Savannah sparrows (Passerculus sandwichensis) nesting close to energy infrastructure alarm call at a lower frequency 
Bridget Antze1, Nicola Koper1,2
1.	Department of Natural Resources Institute, University of Manitoba, Winnipeg, Canada.
2.	Corresponding author: Nicola.koper@umanitoba.ca. ORCiD: 0003-3576-9525.
http://www.bioacoustics.info/article/savannah-sparrows-passerculus-sandwichensis-nesting-close-energy-infrastructure-alarm-call 
Antze, B. and Koper, N. 2018. Savannah sparrows (Passerculus sandwichensis) nesting close to energy infrastructure alarm call at a lower frequency. Bioacoustics. 

Abstract
Many birds rely on alarm calls during nest defense to communicate about risk, but anthropogenic disturbance, including noisy industrial infrastructure, has the potential to disrupt alarm communication. Birds may overcome these effects by altering the structure of vocalizations to improve signal transmission. While previous work has demonstrated that birds can alter the structure of songs in the presence of noise, few have considered effects of noise-producing infrastructure on alarm call structure. Here, we tested whether Savannah sparrows (Passerculus sandwichensis) altered the structure of alarm calls in the presence of natural gas compressor stations or grid-powered or generator-powered screw pump oil wells, and in relation to overall ambient noise levels. Savannah sparrows called at a lower peak frequency and first quartile frequency close to gas compressor stations, but call structure did not differ with proximity to either type of oil well, or in response to ambient noise levels, suggesting that factors beyond noise amplitude, such as the physical footprint of the infrastructure, affected call structure close to compressor stations. Our results demonstrate that even unlearned, structurally simple avian vocalizations can vary in structure, and that the impacts of anthropogenic infrastructure on acoustic communication may be driven by factors beyond just noise.
Keywords: acoustic masking; alarm calls; anthropogenic noise; natural gas; vocal plasticity; vocalization structure

Funding details: Research was supported by funding from the Natural Sciences and Engineering Research Council of Canada under grant CRDPJ 432934 - 12, Cenovus Energy Ltd, Canadian Foundation for Innovation under grant 18787, Manitoba Research and Innovations Fund, and the Clayton H. Riddell Endowment Fund of the University of Manitoba. 
Data availability statement: Data will be made available through the corresponding author.
Word count: 6,308

Introduction
Many birds use alarm calls during nest defense to solicit help from conspecifics in fending off predators (Gill and Sealy 2003), signal to a predator that they have been spotted (Bergstrom and Lachmann 2001) or distract the predator from the nest (Greig-Smith 1980), alert mates of an approaching threat (Bernath-Plaisted and Yasukawa 2011), or to induce anti-predator behaviour in nestlings (McIntyre et al. 2014a). Communicating predation risk during nest defense can have direct consequences for reproductive success: Both American Goldfinches (Spinus tristis) (Knight and Temple 1988) and Red-winged Blackbirds (Agelaius phoeniceus) (Knight and Temple 1986) experienced greater nesting success when they alarm called at higher rates. Alarm calls can encode valuable information about the nature of a threat, including the level of danger (Sloan et al. 2005), the type of predator (Gill and Bierema 2013) or the predator’s behaviour (Griesser 2008). Alarm calls with different characteristics are designed to trigger different behaviours in conspecifics (Sloan et al. 2005, Templeton et al. 2005) so it is important that these characteristics be relayed effectively if conspecifics are to respond appropriately. 
In areas with increasing levels of industrial development, there is growing concern that anthropogenic noise from industrial infrastructure may interfere with acoustic signaling (Habib et al. 2007, Schroeder et al. 2012, McIntyre et al. 2014b). Anthropogenic noise has potential to interfere with acoustic communication by masking or distorting avian vocalizations, which has been shown to impair the communication of mate-attraction (Habib et al. 2007, Blickley et al. 2012), parent-offspring (Leonard and Horn 2012, McIntyre et al. 2014b) and anti-predator  (Templeton et al. 2016) signals. The degree to which noise interferes with acoustic communication is thought to depend on the pitch of vocalizations, with the strongest masking effects occurring when vocalizations overlap in frequency with background noise (Blickley and Patricelli 2012). Because anthropogenic noise is generally loudest at low frequencies, birds with lower frequency vocalizations may be disproportionally affected by noise pollution (Francis et al. 2012). However, noise may also alter avian behaviour (Naguib et al. 2013) and vocalization structure (Hanna et al. 2011) when there is no overlap in frequency between vocalizations and background noise. Furthermore, the impacts of industrialization on acoustic communication may extend beyond acoustic masking. Physical structures associated with urbanization and development can alter the acoustic environment by increasing the density of reflective surfaces, which may degrade the quality or transmission of acoustic signals (Warren et al. 2006).
Birds may alter the structure of vocalizations in several ways that help them to overcome acoustic interference. First, they may simply vocalize louder to increase the signal to noise ratio, a phenomenon known as the Lombard effect (Brumm 2004). This is one of the most effective ways for birds to improve signal transmission in noise (Nemeth and Brumm 2010) and has been documented in the songs of Nightingales (Luscinia megarhynchos) (Brumm 2004) and the alarm calls of Noisy Miners (Manorina melanocephala) (Lowry et al. 2012). Second, birds may raise the frequency of vocalizations to avoid frequency masking from low-frequency anthropogenic (Slabbekoorn and Peet 2003, Wood and Yezerinac 2006) and natural (Lenske and La 2014) background noise. However, if masking is not a concern, birds may improve signal transmission by lowering the frequency of vocalizations (Potvin et al. 2014), as low-frequency sounds travel farther than high frequency sounds (Marten and Marler 1977). Birds may also reduce the bandwidth or entropy of vocalizations, as tonal signals with a narrow bandwidth transmit better in noise (Lohr et al. 2003, Hanna et al. 2011). Finally, birds may increase the rate or duration of vocalizations, to increase the likelihood that conspecifics will detect vocalizations through signal redundancy (Brumm and Slater 2006). 
While numerous studies have demonstrated that a variety of birds alter the structure of territorial and mate-attraction songs in the presence of ambient noise (Slabbekoorn and Peet 2003, Brumm 2004, Wood and Yezerinac 2006, Hanna et al. 2011), only a handful of studies have considered the effects of anthropogenic noise on alarm call structure (Lowry et al. 2012, McIntyre et al. 2014b, Potvin et al. 2014, Templeton et al. 2016). Unlike songs, alarm calls may decrease in pitch as anthropogenic noise increases (Potvin et al. 2014), perhaps because selective pressures on calls differ from selective pressures on songs. While distances over which conspecifics can detect and locate territorial and mate attraction songs should be maximized (Marler and Slabbekoorn 2004), alarm calls may be most effective if they are difficult to locate, and are detectable at a short range, to avoid revealing the caller’s location to predators (Marler 1955, Klump and Shalter 1984). While song structure is learned in oscine songbirds including sparrows (e.g. Marler 1970), alarm call structure is typically thought to be unlearned (e.g. Ter Maat et al. 2014) , and alarm calls usually consist of simple, single syllable notes designed to efficiently communicate danger, so they tend to be less complex and variable than songs (Marler and Slabbekoorn 2004). This has led some authors to question whether alarm calls are sufficiently flexible to be able to adapt to noise (Patricelli and Blickley 2006, Potvin et al. 2014). However, responding appropriately to alarm calls can have life or death consequences, so birds may be under especially high selective pressure to communicate these signals effectively even under sub-optimal conditions.
In the mixed-grass prairies of southern Alberta, increasing development from the oil and gas industry is altering the acoustic and physical landscape. In Western Canada, 11,102 and 11,226 new wells were drilled during 2013 and 2014, respectively, the years that this study took place (CAODC 2015). Oil and gas wells can impact grassland songbirds through increases in roads, traffic, habitat alteration, and noise produced by the infrastructure (Ludlow et al. 2015). However, the relative effects of these different factors are not well understood. While some research has identified anthropogenic noise as a key factor associated with energy-producing infrastructure that can impact songbirds (Bayne et al. 2008), other research has found that impacts of oil and gas infrastructure on nest success (Bernath-Plaisted and Koper 2016) and abundance of some species (Nenninger and Koper 2018) may be independent of noise levels. Furthermore, different types of oil and gas infrastructure differ in terms of the quality and amplitude of ambient noise that they produce, and in terms of the number and size of physical structures associated with them. Thus, each type of infrastructure may affect songbird communication differently (Warrington et al. 2017). 
Savannah Sparrows (Passerculus sandwichensis) are a common grassland generalist breeding throughout all provinces and territories in Canada and much of the Northern United States. Like other grassland songbirds, Savannah Sparrows build their nests on the ground, making them especially vulnerable to predators (Wheelwright and Rising 2008). Parents respond to threats close to their nests by continuously emitting alarm calls, for up to 20 minutes or until the threat goes away (Wheelwright and Rising 2008). They react to these calls by delaying feeding visits, presumably to avoid revealing the location of their nest to predators (Antze and Koper 2018). The acoustic properties of Savannah Sparrow alarm calls are similar to many other avian warning alarm calls, in that they consist of a series of simple, high frequency notes (approx. 7000 – 10 000 Hz, Fig. 1) that are difficult to localize. These characteristics mean that Savannah Sparrow alarm calls do not naturally transmit as far as songs, but also that they occupy a frequency range above which anthropogenic noise is generally loudest. 
The purpose of this study was to determine whether Savannah Sparrows respond to noise-producing oil and gas infrastructure by altering the structure of their alarm calls. The wider geographical distribution and broad range of habitats occupied by Savannah Sparrows may make them more adaptable to disturbance than other grassland endemics. However, the simplicity of alarm call structure may mean that alarm calls are not flexible enough to be altered. 
[Fig. 1 near here]
Materials and methods
Study area
Research took place in native mixed-grass prairies in a 200 km radius surrounding Brooks, Alberta, Canada (50.5642° N, 111.898° W) during May – July of 2013 and 2014. We recorded Savannah Sparrow alarm calls within 800 x 200 m sites centered around one of three different types of infrastructure: gas compressor stations (n = 4 sites), generator-powered screw pumps (n = 4 sites), and grid-powered screw pumps (n = 5 sites). Gas compressor stations are facilities that pressurize natural gas for transport through pipelines, consisting of several noise- producing turbines, motors and engines. These structures were the loudest of the three infrastructure treatments, producing noise at approximately 82 ± 3.1 dB(C) at 10 m. Screw pumps are a type of oil well that uses positive displacement to extract oil from the ground through one or more rotating screws. In remote areas, these structures may be powered by generators (generator-powered screw pumps), while in areas containing transmission lines they may be connected to the power-grid (grid-powered screw pumps). Generator-powered screw pumps produce noise at greater amplitudes (79 ± 3.5 dB(C) at 10 m) than grid-powered screw pumps (59 ± 2.0 dB(C) at 10 m). Most of the acoustic energy is located in the lower frequency ranges for all infrastructure treatments (Koper et al. 2016). Compressor stations produce their greatest amplitudes at the lowest frequencies, followed by generator-powered, then grid-powered screw pumps. Although the difference in amplitude between compressor stations and generator screw pumps was not significant, compressor stations sounded “louder” to the human ear, perhaps due to a higher amplitudes within the lower frequency range, or the more elevated position of noise-producing structures. We also recorded alarm calls on control sites, which were the same size as infrastructure sites, but were located >800 m from paved roads and noise-producing infrastructure (ambient noise was, on average, 52 dB(C) at 10 m, n = 12 sites). 
Alarm call recordings
To determine whether Savannah Sparrows responded to infrastructure noise by altering the structure of anti-predator vocalizations, we recorded alarm calls of 44 free-living adult Savannah Sparrows between May and July of each year. We recorded alarm calls from 11 individuals at compressor stations (1 – 5 per site), 8 individuals at generator-powered screw pumps (2 – 4 per site), 10 individuals at grid-powered screw pumps (1 – 4 per site), and 15 individuals at control sites (1 – 2 per site). Because our study involved focal animals in the field it was not possible to record data blind. To avoid bias, researchers followed exactly the same procedure to collect call recordings at all sites, and used an objective approach to select calls for vocal analysis (Podos 1997), as described below.
Because Savannah sparrows may alter some call features, such as rate (Weatherhead 1979) as investment changes over the nesting cycle, we aimed to record alarm calls from individuals with 5-day nestlings, although because nests were not checked every day actual age may have ranged from 4 – 6 days due to variation in apparent development. An observer stood at the nest, and when an adult approached within approximately 15 m of the nest and commenced alarm calling, the observer used a Zoom H4N Handy Portable Digital Recorder with built in microphone (ZOOM Corporation, Tokyo, Japan), set in an XY stereo microphone configuration (90°) at the maximum recording volume, to record 10 seconds of alarm calls, pointing the microphone directly at the bird. Digital recordings were saved as uncompressed WAV files at a sample rate of 48 kHz with 16-bit resolution. Calls were only recorded once from each nest, and it was assumed that sparrows defending different nests were unique individuals. 
Once the recording was complete, a Garmin eTrex Legend H ® hand-held GPS receiver (3 m precision) was used to measure the distance (in meters) to the perch location where the bird was calling, and 30 seconds of ambient noise were recorded, with the microphone pointed straight up. The distance and direction from the microphone to the bird, and the bird’s distance to the infrastructure were also documented. 
Acoustic analysis
We uploaded recordings of vocalizations into RavenPro 1.5, using a Hann Window Function with a Fast Fourier transformation length of 512 samples, 3 dB Bandwidth set at 135 Hz, and overlap of 50%. Previous research has shown that hand-selecting call features based on visual cues from a spectrogram can introduce bias, as the ability to detect and delineate call features can vary according to spectrogram settings, amplitude of signals, and the signal to noise ratio (reviewed in Zollinger et al. 2012). To avoid these concerns, we selected vocalizations from the power spectra using the methods described by Podos (1997), including only call features that fell with 24 dB below peak power, while everything else was considered to be background noise. This standardized approach is widely used in bioacoustics studies (e.g.  Templeton et al. 2005, Warrington et al. 2017) and has been recognized as an effective means of avoiding issues with spectrogram analysis described above, as it ensures the same acoustic parameters are extracted from signals regardless of these factors (Zollinger et al. 2012). We extracted 7 call parameters that birds may adjust in order to increase signal clarity in the presence of noise from all alarm calls recorded in the 10 s period (15 ± 6 calls per bird) (Table 1). To further ensure objectivity, we used only robust parameters to assess the effects of noise on call frequency, which vary little with the exact boundaries of the selection boxes specified by Raven users, making them more objective (Charif et al. 2010). Average power of each call was normalized to 1 m following Brumm (2004).
[Table 1 near here]
We calibrated average power of calls by playing a recording of white noise of known sound pressure level (SPL), as determined using a Brüel and Kjær 2250 SPL meter and frequency analyzer (C-weighting) (Brüel and Kjær, Nærum, Denmark) 50 cm from the microphone, and uploading the recording into Raven 1.5. We used the difference between the Raven reported sound pressure level and the actual sound pressure level of white noise to calculate the actual sound pressure level of alarm calls.
The average power of ambient noise at the bird’s perch location was measured by selecting all frequencies across the entire 30 second ambient noise recording, as well as two smaller frequency bands: 0-3000 Hz (the frequency range in which infrastructure noise is loudest) and 3000-12000 Hz (which includes the frequency range that overlaps with Savannah Sparrow alarm calls). Frequency bands were separated out in this way to determine whether differences in call features were influenced more by masking frequencies, or by the amplitude of anthropogenic noise, which mostly occupied frequencies <3000 Hz. Average power was measured from power spectra and calibrated with a white noise recording, as above. 
Statistical analysis
We used generalized linear mixed-effects models to assess whether Savannah Sparrows altered alarm call structure in relation to distance from each infrastructure treatment and overall ambient noise (independent of infrastructure treatment). To determine whether changes in call structure were driven by any particular infrastructure type, we modeled effects of infrastructure treatment, (log) distance from the infrastructure, and the interaction between these two variables on each call parameter. To determine whether ambient noise level independently affected call parameters, we conducted 3 sets of analyses modeling the effects of broadband (0 – 24000 Hz), low frequency (0 – 3000 Hz), and high frequency (3000 – 12000 Hz) noise on each call parameter.
Analyses were conducted using SAS 9.1 statistical software (SAS Institue Inc. 2011). We determined the distribution of response variable residuals, and whether we met test assumptions, using diagnostic graphs and deviance/df ratio. In some cases, variables were log-transformed to improve normality. We treated nest ID as a random effect for all analyses performed. We used Akaike Information Criteria (AIC) to determine whether adding site as a random effect would improve model fit. In all cases the AIC values for models including both random variables did not improve model fit (AICsite&nest > AICnest), so we did not include site in the analyses. All other analyses were conducted following a frequentist approach (Mundry 2011). Effects were considered significant at α= 0.05.
Results
Effects of oil and gas infrastructure on alarm structure
Within compressor station sites, closer to the infrastructure Savannah Sparrows called at a lower peak frequency (β = 0.2039, SE = 0.07125, df = 636, t = 2.86, p = 0.0043) and first quartile frequency (β = -0.4432, SE  = 0.1763, df = 636, t = -2.51, p = 0.0122) than on control sites. Although the results were not significant, there was some evidence that closer to compressor stations Savannah sparrows called at a lower third quartile frequency β = 0.1134 SE = 0.05937, df = 636, t = 1.91, p = 0.0566), and alarm calls occupied a larger 90% bandwidth (β = -1.0282, SE = 0.5293 df = 636 t = -1.94, p = 0.0525) than on control sites. Farther from compressor stations, Savannah Sparrows raised the frequency and reduced the bandwidth of alarm calls to frequencies consistent with those observed on control sites (Fig. 2). There was no effect of distance from compressor stations on other call structure parameters (p > 0.10). There was no effect of distance from either grid-powered or generator-powered screw pumps on any of the call parameters examined (p > 0.16)
[Fig. 2 near here]
Effects of ambient noise on alarm structure
	Ambient noise ranged from 41 - 72 dB in the broadband frequency range (mean = 60 dB), and had greater amplitudes in the low frequency range (mean = 68 dB, range: 50 - 81 dB) than in the high frequency range (mean: 30 dB, range: 21 – 48 dB). There was no effect of low, high, or broadband frequency ambient noise on any of the call parameters examined (Table 2). 
[Table 2 near here]
Discussion
	Natural gas compressor stations appeared to affect the structure of Savannah sparrow alarm calls the most strongly, as sparrows living close to these structures concentrated the energy within alarm calls to lower frequencies than those on control sites. Surprisingly, these differences in call structure did not occur in relation to differences in ambient noise level per se. This suggests that the effects of infrastructure on acoustic communication may not be driven by overall ambient noise levels, but by factors (acoustic or otherwise) specific to compressor stations.
One possible reason for the disproportionate effect of compressor stations on alarm call structure is that they were the loudest infrastructure treatment, so noise levels close to the infrastructure may be above a threshold at which Savannah Sparrows can communicate effectively with unaltered alarm calls, while generator- and grid-powered screw pump oil wells, producing more moderate levels of noise, may be sufficiently quiet not to cause interference. However, the types of changes in call structure observed close to compressor stations contrast with results of numerous previous studies, which have found that birds raise, not lower, the pitch of songs when exposed to elevated levels of ambient noise (Slabbekoorn and Peet 2003, Wood and Yezerinac 2006, Lenske and La 2014). In these instances, increasing the frequency of vocalizations is thought to improve signal transmission by reducing overlap with low-frequency masking noise (Slabbekoorn and Peet 2003). However, the songs examined in those studies were much lower in frequency than the alarm calls of Savannah Sparrows. While the minimum frequency of Savannah Sparrow alarm calls ranged from 4044 -9094 Hz, the minimum frequency of Great Tit (Parus major) songs range from 2820 – 3770 Hz (Slabbekoorn and Peet 2003), the minimum frequency of Song Sparrow (Melospiza melodia) songs range from 1200 – 2000 Hz (Wood and Yezerinac 2006) and the minimum frequency of White-throated Sparrow (Zonotrichia albicollis) songs is approximately 2150 Hz (Lenske and La 2014). When the frequency of vocalizations overlaps with low frequency background noise, raising their pitch may be an effective way for many species to avoid acoustic interference. However, given that Savannah Sparrow alarm calls occur well above the frequency range in which infrastructure noise is the loudest, they may derive little benefit from calling at a higher pitch. 
Instead, lowering the frequency of alarm calls may improve signal transmission because low frequency sounds propagate farther than high frequency sounds (Marten and Marler 1977). Given that Savannah Sparrow alarm calls are so high-pitched, they may be able to improve signal transmission by reducing the frequency of alarm calls, without risking increasing masking from low frequency anthropogenic noise. These results echo the finding of two recent studies which found Red-winged Blackbird trills to decrease in third quartile frequency when exposed to non-masking low frequency noise (Hanna et al. 2011), and Silvereye (Zosterops lateralis) alarm calls to decrease in peak frequency, but increase in propagation distance, when exposed to urban noise (Potvin et al. 2014). These findings highlight the importance of considering the acoustic properties of vocalizations and ambient noise in understanding which vocal adjustments might be the most adaptive.
Another possible explanation for the disproportionate effect of compressor stations is that the higher concentration of physical structures associated with compressor stations may change the physical environment in ways that interfere with acoustic communication. In addition to being the loudest infrastructure treatment, compressor stations also have more physical disturbance associated with them than either of the other treatments, including more buildings, fans, fences and other structures. Warren et al. (2006) suggested that the higher density of reflective surfaces from buildings on city streets can cause reverberations that mask or blend call features, thereby degrading the quality of acoustic signals. This effect could be similar to that observed in forested environments (Blumenrath and Dabelsteen 2004); however, concrete is less absorbent than vegetation, so the degrading effect of anthropogenic reflective surfaces is likely stronger than that of forests (Warren et al. 2006).  These reflective surfaces may simultaneously act to amplify anthropogenic noise through a flutter-echo effect (Warren et al. 2006), further interfering with signal transmission. Accordingly, the elevated noise levels associated with compressor stations, combined with the higher density of reflective surfaces from buildings surrounding these structures, may create a particularly challenging environment for acoustic communication.
Lowering the frequency of alarm calls may not only improve signal transmission in noisy sites, but also in the structurally complex environments associated with compressor stations. Sounds that are lower in frequency propagate farther in structurally complex environments as they are less susceptible to absorption, scattering and reverberations than higher frequency sounds (Marten and Marler 1977). Indeed, birds that live in forest habitats tend to sing at a lower frequency than those of open habitats (Boncoraglio and Saino 2007). Given that Savannah Sparrows are grassland generalists and occupy a range of habitats, including lightly treed environments (Wheelwright and Rising 2008), they may be well equipped to adapt to structurally complex acoustic environments.
Alternatively, it is possible that the lower frequency of calls close to compressor stations could be driven by factors unrelated to acoustics, such as elevated predation risk at these stations, which may affect the type of call structure that is most adaptive. As predator abundances were not directly measured in this study it is not possible to rule this out. However, the lower frequency of alarm calls close to compressor stations is consistent with an adaptation to improve transmission, while call features known to change with perceived risk in Savannah sparrows, such as call rate (Weatherhead 1979), were unaffected by proximity to infrastructure. Therefore, the differences in call structure observed in this study more likely represent an acoustic adaptation than a response to predation risk. It is also possible that compressor station infrastructure altered the propagation of calls, rather than the structure of calls produced by the birds. However, if this were the case we would expect to see an increase in echoes and reduced call clarity (Warren et al. 2006), rather than a reduction in call frequency with proximity to infrastructure.
	Our findings suggest that Savannah Sparrows may be capable of altering the structure of alarm calls in the presence of noise-producing oil and gas infrastructure. The ability to make these adjustments is dependent upon a number of factors including body size, the energy (Brumm 2004), to produce louder vocalizations (Oberweger and Goller 2001), and the behavioural flexibility required to detect acoustic interference and make appropriate changes to vocal features (Patricelli and Blickley 2006). This demonstrates that unlearned vocalizations such as alarm calls may be altered to enhance propagation (Patricelli and Blickley 2006, Potvin et al. 2014). Communicating alarm signals effectively is essential for Savannah Sparrows to adequately protect their nests from predation, so the selective pressures to be able to transmit these signals in a variety of acoustic conditions are likely considerable. These findings contribute to the growing evidence (Lowry et al. 2012, Potvin et al. 2014) that the simplicity of unlearned vocalizations such as alarm calls does not limit the ability of some birds, including Savannah Sparrows, from modifying these calls to overcome acoustic interference. Alternatively, Savannah Sparrows with low-frequency calls may preferentially settle near compressor stations; further research is required to understand the mechanisms that explain the observed patterns.
The findings of this study have important implications for the conservation and management of grassland songbirds as oil and gas development continues to grow in the mixed-grass prairies of North America. Given growing concerns about the potential impacts of anthropogenic noise on people and wildlife, infrastructure noise reduction mechanisms have been recommended to reduce the impacts of noise pollution associated with oil and gas wells (Francis et al. 2011). However, the results of this study do not suggest that reducing the amplitude of noise from oil wells would reduce the impacts of infrastructure on songbird communication, as Savannah sparrow alarm call structure close to these structures did not differ from call structure on control sites. While Savannah sparrows close to compressor stations alarm called at a lower frequency, this difference may have been driven more by the physical imprint of the compressor stations rather than noise, as we detected no effect of noise amplitude on call structure. Further research is required to disentangle effects of noise from effects of infrastructure on avian behaviour.
Acknowledgments
We are grateful to James Hare, Miya Warrington and Marty Leonard, who provided comments on an earlier draft of the manuscript. We thank our research assistants Ariel Lenske, Celina Willis, Kathryn Flemming, and Dr. Claire Curry for help with field work. 
Declaration of interests
Neither author has any conflict of interest in this research.
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Table 1. Rationale for call features analyzed to evaluate effects of industrial noise on alarm calls of Savannah Sparrows (Passerculus sandwichensis).
Call feature	Definition	Biological significance	
Call rate (call/s)	The number of calls per second.  Measured by counting the number of calls emitted over a 10 second period from the spectrogram in Raven 1.5, divided by 10.	Birds may increase call rate in noise to improve the chances of conspecifics hearing vocalizations through signal redundancy2.	
Duration (s)	The duration (s) of each individual alarm call. Measured from the selection spectrum in Raven 1.5. 	Birds may increase call duration in noisy environments to improve the chances of conspecifics hearing vocalizations through signal redundancy2.	
Peak Frequency (Hz)	The frequency band (Hz) within a selected alarm call at which the highest power (peak power) is concentrated. Measured from the selection spectrum in Raven 1.5.	Birds may move the energy within alarm calls to higher frequencies in order to avoid acoustic interference from low frequency ambient noise4,9,10 or move the energy within alarm calls to lower frequencies to increase transmission distance6,8.	
90% bandwidth (Hz)	The difference between the 5% frequency and the 95% frequency. Measured from the selection spectrum in Raven 1.5.	Birds may reduce the bandwidth of alarm calls as more tonal signals with a smaller bandwidth transmit better in noise3.	
First quartile frequency (Hz)	The frequency that delineates interval within a selected alarm call that contains the bottom 25% of the energy within the selection. Measured from the selection spectrum in Raven 1.5.	Birds may raise or lower the bottom frequencies of alarm calls in noisy environments, if interference affects only lower frequencies.	
Third quartile frequency (Hz)	The frequency that delineates interval within a selected alarm call that contains 75% of the energy within the selection. Measured from the selection spectrum in Raven 1.5.	Birds may raise or lower the upper frequencies of alarm calls in noisy environments, if interference affects only upper frequencies.	
Aggregate entropy (u)	A measure of the energy distribution or disorder within a selected alarm call. Low entropy sounds are more tonal, with energy concentrated within one frequency band, whereas high entropy sounds have energy scattered across multiple frequency bands. Measured from the selection spectrum in Raven 1.5.	Birds may decrease the entropy of vocalizations in noisy environments, as tonal signals transmit better than broadband signals3,5.	
Average power (1 m) (dB (C))	A measure of loudness, average power was initially measured from the selection spectrum in Raven 1.5 as the spectral density averaged across a selected alarm call, then calibrated to dB(C)7 and normalizing to 1 m1.	Birds may vocalize louder in noisy environments to improve signal transmission by increasing the signal to noise ratio1.	
1Brumm (2004), 2Brumm and Slater (2006), 3Hanna et al. (2011), 4Lenske and La (2014), 5 Lohr et al. (2003), 6Marten and Marler (1977), 7McIntyre et al. 2014b, 8Potvin et al. (2014), 9Slabbekoorn and Peet (2003), 10Wood and Yezerinac (2006) 

Table 2. The effects of broadband, low frequency, and high frequency noise on 8 call features in Savannah Sparrows (Passerculus sandwichensis).
Call Feature	Broadband noise (0 – 24000 Hz)	Low frequency noise (0 – 3000 Hz)			High frequency noise (3000 – 12000 Hz)			
 	β	SE	p	β	SE	p	β	SE	p	
Call rate (call/s)	0.001	0.004	0.887	0.001	0.004	0.889	-0.003	0.004	0.423	
Duration (s)	<0.001	<0.001	0.866	<0.001	0.001	0.785	-0.001	0.002	0.604	
Peak Frequency (Hz)	3.431	9.041	0.705	<0.001	0.001	0.688	0.002	0.001	0.227	
90% bandwidth (Hz)	-0.004	0.008	0.575	-0.004	0.008	0.575	-0.007	0.009	0.437	
First quartile frequency (Hz)	0.001	0.001	0.617	0.001	0.001	0.618	0.002	0.001	0.131	
Third quartile frequency (Hz)	<0.001	0.001	0.968	<0.001	0.001	0.966	0.002	0.001	0.077	

Figure 1. Excerpt from a representative Savannah sparrow alarm call in southern Alberta, Canada, in June of 2013.
Figure 2. The effects of distance from compressor stations on a) peak frequency, b) 90% bandwidth, c) first quartile frequency and d) third quartile frequency of Savannah sparrow alarm calls in southern Alberta, Canada, 2013 and 2014.