The International Journal of Animal Sound and its Recording, 2006, Vol. 15, pp. 289–314
© 2006 AB Academic Publishers
INFORMATION CONTENT OF COYOTE BARKS
BRIAN R. MITCHELL*1
, MAJA M. MAKAGON1, MICHAEL M. JAEGER2 AND
REGINALD H. BARRETT1
1 Department of Environmental Science, Policy and Management, University of
California, Berkeley, 151 Hilgard Hall #3110, Berkeley, CA 94720-3110, USA.
2 National Wildlife Research Center, Department of Forestry, Range, and Wildlife
Sciences, Utah State University, Logan, Utah 84322-5295, USA.
The information content of coyote (Canis latrans)vocalizations is poorly understood,
but has important implications for understanding coyote behaviour. Coyotes probably
use information present in barks or howls to recognize individuals, but the presence
of individually-specific information has not been demonstrated. We found that coyote
barks and howls contained individually specific characteristics: discriminant analysis
correctly classified barks of five coyotes 69% of the time and howls of six coyotes 83%
of the time. We also investigated the stability of vocalization characteristics at
multiple distances from the source. Recordings were played back and re-recorded at
10 m, 500 m, and 1,000 m. Vocalization features were measured at each distance and
analyzed to determine whether characteristics were stable. Most howl characteristics
did not change with distance, and regardless of the distance discriminant analysis
was 81% accurate at assigning howls among six individuals. Bark characteristics,
however, were less stable and it is unlikely that barks could be used for individual
recognition over long distances. The disparate results for the two vocalization types
suggest that howls and barks serve separate functions. Howls appear optimized to
convey information (i.e. data), while barks seem more suitable for attracting attention
and acoustic ranging.
Keywords: bark, Canis latrans, Canidae, communication, coyote, distance effect, howl,
individual differences, ranging
Despite decades of interest in using real or imitated coyote (Canis
latrans) vocalizations for research and management (Alcorn 1946;
*Correspondence and present address: B. Mitchell, Rubenstein School of Environment
and Natural Resources, University of Vermont, 81 Carrigan Drive, Burlington,
Vermont 05405-0088, USA. Email: firstname.lastname@example.org
Fulmer 1990; Beaudette 1996), there are no detailed studies of the
potential information content of coyote vocalizations. “Information” in
this context refers to any data that a listener can obtain about a
vocalizing individual. Coyote long-range vocalizations are
hypothesized to contain cues to the caller’s identity, and may have
characteristics useful for helping listeners localize a call’s source
(Lehner 1978). The ability to recognize individuals and determine
their location based on vocalizations would allow coyotes to use
auditory cues to coordinate social activities (ranging from cooperative
foraging to territorial defense) when conditions do not allow for visual
Coyotes in unexploited populations are generally crepuscular or
nocturnal and they often live in social groups (packs) that consist of
an alpha breeding pair and their offspring (Camenzind 1978; Andelt
& Gipson 1979). These groups can range in size from two to seven
individuals (Camenzind 1978), although we have observed up to nine
individuals in one social group (B. R. Mitchell, personal observation).
Coyotes within a pack are often separated by hundreds of meters;
field observations indicate a median distance between alphas of 402
m (N = 275 for five alpha pairs), between betas of 543 m (N = 99 for
5 beta pairs), and between alphas and betas of 895 m (N = 378 for 13
pairs; B. R. Mitchell, unpublished data). Because coyotes are often
separated and active at night, vocal communication may be even more
important than visual communication in many circumstances.
Showing that barks and howls include individually specific cues is the
first step towards devising field playback experiments that will test
whether coyotes actively distinguish individuals based on their
vocalizations and whether vocal signals convey additional information
that could be used by receivers to coordinate their activities with
Individual vocal characteristics have been documented in a
variety of taxa, from birds (Peake et al. 1998; Walcott et al. 1999) to
various mammalian orders including primates (Dallmann &
Geissmann 2001), ungulates (Reby et al. 1999), rodents (McCowan
& Hooper 2002), elephants (McComb et al. 2000), whales
(McCowan & Reiss 2001), seals (Phillips & Stirling 2000), and
carnivores (McShane et al. 1995; Holekamp et al. 1999). Numerous
studies have taken the additional step of showing that individuals
actually do discriminate between different conspecifics. Examples of
animals using individual vocal cues can be found in birds (Jouventin
et al. 1999), primates (Cheney & Seyfarth 1980; Weiss et al. 2001),
elephants (McComb et al. 2003), whales (Sayigh et al. 1999), and seals
(Charrier et al. 2002). Within the wild canids, individual differences
have been documented in swift foxes (Darden et al. 2003), African
wild dogs (Hartwig 2005), wolves (Theberge & Falls 1967; Tooze et al.
1990), and dholes (Durbin 1998). Frommolt et al. (2003) documented
individuality in barks of a territorial population of arctic foxes and
also showed that foxes respond differently to barks from members of
their own social group than they do to other foxes.
Very few studies have tested whether individually specific
characteristics of long-range vocalizations are stable over distance.
Instead, most researchers assume that discriminating features carry
as far as the sound can be perceived. The problem with this
assumption is illustrated by elephant vocalizations. The infrasonic
component of elephant calls can carry up to 10 km, but useful
discrimination does not occur over these distances – elephants
typically only recognize individuals that are less than 1.5 km away.
This is because elephants recognize individuals based on higher
frequency components of vocalizations that degrade much more
quickly than infrasound (McComb et al. 2003).
Coyote signallers should benefit from producing vocalizations
that allow members of their social group who are out of visual contact
to identify and locate them, because this would facilitate the
coordination of territory defence, cooperative foraging, and group
social activities. Receivers should pay attention to these cues, because
a missed or misinterpreted signal could decrease foraging
opportunities or even lead to the death of siblings or offspring (e.g.,
Camenzind (1978) noted 2 occasions of territorial intrusions resulting
in the death of pups). Vocal characteristics that show strong
reliability regardless of distance should be preferred by receivers
interested in determining the identity of a vocalizing animal (Naguib
& Wiley 2001). Recognition based on features that are stable over
distance would allow receivers to develop a simple, general purpose
perceptual template that could be used for matching vocalizations. If
individually specific features of vocalizations degrade or are altered
with distance, animals attempting to identify the source of a call
would be required to estimate the distance to the source and then
factor in an understanding of how acoustic features change with
distance. Only then would they be able to match the vocalization to a
mental template that had been formed by listening to the sender at
If, however, the signaller is using a long-distance vocalization to
provide location information to receivers, then characteristics that
degrade with distance are preferred. Humans and birds have been
shown to estimate distance to sounds (or “range”) using three
techniques: amount of reverberation, absolute magnitude, and
relative intensity of high-frequency components (Naguib & Wiley
2001). Reverberation is rarely present in animal vocalizations; it is
created as sounds reflect off of features in the environment. Therefore
increased reverberation in a sound almost always indicates a greater
distance to the source. The other types of ranging rely on learned
knowledge of the amplitude and general characteristics of the sound
at its source. Distance estimation based on absolute magnitude takes
advantage of the tendency for more distant sounds to have lower
amplitudes, while ranging based on relative intensity involves judging
the ratio of high to low frequencies in vocalizations. Because high
frequencies are attenuated more rapidly than low frequencies, a low
ratio indicates a distant sound (Naguib & Wiley 2001).
There is therefore a trade-off between vocalization
characteristics useful for information transfer and qualities useful for
ranging. Vocalization types or components used for long-range
communication of content should be stable over distances used by the
species, while vocalizations used for ranging should degrade relatively
We tested whether coyote barks and howls contain individually
specific cues by measuring and analyzing multiple vocalizations
recorded from known individuals. We predicted that discriminant
analysis would demonstrate the presence of individually specific cues
by successfully classifying vocalizations to the correct individual. We
also tested whether individual information in coyote barks and howls
is conserved when transmitted over distances up to 1 km, and we
addressed the possible presence of characteristics useful for ranging.
We predicted that howls, with their long duration, widely spaced
harmonics, and potential for frequency modulation, would be better
suited than barks for conveying individually specific cues over
biologically relevant distances. We predicted that barks, with their
short duration and broad frequency distribution, would be more
suitable for ranging.
Recordings were collected from captive-reared coyotes at the US
Department of Agriculture, Wildlife Services, National Wildlife
Research Center (NWRC) field station in Logan, Utah, between 8 July
1998 and 27 July 1998. We used a Tascam DA-P1 digital tape
recorder (DAT) and a tripod-mounted Sennheiser MKH 70 shotgun
Subject animals were all housed as breeding pairs in 0.1-ha
pens. The coyotes had been housed in these pens for over 6 months,
and had never been involved in behavioural research. Details about
the seven study coyotes are presented in Table 1. Because the
microphone was positioned outside of the pens, recording distances
ranged from 5 to 35 m. There were typically two recording sessions
per day (morning and evening), during times when the coyotes
vocalized regularly and were visually identifiable. Vocalizations
always occurred in response to other coyote vocalizations (either other
captive animals or wild individuals in the surrounding hills), and
were presumed to be agonistic. On any given day, only one pair of
coyotes was recorded. During recording sessions we recorded all
vocalizations while making observations about which coyote of the
subject pair was vocalizing.
Recordings were digitized using DiskRec 1.0 (Engineering
Design, Massachusetts, USA) and a 50 kHz Dart Digital Signal
Processor card (Engineering Design). We isolated and saved
vocalizations along with the identity of the vocalizing subject when
that could be determined. Of the 1,754 vocalizations we recorded, 573
contained single vocalizations from known individuals that
contributed at least 15 vocalizations. The final data set had 293 barks
(from 2 females and 3 males) and 280 howls (from two females and
We used Sound Forge 4.5 (Sonic Foundry, Wisconsin, USA) with
Sonic Foundry Noise Reduction 2.0 to remove excessive background
noise. We then peak-normalized the resulting sounds and produced an
audio playback CD containing each vocalization separated by 4
seconds of silence. Recordings were played using a timer-controlled
playback unit with a 25-watt Johnny Stewart long-range predator
calling speaker (Hunter’s Specialties, Iowa, USA). Speaker height was
50 cm, oriented parallel to the ground, and the sound pressure level
was similar to pressure levels produced by vocalizing coyotes
(approximately 105 dB at 1 m). We selected this speaker because it
was portable and powerful, and the trade-off was an uneven frequency
response. Comparing 15 barks sent to the speaker and re-recorded at
10 m revealed that the speaker overemphasized sound at 4 kHz by
about 15 dB-volts relative to sound at 1 kHz. The playback device was
set in open annual grassland at the Gray Davis Dye Creek Preserve
(DCP), in Tehama County, northern California. The DCP had been
Sample sizes, sex, age, weight, and relationships for coyotes at the NWRC Logan
Field Station, July, 1998
Coyote Barks1Howls1Sex Age Weight (kg) Mate Sibling(s)
F-5414 — 23 F 3 11.0 M-5320 M-5416
F-5438 26 19 F 3 9.1 M-5429
F-5471 96 — F 2 8.4 M-5416
M-5320 91 55 M 5 15.0 F-5414
M-5416 52 61 M 3 14.4 F-5471 F-5414
M-5429 28 39 M 3 14.8 F-5438 M-5430
M-5430 — 83 M 3 12.5 N/A M-5429
1Sample sizes used in discriminant analyses. Dashes indicate fewer than 15
vocalizations and exclusion from analyses.
the site of extensive playback experiments with coyotes over the
previous 2 years (Mitchell 2004). The specific playback site was
selected with the help of GIS software to be isolated and flat. We used
a tripod-mounted microphone (1.2 m) to record the playback CD at
distances of 10 m, 500 m, and 1,000 m. Recordings were made near
dawn, when wind speed was minimal.
The recordings from each distance were digitized and isolated.
The final vocalization library contained four sets of 573 vocalizations:
raw or initial recordings, 10-m recordings, 500-m recordings, and
1,000-m recordings. All recordings were digitized at 25 kHz. The
analysis of individual differences was based on the raw recordings,
while the distance analysis used only the 10-m, 500-m, and 1,000-m
recordings. We expected recordings measured at 10-m to differ from
the raw vocalizations due to processing and playback effects (e.g.,
noise reduction and the speaker’s frequency response), but we felt
that the 10-m recordings adequately incorporated the characteristics
of coyote vocalizations observed at close range. Our distance analysis
therefore assumes that the 10-m recordings are similar to actual
coyote vocalizations and behave the same way when recorded at
Bark measurements and variables
A spectrogram of each bark was displayed in Signal 3.1 (Engineering
Design), using 512-point Fast Fourier Transforms (FFTs), a 0.25 ms
increment between FFTs, a maximum frequency of 4 kHz, and a
Hanning window. The resolution of the cursor used to record
measurements was 0.43 ms and 17 Hz. For each bark spectrogram,
one observer (M. M. Makagon) recorded the start and end time of the
bark based on when the vocalization was within 40 dB of the
maximum amplitude of the recording (Figure 1). She also recorded the
bark structure (chaotic/noisy, intermediate, or harmonic), and the
harmonic structure (frequency contour shape). In this paper, the
terms “chaotic” or “noisy” refer to the presence of broadband sound
energy produced by the subject animal, and not to background
environmental noise. The frequency contour shape was rated on a
5-point scale based on measurements of the lowest (fundamental)
harmonic: “1” if the fundamental could not be detected; “2” if the
frequency increase across the fundamental was less than 100 Hz; “3”
if the frequency increase was more than 100 Hz; “4” if the middle of
the fundamental was more than 100 Hz higher than both ends; and
“5” if the middle of the fundamental was more than 200 Hz higher
than both ends. The measurement thresholds (e.g. 100 Hz) chosen for
this and other vocal characteristics are arbitrary, based primarily on
differences that could be easily distinguished audibly by human
observers. The purpose of this analysis is to demonstrate that coyote
vocalizations have characteristics that are individually specific. We do
not address whether coyotes actually use these characteristics, and
we cannot be certain that the thresholds chosen have biological
significance for coyotes.
We wrote a program for Signal 3.1 that used the methods of
Forrest et al. (1988) to calculate the first four spectral moments
(mean, standard deviation, skewness, and kurtosis) of each bark. We
also calculated an estimate of the Spectral Harmonic-to-Noise Ratio
(HNR) of the barks using methods described in Riede et al. (2005),
and we recorded the frequency where HNR was measured. A final
Signal 3.1 program generated power spectra for each bark using a 16-
k FFT and a 100-Hz moving average for smoothing, and recorded the
Figure 1. Bark spectrogram measurements and their corresponding variables.
Note the presence of echoes in both spectrograms; these were ignored for
determining the end of the bark.
maximum dB level and the frequency where the maximum dB level
Kurtosis and the frequency of the maximum dB were not
important in the analyses reported here, and were excluded from data
tables to save space. Readers interested in the full tables can find
them in Mitchell (2004).
Howl measurements and variables
Spectrograms were displayed in Signal 3.1 using a 5-ms step between
successive FFTs, a 1,024-point FFT size, and a Hanning window.
Spectrograms were zoomed to approximately 1 second by 1 kHz for
measurement, and measurement resolution was at or better than 1.7
ms and 5.0 Hz. Spectrogram measurements were made by two
observers (M. M. Makagon and B. R. Mitchell).
Time and frequency measurements were taken at five points
along the fundamental for each howl: the howl’s start, the end of the
howl’s rising portion, the point of maximum frequency, the start of the
howl’s falling portion, and the end of the howl (Figure 2). The howl’s
start and end were defined at the points where the vocalization was
visibly different from background noise. If one of the five points was
not visible on the fundamental at one or more distances, then the
point was measured on the lowest usable harmonic (almost always the
first harmonic) and the frequency measurement was divided to yield
the equivalent fundamental measurement.
Figure 2. Locations of howl frequency and time measurements.
The frequency and time measurements were converted into
duration (measured in ms) and slope (measured in Hz/ms) variables:
1) of the rising portion; 2) from the start of the middle portion to the
maximum frequency; 3) from the maximum frequency to the end of
the middle portion; and 4) of the falling portion. These eight variables
were used along with the frequency measurements in the statistical
Each howl was assigned a howl type based on the three
frequency measurements from the middle of the howl: “1” for howls
that increased more than 100 Hz, “2” when the howl peaked in the
second half at a value more than 100 Hz above the ends, “3” for a howl
showing less than 100 Hz of change in the midsection, “4” when the
howl peaked in the first half at a value more than 100 Hz above both
ends, and “5” for howls with a midsection that decreased more than
100 Hz. We also documented nonlinear phenomena of howl
spectrograms, specifically subharmonics and chaotic sections (i.e.,
“deterministic chaos” [Fitch et al. 2002]). If one type of nonlinear
phenomenon graded directly into another type (such as a segment of
deterministic chaos transitioning into a section with subharmonics),
we counted two features rather than one. We recorded the number of
nonlinear phenomena in the rising, middle, and falling portions of
We measured frequency modulation of howls by documenting
frequency shifting and wavering. Frequency shifts were found in the
middle section of a howl and were fairly abrupt changes in the
average frequency. Wavers were short frequency-modulated sections
that often gave coyote howls a distinctive “warbling” sound.
Frequency shifts had to be at least 50 Hz, could not be part of a
waver, and could not return to the original frequency for at least 400
ms. Wavers had to be less than 400-ms long, and had to show a
frequency drop of at least 50 Hz relative to the start and end of the
waver. For each howl, we recorded the number of frequency shifts
between 50 and 100 Hz, and the number of shifts greater than 100 Hz.
Wavers were classified according to location (rising portion or middle
of the howl) and size (50 to 100 Hz, 100 to 200 Hz, or greater than 200
Hz). Wavers in the rising portion of the howl were also counted if they
were between 0 Hz and 50 Hz.
Maximum frequency, fall nonlinearities, frequency shifts, rise
wavers less than 50 Hz or greater than 200 Hz, and middle wavers
greater than 200 Hz were not important in the analyses reported
here. These variables were excluded from data tables to save space,
but readers interested in the full tables can find them in Mitchell
We used linear discriminant analysis to examine whether bark and
howl variables from our original recordings could be used to tell
individuals apart. Many researchers suggest excluding variables that
are highly correlated with other variables in the analysis (e.g.
Gouzoules & Gouzoules 2000; Kazial et al. 2001); we used a threshold
of 0.8. Discriminant analysis also performs poorly when there is no
variability within a group (Klecka 1980), so variables were excluded
if multiple individuals showed no variation. In addition, the number
of variables in a discriminant analysis should be less than 0.33 times
the number of observations (Kazial et al. 2001). When this situation
occurred, we chose a subset of variables based on the significance of
Discriminant analysis is an inferential technique based on
sample data, and model validation is based on the data used to create
the model. Therefore the classification accuracy overstates the
discriminant analysis’ true success (Klecka 1980). This bias can be
countered with split-sample validation, so we randomly excluded 25%
of each individual’s vocalizations for use as “test” data to check the
discriminant model built using the remainder of the data. All
discriminant analyses were conducted using SAS 9.1 (SAS Institute,
North Carolina, USA). We used PROC STEPDISC’s stepwise variable
selection process, followed by PROC DISCRIM with proportional
We computed kappa and its associated 95% confidence interval
for each classification according to the procedure in Titus et al. (1984).
Kappa adjusts the percentage accuracy of discriminant analyses to
account for chance and the effect of unequal group sizes. In other
words, kappa corrects for the number of individuals used in the
analysis and the distribution of the data. As with raw classification
accuracies, kappa is only unbiased with test data that were not used
to develop the classification model (McGarigal et al. 2000). However,
estimates of kappa were less precise for the test data because of
smaller sample sizes, and this led to occasional instances where
kappa was lower for the training data.
We used discriminant analysis to classify the original bark and
howl recordings to the individual that produced them. Because the
presence of sex-specific information in vocalizations can change
classification accuracy and alter the importance of different variables
(Bachorowski & Owren 1999), we also used discriminant analysis to
classify individuals within each gender.
We then used repeated measures MANOVA (in JMP IN 4.0,
SAS Institute) to investigate how bark and howl variables changed
with distance. We investigated whether measurements at 10, 500, and
1,000 meters differed by individual, whether measurements differed
over distance, and whether individual and distance interacted. The
MANOVA results were used to generate a list of variables with
minimal distance effects that could be incorporated into a
discriminant analysis. Variables were selected if the F-ratio for an
individual effect was more than double the F-ratio for the distance
effect, which indicated that individual differences outweighed
differences due to distance. Variables were also selected if the F-test
for a distance effect was non-significant given a Bonferroni-corrected
alpha of 0.05/n, where n equalled the number of bark or howl
variables tested. The shortened variable list was used in accordance
with the previously described methods to generate a discriminant
model based on the 10-m training data. The resulting discriminant
functions were checked against the 10-m, 500-m, and 1,000-m test
data. These results were also compared to results from analyses
where there was no attempt to filter out variables with strong
Analysis of original bark recordings
Table 2 lists mean measurements, by individual, for the original bark
recordings. The final discriminant model contained duration,
harmonic structure, mean, standard deviation, HNR, and HNR
frequency. Skewness and kurtosis were excluded because of high
correlations with each other and with mean frequency. The squared
canonical correlations for the four canonical functions were 0.53, 0.35,
0.14, and 0.08; these values indicate the proportion of variability in
each function that is explained by the identity of the barking
individual. The discriminating power of the first 2 functions was
primarily due to bark duration and mean frequency, the third
function was most influenced by bark harmonic structure, and the
power of the final function was most affected by HNR (Table 3).
The classification accuracy of the training data was good, with
an overall 70% accuracy that ranged between 42% and 89% for each
individual (Table 4). The most common mistake was confusion of
mated coyotes M-5416 and F-5471 (22 out of 65 total mistakes). The
test data classification showed a similar overall accuracy (69%), and
more variability in individual success rates (29% to 92%). The
corresponding kappa estimates were 0.59 ± 0.08 ( ± 95% CI) for the
training data and 0.57 ± 0.15 for the test data, indicating a
classification success about 60% better than chance.
Analyzing the three males and two females separately led to
models with high raw accuracy scores, but similar chance-corrected
test model accuracies. The male-only model included duration, bark
Bark data for coyotes recorded at the NWRC Logan Field Station, July, 1998. 1
Variable F-5438 F-5471 M-5320 M-5416 M-5429
Duration (ms) 135 ± 3.3 109 ± 1.7 129 ± 1.7 116 ± 2.3 139 ± 3.2
Bark Structure 2.23 ± 0.12 1.91 ± 0.06 1.70 ± 0.07 2.00 ± 0.09 1.07 ± 0.12
Bark Harmonic Structure 3.46 ± 0.25 2.75 ± 0.13 2.52 ± 0.14 3.25 ± 0.18 1.14 ± 0.25
Max Db (dB-volts) -42.6 ± 1.12 -43.1 ± 0.58 -42.6 ± 0.60 -46.2 ± 0.79 -47.2 ± 1.08
Mean (Hz) 1,220 ± 22 1,295 ± 11 1,108 ± 12 1,328 ± 16 1,380 ± 21
Standard Deviation (Hz) 624 ± 14 705 ± 7.2 594 ± 7.4 681 ± 9.8 658 ± 13
Skewness 1.67 ± 0.07 1.28 ± 0.04 2.01 ± 0.04 1.25 ± 0.05 1.19 ± 0.07
HNR (volts) 10.57 ± 0.76 8.67 ± 0.40 8.35 ± 0.41 6.49 ± 0.54 3.13 ± 0.73
HNR Frequency (Hz) 806 ± 56 719 ± 29 709 ± 30 728 ± 39 867 ± 54
1Values are mean ± standard error. Sample sizes: 26 from F-5438, 96 from F-5471, 91 from M-5320, 52 from M-5416, and 28 from M-5429.
structure, harmonic structure, and mean, while the female-only model
included duration, bark structure, mean, standard deviation, and
HNR. Both skewness and kurtosis were excluded from the males-only
model because of high correlations with other variables, and skewness
was excluded from the female model. The male-only model was 78%
accurate classifying 128 training barks and 72% accurate classifying
43 test barks, with kappas of 0.64 ± 0.12 ( ± 95% CI) and 0.51 ± 0.24,
respectively. The female-only model was 93% accurate classifying 91
training barks and 87% accurate classifying 31 test barks, with
corresponding kappa estimates of 0.81 ± 0.15 ( ± 95% CI) and 0.59 ±
Analysis of original howl recordings
Table 5 lists mean measurements, by individual, for the original howl
recordings. The final discriminant model contained all frequency
measurements except the maximum frequency, all durations except
for the rising portion of the howl, all slope measurements,
nonlinearities in the rise, 50 and 100 Hz wavers in the rise, and 50
Standardized canonical coefficients for discriminant analysis of individual differences
in barks, based on original recordings of 5 individuals.
Variable Function 1 Function 2 Function 3 Function 4
Duration -0.735 0.769 0.265 0.159
Bark Harmonic Structure 0.346 -0.330 0.733 -0.736
Mean 0.856 0.969 0.577 0.145
Standard Deviation 0.321 -0.167 -0.469 0.270
HNR 0.094 -0.144 0.416 1.113
HNR Frequency -0.159 -0.001 0.318 0.421
Training data classification matrix from analysis of individual differences in barks,
based on original recordings
M-5320 M-5416 M-5429 F-5438 F-5471 Percent
M-5320 50 2 5 1 10 74
M-5416 4 17 1 0 17 44
M-5429 2 0 15 2 2 71
F-5438 5 1 1 8 4 42
F-5471 0 5 1 2 64 89
Total 61 25 23 13 97 70
Howl data for coyotes recorded at the NWRC Logan Field Station, July, 1998. 1
Variable F-5414 F-5438 M-5320 M-5416 M-5429 M-5430
Start Frequency (Hz) 394 ± 9.0 446 ± 9.9 380 ± 5.8 392 ± 5.5 370 ± 6.9 374 ± 4.7
End Rise Frequency (Hz) 936 ± 32 1,028 ± 35 1,141 ± 21 1,072 ± 20 673 ± 24 808 ± 17
Start Fall Frequency (Hz) 978 ± 29 1,001 ± 32 1,172 ± 19 1,116 ± 18 671 ± 22 865 ± 15
End Frequency (Hz) 559 ± 34 646 ± 37 1,023 ± 22 504 ± 21 361 ± 26 480 ± 18
Rise Duration (ms) 241 ± 23 234 ± 25 216 ± 15 262 ± 14 191 ± 17 267 ± 12
End Rise to Max Duration (ms) 442 ± 69 312 ± 76 450 ± 45 318 ± 43 397 ± 53 370 ± 37
Max to Start Fall Duration (ms) 440 ± 105 846 ± 115 989 ± 68 631 ± 64 780 ± 81 376 ± 55
Fall Duration (ms) 68 ± 11 73 ± 12 70 ± 7.3 72 ± 6.9 156 ± 8.6 74 ± 5.9
Rise Slope (Hz/ms) 2.67 ± 0.27 2.69 ± 0.29 4.15 ± 0.17 2.84 ± 0.16 2.16 ± 0.20 1.82 ± 0.14
End Rise to Max Slope (Hz/ms) 0.30 ± 0.08 0.52 ± 0.09 0.53 ± 0.05 0.47 ± 0.05 0.44 ± 0.06 0.57 ± 0.04
Max to Start Fall Slope (Hz/ms) –0.34 ± 0.08 –0.19 ± 0.09 –0.23 ± 0.05 –0.23 ± 0.05 –0.27 ± 0.06 –0.62 ± 0.04
Fall Slope (Hz/ms) –6.60 ± 0.58 –5.13 ± 0.64 –2.08 ± 0.38 –10.49 ± 0.36 –2.54 ± 0.45 –5.74 ± 0.31
Rise Nonlinearity 0.48 ± 0.12 0.42 ± 0.13 0.82 ± 0.07 0.51 ± 0.07 0.64 ± 0.09 1.01 ± 0.06
Middle Nonlinearity 0.00 ± 0.00 0.21 ± 0.17 0.00 ± 0.00 0.02 ± 0.09 0.46 ± 0.12 0.92 ± 0.08
50 to 100 Hz Rise Wavers 0.09 ± 0.07 0.32 ± 0.07 0.16 ± 0.04 0.08 ± 0.04 0.05 ± 0.05 0.08 ± 0.04
100 to 200 Hz Rise Wavers 0.17 ± 0.06 0.05 ± 0.06 0.09 ± 0.04 0.11 ± 0.04 0.08 ± 0.04 0.04 ± 0.03
50 to 100 Hz Middle Wavers 0.17 ± 0.15 0.21 ± 0.17 0.42 ± 0.10 0.10 ± 0.09 0.21 ± 0.12 0.64 ± 0.08
100 to 200 Hz Middle Wavers 0.09 ± 0.12 0.00 ± 0.00 0.04 ± 0.08 0.00 ± 0.00 0.33 ± 0.09 0.48 ± 0.06
Howl Type 2.65 ± 0.30 3.37 ± 0.33 3.22 ± 0.20 3.00 ± 0.19 3.38 ± 0.23 2.67 ± 0.16
1Values are mean ±standard error. Sample sizes: 23 from F-5414, 19 from F-5438, 55 from M-5320, 61 from M-5416, 39 from M-5429, and
83 from M-5430.
Hz wavers in the middle. The maximum frequency was excluded from
this analysis because of high correlations with the end of rise and
start of fall frequencies. Nonlinear features of the midsection and end,
frequency shifts between 50 and 100 Hz, and 100 to 200 Hz wavers in
the midsection were excluded because multiple individuals lacked
variability for these variables.
The discriminant analysis of howls from six individuals had
high squared canonical correlations for the first three canonical
functions (0.75, 0.60, and 0.39), suggesting that they would be very
successful at classifying individuals. The remaining functions had
squared correlations of only 0.18 and 0.07. The variables contributing
most strongly to the first function were the end rise, start fall, and
end frequencies. The second function was most strongly affected by
fall slope, with help from the frequency at the start of the fall. The
third function was most influenced by the slope of the rise and the
frequency at the end of the rise (Table 6). In other words, the first
function favored frequency characteristics, the second was most
influenced by the end of the howl, and the third was most affected by
the beginning of the howl.
Classification accuracy for the training data was good, with an
overall 83% accuracy and a chance-corrected accuracy of 0.79 ± 0.07
( ± 95% CI). Accuracy for specific individuals varied from 47% to 92%
(Table 7); the 47% accuracy corresponded to the coyote with the
second-lowest number of howls – only 17 were used in the training
data. The next-lowest individual accuracy was 71%. The most common
classification errors involved the females: 13 of 36 errors involved a
Standardized canonical coefficients for discriminant analysis of individual differences
in howls, based on original recordings of 6 individuals.
Variable Function Function Function Function Function
Start Frequency -0.198 0.064 0.206 0.894 0.094
End Rise Frequency 0.684 -0.237 -0.601 0.507 0.206
Start Fall Frequency 0.541 -0.539 -0.209 0.001 -0.003
End Frequency 0.695 0.245 -0.083 -0.462 0.121
End Rise to Max Duration 0.411 0.051 0.102 0.016 0.099
Max to Start Fall Duration 0.393 -0.082 0.265 -0.259 0.655
Fall Duration 0.206 -0.378 0.453 -0.548 0.363
Rise Slope 0.396 0.187 0.584 -0.472 -0.469
End Rise to Max Slope 0.187 0.115 -0.155 0.186 0.615
Max to Start Fall Slope -0.232 -0.312 0.453 0.016 -0.089
Fall Slope 0.433 1.105 0.043 0.774 -0.309
Rise Nonlinearity -0.170 0.368 -0.322 -0.285 0.112
50 to 100 Hz Rise Wavers 0.327 0.091 0.149 0.023 -0.047
100 to 200 Hz Rise Wavers 0.196 -0.102 0.100 -0.190 -0.561
50 to 100 Hz Middle Wavers -0.058 0.385 -0.464 -0.057 0.001
female being classified as one of the other animals. The analysis
probably included too few howls from the females (14 from F-5438 and
17 from F-5414) for discriminant analysis to fully model their
The discriminant analysis incorporating all individuals yielded
similar results with the test data. Overall accuracy was 83% – with a
corresponding kappa of 0.79 ± 0.11 ( ± 95% CI) – and individual
accuracies varied between 33% and 100%. Out of 12 classification
errors for the test data, seven involved a female’s howls being
classified as belonging to another individual.
The discriminant model that was limited to the four males had
a higher estimated kappa than the model incorporating all
individuals. The training accuracy was 88% and the test classification
accuracy was 93%, with corresponding kappas of 0.84 ± 0.07 ( ± 95%
CI) and 0.91 ± 0.08. The maximum frequency was excluded from the
analysis because of high correlations with other frequency
measurements, and the number of nonlinearities in the end of the
howl was excluded due to lack of variability for multiple individuals.
The final model included the remaining frequency variables (except
start frequency), the duration variables (except start duration), the
slope measurements, the remaining nonlinearity measurements, 50 to
100 Hz wavers in the beginning and middle of the howl, and 100 Hz
wavers in the middle of the howl.
The model based on the two females had lower estimated
kappas than the other models. The 87% training and 82% test
accuracies compared favourably to the model for all individuals, but
because there were only two females and a small sample size (31
training howls) the kappa estimates were lower and had large
confidence intervals: 0.74 ± 0.24 ( ± 95% CI) for the training data and
0.62 ± 0.48 for the test data. Because of the small sample size for this
analysis, it was limited to the 8 variables that showed significant
Training data classification matrix from analysis of individual differences in howls,
based on original recordings.
M-5320 F-5414 M-5416 M-5429 M-5430 F-5438 Percent
M-5320 50 2 5 1 10 74
M-5320 34 1 4 0 2 0 83
F-5414 0 8 3 0 5 1 47
M-5416 0 1 39 3 1 1 87
M-5429 0 0 0 24 5 0 83
M-5430 0 0 2 2 57 1 92
F-5438 1 2 1 0 0 10 71
Total 35 12 49 29 70 13 83
t-tests (at α = 0.05) for differences between the females. The final
model included the frequency of the howl’s start, the duration
between the maximum frequency and the start of the howl’s fall,
wavers up to 50 Hz in the rising portion of the howl, and the howl
Distance effects on coyote vocalizations
Means of bark variables for each distance are provided in Table 8.
Most of these variables had similar values at 500 m and 1,000 m that
differed from the values recorded at 10 m. The exceptions were bark
duration (similar at all distances), HNR frequency (increased with
distance), and skewness and the frequency of the peak dB level (both
varied erratically). The repeated measures MANOVAs of bark
variables showed significant individual, distance, and interaction
effects for all variables, except that duration lacked distance and
interaction effects (Table 9). For every variable except duration, the
distance effect was approximately equal to or larger than the
individual effect, indicating that the effect of distance matched or
exceeded any differences due to the individuals. Bark duration was
the only variable suitable for inclusion in the discriminant analysis of
barks recorded at different distances, and classification accuracy
based on this variable was poor. Accuracy was 50% for the 10-m
training data, 50% for the 10-m test data, 47% for the 500-m test data,
and 49% for the 1,000-m test data. This corresponded to a chance-
corrected accuracy estimate of 0.27 ± 0.10 ( ± 95% CI) for the 10-m
training data, 0.28 ± 0.16 for the 10-m test data, 0.24 ± 0.16 for the
500-m test data, and 0.25 ± 0.17 for the 1,000-m test data.
Bark data at 5 different distances for coyotes recorded at the NWRC Logan Field
Station and re-recorded at the Dye Creek Preserve.1
Variable 10 meters 500 meters 1,000 meters
Duration (ms) 134 ± 1 135 ± 1 132 ± 1
Bark Structure 1.96 ± 0.05 1.72 ± 0.04 1.75 ± 0.04
Bark Harmonic Structure 2.62 ± 0.08 2.14 ± 0.07 2.20 ± 0.07
Max dB (dB-volts) -41.0 ± 0.1 -51.0 ± 0.3 -54.3 ± 0.4
Mean (kHz) 1,492 ± 5 1,275 ± 8 1,299 ± 8
Standard Deviation (Hz) 679 ± 4 609 ± 3 591 ± 6
Skewness 0.98 ± 0.01 1.25 ± 0.02 1.03 ± 0.03
HNR (volts) 6.56 ± 0.25 6.92 ± 0.22 5.34 ± 0.17
HNR Frequency (Hz) 744 ± 16 824 ± 25 957 ± 31
1Values are mean ± standard error for 293 barks from 5 coyotes.
Discriminant analysis results were less consistent when
variables with distance effects were allowed into the bark model. For
a model containing duration, bark structure, harmonic structure,
mean, standard deviation, skewness, and HNR, the training data was
classified with a 63% accuracy rate (kappa of 0.49 ± 0.09). Accuracy
was 50% for the 10-m test data, 35% for the 500-m test data, and 57%
for the 1,000-m test data (with chance corrected accuracies of 0.31 ±
0.16, 0.17 ± 0.14, and 0.42 ± 0.15, respectively).
Means of howl variables for each distance are provided in Table
10. For most variables, the means at each distance were nearly
identical. The exceptions are start and end frequency (both increased
with distance) and rise duration and rise nonlinearities (both
decreased with distance). The repeated measures MANOVA results
for the 26 howl variables showed considerably fewer distance and
interaction effects (Table 11) than the comparable results for bark
measurements. Twenty-one variables had no distance or interaction
effect, and 11 of these had significant individual effects. Of the five
variables with significant distance or interaction effects, only end
frequency had a distance effect F-ratio that was less than half the
individual effect F-ratio. In this case we felt that the individual effect
outweighed any potential distance effect enough that discriminant
analysis would still be stable. The remaining four variables – start
frequency, rise and fall duration, and the number of rise
nonlinearities – were excluded from the distance-independent dis-
criminant analysis. All of these variables showed significant distance
effects with magnitudes similar to or greater than the individual
The accuracy of discrimination among the six individuals was
slightly reduced in the final model, but this model was still successful:
Repeated measures MANOVA results for barks recorded at 10, 500, and 1,000 meters.
Variable Individual Distance Interaction
F4, 288 p(F)1 F2, 287 p(F)1 F28, 576 p(F)1
Duration 25.3 < 0.0001 3.1 0.0463 1.2 0.3229
Bark Structure 16.0 < 0.0001 19.5 < 0.0001 6.0 < 0.0001
Harmonic Structure 19.5 < 0.0001 24.2 < 0.0001 5.9 < 0.0001
Max dB 10.7 < 0.0001 1,340.8 < 0.0001 24.0 < 0.0001
Mean 23.1 < 0.0001 809.1 < 0.0001 4.3 < 0.0001
Standard Deviation 13.7 < 0.0001 143.6 < 0.0001 11.3 < 0.0001
Skewness 18.6 < 0.0001 34.6 < 0.0001 11.7 < 0.0001
HNR 10.3 < 0.0001 23.6 < 0.0001 4.6 < 0.0001
HNR Frequency 10.0 < 0.0001 39.3 < 0.0001 6.5 < 0.0001
1α equals 0.0045
2F-test is Pillai’s Trace
classification accuracy was 76% for the 10-m training data, 81% for
the 10-m test data, 81% for the 500-m test data, and 81% for the
1,000-m test data. This corresponded to a chance-corrected accuracy
estimate of 0.70 ± 0.07 ( ± 95% CI) for the 10-m training data, 0.72
± 0.12 for the 10-m test data, 0.75 ± 0.12 for the 500-m test data, and
0.76 ± 0.12 for the 1,000-m test data.
Allowing the inclusion of howl variables with distance effects
into the discriminant analysis increased the variability of the results,
although accuracy was still high in all of the test data sets. The
classification accuracy was 81% (kappa of 0.76 ± 0.07) for the training
data, 88% (0.84 ± 0.10) for the 10-m test data, 86% (0.82 ± 0.10) for
the 500-m test data, and 81% (0.76 ± 0.11) for the 1,000-m test data.
Individually specific cues in coyote barks and howls
Animal sounds often contain cues that are individually specific. One
source of these cues stems from the physiology of sound production.
The source-filter model of animal acoustics says that the fundamental
Howl data at 5 different distances for coyotes recorded at the NWRC Logan Field
Station and re-recorded at the Dye Creek Preserve. 1
Variable 10 meters 500 meters 1,000 meters
Start Frequency (Hz) 484 ± 3 495 ± 3 506 ± 3
End Rise Frequency (Hz) 939 ± 13 938 ± 13 939 ± 13
Start Fall Frequency (Hz) 974 ± 13 974 ± 13 974 ± 13
End Frequency (Hz) 655 ± 16 665 ± 16 671 ± 15
Rise Duration (ms) 191 ± 6 186 ± 6 180 ± 6
End Rise to Max Duration (ms) 379 ± 20 380 ± 20 380 ± 20
Max to Start Fall Duration (ms) 645 ± 33 645 ± 33 645 ± 33
Fall Duration (ms) 73 ± 4 71 ± 3 70 ± 3
Rise Slope (Hz/ms) 2.76 ± 0.10 2.72 ± 0.09 2.80 ± 0.10
End Rise to Max Slope (Hz/ms) 0.51 ± 0.02 0.52 ± 0.02 0.51 ± 0.02
Max to Start Fall Slope (Hz/ms) -0.36 ± 0.03 -0.36 ± 0.03 -0.36 ± 0.03
Fall Slope (Hz/ms) -5.35 ± 0.23 -5.43 ± 0.25 -5.28 ± 0.23
Rise Nonlinearity 0.55 ± 0.04 0.49 ± 0.04 0.44 ± 0.03
Middle Nonlinearity 0.32 ± 0.05 0.27 ± 0.04 0.31 ± 0.05
50 to 100 Hz Rise Wavers 0.09 ± 0.02 0.09 ± 0.02 0.10 ± 0.02
100 to 200 Hz Rise Wavers 0.08 ± 0.02 0.08 ± 0.02 0.08 ± 0.02
50 to 100 Hz Middle Wavers 0.34 ± 0.04 0.33 ± 0.04 0.34 ± 0.04
100 to 200 Hz Middle Wavers 0.21 ± 0.04 0.22 ± 0.04 0.21 ± 0.04
Howl Type 3.01 ± 0.09 3.01 ± 0.09 2.98 ± 0.09
1Values are mean ± standard error for 280 howls from 6 coyotes.
frequency of animal vocalizations is determined by characteristics of
the sound’s source – the larynx. The acoustic energy generated by the
larynx is then modified by an acoustic filter whose properties are
determined partly by the length, shape, and volume of the
supralaryngeal vocal tract. Certain frequencies (the formants) are
passed with minimal filtering, while other frequencies are strongly
curtailed (Rubin & Vatikiotis-Bateson 1998).
Vocalizations with cues to identity should be the rule rather
than the exception, but the reality is that not all calls are useful for
detecting morphological differences. Calls with low fundamental
frequencies and calls with low-amplitude wideband noise are best for
revealing body size and individuality (Owren & Rendall 2001).
Although minimum fundamental frequency is constrained by
physiology, many mammals can produce a broad range of
fundamental frequencies by varying the rate of vocal fold vibration.
When they use a high fundamental frequency or sound amplitude,
aspects of the individually-specific acoustic filter are more difficult to
detect (Owren & Rendall 2001). Canid growls contain highly specific
cues to size (Riede & Fitch 1999) and probably identity, but barks
(with their high sound amplitudes) and howls (with their high
Repeated measures MANOVA results for howls recorded at 10, 500, and 1,000 meters.
Variable Individual Distance Interaction
F5, 274 p(F)1F2, 273 p(F)1F210, 548 p(F)1
Start Frequency 14.13 < 0.0001 39.21 < 0.0001 3.65 < 0.0001
End Rise Frequency 66.27 < 0.0001 6.39 0.0019 0.79 0.6378
Start Fall Frequency 81.24 < 0.0001 0.08 0.9193 1.58 0.1074
End Frequency 103.50 < 0.0001 29.78 < 0.0001 2.06 0.0263
Rise Duration 4.35 0.0008 16.88 < 0.0001 2.57 0.0048
End Rise to Max Duration 1.27 0.2772 1.75 0.1764 0.77 0.6550
Max to Start Fall Duration 12.04 < 0.0001 0.48 0.6169 1.02 0.4257
Fall Duration 12.24 < 0.0001 7.94 0.0004 3.43 0.0002
Rise Slope 18.39 < 0.0001 4.55 0.0113 1.72 0.0733
End Rise to Max Slope 2.20 0.0550 1.63 0.1973 1.51 0.1307
Max to Start Fall Slope 8.64 < 0.0001 0.16 0.8500 0.81 0.6218
Fall Slope 53.08 < 0.0001 1.17 0.3128 1.44 0.1597
Rise Nonlinearity 8.60 < 0.0001 6.92 0.0012 1.50 0.1368
Middle Nonlinearity 15.34 < 0.0001 1.52 0.2200 2.12 0.0215
50 to 100 Hz Rise Wavers 3.71 0.0029 2.03 0.1339 1.12 0.3460
100 to 200 Hz Rise Wavers 1.37 0.2360 0.27 0.7613 0.40 0.9445
50 to 100 Hz Middle Wavers 5.74 < 0.0001 0.82 0.4415 1.03 0.4191
100 to 200 Hz Middle Wavers 7.66 < 0.0001 0.92 0.3985 0.84 0.5919
Howl Type 2.23 0.0515 3.38 0.0356 1.67 0.0854
1α = 0.0019
2F-test is Pillai’s Trace
fundamental frequencies) are less likely to obviously encode this
Nevertheless, coyote vocalizations clearly contained individually
specific characteristics. The barks of five individuals were correctly
classified about 70% of the time (a 58% chance-corrected accuracy),
and the howls of six individuals were correctly classified 83% of the
time (a 78% chance-corrected accuracy). Individual vocal tract
morphology was not expected to leave a large imprint on barks
because their high amplitude should mask much of the morphological
influence (Owren & Rendall 2001). Some of this influence should
remain, though, and we suspect that many of the individual
differences in spectral moments were due to differences in vocal tract
morphology and sound filtering. The differences among the remaining
bark variables were likely due to individual preference. For example,
duration of barks could be controlled by decisions about the volume
and expulsion rate of air used to form the vocalization.
Howls should be less affected than barks by individual variation
in vocal tract morphology because of their relatively high fundamental
frequency (Owren & Rendall 2001). Frequency measurements could
have been loosely related to individual differences in larynx mor-
phology by representing the range over which each individual was
able to comfortably vocalize, and nonlinear phenomena might have a
physical basis if the threshold controlling the transition to nonlinear
features varies in different coyotes. However, the majority of howl
features that were important for discriminating individuals should be
under voluntary control. These include the duration of the fall,
various slope measurements, and the presence of wavers.
Our results confirm other studies indicating that individuality
is a general feature of canid bark and howl vocalizations. Studies of
swift foxes (Darden et al. 2003), arctic foxes (Frommolt & Gebler
2004), and domestic dogs (Yin & McCowan 2004) used characteristics
of bark sequences in addition to spectral characteristics of individual
barks, and were generally able to obtain higher overall classification
accuracies than we found for coyote barks. The exception is Yin and
McCowan (2004), where they only obtained an average 53% accuracy
(a kappa of approximately 0.50) classifying the barks of 10
individuals. Of the intra-bark measurements made in other studies,
variables relating to duration and the width and shape of the power
spectrum were most important, as they were for this study. For howls,
Tooze et al.’s (1990) wolf study reports a lower accuracy (75%) for the
same number of individuals. Their analysis used many variables that
were similar to the ones we chose, including maximum and end
frequencies, howl duration, nonlinearities, and measures of frequency
modulation of the fundamental. As with our study, they reported
frequency characteristics (e.g. maximum frequency) as being most
important for classifying individuals.
The effect of distance
Barks and howls contain individually specific cues, but characteristics
of these two vocalization types differ in their stability over biologically
relevant distances. Bark features, with the exception of duration, all
had significant distance effects that equalled or exceeded the
individual effect in repeated measures MANOVA. Discriminant
analysis was surprisingly robust to these differences, and was
moderately successful at classifying barks even when variables with
distance effects were included. Nevertheless, the overall discriminant
analysis accuracy for howl characteristics was higher than the
accuracy for bark classifications.
The bark characteristics we chose contained less individually-
specific information and were less stable over distance than the howl
characteristics. While barks may be less suitable for stable
information transmission than howls, they are appropriate for other
purposes, including acoustic ranging. Barks are short, noisy
vocalizations that cover a broad frequency range – from below 500 Hz
to over 2.5 kHz. This type of sound has some distinct advantages
when used in the context of agonistic interactions or as an alarm call.
Barks are likely to trigger the acoustic-startle reflex in nearby
animals, which causes them to increase their alertness and orient
towards the sound source (Owren & Rendall 2001). This would be a
useful response for a coyote that is challenging a conspecific or trying
to alert its pack of danger.
Barks are also well structured for use in distance assessment.
Broadband noisy vocalizations are ideal for determination of distance
via relative intensity changes, and the frequency range of barks is
only slightly lower than the 1-kHz to 4-kHz range needed for
maximum sound transmission distance in most environments (Wiley
& Richards 1978). The abrupt nature of barks, with their sudden
onset and offset, also makes these vocalizations suitable for ranging
based on reverberation (Naguib & Wiley 2001).
Howls are structurally different from barks; they are tonal,
relatively long, frequency modulated vocalizations with a dominant
frequency near 1 kHz. Wiley and Richards (1978) predicted that
optimal information transmission over long distances would be
obtained by tonal, frequency modulated vocalizations with frequencies
between 1 kHz and 4 kHz. Howls therefore meet the criteria for an
optimum information-containing long-distance vocalization. Despite
marked intra-individual variability, each coyote used a particular
combination of howl features in a specific way, which allowed the
howls to be correctly classified to the vocalizing animal over 80% of
the time. When a few variables that showed distance effects were
excluded, a discriminant model based on vocalizations recorded at
10 m classified test howls recorded at 10 m, 500 m, and 1,000 m with
an 81% accuracy rate.
Howls contain a number of individually specific cues that are
transmitted to distances of at least 1,000 m without any noticeable
degradation of information content (Table 10). There was no change in
most howl characteristics with distance, and it is likely that howls are
individually identifiable at even greater distances. The exceptions to
the rule, specifically the features of the start of the howl, may also be
important. It is possible that there are physiological constraints (e.g.,
a need to vocally “ramp up” to a full howl) that create the low
amplitude ascending portion of coyote howls, but it is also possible
that coyotes intentionally maintain this rising portion to attract the
attention of receivers and provide them with additional distance
information (F. Harrington, personal communication).
In addition to individually specific information, howls con-
ceivably contain information about the sex of the howling individual
(Mitchell 2004), plus howls may include more detailed information
about the signaller’s motivational and physical state. Theberge and
Falls (1967) noted that information in howls could be universal
(species-wide) or restricted (limited to a social group). Restricted
communication does not require a private language; it only requires
that individuals alter their vocalizations in consistent ways depending
on context, and that close companions are able to associate the context
with the vocal variation.
Barks and howls probably serve complementary purposes: the
acoustic structure of barks is well suited to ranging, while howls are
better suited to transmitting information over long distances. Coyote
vocal bouts almost universally feature both calls, indicating that the
overall bout may help conspecifics locate and identify the signaller,
and potentially extract additional information about the signaller’s
activities and motivational state.
We thank S. Beissinger, E. Lacey, M. Owren and T. Riede for
reviewing early versions of this research, and F. Harrington and an
anonymous reviewer for their reviews of a later draft. We also thank
R. Mason and the staff of the NWRC’s Logan Field Station for their
support. This study was funded primarily by the United States
Department of Agriculture’s National Wildlife Research Center
through a cooperative agreement with the University of California at
Berkeley (12-03-7405-0235 CA), and a National Science Foundation
Graduate Research Fellowship.
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Received 28 June 2005, revised 6 November 2005 and accepted 10 November 2005.