Nelson et al. 2004), and were digitized at 25 kHz with 16-bit amplitude resolution using

the Signal software package (Engineering Design, Berkeley, CA, U.S.A). Pure tones were created using the sine wave function generator in Signal. Tones and songs were compiled into one wave file, with the amplitude normalized to the same root-mean- square value.

Sound Transmission Trials

Songs were broadcast from a Marantz PMD660 solid-state recorder (Marantz

Professional, Kanagawa, Japan) through a Tivoli PAL (Tivoli Audio, Cambridge, MA,

U.S.A) loudspeaker atop a 1.6-m-high tripod. Songs were recorded using a Sennheiser

ME62 omnidirectional microphone (Sennheiser, Wedemark, Germany) positioned at a height of 1.6 m connected to a Marantz PMD670 solid-state recorder (48 kHz, 16-bit amplitude resolution, wave format). The gain setting on the loudspeaker was held constant across trials, and the microphone gain on the PMD670 was set to the maximum on all trials. Songs were broadcast at an amplitude of 72 dB at 8 m which corresponds to the normal amplitude of white-crowned sparrow song (Nelson & Poesel 2007). As a control, the sound file was broadcast and re-recorded once at a distance of 2 m to create model sounds that account for speaker and microphone induced acoustic variation. The sound file was broadcast and re-recorded twice at distances of 48 and 96 m in each of 17 transects. Ten transects were at Bullard’s Beach State Park where most males sang dialect

1, while 7 transects were at Cape Blanco State Park where males sang dialect 12. Each transect was in habitat occupied by white-crowned sparrows (see above). The 96 m distance approximated the mean distance between the centers of neighboring territories at

Bullard’s Beach (115 + 40 m, n = 83, Nelson & Poesel, unpubl. data).

Trials took place in April 2009 and 2010 in the morning before 1100, when white- crowned sparrows sing most frequently and before strong winds pick up. Trials were run on days with no rain and wind below about 5 km/h.

Sound Analysis

Recorded files were analyzed in Signal. Each tone and phrase was high-pass filtered at 1 kHz then “spliced out” and saved as a separate wave file. Any tone or phrase with overlapping background song was removed from further analysis.

We used spectrogram cross-correlation analysis to estimate sound degradation due to transmission. This procedure compares sounds by sliding two spectrograms across one another on the time axis; assigning a coefficient of similarity for each step based on time, amplitude, and frequency components of the signal (Khanna et al. 1997). The maximum coefficient produced is ultimately assigned to that sound comparison.

Coefficients can range from 0 to 1, with 1 indicating identical sounds.

Each phrase was first trimmed or zero-padded to a constant length. A spectrogram of each phrase was computed using a 256 point FFT (5 ms temporal resolution, 188 Hz frequency resolution), 1000 steps, and with amplitude normalized over the entire phrase.

Only energy between 1500 and 8000 Hz, the range of white-crowned sparrow song, was entered into the spectrogram cross-correlation coefficient. The phrase files from 48 and

96 m were compared to the model phrase of the same type from each of the 13 dialects.

For example, a recorded whistle phrase at 48 m was compared to each of the 13 different whistle models. For statistical analysis, the cross-correlation coefficient for the matching phrase was used (e.g. the cross-correlation coefficient produced from comparing dialect 1

whistle to the model dialect 1 whistle). Any comparison that assigned the highest spectrogram cross-correlation coefficient to the incorrect model was labeled a mismatched case. To measure the degree of dissimilarity among dialects within phrase types, the maximum spectrogram cross-correlation coefficient was subtracted from the next highest coefficient and divided by the standard deviation of all comparisons for each phrase.

Since changes in frequency and amplitude cannot be easily separated in spectrogram cross-correlation analysis, structural measures were also taken from each tone and phrase to further measure attenuation and degradation. From each tone, amplitude (root mean square) was measured during a 100 ms-long period relative to the onset and offset of the tone, one measure was taken after the onset of the tone as well as one after the offset of the tone (Tobias et al. 2010). Reverberation (i.e. tail-to-signal ratio) was expressed as the amplitude after the tone relative to the amplitude during the tone (in dB). From each phrase we measured: maximum amplitude, frequency at maximum amplitude (dominant frequency), and frequency at 15 decibels below the maximum amplitude to get minimum and maximum frequencies. Bandwidth was measured only at

2 and 48 m due to poor signal-to-noise ratio at 96 m. Excess attenuation was measured as the maximum amplitude at 96 m divided by the maximum amplitude at 48 m (in dB) minus 6 dB (Morton 1975).

Prior to statistical analysis, replicates within each distance at each transect were averaged. Sample size varied depending on how many samples could be measured at each distance and location. When comparing minimum and maximum frequency

measures from 2 and 48 m, measures were averaged to the 13 dialect types because the model sounds at 2 m were only recorded once. When aggregating data, if either replicate within a trial at a given distance was misclassified that was counted as 1 mismatch. None of the data were normally distributed based on inspection of residuals, and could not be transformed to a normal distribution. Mann-Whitney U or Kruskal-Wallis tests were used for among-groups analyses. Paired-comparisons were assessed with Wilcoxon Signed

Rank tests with simple Bonferroni corrections. Chi-squared tests were used to determine the difference in mismatches among phrase types with multiple-comparisons by partitioning degrees of freedom (Maxwell 1964). While none of our data were normally distributed, we performed factorial or repeated-measures ANOVA for all comparisons

(except mismatches) on non-transformed data to investigate the interaction of site X phrase type. We report the results from the parametric tests because we obtained similar results with separate non-parametric tests. Statistical analyses were conducted in SPSS

19.0 and R 2.14.2.

Results

Interaction of Sites and Song Phrases

We ran parametric factorial and repeated-measures ANOVAs to investigate interaction effects between sites and song phrases on each of the variables measured.

There were no statistically significant interactions between sites and song phrases for any variable except dissimilarity (Figure 5; Factorial ANOVA, F = 8.028, DF = 3, P < 0.001).

Based on inspection of the Figure 5, dissimilarity among phrases appears to be more variable and lower, especially in the note complex and trill phrases, at Bullard’s Beach than Cape Blanco. We also investigated the possibility of a site and song phrase interaction with minimum frequency because this measure was found to increase in the trill phrase from 2 – 48 m at Bullard’s Beach but not in any other phrase. This indicates that minimum frequency of phrases behaves differently at each site (see results for main effects below). For the trill phrase, minimum frequencies were higher at Bullard’s Beach than Cape Blanco (Mann-Whitney U Z = - 2.71 N NBB = 94, NCB = 63, P = 0.007). At

Bullard’s Beach the minimum frequency of trills increased from 2 – 48 m (Figure 6;

Wilcoxon Signed Ranks, Z = -3.04, N = 13, P = 0.002) but not at Cape Blanco (Wilcoxon

Signed Ranks, Z = -1.64, N = 13, P = 0.101).

Therefore, given the general absence of significant interaction effects, we focus hereafter on the main effects of phrase identity and site.

Song Phrases

Spectrogram cross-correlation analysis

Spectrogram cross-correlation coefficients between the model sound and the distant recordings for each phrase were significantly lower at 96 m than at 48 m with both sites combined (Wilcoxon Signed Ranks; Whistle, Z = -7.74, N = 101, P < 0.001; Buzz,

Z = -8.49, N = 135, P < 0.001; Note Complex, Z = -6.62, N = 123, P < 0.001; Trill, Z = -

7.71, N = 121, P < 0.001). Spectrogram cross-correlation coefficients differed among song phrases at 48 m (Kruskal-Wallis, H = 142.45, N = 708, DF = 3, P < 0.001) but not at

96 m (Kruskal-Wallis, H = 5.05, N = 552, DF = 3, P = 0.169). In particular, at 48 m the

whistle phrase had significantly higher spectrogram cross-correlation values than the other phrases (Figure 7). In transmission from 48 to 96 m, spectrogram cross-correlation coefficients decreased similarly for each phrase (Kruskal-Wallis, H = 6.40, N = 480, DF

= 3, P = 0.094).

The number of mismatches differed among phrases at both 48 and 96 m (48m:

X2 = 19.57, N = 708, DF = 3, P < 0.001; 96m: X2 = 28.18, N = 574, DF = 3, P < 0 .001) with 9.24% of mismatches at 96 m and only 1.41% of mismatches at 48 m. Since there were so few mismatches at 48 m, post-hoc analysis of differences among phrases were investigated only at 96 m by partitioning Chi-squared degrees of freedom (Figure 8). At

96 m, 18.90 % of whistles were misclassified, significantly more than any other phrase whereas only 0.67% of note complexes were misclassified, significantly less than any other phrase. There were no differences in the number of mismatches between the buzz and trill phrase, mismatched 8.97% and 8.45% respectively.

Overall, phrase dissimilarity was greater at 48 m than 96 m (Figure 9; Wilcoxon

Signed Ranks, Z = -11.60, N = 480, P < 0.001). Phrases differed significantly in dissimilarity at both distances (Figure 9; Kruskal-Wallis; 48m: H = 539.95, N = 708, DF

= 3, P < 0.001; 96m: H = 257.21, N = 552, DF = 3, P < 0.001). In particular, the whistle phrase had a significantly smaller dissimilarity than all other phrases while the note complex and trill phrases had comparatively large dissimilarities.

Structural measures

Excess attenuation did not differ among phrases (Kruskal-Wallis, H = 4.58, N =

424, DF = 3, P < 0.206). Between 2 and 48 m, the maximum frequency did not differ for

either the whistle (Figure 10; Wilcoxon Signed Ranks, Z = -0.94, N = 13, P = 0.345) or trill phrases (Wilcoxon Signed Ranks Z = -0.80, N = 13, P = 0.422) but maximum frequency was significantly lower at 48 m in the buzz (Wilcoxon Signed Ranks, Z = -

3.18, N = 13, P = 0.001) and note complex phrases (Wilcoxon Signed Ranks, Z = -3.18,

N = 13, P = 0.001).

Phrases also differed in their minimum frequencies between 2 and 48 m (Figure

11). There was not a significant change in the minimum frequencies of whistle (Wilcoxon

Signed Ranks, Z = -0.73, N = 13, P = 0.463), buzz (Wilcoxon Signed Ranks, Z = -0.04, N

= 13, P = 0.972), or note complex (Wilcoxon Signed Ranks, Z = -1.57, N = 13, P =

0.116) phrases, but the trill phrase did show an increase in its minimum frequency between 2 and 48 m (Wilcoxon Signed Ranks Z = -3.04, N = 13, P = 0.002). Minimum and maximum frequencies were not measured at 96 m due to poor signal-to-noise ratio.

Phrases differed in how the dominant frequency changed between 48 and 96 m

(Figure 12). Whistle (Wilcoxon Signed Ranks, Z = -1.66, N = 84, P = 0.097) and buzz

(Wilcoxon Signed Ranks, Z = -0.63, N = 107, P = 0.532) phrases had similar dominant frequencies from 48 to 96 m while note complex (Wilcoxon Signed Ranks, Z = -2.52, N

= 107, P = 0.012), and trill (Wilcoxon Signed Ranks, Z = -2.14, N = 94, P = 0.032) phrases both had lower dominant frequencies at 96 m.

Sites

Spectrogram cross-correlation analysis

Phrases on average had lower spectrogram cross-correlation coefficients at

Bullard’s Beach than Cape Blanco with distances combined (Figure 13; Mann-Whitney

U Test, Z = -15.26, NBB = 849, NCB = 411, P < 0.001). There was no differences in the number of mismatches between the sites at 48 m (48m: X2 = 1.129, N = 708, DF = 1, P =

0.288) although as stated previously only 1.41% of the cases were misclassified at that distance. There was a significant difference in the number of mismatches between the sites at 96 m (X2 = 17.98, N = 574, DF = 1, P <0 .001) with Bullard’s Beach having

11.96% of mismatches as compared to 0.64% at Cape Blanco. Sites differed significantly in phrase dissimilarity at 48 and 96 m (Figure 14, Mann-Whitney U Test; 48m: Z = -2.56,

NBB = 453, NCB = 255, P = 0.010; 96m: Z = -7.05, NBB = 396, NCB = 156, P < 0.001) with phrases being more similar at Bullard’s Beach than Cape Blanco.

Structural measures

Excess attenuation between 48 and 96m was not significantly different between sites (Mann-Whitney U Test, Z = -0.53, NBB = 312, NCB = 112, P = 0.599). Maximum frequency measures at 48 m did not differ between sites (Mann-Whitney U Test, Z = -

0.81, NBB = 396, NCB = 254, P = 0.417) but minimum frequency measures at 48 m did differ (Mann-Whitney U Test, Z = -2.16, NBB = 396, NCB = 254, P = 0.031) with

Bullard’s Beach showing higher minimum frequencies than Cape Blanco (Figure 15).

Minimum and maximum frequencies were not measured at 96 m due to poor signal-to- noise ratio. Dominant frequencies were significantly lower at Cape Blanco than Bullard’s

Beach at 48 m (Figure 16; Mann-Whitney U Test, Z = -2.28, NBB = 396, NCB = 254, P =

0.023) but there was no differences between the sites at 96 m (Mann-Whitney U Test, Z =

-1.58, NBB = 308, NCB = 155, P = 0.113).

Tone reverberation did not differ between the sites at 48 m (Figure 17; Mann-

Whitney U Test, Z = -0.86 NBB = 21, NCB = 30, P = 0.389) but at 96 m there was more reverberation at Cape Blanco than Bullard’s Beach (Figure 17; Mann-Whitney U Z = -

2.30 NBB = 18, NCB = 30, P = 0.021).

Discussion

The major results are that 1) Phrases are more degraded at 96 m than at 48 m, therefore our methods were sensitive to distance induced effects on structure; 2) Phrases degraded similarly within each site, except for dissimilarity and minimum frequency; 3)

Transmission fidelity differed among phrases, in particular, whistle phrases showed less degradation than other phrases at 48 m; 4) Phrases differed in the dissimilarity measure among phrase examples after transmission. Whistles were very similar to other examples, while trills and note complexes were relatively dissimilar; 5) Transmission of song phrases differed between the two field sites. Phrases were more degraded as well as had lower dissimilarity measures at Bullard’s Beach while there was more reverberation at

Cape Blanco.

Interaction of Sites and Song Phrases

Despite differences in sound transmission between the sites, the main effects of transmission on phrase structure largely stayed consistent at each site. Even in two structurally distinct habitats (open and edge), differences among phrases in degradation were relatively stable. Past research found that habitat structure influences attenuation

and degradation of particular song characteristics, but degradation is more influenced by signal frequency than by habitat type (Marten & Marler 1977; Wiley & Richards 1978).

Regardless of the habitat, high frequencies will attenuate more over distances than low frequencies (Morton 1975; Marten & Marler 1977; Wiley & Richards 1978). Frequencies above 4 kHz show increased attenuation and degradation particularly in habitats with more small reflecting surfaces, like forests (Marten & Marler 1977). In this study, we found no difference in maximum frequencies between the sites even though the maximum frequency of all phrases is above 4 kHz in Z. l. pugetensis. The difference between the vegetative and atmospheric structure of Cape Blanco and Bullard’s Beach may not have been large enough to influence a site-by-phrase interaction in maximum frequency. Research that found differences in attenuation and degradation above 4 kHz compared closed and open habitats (Morton 1975; Marten & Marler 1977), whereas our study compared open versus forest-edge/clearing habitat. Edge habitats appear to have an intermediate amount of attenuation and degradation between closed and open habitats but do not differ significantly from open habitats (Morton 1975).

Although most phrase measures showed similar effects at each site, there were two exceptions: dissimilarity and trill minimum frequency. At Bullard’s Beach, re- recorded phrases were more similar among examples, and dissimilarity was more variable overall, particularly in the note complex and trill phrases. The overall pattern of dissimilarity among phrases appeared consistent at each site. The increased variability and similarity among phrases at Bullard’s Beach may be due to wind effects. Strong winds limit vegetation at Bullard’s Beach to be less than 2-3 m in height, making

Bullard’s Beach an open, windy habitat in contrast to the edge habitat at Cape Blanco.

While we avoided performing trials during high wind (>5 km/h), even a small amount of wind can cause large variation in the received signal (Wiley & Richards 1978). The increased similarity among phrases at Bullard’s Beach may be due to its location on the

Pacific coast with constant broadband background noise from the surf. This background noise may decrease the signal-to-noise ratio at the site whereby making it more difficult for spectrogram cross-correlation analysis to detect similarities among song phrases

(Kime et al. 2000; Brumm & Slabbekoorn 2005).

Trill minimum frequency also showed a site-by-phrase interaction. At Bullard’s

Beach, the minimum frequency of trills increased between 2 and 48 m but not at Cape

Blanco. The reason for this effect is unknown. Trills have the lowest minimum frequencies (avg 2.84 kHz) of the phrases in Z. l. pugetensis song. It is possible that the low-frequency component of trills may have experienced ground attenuation. Although our speaker height was set within the range where boundary reflections occur, this explanation seems unlikely. Ground attenuation is most detrimental to frequencies below

1 kHz (Marten & Marler 1977; Wiley & Richards 1978), and the average minimum frequency of Z. l. pugetensis trills (2.84 kHz) is well above that range.

Phrase Degradation

Transmission fidelity differed among song phrases; with whistles maintaining transmission fidelity better than the other phrases. Whistles showed less degradation than other phrases at 48 m but this difference disappeared at 96 m. Furthermore the whistle phrase did not show any frequency dependent structural changes during transmission

while all other phrases did. The buzz had a decreased maximum frequency while the note complex had decreased maximum and dominant frequencies, and the trill phrase had increased minimum and decreased dominant frequencies at 48 m.

Structurally, the whistle phrase focuses energy in a narrow intermediate frequency range in comparison to the rest of the song, making it resistant to structural changes due to scattering loss. This tonal structure allows the whistle to maintain fidelity longer than phrases that spread out their energy over broader frequency ranges, like the note complex and trill (Wiley & Richards 1978). Note complexes have the highest maximum frequencies and second to highest dominant frequencies (the buzz has the highest). High frequencies are prone to scattering loss (Wiley & Richards 1978); therefore, in accordance with our results, the phrase with the highest average maximum frequency (i.e. note complex) showed the frequency dependent attenuation to their high frequencies.

Except for the whistle phrase, the trill has the lowest average maximum frequency and did not show any high frequency dependent attenuation. Therefore the maximum frequency of the trill was likely low enough to avoid scattering during transmission

(Wiley & Richards 1978). The trill phrase also showed attenuation of its minimum frequencies at Bullard’s Beach as discussed in the Interaction of Sites and Song Phrases section above.

By maintaining fidelity over longer distances, the whistle phrase may help to alert conspecifics to the information following in the note complex and trill phrases. Many avian species start their song with a simple pure tone note that is suggested to serve as a species-specific sign-on to alert receivers of a forthcoming message (Richards 1981b).

There is some evidence that supports this function for the whistle in the white-crowned sparrow. During song learning, young white-crowned sparrows use the whistle as a cue to indicate the species-specific song to learn, and are more likely to copy tutor songs that begin with a whistle phrase (Soha & Marler 2000). As adults, white-crowned sparrows react less aggressively toward whistles than any other phrase, indicating a non-aggressive function (Soha & Whaling 2002). This possible alerting function and structural fidelity of the whistle allows for the rest of the song to contain more information-rich components that may structurally attenuate/degrade over shorter distances.

Phrase Dissimilarity

The degree of similarity among phrases within a particular phase-type may indicate ease of discrimination among phrase examples. The four phrases differed in their similarity to phrases from other dialects. In particular, whistles were very similar to other whistle examples. The simple tonal structure of whistles does not allow for much variation between dialects so a whistle from dialect 1 was very similar to whistles from all other dialects. Trills and particularly note complex phrases are structurally complex with comparatively a lot of variation between dialects and individuals, respectively

(Nelson & Soha 2004; Nelson & Poesel 2007). Trills also have a redundant note structure. Redundancy of signal structure had been found to promote receiver detection and discrimination (Guilford & Dawkins 1991). Therefore, trill redundancy may increase the probability that part of the phrase will maintain structure over transmission for discrimination amongst other trill examples.

Functionally, if the whistle phrase is a species-specific sign-on, a receiver does not use it to discriminate among different individuals or dialects, only to determine species identity. Therefore there may not be selection for variation in the whistle among dialects or individuals. In white-crowned sparrows the trill phrase reflects dialect identity and the note complex signals individual identity (Nelson & Poesel 2007). Since these phrases confer more specific information than the species-specific whistle, their structure needs to be more variable among examples to convey that identifying information

(Marler 1960). That structural variability potentially makes it easier for receivers to discriminate among phrase examples (Marler 1960; Richards & Wiley 1980).

Although whistles on average transmit with less degradation over distance than either note complexes or trills, the simple structure of whistles makes them susceptible to the random effects of scattering. If whistle structure is degraded, a whistle is more likely to resemble another whistle, and therefore potentially be confused with it, than are the structurally more complex note complexes and trills. Despite attenuation of maximum frequencies of these phrases, sufficient redundant information remains that potentially allows receivers to recognize individuals or dialects.

Site Differences

Songs broadcast at Bullard’s Beach experienced higher average degradation, including higher minimum frequencies at 48 m, than Cape Blanco. Although there was less tall vegetation at Bullard’s Beach, higher winds likely contributed to the increased overall degradation at that site (Marten & Marler 1977; Wiley & Richards 1978). In comparison, Cape Blanco had lower dominant frequencies at 48 m and more

reverberation than Bullard’s Beach at 96 m. Reverberations are the result of reflected sound waves arriving at the receiver slightly after the direct wave. Habitats with more reflecting surfaces, like forests, should have more scattering and subsequently more reverberation (Wiley & Richards 1978; Richards & Wiley 1980). Therefore, the vegetation structure of Cape Blanco’s edge habitat provides more reflecting surfaces to promote reverberation relative to the open habitat at Bullard’s Beach.

Conclusion

White-crowned sparrow song contains four structurally and functionally distinct phrases that transmit through the environment with different degrees of degradation.

These differences were largely consistent between two acoustically different habitats.

Overall, whistles maintain structural fidelity over at 48 m better than other phrases and may alert receivers to the more structurally complex, information-rich phrases to follow.

Even though the note complex and trill are more degraded at shorter distances, their variable complex structures may make them potentially easier to detect and discriminate at larger distances.

For long-distance communication to be effective, the receiver must accurately detect and recognize the signal. Even though the note complex and trill were more degraded than the whistle, their structural complexity may allow the receiver to still discriminate the signal from others in their environment. The trill phrase also has note redundancy which can further improve signal perception (Richards & Wiley 1980;

Guilford & Dawkins 1991). Accurate discrimination of complex and/or variable signals over long-distances indicates that there may be less constraint on the evolution of long-

distance signals than previously thought (Morton 1975; Marten & Marler 1977; Wiley &

Richards 1978) and may help to explain why there is so much variation within and among long-distance song in birds. Future research on song perception over transmission would help to shed further light on the evolution of long-distance song structure.

Table 1. Structural description of frequency components (kHz) of Z. l. pugetensis song phrases based on descriptive statistics of model phrases recorded at 2 m (see methods).

Minimum Frequency Dominant Frequency Maximum Frequency Frequency Range Mean Min Max Range Mean Min Max Range Mean Min Max Range Mean Max - Mean Min Whistle 4.11 3.75 4.72 0.97 4.17 3.81 4.79 0.98 4.24 3.86 4.83 0.97 0.12 Buzz 3.59 2.81 4.52 1.71 4.39 3.27 5.19 1.92 5.20 4.39 6.16 1.77 1.61 Note Complex 3.38 2.83 4.24 1.52 4.35 3.36 5.50 2.14 5.73 4.47 6.89 2.42 2.35 Trill 2.84 2.22 3.24 1.02 3.69 3.22 5.32 2.10 4.79 3.86 5.64 1.77 1.95

Figure 1: Spectrogram of Puget Sound white-crowned sparrow (Z. l. pugetensis) song with four phrases identified (whistle (WH), buzz (BZ), note complex (NC), and trill (TR)).

Figure 2. Photo taken in April 2011 of typical habitat along one transect at Bullard’s Beach State Park, OR, U.S.A.

Figure 3. Photo taken April 2010 along one transect at Cape Blanco State Park, OR, U.S.A.

Figure 4. Map depicting the 10 dialects of Z. l. pugetensis song and their approximate location along the Pacific North West coast. Dialects 4, 8, and 9 are not shown.

Figure 5. Box plot of dissimilarity of 4 song phrases (whistle (WH), buzz (BZ), note complex (NC), and trill (TR)) with distances combined at Bullard’s Beach State Park, and Cape Blanco State Park. Statistical significance (P < 0.05) within each site is denoted by dissimilar letters above each box. Whiskers represent the lowest and highest value within 1.5 times the inter- quartile range. Outliers are excluded.

Figure 6. Box plot of minimum frequency (kHz) of the trill phrase at 2 m (model trills) versus the trills recorded at 48 m at Bullard’s Beach State Park, and Cape Blanco State Park. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols.

Figure 7. Box plot of spectrogram cross-correlation coefficients of four song phrases at 48 and 96 m with both sites combined. See Figure 1 for descriptions of plot symbols.

Figure 8. Bar plot of percent mismatches of four song phrases at 96 m with both sites combined. See Figure 1 for descriptions of plot symbols.

Figure 9. Box plot of song phrase dissimilarity at 48 and 96 m with both sites combined. See Figure 1 for descriptions of plot symbols.

Figure 10. Box plot of maximum frequency (kHz) measures of four song phrases at 2 (white boxes) and 48 (gray boxes) m. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols.

Figure 11. Box plot of minimum frequency (kHz) measures of four song phrases at 2 (white boxes) and 48 (gray boxes) m. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols.

Figure 12. Box plot of dominant frequency (kHz) measures of four song phrases at 48 (white boxes) and 96 (gray boxes) m. The ‘*’ denotes statistical significance (P < 0.05) within the phrase. See Figure 1 for descriptions of plot symbols.

Figure 13. Box plot of spectrogram cross-correlation coefficients at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park. The ‘*’ denotes statistical significance (P < 0.05) between the sites. See Figure 1 for descriptions of plot symbols.

Figure 14. Box plot of phrase dissimilarity at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park at 48 and 96 m. The ‘*’ denotes statistical significance (P < 0.05) between the sites. See Figure 1 for description of plot symbols.

Figure 15. Box plot of minimum frequency (kHz) measures at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park. The ‘*’ denotes statistical significance (P < 0.05) between the sites. See Figure 1 for descriptions of plot symbols.

Figure 16. Box plot of dominant frequency (kHz) measures at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park at 48 and 96 m. The ‘*’ denotes statistical significance (P<0.05) between the sites. See Figure 1 for description of plot symbols.

Figure 17. Box plot of reverberation measures at two study sites, Bullard’s Beach State Park, and Cape Blanco State Park at 48 and 96 m. The ‘*’ denotes statistical significance (p<0.05) between the sites. See Figure 1 for descriptions of plot symbols.

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