Ibis (2013), 155, 621–625
Short communication tated urban areas (Skutch 1953). These habitats vary in
noise level, especially anthropogenic noise, which is
higher in urban than in rural habitats (Slabbekoorn &
den Boer-Visser 2006). Anthropogenic noise often over-
Urban noise influences laps with the minimum frequencies of House Wren
songs (1.5 kHz) (e.g. Platt & Ficken 1986), possibly
vocalization structure in reducing the effectiveness of communication.
the House Wren Considering noise constraints on song features (Slab-
bekoorn & den Boer-Visser 2006) and the importance of
Troglodytes aedon vocalizations in social interactions of House Wrens
PRISCILLA REDONDO,1 GILBERT BARRANTES1 & (Johnson & Kermott 1991, Johnson & Searcy 1996,
LUIS SANDOVAL2* Muller et al. 1997), we tested the effect of anthropo-
1Escuela de Biolog
ıa, Universidad de Costa Rica, genic noise on temporal and frequency characteristics of
San Pedro, San Jose
, Costa Rica House Wren songs. We first assessed whether trill fea-
2Department of Biological Sciences, University of tures, a song element well adapted for acoustical trans-
Windsor, Windsor, ON, Canada mission in open areas, differed between urban and rural
habitats. We then tested the effect of changes in noise
levels on the whole song of House Wrens at a popula-
tion level. We predicted that a faster trill rate and songs
with a higher minimum frequency in noisier environ-
Urban habitats are noisy and constrain acoustic commu-
ments would reduce song masking, as has been reported
nication in birds. We analysed the effect of anthropo-
for other bird species (Wood & Yezerinac 2006, Mock-
genic noise on the vocalization characteristics of House
ford & Marshall 2009).
Wrens Troglodytes aedon at two sites with different noise
levels (rural and urban). We measured in each song and
song trill the frequency bandwidth, maximum ampli- METHODS
tude, highest and minimum frequency, and trill rate. In
noisy urban environments, there was a reduction in Study sites
bandwidth and an increase in trill rate relative to qui-
eter, rural environments. The whole song of birds from We sampled birds in two sites in the Costa Rican
both populations increased in minimum frequency as Central Valley that vary in urban development, traffic
noise increased, improving song transmission. density and anthropogenic noise. The rural site is in the
Heredia province (10°01′N, 84°05′W, between 1200
and 1500 m asl) and the urban site is on the Campus of
Keywords: individual response, noise, song, trills, the Universidad de Costa Rica (09°56′N, 84°05′W,
Troglodytidae, urban habitats. 1200 m asl), 17 km away (see Biamonte et al. 2011 for
site descriptions).
In urban habitats, noise often affects communication Song recordings and noise measurements
between birds by masking songs (Slabbekoorn & den
Boer-Visser 2006, Warren et al. 2006). In response, We recorded House Wren songs from 29 March to 14
birds may sing more loudly (Brumm 2004), at night June 2010, using a Marantz PMD 661 digital recorder
(Fuller et al. 2007) or with higher minimum frequencies and a Sennheiser ME66 directional microphone, at a
(Slabbekoorn & Peet 2003, Bermudez-Cuamatzin et al. 44.1-kHz sample rate on WAVE format. All songs were

2011, Halfwerk et al. 2011). These changes often recorded between 06:00 h and 10:00 h, 5–10 m from
increase the energetic costs of song production (Lamb- the focal bird, and at the same recording level. Birds
rechts 1996). Noise and turbulence are thought to have were recorded during partially cloudy or clear days, and
favoured evolution of vocalizations that consist of short low wind conditions. All recordings were deposited in

 

elements that are produced at a fast rate (e.g. trills), the Laboratorio de Bioacustica, Escuela de Biologıa, Uni-
which transmit more efficiently (Slabbekoorn & den versidad de Costa Rica.
Boer-Visser 2006). We recorded only birds that were alone in their terri-
House Wrens Troglodytes aedon inhabit open and tories so as to avoid effects of social interactions on song
semi-open habitats ranging from forest edges to vege- characteristics (Searcy & Beecher 2009). A bird record-
ing was stopped if the bird did not sing for 2 min or
when it flew away. We used only recordings of at least
*Corresponding author. 1 min in our analyses. Birds were not individually
Email: biosandoval@hotmail.com
© 2013 British Ornithologists’ Union
622 P. Redondo, G. Barrantes & L. Sandoval
marked, so one person maintained contact with the bird spectrograms were digitized at 44 100 Hz and 16-bit
just recorded while another located a new bird at a dis- and measurements were obtained using the following
tance of at least 50 m. The minimum distance between parameters: a frequency resolution of 256 samples, a
two individuals recorded on different days was 100 m. grid spacing of 188 Hz and a time grid with 50% over-
This reduces the likelihood of re-recording the same lap using the window Hann function.
individuals, because House Wren territories are smaller
than 1 ha (Johnson 1998).
During a recording session, we registered the noise Statistical analyses
level every minute using a Sper Scientific 840014 mini To test the effects of noise on acoustic characteristics
sound meter (measuring range 32–130 dB, at fast of the trill and song, we used general linear mixed
response on A weight). We calculated the mean of the models (GLMM) following Hanna et al. (2011). The
lowest and the highest noise value measured each minute. normality assumption (Jiang 2007) for each response
variable was verified using Kolmogorov–Smirnov tests
(P > 0.07 all comparisons), so no correction for overdi-
Song measurements
spersion was needed (Zuur et al. 2009). Trill and song
We divided recordings into 1-min intervals to match measurements in 1-min blocks were treated as repli-
with noise measurements. To analyse the effect of noise cates, with bird identity as a random effect. The two
on the whole song, we measured the maximum ampli- study sites (urban and rural) and their interaction with
tude frequency (frequency with more energy), fre- noise level were included in the model as fixed explan-
quency bandwidth and minimum and highest atory variables.
frequencies, using a combination of spectrogram screen We conducted an additional analysis of the effect of
with the power spectrum in RAVEN PRO 1.4 (Fig. 1) noise on the trill. Trill performance is defined by the
(Charif et al. 2004). Frequency values in the power interaction between bandwidth and trill rate, and these
spectrum are not affected by the greyscale settings on variables are inversely correlated. Because noise affects
the spectrogram screen or by background noise (Charif the frequency of vocalizations in birds, we tested explic-
et al. 2004). For each trill, we obtained the same fre- itly the effect of noise on trill performance. In this analy-
quency measurements as for the whole song, trill dura- sis, modelled noise is a function of the residuals of the
tion (Fig. 1) and trill rate (number of trill elements per regression between trill rate and trill bandwidth. Trills
second). For statistical analyses of the whole song, we with high performance are those that have either a
used the minimum value for the minimum frequency slower rate and larger bandwidth (trills with positive
and the highest value for maximum amplitude residuals at the left side of the regression) or trills with
frequency, highest frequency and frequency range in faster rate and smaller bandwidth (trills with negative
each minute. All acoustical analyses were conducted residuals at the right side of the regression). Values
using RAVEN PRO 1.4 on the original recordings. The reported are mean 
 1 se.
(a) (b)
Figure 1. (a) House Wren song spectrogram showing the trill and the minimum and highest frequency measurements used for
analysis. (b) Power spectrum used to define the highest and the minimum frequency in the song. Broken lines show the highest and
minimum frequency limit in the spectrogram and the power spectrum. Maximum amplitude frequency and bandwidth are not repre-
sented in the figure because the maximum amplitude occurs when the bird allocates the maximum energy in the song and this is not
visible in the spectrogram. Bandwidth is the difference between the highest and minimum frequency (the two broken lines).
© 2013 British Ornithologists’ Union
Noise effect on the House Wren vocalization 623
rural site, Wrens produced trills with a higher frequency
RESULTS
bandwidth, and the bandwidth decreased with increasing
noise across sites (site effect: F = 53.42, P < 0.001;
Effect of noise on song between populations 1,117Table 1; noise effect: F1,117 = 7.43, P = 0.007,
In total, we recorded 1371 song-minutes from 20 indi- b = 175.32 
 24.07). At the urban site, Wrens sang
viduals (14 at the rural and six at the urban site). The shorter trills (site effect: F1,117 = 6.93, P = 0.01, Table 1),
mean noise level was lower in the rural site with a lower highest frequency (site effect: F1,117 = 25.60,
(47.26 
 3.54 dB) than in the urban site P < 0.001; Table 1) but within sites these features were
(55.12 
 4.39 dB) (t136 = 10.22, P < 0.001). not affected by noise level (duration: F1,117 = 0.07,
Wrens at the urban site had songs with a lower high- P = 0.80, b = 0.007 
 0.003; highest frequency:
est frequency (F1,135 = 7.15, P = 0.008, Table 1) and F1,117 = 0.22, P = 0.64, b = 89.68 
 22.95). Wrens at
narrower bandwidth (F1,117 = 8.33, P = 0.005, Table 1) both sites sang trills with similar maximum amplitude fre-
but noise had no additional effect on these characteris- quency (site effect: F1,117 = 0.50, P = 0.48; Table 1), and
tics (highest frequency: F1,135 = 0.09, P = 0.76, this feature increased with noise within sites
b = 38.49 
 20.31; bandwidth: F1,135 = 3.01, (F1,117 = 6.46, P = 0.01, b = 27.84 
 10.55).
P = 0.08, b = 92.77 
 19.70). Songs had a similar House Wrens showed a negative relationship
minimum frequency in both sites (F1,135 = 0.13, between frequency bandwidth and trill rate (linear
P = 0.72; Table 1), but increased with noise across sites regression: F1,97 = 18.67, P < 0.001; Fig. 2a). Wrens
(F1,135 = 39.18, P < 0.001, b = 54.28 
 6.26). Songs varied the trill structure to maintain a high performance
had a similar maximum amplitude frequency in both in response to the noise levels. At low noise levels,
sites (F1,135 = 0.11, P = 0.75; Table 1), with no within- Wrens sang trills at a slow rate and larger bandwidth,
site effect of noise (F1,135 = 0.41, P = 0.52, but when the noise level increased, the trill bandwidth
b = 8.16 
 14.32). decreased and the rate increased (linear regression:
F1,97 = 11.94, P < 0.001; Fig. 2b).
Effect of noise on trills
DISCUSSION
We analysed 792 trills from 20 individuals: 39.6 
 6.6
trills per individual. Wrens at the rural site sang trills with The characteristics of House Wren trills (rate and fre-
a lower minimum frequency, and the minimum frequency quency range) were influenced by noise levels. Wrens
increased with noise across sites (site effect: sang trills with a wider frequency range and slower rate
F = 11.67, P = 0.001; Table 1; noise effect: at the less noisy site (rural) than the noisy site (urban).1,117
F = 14.10, P < 0.001, b = 85.62 
 12.47). At the Wrens also increased trill rate and reduced frequency1,117
bandwidth within sites when noise levels increased.
Increasing trill rate often produces more reverberations,
Table 1. Variation in the structure of House Wren songs and which enhance vocal signal transmission in noisy envi-
trills in two sites that differ in intensity of ambient noise. The ronments (Slabbekoorn et al. 2002, Naguib 2003). Like-
values reported are the mean 
 1 se. wise, reducing trill bandwidth increases signal tonality,
and this transmits better through noisy environments
Rural Urban than do broadband signals (Lohr et al. 2003, Hanna
et al. 2011). These results suggest that House Wrens
Songs modify trill structure to reduce noise masking and
Minimum 1604.22 
 65.87 2050.86 
 48.07 improve signal transmission.
frequency (Hz)

 
 Differences in trill features between sites probablyHighest 8848.49 172.88 8069.93 136.44
frequency (Hz) reflect birds adjusting to different noise environments,
Maximum 7244.28 
 183.28 6019.06 
 129.51 rather than there being different dialects in urban and
amplitude (Hz) rural populations, because the study sites are demo-
Bandwidth (Hz) 3861.61 
 179.53 3883.78 
 81.51 graphically connected. However, the presence of micro-
Trill dialects without geographical barriers in this species is a
Minimum 1249.23 
 31.30 2221.46 
 100.28 possibility that should be investigated.
frequency (Hz) Trill characteristics (rate and bandwidth) have been
Highest 7667.78 
 158.80 6059.50 
 149.44 related to female preference, diet and, primarily, bill
frequency (Hz)

 
 morphology (Podos 2001, Ballentine et al. 2004, Ballen-Maximum 3461.77 78.80 3583.47 78.52 tine 2006). There is a mechanical limitation that pre-
amplitude (Hz)
Bandwidth (Hz) 6418.55 
 165.57 3838.58 
 145.50 vents larger bills from producing faster trills (Podos &
Duration (s) 0.71 
 0.03 0.58 
 0.02 Nowicki 2004), or trills at higher performance levels
(Podos 2001). We did not measure bills to exclude their
© 2013 British Ornithologists’ Union
624 P. Redondo, G. Barrantes & L. Sandoval
(a) These results contrast with those of Dowling et al.
(2012) who proposed that birds cannot increase the
minimum frequency and reduce the highest frequency
at the same time. Hence, it is possible that species differ
in their response to noise environments as a conse-
quence of differences in the functioning of their vocal
apparatus. In conclusion, House Wrens reduce the
noise-masking effect of signal transmission by adjusting
the acoustical and temporal characteristics of the song
and trill in response to changes in noise level.
We thank Edgardo Arevalo, Roberto Sosa, Julie Koloff and Agu-
(b) stin Vega for helpful suggestions on earlier versions of the manu-
script. We also thank Jeremy Wilson, Ian Hartley and two
anonymous reviewers for all the valuable comments offered dur-
ing the review process. We also thank Agustin Vega for field assis-
tance and for helping with song analysis. The Ministerio de
Ciencia y Tecnolog
ıa (MICIT), the Consejo Nacional para
Investigaciones Cient
ıficas y Tecnolo
gicas (CONICIT) of Costa
Rica, the Government of Ontario, Canada, and the University of
Windsor, Ontario, Canada, provided financial support for L.S.
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