1. No category

Citation as online first paper: North-western Journal of Zoology 10: art.141504
NORTH-WESTERN JOURNAL OF ZOOLOGY 10 (2): online-first
Article No.: 141504
©NwjZ, Oradea, Romania, 2014
http://biozoojournals.ro/nwjz/index.html
Effects of traffic noise on the locomotion activity and vocalization
of the Marsh Frog, Pelophylax ridibundus
Simeon LUKANOV1,*, Daniela SIMEONOVSKA-NIKOLOVA1, Nikolay TZANKOV2
Received: 21. December 2013 / Accepted: 01. May 2014 / Available online: 15. October 2014 / Printed: December 2014
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1. Department of Ecology and Protection of Nature, Faculty of Biology, Sofia University “St. Kliment Ohridski”,
blvd. Dragan Tcankov 8, 1164 Sofia, Bulgaria.
2. National Museum of Natural History, Bulgarian Academy of Sciences, blvd. Tzar Osvoboditel 1, 1000 Sofia, Bulgaria.
* Corresponding author, S. Lukanov, E-mail: [email protected]
Introduction
growth and existence. Frogs are associated with a
variety of natural habitats, from intact forest to
disturbed areas. This difference in habitat association could predict their different behavioral response to anthropogenic noise. However, whether
the effect of anthropogenic noise on amphibian
calling is habitat-dependent is yet unclear (Pellet
et al. 2004, Eigenbrod et al. 2008).
Many studies have shown that roads have a
negative influence on global biodiversity in general (Forman & Alexander 1998, Trombulak &
Frissell 2000, Coffin 2007). Roads with high levels
of traffic and engine noise, for example, have a
certain negative impact on amphibians living in
otherwise undisturbed areas (Kentula et al. 2004).
A study by Sun & Narins (2005) on several Thailand species found that while some decreased
their calling activity in response to low-frequency
motorcycle sounds, others increased it. There are
three main consequences of the road effect: direct
mortality caused through traffic, movement barrier leading to population subdivision and resource inaccessibility and reduced populations
through habitat loss (Jaeger et al. 2005). Traffic
noise seems the most important factor for the latter two, although visual disturbance, pollutants,
and predators moving along a road are alternative
hypotheses as the cause of avoidance (Forman &
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Studies have demonstrated that anthropogenic
noise has an adverse effect on acoustically communicating organisms (Wells 2007). It generally
decreases the ability of a receiver to respond to information in a sender’s message (Narins & Zelick
1988). In order to overcome this negative effect,
many species exhibit behavioral and physiological
adaptations (Narins 1982, Brumm & Slabbekoorn
2005).
Although frogs are among the most vocal vertebrates, only recently have they become a focus
for studies of behavioral responses to anthropogenic noise. Some studies have shown that noise
affects frog breeding patterns by altering mate
preferences in females (Bee & Swanson 2007) and
it may also cause males to modulate their call rate
or call frequency (Sun & Narins 2005, Kaiser &
Hammers 2009, Parris et al. 2009). Given the
physiological costs associated with frog vocalizations, species that increase calling effort in response to anthropogenic noise may alter other behaviors to compensate for increased energy expenditure associated with increased vocal output
(e.g., call duration or call rate) (Kaiser et al. 2011).
The cumulative effect of such behavioral changes
over time may negatively affect population
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Key words: anthropogenic noise, call characteristics, locomotion,
Marsh Frog, Pelophylax ridibundus, spectrograms.
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Abstract. Anthropogenic noise can induce different responses among acoustically communicating
amphibians, such as leaving their burrows in response to high-decibel sounds or changing their locomotion
activity when subjected to higher levels of stress. To understand how the Marsh Frog Pelophylax ridibundus
(Pallas 1771) responds to anthropogenic noise in its natural habitats, we conducted laboratory and field
experiments. In laboratory experiments we tested whether their locomotion is affected by different levels of
exogenous noise, and in the field - whether proximity to noise source (e.g. a busy road) influences call
characteristics. In the laboratory we observed the locomotion activity of 25 Marsh Frogs and recorded the
distance covered during three 10-minute periods: control, noise level of 50 dB and noise level of 70 dB. The
results from the laboratory experiments indicated that the animals’ locomotion activity was affected during
periods of noise influence. A tendency for differentiation between calls of frogs inhabiting ponds with
different proximity to anthropogenic noise sources was established as well. The effect of anthropogenic noise
and the adaptations of the frogs are discussed.
Citation as online first paper: North-western Journal of Zoology 10: art.141504
S. Lukanov et al.
Materials and methods
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in populated areas (Biserkov et al. 2007). These
characteristics make the species a suitable model
for studies on the effects of traffic noise on locomotion activity and vocal signalization. We hypothesize that traffic noise will affect locomotion
activity in the Marsh Frog. We also expected differences in calling characteristics depending on
the distance to traffic noise source.
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Laboratory experiments
We tested 25 adult specimens, 12 males and 13 females.
The snout-vent length (SVL) was measured in millimeters
with a standard ruler; weight was measured in grams
with a Durascale D2 capacity pocket scale (accuracy of
0.01g). For males mean SVL = 70 mm (59-89 mm), and for
females mean SVL = 81 mm (49-98 mm). Mean weight
was 38.9 g (16.5-74.2 g) and 59.4 g (11.7-100.5 g) respectively. All captured animals were from the Lesnovska
River near the village of Chelopechene, situated in the
Sofia valley, Western Bulgaria (Table 1). They were
housed in two glass cages with water tanks deep enough
for them to swim at least one week before the beginning
of the experiment. The frogs were kept in the laboratory
at temperatures of 20 Cº ± 2 Cº and were fed mealworms
ad libitum every other day. The temperature was maintained through the central heating installation, according
to the outside temperature. Tests were carried out between 1600 and 1900 h. All animals were tested during
the period March-May.
Experiments were performed in a glass cage with
dimensions 100x100x40cm. An X-mini loudspeaker connected to a laptop was positioned at the upper right corner of the cage. A Canon A590-IS digital camera was positioned above the cage in order to record the animals’ activity. For stimuli preparation we used actual recordings
of traffic noise and calibrated them to decibel levels of 50
and 70 dB. Studies on anuran physiology reveal that their
auditory perception is very similar to that of mammals
and birds (Bee 2012). Frog vocalizations can reach up to of
90 dB and frogs in choruses commonly call in ambient
noise levels in excess of 50dB. Hence, for stimuli preparation we used actual recordings of traffic noise and calibrated them to decibel levels of 50 and 70 dB. To minimize stress, one day before the start of the experiments
the animals were placed in the glass cage so they could be
familiarized with it. All animals were tested individually
and released in the center of the glass cage, upon which
the camera was switched on. Cage walls were covered
with cloth in order to minimize any potential side effects
which the laboratory environment could have on the animals. During the actual testing there was no human presence in the room.
The experiment consisted of three 10-minute intervals – control (the animal was left to explore the glass
cage in silence), track 1 (the animal was left to explore the
cage in traffic noise at 50dB) and track 2 (the animal was
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Alexander 1998). Modeling work by Jaeger &
Fahrig (2004) shows that direct mortality generally
has a greater negative effect on wildlife populations than barrier effects caused by road or traffic
avoidance. However, studies on some bird species
have shown that population densities begin to decline at noise levels as low as 35 dB (Reijnen et al.
1996). Whether traffic noise has a significant effect
on individuals of a particular population of amphibians remains to be clarified. The impact depends on the behavioral response of the animal to
the traffic – for example a high avoidance of traffic
noise could increase the migration and barrier effects, while a low avoidance of traffic noise increases the mortality caused by passing vehicles
(Forman & Alexander 1998). Therefore, this could
have a detrimental effect, especially if it occurred
at the wrong time of year. Moreover amphibians
are among the most vulnerable groups overall because of their relatively low movement speed (Eigenbrod et al. 2009).
Any type of movement carries potential costs
for the amphibian, including spent energy, risk of
predation, and exposure to drought or other unfavorable climatic conditions. Consequently, amphibians move only when absolutely necessary
(Wells 2007). Movement could also be related to
stress – some studies show that frogs increase
their jump distance when subjected to greater levels of stress (Johansson et al. 2010). According to
this study, the Common Frog (Rana temporaria)
makes longer jumps when subjected to higher levels of noise, and consequently changes in frog locomotion can be used to measure the response towards anthropogenic stress. Nevertheless, studies
on the effects of noise on amphibian activity are
too scarce.
To understand how the Marsh Frog Pelophylax
ridibundus (Pallas 1771) responds to anthropogenic
noise, we conducted a series of laboratory and
field experiments to test whether frog behavior is
affected in respect to locomotion activity and if
call characteristics differed according to distances
to a noise source. The species is widely distributed
across most of Europe and some parts of Africa
and Asia - the northern margin of the range runs
from Latvia through Russia, and the southern
reaches Western Turkey, Northern and Central
Iran, Afghanistan, Kirgizia, and Northwestern
China (AmphibiaWeb 2013). It is ubiquitous
across Bulgaria and can occur in all types of fresh
water ponds, as well as in brackish waters with
low salinity, and is also commonly found in ponds
Citation as online first paper: North-western Journal of Zoology 10: art.141504
Effects of traffic noise on the Marsh Frog
Table 1. Coordinates of localities.
42°43´53˝
23°27´55˝
525
42°19´10˝
26°52´11˝
164
left to explore the cage in traffic noise at 70dB). We tested
for significant differences between the three periods of
the laboratory experiment in relation to distance covered
by the animals and type of locomotion used (jump, walk,
climb). A preliminary analysis did not reveal any correlation between the animals’ gender, weight, length or body
index (length/weight) and the distance they covered during the experiment as well as the types of locomotion.
Therefore, all animals were treated as one sample.
To check for significant differences between the three
periods of the laboratory experiment in relation to distance covered by the animals and type of locomotion we
used a repeated measures ANOVA in conjunction with
Tukey’s HSD test, with period as a grouping variable
(control, noise period 1 and noise period 2).
The study complied with the national laws of Bulgaria and the international requirements for ethical attitude towards animals (Lehner 1996). Upon completion of
the experiments all tested frogs were released back in
their habitat.
Popovski heights
43°21´28˝
26°00´22˝
324
tory experiments, animal size was measured in millimeters using a standard ruler.
The vocal activity was recorded using an Olympus
LS-5 linear PCM recorder and an Olympus ME-31 shotgun microphone. We recorded the mating calls in a WAVPCM mode with sampling frequency 44.1 kHz, 20 - 21.000
Hz and 24-bit resolution. Recordings with lesser quality
(i.e. strong background noise) were not selected for the
analyses. The rest were processed with the computer program Soundruler v. 0.9.6.0. (Gridi-Papp et al. 2007). For
the statistical analyses we used the following sound characteristics: energy between final peak: 90% amplitude
(Ener_Peak-90_End), dominant frequency of the pulse
(PulseDomFreq), prop of duration to reach half frequency
modulation (PulseHalfFM), tuning: peak frequency
/bandwidth at 50% peak amplitude (Tuning-6dBSPL),
tuning: peak frequency /bandwidth at 10% peak amplitude (Tuning-20dBSPL), relative amplitude of harmonic 1
(relAmpl_H1), relative amplitude of harmonic 3 (relAmpl_H3), relative amplitude of harmonic 4 (relAmpl_H4), relative amplitude of harmonic 5 (relAmpl_H5). The data were not normally distributed and
was log10-transformed for ANOVA, which did not reveal
significant differences between call characteristics based
on animal size/weight and air/water temperature. To
avoid log-transformation, for further analysis we used a
Mann-Whitney U test to compare samples from “noisy”
(adjacent to busy roads) and “silent” (with no traffic
noise) sites, using the site type as a grouping variable.
All analyses were carried out using the computer
program Statistica v.7.0 (StatSoft 2004).
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Field experiments
To test whether proximity to noise source (e.g. a busy
road) influences call characteristics, we visited 7 sites with
different proximity to busy roads (Table 1). All studied
sites were small ponds and slow flowing canals, which
provided similar conditions in terms of their size, depth,
vegetation and temperature.
On average from each site we made 5 recordings.
The distance to the roads varied from 10 m at the noisiest
to 5 kilometers at the quietest site. The estimated trafficnoise levels at our study sites ranged from 45 to 80 dB(C).
We chose the C-weighting method over the A-weighting
because the latter effectively cuts off the lower and higher
frequencies (i.e. those not perceived by the human ear)
(Ladefoged 2001). We recorded only individual calls and
not choruses, for an interval of five minutes. Because generally larger males produce lower-frequency calls (Wells
2007), we caught and measured the SVL of P. ridibundus
at a subset of sites. Recorded males had mean SVL of 75
mm and mean weight of 49.5g.
All field tests were performed during the mating season (April-June 2013). Аir (near ground level) and water
temperatures of the localities were measured because
they have an influence on amphibian activity and call parameters. The temperature was measured using a digital
water-resistant thermometer with an accuracy of 0.1 degree. Average temperature of air and water was 17.6ºC
(14.4-20.8ºC) and 18.2ºC (16.3-20.1ºC) respectively. Call
recordings and measurements were taken once per day
during the midday hours (1400-1600). As in the labora-
Strandza
Kolets
Silent
42°04´31˝ 41°51´23˝
27°00´20˝ 25°20´21˝
450
307
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42°12´18˝
24°33´52˝
190
Aleksandrovo
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North
East
Meters (a.s.l.)
Chelopechene
PR
Locality
Ribaritsa
Noisy
42°51´00˝
24°21´24˝
562
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Tsalapitsa
Results
Laboratory experiments
After release of the experimental animals in the
glass cage, their first reaction was to jump towards
a corner or a wall. Generally the subjects moved
along the walls and avoided the open space in the
center of the glass cage. Results regarding displayed locomotion activity and distance covered
by the animals during the test are presented with
mean values and are given in Table 2.
The repeated measures ANOVA revealed significant differences between the control and the
noise periods in relation to distance covered. The
same was also true for the number of jumps and
climbs, but not for the number of walks (Table 2).
Citation as online first paper: North-western Journal of Zoology 10: art.141504
S. Lukanov et al.
Distance (cm)
F(2, 72)=8.04
Jump
F(2, 72)=6.46
Climb
F(2, 72)=4.36
Walk
F(2, 72)=2.15
Control
n = 25
487.28 ± 321.96
p < 0.01(CxT1)
7.04 ± 4.82
p < 0.05(CxT1)
3.12 ± 3.73
p < 0.05(CxT1)
3.88 ± 3.02
Ns (CxT1)
noise level of 50 dB
n = 25
252.08 ± 269.39
p < 0.01(CxT2)
3.60 ± 4.79
p < 0.01(CxT2)
1.24 ± 1.69
p < 0.05(CxT2)
4.56 ± 4.27
Ns (CxT2)
noise level of 70 dB
n = 25
201.48 ± 201.43
Ns (T1xT2)
2.80 ± 3.57
Ns (T1xT2)
1.28 ± 1.74
Ns (T1xT2)
2.60 ± 2.68
Ns (T1xT2)
Discussion
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The two noise treatments did not demonstrate
significant differences between each other (Table
2).
All animals decreased their locomotion activity when subjected to anthropogenic noise but
there was not a significant difference between the
two noise periods. The highest number of jumps
and climbs was during the control period, while
the highest number of walks was during the track
1 period.
Mann-Whitney results
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p-value
6709
-10.501
< 0.01
14334.5
-4.316
< 0.01
17861
1.456
Ns
11840
-6.339
< 0.01
16284
-2.735
< 0.01
11692
6.459
< 0.01
13585
4.924
< 0.01
13276
5.174
< 0.01
13164
5.265
< 0.01
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Ener_Peak-90_End
PulseDomFreq
PulseHalfFM
Tuning-6dBSPL
Tuning-20dBSPL
relAmpl_H1
relAmpl_H3
relAmpl_H4
relAmpl_H5
Noisy sites
Silent sites
Mean ± SD
Mean ± SD
0.082 ± 0.166
0.180 ± 0.181
1772.511 ± 729.731 2121.828 ± 402.500
0.528 ± 0.343
0.453 ± 0.195
18.296 ± 10.021
23.917 ± 6.105
4.557 ± 2.840
4.895 ± 1.171
-17.389 ± 7.411
-21.939 ± 3.362
-18.174 ± 7.279
-21.450 ± 4.718
-20.121 ± 7.556
-24.149 ± 6.223
-29.091 ± 9.701
-34.918 ± 8.606
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Table 3. Mean and SD of studied call variables of animals from “silent” and “noisy” sites,
and differences revealed by Mann–Whitney U-test. N=25. Ns – not significant.
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Field experiments
Overall calls from noisier sites had lower energy,
frequency and amplitude (Table 3).
Results from a Mann-Whitney U test demonstrated significant differences between most call
characteristics of animals from “silent” and
“noisy” sites (Table 3). There were pronounced
differences in parameters related to call frequency,
energy and amplitude. There was an average decrease in call dominant frequency of 349 Hz. The
relative amplitudes of the harmonics were also
lower in calls from the silent sites, with average
difference of 4.42 dB.
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Table 2. Mean and SD of distance covered and types of locomotion. The significance of
differences revealed by repeated measures ANOVA is shown. Explanation: C – control;
T1 – noise period 50 dB; T2 – noise period 70 dB; n – sample size; Ns – not significant.
The results obtained demonstrated that traffic
noise decreased locomotion activity in P. ridibundus. It has been suggested that jumping performance increases survival (Henein & Hammond
1997). In our tests the “walk” type of locomotion
was most common during the “track 1” period, as
opposed to “jump” being the most common during the control. This could indicate that noise has
an adverse effect on the animals’ locomotion.
Nash et al. (1970) found that Leopard frogs (Lithobates pipiens) exposed to loud noise (a 120 dB signal horn, 8 cm from the frog for one second) before being manually restrained remained immobile almost eight times longer than did the control
group. It could be suggested that sound increased
the level of fear, which consequently affected the
animals mobility. Similar results, in which the
animals reduced or even suspended their activity
in response to anthropogenic noise, have also been
demonstrated for other species – e.g. disrupted
feeding and slower return to shelter in crabs (Wale
et al. 2013). A study by Brattstrom & Bondello
(1983) showed that during aestivation Spadefoot
Toads (Scaphiopus couchii) leave their burrows in
Citation as online first paper: North-western Journal of Zoology 10: art.141504
Effects of traffic noise on the Marsh Frog
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acteristics. A frog population exposed to traffic
noise could develop a frequency shift as an evolutionary adaptation to noisy conditions. However,
this would take a number of generations to manifest, depending on the strength of the selection
pressure. Sun & Narins (2005) suggest that traffic
noise suppresses calling behavior in one set of
species which in turn stimulates calling in other
species. Our results showed that calling activity
was neither suppressed nor stimulated by traffic
noise – a fact that could be explained by the high
plasticity of P. ridibundus, which has made the
species ubiquitous across its range. The registered
differences in relative amplitudes could also be attributed to anthropogenic noise influence. Similar
results have been obtained for other species of vertebrate animals. A study by Brumm (2004) demonstrated that some birds sing with higher amplitudes in order to overcome noise interference.
Therefore it could be suggested that the artificial
noise exerts some kind of masking interference effect on the signals of P. ridibundus. So, further field
and laboratory studies are required to clarify better behavioral response of the marsh frog to anthropogenic noise and the factors that influence
calling differences.
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response to motorcycle sounds (up to 95 dB).
Therefore it could be assumed that anthropogenic
noise has different influence on the activity of different species. Kight and Swaddle (2011) point out
that different species sometimes react differently
to the same noise stimuli and the reasons behind
these differences are still unclear. In all tests periods “track 1”and “track 2” did not demonstrate
any differences between one another, leading us to
conclude that the difference of 20 dB had no role
on the animals’ reactions during the experiments.
It has to be noted that much additional work is
still needed in order to determine whether the observed patterns are directly applicable to wildlife.
Field experiments revealed that anthropogenic
noise also affected call parameters. The characteristics of the calls recorded at noisier sites differed
significantly from those at quieter sites in almost
all studied variables. A study on the Brown Tree
Frog (Litoria ewingii) showed an increase of approximately 125 Hz in the dominant frequency of
calls between the sites with the lowest and highest
levels of traffic noise (Parris et al 2009). The authors hypothesize that this may be due to traffic
noise interference. However, the same study
found no such increase in the Common Eastern
Froglet (Crinia signifera). Both of these species call
with a dominant frequency above 2 kHz – 2.22.6kHz for L. ewingii and up to 5 kHz for C. signifera (Parris 2009), while for P. ridibundus energy in
our study was concentrated in the frequency
ranges of 1.6-2.5 kHz. This difference in frequency
ranges may explain why the Marsh Frog tends to
lower its dominant frequency due to traffic noise
influence. However, Barrass (1985) observed an
increase in the fundamental frequency of both Bufo
(Anaxyrus) woodhousii and Hyla cinerea calls in
noisier sites compared to calls in more silent sites.
Considering that B. woodhousii calls in the region
of 1.4 kHz (Sullivan & Leek 1987) and the dominants frequency of H. cinerea varies between 0.9
and 3 kHz (Moss & Simmons 1986), it may be difficult to explain our results based solely on frequency range. What (if any) additional factors
may play a role remains to be clarified in subsequent studies. For instance, a study on the acoustic
variation in Scinax squalirostris demonstrated that
longitude, latitude and altitude had an effect only
on temporal call properties like duration and
number of pulses but not on frequency (Faria et al.
2013). Frog calls are considered to be innate rather
than learned (Wells 2007) and for that reason we
cannot expect great differences between call char-
Acknowledgements. This work was supported by a grant
№ 24/2013 from the Sofia University “St. Kliment
Ohridski”. We would like to thank the two referees, Prof.
Dr. Ulrich Sinsch and Dr. Bruno Gingras, for their
comments and recommendations for improving this
manuscript.
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