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The communication space of humpback whale social sounds in wind-dominated noiseRebecca A. Dunlop
Citation: The Journal of the Acoustical Society of America 144, 540 (2018); doi: 10.1121/1.5047744View online: https://doi.org/10.1121/1.5047744View Table of Contents: http://asa.scitation.org/toc/jas/144/2Published by the Acoustical Society of America
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The communication space of humpback whale social sounds inwind-dominated noise
Rebecca A. Dunlopa)
Cetacean Ecology and Acoustics Laboratory, School of Veterinary Science, University of Queensland, GattonCampus, Queensland, QLD 4343, Australia
(Received 1 May 2018; revised 1 July 2018; accepted 3 July 2018; published online 1 August 2018)
In animal social networks, a large acoustic communication space tends to involve complex net-
works. Signal masking may reduce this space, leading to detrimental effects on the animal’s ability
to obtain important social information. Humpback whales use acoustic social sounds (vocal sounds
and surface-generated sounds from breaching or fin slapping) for within- and between-group com-
munication. In this study, changes in various sound parameters (e.g., signal-above-noise and fre-
quency content) of received humpback whale social sounds were statistically modeled against the
combined effect of increasing wind-dominated noise and distance from the source (whale) to pro-
duce masking models. Behavioral data on vocalizing groups were also used to inform these models.
The acoustic communication space, in this shallow water (<50 m) environment, extended to
approximately 4 km from the signaler in median wind noise. However, the majority of behavioral
interactions occurred within 2 km of the signaler. Surface-generated signals propagated better and
likely function to maintain this space in higher wind noise. This study provides a basic wind-noise
masking model for social communication signals in humpback whales which can be updated as
more information on humpback auditory capabilities, and potential masking effects of anthropo-
genic noise sources, becomes available. VC 2018 Acoustical Society of America.
https://doi.org/10.1121/1.5047744
[WWA] Pages: 540–551
I. INTRODUCTION
The area over which an acoustic signal transmits, and is
audible to the intended receiver, is known as the active or
communication space (Brenowitz, 1982; Janik, 2000; Clark
et al., 2009). The dimensions of this area depend on the tem-
poral and spectral structure of the signal, the characteristics
of the environment, and the receiver’s ability to detect (the
detection threshold) and discriminate the signal from back-
ground noise (Wiley and Richards, 1978; Clark et al., 2009).
Acoustic signals that propagate over relatively long distan-
ces relative to the distribution of conspecifics can create a
large communication space (McGregor and Krebs, 1984;
Morton, 1986; Janik, 2000). Within this space, for any given
signaler, there may be a number of other intended, and/or
unintended, receivers and together these form a communica-
tion network (McGregor, 1993; McGregor and Horn, 2015).
Intended receivers are those at which the signals are directed
whereas unintended receivers, such as eavesdroppers, can
obtain beneficial information from exchanges between the
signaler and intended receiver (McGregor, 1993; McGregor
et al., 2001; Otter et al., 1999; Peake et al., 2001). Within
the context of breeding interactions, a male eavesdropper
can assess male competitors and therefore make decisions as
to whether or not to compete for the female (e.g., Siamese
fighting fish, Betta splendens, Matos et al., 2003; great tits,
Parus major, Peake et al., 2001; wolf spiders, Lycosidaespp., Clark et al., 2015). For breeding females, eavesdrop-
ping may allow assessment of potential mates without
having to directly interact with the senders (e.g., black-
capped chickadees, Poecile atricapilla, Mennill et al., 2002;
crayfish, Procambarus clarkia, Aquiloni et al., 2008;
Siamese fighting fish, Betta splendens, Doutrelant and
McGregor, 2000). Any reduction in the communication
space, due to increased background noise, may therefore
have detrimental effects on the ability of animals to obtain
breeding information within a network leading, ultimately,
to a reduced breeding success.
In the marine environment there are several natural
sources of noise, such as wind, wave, rain, and biological,
and these noise sources can limit the communication space
used by animals through masking (Clark et al., 2009).
Masking is the process by which the threshold of hearing for
one sound is raised by the presence of another (masking)
sound, expressed in dB (American National Standards
Institute, 2008). In the case of signalers and receivers in the
underwater environment, the sound would be from the sig-
naler, and the masking sound would be natural underwater
noise such as wind noise. Underwater noise can also be
anthropogenic in origin (e.g., noise from vessel activity, oil
and gas exploration, naval sonar activity or construction),
and, in some circumstances, this noise may result in further
masking of the animal’s acoustic signals compared to mask-
ing effects of natural noise. The effect of various sources of
noise, with regards to masking, however, is difficult to quan-
tify, given it is essential to know the auditory capabilities of
the animal.
Many masking studies relate a change in a particular
signal feature, most commonly the signal level in noise (i.e.,
a measure of signal-to-noise, or SNR), to increasing distancea)Electronic mail: [email protected]
540 J. Acoust. Soc. Am. 144 (2), August 2018 VC 2018 Acoustical Society of America0001-4966/2018/144(2)/540/12/$30.00
from the receiver to determine the distance at which a signal
is just detectable in noise. A decrease in this detectable dis-
tance can then be related to an increase in signal masking
due to an increase in critical bandwidth noise. Critical band-
width is defined as the bandwidth of noise at which the
detection threshold of a tone signal at the centre of the noise
band ceases to increase with increasing width of the noise
band and is therefore the noise band that is applicable to
masking studies. However, the detection of a signal may not
necessarily equate to recognition (Franklin et al., 2006).
Some species of bird require a signal excess of 2–3 dB above
the critical ratio (the audible threshold of a pure tone in noise
of constant spectral density; American National Standards
Institute, 2008) for successful discrimination of conspecific
calls and a further 2–3 dB for call recognition (Dooling and
Blumenrath, 2016; Lohr et al., 2003). This is likely because
signal characteristics, such as frequency and temporal
parameters, also change as the signal propagates through the
environment. In seawater high frequencies are readily
absorbed (Francois and Garrison, 1982), meaning high-
frequency components of a signal are likely to attenuate
more rapidly with distance compared to lower frequency
components. In shallow water, low frequencies propagate
poorly, leading to increased loss of low-frequency compo-
nents with distance. Therefore, as the signal travels through
the environment, it is subject not only to loss in signal level,
but to changes in signal structure meaning features of the
signal should be measured at the receiver, as well as at the
source.
Humpback whales use two different signal types; song,
which is audible over 10’s of kilometres (Au et al., 2006),
and social sounds, which are likely audible over a few kilo-
metres (Dunlop et al., 2013a). Song is a broadcast signal and
therefore results in a large (10’s of kilometres) and complex
communication network, likely involving multiple signalers,
intended receiver(s) and unintended receiver(s). Intended
receivers, for example, may be females (Winn and Winn,
1978; Tyack, 1981; Chu and Harcourt, 1986; Smith et al.,2008) or other male singing whales (Darling and B�erub�e,
2001; Darling et al.,. 2006; Frankel et al., 1995) and unin-
tended receivers may be other non-singing competitive
males in the area (Dunlop, 2016a; Dunlop and Noad, 2016).
Social sounds can be vocal signals (in this paper referred to
as social vocalizations) or surface-generated signals from
breaching or fin slapping. As these signals are significantly
quieter compared to song (Dunlop et al., 2013a; Dunlop
et al., 2013b), the communication network is likely to be kil-
ometres rather than 10’s of kilometres. Behavioral studies
suggest they are likely to be used as within-group signals as
well as between-group signals (Dunlop et al., 2008; Dunlop
et al., 2010; Dunlop, 2017; Parks et al., 2014; Rekdahl et al.,2015; Silber, 1986; Thompson et al., 1977).
Humpback whale social vocalizations range from long,
low frequency (<80 Hz) “grumbles” to short, very high fre-
quency (�2 kHz) “squeaks,” with many of the sound types
forming a continuum rather than being discretely different
signals (Dunlop et al., 2007; Dunlop, 2017, Fournet et al.,2015; Stimpert et al., 2011). Given this variation in sound
structure, discrete sound types are likely to give information
on group membership, changes in group membership, and,
as lone animals also use social sounds, perhaps the sex, size
and location of the signaler (Dunlop et al., 2008). The con-
tinuum in structure between different sound types (gradual
changes in frequency, duration, and bandwidth) likely pro-
vides additional information to the intended receiver on
(changeable) signaler features such as its motivation
(Dunlop, 2017). Humpback whale signaling behavior also
changes in response to other (likely unintended) receivers in
the area, known as the audience effect (Dunlop, 2016a).
Taken together, the communication network of humpback
whales likely extends beyond the immediate group to
encompass other groups in the area.
Before understanding the consequences of signal mask-
ing by increased anthropogenic noise, the size of the com-
munication space must first be determined, as well as any
changes its size due to increases in natural noise sources
such as wind-dominated noise. Humpback whales compen-
sate for increases in wind-dominated noise by both switching
from vocal sounds to surface-generated sounds (Dunlop
et al., 2010) and increasing their vocal source level (Dunlop
et al., 2014). Though responses are likely to maintain, to
some extent, the size of the communication space in noisier
conditions, it is currently unknown how large this network is
and what, if any, effects of increased natural wind-noise lev-
els on has on this space. In light of this, this study first aims
to measure and statistically model changes in received signal
level above noise, and structural parameters of different cat-
egories of social sounds, with both increasing proximity to
the source (signaling group) combined with increasing wind-
noise. This will provide information on the communication
space of humpback whale social sounds within a shallow-
water (<50 m) environment for different signal types.
Second, the analysis will be repeated whilst accounting for
the Lombard response, to determine how effective this
Lombard response is in maintaining this space during peri-
ods of increased wind-dominated noise. Third, behavioral
data on the interactions between signaling groups within the
communication network will be used to inform these statisti-
cal models. The results of this study will provide baseline
information that can be used to compare the size of the
humpback whale communication space in natural noise, with
the size of the space during periods of increased anthropo-
genic noise such as noise from vessels, to determine by how
much this space is potentially reduced during periods of high
vessel noise.
II. METHODS
A. Visual and acoustic data collection
Data were collected during the southwards (from their
breeding ground in the Great Barrier Reef, towards their
Antarctic feeding grounds) migration of the eastern
Australian humpback whale (September/October of
2002–2004 and 2008). Acoustic recordings of were made
using an array of five hydrophone-buoy systems where each
hydrophone-buoy consisted of a surface buoy, a pre-
amplifier (þ20 dB) and VHF radio transmitter anchored in
20–28 m of water. At the seabed, a High Tech HTI-96-MIN
J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop 541
hydrophone with built-in þ40 dB pre-amplifier was sus-
pended about a metre of the sea floor and cabled to the sur-
face buoy. Buoys 1–3 were 1.5 km from the beach, parallel
to the shoreline, and approximately 0.7 km apart. Buoys 4
and 5 extended seaward from buoy 2, in a line perpendicular
to the shore and were approximately 0.5 km apart. Accurate
positions of hydrophones were obtained by a shore-based
theodolite survey of the surface buoys (Noad et al., 2004).
Water depth in the study site (where most of the groups were
recorded) varied from 25 to 35 m.
Radio transmissions from the buoys were received in
real time at a base station just behind the beach using a verti-
cally orientated Yagi antenna attached to a four channel, low
noise, VHF receiver (type 8101), and a Winradio receiver.
Two computers, equipped with National Instruments E-
series data acquisition cards and using ISHMAEL software
(Mellinger, 2001), were used to record and track the acoustic
signals (at a sampling rate of 22.05 kHz). Signal sources
were tracked in real-time (or post-field if required) using the
arrival time differences of the signals. Bearings to the signal
sources calculated by ISHMAEL were accurate, however, there
were small errors in the range estimates (less than 50 m at
2 km and less than 1 km at 10 km from the array). Signal
location accuracy was significantly improved by using the
mean position of several estimates calculated over a brief
period. Array validation was carried out by comparing
acoustically calculated positions with accurate theodolite
positions of visually identified singing whales within the
study area (for further validation experiments see Noad
et al., 2004). The hydrophone with a built-in preamplifier
was calibrated at the Defence Science and Technology
Organisation calibration facility in Woronora Dam. The
remainder of the recording chain was calibrated by inserting
tones and white noise of known levels into the amplifier in
the buoy in place of the hydrophone. Full system sensitivity
varied by 1.5 dB over the frequency range 40 to 10 000 Hz.
Hydrophone sensitivity was �164 dB re 1 V lPa–1, which
included þ40 dB gain for the built-in preamplifier.
Land-based tracking of the groups occurred simulta-
neously using a theodolite linked to a visual tracking pro-
gramme (CYCLOPES; developed by Eric Kniest, University of
Newcastle). Each position was annotated with the social
composition (number of adults within the group and whether
or not there was a calf) and social behavior (splitting of ani-
mals from, and joining of animals to, the group). Acoustic
tracks of vocalizing whales (from ISHMAEL) were overlaid on
the visual tracking map in CYCLOPES and the combined acous-
tic/visual data were shared between the base and hilltop sta-
tions using a wireless network providing a real-time
superposition of acoustic and visual tracks. There were
rarely more than six groups migrating through the study area
at any one time, and these were usually widely dispersed
(unless the groups were joining together). Given the accu-
racy of the system, and the way in which groups could be
simultaneously visually and acoustically tracked in real-
time, social sounds could be assigned at a group level,
though not at an individual level. Using this combination of
visual and acoustic tracking data, the distance of each signal-
ing group to each hydrophone-buoy receiver, as well as the
distance of each signaling group to other groups in the area,
could be measured for each recorded social sound.
B. Signal measurements
Spectrograms of social vocalizations and surface-
generated sounds (signals) were produced using RAVEN 1.2
(Cornell Lab of Ornothology) with the DFT size set at 4096
samples, Hamming window, and 80% overlap. Each signal
was isolated from three different channels (three different
receivers at a range of distances from the signaling group)
and saved as a separate file along with a sample of back-
ground noise. If a signal could not be isolated from one of
the three channels (in that the signal was below noise and no
part was visible on the spectrogram), only two channels
were used in the analysis.
Social vocalizations were audibly and visually (by
inspecting the spectrogram) classified into a series of vocali-
zation types based on a previous study at this site (Dunlop
et al., 2007). Various parameters, including estimates of
received and source level, were then measured using a
custom-made MATLAB script. First, a noise-correction was
made on each vocalization by subtracting the spectrum of
the noise file from that of the associated vocalization file, see
below. Then, various temporal and frequency measurements
were made on the vocalization (Table I). All frequency
parameters were log-transformed to better represent the
mammalian perception of pitch (Evans, 1992).
To measure the received level, each signal was divided
into 743 ms segments with a 50% overlap, multiplied by a
Hanning window and a discrete Fourier transform (DFT) cal-
culated [using a fast Fourier transform (FFT) size of 16 384
with a 1.35 resolution]. The power spectrum (calibrated to
account for the sensitivity of the hydrophone and the gain of
the system) for each segment was extracted as the squared
magnitude of the DFT. A noise-correction was made on each
segment and the resulting power spectrum used as the sound
received level for each 1.35 Hz frequency band for each
segment.
Transmission loss (TL) was measured at the site as
described in Dunlop et al. (2013a), using a boat as a source
(running various transects towards and away from the
array) and playback of octave band limited white noise at
three positions. This gave various regression lines of
received levels as a function of distance from the sources in
the form
TL ¼ aþ b logðxÞ; (1)
where b is the slope of the regression line, x is distance
(meters) and a is a constant (which may be frequency depen-
dent). The horizontal distance was approximated as the slant
range since water depths of the transmission paths were less
than 40 m and thus very small compared with the distances.
For most frequencies, b varied with distance but could be
well approximated by two values, one applying to distances
less than, and the other greater than, a cross over value
where the slope changed. Values of a and b and the cross
over distances are given in Dunlop et al. (2013a). Both a and
542 J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop
b varied with frequency, so Eq. (1) values were calculated
for each octave band.
The square root of the power spectrum was converted
back into a waveform by applying an inverse Fourier trans-
form. All processed segments within each signal were then
concatenated to reconstruct the signal. This was filtered from
35 to 5623 Hz, corresponding to the lower and upper limit of
the 1/3 octave band centred at 40 and 5000 Hz, respectively,
to eliminate low frequency turbulence noise and high fre-
quency system noise, whilst capturing the peak frequencies
of the signals. The 1/3 octave band containing the most
energy was used as the RLrms for the analysis. From the
received level of each signal (RLrms over 35 to 5623 Hz), the
source level was estimated as
SLrms ¼ RLrms þ TL: (2)
To estimate broadband wind-dominated background
noise levels (NLbb), a 10-min file was used for each buoy
where each file began approximately when the first signal
for the group was audible on one of the hydrophone-buoys
(excluding any signals when measuring the noise). For each
file, mean square pressures were summed over the 40 Hz to
2.5 kHz 1/3 octave bands (actual band 36 Hz to 2.8 kHz) and
converted to decibels to give the broadband noise level. This
bandwidth was chosen because almost all the energy in the
signals lies within this band and this encompassed the peak
frequencies for wind-dominated noise. Groups were only
included in this analysis if there were no boats audible on
the array (and there were no sighted boats traversing the
study site) as well as no audible singing whales (so that sing-
ers would have been more than 10 km away and would not
have contributed significantly to the background noise at the
group) at the time the group was vocalizing. On a few occa-
sions, there was very faint song audible but this singer noise
contributed less than 1 dB to the broadband noise level. This,
and snapping shrimp at higher frequencies, were the only
other two sources of noise in the study area during times
measurements were made. There was no evidence that dis-
tant shipping noise contributed to the measured noise. In the
absence of singer and vessel noise, the measurements were
similar to those observed for wind-dependent noise in
Australian waters up to 800 Hz, showing the general
decrease in noise level with increasing frequency (Cato,
1997). Since the wind speed was stable over the study site,
the background noise measured at the array could be consid-
ered to be similar to that at the signaling whales. The system
electronic noise over the 36 Hz to 2.8 kHz band had an
equivalent input level of 77 dB re 1 lPa (using the type 8101
receiver). Ambient background noise levels included in this
analysis were well above this level meaning there was no
contribution of system noise. The received (at the hydro-
phone) signal-to-noise level (SNR) of each sound was then
estimated as
rSNR ¼ RLrms � NLbb; (3)
where the RLrms was measured in the 1/3 octave containing
the most energy and the NLbb was measured over 36 Hz to
2.8 kHz.
TABLE I. Measured frequency and duration parameters used in the CART analysis. Frequency parameters were logged for use in data analyses procedures.
Measurement Abbreviation Description
Centre time peak (s) pTC The time at which the peak of the sound is divided into two intervals containing equal energy
First quartile time peak (s) pTQ1 Time containing the first 25% of the energy of the sound peak.
Third quartile time peak (s) pTQ3 Time containing 75% of the energy of the sound peak.
Inter-quartile time peak (s) pTIQ Difference between the 3rd and 1st quartile times of the sound peak.
10th centile time peak (s) pTC10 Time containing the first 10% of the energy of the sound peak.
90th centile time peak (s) pTC90 Time containing 90% of the energy of the sound.
Inter-centile time peak (s) pTIC Time between the 10th and 90th centile times of the sound peak.
Centre time (s) TC Time at which the sound is divided into two intervals containing equal energy.
First quartile time (s) TQ1 Time containing the first 25% of the energy of the sound.
Third quartile time (s) TQ3 Time containing 75% of the energy of the sound.
Inter-quartile time (s) TIQ Difference between the 3rd and 1st quartile times.
10th centile time (s) TC10 Time containing the first 10% of the energy of the sound.
90th centile time (s) TC90 Time containing 90% of the energy of the sound.
Inter-centile time (s) TIC Time between the 10th and 90th centile times.
Peak frequency (Hz) FP The frequency at maximum level
Centre frequency (Hz) FC Frequency at which the sound is divided into two components of equal energy
First quartile frequency (Hz) FQ1 Frequency that divides the sound into two components containing 25% and 75% of the sound’s energy.
Third quartile frequency (Hz) FQ3 Frequency that divides the sound into two components containing 75% and 25% of the sound’s energy.
Inter-quartile frequency (Hz) FIQ Difference between the 3rd and 1st quartile frequencies.
10th centile frequency (Hz) FC10 Frequency that divides the sound into two intervals containing 10% and 90% of the sound’s energy
(�MinF).
90th centile frequency (Hz) FC90 Frequency that divides the sound into two intervals containing 90% and 10% of the sound’s energy
(�MaxF).
Inter-centile frequency (Hz) FIC The difference between the 90th and 10th centile frequencies.
First quartile time frequency (HzHz) TFQ1 Frequency at TQ1
Third quartile time frequency (Hz) TFQ3 Frequency at TQ3
Frequency trend FTrend Calculated as FQ1T / FQ3T.
J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop 543
C. Call categorization
Classification and regression tree (CART) analysis was
used following methods of Garland et al. (2012) and
Rekdahl et al. (2013). This was carried out on vocalizations
(not surface-generated signals) using the rpart package in R
(R Core Team, 2013). As each vocalization was measured
up to three times (from three different hydrophone
receivers), the one closest to the receiver (being the sound
with the highest received signal-to-noise) was selected to
develop the initial CART. To minimize propagation effects
on sound structure, sounds beyond 1.5 km of the receiver
were excluded as well as those under 3 dB SNR (calculated
as the signal RLrms minus the NLrms of the noise sample used
for the noise correction). Measured frequency and temporal
parameters from Table I were used, where each branch
(split) uses the parameter that best differentiates the resulting
nodes, based on a measure of “goodness of fit”; the Gini
index (Breiman et al., 1996). In brief, the split with the low-
est splitting error is chosen to continue tree growth resulting
in homogeneous nodes equating to similar sound categories,
and eventually through further splitting, vocalization types.
Splitting continues until all vocalizations have been used, or,
subsequent split results in too few cases in the terminal node
(set to five). The minimum number of observations required
in a node for a split to be attempted was set to 10. The initial
tree was cross-validated (V-fold cross validation with 50
subsets) and then pruned until a final decision tree, with the
smallest estimated error and lowest misclassification rate,
was reached.
Using the final CART output as a guide, vocalizations
were categorized into broad categories, with the assumption
that each category contained relatively similar vocalization
types. A randomForest analysis (randomForest package in R;
Liaw and Wiener, 2002) was then run on these categories.
randomForest creates a forest of trees that internally calcu-
lates misclassification errors during tree construction. Within
each tree branch, a predefined number of parameters are ran-
domly selected for each branching event (¼10) and a num-
ber of different trees are constructed (¼1000) and compared.
The variable importance is then ranked (using the Gini
index) and an overall misclassification estimated for each
sound category. This “out-of-bag” (OOB) error was used to
assess how well the broad categorizations performed.
First, a randomForest analysis was performed only on
those vocalizations selected for the CART analysis (i.e.,
excluding sounds beyond 1.5 km of the receiver and those
under 3 dB SNR). Next, a second randomForest analysis was
performed on the full vocalization dataset. Here all aurally
classified vocalization types were grouped into their respec-
tive categories based on their initial subjective categoriza-
tion. randomForest then assessed suitability of this broad
classification scheme irrespective of the distance of the
vocalization from the receiver (equating to likely differences
in structure) and level above noise.
D. Data analysis
A generalised additive model (GAM) framework was
used to statistically model the response variables using R
software with the MRSEA (Scott-Hayward et al., 2014) and
GEEPACK (Yan and Fine, 2004; Højsgaard et al., 2006) pack-
ages for model fitting and selection. The response variables
were received signal-to-noise level (rSNR) or a representa-
tive measure of sound frequency. A Gaussian distribution
was appropriate for all response models. Covariates in all
response models were SLrms, a measure of frequency and/or
a measure of duration, to account for differences in signal
propagation due to source level and signal structure. These
covariates were considered as one-dimensional smooth
terms.
A complex region spatial smoother (CReSS) (Scott-
Hayward et al., 2014) was then used to fit a two-dimensional
smooth surface to the interaction between broadband wind-
dominated noise (x) and the distance from the receiver (y).
This procedure statistically models the two-dimensional (x
and y) surface using a spatially adaptive local smoothing
algorithm (SALSA) (Walker et al., 2011). In short, the 2D
surface contains a number of knots, being sources of flexibil-
ity of the surface which can raise or lower the surface
according to the relationship between x and y. A knot-
selection process (SALSA) is used to select the position of
the knots and a smoothing method (CReSS) is used to
manipulate the flexibility of the surface. Bayesian
Information Criteria was used for selection of number and
location of knots.
This surface model was then used as one of the covari-
ates within the analysis, where each measured signal param-
eter now had an associated integrated measure of broadband
wind-noise and distance of the signaling group from the
receiver. Model selection of covariates (SLrms, frequency or
duration measures, 2D surface) used a five-fold cross-valida-
tion (CV) procedure, where a smaller number indicated a
better fitting model. The final optimal model included the
combination of covariates which best explained the variation
in response data. This final model was rerun in a generalized
estimating equation (GEE) (Hardin, 2005) with “Signal ID”
as the panel structure as each signal was measured three
times on three different hydrophones at three different dis-
tances from the signaling group. Predictions were then made
and displayed as figures. These figures illustrate the inte-
grated relationship between measured signal parameters at
the receiver, the distance of the receiver to the source (sig-
naling group), and the broadband wind-noise, whilst control-
ling for variation due to differences in source level and
frequency content.
In the first set of analyses, SLrms and measures of signal
structure (frequency or duration) were included as smooth
terms to control for propagation differences due to level and
structure (aim 1). In the Lombard response analysis (aim 2),
these smooth terms were not included. Here both the SLrms
and the rSNR were statistically modelled as a function of dis-
tance and wind-noise to display the Lombard response
(SLrms) and then to account for the Lombard response on
received signal-to-noise levels (rSNR).
For comparison between the results of the different
models, a nominal rSNR of 0 was used. It is unknown, due to
the lack of information on how humpback whales detect con-
specific signals in noise, whether this equates to the
544 J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop
detection limit for a humpback whale’s hearing in noise.
Currently, there is also no available information on mecha-
nisms of masking release in humpback whales. However, as
a range of rSNRs are presented, the comparison measure can
be updated when more information on hearing and masking
release parameters becomes available.
For the behavioral analysis (aim 3), each signaling
group was annotated with three behavioral variables at the
beginning of every 10 min time period (beginning with the
first audible social sound) until signaling ceased. These
were:
(1) the number of other groups within a 5 km radius of the
signaling group,
(2) the distance of each of the other groups from the signal-
ing group, noting if it was the nearest neighbor, 2nd
nearest neighbor and so on and,
(3) interaction state; where a 1 was given if the group was
actively approaching or was being approached by
another group to result in them joining together or com-
ing within 500 m of each other (otherwise 0).
The probability of an interaction occurring (1 or 0) was
modelled using a GLM (binomial regression model) with vari-
ables 1 and 2 (above) and wind-dominated noise levels as pre-
dictor variables. The “behavioral” communication distance
was then determined by comparing the range of neighbor dis-
tances from the signaling group in which group interactions
were most likely to occur, with the range of neighbor distan-
ces in which interactions were not likely to occur.
III. RESULTS
A. Sound categorization
Signals were initially qualitatively (aurally and visually
using the spectrogram) separated into 31 different types
(based on Dunlop et al., 2007) including two types of
surface-generated sound. The initial CART analysis (n¼ 449
sounds with 29 vocalization types) separated the vocaliza-
tions into 10 different types (Fig. 1). The 1st split separated
vocalizations according to duration (TQ3 less than or greater
than approximately 0.5 s) and the 2nd split produced four
broad categories:
(1) long (TIC >¼ 0.84 s), low-frequency (log FC10
>¼ 2.1 Hz) vocalizations, referred to as “long-low”
(containing low-frequency unmodulated “grumbles,”
broadband “screeches,” “growls,” and “purrs” and likely
blow-hole associated sounds; see Dunlop et al., 2007 for
spectrograms),
(2) mid-length (TIC< 0.84 s), low-frequency vocalizations,
referred to as “mid-low” (containing frequency-modulated
“moans,” “groans,” “wops,” and “thwops”; Dunlop et al.,2007),
(3) short and low-frequency (log FC< 2.2 Hz) referred to as
“short-low” vocalizations (containing short low-
frequency “snorts,” “grunts,” and “barks; Dunlop et al.,2007), and
(4) short high-frequency (log FC> 2.2 Hz) referred to as
“short-high” vocalizations (containing “yaps” and
high-frequency “squeaks” and “barks”; Dunlop et al.,2007).
Each vocalization was assigned to one of the four catego-
ries, according to which branch the majority of the sounds
within each type were grouped into (e.g., “grumbles,” despite
appearing in two categories, were assigned to category 1).
Surface-generated sounds were placed into a separate category.
A randomForest analysis was ran using these four catego-
ries (Table II) and the out-of-bag estimate of error rate was
4.69% indicating a low mis-classification rates within all cate-
gories. All measured vocalizations (n¼ 2010) were then clas-
sified, according to their original subjective classification, into
one of the four categories and the randomForest analysis re-
ran (Table II). The out-of-bag estimate of error rate was 0.3%
and therefore the use of these four broad categories to be
appropriate.
B. The combined effect of distance and wind-noiseon received signal-to-noise
Signals within the various categories were recorded out
to approximately 3.5 to 5 km from the array (depending on
the category) in wind-dominated noise levels ranging from
FIG. 1. The output of the CART analysis showing the frequency or temporal
parameter used at each split (squares), the cut-off value used for each
parameter, and the resulting ten different sound types (circled). The second
CART split produced four broad categories (named by the most common
sound within this category; “grumbles,” “wops,” “snorts,” and “yaps”), re-
labelled to “long-low,” “mid-low,” “short-low,” and “short-high”).
TABLE II. The number of vocalizations classified by the RANDOMFOREST
analysis into one of the four categories. The left column is the number of
aurally-classified vocalizations within each category. The four right hand
columns are the number of vocalizations classified by the RANDOMFOREST
analysis into each category. Numbers in normal font are sounds used in the
initial CART analysis. Numbers in italics are the full sample size (i.e.,
including all sounds regardless of distance from the receiver, SNR, and
hydrophone-buoy the recording came from).
Long-low Mid-modulated Short-low Short-high
Long-low (125, 490) 114 2 7 2
487 1 2 0
Mid-low (109, 660) 2 104 3 0
0 660 0 0
Short-low (155, 648) 2 2 150 1
0 0 648 0
Short-high (59, 208) 0 0 0 59
0 1 2 205
J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop 545
91 to 113 dB re 1 lPa (equating to wind speeds of 7 to
20 kn). Low wind-dominated noise levels were considered to
be less than 95 dB re 1 lPa, median levels were 100 dB re1 lPa (wind speed of approximately 12 kn) and high levels
were considered to be over 105 dB re 1 lPa.
Within all four vocalization categories, there was a sig-
nificant relationship between the rSNR, wind noise, and dis-
tance from the receiver. The final statistical model outputs are
presented below, with the measured response variable on the
left, and various significant predictor variables (including the
combined effect of broadband wind noise and distance from
the receiver with degrees of freedom, d.f.) on the right. The
figures (Fig. 2) display the relationship between the distance
and wind noise variables (x and y) and the response variable
(rSNR) whilst controlling for source level and frequency. The
final model outputs are a representation of how the signals
would be received according to signaler distance combined
with wind noise:
(1) “Long-low”: rSNR � s (wind noise, distance, d.f.¼ 6)
þ SLrms þ log FQ3,
(2) “Mid-low”: rSNR � s (wind noise, distance, d.f.¼ 6)
þ SLrmsþlog FC10,
(3) “Short-low”: rSNR � s (wind noise, distance, d.f.¼ 6)
þ SLrms þ log FC90,
(4) “Short-high”: rSNR � s (wind noise, distance, d.f.¼ 4)
þ SLrms þ log FC10.
The rSNR remained above 0 out to, and potentially
beyond, 4 km in “long-low” [490 observations of 170 vocal-
izations; Fig. 2(a)], “mid-low” [660 observations of 226
vocalizations; Fig. 2(b)] and “short-low” vocalizations [648
observations of 226 vocalizations; Fig. 2(c)] in low wind-
noise. In other words, the receiving whale is likely to hear
these vocalizations from signalers at least 4 km away. These
categories had the similar source level ranges (“long-low”
sounds ranged in SLrms from 131 to 188 dB re 1 lPa @ 1 m,
“mid-low” ranging from 125 to 195 dB re 1 lPa @ 1 m and
“short-low” from 127 to 179 dB re 1 lPa @ 1 m). “Short-
high” vocalizations [208 observations of 84 vocalizations;
Fig. 2(d)] were the least common category and lower in
source level (128 to 167 dB re 1 lPa @ 1 m). The
rSNRremained above 0 until approximately 2.5 km in low
wind-noise conditions, suggesting poorer propagation of
these higher frequency vocalizations after controlling for dif-
ferences in source level.
In median wind-noise levels, the rSNR remained above 0
to approximately 3 km in “long-low,” 4 km in “mid-low” and
“short-low” vocalizations, and reducing to 2.5 km in “short-
high” vocalizations [Figs. 2(a), 2(b), 2(c), and 2(d)].
However, in high wind-noise, the rSNR of “long-low,” “mid-
low,” and “short-low’ vocalizations remained above 0 until
approximately 1 km, whereas “short-high” vocalizations
remained above 0 until approximately 500 m [Figs. 2(a), 2(b),
2(c), and 2(d)]. This suggests a significant reduction in the
FIG. 2. The relationship between the
distance of the signaling group from
the receiver (x axis), wind-dominated
noise levels (y axis), and measured
rSNRfor “long-low” (a), “mid-low”
(b), “short-low” (c), “short-high” (d),
and surface-generated (e) categories.
The SLrms for each category was set at
the mean (154, 159, 154, 150, and
161 dB re 1 lPa at 1 m for the five cat-
egories, respectively) to standardize
for source level. The representative
frequency measures used were FQ3 [set
at 2.30 Hz (a)], log FC10 [set at 2.05 Hz
(b)], log FC90 [set at 1.97 Hz (c)], log
FC10 [set at 2.60 Hz (d)], and the log
FC [set at 2.24 Hz (e)] to control for the
effects of frequency on the sound
propagation.
546 J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop
signaler/receiver vocalization communication space due to
increased masking.
Surface-generated signals [136 observations of 46
sounds; Fig. 2(e)] ranged in SLrms from 145 to 177 dB re1 lPa @ 1 m. Although these signals also significantly
reduced in rSNR with increased signaler distance and wind-
noise, they were of a higher rSNR compared to vocalizations
in all conditions (Fig. 2). Even in high wind-noise condi-
tions, these signals remained above 0 rSNR until at least
3 km from signaler, suggesting these sounds were less likely
to be masked in higher noise [Fig. 2(e)].
C. The combined effect of receiver distanceand wind-noise on received signal frequency
The full dataset (n¼ 2006 observations of 705 vocaliza-
tions and 134 observations of 81 surface-generated signals)
was used for this analysis. “Short-low,” “mid-low,” and
“long-low” categories were combined given these were of a
similar frequency range (i.e., all low-frequency vocaliza-
tions; Fig. 1). The frequency measure chosen was a measure
of approximate minimum (FC10) and a time variable was
included as a smoother to standardize for changes in signal
duration with frequency (TQ3 ranged from 0.03 to 11.80 s
with a mean of 0.89 s). Vocalizations in the “short-high” cat-
egory were analyzed separately using a measure of approxi-
mate maximum frequency (FC90) and standardized for
duration (TQ3 ranged from 0.03 to 2.10 s with a mean of
0.25 s). The peak frequency (FP) of surface-generated signals
was used as a measured of frequency content. Source level
was also standardized in all statistical models as before.
The log FC10 of low-frequency vocalizations remained
relatively stable out to approximately a 2 km from the sig-
naler in median wind-noise then progressively disappeared
in increased noise and with greater separation distances. The
high-frequency components of “short-high” vocalizations
were also progressively lost at distances greater than 500 m
and wind noise levels above median (Fig. 3). Note, there was
insufficient data at close distances and in low-noise therefore
these are predicted outputs within 500 m and below 100 dB
re 1 lPa. In contrast, the FP of surface-generated sounds,
even in high wind-noise, remained stable out to approxi-
mately 3 km from the signaler (Fig. 3).
(1) “Low-frequency”: log FC10 � s (wind noise, distance,
d.f.¼ 5) þ SLrms þ TQ3,
(2) “high-frequency”: log FC90 � s (wind noise, distance,
d.f.¼ 5) þ SLrms þ TQ3,
(3) “surface-generated”: log FP � s (wind noise, distance,
d.f.¼ 5) þ SLrms þ TQ3.
D. Including the vocal Lombard effect
The Lombard hypothesis (an animal will increase its
vocal level in response to increasing noise levels; Lombard,
1911) can be measured from an acoustic array. There are two
problems with this approach. The first is that the further the
source is from the array, the more likely it is that lower source
level sounds will be missed in the recordings. The second is
that increased background noise will increase the proportion
of lower source level sounds missed, thus biasing the data to
higher source levels in higher noise. These biases are clearly
shown in Fig. 4. A previous study accounted for these issues
and found groups to maintain about a 60 dB excess above
wind noise, though not in the highest noise levels (Dunlop
et al., 2014). The current study measured signal-to-noise in adifferent way, but found that, at 1 km from the receiver, where
the effects of the biases are minimal, the measured signal
excess at the signaling group remained at 50 dB above noise
up to approximately 105 dB re 1 lPa of noise [Fig. 4(a)].
This signaler Lombard response resulted in vocalization
rSNRs of 5 to 10 dB above noise from signalers within 3 km,
and just above 0 from signalers out to 4 km [Fig. 4(b)]. In
high wind-noise, however, the vocal Lombard response in sig-
naling whales did not compensate for the masking effect of
increased wind noise. Although there is a bias in measuring
the Lombard response, in that only the louder sounds will be
recorded, this bias could be considered to be a representation
FIG. 3. The relationship between the distance of the signaling group from the
receiver (x axis; note the different ranges), wind-dominated noise levels (y
axis), and measured approximate minimum (FC10) for “long-low” (a), approx-
imate maximum (FC90) for “short-high” sound categories and peak frequency
(FP) for surface-generated sounds (c). The SLrms for each sound category was
set at the mean (156, 149, and 161 dB re 1 lPa at 1 m for the three categories,
respectively) to standardize for source level. The representative temporal mea-
sure was TQ3 [set at 0.89, 0.26, and 0.20 s for (a), (b), and (c), respectively] to
control for the effects of duration on the frequency propagation.
J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop 547
of the reception of the signals by a whale receiver, in that
whale receiver will only hear louder signals in higher noise.
(1) SLrms � s (wind noise, distance, d.f.¼ 4),
(2) rSNR � s (wind noise, distance, d.f.¼ 4).
E. Behavioral responses within the communicationnetwork
Social sounds were not recorded from signaling
groups beyond 5 km from the array (the detection limits of
the system) therefore the number of potential receivers
was counted within 5 km from the signaling group. This
ranged between 1 and 4 (with an average of 2 to 3) at the
time of signaling. The distance of the nearest neighbor
was 300 m to 4.8 km from the signaling group (depending
on the number of other groups in the area), the 2nd and
3rd neighbors were 1 km to 5 km, and the fourth neighbor
was always beyond 3 km.
Signaling groups, if joining with another group (n¼ 6),
always joined the nearest neighbor. The separation between
two joining groups at the beginning of the interaction ranged
from 500 m to 2.4 km in wind-dominated noise levels up to
101 dB re 1 lPa. One of the groups also joined the 2nd near-
est neighbor, which approached it from 4.1 km away. Three
additional signaling groups were approached by the nearest
neighbour from approximately 1.5 km away, but did not
join.
The probability of a signaling group interacting with
another group was significantly dependent on the distance of
the nearest neighbor [p¼ 0.03; Fig. 5(a)] and the total num-
ber of other groups (other potential receivers) within the
5 km radius [p¼ 0.005; Fig. 5(b)] but not the wind-
dominated noise level or the distance of the other (2nd, 3rd,
or 4th) groups in the area. Specifically, signaling groups
were more likely to join with another group if the nearest
neighbor was within 2 km when signaling and unlikely to
join if they were more than 2.5 km from their nearest neigh-
bour [Fig. 5(a)]. Signaling groups were more likely to have a
close-by neighbor as the number of groups in the area
increased and therefore more likely to interact [Fig. 5(b)].
These observations suggest the signals are audible about
to at least 4 km, where the received level above noise (as
measured in this study) of low-frequency vocalizations was
approximately 0 in median wind conditions. However, inter-
actions between groups within this communication space
were more likely to occur within 2 km of the signaling
group. Here, received signal-above-noise levels were þ5 to
þ10 dB for low-frequency vocalizations, with no significant
loss of low-frequency components. For high-frequency
vocalizations, received signal-above-noise levels were just
above 0 but with some loss of high-frequency components.
IV. DISCUSSION
The received levels and frequency components of each
measured social sound category varied significantly with
increasing levels of wind-dominated noise and separation
distance between the signaling group and receiver. In rela-
tively low wind-noise (less than 95 dB re 1 lPa), the signal-
to-noise of low-frequency vocalizations remained above 0,
with no loss of low-frequency components out at least 4 km.
High-frequency vocalizations, however, had poor propaga-
tion in that even in low wind-noise, the signal-to-noise
remained above 0 out to 2.5 km with some loss of high-
frequency components. The behavioral data suggest the
signals were audible out to at least 4 km (likely the low-
frequency vocalizations and surface-generated signals) but
group interactions were more likely to occur within 2 km,
where the higher-frequency signals were more likely to
FIG. 4. The relationship between the distance of the signaling group from
the receiver (x axis), wind-dominated noise levels (y axis), and measured
SLrms (a) and rSNR (b) for vocal sounds.
FIG. 5. Boxplot (displaying the median, inter-quartile ranges and minimum/
maximum of the distributions) comparing the distance of the nearest neigh-
bor (a) and number of groups within 5 km (b) between non-interacting to
interacting signaling groups.
548 J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop
become audible. In high wind-noise (þ105 dB re 1 lPa) con-
ditions, however, the vocalization communication space was
likely to be reduced. Low-frequency vocalizations remained
above 0 SNR to only 1 km from the receiver, and high-
frequency vocalizations, less than 1 km, without a Lombard
response. When accounting for the Lombard response, the
measured rSNR of vocalizations was almost constant up to,
but not above, noise levels of 105 dB re 1 lPa. Frequency
components also changed, meaning the received signal was
likely progressively more distorted as the wind-noise
increased resulting in a loss of information such as signaler
motivation. However, surface-generated signals, in higher
wind-noise, remained above 0 rSNR out to at least 3 km and
with a constant peak frequency meaning they are likely to be
audible and less prone to distortion. A previous study found
that humpback whales switch using primarily surface-
generated signals in higher wind-noise (Dunlop et al., 2010)
and the results of this study suggest this switch would main-
tain their communication space in high wind-noise
conditions.
It should be noted that this measure of signal-to-noise
(rSNR) does not assume audibility of signal in noise, in that
an rSNR of 0 does not necessarily mean the signal becomes
inaudible to the whale receivers. As yet, there are no empiri-
cal measures of humpback hearing in noise and an rSNR of 0
(as measured in this study) was chosen as a level with which
to compare the performance of different sound categories.
These models can be updated when more information on
humpback whale hearing becomes available. The choice of
the signal and noise measurement used in this study was
based on what was considered to be a reasonable approxima-
tion of how whales might hear their own signals in noise.
The received level was measured in the 1/3 octave band, as
most of the energy of each social sound type is contained
within the 1/3 octave band and is presumably what a hump-
back whale receiver would hear. The noise bandwidth was
chosen because almost all the energy in the vocalizations (if
taking the social vocalization repertoire as a whole and not-
ing that signals can occur in bouts containing multiple signal
types; Rekdahl et al., 2015) lies within this band, meaning
this is presumably the noise band the whales are listening to.
This bandwidth also encompassed the peak frequencies for
wind-dominated noise.
Critical ratios for marine mammals are summarised by
Richardson et al. (1995), Southall et al. (2007), and Erbe
et al. (2016), where the values at 2 kHz range from approxi-
mately 19 to 26 dB. An earlier humpback whale playback
study at this study site, using this population of whales,
played 2 kHz tones to groups of migrating humpback whales
and measured their behavioral response. The masked thresh-
old of the 2 kHz tone stimulus was assumed to be �7.7 to
�0.7 dB (tonal signal measured in 1/3 octave noise; Dunlop
et al., 2013c), which was equivalent to a critical ratio of
19–26 dB. Critical ratios, however, are measured for tonal
signals and there does not appear to be measurements appli-
cable to signals like social sounds, where the energy is dis-
tributed over multiple frequencies. The peak frequencies of
social vocalizations varied from 43 Hz to 2.8 kHz with the
peak energy for the majority of signals below 150 Hz. Most
of the available data on critical ratios at low frequencies
comes from pinniped species, and is approximately 15 dB at
100 Hz and 10 to 18 dB at 200 Hz (summarised in Erbe
et al., 2016, using tone signals). This would be equivalent to
1 to �2 dB for 100 and 200 Hz tones, respectively, if mea-
sured in 1/3 octave noise. Given the lack of information for
baleen whale hearing of their own signals in noise, and fol-
lowing the argument above, 0 dB rSNR (as measured in this
study) was assumed to be close to the audible limits of the
receiver.
Behavioral evidence also suggests 0 dB rSNR may be
a reasonable value to use to estimate the size communica-
tion space for humpback whales in this environment. First,
the switch to surface-generated sounds occurred in higher
wind-noise (Dunlop et al., 2010) when vocalization
received levels above noise fell below 0 SNR. Second, the
Lombard response resulted in vocalizations remaining
above 0 rSNR until 4 km in noise levels up to 105 dB re1 lPa, after which, surface-generated signals, which propa-
gated further, were more commonly used for lower fre-
quency sounds. This agrees somewhat with estimates by
Cholewiak et al. (2018), where masking levels were mod-
elled for whale receivers and agent-based model used to
calculate changes in communication. Here the communica-
tion of humpback social sounds in ambient noise was esti-
mated to be 3.4 6 1.6 km. Third, in median wind-noise,
the rSNR and signal structure of high-frequency vocaliza-
tions was relatively unchanged out to approximately 2 km.
Interactions between groups were significantly more likely
to occur within this separation distance suggesting these
higher frequency sounds may function to mediate groups
joining together.
Fin and blue whales (Balaenoptera spp.) produce ste-
reotyped and redundant songs as a way to increase the
communication range of their signals in a noisy underwa-
ter environment (Clark et al., 2009). In addition, the fre-
quency range and peak frequencies of fin and blue whale
song from shallow water coastal environments are higher
than song from pelagic environments to compensate for
differences in sound propagation (Clark and Ellison,
2004). Humpback whale social sounds, however, are not
structurally stereotyped and modifying the frequency con-
tent may change the signaler message (Dunlop, 2017). The
analyses presented here suggest low-frequency vocal sig-
nals remain above noise out to approximately 4 km and up
to wind noise levels of 105 dB re 1 lPa. In higher noise,
surface-generated signals likely function to maintain this
space. Behavioral interactions more likely to occur within
2 km where higher frequency signals remained above
noise. Humpback whales neither increase their source lev-
els, nor switch to surface-generated signals, in the presence
of traversing fishing vessels (Dunlop, 2016b). The received
vessel noise levels were, in some instances, well above
high wind-noise levels. Given that this study has provided
masking models (in terms of changes in SNR and fre-
quency content with noise and distance), the next step will
be to model changes in this parameter with increasing ves-
sel noise. Without any compensation, it is likely that signal
masking will occur at closer distances than in wind noise,
J. Acoust. Soc. Am. 144 (2), August 2018 Rebecca A. Dunlop 549
more-so if humpback whales do not compensate. These
models will not account for any release from masking
mechanisms, given there is currently no available data.
They can, however, be updated as research in this area
progresses.
ACKNOWLEDGMENTS
The author would like to thank everyone involved in the
Humpback Acoustic Research Collaboration (HARC)
(funded by the U.S. Office of Naval Research and the
Australian Antarctic Divison), in particular the numerous
volunteers who donated their time and energy to this project.
The author also thanks David Paton for his invaluable field
expertise and Eric Kniest for his continued support in the
development of CYCLOPES. The author would particularly like
to acknowledge Associate Professor Michael Noad for
leading the HARC work (without which, this study would
not have been possible) and Dr. Douglas Cato for his
continued support and mentorship.
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