Fig 2 - uploaded by Molly Grace
Content may be subject to copyright.
Illustration of how max and peak frequencies were measured using amplitude spectra. The horizontal dotted line indicates the cut-off point used in our measurements, 36 dB below the peak amplitude of the call. Peaks below the frequency of component 1 are background noise. Each call was high-pass filtered at 1105 Hz before measurements were taken. This call is shown without filtering to show the relative noise level
Source publication
Loud, low-frequency traffic noise can mask songbird vocalizations, and populations of some urban songbird species have shifted the frequency of their vocalizations upward in response. However, the spectral structure of certain vocalization elements may make them resistant to masking, suggesting that species that use these notes could be more succes...
Contexts in source publication
Context 1
... measured characteristics of D notes for all calls using Signal 4.0 software (Engineering Design, Belmont, MA, USA). Each call was high-pass filtered at 1105 Hz, as in Freeberg (2012), in order to remove some of the low-frequency background noise (primarily from traffic) without removing any part of the call. To minimize bias, all note measurements were made blind to the noise level in which the bird was recorded. We made note measurements for all birds from which we recorded at least two calls containing “ D ” notes ( n =23). For each “ D ” note, we measured the frequencies of three spectral parameters: peak frequency, maximum frequency, and minimum frequency. Peak and maximum frequencies were measured using amplitude spectra (Zollinger et al. 2012; Fig. 2). We defined peak frequency as the frequency of the peak of greatest amplitude within the call (after filtering at 1105 Hz, the peak frequency was always part of the call, never the noise) and the maximum frequency as the highest frequency peak with amplitude greater than − 36 dB relative to the peak amplitude (Nowicki 1989; Bloomfield et al. 2005; Fig. 2). Measurements from all of a bird ’ s recorded notes were averaged to yield one value for each parameter. Before making “ D ” note measurements from our recordings, visual inspection of note spectrograms and amplitude spectra revealed that in many cases, moderate to high levels of low-frequency traffic noise obscured the lowest overtones, including but not limited to the fundamental frequencies, of “ D ” notes (Fig. 1). The difficulty of making accurate measurements of lower-frequency vocalizations recorded in noise has been noted previously (Nemeth and Brumm 2009; Zollinger et al. 2012) and we surmised that noise masking would interfere with our ability to accurately measure the frequency of the lowest components of “ D ” notes. Specifically, the problem lies in the fact that as traffic noise level increases, noise masking spreads to higher frequencies obscuring increasingly higher frequency overtones. This means that the lowest overtones detectable for measurement would increase with increasing noise level, creating the ap- pearance of a positive relationship between minimum frequency and noise that, in fact, may not exist. Because the fundamental frequencies were masked by noise, we calculated the minimum frequencies of “ D ” notes based on overtone spacing. The consistent frequency difference between overtones (hereafter “ overtone interval ” ) in “ D ” notes allowed us to calculate the minimum frequency of each note, even if it was not identifiable on the amplitude spectrum due to noise. This is because the interval between the first two frequency components (which typically occur at about 1680 and 2015 Hz for Carolina chickadees; Bloomfield et al. 2005; Fig. 1) is reflected in the interval of all higher components (Nowicki 1989). Therefore, if the frequency of at least one overtone and the overtone interval of a note are known, it is possible to calculate the expected frequencies of the note ’ s other overtones. Using this rationale, we calculated estimated values for each “ D ” note ’ s minimum frequency by subtracting its overtone interval from the note ’ s peak frequency. Peak frequency was chosen as the starting point for our calculations because this peak (the peak of highest amplitude) always appeared clearly and unambiguously on the amplitude spectrum. Four overtone intervals, located between the five adjacent peaks of highest amplitude on the note ’ s amplitude spectrum (the peak frequency was always included), were averaged to yield an overtone interval value for that note. From each note ’ s peak frequency, we subtracted the note ’ s overtone interval value until we obtained a value that fell within the published range for the minimum frequency of Carolina chickadee “ D ” notes (average 1851 Hz ±163 Hz (SD), range 1688 – 2014 Hz; Bloomfield et al. 2005), plus or minus 10 Hz. Therefore, the calculated minimum frequency always fell between 1678 and 2024 Hz. This calculation led to an unambiguous estimate of minimum frequency — there was never a case in which our calculation for a particular note yielded more than one value within this range. We also recorded the number of “ D ” notes in each call and calculated the duration of each individual “ D ” note using spectrograms scaled to − 24 dB relative to peak amplitude. Playback test: low-frequency traffic noise and accurate note frequency measurements The practice of using spectrograms to measure note frequencies has long been criticized for producing inaccurate results (see Greenewalt 1968; Beecher 1988; Zollinger et al. 2012), but the practice persists. It is particularly problematic when used to measure frequencies of calls recorded in noise. Many previous studies that have report- ed finding a correlation between noise and song minimum frequency used measurements from spectrograms, which raises questions about the validity of the results (discussed in Zollinger et al. 2012). We conducted a playback experiment to test our ability to accurately measure the minimum frequency of calls recorded in noise using spectrograms. We played synthesized chick-a-dee calls (Fig. 3) through a loudspeaker at sites with a range of traffic noise levels, and recorded them using the same equipment we used to record wild chickadee calls. This experiment mir- rored the way that we recorded the calls of wild chickadees; but by recording synthesized calls with known minimum frequency, we could test whether loud traffic noise affected our ability to make accurate measurements. We synthesized five chick-a-dee calls with different minimum frequencies (1300 – 2980 Hz) using Signal 4.0 software. Each call was constructed based on the spectral structure of a “ D ” note recorded in the local chickadee population. The frequencies of the lowest spectral components, the relative sound energies of the overtones, and the overtone intervals all mimicked the natural call on which it was based. Constructing “ D ” notes rather than using the original recorded calls allowed us to create playback files that had excellent signal-to-noise ratio (free from attenuation and from other environmental sounds such as other birds, traffic noise, and wind) and had clearly discernable spectral components in the lower frequencies. We then played the calls at ten sites characterized by varying levels of background noise (40.3 – 65.0 dB (A)). At each site, we played the five synthesized calls using a Marantz PMD 670 digital recorder connected to an AV70 Powered Partners loudspeaker. Recordings were made in flat open areas, free of dense vegetation or trees that might affect sound transmission, and ground cover was either grass or leaves (not concrete). The speaker was elevated so that its center was 1.1 m above the ground to minimize “ ground effects ” (Wiley and Richards 1982). We played calls at a 65 – 75 dB, SPL measured at 1 m from the speaker using a VWR Type 2 sound level meter (set to A-weighting, Lo range, slow response). We recorded the synthesized calls at a distance of 10 m from the speaker, and made ambient noise level measurements using the previously described protocol. We made minimum frequency measurements of “ D ” notes from spectrograms scaled to − 24 dB relative to the note ’ s peak amplitude, and calculated ...
Context 2
... measured characteristics of D notes for all calls using Signal 4.0 software (Engineering Design, Belmont, MA, USA). Each call was high-pass filtered at 1105 Hz, as in Freeberg (2012), in order to remove some of the low-frequency background noise (primarily from traffic) without removing any part of the call. To minimize bias, all note measurements were made blind to the noise level in which the bird was recorded. We made note measurements for all birds from which we recorded at least two calls containing “ D ” notes ( n =23). For each “ D ” note, we measured the frequencies of three spectral parameters: peak frequency, maximum frequency, and minimum frequency. Peak and maximum frequencies were measured using amplitude spectra (Zollinger et al. 2012; Fig. 2). We defined peak frequency as the frequency of the peak of greatest amplitude within the call (after filtering at 1105 Hz, the peak frequency was always part of the call, never the noise) and the maximum frequency as the highest frequency peak with amplitude greater than − 36 dB relative to the peak amplitude (Nowicki 1989; Bloomfield et al. 2005; Fig. 2). Measurements from all of a bird ’ s recorded notes were averaged to yield one value for each parameter. Before making “ D ” note measurements from our recordings, visual inspection of note spectrograms and amplitude spectra revealed that in many cases, moderate to high levels of low-frequency traffic noise obscured the lowest overtones, including but not limited to the fundamental frequencies, of “ D ” notes (Fig. 1). The difficulty of making accurate measurements of lower-frequency vocalizations recorded in noise has been noted previously (Nemeth and Brumm 2009; Zollinger et al. 2012) and we surmised that noise masking would interfere with our ability to accurately measure the frequency of the lowest components of “ D ” notes. Specifically, the problem lies in the fact that as traffic noise level increases, noise masking spreads to higher frequencies obscuring increasingly higher frequency overtones. This means that the lowest overtones detectable for measurement would increase with increasing noise level, creating the ap- pearance of a positive relationship between minimum frequency and noise that, in fact, may not exist. Because the fundamental frequencies were masked by noise, we calculated the minimum frequencies of “ D ” notes based on overtone spacing. The consistent frequency difference between overtones (hereafter “ overtone interval ” ) in “ D ” notes allowed us to calculate the minimum frequency of each note, even if it was not identifiable on the amplitude spectrum due to noise. This is because the interval between the first two frequency components (which typically occur at about 1680 and 2015 Hz for Carolina chickadees; Bloomfield et al. 2005; Fig. 1) is reflected in the interval of all higher components (Nowicki 1989). Therefore, if the frequency of at least one overtone and the overtone interval of a note are known, it is possible to calculate the expected frequencies of the note ’ s other overtones. Using this rationale, we calculated estimated values for each “ D ” note ’ s minimum frequency by subtracting its overtone interval from the note ’ s peak frequency. Peak frequency was chosen as the starting point for our calculations because this peak (the peak of highest amplitude) always appeared clearly and unambiguously on the amplitude spectrum. Four overtone intervals, located between the five adjacent peaks of highest amplitude on the note ’ s amplitude spectrum (the peak frequency was always included), were averaged to yield an overtone interval value for that note. From each note ’ s peak frequency, we subtracted the note ’ s overtone interval value until we obtained a value that fell within the published range for the minimum frequency of Carolina chickadee “ D ” notes (average 1851 Hz ±163 Hz (SD), range 1688 – 2014 Hz; Bloomfield et al. 2005), plus or minus 10 Hz. Therefore, the calculated minimum frequency always fell between 1678 and 2024 Hz. This calculation led to an unambiguous estimate of minimum frequency — there was never a case in which our calculation for a particular note yielded more than one value within this range. We also recorded the number of “ D ” notes in each call and calculated the duration of each individual “ D ” note using spectrograms scaled to − 24 dB relative to peak amplitude. Playback test: low-frequency traffic noise and accurate note frequency measurements The practice of using spectrograms to measure note frequencies has long been criticized for producing inaccurate results (see Greenewalt 1968; Beecher 1988; Zollinger et al. 2012), but the practice persists. It is particularly problematic when used to measure frequencies of calls recorded in noise. Many previous studies that have report- ed finding a correlation between noise and song minimum frequency used measurements from spectrograms, which raises questions about the validity of the results (discussed in Zollinger et al. 2012). We conducted a playback experiment to test our ability to accurately measure the minimum frequency of calls recorded in noise using spectrograms. We played synthesized chick-a-dee calls (Fig. 3) through a loudspeaker at sites with a range of traffic noise levels, and recorded them using the same equipment we used to record wild chickadee calls. This experiment mir- rored the way that we recorded the calls of wild chickadees; but by recording synthesized calls with known minimum frequency, we could test whether loud traffic noise affected our ability to make accurate measurements. We synthesized five chick-a-dee calls with different minimum frequencies (1300 – 2980 Hz) using Signal 4.0 software. Each call was constructed based on the spectral structure of a “ D ” note recorded in the local chickadee population. The frequencies of the lowest spectral components, the relative sound energies of the overtones, and the overtone intervals all mimicked the natural call on which it was based. Constructing “ D ” notes rather than using the original recorded calls allowed us to create playback files that had excellent signal-to-noise ratio (free from attenuation and from other environmental sounds such as other birds, traffic noise, and wind) and had clearly discernable spectral components in the lower frequencies. We then played the calls at ten sites characterized by varying levels of background noise (40.3 – 65.0 dB (A)). At each site, we played the five synthesized calls using a Marantz PMD 670 digital recorder connected to an AV70 Powered Partners loudspeaker. Recordings were made in flat open areas, free of dense vegetation or trees that might affect sound transmission, and ground cover was either grass or leaves (not concrete). The speaker was elevated so that its center was 1.1 m above the ground to minimize “ ground effects ” (Wiley and Richards 1982). We played calls at a 65 – 75 dB, SPL measured at 1 m from the speaker using a VWR Type 2 sound level meter (set to A-weighting, Lo range, slow response). We recorded the synthesized calls at a distance of 10 m from the speaker, and made ambient noise level measurements using the previously described protocol. We made minimum frequency measurements of “ D ” notes from spectrograms scaled to − 24 dB relative to the note ’ s peak amplitude, and calculated ...
Citations
... For the UCSD 2006/2007 comparison to UCSD 2018-2020 and to UCLA frequency measurements, we used spectrogram analysis and created a bound selection box where we visually identified the lower and upper limits by finding the lowest and highest frequency measurement. We understand the implications of using spectrogram analysis over mean power spectrum analysis for this comparison, as noted in many studies [86][87][88][89]. Visually determining measurements from a spectrogram can lack repeatability between individuals and, therefore, account for a larger variation in the dataset than exists in the population. ...
Urbanization can affect species communication by introducing new selection pressures, such as noise pollution and different environmental transmission properties. These selection pressures can trigger divergence between urban and non-urban populations. Songbirds rely on vocalizations to defend territories and attract mates. Urban songbirds have been shown in some species and some populations to increase the frequencies, reduce the length and change other temporal features of their songs. This study compares songs from four urban and three non-urban populations of dark-eyed juncos ( Junco hyemalis ) throughout Southern California. We examined song length, trill rate, minimum frequency, maximum frequency, peak frequency and frequency bandwidth. We also compared songs recorded from one urban junco population in San Diego nearly two decades ago with recently collected data in 2018–2020. Over all comparisons, we only found significant differences between UCLA and the 2006/2007 UCSD field seasons in minimum and maximum frequencies. These findings partially support and are partially in contrast to previous urban song studies. As urban areas expand, more opportunities will arise to understand urban song divergence in greater detail.
... Similarly, it had been suspected that birds might also regulate their song pitch, i.e., that they temporarily shift their songs upwards when their song frequency is overlapped by noise (Brumm and Slabbekoorn 2005). However, the current evidence is mixed, with some studies supporting this hypothesis (Verzijden et al. 2010;Bermúdez-Cuamatzin et al. 2011;Goodwin and Podos 2013;LaZerte et al. 2016) while others do not (Grace and Anderson 2015;Potvin and MacDougall-Shackleton 2015;Zollinger et al. 2017;Rios-Chelen et al. 2018). Behavioral plasticity of song frequencies could either be a direct regulation to mitigate masking or a non-functional by-product of the birds' attempts to sing louder in noise. ...
It has often been observed that birds sing at a higher pitch in cities and other areas that are polluted with intense low-frequency noise. How this pattern arises remains unclear though. One prevailing idea is that songbirds adjust song frequencies to environmental noise profiles through developmental plasticity via vocal learning. However, the conclusions of previous studies testing this hypothesis are inconsistent. Here we report the findings from two song learning experiments with zebra finches (Taenopygia guttata), in which we exposed young birds to anthropogenic noise during their sensitive vocal learning period. Unlike previous studies that addressed this issue, we did not use constant synthetic noise but natural urban noise with its typical amplitude fluctuations that was broadcast at realistic sound levels. We found that noise-exposed males in neither experiment developed higher pitched songs compared to control males. This suggests that the natural fluctuations between higher and lower noise levels in cities may allow young birds to exploit relatively quiet moments to hear their tutors and themselves, permitting them to make accurate copies of even low-frequency song elements.
Significance statement
If animals are to persist in urban habitats, they often must adjust their behavior to the altered conditions. Birds in cities are often observed to sing at a higher pitch, but we are largely ignorant of how this phenomenon arises. We investigated whether low-frequency traffic noise interferes with the song learning of birds so that they develop higher pitched songs. Accordingly, we played back natural traffic noise from urban bird habitats to young birds during their learning period and then analyzed their adult songs. We found that birds that learned their songs in noise did not sing at higher frequencies compared to control males that learned their song with no noise exposure. Our results show that typical traffic noise in cities may not be sufficient to interfere with vocal learning in a way that birds develop higher-pitched songs.
... Many birds use low-frequency songs and calls to transmit information over long distances and against environmental obstructions because higher frequency sounds attenuate faster [2]. Traffic noise usually occupies lower frequency ranges [8,9], and overlap between human and animal noise can lower the threshold at which birds are able to detect and discriminate between vocalizations (i.e., 'masking') [2,3,10] and appropriately respond [7,11,12]. To combat this issue, birds can: shift vocalization frequencies (i.e., 'the acoustic adaptation hypothesis'); reviewed by Roca et al. [13], increase the amplitude of vocalizations (i.e., 'the Lombard Effect') [7,14], shift calling to times of the day when anthropogenic noise is less prevalent [15], repeat portions of their vocalizations [16], and alter the duration of vocalizations [17,18]. ...
... To date, however, it is unclear how anthropogenic noise affects alarm calls of Blackcapped Chickadees. Grace and Anderson [9] reported that the frequency parameters of Dnotes in similar Carolina Chickadee (Poecile carolinensis) alarm calls did not change in response to a traffic noise gradient. Jung et al. [35] also reported that Carolina Chickadees responded similarly to a Screech-Owl playback in environments of varying noise levels, potentially indicating that noise was not interfering with information transfer. ...
... Field sites were located >800 m from each other to minimize the chance of recording the same birds at different locations [9] and trials were conducted between 0800-1600 each day, with at least 48 hours between trials to prevent habituation. During trials, we placed a taxidermic mount of an Eastern Screech-Owl (Megascops asio) on a platform 1 m away from the bird feeder to elicit alarm calls [31, 37, 38]. ...
Anthropogenic noise is an often-overlooked byproduct of urbanization and affects the soundscape in which birds communicate. Previous studies assessing the impact of traffic noise have focused on bird song, with many studies demonstrating the ability of birds to raise song frequency in the presence of low-frequency traffic noise to avoid masking. Less is known about the impact of traffic noise on avian alarm calls, which is surprising given the degree to which predator information within alarm calls may impact fitness. The objective of this study was to assess the impacts of traffic noise on the Black-capped Chickadees (Poe-cile atricapillus), a small non-migratory songbird with a well-studied and information-rich alarm call. We studied birds at eight locations in Stark County, Ohio, from 15 January to 7 March 2016, and used a taxidermic mount of an Eastern Screech-Owl to elicit alarm calls. In half of the trials, a pre-recorded traffic noise track was also broadcasted at 50 decibels. In noise trials, chickadee calls contained more introductory notes (P < 0.001), more total notes (P < 0.001), were of longer duration (P < 0.001), and had lower introductory and D-note peak frequencies (P = 0.032 and P = 0.041, respectively). No differences were noted in the number of D-notes per call between noise and control trials. Modifying alarm call duration and frequency, without changing the number of D-notes, may be a strategy that chickadees use to convey predator information and to coordinate a threat-appropriate mobbing response when it is not possible to change call type. Our results add to the small, but growing , literature documenting the effects of anthropogenic noise on avian alarm calls, demonstrate the flexibility and complexity of chickadee calls given in response to predators, and may partially explain why chickadees adapt well to urban areas.
... Results of prior research have been inconclusive as to whether or not birds do alter their vocalisation to overcome the effects of acoustic masking in areas with high anthropogenic sound disturbance. About half of previous studies have shown vocalisation frequency shifts in bird vocalisations to higher frequencies (Brumm 2004; Kight and Swaddle 2015; Grace and Anderson 2015) in response to acoustic masking. The Black-capped Chickadee (Poecile atricapillus), a closely related species to the Carolina Chickadees we studied, is among those that increase vocalisation frequency (Oden et al. 2015). ...
... However, we did not find similar vocalisation shifts in response to changes in the soundscape as a whole. The inconsistency of results in this and previous studies (Brumm 2004;Kight and Swaddle 2015;Grace and Anderson 2015) calls to attention the need to test behaviours for functional advantages in overcoming anthropogenic disturbances. In our results we found that vocalisation frequency shifts did not increase the effectiveness of the communication in the presence of anthropogenic sound disturbances from roads. ...
Anthropogenic sound disrupts animal communication, but little research has both quantified how this disruption changes the soundscape and connected these changes to shifts in avian behaviour. We examined the effects of anthropogenic sound on soundscapes and avian vocalisation and flight behaviour. We recorded Carolina Chickadee (Poecile carolinensis) vocalisations, tested if vocalisations differed with varying levels of anthropogenic sound and overall variation in soundscape, and observed behavioural responses to differing vocalisation playbacks to test the functionality of possible vocalisation shifts. We found that anthropogenic sound disturbance predicted change in soundscape indicators. Vocalisation frequency was higher in areas of high disturbance but did not differ with changes in soundscape. Vocalisation frequency shifts did not provide a functional advantage, possibly explaining the lack of vocalisation changes in response to disturbed soundscapes. These analyses demonstrate that anthropogenic sound does change the surrounding soundscape, but the interaction between these changes and animal behaviour is multidimensional.
• Key policy insights
• By quantifying the effects of sound from roads on the surrounding soundscape, we demonstrated how anthropogenic sound disturbance impacts the surrounding ecosystem, not just the immediate site of the generated sound.
• Increasing anthropogenic sound disturbance affected avian vocalisation. Birds vocalised at a higher frequency in the presence of higher anthropogenic sound disturbance.
• Vocalisation frequency shifts did not provide an advantage in overcoming acoustic masking from roads.
• The assessment of vocal responses to anthropogenic sound needs to extend to include complex assessments of behaviours. Without these behavioural assessments we may not be able to properly evaluate the effects of anthropogenic sound disturbances on avian communication and behaviour.
... Not all studies, however, have shown that birds raise their frequencies in noisy environments (reviewed in Brumm and Zollinger 2013). Several studies, for example, have found no changes in song frequency from study species exposed to chronic background noise (Grace andAnderson 2015, Rios-Chelen et al. 2015), whereas others have found species to decrease the frequencies or frequency bandwidths of their songs in noisy environments (Potvin et al. 2014, Luther et al. 2015, Potvin and MacDougall-Shackleton 2015. More specifically, species with high-pitched songs situated above the noise threshold often display considerably less frequency-shifting behavior than lower-pitched species found in the same study sites Schneider 2009, Dowling et al. 2012). ...
The Northern Mockingbird (Mimus polyglottos) is a successful urban adaptor known to display flexibility in foraging, nesting, and anti-predator behavior. Its vocal behavior is also complex, with a breeding song composed of a wide variety of non-mimetic and mimetic elements, or “syllable types.” We tested the hypothesis that Northern Mockingbird adaptation to urban settings includes changes in its vocal behavior in noisy urban environments. We studied an urban/suburban mockingbird population to test the effect of urban background noise on breeding song frequency and syllable-type composition. Given that urban noise overlaps most strongly with low-frequency vocalizations, a phenomenon known as “signal masking,” we predicted a positive association between noise levels and mockingbird average peak frequency (a measure of vocalization power). We further predicted a positive effect of noise levels on the peak frequency of the lowest-pitched syllable type in a mockingbird’s song, no effect on the peak frequency of the highest-pitched syllable type, and thus a negative effect on mockingbird peak frequency range. Lastly, we predicted a negative effect of background noise on the use of syllable types experiencing heavy signal masking and, conversely, a positive effect on the use of syllable types experiencing minimal signal masking. We found a significant positive effect of noise levels on both average peak frequency and peak frequency of the lowest-pitched syllable type, but no effect on the peak frequency of the highest-pitched syllable type and peak frequency range. In addition, as background noise levels increased, we found significant declines in the percentages of heavily masked syllable types (1–3 kHz) and significant increases in the percentages of syllable types in the 3–5 kHz range; percentages of syllable types >5 kHz were, however, unaffected by background noise. These results were consistent with the hypothesis that Northern Mockingbird breeding songs change in pitch and syllable-type composition in noisy settings, providing further evidence that songs of urban-adapting species differ in noisy environments.
... While there is no objectively correct scale at which to study and manage ecosystems (Levin 1992), including the soundscape, consideration of multiple scales has proven valuable in research and practice (e.g., Quinn et al. 2014). The spatial and temporal scale of research in acoustic and soundscape ecology varies, including studies on small populations at local scales (e.g., Grace and Anderson 2015), snapshots at larger scales at a given time and place (e.g., Rodriguez et al. 2014;Oden et al. 2015;Buxton et al. 2017), and longer monitoring efforts (e.g., Frommolt and Tauchert 2014). ...
... Here, however, V. sinensis did not increase the peak frequencies of any syllables in response to traffic noise. Several previous studies have also shown that call frequency did not increase significantly in response to increased background noise (Grace and Anderson 2015;Proppe et al. 2012). For example, the F 0 of the calls of the Carolina chickadee (Poecile carolinensis) did not shift in response to traffic noise (Grace and Anderson 2015), although increased background noise did cause an F 0 shift in black-capped chickadees (P. ...
... Several previous studies have also shown that call frequency did not increase significantly in response to increased background noise (Grace and Anderson 2015;Proppe et al. 2012). For example, the F 0 of the calls of the Carolina chickadee (Poecile carolinensis) did not shift in response to traffic noise (Grace and Anderson 2015), although increased background noise did cause an F 0 shift in black-capped chickadees (P. atricapillus) (Proppe et al. 2012). ...
Natural background noises are common in the acoustic environments in which most organisms have evolved. Therefore, the vocalization and sound perception systems of vocal animals are inherently equipped to overcome natural background noise. Human-generated noises, however, pose new challenges that can hamper audiovocal communication. The mechanisms animals use to cope with anthropogenic noise disturbances have been extensively explored in a variety of taxa. Bats emit echolocation pulses primarily to orient, locate and navigate, while social calls are used to communicate with conspecifics. Previous studies have shown that bats alter echolocation pulse parameters in response to background noise interference. In contrast to high-frequency echolocation pulses, relatively low-frequency components within bat social calls overlap broadly with ambient noise frequencies. However, how bats structure their social calls in the presence of anthropogenic noise is not known. Here, we hypothesized that bats leverage vocal plasticity to facilitate vocal exchanges within a noisy environment. To test this hypothesis, we subjected the Asian particolored bat, Vespertilio sinensis, to prerecorded traffic noise. We observed a significant decrease in vocal complexity (i.e., an increased frequency of monosyllabic calls) in response to traffic noise. However, an increase in the duration and frequency of social calls, as have been observed in other species, was not evident. This suggests that signal simplification may increase communication efficacy in noisy environments. Moreover, V. sinensis also increased call amplitude in response to increased traffic noise, consistent with the predictions of the Lombard effect.
... Spectral changes have been reported for quite a few bird species that sing their songs at higher minimum or peak frequency when exposed to elevated levels of ambient noise, by which they escape lowfrequency masking at least to some extent (Bermúdez-Cuamatzin et al., 2009Gross et al., 2010;Halfwerk and Slabbekoorn, 2009;Ripmeester et al. 2010;Slabbekoorn and den Boer-Visser, 2006;Verzijden et al., 2010). However, there have also been other studies that have reported a lack of such noise-dependent spectral shifts, just like in our current data set (Gough et al., 2014;Grace and Anderson et al., 2015;Ríos-Chelén et al., 2013;. Parris and Schneider (2009) for example, studied two species, one that adjusted and one that did not. ...
Many bird species adjust their songs to noisy urban conditions by which they reduce masking and counteract the detrimental impact on signal efficiency. Different species vary in their response to level fluctuations of ambient noise, but it remains unclear why they vary. Here, we investigated whether noise-dependent flexibility may relate to singing style and signal function of the flexible acoustic trait. Species with highly variable songs may generally be more flexible and strongly repetitive singers may be more limited to stray from their stringent patterns. We exposed males of four passerine species with contrasting singing styles (repertoire size, immediate or eventual variety singing and syllable diversity) to three experimental sound conditions: 1) continuous urban noise; 2) intermittent white noise and 3) conspecific song playback. We found no spectral or temporal changes in response to experimental noise exposure in any of the four species, but significant temporal adjustment to conspecific playback in one of them. We argue that the consistency in song frequency and timing may have signal value, independent of singing style, and therefore be an explanation for the general lack of noise-dependent flexibility in the four species of the current study.
... The only other study that we know of, that explicitly tested for vocal plasticity with noise in a suboscine, is Gentry et al. (2017)'s; by comparing moments of relatively high and low urban noise levels, they provided correlative evidence that the Eastern wood pewee, can flexibly increase the minimum frequency of their songs, and decrease song duration, with noise. Gentry et al. (2017) measured the minimum and maximum frequency of songs by eye on spectrograms, a practice that can be biased by noise Grace and Anderson 2015;Ríos-Chelén et al. 2016Brumm et al. 2017). However, it is likely that the frequency measures in Gentry et al.'s study were not noise-biased to an extent that their results were compromised because the minimum frequency of Eastern wood pewee songs are relatively high pitched (3000-5000 Hz, Gentry et al. 2017) and, thus, likely not biased by low-frequency noise (Ríos-Chelén et al. 2017). ...
... However, it is important to keep in mind that several of these studies used the subjective by-eye practice (i.e. BEP, measuring by eye on spectrograms) to measure the minimum frequency which can potentially lead to noise-biased results Grace and Anderson 2015;Ríos-Chelén et al. 2016Brumm et al. 2017), meaning that some studies in oscines might have found a positive relation between vocal minimum frequency and noise when no one exist, or might have overestimated the extent to which they vocalize at a higher pitch in noise. In this study, we used the objective Bthreshold method^(which uses a power spectrum and a pre-set energy threshold, Podos 1997Podos , 2001Ríos-Chelén et al. 2016 with two thresholds. ...
Vermilion flycatchers, a suboscine, sing songs with more elements in territories with higher urban noise levels. We tested the hypothesis that this pattern is achieved through vocal flexibility, by which individuals add elements to their songs when noise increases; we also tested whether males modulate other song attributes and song output with noise. To this end, we did a playback experiment with free-living males where we recorded their songs during three noise treatments: first ambient noise (FAN), high urban noise (HUN), and second ambient noise (SAN) treatments. We counted the number of song elements and measured acoustic attributes both in the whole song and in the song terminal element (T). Males did not modify the number of song elements, nor song minimum frequency, with noise. The T minimum frequency slightly increased during SAN when compared to the FAN and HUN treatments, but it did not differ between the HUN and FAN treatments. Thus, we interpret these results as a lack of reliable evidence of immediate noise-induced song flexibility in frequency parameters. Song entropy decayed during the trials, but this seemed to be an effect of time and not a noise-induced change. Vermilion flycatchers appear to be less capable of modulating spectral song attributes to cope with noise than many oscines. We discuss other potential strategies that this species may use to deal with noise and a possible mechanism by which males end up singing longer songs in noisier territories (natural selection).
... During exposure to experimental noise mountain chickadees increased the frequency of dee notes by an average of 30 Hz. Black-capped chickadees are generally capable of adjusting dee-note frequencies, as seen by differential learning in juveniles (Hughes et al. 1998) and convergence in frequency characteristics in winter flocks (Mammen and Nowicki 1981;Nowicki 1989). However, in Carolina chickadees (a close relative of mountain chickadees) Grace and Anderson (2014) observed no relationship between the minimum frequency of dee notes and local levels of traffic noise. Similarly, we also observed no change in dominant dee-note frequencies with local ambient noise levels; yet, in our experimental noise treatment, the frequency of dee notes increased during noise exposure among all males, but returned to preexposure levels in the 5-min period after experimental noise exposure. ...
Vocal plasticity may allow birds to reduce masking effects of noise pollution arising from urbanization. Mountain chickadees (Poecile gambeli) use both songs and calls during the dawn chorus, which vary in masking susceptibility. Thus, increasing song or call frequency, or switching between vocalization types are all potential mechanisms to reduce masking during fluctuating noise conditions. Further, prior experience with noise pollution may be a necessary precursor to allow birds to alter signals in response to sudden noisy conditions. To determine how mountain chickadee songs, calls, and chorus composition are affected by noise, we recorded 55 males across gradients of local ambient noise and habitat urbanization in three cities in British Columbia, Canada. Of these individuals, 31 were also exposed to 5-min experimental noise treatments. Habitat urbanization was quantified through a continuous index reflecting properties of urbanized areas. Only song frequency increased with local ambient noise, and this effect varied regionally. In response to experimental noise exposure, males increased the frequency of their calls (but not of their songs), and varied their use of songs vs. calls. Interestingly, this response was dependent on local ambient noise levels: males in noisy areas shifted to using relatively more songs, whereas males in quiet areas shifted to using relatively more calls. These findings may suggest that although mountain chickadees are capable of adjusting their vocalizations, choosing a response which can lead to masking release may require prior exposure to high levels of ambient noise.
