Behavioral Ecology
doi:10.1093/beheco/arr176
Advance Access publication 11 November 2011
Original Article
Comparative effects of urban development and
anthropogenic noise on bird songs
J.L. Dowling,a,b D.A. Luther,c and P.P. Marraa
aSmithsonian Migratory Bird Center, National Zoological Park, 3001 Connecticut Avenue, Washington,
DC 20008, USA, bDepartment of Neurobiology and Behavior, W343 Mudd Hall, Cornell University,
Ithaca, NY 14853, USA, and cBiology Department, George Mason University, 4400 University Drive, MS
3E1, Fairfax, VA 22030, USA
Many avian species live, breed, and communicate in urban areas. To survive and reproduce in these areas, birds must transmit
their signals to intended receivers. As an arena for acoustic communication, 2 salient features of the urban environment are an
abundance of reflective surfaces and a high level of low-frequency anthropogenic noise. Each presents unique communication
challenges, with hard surfaces reflecting and distorting high frequencies and noise masking low-frequency song components.
Based on this, we predicted that noise level would affect minimum song frequency and urban development (percentage of
impervious surface) would affect maximum frequency and frequency range. We compared the effects of urban development and
noise on songs of 6 bird species at 28 sites along an urban to rural gradient, across a broad range of noise levels. We found that
minimum song frequency increased as noise level increased for 2 of 6 species, with 5 of 6 species showing a strong trend in the
predicted direction. Species with lower frequency songs were more affected by noise. Maximum frequency and frequency range
decreased for 2 of 6 species as urban development increased, and this effect was stronger for species with higher frequency songs.
For some species, minimum frequency only increased with noise at less urban sites and similarly, maximum frequency and
frequency range only decreased with urbanization at quiet sites, suggesting a trade-off between different vocal adjustments. Ours
is the first study to investigate how noise and urban development affect song frequency characteristics of multiple bird species.
Key words: acoustic adjustment, acoustic interference, anthropogenic noise, bird song, signal transmission, urbanization. [Behav
Ecol 23:201–209 (2012)]
INTRODUCTION
With human population growth, landscapes become in-creasingly developed and habitats important to wildlife
become permanently altered (Marzluff 2001). Anthropogenic
modification of habitats can have a particularly strong effect
on acoustic communication in animals because their vocaliza-
tions are specifically adapted to both the structural and the
acoustic characteristics of their local environment (Orejuela
and Morton 1975; Wiley and Richards 1978; Hunter and
Krebs 1979; Rabin and Greene 2002; Warren et al. 2006).
The impervious surfaces of developed areas affect animal sig-
nals by scattering sound waves and creating multiple reverber-
ations that can cancel and distort portions of the signal (Truax
1978; Slabbekoorn et al. 2007). These effects depend on the
frequency of the signal. Scattering, reverberation, and atmo-
spheric and vegetative absorption are stronger for high-
frequency than for low-frequency signals (Wiley and Richards
1982; Boncoraglio and Saino 2007). Reverberations of high
frequencies create many overlapping echoes that decay ran-
domly, whereas low frequency sounds often form one discrete
echo (Richards and Wiley 1980). Multiple overlapping echoes
with unpredictable arrival times can mask, cancel, or distort
structural features of the signal (Richards and Wiley 1980;
Slabbekoorn et al. 2007). This effect is especially strong for
broad bandwidth signals (Boncoraglio and Saino 2007). In
addition, large barriers in urban habitat (such as buildings)
absorb and reflect high-frequency sounds, whereas low-frequency
sounds are diffracted around barrier edges and continue to
propagate (Truax 1978; Rossing and Fletcher 2004).
An additional effect of increased human population density
and continued urban development is an increasing level of an-
thropogenic noise. Background noise presents a challenge for
animal communication because it increases the masked hear-
ing threshold (threshold of audibility for a specified sound in
the presence of another sound) of receivers (Yost 2000; Barber
et al. 2010) and thereby limits the signal’s active space (dis-
tance over which a sound can be heard). Most anthropogenic
noise (including air and road traffic noise) occupies a fre-
quency range below 2000 Hz (Slabbekoorn and Peet 2003).
This low-frequency noise disrupts the transmission of low-
frequency signal components by masking that region, which
changes the frequency range that the receiver hears and in-
terferes with the receiver’s ability to hear the signal’s original
content (Lohr et al. 2003; Brumm and Slabbekoorn 2005;
Halfwerk et al. 2011).
Animals use acoustic signals for functions like species recog-
nition, mate attraction, and territory defense, making them
central to their reproductive success and survival (Kroodsma
and Byers 1991; Brumm and Slabbekoorn 2005). A signal
must be well matched to the acoustic environment in order
to reach the receiver and maintain information content. In
highly reverberant structural environments, we would expect
low-frequency songs with narrow frequency ranges to be
Address correspondence to J. Dowling. E-mail: jld276@cornell.edu.
Received 27 January 2011; revised 28 July 2011; accepted 22
September 2011.

 The Author 2011. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved.
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favored, whereas we would expect songs with a high minimum
frequency bound to be favored in areas where anthropogenic
noise is present. Because different bird species have different
song frequency and structural characteristics, we would also
expect the effects of noise and urbanization on song to differ
by species. Specifically, in highly reverberant urban environ-
ments, we would expect species with high-frequency broad
bandwidth songs to be more impacted by reverberation and we
expect them to adjust songs more in response. We would
expect species whose songs have a lower minimum frequency
bound to be more impacted by masking in areas with high
noise levels and we expect them to adjust songs more in re-
sponse. Numerous observational and experimental studies
have shown that several bird species sing with a higher mini-
mum frequency when low-frequency ambient noise is present
(Slabbekoorn and Peet 2003; Ferna´ndez-Juricic et al. 2005;
Wood and Yezerinac 2006; Nemeth and Brumm 2009; Bermu´-
dez-Caumatzin et al. 2010). To our knowledge, no studies have
investigated how both urban development and noise level
affect bird song in more than one species.
In this study, we investigated how the frequency characteris-
tics of the songs of 6 bird species vary with urban development
and with the decibel level of the background noise. Based on
both sound transmission and masking theory outlined above,
we made the following predictions: 1) The minimum fre-
quency of bird songs will be affected by background noise level
and this affect will be stronger for species with lower frequency
songs. We expect urban development will have little or no ef-
fect on minimum frequency. 2) The maximum frequency of
bird songs will be affected by the amount of urban develop-
ment and this affect will be stronger for species with higher
frequency songs. We expect noise level will have little or no ef-
fect on maximum frequency. 3) The frequency range of bird
songs will be affected by the amount of urban development and
this affect will be stronger for species with higher frequency
songs. We expect noise level will have little or no effect on
frequency range.
MATERIALS AND METHODS
Research was conducted at 28 study sites across an urban to
rural land-use gradient in the greater Washington, DC and
Baltimore metropolitan areas. Study sites were part of
the Smithsonian Neighborhood Nestwatch citizen science
project (http://nationalzoo.si.edu/ConservationAndScience/
MigratoryBirds/Research/Neighborhood_Nestwatch). In this
project, participating citizens volunteer their private property
for use as study sites and can also participate in data collection
for some projects.
At each study site, a center point was established at the front
left corner of the central structure of the property (typically
a house). This center point was used for land cover measure-
ments and ambient noise measurements. All birds were
recorded within 400 m of this point. We measured ambient
noise level using an Extech 407730 sound level meter. Measure-
ments were C weighted and taken for 5 min total in 4 cardinal
directions, and the highest readings (that were not the result of
wind or abrupt noises) from each direction were recorded and
averaged. We took readings between 6:30 AM and 9:00 AM
(active time of day for singing for all 6 species) on the same
day that birds were recorded at that location. To determine the
level of urban development, the percentage of impervious sur-
face within a 400 m radius around the site center was measured
for each site. We chose a radius size of 400 m because bird
communities are significantly affected by traffic noise up to
a distance of about 400 m (Reijnen and Foppen 1997; Weiserbs
and Jacob 2001; Reijnen and Foppen 2006). Percentage of
impervious surface was determined using 30m–resolution Na-
tional Land Cover Data 2001 (Homer et al. 2004) in ArcGIS 9.2
(Environmental Systems Research Institute 1999–2006).
We recorded songs using a Sony TCM-5000EV tape recorder
(Sony, New York, NY), Maxell UR type 1 90-min tapes (Maxell,
Atlanta, GA), and a Sennheiser ME 66 shotgun microphone
(Sennheiser, Old Lyme, CT). Songs from the following species
were recorded: American Robin (Turdus migratorius), Carolina
Wren (Thryothorus ludovicianus), Gray Catbird (Dumetella caro-
linensis), House Wren (Troglodytes aedon), Northern Cardinal
(Cardinalis cardinalis), and Song Sparrow (Melospiza melodia).
We chose these species because they are present in rural, sub-
urban, and urban areas and can be readily recorded at all
sites. All 6 species vocalize at frequencies low enough to be
masked by low-frequency anthropogenic noise (Cimprich and
Moore 1995; Haggerty and Morton 1995; Johnson 1998; Halkin
and Linville 1999; Sallabanks and James 1999; Arcese et al.
2002) and at frequencies high enough to be attenuated and
distorted in reverberant environments (Morton 1975).
We recorded a total of 145 individual birds (7 American Rob-
ins, 33 Carolina Wrens, 8 Gray Catbirds, 16 House Wrens, 45
Northern Cardinals, and 36 Song Sparrows). Each individual
bird was recorded once, and an average of 6 birds were re-
corded per site. The observer approached the singing bird as
close as possible during recordings (I attempted to maintain
a maximum recording distance of 10 horizontal meters).
When more than one individual of the same species was re-
corded at a site, a minimum distance of 50 m between indi-
viduals was assured. Number of songs recorded from each
individual bird ranged from 2 to 20 (average 8.5 1 4.7 songs).
We recorded birds between 30 May 2008 and 11 August 2008,
which is during the breeding season for all species studied
(Cimprich and Moore 1995; Haggerty and Morton 1995;
Johnson 1998; Halkin and Linville 1999; Sallabanks and James
1999; Arcese et al. 2002). Birds were recorded between 6:30
AM and 11:30 AM, with most (75%) of the songs recorded
before 10 AM. We collected information on weather at the
time of each recording. Bird song playback from a separate
project occasionally occurred at the same site where I was
recording. This was true for recordings of only 4 individuals.
We determined the effect of weather on the dependent
variables in the study using a simple linear regression.
Recordings were digitized from tapes using sound recording
program Wildspectra Live (http://www.unc.edu/;rhwiley/
wildspectra/) at a sampling rate of 22 050 Hz, a frequency
resolution of 344 Hz, and a temporal resolution of 2.9 ms.
We used the sound analysis program Wildspectra to generate
a chart of frequency and spectral intensity for each song an-
alyzed. We used this chart to determine maximum frequency
(the highest frequency on the chart), minimum frequency
(the lowest frequency on the chart), and frequency range
(maximum frequency minus minimum frequency). We ana-
lyzed songs at a frequency resolution of 344 Hz and a temporal
resolution of 2.9 ms. Songs were normalized (a method of
standardization that sets the largest amplitude in a sound
equal to the maximal value and scales other amplitudes
proportionately) and 2 songs from each individual were
measured and the values averaged.
Before statistical analyses were performed, outliers were
located in each analysis using the Fourth-Spread method
(Hoaglin et al. 1991) and the outlying songs were examined
for irregularities and all were reanalyzed to control for possible
measurement error. One extreme outlier was removed from
analysis, which had no effect on the results. To determine the
effects of ambient noise level and urban development on the
acoustic features of bird song, we used linear mixed-effect mod-
els (LMMs) for each species. The site where birds were re-
corded was set as a random effect. Minimum frequency,
maximum frequency, and frequency range of recorded songs
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were set as dependent variables and ambient noise level, per-
centage of impervious surface, and the interaction of noise
level by impervious surface were set as independent variables.
For each species, 1 model was run for each of the 3 dependent
variables.
The residuals from each model were tested for normality us-
ing a Shapiro–Wilk W test. We determined the effect of col-
linearity of explanatory variables on the model estimates by
calculating the variance inflation factor for each model. The
statistical program JMP 9.0.0 was used for all statistical analy-
ses. We corrected for multiple comparisons by calculating the
false discovery rate (Benjamini and Hochberg 2000) based on
18 tests (1 model for each of 3 dependent variables for 6
species).
There was a significant interaction between ambient noise
level and percentage of impervious surface for several of the
models. We determined the nature and direction of the in-
teraction in these models by grouping the data into discrete
categories of one of the predictor variables (noise level or
level of urban development). We then determined the rela-
tionship between the other predictor variable and the re-
sponse variables within each grouped category. We grouped
the data into 2 categories for noise level (high noise or low
noise) and 3 categories for urban development (rural, sub-
urban, or urban) using K-means clustering. We determined
the direction of relationships at each level of the predictor
using a simple linear regression.
To determine which species were more affected by noise and
urbanization, based on their song frequency characteristics, we
calculated the species-typical minimum and maximum fre-
quency for each species. We did this by calculating the average
minimum and maximum frequency of birds recorded at non-
urban sites (sites binned at rural or suburban by K-means
clustering). The strength of the effect of noise and urbaniza-
tion on song characteristics was determined by the size of the
P value (smaller P value, stronger effect).
RESULTS
The ambient noise level across study sites varied from 60.7 to
74.3 dB (65.41 4.04 dB, N ¼ 28). Air, road and rail traffic, and
construction machinery and appliances such as pool pumps
and air conditioning units were the primary sources of back-
ground noise at our study sites. Background noise at all sites,
including those that were rural, could be attributed to anthro-
pogenic sources. Weather had no effect on maximum fre-
quency (analysis of variance [ANOVA]: N ¼ 145, R2 ¼ 0.02,
F ¼ 3.06, P ¼ 0.083), minimum frequency (ANOVA: N ¼ 145,
R2 ¼ 0.02, F ¼ 3.52, P ¼ 0.063), or frequency range (ANOVA:
N ¼ 145, R2 ¼ 0.01, F ¼ 2.20, P ¼ 0.141).
The species-typical song range in our study for American
Robin was 1808 Hz (average minimum frequency) to 3729
Hz (average maximum frequency), 1671–5260 Hz for Caroli-
na Wren, 1377–7398 Hz for Gray Catbird, 1892–7195 Hz for
House Wren, 1450–4666 Hz for Northern Cardinal, and 2114–
7828 Hz for Song Sparrow (see spectrograms—Figure 1).
The residuals for each LMM were normally distributed, with
the exception of the model for frequency range for Northern
Cardinal, which became normally distributed after log trans-
formation, and the maximum frequency models for Carolina
Wren and House Wren, which also became normally distrib-
uted after transformation. Ambient noise levels increased sig-
nificantly as the percentage of impervious surface increased
(pairwise correlation: N ¼ 145, r ¼ 0.57, P 
 0.0001), but this
collinearity had a negligible effect on model variance (the
variance inflation factor for all models was below 2.8).
Minimum song frequency increased as ambient noise level
increased for 2 of 6 species, with 5 of 6 species showing a strong
increasing trend (Figures 2 and 3), whereas urban develop-
ment had no effect on minimum song frequency. American
Robin (LMM: F1 ¼ 2.29, R2 ¼ 0.67, P ¼ 0.0837, N ¼ 7), Gray
Catbird (ANOVA: F1 ¼ 70.32, R2 ¼ 0.97, P ¼ 0.0011, N ¼ 8),
Northern Cardinal (ANOVA: F1 ¼ 3.98, R2 ¼ 0.23, P ¼ 0.053,
N ¼ 45), and House Wren (LMM: F1 ¼ 2.47, R2 ¼ 0.19, P ¼
0.14, N ¼ 16). For Carolina Wren, there was a significant effect
of the interaction between percentage of impervious surface
and ambient noise level (LMM: impervious 3 ambient: F1 ¼
5.1, R2 ¼ 0.30, P ¼ 0.0315, N ¼ 33, Figure 3). When this
interaction was further investigated, lowest frequency in-
creased with ambient noise at rural sites, but there was no
effect at suburban or urban sites (Figure 4).
Maximum frequency decreased with percentage of impervi-
ous surface for 2 of 6 species (Figures 5 and 6), Northern
Cardinal (LMM: F1 ¼ 4.24, R2 ¼ 0.24, P ¼ 0.047, N ¼ 45)
and Gray Catbird. For Gray Catbird, there was a significant
effect of the interaction between percentage of impervious
surface and ambient noise level (LMM: impervious 3 ambi-
ent: F1 ¼ 8.75, R2 ¼ 0.74, P ¼ 0.042, N ¼ 8). There was also
a significant effect of the interaction between percentage of
impervious surface and ambient noise level for Northern Car-
dinal (LMM: impervious 3 ambient: F1 ¼ 5.32, R2 ¼ 0.24, P ¼
0.0274, N ¼ 45), but this effect became only marginally signif-
icant after controlling for multiple comparisons. When the
interaction between percentage of impervious surface and
ambient noise level for Northern Cardinal and Gray Catbird
was further investigated, maximum frequency decreased with
impervious surface at low noise sites, but there was no effect at
high noise sites. For American Robin, we found the opposite
pattern, maximum frequency increased with impervious sur-
face (LMM: impervious: F1 ¼ 18.35, R2 ¼ 0.84, P ¼ 0.0013,
N ¼ 7).
Frequency range decreased with percentage of impervious
surface for 2 of 6 species (Figure 7), Northern Cardinal
(LMM: F1 ¼ 4.2, R2 ¼ 0.20, P ¼ 0.048, N ¼ 45) and Gray
Catbird. For Gray Catbird, there was a significant effect of
the interaction between percentage of impervious surface
and ambient noise level (LMM: impervious 3 ambient: F1 ¼
8.97, R2¼ 0.71, P¼ 0.0401, N¼ 7). There was also a significant
effect of the interaction between percentage of impervious
surface and ambient noise level for Northern Cardinal
(LMM: impervious 3 ambient: F1 ¼ 4.44, R2 ¼ 0.198, P ¼
0.0426, N ¼ 33), but this effect became only marginally signif-
icant after controlling for multiple comparisons. When the
interaction between percentage of impervious surface and
ambient noise level for Northern Cardinal and Gray Catbird
was further investigated, frequency range decreased with im-
pervious surface at low noise sites, but there was no effect at
high noise sites. For American Robin, we found the opposite
pattern, frequency range increased with impervious surface
(LMM: impervious: F1 ¼ 26.3, R2 ¼ 0.87, P ¼ 0.0068, N ¼ 7).
For Song Sparrow, neither percentage of impervious surface
nor ambient noise level affected minimum frequency (LMM:
noise: F1 ¼ 1.09, R2 ¼ 0.49, P ¼ 0.322, N ¼ 36; impervious:
F1 ¼ 2.44, R2 ¼ 0.49, P ¼ 0.14, N ¼ 36), frequency range
(LMM: noise: F1 ¼ 0.14, R2 ¼ 0.64, P ¼ 0.721, N ¼ 36; imper-
vious: F1 ¼ 2.9, R2 ¼ 0.64, P ¼ 0.12, N ¼ 36), or maximum
frequency (LMM: noise: F1 ¼ 0.03, R2 ¼ 0.56, P ¼ 0.87, N ¼ 36;
impervious: F1 ¼ 1.54, R2 ¼ 0.56, P ¼ 0.23, N ¼ 36).
The strength of the effects of noise and urbanization on bird
song differed by species. For species with a low species-typical
minimum frequency (Gray Catbird, Northern Cardinal, and
Carolina Wren, Figure 2) noise level had a strong effect on
minimum frequency (P values, 0.011, 0.053, and 0.032, respec-
tively). For species with a high species-typical minimum fre-
quency (American Robin, House Wren, and Song Sparrow),
noise level had little or no effect (P values, 0.084, 0.14, and
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0.322, respectively). The effect of noise increased as the species-
typical minimum frequencies became lower. Gray Catbird,
the species with the lowest minimum frequency, showed the
strongest effect.
For the species with the highest species-typical maximum fre-
quency (Gray Catbird), urban development had the strongest
effect on maximum frequency (Figure 5) and frequency range
(Figure 6), whereas there was little or no effect of urbaniza-
tion for species with a lower maximum frequency (Carolina
Wren and American Robin). Although, this pattern was not
consistent for all species, there was a strong effect of urbani-
zation for Northern Cardinal, a species with a low maximum
frequency and there was no effect of urbanization for House
Wren, a species with a high maximum frequency. For Ameri-
can Robin, the species with the lowest maximum frequency
and bandwidth, the maximum frequency and bandwidth
increased with urbanization (which is opposite to the pattern
for Gray Catbird and Northern Cardinal).
DISCUSSION
We found that 2 salient features of the urban environment,
abundant impervious surfaces and anthropogenic noise, both
affect song characteristics and do so in different ways. As ambi-
ent noise level increased, minimum frequency of bird songs
increased, whereas urban development had no effect, support-
ing our first hypothesis. As urban development increased, max-
imum frequency and bandwidth of songs decreased, whereas
noise had no effect, supporting our second and third hypothe-
ses. Support for these hypotheses indicates that these species
make the adjustments we would expect for optimal transmission
in loud reverberant urban environments. Although numerous
Figure 1
Spectrograms for 6 study spe-
cies with species-typical fre-
quency range (average
minimum frequency to aver-
age maximum frequency for
each species, calculated from
individuals recorded at nonur-
ban sites).
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previous studies provide evidence that birds make vocal adjust-
ments in response to noise, ours is the first to investigate the
effects of reverberation and refraction from urban structures,
which we expect to equally constrain communication.
Species comparisons
Our study is also one of the very few urban song studies that
include more than one bird species (but see Hu and Cardoso
2009, 2010). We included multiple species because birds with
different song frequency characteristics are expected to re-
quire different degrees of adjustment for their songs to be
well suited to urban environments. Indeed, we found that
the strength of the effects of noise and urbanization on bird
song differed by species. The lower the species-typical mini-
mum frequency, the stronger the effect of noise level on min-
imum frequency. Similarly, the species with the highest
maximum frequency was the species most affected by urbaniza-
tion, whereas species with lower maximum frequencies were
not affected by urbanization.
These results may indicate that species with songs that are
more susceptible to the effects of urbanization and noise
may be more likely to adjust their songs. Indeed, the above in-
terpretation may explain the results for American Robin that
Figure 2
Results of LMM for the effect
of noise level on minimum fre-
quency. Table lists how the
strength of the effect varied
with species-typical minimum
frequency and also whether re-
sults support hypothesis 1.
Figure 3
Results for minimum fre-
quency. Simple linear regres-
sion of ambient noise level
(dB) on minimum song fre-
quency (Hz) for 4 species. Sta-
tistics listed are from LMM.
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are inconsistent with other species. For American Robin, the
maximum frequency and bandwidth increased with urbaniza-
tion (the pattern was opposite for Gray Catbird and Northern
Cardinal). American Robin has the lowest maximum fre-
quency of all species studied (several thousand hertz lower),
so their songs may be least susceptible to the effects of rever-
beration (which increase with frequency), and they may not be
constrained to keep their maximum frequency low. Maximum
frequency and bandwidth may increase with urbanization be-
cause noise in cities may pressure for an increase in minimum
frequency, causing the entire song to shift up (which would be
possible for American Robins, and not other species studied,
because they have ‘‘room for adjustment’’).
Our results for minimum frequency are consistent with
those of Hu and Cardoso (2010). They found that species with
intermediate species-typical minimum frequencies (1000–1500
Hz) adjusted songs more in response to noise than species
with higher minimum frequencies. Our results additionally
show that within those species that do make adjustments, the
lower the minimum frequency, the stronger the adjustment.
Hu and Cardoso also found that species very susceptible to
masking by noise (those with calls/songs below 1000 Hz) did
not make adjustments, which may indicate that birds adjust
more with increased masking only to a certain point. This
has important implications for which species will be excluded
from urban bird communities. If species whose songs are
masked more by noise adjust their songs more in response
(as our results suggest), then many species would have the ca-
pacity to adapt their songs for transmission in noise instead of
being excluded from noisy areas (or relying on different kinds
of adaptations).
Because our study did not include species with songs below
1000 Hz, we do not know if this is the case or if adjustment will
continue only to a certain point (as Hu and Cardoso’s results
suggest). Future studies that investigate song adjustment
across several species with widely varying song frequency char-
acteristics (from below 1000 Hz to above 2500 Hz) would
further inform this question. It is also important that future
comparative studies include repertoire-singing species, like
Great Tits, that may behaviorally respond to noise by switching
Figure 4
Results for minimum frequency for Carolina Wren. Simple linear regression of ambient noise level (dB) on minimum song frequency (Hz).
Statistics listed are from LMM.
Figure 5
Results of LMM for the effect of
percentage of impervious sur-
face on maximum frequency.
Table lists how the strength of
the effect varied with species-
typical maximum frequency
and also whether results sup-
port hypothesis 2.
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to song types with different frequency characteristics (Halfwerk
and Slabbekoorn 2009). This may represent an alternative
mechanism for adapting song to urban environments but
may be subject to similar limits of adjustment above and below
certain frequencies.
We found no effect of noise or urban development on song
for the Song Sparrow. This result differs from the results of
Wood and Yezerinac (2006), who found that Song Sparrows
in louder areas sang with higher frequency low notes. In our
study, Song Sparrows have the highest species-typical mini-
mum frequency of all species studied, so we predicted that
they would be least affected by noise. The minimum frequen-
cies of the Song Sparrows in our study were higher (between
1600 and 2800 Hz, average 2095 1 338 Hz) than those re-
corded in Wood and Yezerinac’s study (between 1100 and
1900 Hz), even in rural areas. This may represent a geographic
difference in dialects (Wood and Yezerinac’s study was con-
ducted in Oregon, ours in and around Washington, DC).
Song Sparrows near Washington, DC may not have their songs
masked by noise to the same extent as Western Song Sparrows
and may not need to make adjustments.
This could be an example of cultural evolution of dialects
in response to urban noise. A recent study by Luther and
Baptista (2010) found that White-crowned Sparrow dialects
with higher minimum frequencies spread and replaced other
dialects over the last 30 years in San Francisco, a period over
which population levels and noise levels increased. A similar
phenomenon may have occurred in the Washington, DC met-
ropolitan area (the 9th most populous metropolitan statistical
area, United States Census Bureau 2010) but may not have
occurred in Portland (the 101st most populous metropolitan
statistical area, United States Census Bureau 2010).
Trade-off of adjustment to noise or reverberation
Several species seem unable to make song frequency adjust-
ments in response to one pressure (either urbanization or
noise) when the second is also present. For the Carolina Wren,
birds recorded in loud environments had a higher minimum
frequency if that environment was rural but not if that envi-
ronment was urban or suburban. For Northern Cardinal and
Gray Catbird, birds recorded in urban environments had a de-
creased maximum frequency and bandwidth if that environ-
ment was quiet but not if it was loud (although, for Northern
Cardinal, this pattern was only marginally significant after
multiple comparison correction).
We suggest that these results indicate a trade-off between 2
different types of song adjustment, decreasing the maximum
Figure 6
Results for maximum frequency. Simple linear regression of percentage of impervious surface and maximum frequency (Hz) at 2 different noise
levels for 2 species. Statistics listed are from LMM. Results for frequency range are not presented because the same patterns for the same species
are shown in these maximum frequency plots.
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frequency and increasing the minimum frequency, in re-
sponse to 2 different environmental factors, urban structures
and ambient noise. In other words, birds may decrease their
maximum song frequency in response to an urban structural
environment but may be less able to do so in the presence of
noise. Similarly, birds may increase their minimum song fre-
quency in response to noise but may be less able to do so
when urban structures are present.
This may be the result of an environmental constraint. Birds
singing in noisy environments would improve their song trans-
mission by increasing their minimum song frequency, but
when that environment is also urban, an increase in frequency
would worsen transmission (due to higher sound reflection,
absorption, and diffusion), thus constraining the minimum
frequency adjustment. Similarly, birds in urban environments
would improve their song transmission by decreasing their
maximum song frequency and bandwidth, but when that en-
vironment is also loud, a decrease in frequency would worsen
transmission (due to masking of low-frequency song por-
tions), thus constraining the maximum frequency adjustment.
This result may indicate that noise and reverberation pres-
sure for adjustments that are not simultaneously possible. This
would prevent birds from optimally adjusting their songs and
affect their ability to communicate in areas where both factors
are present (like most urban areas). Although, if birds make
shorter term real-time adjustments to song frequency and
adjust their songs depending on the prevailing environmental
pressure (noise level at the instant of singing, reverberation
given the bird’s singing position), then they would avoid this
problem (at least in environments where noise is not con-
stant). Indeed, Bermu´dez-Caumatzin et al. (2010) found that
birds made real-time adjustment of minimum song frequency
when noise was experimentally presented. Because each bird
in our study was only recorded once, instead of multiple times
under different noise conditions and in different environmental
conditions, and because noise and urbanization level at each
site was considered fixed in our analyses, we were unable to
fully investigate this possibility. Future studies are needed that
investigate characteristics of urban bird songs on a finer scale,
with repeated recordings of individuals and careful measure-
ment of noise and environmental conditions at the moment
of each recording, either in the field or under controlled
laboratory conditions.
To date, ecology in urban areas has been largely under-
studied (Martin et al 2010) and although many recent studies
have described the effect of urbanization on bird song, all con-
sider only one urban feature, noise. Future research should
consider urban habitat as a sound transmission environment
(with both an acoustic and structural component). The land-
scape worldwide is being rapidly developed and as a result,
habitats are fragmented, ambient noise levels are increased,
and reverberant surfaces and barriers are introduced. This in
turn affects animal communication by restructuring the spac-
ing of individuals and disrupting the transmission of signals
through reverberation and masking. We urge researchers to
further investigate how a modified communication environ-
ment affects wildlife. An important next step would be to in-
vestigate whether species with certain signal characteristics are
excluded from urban areas and if those species that remain to
breed in these areas (such as urban adapted species) show
a reduction in fitness.
FUNDING
Wallace Genetic Foundation Friends of the National Zoo and
the Mars Family.
We thank the Wallace Genetic Foundation, Friends of the National
Zoo, and the Mars Family for financial support. This research was only
possible because of the generosity and open access provided by the
Neighborhood Nestwatch participants to their property. We thank
Gillian Kruskall and Lauren Pulz for field assistance. Brian Evans
for performing spatial analyses, helping with fieldwork, and providing
helpful comments on the manuscript.
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