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Vocal sequences in narwhals ( Monodon monoceros )

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  • University of Windsor (www.husseylab.com)

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Sequences are indicative of signal complexity in vocal communication. While vocal sequences are well-described in birds and terrestrial mammals, the extent to which marine mammals use them is less well understood. This study documents the first known examples of sequence use in the narwhal (Monodon monoceros), a gregarious Arctic cetacean. Eight female narwhals were fitted with animal-borne recording devices, resulting in one of the largest datasets of narwhal acoustic behaviour to date. A combination of visual and quantitative classification procedures was used to test whether subjectively defined vocalization patterns were organized into sequences. Next, acoustic characteristics were analyzed to assess whether sequences could disclose group or individual identity. Finally, generalized linear models was used to investigate the behavioural context under which sequences were produced. Two types of sequences, consisting of “paired” patterns and “burst pulse series,” were identified. Sequences of burst pulse series were typically produced in periods of high vocal activity, whereas the opposite was true for sequences of paired patterns, suggesting different functions for each. These findings extend the set of odontocetes which are known to use vocal sequences. Inquiry into vocal sequences in other understudied marine mammals may provide further insights into the evolution of vocal communication.
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Vocal sequences in narwhals (Monodon monoceros)
Sam F. Walmsley, Luke Rendell, Nigel E. Hussey, and Marianne Marcoux
Citation: The Journal of the Acoustical Society of America 147, 1078 (2020); doi: 10.1121/10.0000671
View online: https://doi.org/10.1121/10.0000671
View Table of Contents: https://asa.scitation.org/toc/jas/147/2
Published by the Acoustical Society of America
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Vocal sequences in narwhals (Monodon monoceros)
a)
Sam F. Walmsley,
1,b)
Luke Rendell,
1,c)
Nigel E. Hussey,
2
and Marianne Marcoux
3
1
Sea Mammal Research Unit, School of Biology, University of St Andrews, Sir Harold Mitchell Building, St Andrews, KY16 9TH, Scotland
2
Integrative Biology, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada
3
Arctic Aquatic Research Division, Fisheries and Oceans Canada, Winnipeg, Manitoba, R3T 2N6, Canada
ABSTRACT:
Sequences are indicative of signal complexity in vocal communication. While vocal sequences are well-described in
birds and terrestrial mammals, the extent to which marine mammals use them is less well understood. This study
documents the first known examples of sequence use in the narwhal (Monodon monoceros), a gregarious Arctic ceta-
cean. Eight female narwhals were fitted with animal-borne recording devices, resulting in one of the largest datasets
of narwhal acoustic behaviour to date. A combination of visual and quantitative classification procedures was used
to test whether subjectively defined vocalization patterns were organized into sequences. Next, acoustic characteris-
tics were analyzed to assess whether sequences could disclose group or individual identity. Finally, generalized lin-
ear models were used to investigate the behavioural context under which sequences were produced. Two types of
sequences, consisting of “paired” patterns and “burst pulse series,” were identified. Sequences of burst pulse series
were typically produced in periods of high vocal activity, whereas the opposite was true for sequences of paired pat-
terns, suggesting different functions for each. These findings extend the set of odontocetes which are known to use
vocal sequences. Inquiry into vocal sequences in other understudied marine mammals may provide further insights
into the evolution of vocal communication. V
C2020 Acoustical Society of America.
https://doi.org/10.1121/10.0000671
(Received 16 July 2019; revised 4 January 2020; accepted 13 January 2020; published online 13 February 2020)
[Editor: Rebecca A. Dunlop] Pages: 1078–1091
I. INTRODUCTION
Species in many taxa combine vocalizations according
to stereotyped organizational principles, producing vocal
sequences (Kershenbaum et al., 2016). Sequences can be
composed of repetitions of the same unit or combinations
of different units (Collier et al., 2014; Kershenbaum et al.,
2016) and may be produced by a single individual or multi-
ple individuals, as in the overlapping songs of great tits
(Parus major;Krebs et al., 1981). Many species’ vocal
production capacities are largely innate, meaning they can
only use a small repertoire of sounds (Podos, 1996).
However, a set of vocal signals combined into structured
sequences can contain more information than the same
sounds produced in isolation. As such, constraints on vocal
production are hypothesized to be an evolutionary driver
sequence use, as in Campbell’s monkeys (Cercopithecus
campbelli; Ouattara et al., 2009). In species with more
flexible vocal production, sequences may still contribute to
information-rich communication, as is the case for human
language. While possible relationships between the use of
sequences and the evolution of language are not resolved
(Kershenbaum et al., 2014;Ouattaraet al., 2009; Scott-
Phillips et al., 2014), it is agreed that vocal sequences are
indicative of signal complexity. Even when individual
units do not have specific meanings, the “developmental
stress hypothesis” states that the complexity of a sequence
(e.g., birdsong) can serve as an otherwise arbitrary indica-
tor of mate quality (Nowicki and Searcy, 2004). Thus,
without attention to the sequential structure of signals, the
complexity and functions of vocal repertoires may be
underestimated (Collier et al., 2014;Kershenbaum et al.,
2012;Lu
ıs et al., 2018).
Toothed whales are important model systems for
studying the evolution of vocal communication. For example,
bottlenose dolphins [Tursiops species (sp)] are the only non-
human species for which vocal production learning, func-
tional reference, signal innovation, and the capacity to
understand syntax have all been demonstrated (Herman,
2006;Janik, 2013). However, in contrast to the complex
sequences described in songbirds, terrestrial mammals, and
mysticetes (Payne and McVay, 1971;McDonald et al., 2006;
Berwick et al., 2011), most examples of vocal sequences in
odontocetes are repetitions of a single call type. Existing
work has identified nonrandom transitions between calls of
different types in short-finned pilot whales (Globicephala
macrorhynchus;Sayigh et al.,2013), killer whales (Orca
orcinus;Saulitis et al.,2005), and bottlenose dolphins
(Ferrer-i-Cancho and McCowan, 2012). Bottlenose dolphins
a)
Portions of this work were presented in “Using biologging technology to
study vocal sequences in narwhals” at the Easter meeting of the Association
for the Study of Animal Behaviour, University of York, York, England,
2019, and “Rhythmically repeated patterns of pulsed vocalizations in wild
narwhals,” MSc dissertation, University of St. Andrews, 2018.
b)
Electronic mail: sjf.walmsley@gmail.com, ORCID: 0000-0001-8577-6884.
c)
ORCID: 0000-0002-1121-9142.
1078 J. Acoust. Soc. Am. 147 (2), February 2020 V
C2020 Acoustical Society of America0001-4966/2020/147(2)/1078/14/$30.00
ARTICLE
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are known to produce feeding-associated bray sequences com-
posed of repeated pulsed units, as well as “multi-looped”
sequences of signature whistles (Janik, 2000;Lu
ıs et al., 2018;
Esch et al.,2009). Finally, sperm whales (Physester macroce-
phalus) are known to produce temporally stereotyped click
codas (Watkins and Schevill, 1977). However, the extent to
which other odontocete species organize their vocalizations
into sequences is poorly understood (Janik, 2009).
Technology has traditionally been a limiting factor in
the study of marine mammal communication (Lammers and
Oswald, 2015, p. 125), given that most behaviour occurs
underwater, and vocalizations are often produced at frequen-
cies beyond the range of human hearing (Mann, 2000). This
has resulted in biases for the study of accessible (e.g.,
coastal) species or species that are readily kept in captivity.
It is possible, then, that the apparent lack of vocal sequences
in odontocetes is an artifact of methodological limitations.
Alternatively, an advanced ability to produce and learn new
signals (e.g., Abramson et al., 2018) might allow odonto-
cetes to produce large enough repertoires of singular signals
to satisfy their communication needs without recourse to
sequences.
Here, we used high-bandwidth animal-borne recording
devices to investigate the vocal behaviour of individual nar-
whals (Monodon monoceros), a species for which the nature
and function of communication signals remain understudied
(Morisaka, 2012;Morisaka et al., 2013). In winter, the nar-
whal’s environment is characterized by dense ice-cover and
darkness (Laidre and Heide-Jørgensen, 2011), making
acoustic signals critical for both sensing the environment
and social interaction. Narwhals have been recorded produc-
ing a range of plausibly social sounds, typically classified as
“pulsed calls” composed of clicks with very short inter-click
intervals, as well as “tonal calls” or whistles, and “mixed
calls,” which include overlaid pulsed and tonal components
(Marcoux et al., 2012;Stafford et al., 2012;Shapiro, 2006).
Initial exploration of a subset of our recordings revealed two
types of possible sequences, one consisting of “paired” pat-
terns of pulsed calls, and the other consisting of sets of short
burst pulses, hereafter, termed “burst pulse series.”
To investigate the vocalizations in more depth, we first
asked whether they were produced according to stereotyped
organizational principles, satisfying the definition of sequen-
ces. Without being able to locate an individual using multi-
ple simultaneous recording devices, it is challenging to
attribute vocalizations to specific individuals, even when
instrumented—for example, an animal swimming alongside
a tagged individual might have its vocal production appara-
tus closer to the tag hydrophone than the tagged individual
itself (Sayigh et al., 2013). This makes it difficult to distin-
guish between sequences produced by single individuals and
those produced in call exchanges (Kershenbaum et al.,
2014). For this reason, we then investigated frequency and
amplitude characteristics of these patterns to understand
whether differences in calls recorded from different individ-
uals could be indicative of individual or group-specific sig-
nals. Finally we used generalized linear models (GLMs) to
investigate the behavioural context of sequences in order to
make inferences about their function.
II. METHODS
A. Study system and data collection
Narwhals were captured and equipped with instrument
packages that recorded acoustic and depth data (Acousonde
TM
Model 3B tags, Santa Barbara, California, USA) in Tremblay
Sound, Nunavut (7228049.558800 N, 8054018.259200 W)
between August 13 and September 11, 2017. Tags were
attached adjacent to the left side of the dorsal ridge by suction
cup, allowing them to be released after several days and float
to the surface for recovery. The Acousonde
TM
tags were pro-
grammed to alternate every 30 min between low-frequency
(LF) and high-power (HP) channels with sampling rates of
25.8 kHz and 232.3 kHz, respectively (see Wiggins, 2013,for
details). The HP channel included a 22 dB low-pass anti-alias
filter at 100 kHz, allowing for high quality representations of
the primary bandwidth of narwhal clicks (20–70 kHz; Marcoux
et al., 2012;Koblitz et al., 2016). Two tags (NW08, NW09)
were programmed with an additional 20 dB gain, which was
subtracted to match the relative amplitudes of the other six tags
prior to acoustic analyses.
Because one aim of the overall tagging effort was to
investigate year-round movements, narwhals recorded in this
study were also fitted with “backpack” telemetry tags during
capture, which were secured with pins through the dorsal
ridge. Remotely deployed tags on similar species tend to last
only a few weeks to months (Andrews et al.,2008), making
net-based capture and tagging an important tool in their study
(Gonzalez, 2001;Blackwell et al.,2018). Acousonde
TM
tags
were sometimes secondarily secured by cable to these back-
pack tags, although the resulting telemetry data were not
used in this analysis.
B. Exploratory analyses
1. Sound auditing and initial call selection
Sound files of the HP channel were visually and aurally
audited in 15-s viewing frames in Raven Pro 1.3 [2007; fast
Fourier transform (FFT) length 4096, 50% overlap, Hann
window maximum frequency display 80 kHz]. This audit
informed the design of a simple pre-classification procedure,
where calls with sufficient signal-to-noise ratio (SNR) to be
visually and aurally discernable were annotated and
extracted. A small amount of overlap by surfacing noise,
clicks, or other vocalizations was permitted (Kaplan et al.,
2014). Initial call classes included vocalizations broadly
classified as tonal, pulsed, or mixed (containing both tonal
and pulsed components). Echolocation trains and buzzes
were readily distinguishable from other vocalizations by dif-
ferences in click rate (Lammers et al., 2004;Arranz et al.,
2016) and were typified by clicks at relatively low inter-
click intervals (approximately 200 ms) speeding up to very
high repetition rates with inter-click intervals of approxi-
mately 3 ms (Rasmussen et al.,2015;Blackwell et al.,2018).
J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al. 1079
https://doi.org/10.1121/10.0000671
Given our interest in identifying repeated call types,
some of which are produced when animals are isolated
(Janik and Slater, 1998), we included all recordings from
the point of release onward for these analyses. We had no
knowledge regarding social affiliations between individuals.
Furthermore, while all recordings used in this analysis came
from tags attached to female narwhals, our inability to fully
discriminate between focal and non-focal vocalizations
meant that we made no assumptions regarding the sex of
calling animals.
2. Two types of patterns: Classification of paired
patterns and burst pulse series
Given that patterns of pulsed vocalizations have not yet
been described in narwhals, we had to create bespoke defini-
tions of sequence types. We defined paired patterns as two
stereotyped, pulsed (i.e., click-based) units that co-occur in
the same order within a short time interval (<2 s), the pair
of which is repeated at least three times within 30 s (e.g.,
“A-B–A-B–A-B”; Fig. 1).
We also identified a series of short pulsed vocalizations,
possibly similar to the “chitter” described by Stafford et al.
(2012). These were termed as burst pulse series (Fig. 2). We
limited our analysis to sets of at least three burst pulse series,
each produced within 10 s of the next. Subunits greater than
0.5 s apart were assumed to belong to separate series.
Time and repetition thresholds were informed by
exploratory analyses as well as the timing of rhythmically
repeated vocalizations in other odontocetes (Janik and
Sayigh, 2013;Riesch et al., 2006;Zwamborn and
Whitehead, 2017). Quantitative support for the distinction
between paired patterns and burst pulse series was verified
retrospectively with a two-sample t-test comparing the
duration of subunits of each type (i.e. the AB units in Fig. 1
compared to the sub-units in Fig. 2).
C. Testing stereotyped organization of units
1. Visual classification of paired patterns
We used a visual classification task with independent
human observers to test our prediction that narwhals combined
recognizable types of “A-B” pairs into repetitive sequences
(see Appendix A for details of the classification task). Fleiss’
kappa statistic was used to quantify agreement between
observers (irr Package in R;Gamer et al.,2015;Landis and
Koch, 1977). To assess the distribution of call types across
time and recording devices, we labelled each pair according to
the type matched by the majority of raters if at least 4/6 agreed
on the same type. Any remaining patterns were excluded from
further analyses.
2. Discriminant function analysis of paired patterns
In principle, animals could produce vocalizations con-
taining gaps of silence that are still perceived as single calls.
Support for the interpretation that paired patterns are combi-
nations of units would come from the finding that their con-
stituent units are also produced alone. To this end, we
performed an initial classification of any “lone” calls that
appeared to match a single unit of a paired pattern. These
matches were verified with a discriminant function analysis
(DFA), classifying the “A” units of all stereotyped patterns
(as classified in the visual classification task), including the
additional “lone units.”
For the DFA, we first filtered each A unit using a
Butterworth four-pole bandpass filter bounded by user-
defined LF and high-frequency limits from the selection pro-
cess, similar to Marcoux et al. (2012). Individual clicks
FIG. 1. Sample spectrogram showing definition of repeated “paired” vocalizations (FFT length, 4096; window, Hann; overlap, 50%).
1080 J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al.
https://doi.org/10.1121/10.0000671
were located using the “findpeaks” function from the Signal
Processing Toolbox in MATLAB (R2017b; The
MathWorks, Natick, MA). Starting and ending pulse repeti-
tion rate (PRR, measured in Hz) were then calculated from
the initial and final quartiles of the call, respectively. These
PRR measurements and duration (s) were checked for nor-
mality and homogeneity of variance and then entered into a
DFA. (HH Package in R;Heiberger, 2018). Only paired pat-
tern types representing four or more A-B pairs were used so
that the number of variables in the DFA did not exceed the
size of the smallest group.
3. Levenshtein distance analysis of burst pulse series
Using a custom MATLAB (R2017b) script, we manu-
ally marked the start- and end-times of each subunit in each
series. These data were transformed into a binary string that
represented, with one value per recording sample, the pre-
cise timing of the burst pulse series as “on” (1) or “off” (0).
These strings were then down-sampled to every 500th value,
allowing us to reduce the computational load of subsequent
analyses while retaining high temporal resolution (465 val-
ues/s).
Levenshtein distance (LD) is the minimum number of
insertions, deletions, and substitutions, required to transform
one sequence into another, and has been used to compare
sequences as diverse as deoxyribonucleic acid (DNA), lan-
guage (Petroni and Serva, 2010), and humpback song
(Garland et al.,2012;Kershenbaum and Garland, 2015). To
test for repetition in sequences of burst pulse series, we calcu-
lated LD between series using the binary call representations,
first for observed transitions, defined as adjacently produced
burst pulse series within originally identified sequences and
then also for an equal number of random comparisons,pair-
wise comparisons of the same dataset of burst pulse series
reshuffled in random order. Random comparisons were calcu-
lated within samples from each tag to accommodate for any
individual-specific differences in vocal behaviour, which
could bias subsequent estimates of stereotypy. We interpreted
lower LD (i.e., higher similarity) values for observed com-
pared to random transitions as evidence of repeated use of
stereotyped sequences.
We fit a Poisson family GLM using the canonical link
function to compare LDs between observed and random
transitions according to the following equations:
LD ¼exp b0þ/Randomb1
ðÞ
;
LD PoissonðLDÞ;
where LD represents actual measures of LD and LD repre-
sents expected measures. b0is the model intercept, repre-
senting mean LD for observed transitions between burst
pulse series. b1is a contrast representing the difference
between LD measures of random comparisons relative to
observed transitions. We also fit an identical model with the
quasi-Poisson family to check for overdispersion in LD val-
ues. Only recordings with more than one sequence of burst
pulse series were included.
D. Inferring patterns of production across individuals
1. Discriminating between sequences and call
exchanges
A lack of overlap between repeated signals is often
used to support inferences that a sequence is produced by a
FIG. 2. Sample spectrogram showing the definition of the burst pulse series (FFT length, 4096; window, Hann; overlap, 50%).
J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al. 1081
https://doi.org/10.1121/10.0000671
single individual (Lu
ıs et al., 2018;Sayigh et al., 2013;
Zwamborn and Whitehead, 2017). However, some species
produce call exchanges with precise and stereotyped timing
(Mann et al., 2006;Pika et al., 2018), meaning that nonover-
lapping sequences can also result from exchanges between
multiple individuals (Miller et al., 2004). To assess whether
sequences of vocalizations are produced by a single animal,
we examined not only overlap, but also variation in ampli-
tude and frequency of pulsed vocalizations produced in
sequences, expecting that the exceptionally directional
clicks of narwhals (Koblitz et al., 2016) should not be con-
sistently recorded with the same acoustic characteristics
from multiple individuals.
2. Exclusion of patterns produced by non-focal
individuals
Clicks recorded from suction-cup-tagged cetaceans are
hypothesized to include additional LF components that are
conducted through the body (Johnson et al., 2006;Johnson
et al., 2009;Zimmer et al., 2005;Blackwell et al., 2018).
We examined the frequency spectra of clicks from each call
sequence to determine which were unlikely to be produced
by the focal individual (see Appendix B for details of the
discrimination method). We compared the remaining
“possibly focal” sequences across tag recordings to consider
evidence of individual- or group-specific call characteristics.
E. Modeling the behavioural context of sequence
production
We fit GLMs to explore the context, and thereby poten-
tial function, of narwhal call sequences. Models were fit
according to the following equations:
S
ðÞ
¼expðb0þb1vþb2dþb3/ZÞ
1þexpðb0þb1vþb2dþb3/ZÞ;
SBinomialð
SÞ;
where
Srepresents the expected probability of a sequence
occurring in a given 60 second period. b0is the intercept of
the linear predictor. b1represents the effect of the number of
other calls (vÞ,b2represents the effect of depth (dÞin meters,
and b3represents the difference in model intercept when
buzzes were detected. Call counts and depth values were
log
10
-transformed as the raw data were positively skewed. We
expected that a logarithmic representation would be biologi-
cally appropriate because a change in depth of 1 m vs 5 m rep-
resents a proportionally much larger difference in pressure
than 100 m vs 105 m, for example. Depth profiles were cali-
brated by zero-offset correction using a custom script in
MATLAB from the Animal Tag Tools Calibration Toolkit.
1
This model structure was applied separately to patterns
of pulsed calls and burst pulse series, and for each, accord-
ing to the 60 s preceding a call (“previous activity models”),
and the 60 s following a call (“subsequent activity models”).
For pseudo-absences, we randomly selected 60 additional
60-s periods from the same tags where the sequences in
question were identified but in which no sequences were
heard. These pseudo-absences were only selected from peri-
ods following the first vocalization identified on a tag
recording and prior to tag detachment as determined from
the dive profile. All four models were also fit using GLMs
with quasi-binomial families to check for overdispersion,
and diagnostic plots were used to validate all model struc-
tures prior to consideration of estimated parameters.
Statistical analyses were done in R3.4.2 (R Core Team
2017). Analysis scripts and basic datasets are available via
the Open Science Framework.
2
III. RESULTS
A. General features of recordings
Eight female narwhals were equipped with hydrophone
tag packages. We identified 3261 vocalizations from the
resulting high-frequency recordings, including tonal calls,
pulsed calls, mixed calls, and patterns of pulsed calls (Table
I). Most tags (7/8) detached from the narwhal prior to maxi-
mum recording duration, resulting in varying recording
lengths per tag (Table I). Periods of multiple overlapping
vocalizations at rates of up to 3 calls/s were identified on 4/
8 tags, and presumed to be the result of narwhals aggregat-
ing into larger herds. Consistent with other studies, pulsed
vocalizations outnumbered whistles (Stafford et al., 2012;
Miller et al., 1995), although we did note several periods
when many overlapping whistles were produced, as
described by Ford and Fisher (1978).
Contrary to suggestions that narwhals tend not to feed
in the summertime (Stafford et al., 2012), buzzes indicative
of prey capture attempts were identified on all but one tag
(7/8; Table I). Buzzes tended to occur in series with a short
gap after the terminal buzz, and occurred at very high rates
on some recordings (up to 11 buzzes/min). Narwhals did not
appear to withhold from vocalizing immediately after tag-
ging, as has been found in other studies. Even when sam-
pling 50% of possible recordings (only the HP channel), we
found that narwhals used buzzes comparatively soon after
tagging [8.5 62.8 [standard error (SE)] hours; N¼7,
excluding one animal that did not buzz at all] compared to
22.7 65.1 h of post-release silence as found by Blackwell
et al. (2018;N¼6).
B. Summary of identified patterns
We identified eight plausible call types consistent with
our definition of paired patterns. We also included one pat-
tern of two stereotyped pulsed calls with an additional small
set of clicks preceding a clear pair of calls (type I; Fig. 3)
and one pattern for which both units were linked by a tonal
“bridge” (type VI; Fig. 3). Each of these ten patterns was
included as a template in the subsequent matching task. This
can be seen in Mm. 1.
Mm. 1. Combined sound file of ten templates. This is a file
of type “wav” (13.3 Mb).
1082 J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al.
https://doi.org/10.1121/10.0000671
Auralassessmentat0.1speed confirmed that the burst
pulse series consisted of sets of clicks interspersed with clearly
defined periods of silence. This can be seen in Mm. 2. Burst
pulse series were detected on nearly all tag recordings (7/8)
but were distributed unevenly. Sequences of at least three burst
pulse series with intervals of less than 10 s between each (e.g.,
Fig. 2) were identified on fewer tag recordings (3/8) but still
comprised approximately 40% of all burst pulse series
detected. Within these sequences, we also noticed that single
subunits matching the amplitude, frequency, and duration of
subunits in the more typical burst pulse series were sometimes
produced. These singletons, while rare (7% of all burst pulse
series), were also included in the analysis.
Mm. 2. Example of burst pulse series. This is a file of type
“wav” (3.6 Mb).
In total, we isolated 36 sequences comprising varying
numbers of burst pulse series (N
series
¼212). Aural assess-
ment suggested that PRR was relatively consistent within
and across the burst pulse series. Some sequences were com-
posed of series with different numbers and temporal organi-
zation of subunits. However, most sequences appeared to be
repetitive with small modifications such as the addition or
subtraction of a single set of clicks (Fig. 2). The length of
individual subunits in the burst pulse series often appeared
to increase over the course of the call, and some (approxi-
mately 14%) of the burst pulse series were initiated by an
especially short subunit (<50 ms).
Subunits of paired patterns [mean ¼515.8 640.8 (SE)
ms; N¼21] were significantly longer than those of the
burst pulse series [mean 104.9 61.5 (SE) ms; N¼900;
two-sample t-test; t(20.06)¼10.05, p<0.001], supporting
TABLE I. Details of female M. monoceros fit with Acousonde
TM
acoustic tags in Tremblay Sound, Nunavut. Here, “pattern” refers to any plausible stereo-
typed patterns of pulsed calls as well as series of burst pulses. Note that “time to vocalize” was calculated using the LF channel as well, so may exceed the
duration of the HP channel recording. No buzzes were detected on the recording from NW14.
Narwhal ID Deployment date
Recording length
of HP channel
Time to vocalize (min) Calls detected
Call Buzz Tonal Pulsed Mixed Pattern
NW08 Aug 13, 2017 41 h 53 30 15 110 0 199
NW09 Aug 16, 2017 16 h 38 246 2 17 0 0
NW11
a
Aug 30, 2017 4 h <5 158 4 36 1 23
NW12
a
Sept 2, 2017 4 h <5 754 408 696 240 583
NW14 Sept 3, 2017 12h 10 N/A 45 91 8 46
NW18
a
Sept 11, 2017 11 h 34 870 12 99 5 10
NW19
b
Sept 11, 2017 8h 38 271 23 41 0 5
NW20
b
Sept 11, 2017 16 h 31 1231 13 119 27 32
a
Associated with a calf.
b
Captured together.
FIG. 3. Templates of paired pattern types used in the visual classification task. All templates are shown with identically calculated spectrograms, 80 kHz
maximum frequency, and 3 s duration (FFT length, 4096; window, Hann; overlap, 50%).
J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al. 1083
https://doi.org/10.1121/10.0000671
our decision to split them into distinct classes of vocal
sequences.
3
C. Test of stereotyped organization of units
Six participants naive to the original order and context
of vocalizations consistently matched paired patterns to ten
templates, although interobserver agreement varied between
types (Table II). Quantitative classification was also suc-
cessful: using jackknifed cross-validation, the DFA classi-
fied paired patterns with 80.6% accuracy [Wilk’s K¼0.04,
X2 ¼205.5, DF (degrees of freedom) ¼21.0, p<0.001].
3
Here, classification accuracy was dependent on pattern type:
four classes had very high accuracies (I, 100%; II, 100%;
IV, 100%; VIII, 94%; IX, 88.9%), and three classes were
not accurately discriminated (III, 0%; V, 50%; VI, 0%).
We identified 29 sequences of paired patterns, consist-
ing of 72 pairs in total. Classifiers confirmed that each
sequence contained just a single type of A-B pair (Fig. 3).
We identified lone units matching the A units of two types
of paired patterns (II and IX). These were classified with
their expected types with 100% accuracy in the DFA,
confirming that individual units of the paired patterns are
sometimes produced alone. We detected no lone “B” units
for any patterns. Lone A units were sometimes rhythmically
repeated (Fig. 4) and often produced in close proximity to
full “pairs” (Fig. 5). When both units of a pattern were pro-
duced together, they were always produced in the same
order. We also noticed that the mean PRR of the B unit was
higher than that of the A unit for 9/10 types.
Measures of LD between burst pulse series were overdis-
persed (dispersion parameter 104.9), so we interpreted effect
coefficients from the quasi-Poisson GLM. Pairs of burst pulse
series from observed transitions were more similar than pairs
from randomly generated transitions (mean
LD
Observed
¼150.7, mean LD
Random
¼200.7, p<0.001).
Burst pulse series did not appear to change gradually across
the study period,
3
indicating that narwhals combine burst
pulse series into repetitive “sequences of series.”
D. Patterns of production across individuals
Frequency and amplitude were highly consistent
between units of paired patterns and across subsequent A-B
TABLE II. Summary statistics of Fleiss’ kappa test applied to multiple observer classification of patterns of M. monoceros paired patterns. Tag specificity
indicates the recording on which each pattern was identified with the number of A-B pairs in parentheses. Bolded tag identifications (IDs) designate calls
that were likely produced by the tagged animal. Reliability descriptors are taken from Landis and Koch (1977).
kz pReliability descriptor Tag specificity (N)
Pattern
I 0.813 31.472 <0.001 Near-perfect NW08 (8)
II 0.921 35.683 <0.001 Near-perfect NW12 (7)
III 0.399 15.469 <0.001 Fair NW12 (5)
IV 0.757 29.316 <0.001 Substantial NW12 (5)
V 0.660 25.551 <0.001 Substantial NW12 (8)
VI 0.492 19.060 <0.001 Moderate NW12 (1), NW14 (5)
VII 0.533 20.633 <0.001 Moderate NW08 (4)
VIII 0.772 29.893 <0.001 Substantial NW08 (16), NW08 (1), NW11 (1)
IX 0.607 23.525 <0.001 Substantial NW11 (6), NW12 (2)
X 0.373 14.439 <0.001 Fair NW12 (3)
No match 0.321 12.447 <0.001 Fair (All)
Overall
All classes 0.584 63.4 <0.001 Substantial
FIG. 4. Spectrogram showing lone A units of a stereotyped pattern of calls repeated in rapid succession (type IX; FFT length, 4096; window, Hann; overlap,
50%). Classified visually (kappa statistic 0.607, p<0.001) and by DFA (88.9% classification for type IX).
1084 J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al.
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pairs in the same sequence. There was one exception: a
sequence of sub-type VIII included one pair produced with
clicks of higher frequency than others in the sequence.
3
We
did not identify any instances of overlapping A and B units
of paired patterns, nor was there any overlap of repeated
pairs. Sequences of burst pulse series were generally pro-
duced with consistent frequencies and amplitudes, although
we detected two sequences with overlapping series.
When excluding calls attributable to non-focal individu-
als, 98% (45/46) of remaining paired patterns were identified
on a single recording only (bold sequences in Table II).
These tag-specific patterns appeared to be stable over our
recording durations: types I, II, VIII, and IX were identified
7.2, 10.2, 38.6, and 40.7 h apart within their respective
recordings. Although sequences of burst pulse series (as
defined above) were only detected on three tags, and only
two tags had more than one sequence, we detected tag-
specific differences in burst pulse series characteristics: series
recorded from NW08 had fewer subunits than series recorded
from NW12 (two-sample t-test; mean subunits
NW08
¼3.29,
mean subunits
NW12
¼4.79; t(207) ¼6.1, p<0.001).
E. Behavioural context of sequence production
Relative to randomly selected samples, paired patterns
were 50% more likely, and burst pulse series 77% less
likely, respectively, to be followed by buzzes, although
these estimates were associated with very large standard
error and were not statistically significant (Table III). Depth
did not appear to influence the likelihood of detecting vocal
sequences, matching our observation that sequences were
produced at various positions in the water column, except
the deepest dives (i.e., below 300 m; Table III, Fig. 6).
A one-unit increase in log
10
-transformed counts of
vocalizations in a 60-s recording sample corresponded to a
330% increase in the probability of the sample being pre-
ceded (and 460% increase of being followed) by a burst
pulse series, suggesting they are associated with vocal
exchanges generally (Table III). In contrast, paired patterns
were significantly less likely to be produced in the context
of other calls (Fig. 7). Three sequences of burst pulse series
from one tag (NW12) were excluded from behavioural con-
text models as pressure sensors on the tag failed midway
through the deployment.
IV. DISCUSSION
We have shown that narwhals produce at least two
kinds of vocal sequences. Paired patterns are composed of
distinct units combined into strictly ordered sets, providing
parallels to vocal sequences in avian and other mammalian
species. Burst pulse series, similar to vocalizations in other
odontocetes, are shown to be combined into repetitive
sequences of series.
FIG. 5. Spectrogram showing a stereotyped pattern of calls followed by two additional lone A units (type II; FFT length, 4096; window, Hann; overlap,
50%). Classified visually (kappa statistic 0.921, p<0.001) and by DFA (100% classification).
TABLE III. Parameter estimates for GLMs relating the occurrence of
sequences of paired patterns (n¼29) or burst pulse series (n¼21) produced
by M. monoceros to the behavioural context preceding and following the
call. Call occurrences were supplemented with 60 randomly selected 60-s
samples to provide pseudo-absences.
Estimate SE zp
Paired patterns
Previous activity model
Intercept 0.304 0.488 0.624 0.532
Calls 0.865 0.393 2.202 0.028
Depth 0.005 0.351 0.014 0.989
Feeding buzzes 0.489 0.688 0.711 0.477
Subsequent activity model
Intercept 0.550 0.493 1.116 0.265
Calls 0.476 0.388 1.228 0.219
Depth 0.205 0.327 0.626 0.531
Feeding buzzes 0.407 0.574 0.709 0.478
Burst pulse series
Previous activity model
Intercept 1.790 0.679 2.637 0.008
Calls 1.231 0.499 2.469 0.014
Depth 0.484 0.496 0.975 0.329
Feeding buzzes 0.488 0.912 0.535 0.593
Subsequent activity model
Intercept 2.281 0.819 2.786 0.005
Calls 1.526 0.533 2.863 0.004
Depth 0.835 0.495 1.688 0.091
Feeding buzzes 1.472 0.868 1.697 0.090
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Consensus between the visual classification task and the
DFA for most types of paired patterns demonstrates that
they are stereotyped and rhythmically repeated. The finding
that the first units of two types (II, IX) were also produced
alone provides further support for the interpretation of these
as combinations of calls, rather than single calls with gaps
of silence (Kershenbaum et al., 2016). We are unable to
conclude with certainty that other types are also composed
of divisible units as we did not identify any candidate cases.
However, this may have largely been an artefact of the rarity
FIG. 6. Distributions of depths registered on the tags (left) compared to depths at which sequences of paired patterns (middle) and burst pulse series (right)
were produced.
FIG. 7. Marginal effects of other vocalizations preceding the occurrence of sequences of pulsed vocalizations. Effects were estimated using binomial family
GLMs.
1086 J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al.
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of certain types as the two patterns for which we identified
lone units were relatively common (Table I). To our knowl-
edge, there are only two examples of ordered sequences in
odontocetes: the “N7-N8” calls of Northern resident killer
whales and the bray sequences of bottlenose dolphins (Ford,
1988,Lu
ıs et al., 2018). Structurally similar combinations
of vocalizations are found more commonly in other taxa,
where they have been linked to communicative complexity.
For example, meerkats (Suricata suricatta) produce similar
“di-drrr” calls composed of two units, the pair of which are
rhythmically repeated, and interpreted as evidence for hier-
archical communication (Collier et al., 2014). Putty-nosed
monkeys (Cercopithecus nictitans) produce “pyow” and
“hack” calls, which are regularly combined into ordered
sequences but are also sometimes produced alone. Playback
experiments have demonstrated that the ordering of these
calls conveys distinct meaning and is considered a rare
example of syntax in a wild organism (Arnold and
Zuberb
uhler, 2008;Kershenbaum et al., 2016). As such, the
finding that B units of paired patterns were only ever found
following A units is noteworthy as it is indicative of a
“finite-state” grammar similar to those documented in pri-
mate and avian taxa (Berwick et al., 2011;Fitch and
Hauser, 2009;Shettleworth, 2010). While inflexible A-B
grammars are fundamentally different from the recursive
grammatical structures that support human language
(Berwick et al., 2011;Fitch and Hauser, 2009), they can be
important vehicles for the transfer of information (Bradbury
and Vehrencamp, 1998, p. 395). Of course, it remains to be
tested whether the rule-like structure of narwhal paired pat-
terns supports compositional communication, i.e., whether
A and B units convey distinct information when combined
(Zuberb
uhler, 2018).
Whereas burst pulse series are varied (e.g., in number
of subunits), narwhals combine similar series into repetitive
sequences. Vocalizations similar to burst pulse series have
been detected in recordings of northern right whale dolphins
(Lissodelphis borealis;Rankin et al., 2007), dusky dolphins
(Lagenorhynchus obscurus;Vaughn-Hirshorn et al., 2012),
pacific white-sided dolphins (Lagenorhynchus obliquidens;
Henderson et al., 2011), and Heaviside’s dolphins
(Cephalorhynchus heavisidii;Martin et al., 2019), although
there are nontrivial differences between species. For exam-
ple, the “patterned burst pulses” in northern right whale dol-
phins are the only other series that appear to be rhythmically
repeated, and the “burst pulse sequences” produced by
dusky dolphins rarely had more than two subunits. Calves of
Araguaian river dolphins (Inia araguaiaensis) produce calls
similar to the narwhals’ burst pulse series, although it
remains to be tested whether they are combined into sequen-
ces of series (Melo-Santos et al., 2019).
We are unable to rule out the possibility that sequences
were sometimes produced by multiple individuals in coordi-
nated call exchanges, which has important implications for
the interpretation of communicative complexity (as dis-
cussed above). However, several findings were inconsistent
with this explanation. First, the strong directionality of
narwhal clicks makes it unlikely that multiple individuals
would be able to consistently produce clicks resulting in
near-identical frequency ranges on an animal-attached tag
(Koblitz et al., 2016). Second, overlap between burst pulse
series was very rare and was not identified in paired pat-
terns, supporting the interpretation that they were produced
by a single individual (Sayigh et al., 2013). If these sequen-
ces were the result of call exchanges, they would imply a
high degree of temporal synchrony, akin to duetting or even
“turn taking,” given the highly stereotyped time intervals
between units (Pika et al., 2018). Therefore the most parsi-
monious interpretation is that sequences of paired patterns
were produced by single individuals. The same can be con-
cluded for sequences of burst pulse series, whereas the
detection of some cases of overlap suggests that they may
also be produced in call exchanges.
If sequences are typically produced by a single individ-
ual, the variation in sequence characteristics between tags
opens the possibility that they support individual or group
recognition, as has been suggested for other narwhal vocal-
izations (Shapiro, 2006). For example, call types associated
with specific tag recordings have been taken as preliminary
evidence for individual-specific signals in short-finned pilot
whales (G. macrorhynchus;Quick et al., 2018). We identi-
fied possible individual specificity in the paired patterns,
which were re-produced on the same tags up to 40 h apart.
However, two considerations suggest that these patterns
should not be interpreted as “signature sequences.” First, we
would expect a signature signal to be the predominant vocal-
ization in an individual’s repertoire (Cook et al., 2004),
whereas paired patterns were relatively rare. Second, for
species with large vocal repertoires, a subsampling should
result in recording-specific differences, even if repertoire
composition is identical across individuals, making us cau-
tious to label these as individual specific without further
inquiry. We were surprised to find that burst pulse series
recorded from two different individuals had different and
continuous (i.e., not obviously incomplete or under-sam-
pled) distributions of numbers of subunits. Nevertheless,
these patterns seem unlikely to disclose individual identity,
given the wide range of numbers of subunits used.
In addition to the consideration of signal properties,
linking acoustic cues to behaviour can help to provide
insights into their function (Papale et al., 2017). Burst pulse
series were more likely to be produced in contexts of high
vocal activity. This matches well with theory that graded
signals are primarily used in social contexts where animals
are in close proximity (Ford, 1988). We detected the oppo-
site effect for paired patterns. These calls were often pro-
duced in very quiet periods. Accordingly, it may be that the
highly stereotyped paired patterns may serve a role in
longer-distance communication. Neither paired patterns nor
burst pulse series were significantly related to prey-capture
attempts, and were not recorded when individual narwhals
were below 300 m (Fig. 6), suggesting that they are not
related to feeding, as are the “bray sequences” of bottlenose
dolphins (Janik, 2000;King and Janik, 2015). Combining
J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al. 1087
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our findings with visual observations (e.g., from shore or
uncrewed aerial vehicle) might reveal further associations
between context and sequence use. For example, call
sequences may facilitate contact between mothers and
calves (Smolker et al., 1993), like the “type A” pulsed
vocalizations of belugas (Vergara et al., 2010). Female nar-
whals give birth in midsummer (Furgal and Laing, 2012;
Heide-Jørgensen, 2009), and paired patterns were recorded
on all tags attached to narwhals observed with calves during
capture (Table I).
Both paired patterns and burst pulse series are charac-
terized by rhythmic repetition. While the energetic cost of
vocalizing underwater may not be a significant limitation
(Jensen et al., 2012), the risk of detection by eavesdropping
acoustic predators (e.g., killer whales) is likely important for
narwhals (Deecke et al., 2002;Breed et al., 2017;Furgal
and Laing, 2012;Laidre et al., 2006), making the use of
repeated signals surprising. One benefit of repetitive signal-
ing is to increase the likelihood of successful transmission,
perhaps especially important in the icy, reverberation-prone
environments inhabited by narwhals (Brumm and Slater,
2006;Ey and Fisher, 2009;Vergara et al., 2010). For exam-
ple, links between noise and signal redundancy have been
identified in blue whales (Balaenoptera musculus;Miller
et al., 2000) and killer whales (Foote et al., 2004).
Alternatively, the repetitive nature of these sequences may
suggest that call production is sustained until a response is
achieved in a target animal or the number of repetitions
itself encodes specific information (Payne and Pagel, 1997;
Janik and Sayigh, 2013;Sloan and Hare, 2004). For exam-
ple, the number of “dee” units in call sequences of black-
capped chickadees (Poecile atricapilla) is inversely propor-
tional to the size of an encroaching predator (Templeton
et al., 2005). Encoding information in the temporal structure
of a signal is also consistent with the optimization of trans-
mission success in noisy habitats; these features should
degrade less severely than other acoustic properties over
long distances (Bradbury and Vehrencamp, 1998, p. 129).
This reasoning is proposed for the use of temporally stereo-
typed vocalizations in white-eyelid mangabeys (Cercocebus
sp.; Waser, 1982, p. 134), sperm whales (Gero et al., 2016),
and forest-dwelling chingolo sparrows (Zonotrichia capen-
sis;Bradbury and Vehrencamp, 1998, p. 136).
V. CONCLUSIONS
We conclude that narwhals produce at least two kinds
of vocal sequences. The paired patterns appear to be one of
few ordered, multiunit sequences in odontocetes, and to our
knowledge, the first in the Monodontidae. Whereas we pro-
pose that they may play a role in long-distance communica-
tion, their specific function remains unknown. The use of
repetition in successive burst pulse series suggests that their
signaling benefit outweighs the costs associated with redun-
dant signaling. Further inquiry into the function and
population-distribution of sequence use (e.g., whether they
are produced by narwhals in East Greenland and Western
Hudson Bay) could lead to insights regarding the ecological,
genetic, and cultural development of narwhal communica-
tion (Garland et al., 2011;Janik, 2000;May-Collado and
Wartzok, 2008;Rendell and Whitehead, 2001). Together,
our findings provide a small contribution to the understand-
ing of the phylogenetic distribution of vocal sequence pro-
duction and suggest that methodological limitations may
explain the apparent paucity of vocal sequences in other
odontocetes. We hope that future inquiry will continue to
elucidate the function(s) of these sequences in narwhals and
widen the set of species in which sequence use is tested, ulti-
mately supporting comparative studies to better understand
evolution of vocal communication.
ACKNOWLEDGMENTS
We thank Patrick Miller and Katie Harris for providing
helpful comments on early iterations of this work. We also
thank Mikhail Barabanov, Vanessa Simons, Rowan Gardner,
Nick Jones, and Diana Mirella Pab
on Figueroa for
participating in the visual classification task. Funding for
S.F.W. was provided by an R and A Ransome Scholarship.
Data used in this analysis were collected by the Ecosystem
Approach to Tremblay Sound project led by the Department of
Fisheries and Oceans, Canada. We thank Robert Hodgson and
all participants involved in this project and the associated
funding bodies, including the Nunavut Wildlife Management
Board, the World Wildlife Fund, Golder Associates, and
Ocean Wise. We are grateful to the Mittimatalik Hunters &
Trappers Organization and the Mittimatalik Inuit Community
for their support and involvement in this project. We also
thank the Polar Continental Shelf Program, the Government of
Nunavut, Parks Canada, the Ocean Tracking Network,
l’Universit
eduQu
ebec
a Rimouski, the University of
Windsor, the University of Manitoba, the University of
Calgary, l’Universit
e de Montr
eal, the University of British
Columbia, and the University of New Hampshire for their
partnership. Finally, we thank two anonymous reviewers
whose comments improved the manuscript.
APPENDIX A: MULTIPLE-OBERVER CLASSIFICATION
Visual classification has been shown to be an effective
method for the classification for vocal sequences (Janik and
Sayigh, 2013;Kershenbaum et al., 2016). While spectro-
graphic visualizations of pulsed signals are highly contin-
gent on FFT and time dimensions used, differences in click
rate (the expected form of stereotypy for paired patterns)
should be consistently distinguishable as long as spectro-
gram parameters are kept constant.
One randomly selected pair from each of the paired pat-
tern types we defined served as a template (Fig. 3). We then
printed 100 patterns of pulsed calls to be matched to these
templates. This number of patterns was chosen as a balance
between including adequate variation to assess patterns
across tags, while not being overly cumbersome for human
classifiers, which could result in reduced performance
(Rendell and Whitehead, 2003). These included the
1088 J. Acoust. Soc. Am. 147 (2), February 2020 Walmsley et al.
https://doi.org/10.1121/10.0000671
remaining paired patterns from the original sequences, as
well as possible pair-like patterns identified elsewhere in the
classification procedure, but which were not rhythmically
repeated. Given the propensity of narwhals to aggregate in
groups where many (and often overlapping) vocalizations
are heard, this test sample was likely to include pulsed calls
that were produced close together in time by chance, i.e.,
“null” patterns.
Six participants matched call patterns to one of ten pos-
sible templates or indicated that no match was found.
Participants had varying degrees of expertise in bioacoustics
(range 1–20 yr), although none had previous experience
classifying narwhal vocalizations. Participants were
instructed to ignore any background noise or differences in
frequency range for the classification task. Index numbers
for calls were randomized prior to the task.
APPENDIX B: DISCRIMINATING FOCAL AND
NON-FOCAL VOCALIZATIONS
To improve our ability to make inferences regarding the
individual specificity of call types, we devised a threshold
for “unusual” low-sound frequency based on previous
reports of narwhal click frequency ranges. Estimates of the
peak frequency of narwhal clicks using standard hydro-
phones include 19 kHz (Miller et al., 1995) and 12–20 kHz
(Marcoux et al., 2012). Others have reported that narwhals
produce clicks at variable frequency ranges, the lowest hav-
ing a peak frequency of 3.5–5 kHz and no energy below
3 kHz [Fig. 2(a) in Stafford et al., 2012]. In contrast, record-
ings of tagged narwhals contain clicks with substantial
energy below these frequencies (Shapiro, 2006;Blackwell
et al., 2018). As such, pulsed patterns with clearly visible
energy below 3 kHz were identified as possibly focal.
On average, clicks produced by non-focal individuals
should have lower amplitudes than those plausibly produced
by the tagged animal, acknowledging that non-focal vocaliza-
tions may sometimes produce higher received levels
(Johnson et al.,2009). We used a two-sample t-testtotestthe
prediction that pulsed vocalizations with visible LF energy
(<3 kHz) should have greater root mean square (RMS)
amplitudes, using the first unit of each paired pattern. A sig-
nificant difference would provide further support for the
hypothesis that click-based sounds lacking energy below
3 kHz are produced by non-focal whales. Units were filtered
with a 3 kHz high-pass filter to remove the confounding
effects of the LF energy, itself, influencing amplitude. As pre-
dicted, we found that pulsed vocalizations with energy below
3 kHz had higher amplitudes than calls lacking energy below
3 kHz (two-sample t-test; mean amplitude
lowFreq
¼156.8 dB
RMS, mean amplitude
noLowFreq
¼142.4 dB RMS, t(108)
¼9.57, p<0.001), even when energies below 3 kHz were fil-
tered out, supporting the hypothesis that calls lacking LF
energy were produced by non-focal whales.
That being said, it is thought that high-amplitude pulsed
vocalizations produced by nearby, non-focal individuals can
also result in additional LF energy on a tag recording
(Blackwell et al., 2018). As in-depth consideration of the
conduction and propagation of LF energy was beyond the
scope of this analysis, we assumed that our discrimination
process should allow for an increased (but not total) ability
to relate sequences of calls to specific individuals.
1
See http://www.animaltags.org/doku.php?id¼tagwiki:tools:calibration (Last
viewed 1 June 2018).
2
See osf.io/wk9a5 (Last viewed 31 January 2020).
3
See supplementary material at https://doi.org/10.1121/10.0000671 for dis-
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... Introduction Narwhals (Monodon monoceros) are deep-diving, medium-sized toothed whales endemic to the Arctic waters of Greenland and Northeastern Canada [e.g., 1,2]. Like other toothed whales, narwhals are a vociferous species, using echolocation to navigate and forage within their environment [3][4][5][6][7][8] and produce a diverse repertoire of sound types for communication with conspecifics [3,6,[9][10][11][12]. Sound types are classified into general groups of signals which share similar characteristics [13]. ...
... Like other toothed whales, narwhals are a vociferous species, using echolocation to navigate and forage within their environment [3][4][5][6][7][8] and produce a diverse repertoire of sound types for communication with conspecifics [3,6,[9][10][11][12]. Sound types are classified into general groups of signals which share similar characteristics [13]. Those occurring in the narwhal vocal repertoire include tonal and pulsed sounds [3,6,[9][10][11][12] and sounds which combine both tonal and pulsed components [10,12]. ...
... Like other toothed whales, narwhals are a vociferous species, using echolocation to navigate and forage within their environment [3][4][5][6][7][8] and produce a diverse repertoire of sound types for communication with conspecifics [3,6,[9][10][11][12]. Sound types are classified into general groups of signals which share similar characteristics [13]. Those occurring in the narwhal vocal repertoire include tonal and pulsed sounds [3,6,[9][10][11][12] and sounds which combine both tonal and pulsed components [10,12]. ...
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... The present study aimed to characterize the movements of Arctic char within Tremblay Sound, Nunavut, Canada, a model ecosystem for investigating the impacts of sea-ice phenology on consumer movements. Spring ice breakup here draws not only char but high biomass of other top-level consumers such as narwhal Monodon monoceros and Greenland sharks Somniosus microcephalus into the system (Heide-Jørgensen et al. 2002, Barkley et al. 2020, Walmsley et al. 2020. Moreover, char are abundant in Tremblay Sound in the summer, and harvesting by local communities provides an important subsistence resource. ...
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... Whistles are narrow-band and frequency-modulated tonal vocalizations 21,28 . Finally, combined signals, or "mixed calls, " include overlaid or paired pulsed and tonal sounds 23,29,30 . Acoustic classifiers may use a single call type or multiple call types to differentiate species 31 . ...
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Language, humans’ most distinctive trait, still remains a ‘mystery’ for evolutionary theory. It is underpinned by a universal infrastructure — cooperative turn-taking — which has been suggested as an ancient mechanism bridging the existing gap between the articulate human species and their inarticulate primate cousins. However, we know remarkably little about turn-taking systems of nonhuman animals, and methodological confounds have often prevented meaningful cross-species comparisons. Thus, the extent to which cooperative turn-taking is uniquely human or represents a homologous and/or analogous trait is currently unknown. The present paper draws attention to this promising research avenue by providing an overview of the state of the art of turn-taking in four animal taxa — birds, mammals, insects and anurans. It concludes with a new comparative framework to spur more research into this research domain and test which elements of the human turn-taking system are shared across species and taxa.
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