rspb.royalsocietypublishing.org

ResearchCite this article: Martin MJ, Gridley T, Elwen

SH, Jensen FH. 2018 Heaviside’s dolphins

(Cephalorhynchus heavisidii) relax acoustic

crypsis to increase communication range.

Proc. R. Soc. B 285: 20181178.

http://dx.doi.org/10.1098/rspb.2018.1178

Received: 29 May 2018

Accepted: 26 June 2018

Subject Category:Behaviour

Subject Areas:behaviour, evolution, ecology

Keywords:acoustic crypsis, active space, communication,

echolocation, Heaviside’s dolphin, narrowband

high-frequency clicks

Author for correspondence:Morgan J. Martin

e-mail: mjmartin@sandiego.edu

Electronic supplementary material is available

online at https://dx.doi.org/10.6084/m9.

figshare.c.4154456.

Heaviside’s dolphins (Cephalorhynchusheavisidii) relax acoustic crypsis toincrease communication range

Morgan J. Martin1, Tess Gridley2, Simon H. Elwen1 and Frants H. Jensen3,4

1Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, c/o Sea SearchResearch and Conservation NPC, 4 Bath Rd, Cape Town 7945, South Africa2Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University ofCape Town, c/o Sea Search Research and Conservation NPC, 4 Bath Rd, Cape Town 7945, South Africa3Aarhus Institute of Advanced Studies, Aarhus University, 8000 Aarhus C, Denmark4Biology Department, Woods Hole Oceanographic Institution, 266 Woods Hole Rd, Woods Hole, MA 02543, USA

MJM, 0000-0002-3556-6632; TG, 0000-0001-6167-9270; SHE, 0000-0002-7467-6121;FHJ, 0000-0001-8776-3606

The costs of predation may exert significant pressure on the mode of com-

munication used by an animal, and many species balance the benefits of

communication (e.g. mate attraction) against the potential risk of predation.

Four groups of toothed whales have independently evolved narrowband

high-frequency (NBHF) echolocation signals. These signals help NBHF

species avoid predation through acoustic crypsis by echolocating and com-

municating at frequencies inaudible to predators such as mammal-eating

killer whales. Heaviside’s dolphins (Cephalorhynchus heavisidii) are thought

to exclusively produce NBHF echolocation clicks with a centroid frequency

around 125 kHz and little to no energy below 100 kHz. To test this, we

recorded wild Heaviside’s dolphins in a sheltered bay in Namibia. We

demonstrate that Heaviside’s dolphins produce a second type of click with

lower frequency and broader bandwidth in a frequency range that is audible

to killer whales. These clicks are used in burst-pulses and occasional click

series but not foraging buzzes. We evaluate three different hypotheses and

conclude that the most likely benefit of these clicks is to decrease transmission

directivity and increase conspecific communication range. The expected

increase in active space depends on background noise but ranges from 2.5

(Wenz Sea State 6) to 5 times (Wenz Sea State 1) the active space of NBHF

signals. This dual click strategy therefore allows these social dolphins to

maintain acoustic crypsis during navigation and foraging, and to selectively

relax their crypsis to facilitate communication with conspecifics.

1. IntroductionSocial animals inevitably need to balance effective communication with

conspecifics against the costs associated with communication, including

eavesdropping and potential detection by predators and prey [1]. Trade-offs

to decrease predator detection often involve shifting communication to periods

or locations with lowered predation risk [2], but such acoustic avoidance can be

costly if the social or ecological functions of communication are not fulfilled

[3]. Alternatively, animals may use quiet, low-amplitude or high-frequency

signals with short detection ranges for social interactions [4], which can

be difficult for predators to locate [5].

In the aquatic environment, where light diminishes quickly, cetaceans

(whales, dolphins and porpoises) rely on sound as the primary medium for

orientation, foraging and communication [6]. In water, sound travels faster

and attenuates less than in air [7], increasing the necessity of balancing com-

munication with the associated risk of distant eavesdroppers. Mammal-eating

killer whales (Orcinus orca) have been shown to fall silent as they hunt so as

& 2018 The Author(s) Published by the Royal Society. All rights reserved.

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not to alert their acoustically sensitive prey [8]. Antipredator

strategies that decrease the risk of passive detection by preda-

tors have potentially large benefits because echolocation used

by all toothed whales puts them at heightened risk of detec-

tion by eavesdroppers [9]. For example, Blainville’s beaked

whales (Mesoplodon densirostris) only produce sound at

depth and remain silent within several hundred metres of

the surface, and this has been proposed to represent a strat-

egy to reduce risk of detection by killer whales, which tend

not to dive deeper than a few tens of metres [10]. Addition-

ally, delphinids [11] and seals [12] seem to suppress vocal

activity in the presence of killer whales.

Toothed whales are grouped into four acoustic categories

by the type of biosonar pulses they emit [13,14]. While most

delphinids produce broadband, extremely short biosonar

clicks, 13 species from four separate clades (Kogiidae, Phocoenidae,Pontoporiidae and 6 delphinid species from the genera Cepha-lorhynchus and Lagenorhynchus) have evolved a narrowband,

high-frequency (NBHF) click type [15,16] with energy almost

exclusively above 100 kHz [17]. These four independent

cases of convergent evolution have spurred several hypoth-

eses regarding the evolution of NBHF signals [16]. Some

authors have argued that NBHF signals exploit a natural

low noise window occurring at frequencies above 100 kHz

to favour detection in an otherwise noisy environment [18].

Other authors propose that the evolution of NBHF signals

and the concurrent loss of producing lower-frequency whis-

tles is evidence for an ‘acoustic crypsis’ strategy [15,19]

where NBHF species have shifted their acoustic signals to

frequencies above the hearing limit of killer whales which

cuts off around 100 kHz [20]. The ‘acoustic crypsis’ hypo-

thesis has become a commonly accepted explanation for

the evolution of NBHF signals [14,21,22].

This cryptic biosonar strategy has had consequences for

communication and social behaviour in NBHF species.

Many broadband delphinid species produce a wide variety

of communication signals [23,24] including low-frequency

calls and whistles that can travel several kilometres under-

water [25,26] and are easily distinguished from foraging

sounds. By contrast, NBHF species seem to have lost the abil-

ity to whistle [15] and communication is therefore limited to

clicks. Both harbour porpoises (P. phocoena) [21,27] and Hec-

tor’s dolphins (C. hectori) [28] are NBHF species which have

been shown to communicate acoustically with short, iso-

lated burst-pulses during social and aggressive encounters.

However, there are socio-ecological drawbacks for species

constrained to producing NBHF signals for both echolocation

and communication. First, the signal repertoire and thus com-

munication complexity [29] are limited, potentially reducing

options for resolving and differentiating social interactions

with sound. Second, communicating with signals that are

also used for echolocation and foraging may increase signal

ambiguity for a receiver [30] which then needs to differentiate

communication from foraging signals. Finally, as NBHF clicks

are highly directional and attenuate rapidly with distance due

to high-frequency-dependent absorption [22], the detection

range for nearby conspecifics is typically short (less than

1 km) and dependent on the relative orientation of the source

and the receiver [21].

Heaviside’s dolphins (Cephalorhynchus heavisidii) are small

(less than 1.7 m) delphinids endemic to the west coast of

southern Africa. They are typically found in shallow coastal

waters to approximately 100 m depth [31] in small groups;

however, group sizes tend to be slightly larger, with more

socializing activity than described for other NBHF species

[32]. Heaviside’s dolphins have only been reported to pro-

duce NBHF clicks with little to no energy below 100 kHz,

like other NBHF species [33]. Here we present evidence

that Heaviside’s dolphins produce lower-frequency broad-

band signals, despite residing in an area with killer whale

predation risk. We show that burst-pulses are generally com-

posed of these lower-frequency broadband signals, and thus

present evidence of a NBHF species with a dual click type

strategy. We discuss three possible theories to explain how

the production of these lower-frequency broadband signals

may help this species compensate for the socio-ecological

trade-offs imposed by communicating with NBHF signals.

We use an acoustic model to show that a major advantage

of communicating using lower-frequency clicks is that trans-

mission directivity is lower and active space is larger over a

wide range of noise levels, thus facilitating social interactions

over a greater area.

2. Material and methodsTwenty-five hours of acoustic recordings of Heaviside’s dolphins

were collected in Shearwater Bay, Namibia (2268 370 S, 158 050 E),

over 12 days during April and May 2016. Recordings were

made by deploying two hydrophones (SoundTrap 300 HF;

Ocean Instruments, New Zealand) mounted 1 m apart and

suspended 1.5 m below an ocean kayak. Only data from a

single hydrophone were analysed for this study. Sound was

digitized at a sampling rate of 576 kHz with a 16-bit resolution

(sensitivity: 2171 dB re 1 V mPa21, flat frequency response:

400 Hz–150 kHz+3 dB). Behaviour and group size information

were collected concurrently with sound recordings (see electronic

supplementary material). A land-based observer team stationed

at a vantage point (20 m elevation) monitored the presence of

cetaceans within the bay.

(a) Acoustic data extractionRecordings made within a visually estimated 50 m range of dol-

phins were selected for analysis. Acoustic signals produced by

Heaviside’s dolphins were identified through visual inspection

of a spectrogram display in Adobe AUDITION CC (Adobe Systems

Inc.). Heaviside’s dolphin NBHF echolocation clicks have been

previously described [33], and only a subset were selected for

analysis. We defined three functional groups of signals based

on signal context and interclick intervals (ICI, calculated as the

time between subsequent clicks [9]). Click trains were defined

as series of clicks with ICI exceeding 10 ms. Such click trains

are likely to be echolocation signals produced by the animals.

A subset of click trains were composed of lower-frequency,

broader-bandwidth signals than previously described [33], and

we therefore divided click trains into NBHF click trains and

broadband click trains by inspecting spectrograms (figure 1).

Foraging buzzes are used during prey capture by echolocating

animals [34,35], including NBHF species [36]. These were defined

as click series with ICIs less than 10 ms, which were preceded by

a slower click train. Since buzzes occurred at the end of a click

train, we defined the start of a buzz as the point when the ICI

first decreased below 10 ms and the end of the buzz as the

point where the click train ended or where the ICI increased to

greater than 10 ms. Finally, we defined burst-pulse signals as

discrete, isolated series of high repetition rate clicks that began,

persisted and generally ended with interclick intervals less than

10 ms following Lammers et al. [37]. Burst-pulses are commonly

considered to have an intra-specific communicative function

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[24,37,38], including in NBHF species [21]. Only distinguishable,

high-quality pulsed signals measuring more than 10 dB above

the background noise measured immediately before the signal

were selected for further analysis.

(b) Acoustic feature extractionTo quantify temporal differences in repetition rate across signals,

we used a click detection algorithm developed in MATLAB 2013B

(MathWorks, USA). We first filtered the input signal with a six-

pole Butterworth bandpass filter (20–275 kHz), calculated the

signal envelope, and extracted peaks in the envelope that were

separated by more than 0.5 ms. Click detections were visually

inspected and manually corrected for missed detections. To

compare signals with highly variable numbers of clicks, we

finally calculated the 5th, 50th and 95th percentile ICI across

each click series.

To quantify temporal and spectral differences of component

clicks, we extracted the highest amplitude click from each click

series following the methods for on-axis click analysis [39,40].

While these signals were recorded from an unknown aspect,

the minute difference in the waveform and spectrum of NBHF

clicks across varying off-axis angles [41] means that spectral par-

ameters are likely reasonably close to on-axis signals. Individual

signals were filtered in MATLAB with a four-pole Butterworth

bandpass filter between 20 and 275 kHz. Individual click

60

40

NBHF click train(i)

(ii)

(iii) (iv)

(i)

(ii)

(iii) (iv)

(i)

(ii)

(iii) (iv)

(i)

(ii)

(iii) (iv)

foraging buzz

40

20

20

250

200

150

100

50

0

250

200

150

100

50

0 0.2 0.4 0.6 0.8time (s)

1.2 0 0.2 0.4 0.6time (s)

0.8 1.0 1.2

0.5

0

–10 0.5

1.0 0

–10

–0.5

–1.0

1.0

0

–100 0 100 200

–20

–30

–400 100 200 300

0

–0.5

–1.0

–100 0 100 200

–20

–30

–400 100 200 300

150

100

time (ms)

BB click train

frequency (kHz)

freq

uenc

y (k

Hz)

freq

uenc

y (k

Hz)

freq

uenc

y (k

Hz)

freq

uenc

y (k

Hz)

time (ms)

burst-pulse call

1.6

1.4

frequency (kHz)

50

250

200

150

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50

1.2

250

200

150

100

50

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4time (s)

01.6 1.8 0.2 0.4 0.6 0.8 1.0time (s)

0.5

0

–0.5

–1.0

1.00

–10

–30

–20

–40

0.5

1.0

0

–0.5

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0

–10

–20

–30

–40100–100 0 200 0 100 200 300 –100 0 100 200 0 100 200 300

time (ms) frequency (kHz) time (ms) frequency (kHz)

1.0

ICI

(ms)

ICI

(ms)

ICI

(ms)

ICI

(ms)

ampl

itude

ampl

itude

pow

er (

dB)

pow

er (

dB)

ampl

itude

ampl

itude

pow

er (

dB)

pow

er (

dB)

(a) (b)

(c) (d)

Figure 1. Examples of Heaviside’s dolphin pulsed signal types. (a) Narrowband high-frequency (NBHF) click train, (b) foraging buzz, (c) broadband (BB) click trainand (d ) burst-pulse call. For each signal, panels represent (i) the interclick intervals throughout the signal, (ii) the spectrogram of the signal (512 pt FFT, Hammingwindow, 50% overlap), (iii) the normalized waveform (solid line) and envelope (dashed line) of a single click extracted from the pulsed signal shown in panel (ii)and (iv) the normalized power spectrum of the extracted click (512 pt rectangular window, 576 kHz sampling rate).

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power spectra were calculated with a 512-point 50% Tukey

window centred on the peak envelope of each click. Spectral

and temporal click parameters were calculated according to

methods for measuring on-axis click parameters [9,42].

(c) Statistical analysis of signal discriminationSignal parameters, including spectral and temporal click par-

ameters as well as interclick intervals, were compared across

signal categories using a non-parametric Kruskal–Wallis test

and subsequent Dunn’s post-hoc tests for pairwise comparisons

in R v. 3.4.2 [43,44]. We then used a random forest classifier [45]

to measure prediction accuracy as a function of buzz and burst-

pulse signal categories using either ICI parameters (5th, 50th and

95th ICI percentiles for each click series), spectral and temporal

individual click parameters, or all signal parameters combined

to test the potential benefit of spectral differences in decreasing

signal ambiguity. The random forest classifier was built in

MATLAB 2017b using a ‘bagged trees’ ensemble classifier with

30 learners [45]. Prediction accuracy was measured using 5-fold

cross-validation to prevent overfitting. To measure consistency

in prediction accuracy, a classifier was trained 100 times and

prediction accuracy measured for each iteration.

(d) Acoustical modelling of detection rangeTo test the potential benefit for communication, we modelled the

detection range for typical NBHF clicks and for lower-frequency

clicks extracted from burst-pulses. We first filtered the input signal

with a six-pole Butterworth bandpass filter (10–150 kHz), and we

used a piston model [46] to estimate changes in transmission

beam and empirical measurements of hearing sensitivity of a har-

bour porpoise [47] to estimate changes in directional hearing. We

modelled the detection range (m) for a noise-limited scenario with

Wenz Sea State 2 noise levels, and we accounted forchanges in trans-

mission loss due to lower frequency-specific absorption. A separate

sensitivity analysis was conducted across a 25 dB variation in wind-

generated ambient noise (reflecting calm sea conditions to storms)

and a 25 dB variation in signal source levels (reflecting the full

distribution of on-axis source levels from Heaviside’s dolphins

[33]) to examine how varying noise conditions and output

levels affect the relative change in active space between the two

signal types. The full model and sensitivity analysis are described

in electronic supplementary material.

3. ResultsAcoustic data were collected during recording sessions with

Heaviside’s dolphins during which foraging, resting, socializing,

interacting with the kayak and travelling behaviours were

observed. No other cetacean species were sighted visually

or detected acoustically during recording sessions. A total

of 90 broadband click trains, 706 buzzes and 954 burst-

pulses and a subset of 33 NBHF click trains were indexed

from recordings made when Heaviside’s dolphins were

within 50 m of the kayak.

Broadband click trains and burst-pulse signals were

composed of clicks with lower frequency and broader band-

width (figure 1) compared to typical NBHF signals (table 1).

Q-ratios (centroid frequency/RMS bandwidth) are an indi-

cator of click type, and generally burst-pulse signals and

broadband click trains had Q-ratios less than 5, whereas

NBHF click trains and buzz signals had Q-ratios greater than

7 (table 1; electronic supplementary material, figure S1A).

Initially, buzz and burst-pulse signals were visually differ-

entiated by the presence or absence of a preceding click train

as burst-pulses occur as isolated signals. The measured

signal parameters confirmed there were significant differences

in both ICI parameters and spectral parameters between these

two signal types (figure 2; see electronic supplementary

material for a full comparison of different signal types).

Based on these findings, a random forest classification algor-

ithm was implemented to evaluate importance of different

parameters and test if discrimination of communication sig-

nals (burst-pulses) from feeding signals (buzzes) benefits

Table 1. Biosonar parameters of pulsed signal types produced by Heaviside’s dolphins. The median and 5th – 95th percentile values are reported for eachparameter. ICI5th, ICIMED and ICI95th represent 5th, median (50th) and 95th percentile interclick intervals, respectively. FP, peak frequency; FC, centroid frequency;BW3dB, 23 dB bandwidth; BW10dB, 210 dB bandwidth; BWRMS, root mean square bandwidth; QRMS, FC/BWRMS; Dur10dB, 210 dB click duration.

NBHF click train BB click train buzz burst-pulse

n 5 33 n 5 28 n 5 40 n 5 58

sourceparameters median (5 – 95%) median (5 – 95%) median (5 – 95%) median (5 – 95%)

ICI5th (ms)a 23.5 (14.9 – 41.2) 24.8 (7.8 – 78.9) 6.0 (2.1 – 9.9) 1.5 (1.2 – 1.9)

ICIMED (ms)a 28.9 (22.3 – 55.4) 28.8 (11.7 – 110.1) 7.2 (3.0 – 11.2) 1.6 (1.3 – 2.2)

ICI95th (ms)a 46.1 (29.4 – 104.8) 40.9 (17.4 – 215.8) 10.0 (5.0 – 13.0) 1.7 (1.4 – 3.2)

FP (kHz)b 127.1 (121.5 – 136.6) 113.6 (78.4 – 141.3) 123.8 (115.8 – 137.3) 112.5 (90.0 – 133.1)

FC (kHz)b 131.3 (125.3 – 136.9) 110.8 (87.2 – 146.8) 132.4 (124.9 – 143.3) 119.5 (94.4 – 149.0)

BW3dB (kHz)b 15.8 (9.5 – 22.7) 21.4 (4.9 – 79.1) 12.4 (3.3 – 23.7) 16.3 (3.2 – 62.2)

BW10dB (kHz)b 31.5 (22.7 – 69.8) 79.9 (38.1 – 142.5) 37.1 (21.2 – 86.1) 75.4 (31.0 – 137.9)

BWRMS (kHz)b 12.8 (8.2 – 23.2) 27.5 (17.1 – 38.6) 18.3 (10.2 – 31.6) 26.6 (18.1 – 38.7)

QRMSb 10.2 (5.9 – 15.6) 4.1 (2.8 – 7.1) 7.2 (4.3 – 12.5) 4.4 (3.0 – 6.8)

Dur10dB (mm)b 63.9 (50.6 – 85.1) 37.0 (16.6 – 50.7) 71.1 (42.6 – 129.0) 41.1 (21.1 – 82.3)aParameters measured across a click series.bParameters measured for an individual click.

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from spectral differences. The random forest classifier demon-

strated that ICI parameters were most important for accurate

classification of buzz and burst-pulse signal categories

(figure 2c). Signal categories could be predicted with 97%

accuracy using all available parameters (figure 2d). Classifi-

cation accuracy decreased only marginally (95% prediction

accuracy) when only interclick interval parameters were

included in the model, whereas a larger drop in accuracy

(d)(c)

(b)

(a)16

14 foraging buzzburst-pulse

12

10

8

6

4

280 90 100 110 120 130 140 150 160

centroid frequency (kHz)

1.8

1.6

1.4

log 10

RM

Sba

ndw

idth

(kH

z)

1.2

1.0

0.80 0.2 0.4 0.6

log10 median ICI (ms)0.8 1.0 1.2

ICIMED

ICI95th

importance of acoustic parameters fordiscrimination (ranked)

100

90

discriminating of foraging buzz fromburst-pulse

ICI5th

FC

QRMS

QR

MS

Dur10dB

FP

BWRMS

BW10dB

0 0.02 0.04 0.06 0.08feature importance

80

clas

sifi

catio

n ac

cura

cy(%

)

70

60

50all ICI spectral

acoustic parameters for classification

Figure 2. Signal parameters and discrimination of buzz and burst-pulse signal types. (a) Q-ratio (centroid frequency/RMS bandwidth) as a function of centroidfrequency. (b) Log-transformed RMS bandwidth as a function of log-transformed median ICI. (c) Relative feature importance of acoustic signal parameters forclassification accuracy. (d ) Random forest classification accuracy following three scenarios: discrimination using all signal parameters (All); discrimination using inter-click intervals (ICI); or discrimination using spectral and temporal click parameters (spectral). For plots (c,d), values are reported as mean (+s.d.) for 100independently trained random forest models. Both feeding buzzes and burst-pulse calls can be accurately classified by interclick intervals without includingfrequency, bandwidth or other individual click parameters. (Online version in colour.)

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was seen when only spectral and temporal click parameters

were included in the model (86% prediction accuracy).

The effect of signal type on beamwidth was two-fold:

first, the sidelobes seen in NBHF signals were suppressed

because of the broader bandwidth of burst-pulse signals;

second, the transmission directivity was lower and conse-

quently sound intensity away from the centre of the sound

beam was higher (figure 3a). The detection range for NBHF

clicks and burst-pulse signals was modelled for a typical

130 kHz NBHF signal and for a burst-pulse signal with a cen-

troid frequency of 80 kHz. While detection range depends on

the modelled noise levels as well as source and receiver geo-

metry, the estimated detection range was consistently greater

for burst-pulse signals at all estimated source and receiver

angle combinations (figure 3b). The potential gain in active

space depended on noise level but was relatively unaffected

by large changes in sound source level (figure 3c). At wind-

generated noise levels corresponding to Wenz Sea State 1

(approximately 4–6 knots of wind), the active space of a

burst-pulse signal would be around five times greater than

the active space of a NBHF click (figure 3c). At an estimated

wind-generated noise level corresponding to Wenz Sea State

6 (approximately 28–47 knots of wind), the active space

would be approximately 2.5 times greater than for a NBHF

click (figure 3c).

4. DiscussionMembers of the genus Cephalorhynchus are thought to have

evolved the exclusive use of NBHF biosonar signals to

become acoustically cryptic, thereby reducing predation risk

by killer whales [15]. This has consequences for the evolution

and function of communication signals within the genus,

because acoustic communication is thought to be limited to

taking place through click series [21,28]. Here, we show

that Heaviside’s dolphins produce a second click type that

is distinct from normal NBHF clicks by having a lower-

frequency content and broader bandwidth which circumvents

some of the limitations of communicating with NBHF clicks.

Heaviside’s dolphins produce these lower-frequency broad-

band signals occasionally in the form of slow click trains

but predominately in the form of burst-pulses, presumably

used for communication [21,24,27,28,37,38].

Communication with burst-pulses is normally achieved

using clicks that are nearly indistinguishable from echoloca-

tion clicks in delphinids [28,37] and phocoenids [21,48],

apart from low-frequency pulsed signals such as bottlenose

dolphin (Tursiops sp.) pops [49] or jaw claps [50]. However,

in Heaviside’s dolphins, clicks comprising most burst-pulses

appear to be a modified and clearly distinguishable version

(86% classification success based only on spectral differences:

change in beamwidth0

–580 90 100 110 120 130

change in active space

500

–10

–15

–20

–25

–30

–35

rela

tive

inte

nsity

(dB

) Fc (kHz) 450

400

350

300

250

200

pote

ntia

l cha

nge

in a

ctiv

e sp

ace

(%)

SLRMS:

174 dB (max)166 dB (+1 s.d.)161 dB (mean)156 dB (–1 s.d.)

–40 150WenzSS6

WenzSS4

WenzSS2

WenzSS1

149 dB (min)

0 10 20 30 40 50 60angle (°)

70 80 90 50 55 60 65 70NL1kHz¢ dB re 1mPa2 Hz–1

75 80

change in detection range

receiver aspect 0°

(head-on) source angle

800 m

receiver aspect 45° receiver aspect 90°

(broadside)

600 m

400 m

30°

0°

60°

–30°

–60°

source angle

800 m

600 m

400 m

30°

0°

60°

–30°

–60°

source angle

800 m

600 m

400 m

30°

0°

60°

–30°

–60°BPNBHF

BPNBHF

BPNBHF

(a)

(b)(i) (ii) (iii)

(c)

Figure 3. Switching to lower-frequency burst-pulse signals increases beamwidth and active space. (a) Transmission beam modelled for 10 standard NBHF clicks and9 burst-pulse clicks of varying frequency using a circular piston model. (b) Detection range modelled for a typical NBHF signal (blue solid line) and a lower-frequency(80 kHz) burst-pulse signal (green dashed line) under Wenz Sea State (SS) two noise conditions and with 161 dB RMS source level, with receivers oriented (i)towards the source, (ii) at a 458 angle to the source or (iii) at a 908 angle to the source. (c) Relative active space for burst-pulse signals compared to theactive space of NBHF signals. Note that while detection range will depend on specific model parameters, the qualitative relationship between the detectionrange of NBHF and burst-pulse signals is consistent under a wide range of noise levels (including Wenz SS1 to Wenz SS6 wind-generated noise, with thermalnoise constant) and source levels (covering full range of source levels measured for Heaviside’s dolphins in [33]).

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figure 2d). Most of the burst-pulses analysed (63%) contained

energy beginning at approximately 50 kHz, which is an

octave lower than signals reported for other NBHF species

[21,22,28,51]. Consequently, most of the recorded broadband

signals are well within the hearing limit of killer whales

(upper limit at approximately 100 kHz) [20]. This makes

these signals risky to produce, especially in Namibia where

killer whales are known to occur and predate on cetaceans

[52], including Heaviside’s dolphins in the study area (J.-P.

Roux 2016, personal communication).

One explanation for the use of lower-frequency broadband

signals could be to reduce signal ambiguity by allowing con-

specifics to differentiate communication signals from foraging

buzzes. We addressed this theory by using a cross-validated

random forest classification algorithm with feature vectors

containing only ICI parameters, only spectral and temporal

click parameters, or containing all parameters combined.

Both burst-pulses and foraging buzzes were accurately classi-

fied (95% accuracy) by interclick intervals without including

spectral and temporal click parameters, so these do not

seem to be necessary for accurate discrimination of burst-

pulses from foraging buzzes. Rather, it seems likely that

ICIs by themselves may allow animals to identify communi-

cation signals and it will be interesting to see if that is the

case for other NBHF species as well.

A second, similar explanation for the use of lower-

frequency broadband signals is to increase signal complexity

in the repertoire, thus allowing for encoding a greater variety

of messages. Repertoire complexity could be augmented

either by producing non-NBHF communication signals at

repetition rates that are also used for foraging signals, or

by composing communication signals with different click

types. However, we see only little evidence for either of

these explanations: burst-pulses were composed predomi-

nantly of lower-frequency clicks, with no evidence of

burst-pulses composed of different click types, and with rep-

etition rates consistently higher than for other signal types

such as click trains or foraging buzzes. However, the lower-

frequency cut-off did vary between burst-pulses, and it

is unclear how much of this is due to off-axis distortion

[46,53] or could be used to encode information.

Finally, a third possible explanation for the use of these

signals is that the lower frequency helps to increase the detec-

tion range and thus favours signal detection for nearby

conspecifics. High-frequency signals suffer from increased

sound absorption as they propagate through water, and

thus attenuate faster than lower frequencies [7]. By reducing

the predominant frequency, signals will suffer less frequency-

dependent absorption and thus travel farther underwater

[51]. At the same time, both transmission directivity and

receiving directivity will be lower (figure 3a), and thus

energy will be more equally distributed around the vocaliz-

ing animal [47,54]. The modelled detection ranges of NBHF

and burst-pulse signals support this hypothesis and show

that significant improvements in detection range are poss-

ible by switching to lower-frequency burst-pulse signals,

especially for receivers that are oriented away from or

located outside the centre of the sound beam (figure 3b).

The relative change in active space is driven mostly by the

change in sound radiation and partly by a lower sound

absorption and thus is relatively independent of the actual

source level and the absolute detection range of the animal

(figure 3c). Since the noise at NBHF signal frequencies is

primarily thermal noise, increasing wind-generated ambient

noise decreases the potential gain in active space, but

active space remains higher for burst-pulse signals across

the entire range of modelled noise levels from Wenz Sea

State 1 through Wenz Sea State 6 conditions (figure 3c).

Furthermore, the change in active space may be greater if

animals simultaneously change transmission aperture

through manipulations of air sacs or soft tissue structures,

such as suggested for echolocating delphinids [46] or harbour

porpoises emitting foraging buzzes [55]. Thus, the most likely

reason for Heaviside’s dolphins to use risky, lower-frequency

broadband signals is to circumvent the restrictions in com-

municating with a short-range, highly directional NBHF

signal imposed by shifting their biosonar above the hearing

range of killer whales. The estimated increase in active

space achieved by the lower-frequency broadband signals is

still far less than could be achieved by using whistles [26],

thus this secondary click type represents a compromise

between remaining acoustically cryptic (especially when

foraging) and possessing the ability to communicate over a

greater range when necessary.

It is possible that other NBHF species may take advantage

of selectively increasing their active space. Neonatal phocoe-

nids have been reported to produce pulsed signals with a

strong low-frequency (approx. 1–3 kHz) content just after

birth and begin to exclusively produce NBHF clicks between

four [56] and 20 [57] days postnatal. It is not yet understood

if this is related to morphological changes or learned call

behaviour. Regardless, calves’ ability to produce lower-

frequency signals with greater active space may be useful

for mother–offspring cohesion during the first days of life.

Additionally, sporadic broadband clicks and low-frequency

(4–16 kHz) whistle sounds have been recorded in the pres-

ence of mother and calf pairs of Commerson’s dolphins

(C. commersonii) [58]. Thus, we should not unequivocally dis-

miss the possibility of finding lower-frequency communication

signals in species that are considered acoustically cryptic

NBHF species.

Ethics. This research was conducted by the ‘Namibian Dolphin Project’with permission from the Namibian Ministry of Fisheries and MarineResources and with ethics 252 clearance from the University of PretoriaAnimal Use and Care Committee (Reference: ec061-09 AUCC).

Data accessibility. The datasets supporting this article have beenuploaded as part of the electronic supplementary material and inDryad Digital Repository (doi:10.5061/dryad.64048p0) [59].

Authors’ contributions. M.J.M., T.G. and S.H.E. conceived and designedthe experiments. M.J.M. performed the experiments and collecteddata. M.J.M. and F.H.J analysed the data. M.J.M., T.G., S.H.E. andF.H.J. contributed materials and analysis tools. F.H.J. contributed toacoustic modelling. M.J.M., T.G., S.H.E. and F.H.J. wrote the paperand approved final submission.

Funding. This research was supported by a Fulbright U.S. ResearchFellowship, the National Geographic Society’s Emerging ExplorersGrant in conjunction with the Waitt Foundation (38115) and theUniversity of Pretoria’s Zoology Department. T.G. was funded bythe Claude Leon Foundation, and S.H.E. was funded by the SouthAfrican National Research Foundation. F.H.J. acknowledges fundingfrom the Office of Naval Research (N00014-1410410) and an AIAS-COFUND fellowship from Aarhus Institute of Advanced Studies.

Competing interests. We declare we have no competing interests.

Acknowledgements. The authors gratefully acknowledge Heiko Metzger,Dr J.-P. Roux, Jeff Slater, Melissa Nel, Robert Sparg, the leaders of theSNAK Acoustic Communication Course, Aarhus University, andfour anonymous reviewers for providing comments and helpfulfeedback to improve this manuscript.

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References

1. Bradbury J, Vehrencamp S. 1998 Signal designrules. In Principles of animal communication,pp. 571 – 618. Sunderland, MA: SinauerAssociates.

2. Curio E. 1976 The ethology of predation. Berlin,Germany: Springer-Verlag.

3. Ruxton GD. 2009 Non-visual crypsis: a review of theempirical evidence for camouflage to senses otherthan vision. Phil. Trans. R. Soc. B 364, 549 – 557.(doi:10.1098/rstb.2008.0228)

4. Wilson DR, Hare JF. 2006 The adaptive utility ofRichardson’s ground squirrel (Spermophilusrichardsonii) short-range ultrasonic alarm signals.Can. J. Zool. 84, 1322 – 1330. (doi:10.1139/Z06-120)

5. Jones KJ, Hill WL. 2001 Auditory perception ofhawks and owls for passerine alarm calls. Ethology107, 717 – 726. (doi:10.1046/j.1439-0310.2001.00698.x)

6. Tyack P. 1998 Acoustic communication under the sea.In Animal acoustic communication (eds SL Hopp, MJOwren, CS Evans), pp. 163 – 220. Berlin, Germany:Springer. (doi:10.1007/978-3-642-76220-8_6)

7. Clay CS, Medwin H. 1977 Acoustical oceanography:principles and applications. New York, NY:Wiley-Interscience.

8. Deecke VB, Ford JKB, Slater PJB. 2005 The vocalbehaviour of mammal-eating killer whales:communicating with costly calls. Anim. Behav. 69,395 – 405. (doi:10.1016/j.anbehav.2004.04.014)

9. Au W. 1993 The sonar of dolphins. New York, NY:Springer-Verlag. (doi:10.1007/978-1-4612-4356-4)

10. Aguilar de Soto N, Madsen PT, Tyack P, Arranz P,Marrero J, Fais A, Revelli E, Johnson M. 2012 Noshallow talk: cryptic strategy in the vocalcommunication of Blainville’s beaked whales. Mar.Mamm. Sci. 28, 75 – 92. (doi:10.1111/j.1748-7692.2011.00495.x)

11. Rankin S, Archer F, Barlow J. 2013 Vocalactivity of tropical dolphins is inhibited by thepresence of killer whales, Orcinus orca. Mar.Mamm. Sci. 29, 679 – 690. (doi:10.1111/j.1748-7692.2012.00613.x)

12. Thomas JA, Ferm LM, Kuechle VB. 1989 Silence asan antipredation strategy by weddell seals. SanDiego, CA: Naval Ocean Systems Center.

13. Wahlberg M, Beedholm K, Heerfordt A, Møhl B.2011 Characteristics of biosonar signals from thenorthern bottlenose whale, Hyperoodon ampullatus.J. Acoust. Soc. Am. 130, 3077 – 3084. (doi:10.1121/1.3641434)

14. Fenton BM, Jensen FH, Kalko EK, Tyack PL. 2014Sonar signals of bats and toothed whales. InBiosonar (eds A Surlykke, P Nachtigall, R Fay,A Popper), pp. 11 – 59. New York, NY: Springer.(doi:10.1007/978-1-4614-9146-0_2)

15. Morisaka T, Connor RC. 2007 Predation by killerwhales (Orcinus orca) and the evolution of whistleloss and narrow-band high frequency clicks inodontocetes. J. Evol. Biol. 20, 1439 – 1458. (doi:10.1111/j.1420-9101.2007.01336.x)

16. Morisaka T. 2012 Evolution of communicationsounds in odontocetes: a review. Int. J. Comp.Psychol. 25, 1 – 20.

17. Au WWL. 1997 Echolocation in dolphins with adolphin-bat comparison. Bioacoustics 8, 137 – 162.(doi:10.1080/09524622.1997.9753357)

18. Møhl B, Andersen S. 1973 Echolocation: high-frequency component in the click of the harbourporpoise (Phocoena ph.). J. Acoust. Soc. Am. 54,1368 – 1372. (doi:10.1121/1.1914435)

19. Andersen S, Amundin M. 1976 Possible predator-related adaption of sound production and hearingin the harbour porpoise (Phocoena phocoena).Aquat. Mamm. 4, 56 – 58.

20. Szymanski MD, Bain DE, Kiehl K, Pennington S,Wong S, Henry KR. 1999 Killer whale (Orcinus orca)hearing: auditory brainstem response andbehavioral audiograms. J. Acoust. Soc. Am. 106,1134 – 1141. (doi:10.1121/1.427121)

21. Clausen KT, Wahlberg M, Beedholm K, DeRuiterSL, Madsen PT. 2010 Click communication inharbour porpoises Phocoena phocoena.Bioacoustics 20, 1 – 28. (doi:10.1080/09524622.2011.9753630)

22. Kyhn LA, Tougaard J, Beedholm K, Jensen FH, AsheE, Williams R, Madsen PT. 2013 Clicking in a killerwhale habitat: narrow-band, high-frequencybiosonar clicks of harbour porpoise (Phocoenaphocoena) and Dall’s porpoise (Phocoenoides dalli).PLoS ONE 8, e63763. (doi:10.1371/journal.pone.0063763)

23. Tyack P. 1986 Population biology, social behaviorand communication in whales and dolphins. TrendsEcol. Evol. 1, 144 – 150. (doi.org/10.1016/0169-5347(86)90042-X)

24. Herzing DL. 1996 Vocalizations and associatedunderwater behavior of free-ranging Atlanticspotted dolphins, Stenella frontalis, and bottlenosedolphins, Tursiops truncatus. Aquat. Mamm. 22,61 – 79.

25. Miller PJ. 2006 Diversity in sound pressure levelsand estimated active space of resident killer whalevocalizations. J. Comp. Physiol. A 192, 449. (doi:10.1007/s00359-005-0085-2)

26. Jensen F, Beedholm K, Wahlberg M, Bejder L,Madsen PT. 2012 Estimated communication rangeand energetic cost of bottlenose dolphin whistlesin a tropical habitat. J. Acoust. Soc. Am. 131,582 – 592. (doi:10.1121/1.3662067)

27. Amundin M. 1991 Sound production in odontoceteswith emphasis on the harbour porpoise Phocoenaphocoena. PhD dissertation, Stockholm, Sweden.

28. Dawson SM. 1991 Clicks and communication: thebehavioral and social contexts of Hector’s dolphinvocalizations. Ethology 88, 265 – 276. (doi:10.1111/j.1439-0310.1991.tb00281.x)

29. Fischer J, Wadewitz P, Hammerschmidt K. 2016Structural variability and communicative complexityin acoustic communication. Anim. Behav. 134,229 – 237. (doi:10.1016/j.anbehav.2016.06.012)

30. Wiley, RH. 2013 Communication as a transfer ofinformation: measurement, mechanism, andmeaning. In Animal communication theory:information and influence (ed. U Stegmann),pp. 113 – 129. Cambridge, UK: Cambridge UniversityPress.

31. Elwen S, Meyer MA, Best PB, Kotze PGH, ThorntonM, Swanson S. 2006 Range and movements offemale Heaviside’s dolphins (Cephalorhynchusheavisidii), as determined by satellite-linkedtelemetry. J. Mammal. 87, 866 – 877. (doi:10.1644/05-MAMM-A-307R2.1)

32. Behrmann CA. 2011 Occurrence and groupdynamics of Heaviside’s dolphins (Cephalorhynchusheavisidii) in Table Bay, Western Cape, South Africa.MSc thesis, University of Pretoria, Pretoria,South Africa.

33. Morisaka T, Karczmarski L, Akamatsu T, Sakai M,Dawson S, Thornton M. 2011 Echolocation signals ofHeaviside’s dolphins (Cephalorhynchus heavisidii).J. Acoust. Soc. Am. 129, 449 – 457. (doi:10.1121/1.3519401)

34. Griffin DR, Webster FA, Michael CR. 1960 Theecholocation of flying insects by bats. Anim. Behav.8, 141 – 154. (doi:10.1016/0003-3472(60)90022-1)

35. Miller LA, Pristed J, Møshl B, Surlykke A. 1995 Theclick-sounds of narwhals (Monodon monoceros) inInglefield Bay, Northwest Greenland. Mar. Mamm.Sci. 11, 491 – 502. (doi:10.1111/j.1748-7692.1995.tb00672.x)

36. Wisniewska DM, Johnson M, Teilmann J, Rojano-Donate L, Shearer J, Sveegaard S, Miller LA, SiebertU, Madsen PT. 2016 Ultra-high foraging rates ofharbor porpoises make them vulnerable toanthropogenic disturbance. Curr. Biol. 26,1441 – 1446. (doi:10.1016/j.cub.2016.03.069)

37. Lammers MO, Au WWL, Aubauer R, Nachtigall PE.2004 A comparative analysis of the pulsedemissions of free-ranging Hawaiian spinner dolphins(Stenella longirostris). In Echolocation in bats anddolphins (eds JA Thomas, CF Moss, M Vater),pp. 414 – 419. Chicago, IL: The University ofChicago Press.

38. Blomqvist C, Amundin M. 2004 High-frequencyburst-pulse sounds in agonistic/aggressiveinteractions in bottlenose dolphins, Tursiopstruncatus. In Echolocation in bats and dolphins(eds JA Thomas, CF Moss, M Vater), pp. 425 – 431.Chicago, IL: The University of Chicago Press.

39. Madsen PT, Wahlberg M. 2007 Recording andquantification of ultrasonic echolocation clicks fromfree-ranging toothed whales. Deep Sea Res. 1Oceanogr. Res. Pap. 54, 1421 – 1444. (doi:10.1016/j.dsr.2007.04.020)

40. Jensen FH, Rocco A, Mansur RM, Smith BD, JanikVM, Madsen PT. 2013 Clicking in shallow rivers:short-range echolocation of Irrawaddy and Gangesriver dolphins in a shallow, acoustically complexhabitat. PLoS ONE 8, e59284. (doi:10.1371/journal.pone.0059284)

rspb.royalsocietypublishing.orgProc.R.Soc.B

285:20181178

8

on July 18, 2018http://rspb.royalsocietypublishing.org/Downloaded from

41. Au WWL, Kastelein RA, Rippe T, Schooneman NM.1999 Transmission beam pattern and echolocationsignals of a harbor porpoise (Phocoena phocoena).J. Acoust. Soc. Am. 106, 3699 – 3705. (doi:doi.org/10.1121/1.428221)

42. Madsen PT, Kerr I, Payne R. 2004 Source parameterestimates of echolocation clicks from wild pygmykiller whales (Feresa attenuata). J. Acoust. Soc. Am.116, 1909 – 1912. (doi:10.1121/1.1788726)

43. Fox J, Weisberg S. 2011 An R companion to appliedregression, 2nd edn. Thousand Oaks, CA: SagePublications.

44. Ogle D. 2017 FSA: Fisheries Stock Analysis. Rpackage version 0.8.16.

45. Breiman L. 2001 Random forests. Mach. Learn. 45,5 – 32. (doi:10.1023/A:1010933404324)

46. Jensen FH, Wahlberg M, Beedholm K, Johnson M,de Soto NA, Madsen PT. 2015 Single-click beampatterns suggest dynamic changes to the field ofview of echolocating Atlantic spotted dolphins(Stenella frontalis) in the wild. J. Exp. Biol. 218,1314 – 1324. (doi:10.1242/jeb.116285)

47. Kastelein RA, Janssen M, Verboom WC, de Haan D.2005 Receiving beam patterns in the horizontalplane of a harbor porpoise (Phocoena phocoena).J. Acoust. Soc. Am. 118, 1172 – 1179. (doi:10.1121/1.1945565)

48. Soerensen PM, Wisniewska DM, Jensen FH,Johnson M, Teilmann J, Madsen PT. 2018Click communication in wild harbour porpoises(Phocoena phocoena). Sci. Rep. UK 8, 9702.

49. Connor RC, Smolker RA. 1996 ‘Pop’ goesthe dolphin: a vocalization male bottlenosedolphins produce during consortships.Behaviour 133, 643 – 662. (doi:10.1163/156853996X00404)

50. Caldwell MC, Haugen RM, Caldwell DK. 1962 High-energy sound associated with fright in the dolphin.Science 138, 907 – 908. (doi:10.1126/science.138.3543.907)

51. Madsen PT, Carder D, Bedholm K, Ridgway S. 2005Porpoise clicks from a sperm whale nose:Convergent evolution of 130 kHz pulses in toothedwhale sonars? Bioacoustics 15, 195 – 206. (doi:10.1080/09524622.2005.9753547)

52. Best PB, Meyer MA, Lockyer C. 2010 Killer whales inSouth African waters: a review of their biology.Afr. J. Mar. Sci. 32, 171 – 186. (doi:10.2989/1814232x.2010.501544)

53. Wahlberg M et al. 2011 Source parameters ofecholocation clicks from wild bottlenose dolphins(Tursiops aduncus and Tursiops truncatus).J. Acoust. Soc. Am. 130, 2263 – 2274. (doi:10.1121/1.3624822)

54. Jakobsen L, Ratcliffe JM, Surlykke A. 2013Convergent acoustic field of view in echolocatingbats. Nature 493, 93 – 96. (doi:10.1038/nature11664)

55. Wisniewska DM, Ratcliffe JM, Beedholm K, ChristensenCB, Johnson M, Koblitz JC, Wahlberg M, Madsen PT.2015 Range-dependent flexibility in the acoustic fieldof view of echolocating porpoises (Phocoenaphocoena). Elife 4, e05651. (doi:10.7554/eLife.05651)

56. Delgado-Garcia L. 2016 Acoustic development andbehaviour of odontocete calves. PhD thesis,University of Southern Denmark, Odense, Denmark.

57. Li S, Wang K, Wang D, Dong S, Akamatsu T. 2008Simultaneous production of low-and high-frequencysounds by neonatal finless porpoises. J. Acoust. Soc.Am. 124, 716 – 718. (doi:10.1121/1.2945152)

58. Reyes Reyes MV, Tossenberger VP, Iniguez MA,Hildebrand JA, Melcon ML. 2016 Communicationsounds of Commerson’s dolphins (Cephalorhynchuscommersonii) and contextual use of vocalizations.Mar. Mamm. Sci. 32, 1219 – 1233. (doi:10.1111/mms.12321)

59. Martin MJ, Gridley T, Elwen SH, Jensen FH. 2018Data from: Heaviside’s dolphins (Cephalorhynchusheavisidii) relax acoustic crypsis to increasecommunication range. Dryad Digital Repository.(doi:10.5061/dryad.64048p0)

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Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

1

Heaviside’s dolphins (Cephalorhynchus heavisidii) relax acoustic

crypsis to increase communication range

Morgan J. Martin1, Tess Gridley2, Simon H. Elwen1 and Frants H. Jensen3,4

1Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria.

C/o Sea Search Research and Conservation NPC, 4 Bath Rd, Cape Town 7945, South Africa

2 Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical

Sciences, University of Cape Town. C/o Sea Search Research and Conservation NPC, 4 Bath Rd,

Cape Town 7945, South Africa.

3Aarhus Institute of Advanced Studies, Aarhus University, Aarhus 8000, Denmark

4Biology Department, Woods Hole Oceanographic Institution, 266 Woods Hole Rd, Woods Hole,

MA 02543, USA

Contact: mjmartin@sandiego.edu

Field site and data collection

The study area, Shearwater Bay, located near Lüderitz in southern Namibia (-26° 37' S, 15° 05' E),

is a small bay (6.5 km2) which consists of shallow water with a maximum depth of 12 m. Data

analysed in this study were collected from wild Heaviside’s dolphins located in Shearwater Bay

during April and May 2016.

Underwater acoustic recordings of Heaviside’s dolphin vocalisations were made under calm

weather conditions (Beaufort sea state ≤ 2) using two high frequency recording hydrophones

(SoundTrap 300 HF; www.oceaninstruments.co.nz). The hydrophones were mounted 1 m apart

and suspended 1.5 m below a 4.2 m fiberglass ocean kayak. Sound was digitised at a sampling rate

of 576 kHz with a 16-bit resolution, and settings were configured to include high gain (+12 dB)

and a high pass filter (400 Hz), effective sensitivity: -171 dB re 1 V/µPa, flat frequency response:

400 Hz – 150 kHz ± 3 dB. A built in anti-aliasing filter exists at 150 kHz. Recordings were stored

as compressed 30-min SUD files on the SoundTraps.

The kayak and hydrophone array were deployed when Heaviside’s dolphins were observed from

shore and weather conditions permitted. When an individual or group of dolphins was sighted, the

observer on board the kayak would attempt to approach with minimal disturbance. A group was

defined as two or more dolphins in close proximity (< 50 m radius), generally carrying out the

same activity. Behaviour and focal group information were collected concurrently with sound

recordings using a Dictaphone. A visual survey group-follow with incident sampling protocol [1,

2] was used to record surface behaviour along with group size, group composition (presence or

absence of calves), group spacing, and estimated distance from the hydrophone array. Definitions

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

2

of behavioural states and events were adapted from [3, 4]. A secondary visual survey method was

implemented from shore (20 m elevation) using two observers with walkie talkies and a Sony

Handycam camcorder to assist the kayak-based observer to locate and maintain focal groups,

monitor other Heaviside’s dolphin groups present in the bay, provide information on behaviour

and to assess inter-observer reliability [5].

Statistical analyses

All high-quality measured signals visually classified into the four proposed categories were

evaluated to examine the ability to quantitatively distinguish pulsed signal types. Signal parameters

were compared across signal categories using non-parametric Kruskal-Wallis tests and subsequent

Dunn’s post-hoc tests for pairwise comparisons in R version 3.4.2 [6, 7] (Suppl. Table 1). Further

in R, all high-quality signals were evaluated with a principal component analysis (PCA) as it is

robust to correlated variables. The PCA was used to identify the most influential parameters for

signal classification. Nine parameter variables were included in the PCA: 5th, median (50th) and

95th percentile interclick intervals (ICI), peak frequency, centroid frequency, -10 dB bandwidth,

RMS bandwidth, Q-ratio, and -10 dB click duration (Suppl. Fig. 1). All values were log-

transformed prior to the analysis. The Kaiser criterion was used to identify the number of principal

components to retain and was determined by eigenvalues > 1 (Suppl. Table 2). We then used a

Random Forest classifier [8] to measure prediction accuracy as a function of buzz and burst-pulse

signal categories using either interclick intervals (5th, 50th and 95th percentiles for each signal),

spectral and temporal click parameters (peak frequency, centroid frequency, -10 dB bandwidth,

RMS bandwidth, Q-ratio, and -10 dB click duration), or all parameters combined as features to

test the potential benefit of spectral differences in decreasing signal ambiguity in the repertoire.

The Random Forest classifier was built in MATLAB 2017b using a ‘bagged trees’ ensemble

classifier with 30 learners. Prediction accuracy was measured using 5-fold cross-validation to

prevent overfitting. To measure consistency in prediction accuracy, the classifier was trained 100

times and prediction accuracy was measured for each iteration.

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

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Supplementary Table 1. Dunn’s post-hoc tests of measured parameters across signal categories. All parameters were log-transformed

before statistical analysis. Note that initially click trains were differentiated visually from buzzes and burst-pulses using click rates with

interclick intervals exceeding 10 ms. A subset of click trains were composed of lower-frequency, broader bandwidth signals than

previously described [9], and we therefore divided click trains into NBHF click trains and broadband click trains by inspecting

spectrograms. Initially, buzz and burst-pulse signals were visually differentiated by the presence or absence of a preceding click train as

burst-pulses occur as isolated signals.

ICI5th ICIMED ICI95th FP FC BW10dB BWRMS QRMS Dur10dB

Signal Comparison p p p p p p p p p

NBHF Train : BB Train 0.9695 0.8520 0.6452 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

NBHF Train : Buzz < 0.0001 < 0.0001 < 0.0001 0.1466 0.7288 0.3543 0.0062 0.0189 0.4201

NBHF Train : Burst-pulse < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

Buzz : Burst-pulse < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

Buzz : BB Train < 0.0001 < 0.0001 < 0.0001 0.0029 < 0.0001 < 0.0001 0.0002 < 0.0001 < 0.0001

Burst-pulse : BB Train < 0.0001 < 0.0001 < 0.0001 0.7380 0.4669 0.5611 0.9823 0.6735 0.0459

α = 0.05, p-values below this threshold are shown in boldface

Abbreviations: NBHF Train = narrowband high-frequency click train; BB Train = broadband click train; ICI5th, ICIMED and ICI95th = 5th,

median (50th) and 95th percentile interclick intervals (ms); FP = peak frequency (kHz); FC = centroid frequency (kHz); BW10dB = -10 dB

bandwidth (kHz); BWRMS = root mean square bandwidth (kHz); QRMS = FC/BWRMS; Dur10dB = -10 dB click duration (µs)

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

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Supplementary Table 2. PCA output of the nine measured parameter variables from 159 signals.

All parameter values were log-transformed prior to the PCA. Parameter abbreviations: ICI5th,

ICIMED and ICI95th = 5th, median (50th) and 95th percentile interclick intervals (ms); FP = peak

frequency (kHz); FC = centroid frequency (kHz); BW10dB = -10 dB bandwidth (kHz); BWRMS =

root mean square bandwidth (kHz); QRMS = FC/BWRMS; Dur10dB = -10 dB click duration (µs)

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9

Importance of Components

Standard Deviation 2.099 1.545 1.088 0.794 0.461 0.383 0.172 0.058 0.010

Prop. of Variance 0.490 0.265 0.132 0.070 0.024 0.016 0.003 0.000 0.000

Cumulative Prop. 0.490 0.755 0.886 0.956 0.980 0.996 1.000 1.000 1.000

Loadings with Rotation = (9 x 9)

ICI5th -0.326 -0.460 0.104 -0.037 0.063 0.050 -0.602 -0.548 -0.005

ICIMED -0.329 -0.459 0.116 -0.056 0.045 0.031 -0.166 0.797 0.002

ICI95th -0.328 -0.451 0.126 -0.058 -0.009 -0.010 0.777 -0.255 0.003

FP -0.254 0.332 0.514 0.231 0.704 -0.107 0.026 -0.002 -0.001

FC -0.226 0.306 0.619 -0.160 -0.585 0.252 -0.035 -0.002 0.201

BW10dB 0.369 -0.218 0.398 0.000 -0.173 -0.791 -0.048 -0.001 0.001

BWRMS 0.405 -0.139 0.328 -0.361 0.144 0.359 0.018 -0.002 -0.656

QRMS -0.426 0.210 -0.124 0.280 -0.292 -0.255 -0.019 0.007 -0.728

Duration10dB -0.284 0.244 -0.179 -0.840 0.141 -0.320 -0.017 -0.008 0.000

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Suppl. Fig. 1: Signal parameters and discrimination of signal types. A: Q-ratio (centroid frequency

/ RMS bandwidth) as a function of centroid frequency. B: Log-transformed RMS bandwidth as a

function of log-transformed median ICI. C: Principal component analysis of signal types including

nine parameter variables. Each data point represents one measured pulsed signal. PC 1 primarily

represents RMS bandwidth and Q-ratio parameters. PC 2 represents click rate parameters (5th, 50th

and 95th percentile interclick intervals).

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

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Acoustical modelling of detection range and impact on active space

To investigate how click type affects conspecific detection range and active space, we built an

acoustic model of detection range under a noise-limited scenario using the passive sonar equation

[10] and assuming successful detection when received sound energy exceeded masking noise

energy integrated across auditory bandwidth:

Eq. 1: 𝑅𝐿 = 𝑆𝐿 − 𝑇𝐿 > 𝑁𝐿

Here, RL is the received echo level, SL is the source level measured in energy flux density, TL is

the transmission loss between source and receiver, NL is the masking noise level; all in decibels.

Since toothed whales have directional sound emission and directional hearing, we modelled

detection range explicitly as a function of the outgoing source angle 𝜃𝑆 and the incoming receiver

aspect 𝜃𝑅. Directional sound emission was modelled through a transmission gain (TG), the

difference between off-axis apparent source level and the on-axis source level, with values always

negative. Directional hearing was modelled through an auditory gain (AG), the difference between

off-axis hearing sensitivity and on-axis hearing sensitivity, with values always negative.

Eq. 2: Successful detection when: 𝑆𝐿 + 𝑇𝐺(𝜃𝑆) − 𝑇𝐿 + 𝐴𝐺(𝜃𝑅) > 𝑁𝐿

Source level: On-axis source level for Heaviside’s dolphin NBHF clicks has been measured to

161±5 dB [min 149, max 174] re. 1 µPa RMS for a -10 dB duration of 74 µs [9]. To reflect the

temporal integration of the auditory system, we corrected these source levels for a temporal

integration time of 264 µs for a bottlenose dolphin [11] by adding 10𝑙𝑜𝑔10(74𝜇𝑠 264𝜇𝑠⁄ ).

Directional sound emission: Off-axis apparent source level was modelled using a circular,

symmetric piston which has frequently been used to approximate the sonar beam of toothed whales

[12]. Transmission beams were calculated for 10 NBHF clicks and 9 burst-pulse clicks using a

piston size of 6.4 cm diameter and a waveform filtered with a 10 kHz - 150 kHz 6-pole Butterworth

bandpass filter (Fig. 3A). This resulted in a directivity index (DIT) of 24 dB for NBHF clicks,

similar to that of other NBHF species [13-15] and decreasing to 20 dB for burst-pulse clicks. For

the rest of the paper, we used a model burst-pulse click with a centroid frequency of 80 kHz1 to

calculate the possible change in detection range. We assumed that animals were energy limited

and that a change in directivity would therefore lead to a lower on-axis source level, so on-axis SL

for burst-pulse clicks was set 4 dB lower than for NBHF clicks.

Transmission loss: We estimated transmission loss as the combination of spherical spreading loss

and frequency dependent absorption, so that TL = 20 log10(R) + αR. Here, R was the range to the

target (m), and the absorption coefficient α was calculated using the centroid frequency of each

1 Note that click parameters reported in manuscript are for signals filtered with a wider bandwidth Butterworth filter

(20 kHz – 275 kHz), and centroid frequency measurements here are therefore similar but not directly comparable.

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

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click type [16], resulting in an absorption coefficient of 0.40 dB/m for a 128 kHz NBHF click and

0.22 dB/m for an 80 kHz burst-pulse click.

Directional hearing: No data were available for hearing directivity at the exact frequencies

required. Instead, we used auditory sensitivity measurements as a function of angle reported for a

harbour porpoise (Phocoena phocoena) at 64 kHz and 100 kHz [17], which represents a similar

shift in frequency (a little over half an octave) as the difference between NBHF and burst-pulse

clicks. While this study found a slightly asymmetric receiving beam, we simplified this by taking

the mean acoustic sensitivity between the left and right side, and then interpolated across values

using a piecewise cubic interpolation. This resulted in a receiver directivity index (DIR) of 8.8 dB

(100 kHz) and 5.1 dB (64 kHz; Suppl. Fig. 2).

Masking noise level: The masking noise energy was estimated as the spectral noise level N0 (in dB

re 1 µPa2Hz-1, i.e. noise intensity per Hz bandwidth) integrated over the auditory filter bandwidth

of the animal and suppressed by the auditory directivity of the animal. Since we did not have

reliable estimates of auditory filter bandwidth for clicks, we assumed a simple 1/3rd octave

bandwidth similar to terrestrial mammals:

Eq. 3: 𝑁𝐿 = 𝑁0(𝐹𝑐) + 10𝑙𝑜𝑔10(0.23 ∗ 𝐹𝐶) − 𝐷𝐼𝑅

The spectral noise level was estimated as Wenz Sea State 2 deep-water noise levels (approximately

58 dB at 1 kHz, with a gradual decrease of 17 dB per decade increase in frequency), plus the

addition of thermal noise (generally at frequencies above 100 kHz) (both in dB re. 1 µPa2Hz-1):

Eq. 4: 𝑁0(𝐹𝑐) = 𝑁0(1 𝑘𝐻𝑧) − 17𝑙𝑜𝑔10 (𝐹𝑐

1 𝑘𝐻𝑧) − 75 + 20𝑙𝑜𝑔10(𝐹𝑐)

----(wind generated noise)--- ---(thermal noise)---

Suppl. Fig. 2: Aspect dependent

hearing sensitivity (implemented

here as auditory gain AG) based on

measurements from a harbour

porpoise [17].

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

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Equations from http://www.usna.edu/Users/physics/ejtuchol/documents/SP411/Chapter11.pdf

Detection range: We solved equation 2 numerically in MATLAB 2013b to find the maximum

detection range where the received level exceeded the masking noise level. The detection range

was calculated as a function of source angle (θS) and receiver aspect (θR), for both a NBHF click

and an 80 kHz burst-pulse click (Suppl. Fig. 3).

Active space: To calculate active space, we assumed a 2D habitat with conspecifics located at the

water surface. We then calculated the total detection area A (m2) by integrating detection range R

as a function of source angle from 0 to 180 degrees and assuming rotational symmetry:

Eq. 5: 𝐴 = 2 ∫ 𝑅(𝜃𝑠) sin 𝑑𝜃𝑠𝜋

0

Since detection range depends both on source angle θS and receiver aspect θR, we assumed an

equal probability of receiver aspect and used the mean detection range as a function of receiver

aspect, so only source angle appears in equation 5.

Sensitivity analysis: The two most important parameters for detection range are source level and

noise level. We therefore conducted a sensitivity analysis to measure the change in total detection

area (ABP/ANBHF) across a wide range of possible source levels and noise levels. We varied wind-

generated noise [N0(1kHz)] from 50 to 75 dB re. 1 µPa2Hz-1 while keeping thermal noise constant,

thus increasingly favouring NBHF signals that are primarily limited by thermal noise. We used 5

different source levels reflecting the full range of NBHF source levels measured empirically from

Heavisides dolphins [9]. For all simulations, the modelled active space for burst pulse signals was

at least twice as large, and for quieter (Sea State 1 or Sea State 2 conditions) as high as 4 to 5 times

as large as for NBHF signals.

Suppl. Fig. 3:

Modelled conspecific

detection range for a

Heaviside’s dolphin

NBHF click and an

80 kHz burst-pulse

click, as a function of

both source angle and

receiver aspect.

Martin et al. 2018 Supplementary Methods Proceedings of the Royal Society B

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References

1. Altmann J. Observational study of behaviour: sampling methods. Behaviour. 1974. 49: 227-

67.

2. Mann J. Behavioural sampling methods for cetaceans: A review and critique. Mar. Mamm.

Sci. 1999. 15: 102-22.

3. Herzing DL. Vocalizations and associated underwater behavior of free-ranging Atlantic

spotted dolphins, Stenella frontalis, and bottlenose dolphins, Tursiops truncatus. Aquat.

Mamm. 1996. 22: 61-79.

4. Henderson EE, Hildebrand JA, Smith MH, Falcone EA. The behavioral context of common

dolphin (Delphinus sp.) vocalizations. Mar. Mamm. Sci. 2011. 28: 439-60.

(doi:10.1111/j.1748-7692.2011.00498.x)

5. Kaufman AB, Rosenthal R. Can you believe my eyes? The importance of interobserver

reliability statistics in observations of animal behaviour. Anim. Behav. 2009. 78: 1487-91.

6. Fox J, Weisberg S. An R Companion to Applied Regression, 2nd Ed. Thousand Oaks,

California: Sage Publications. 2011.

7. Ogle D. FSA: Fisheries Stock Analysis. R package version 0.8.16. 2017.

8. Breiman L. Random Forests. Machine learning. 2001. 45: 5-32.

(doi:10.1023/A:1010933404324)

9. Morisaka T, Karczmarski L, Akamatsu T, Sakai M, Dawson S, Thornton M. Echolocation

signals of Heaviside's dolphins (Cephalorhynchus heavisidii). J. Acoust. Soc. Am. 2011.

129: 449-57. (doi:10.1121/1.3519401)

10. Au W. The Sonar of Dolphins. New York: Springer-Verlag. 1993. (doi:10.1007/978-1-4612-

4356-4)

11. Au WW, Moore PW, Pawloski DA. Detection of complex echoes in noise by an echolocating

dolphin. J. Acoust. Soc. Am. 1988. 83: 662-8.

12. Jensen FH, Wahlberg M, Beedholm K, Johnson M, de Soto NA, Madsen PT. Single-click

beam patterns suggest dynamic changes to the field of view of echolocating Atlantic

spotted dolphins (Stenella frontalis) in the wild. J. Exp. Biol. 2015. 218: 1314-24.

(doi:10.1242/jeb.116285)

13. Kyhn LA, Tougaard J, Jensen F, Wahlberg M, Stone G, Yoshinaga A, et al. Feeding at a high

pitch: source parameters of narrow band, high-frequency clicks from echolocating off-

shore hourglass dolphins and coastal Hector's dolphins. J. Acoust. Soc. Am. 2009. 125:

1783-91. (doi:10.1121/1.3075600)

14. Kyhn LA, Jensen FH, Beedholm K, Tougaard J, Hansen M, Madsen PT. Echolocation in

sympatric Peale's dolphins (Lagenorhynchus australis) and Commerson's dolphins

(Cephalorhynchus commersonii) producing narrow-band high-frequency clicks. J. Exp.

Biol. 2010. 213: 1940-9. (doi:10.1242/jeb.042440)

15. Kyhn LA, Tougaard J, Beedholm K, Jensen FH, Ashe E, Williams R, et al. Clicking in a

killer whale habitat: narrow-band, high-frequency biosonar clicks of harbour porpoise

(Phocoena phocoena) and Dall's porpoise (Phocoenoides dalli). PLOS ONE. 2013. 8:

e63763. (doi:10.1371/journal.pone.0063763)

16. Kinsler LE, Frey AR, Coppens AB, Sanders JV. Fundamentals of Acoustics, 4th Ed. Wiley-

VCH. 1999. p. 560.

17. Kastelein RA, Janssen M, Verboom WC, de Haan D. Receiving beam patterns in the

horizontal plane of a harbor porpoise (Phocoena phocoena). J. Acoust. Soc. Am. 2005.

118: 1172-9. (doi:10.1121/1.1945565)

Martin et al. 2018 Supplementary Appendix Proceedings of the Royal Society B

1

Heaviside’s dolphins (Cephalorhynchus heavisidii) relax acoustic

crypsis to increase communication range

Morgan J. Martin1, Tess Gridley2, Simon H. Elwen1 and Frants H. Jensen3,4

1Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria.

C/o Sea Search Research and Conservation NPC, 4 Bath Rd, Cape Town 7945, South Africa

2 Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical

Sciences, University of Cape Town. C/o Sea Search Research and Conservation NPC, 4 Bath Rd,

Cape Town 7945, South Africa.

3Aarhus Institute of Advanced Studies, Aarhus University, Aarhus 8000, Denmark

4Biology Department, Woods Hole Oceanographic Institution, 266 Woods Hole Rd, Woods Hole,

MA 02543, USA

Contact: mjmartin@sandiego.edu

Appendix S1: Measured parameters from 159 Heaviside’s dolphin pulsed signals grouped by

signal type. Parameter abbreviations: ICI5th, ICIMED and ICI95th = 5th, median (50th) and 95th

percentile interclick intervals (ms); FP = peak frequency (kHz); FC = centroid frequency (kHz);

BWRMS = root mean square bandwidth (kHz); BW10dB = -10 dB bandwidth (kHz); QRMS =

FC/BWRMS; Dur10dB = -10 dB click duration (µs)

Signal Type ICI5th ICI50th ICI95th FP FC BWRMS BW10dB QRMS Dur10dB

NBHF Click Train 24.4 27.9 33.0 121.5 130.5 12.8 27.0 10.2 60.8

NBHF Click Train 30.4 50.2 98.6 130.5 132.9 11.8 32.6 11.2 84.6

NBHF Click Train 25.2 29.0 65.2 131.6 132.3 10.6 25.9 12.4 61.3

NBHF Click Train 23.5 31.9 48.3 130.5 131.0 13.9 24.8 9.5 61.3

NBHF Click Train 24.1 28.9 49.5 121.5 124.1 8.5 19.1 14.5 70.3

NBHF Click Train 31.4 38.6 60.1 127.1 134.1 19.9 58.5 6.8 74.1

NBHF Click Train 15.5 29.8 58.5 127.1 131.3 15.8 57.4 8.3 85.9

NBHF Click Train 21.3 29.0 42.5 128.3 127.6 9.6 21.4 13.3 77.8

NBHF Click Train 25.6 30.1 36.7 136.1 132.4 9.7 31.5 13.7 53.7

NBHF Click Train 27.1 34.5 63.8 122.6 136.6 20.4 61.9 6.7 72.2

NBHF Click Train 21.0 25.7 29.0 127.1 129.4 10.5 25.9 12.4 54.9

NBHF Click Train 33.9 53.8 141.2 127.1 128.8 10.4 31.5 12.4 51.7

NBHF Click Train 42.8 67.2 114.1 123.8 126.0 7.1 23.6 17.8 63.9

NBHF Click Train 22.4 28.1 34.3 128.3 130.1 8.5 23.6 15.3 69.6

NBHF Click Train 14.0 22.3 67.0 122.6 135.0 26.7 93.4 5.1 39.8

NBHF Click Train 26.6 33.8 44.4 128.3 123.2 7.7 27.0 15.9 59.4

NBHF Click Train 17.3 23.5 61.5 137.3 131.3 10.4 31.5 12.6 60.1

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NBHF Click Train 40.1 57.0 87.5 122.6 139.6 27.8 69.8 5.0 74.7

NBHF Click Train 24.1 28.7 34.3 127.1 131.3 13.6 33.8 9.7 51.2

NBHF Click Train 10.6 13.5 21.9 122.6 130.2 14.5 37.1 9.0 49.8

NBHF Click Train 25.2 27.7 31.9 135.0 130.7 10.0 23.6 13.1 72.1

NBHF Click Train 26.1 35.6 73.4 122.6 131.9 14.2 23.6 9.3 70.3

NBHF Click Train 22.9 27.9 42.6 130.5 130.5 9.0 28.1 14.5 65.5

NBHF Click Train 20.9 25.2 36.9 138.4 136.0 16.3 33.8 8.3 51.0

NBHF Click Train 17.6 26.6 42.7 121.5 134.0 20.8 69.8 6.5 51.2

NBHF Click Train 23.2 28.7 46.1 136.1 130.5 14.0 28.1 9.3 56.8

NBHF Click Train 18.7 25.0 49.7 127.1 127.2 11.1 23.6 11.5 64.9

NBHF Click Train 21.2 25.2 42.3 130.5 131.7 15.9 33.8 8.3 54.3

NBHF Click Train 19.8 30.4 49.3 121.5 132.4 15.4 32.6 8.6 63.7

NBHF Click Train 17.1 22.2 29.7 136.1 131.6 11.5 31.5 11.4 67.9

NBHF Click Train 18.1 27.3 37.7 131.6 137.3 20.9 69.8 6.6 77.6

NBHF Click Train 27.3 29.9 41.4 121.5 133.8 20.3 46.1 6.6 65.3

NBHF Click Train 47.9 54.3 61.2 122.6 130.3 11.7 30.4 11.1 91.5

Buzz 6.4 7.0 10.9 137.3 136.6 14.5 30.4 9.4 55.2

Buzz 9.0 9.5 10.1 130.5 132.6 13.2 43.9 10.1 55.9

Buzz 4.2 5.4 7.7 121.5 143.2 31.4 77.6 4.6 39.8

Buzz 2.6 4.4 7.9 123.8 132.3 23.1 40.5 5.7 62.0

Buzz 2.1 2.3 4.6 122.6 130.2 12.7 36.0 10.3 76.6

Buzz 11.2 13.5 15.0 118.1 150.5 39.9 113.6 3.8 54.2

Buzz 5.6 6.6 10.4 122.6 134.0 25.6 51.8 5.2 95.5

Buzz 2.9 3.3 4.4 128.3 132.6 11.3 38.3 11.8 42.7

Buzz 6.7 10.2 11.0 137.3 134.9 17.4 47.3 7.8 54.9

Buzz 4.9 6.3 8.2 126.0 128.0 9.9 18.0 12.9 83.5

Buzz 8.8 9.0 10.3 135.0 138.6 17.2 39.4 8.0 84.6

Buzz 6.5 7.9 9.3 120.4 138.8 25.4 85.5 5.5 71.2

Buzz 6.7 7.7 11.4 135.0 146.1 16.8 50.6 8.7 51.0

Buzz 7.2 7.4 9.2 130.5 135.9 21.8 69.8 6.2 74.3

Buzz 9.9 11.1 13.0 115.9 125.3 21.6 24.8 5.8 145.3

Buzz 6.7 7.8 9.1 111.4 125.4 25.4 33.8 4.9 135.9

Buzz 6.1 7.3 9.0 121.5 125.0 15.1 10.1 8.3 128.7

Buzz 6.2 6.6 9.5 115.9 130.7 25.0 49.5 5.2 79.3

Buzz 8.1 8.7 9.9 124.9 129.0 14.8 21.4 8.7 121.2

Buzz 3.8 4.8 12.9 124.9 126.9 14.8 24.8 8.6 83.7

Buzz 5.9 8.9 12.4 118.1 127.9 18.1 21.4 7.1 79.5

Buzz 5.8 6.7 10.5 121.5 128.5 10.3 27.0 12.5 55.0

Buzz 9.4 10.6 12.3 121.5 124.0 12.9 27.0 9.6 54.9

Buzz 4.3 4.7 6.6 124.9 130.4 19.5 25.9 6.7 62.0

Buzz 8.0 8.3 8.7 120.4 108.5 23.3 68.6 4.7 30.4

Buzz 10.7 11.2 12.6 126.0 131.5 16.3 33.8 8.1 66.5

Buzz 5.7 7.0 8.2 128.3 128.0 13.4 24.8 9.6 78.1

Buzz 4.4 4.5 5.0 113.6 136.8 32.1 97.9 4.3 86.3

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Buzz 8.0 9.8 11.3 122.6 133.7 24.9 32.6 5.4 66.2

Buzz 2.1 2.9 5.5 119.3 138.1 24.9 67.5 5.6 52.3

Buzz 4.3 5.9 8.2 119.3 133.2 21.5 57.4 6.2 80.2

Buzz 5.3 7.4 11.3 126.0 135.8 18.9 25.9 7.2 102.1

Buzz 3.4 6.1 12.9 138.4 134.0 21.0 40.5 6.4 71.0

Buzz 1.8 3.0 5.7 122.6 136.0 23.7 52.9 5.7 68.9

Buzz 3.6 6.6 11.3 123.8 131.5 18.5 27.0 7.1 117.7

Buzz 8.0 10.1 11.7 124.9 131.0 12.3 47.3 10.6 71.7

Buzz 3.3 6.2 7.7 128.3 129.4 13.6 30.4 9.5 55.2

Buzz 8.5 8.9 10.7 130.5 134.7 15.8 28.1 8.6 71.5

Buzz 7.0 7.6 12.4 122.6 132.6 31.6 83.3 4.2 57.8

Buzz 3.3 3.5 8.4 137.3 131.5 9.2 28.1 14.3 56.8

Burst-pulse 1.0 1.1 1.1 122.6 169.9 39.1 132.8 4.3 29.5

Burst-pulse 1.8 2.1 2.7 120.4 132.5 34.4 110.3 3.8 35.4

Burst-pulse 1.3 1.6 11.2 99.0 122.3 36.3 126.0 3.4 32.8

Burst-pulse 1.4 1.8 2.1 119.3 127.1 11.3 31.5 11.3 75.5

Burst-pulse 1.4 1.5 1.6 108.0 119.5 23.7 78.8 5.1 57.3

Burst-pulse 1.4 1.4 1.6 124.9 148.6 36.6 88.9 4.1 60.6

Burst-pulse 1.6 1.6 1.8 97.9 120.9 40.1 133.9 3.0 81.9

Burst-pulse 1.6 1.7 1.8 126.0 131.6 29.9 56.3 4.4 57.8

Burst-pulse 5.4 6.7 7.7 122.6 129.2 18.3 28.1 7.1 58.3

Burst-pulse 1.6 1.7 1.8 145.1 121.4 24.4 81.0 5.0 29.0

Burst-pulse 1.4 1.6 1.7 136.1 122.0 30.4 115.9 4.0 26.4

Burst-pulse 1.5 1.5 1.5 126.0 125.6 24.0 31.5 5.2 63.2

Burst-pulse 1.5 1.6 1.6 91.1 95.2 24.7 47.3 3.9 47.6

Burst-pulse 1.6 1.6 1.7 124.9 124.1 19.9 56.3 6.2 34.7

Burst-pulse 1.3 1.3 1.3 112.5 123.6 20.5 66.4 6.0 53.0

Burst-pulse 1.3 1.3 1.6 135.0 135.5 14.3 14.6 9.5 96.9

Burst-pulse 1.6 1.6 1.8 120.4 112.7 16.7 56.3 6.7 40.8

Burst-pulse 1.4 1.4 1.6 120.4 123.9 21.0 60.8 5.9 33.7

Burst-pulse 1.6 1.6 1.7 105.8 111.5 18.4 45.0 6.0 71.0

Burst-pulse 1.5 1.6 1.7 109.1 110.5 24.7 63.0 4.5 55.4

Burst-pulse 1.4 1.4 1.4 115.9 109.4 36.0 68.6 3.0 36.6

Burst-pulse 1.5 1.6 1.9 104.6 105.3 23.3 40.5 4.5 35.1

Burst-pulse 1.3 1.5 1.6 132.8 121.4 32.3 132.8 3.8 32.3

Burst-pulse 2.7 2.9 4.8 109.1 122.9 34.3 91.1 3.6 57.5

Burst-pulse 1.4 1.4 1.5 106.9 151.5 40.9 129.4 3.7 15.5

Burst-pulse 1.4 1.4 1.5 91.1 105.2 28.6 79.9 3.7 40.1

Burst-pulse 1.6 1.6 1.9 111.4 106.8 27.9 61.9 3.8 31.1

Burst-pulse 1.1 1.2 1.7 105.8 121.7 36.3 112.5 3.4 19.6

Burst-pulse 1.6 1.6 1.9 113.6 105.2 32.8 141.8 3.2 24.7

Burst-pulse 1.6 1.7 2.1 112.5 105.8 29.6 137.3 3.6 29.2

Burst-pulse 1.2 1.3 1.5 102.4 102.7 22.9 51.8 4.5 30.0

Burst-pulse 1.4 1.4 1.6 110.3 105.0 19.1 49.5 5.5 29.5

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Burst-pulse 1.4 1.4 1.7 118.1 113.5 21.2 57.4 5.3 41.5

Burst-pulse 1.5 1.6 1.8 131.6 117.8 37.8 126 3.1 38.4

Burst-pulse 1.4 1.5 1.5 126.0 115.0 26.2 77.6 4.4 27.6

Burst-pulse 1.5 1.6 1.7 127.1 122.1 20.5 74.3 6.0 19.3

Burst-pulse 1.6 1.6 1.8 105.8 121.4 38.7 118.1 3.1 33.0

Burst-pulse 1.5 1.6 1.7 83.3 89.8 36.6 154.1 2.5 22.7

Burst-pulse 1.3 1.6 1.8 108.0 104.0 28.3 72.0 3.7 67.0

Burst-pulse 1.5 1.5 1.5 74.3 89.2 28.3 84.4 3.2 25.4

Burst-pulse 1.4 1.4 1.5 115.9 115.9 26.9 94.5 4.3 41.3

Burst-pulse 1.2 1.2 1.3 104.6 103.7 22.8 34.9 4.6 79.9

Burst-pulse 1.6 1.6 1.7 103.5 107.6 24.1 76.5 4.5 40.3

Burst-pulse 1.4 1.5 1.6 94.5 95.4 31.1 78.8 3.1 21.4

Burst-pulse 1.6 1.6 1.7 120.4 119.5 18.6 37.1 6.4 44.3

Burst-pulse 1.6 1.7 1.9 111.4 133.3 38.3 124.9 3.5 49.0

Burst-pulse 1.5 1.6 1.6 124.9 128.6 28.7 95.6 4.5 53.7

Burst-pulse 1.4 1.6 1.6 112.5 122.1 35.7 78.8 3.4 46.5

Burst-pulse 1.4 1.6 1.7 103.5 112.8 21.1 52.9 5.3 56.8

Burst-pulse 1.5 1.5 1.6 101.3 116.6 38.5 147.4 3.0 33.7

Burst-pulse 1.6 1.6 1.7 91.1 104.3 38.3 123.8 2.7 43.2

Burst-pulse 1.3 1.5 1.7 130.5 131.8 24.4 25.9 5.4 62.5

Burst-pulse 1.3 1.6 1.7 112.5 113.5 22.7 55.1 5.0 84.2

Burst-pulse 1.4 1.4 1.5 122.6 118.9 23.8 37.1 5.0 49.7

Burst-pulse 1.8 1.9 2.0 74.3 84.2 24.7 73.1 3.4 61.6

Burst-pulse 1.4 1.5 1.5 117.0 124.7 26.1 43.9 4.8 62.0

Burst-pulse 1.8 1.8 2.0 119.3 126.9 25.6 74.25 5.0 95.5

Burst-pulse 1.9 2.3 2.9 126.0 151.2 36.0 102.4 4.2 33.0

BB Click Train 15.4 24.0 34.1 113.6 110.7 37.9 142.9 2.9 27.4

BB Click Train 8.1 13.7 17.8 180.0 172.3 39.1 141.8 4.4 22.1

BB Click Train 13.8 16.0 20.1 130.5 129.2 18.7 23.6 6.9 50.9

BB Click Train 15.0 18.7 27.1 113.6 155.3 41.9 156.4 3.7 27.4

BB Click Train 42.9 53.4 87.3 79.9 101.3 25.5 50.6 4.0 43.4

BB Click Train 16.5 20.5 23.4 142.9 108.3 37.9 129.4 2.9 25.7

BB Click Train 26.2 27.9 30.7 109.1 114.5 21.5 51.8 5.3 31.1

BB Click Train 19.8 25.6 41.2 82.1 84.0 21.0 67.5 4.0 20.7

BB Click Train 64.3 83.8 133.3 54.0 89.8 29.9 118.1 3.0 19.4

BB Click Train 104.0 118.1 254.7 91.1 86.6 29.4 78.8 2.9 34.6

BB Click Train 32.9 79.2 194.4 77.6 88.4 34.2 129.4 2.6 41.2

BB Click Train 57.4 125.7 227.3 82.1 104.0 33.2 108.0 3.1 41.3

BB Click Train 23.6 26.9 28.6 119.3 117.4 16.3 33.8 7.2 46.2

BB Click Train 33.0 43.4 127.2 136.1 129.2 14.9 55.1 8.7 32.3

BB Click Train 7.7 10.7 17.1 112.5 110.9 28.0 81.0 4.0 36.1

BB Click Train 36.5 39.2 48.4 119.3 101.0 23.7 69.8 4.3 69.4

BB Click Train 20.0 21.3 23.1 133.9 118.6 21.2 50.6 5.6 40.5

BB Click Train 52.7 70.1 135.2 121.5 130.5 27.0 81.0 4.8 37.9

Martin et al. 2018 Supplementary Appendix Proceedings of the Royal Society B

5

BB Click Train 15.6 21.6 75.9 138.4 128.1 27.4 113.6 4.7 46.2

BB Click Train 22.6 31.4 57.6 100.1 91.7 30.4 87.8 3.0 22.6

BB Click Train 86.8 95.1 118.8 108.0 105.5 24.5 72.0 4.3 43.6

BB Click Train 30.2 36.0 47.4 94.5 104.1 30.0 61.9 3.5 33.3

BB Click Train 26.1 29.6 39.5 118.1 131.0 24.5 46.1 5.3 50.4

BB Click Train 30.2 34.7 40.5 79.9 101.3 25.6 50.6 4.0 43.6

BB Click Train 18.8 22.6 24.4 122.6 120.4 19.9 78.8 6.0 46.7

BB Click Train 33.4 41.8 47.0 120.4 129.2 27.9 101.3 4.6 45.1

BB Click Train 3.6 4.1 4.6 92.3 88.8 31.9 117.0 2.8 15.1

BB Click Train 21.3 22.3 24.6 135.0 122.3 27.5 101.3 4.4 13.9