Dolphin whistle repertoires around São Miguel (Azores): Are you common or spotted?

Applied Acoustics 161 (2020) 107169

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Dolphin whistle repertoires around São Miguel (Azores): Are you common or spotted? Alexandre Gannier a,⇑, Sandra Fuchs a, Adrien Gannier a, Marc Fernandez b, José M.N. Azevedo b a b

Groupe de Recherche sur les Cétacés, BP715, 06633 Antibes, France Centre for Ecology, Evolution and Environmental Changes/Azorean Biodiversity Group, and Faculdade de Ciências e Tecnologia, Universidade dos Açores. Azores, Portugal

a r t i c l e

i n f o

Article history: Received 4 December 2016 Received in revised form 15 November 2019 Accepted 26 November 2019

Keywords: Whistles Dolphins Spectrogram Classification Atlantic ocean

a b s t r a c t Short-beaked common dolphins (Delphinus delphis) and Atlantic spotted dolphins (Stenella frontalis) are both common in the Azores archipelago during summer. Because both species are sympatric, at least in part of their range, they may use acoustic features to recognize conspecifics and maintain school cohesion throughout their different activities. Delphinid whistles were recorded with a 96-kHz sampling rate using towed hydrophone system during surveys held in summer of 2013 and 2014 around São Miguel Island (Azores, Portugal). A total of 256 whistles attributed to either short-beaked common dolphin (n = 133) or Atlantic spotted dolphin (n = 123) were selected and processed with a contour extraction software. Elementary statistical analysis showed that duration, frequency and slope variables were significantly different for both species, although in most cases their range overlapped. We performed a discriminant analysis to test species classification: the dataset was randomly split into one calibration subset (186 whistles) and one validation subset (70 whistles). The discriminant analysis retained four variables (global slope, duration, minimal and final frequencies) as useful for classification. The discriminant function resulted in correct classification rates of 78.5% (calibration subset) and 81.4% (validation subset). Common dolphin whistles were better classified than Atlantic spotted dolphin whistles (83.4% and 74.8%) respectively. This study shows that reliable species identification can be achieved for common and spotted dolphins using their whistle repertoire characteristics. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Most delphinid species routinely use various pure-tone or harmonic vocalisations, called whistles, which together constitute a repertoire with a particular pattern [1]. Delphinid whistle repertoires depend on physiological traits [2], evolutionary factors [3] or social aspects of life history: delphinids with higher sociality levels use more complex tonal vocalisations [1]. Acoustic methods can be used to improve species identification during shipboard surveys, although repertoire classification can be problematic for similar-sized social delphinids [4,5]. Several studies showed that wide ranging species, such as the bottlenose dolphin (Tursiops truncatus and T. aduncus), the shortbeaked common dolphin (Delphinus delphis), the striped dolphin (Stenella coeruleoalba), or the Atlantic spotted dolphin (S. frontalis) have significantly distinct whistle repertoires in distant regions [6– 10]. In the Gulf of Mexico and western Atlantic, Baron et al. [7] showed that Atlantic spotted and bottlenose dolphins exhibited ⇑ Corresponding author. E-mail address: [email protected] (A. Gannier). https://doi.org/10.1016/j.apacoust.2019.107169 0003-682X/Ó 2019 Elsevier Ltd. All rights reserved.

clear inter-regional differences in several contour variables. Whistles frequency variables were also significantly different, although in the same range, for short-beaked common or striped dolphins inhabiting the eastern Atlantic (Canary Islands and Azores archipelago) and Mediterranean Sea [9,10]. At a regional scale in the Mediterranean Sea, striped dolphin repertoires collected in the eastern and western basins showed differences in frequency parameters [11]. In some cases, frequency variables were more similar for adjacent populations than for distant ones, as for bottlenose dolphins [6] or estuarine dolphin (Sotalia guianensis) [12]. In other cases, the degree of inter-regional plasticity seemed to be related to different ambient noise levels [13,14]. Consequently, the recent literature suggests that reliable identification of a species from its whistle repertoire may only be obtained at a regional scale [15,16]. In addition to repertoire plasticity, temporary shifts in whistle variables (such as duration, maximal frequency or number of inflexions) have been observed to result from short term increase of anthropic noise caused by the proximity of boats [8] or to be dependent on group behavior [6,17]. This kind of reversible changes, which may be termed repertoire elasticity, could also

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affect whistle classification results when different regional species are recorded in various noise contexts [18]. Such an elasticity was suggested by May-Collado and Wartzog [8] when comparing bottlenose dolphin whistles recorded in the same location, in presence of variable numbers of dolphin watch boats: the duration, number of inflexions, as well as different frequency variables, varied significantly in relation to this factor. Repertoire elasticity may also emerge in response to the co-occurrence of another dolphin species: May-Collado [19] showed that estuarine dolphins (Sotalia guianensis) modified their whistles when interacting with bottlenose dolphins. Furthermore, group structure, and in particular calf presence, may also influence the recorded repertoire, neonates tend to produce longer and less modulated whistles, as shown for bottlenose dolphins [20]. Although not often investigated, the influence of activity on repertoire was shown for Indo-Pacific bottlenose dolphins: whistle tonal class (modulation type) was linked to different behavioural states, such as traveling and socialization [17]. In the Celtic Sea, common dolphin whistle emission rates showed a clear diel pattern, and were strongly related to feeding activity [21]. Moreover, some whistle repertoire characteristics may also change across successive recording years in a given region, as shown for Indo-Pacific bottlenose dolphins [22]. Therefore, when performed for the purpose of clearly recognizing different dolphin species, whistle classification studies may be blurred by a number of potential issues, partly inherent to the noncontrolled nature of open sea dolphin populations. Several dolphin species inhabit the Azores archipelago. The short-beaked common dolphin, bottlenose dolphin and Risso’s dolphin (Grampus griseus) are relatively abundant and present all year around, while Atlantic spotted dolphins are regular summer visitors [23]. The common and spotted dolphins are commonly observed around São Miguel Island (Azores) during summer [24,25]. Genetic investigations showed that each species was homogeneous in terms of population structure, within the Azores archipelago and at a larger scale [26]. Schools of both species are often observed in close proximity, sometimes forming mixedspecies schools usually associated with foraging activities [27]. As both species share the same habitat in summer, we assume that interspecific differences will occur in their social vocalizations, particularly whistles, given the importance of conspecific recognition. Our study aimed to test this assumption. Whistles from recordings collected during summer surveys held in 2013 and 2014 were examined with a contour analysis software and extracted data were processed with a discriminant analysis. As our recordings were collected during the same field seasons, from the same platform, in a regional context and away from whale-watching boats, this study provided an opportunity to minimize several incertitudes potentially affecting whistle classifications results.

2. Field methods Data were collected from a dedicated motorized sailboat. Systematic transects were carried out in July-August 2013 and July 2014, recordings were collected during 21 days. Surveys were constrained by wind and swell conditions (respectively less than Beaufort 4 and <2-meter, in general). At least three visual observers and one secretary/acoustic operator were active during surveys. Boat speed was 5 knots in average, the diesel engine being used to maintain course whenever wind conditions were not favourable. A wide-band stereo towed hydrophone (Ecologic Ltd) was used in high frequency mode (two spherical 2–150 kHz elements potted with preamplifiers with 2 kHz high pass filter, global sensitivity
160 dB re1V/lPa), connected to a Magrec HP/27ST analog amplifier/conditionner unit (amplifying gain adjusted to 30 dB, high pass filter set to 400 Hz), and to a Fireface 400 analog–digital converter

(24 bit resolution, 110 dB signal to noise ratio, frequency response < 1 dB, 5 Hz-90 kHz). An on-board computer monitoring and recording system [28] was used to record all survey data and control acoustic signals. During our study the sampling rate was set to 96 kHz, in order to record and visualize whistle harmonics up to 48 kHz [29]. Upon detection, whenever necessary, dolphin schools were approached to confirm species. Acoustic data with visual confirmation were recorded with a file name indicating the observed species (Fig. 1). Recordings from mixed-species school were not included in this study. Sound files were stored in .wav format of variable duration, depending on their signal-tonoise quality; during long duration sightings multiple recordings were collected. 3. Material During the two consecutive summer surveys, common dolphins were observed more frequently than Atlantic spotted dolphins, but in comparable proportions: 113 common dolphin sightings (55 in 2013 and 58 in 2014), and 68 spotted dolphin groups (36 in 2013, and 32 in 2014). A total of 256 whistles of 44 dolphin schools (24 common dolphin and 20 spotted dolphin groups) were used in the present study, comprising 133 common dolphin and 123 Atlantic spotted dolphin whistles (Table 1). Not all collected recordings could be used for whistles extraction and analysis: common or spotted dolphins were sometimes observed within a short amount of time. In order to use recordings that were only from the focal species, or at least to minimize this risk of having recordings contaminated with other species whistles, we used a buffer time criterion: only sightings obtained >15 min after the previous observation, or 15 min before the following one, were selected for whistle analysis, when the previous or next sighting was of a species different from the focal one. However, for reason of data availability, one exception was tolerated, that of a large spotted dolphin school (ca. 200 individuals) recorded five minutes after a three-individual common dolphin group. Depending on the sighting duration, one to three recordings were collected, each of variable length. To select whistles for processing, the whole recording spectrogram was visualized with Cool Edit Pro 2 software and whistles were selected by scrolling the spectrogram. More than one whistle sample was generally taken from a given recording, in relation to its duration (Table 1), although no fixed rule was adopted. Not all whistles were extracted from a recording, because a given whistle type could sometimes be repeated many times. In case of series of similar whistles, only one whistle was selected from the series in order to avoid the sample replication. For example, common dolphin sighting 2013–054 was documented with three recordings, whose durations were 557 s., 128 s. and 129 s. (Table 1) and from which 17, 2 and 3 whistles, respectively, were extracted for analysis. Two datasets were randomly generated, both for common and spotted dolphin whistles: a main dataset which was used for the discriminant model calibration, and a second dataset which was used for model validation. Random selection was carried out with a program written in Matlab language which selected whistles based on their individual file number: different whistles extracted from a given recording could be attributed either to the calibration or the validation data set. The main dataset comprised 186 whistles (98 of common dolphins and 88 of spotted dolphins) and the validation dataset 70 samples (35 whistles for each species). 4. Analysis Individual whistles were processed with Seafox [30], a semiautomatic custom contour and variables extraction software. Sea-

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Fig. 1. Locations around São Miguel Island where whistles of short-beaked common dolphins (triangles) and Atlantic spotted dolphins (circles) were recorded. 500 m, 1000 m, 1500 m and 2000 m isobaths are drawn. Contours from GEBCO digital atlas (BODC, 2003).

fox was written in Matlab 6.0 and based on a 512 point Fast Fourier Transform of whistles sampled at 96 kHz, using a Hanning window with 25% overlap. An initial spectrogram was obtained after the whistle beginning and end were mouse-pointed on the selected recording spectrogram (Fig. 2a). The whistle spectrogram was improved by running a variable threshold routine and a high pass/low pass filtering option. Frequencies of highest amplitude were then extracted for every time window and stored for initial contour plotting (Fig. 2b); a pulse removing option complemented this stage. This first extracted frequency contour could then be improved by using a mouse-driven suppression of contour accidents eventually subsisting due to interferences with other whistles or pulses. A smoothing operation based on a moving average produced the final whistle contour (Fig. 2c), from which 14 frequency and modulation variables were extracted (Fig. 3). Initial and final frequency slopes were calculated respectively from the seven initial and seven final frequency points. Maximal and mini-

mal frequency slopes were calculated from a three-point moving slope computed over the entire whistle duration. Only 11 variables were kept for statistical analysis (Table 2): the remaining variables (maximal negative and positive slopes, final frequency slope) were affected by too many undetermined values. Maximal and minimal slopes were sometimes driven to infinite values due to whistle steps or high intensity pulses scattered along whistle duration. Final frequency slope could be impossible to determine because whistles progressively faded at the end instead of clearly stopping. In these cases final frequency values were not extracted by the software, causing final slope as well as mean frequency to be declared as ’missing’. Statistical analyses were carried out with Statistica 6 software (www.statsoft.com), and conducted in three stages: first, every continuous variable was examined for normality with a Kolmogorov-Smirnov test modified to account for empirical estimation of mean and variance [31]. Second, in order to have a sim-

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Table 1 Description of the data set used in this study. Sighting ID

Date

Number and duration of recordings (sec.)

Number of extracted whistles

School size

Juveniles

Activity state

Dd 2013-053 Dd 2013-054 Dd 2013-055 Dd 2013-063 Dd 2013-068 Dd 2013-073 Dd 2013-079 Dd 2013-080 Dd 2013-086 Dd 2013-088 Dd 2013-098 Dd 2013-112 Dd 2013-130 Dd 2013-131 Dd 2013-133 Dd 2013-138 Dd 2013-149 Dd 2013-155 Dd 2013-161 Dd 2014-045 Dd 2014-055 Dd 2014-100 Dd 2014-102 Dd 2014-184 Sf 2013-083 Sf 2013-089 Sf 2013-092 Sf 2013-093 Sf 2013-100 Sf 2013-109 Sf 2013-116 Sf 2013-122 Sf 2013-124 Sf 2013-137 Sf 2013-151 Sf 2013-152 Sf 2013-156 Sf 2013-172 Sf 2013-174 Sf 2013-181 Sf 2014-039 Sf 2014-044 Sf 2014-046 Sf 2014-054

13/07/2013 13/07/2013 13/07/2013 14/07/2013 15/07/2013 20/07/2013 21/07/2013 21/07/2013 24/07/2013 24/07/2013 28/07/2013 03/08/2013 07/08/2013 07/08/2013 08/08/2013 08/08/2013 12/08/2013 18/08/2013 18/08/2013 06/07/2014 07/07/2014 15/07/2014 15/07/2014 20/08/2014 21/07/2013 24/07/2013 25/07/2013 25/07/2013 28/07/2013 03/08/2013 03/08/2013 06/08/2013 06/08/2013 08/08/2013 17/08/2013 18/08/2013 18/08/2013 21/08/2013 21/08/2013 22/08/2013 06/07/2014 06/07/2014 06/07/2014 06/07/2014

1 3 1 1 1 2 1 1 3 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 4 1 1 1 1 1 1 1 1

2 22 5 5 5 4 1 2 12 2 5 4 19 4 1 11 3 4 5 6 4 5 1 1 1 3 17 10 1 6 8 2 4 4 7 20 5 11 5 6 1 8 2 2

25 120 30 120 85 90 60 150 800 25 60 20 25 20 120 50 38 35 350 30 60 12 12 25 30 12 35 60 25 400 10 20 20 200 10 400 150 20 350 40 80 50 15 12

Y Y Y Y Y Y Y ? ? Y Y ? ? ? Y ? ? Y Y N ? N ? Y N Y Y Y Y Y Y N N Y ? Y ? Y Y Y N Y ? Y

resting feeding ? feeding travelling travelling resting feeding feeding feeding feeding feeding feeding feeding feeding travelling ? feeding feeding travelling ? feeding feeding travelling feeding resting ? resting ? socializing resting feeding feeding feeding travelling feeding feeding feeding travelling travelling travelling socializing ? travelling

(153) (557,128,129) (170) (437) (132) (314,217) (131) (159) (279,289,496) (141) (239,129) (389) (721) (191) (332) (129) (239) (524) (681) (188) (385) (148) (257) (192) (322) (401) (21,438) (565) (131) (249,216) (478) (38) (129) (349) (619) (258,212,128,131) (129) (551) (191) (171) (518) (155) (188) (75)

ple between-species comparison summary, all variables were compared using a Mann-Whitney test. Finally, a discriminant analysis was conducted, for which variables tested as gaussian or successfully transformed were used. The discriminant function was calibrated with the main dataset: a step-by-step ascending mode was retained, controlled with inclusion/exclusion test on Wilk’s lambda function [31] as implemented in Statistica software. Wilk’s lambda (k) was used to measure global discriminant function efficiency at each step, as well as the discrimination contribution of each candidate variable, which was considered on the basis of F statistics:

  F ¼ ½ðn
p
qÞ=ðq
1Þx ð1
kp Þ=kp where n is number of samples, p number of variables, q the number of groups (q = 2), and kp is the relative increase of lambda due to the candidate variable. F values were set at 2.0 and 0.5 for inclusion and exclusion, respectively. Candidate variables tested as strongly correlated (R2 > 95%) with those already included in the discriminant function were not retained in the model. The final discriminant function was characterized by its global Wilk’s lambda, whose value theoretically lies between 0 (perfect discrimination power) and 1 (no discriminant power at all). Two classification functions were issued by the discriminant analysis, one for each species. They provided a constant and coefficients for each selected variable and

could be used to classify whistles either a posteriori (for samples selected in a calibration subset) or a priori (for whistles selected in a validation subset). Whistles classification was given global performance on the basis of correct species attribution for each whistle, for both calibration and validation data sets, hence resulting in two distinct scores. Statistica 6 also provided Mahalanobis distances for each sample, as well as classification probabilities, ie probabilities for every whistle to belong to either species category, as derived from Mahalanobis distances to each species centroïd. This statistic enabled to sort whistles which were strongly misclassified by the discriminant model. 5. Results Although both species were sometimes observed <2 km apart, true mixed schools were encountered only on four occasions in 2013 and two in 2014. Average estimated school sizes were lower in 2014, for both species. Common dolphins group size varied from 73.2 (SD = 121.2) in 2013 to 25.2 (SD = 31.5) in 2014. A similar trend was observed for spotted dolphins with schools averaging 81.8 individuals (SD = 109.8) in 2013, and 29.2 dolphins (SD = 37.3) in 2014. Juveniles or calves were present in 13 schools out of 24 common dolphin groups, whereas for spotted dolphins 13

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dolphins were mostly recorded as ’feeding’, and ’socializing’ was only noticed for spotted dolphins (Table 1). Seven variables departed significantly from the normality hypothesis (Kolmogorov-Smirnov, d > 0.095): the whistle duration, initial, final, minimal and maximal frequencies, the frequency range and number of inflexions. Simple square root transformations enabled to use them in the discriminant analysis, with the exception of the number of inflexions (Table 2). When comparing the two species with a Mann-Whitney test, eight of the continuous variables were significantly different (p < 0.05) (Table 3): the duration, initial, final, minimal and mean frequencies, frequency range, and the initial and global slopes. Whistles were significantly longer for common dolphins (average 0.94 s) than for Atlantic spotted dolphins (average 0.65 s). Initial, minimal and mean frequencies were significantly higher for the common dolphin, while final frequency and frequency range were higher for the spotted dolphin (Table 3). For example the average minimal frequency was 8171 Hz (SD = 1911) for common dolphins and 7290 Hz (SD = 2024) for spotted dolphins. Common dolphin whistles showed an average down-sweep modulation (global slope =
1502 Hz/s), while the spotted dolphin whistles were globally up-swept (global slope = 4335 Hz/s), although for both species down-sweep and up-sweep modes were encountered. Spotted dolphin whistles were characterized by strong positive initial modulation, while those of common dolphins were weakly negative (Table 3). Maximal frequencies were not significantly different (>16 kHz in average for both dolphins) and harmonics were present in 82% of whistles of both species. On average whistles showed more than two inflexions for both species, however the number tended to be lower for common dolphins (2.34, SD = 1.92) compared to spotted dolphins (2.67, SD = 2.43). In summary, short-beaked common and Atlantic spotted dolphins encountered around São Miguel used grossly similar whistle repertoires in terms of frequency, although several variables were significantly different between species. Discriminant analysis was conducted with seven variables (Table 2), the step-by-step process retaining four of them, in their transformed form, for the discriminant function: the global slope (p < 10
6), the duration (p < 10
5), the final frequency (p < 10
3), and the minimal frequency (p < 0.01). The discriminant model resulted in a Wilk’s lambda value of 0.643 (Table 4): global slope and duration accounted for much of the classification power (lambda p = 0.833 and 0.891, respectively). Two classification functions were computed by the discriminant analysis, one for each species. The classification functions had a success rate of 78.5% for the calibration dataset, 16 common dolphin whistles (16.3%) and 24 spotted dolphin whistles (27.2%) being misclassified. A slightly higher success rate was obtained for the validation dataset, with 81.4% correct classification: six common dolphin and seven spotted dolphin whistles were attributed to the wrong species (respectively 17.1% and 20.0% of the samples). Among mis-classified whistles, eight common dolphin and 10 spotted dolphin samples were attributed to the wrong species with a high probability (>80%). In spite of some misclassifications, success rates were moderately good, stable. They were somewhat better for common dolphin than for Atlantic spotted dolphin whistles. Fig. 2. Whistle contour processing with Seafox (software-generated labels are in French). (top) Raw spectrogram of a common dolphin whistle (FFT 512 points, Hanning, 25% overlap). (middle) Initial contour plotting, with one contour accident remaining. (bottom) Final contour plotting from which variables are extracted.

schools out of 20 included calves and/or juveniles. The four basic dolphin activity categories identified by Ballance [32] (i.e. feeding, travelling, socializing and resting) were present, although common

6. Discussion A moderately good classification rate was obtained in our study, with overall correct species identification in 79.3% of the cases, 83.4% for common dolphins and 74.8% for Atlantic spotted dolphins. A correct success rate was maintained with a separate validation data set comprising whistles extracted from the recordings

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Fig. 3. Whistle contour variables as extracted by Seafox software.

Table 2 Summary of contour variables used in the discriminant analysis (DA). Variable

validated

Transformation

used for DA

Duration (sec) Initial frequency (Hz) Final frequency (Hz) Minimal frequency (Hz) Maximal frequency (Hz) Mean frequency (Hz) * Frequency range (Hz) Initial slope (Hz/sec) Global slope (Hz) Number of inflexions Harmonics (Y/N)

Y Y Y Y Y Y Y Y Y Y Y

sqrt sqrt sqrt sqrt – – sqrt sqrt N N N

Y Y Y Y N N Y Y Y N N

(x) (x + 3000) (x) (x-500)

(x) (x)

(*) Due to many missing values, the mean frequency was not used in the discriminant analysis.

used to extract calibration data, suggesting that short term variations did not affect our species classification. Inter-specific differentiation of whistle repertoires can be challenging for small oceanic delphinids inhabiting the same region [5,16]. As a matter of fact, almost all key variables showed wide variability for both species, with overlapping ranges (Table 3). Hence, some degree of misclassification should be viewed as normal, as shown by previous studies of similarly-sized delphinids [5,16]. Very large dolphin schools were not uncommon during our surveys (Table 1). Although positively identified to species level, those schools might eventually have included unnoticed individuals belonging to the other species, with some whistles contaminating the focal species data set. However, this possibility could be excluded in most cases, because our boat approached the focal dolphin group closer than 100 m 95% of the time (there was one minimal sighting distance of 150 m and one of 100 m), thus allowing a very reliable identification. Furthermore, misclassified whistles were present in recordings of both small and large dolphin schools. Hence, we remained confident that our misclassification cases were not caused by a small amount of species confusion. Our analysis selected global slope, duration and minimal frequency as the main contributors to the discriminant function. A previous study in the Mediterranean Sea also selected maximal frequency as an important variable for classification [16]. However, that study dealt with delphinids of different sizes, including the

long-finned pilot whale, hence the importance of frequency in the discriminant function [2]. In a study of eight species inhabiting the tropical Pacific, Oswald et al. [5] obtained variable success rates: among four similarly-sized species, an identification rate of 64.3% was reached for rough-toothed dolphins (Steno bredanensis), while identification success was poor (15.8%) for striped dolphin whistles. In the Mediterranean Sea, identification rates were also variable for five delphinids [30], higher success scores being obtained for striped dolphins, common dolphins and long-finned pilot whales (Globicephala melas), with respectively 79.8%, 60.8% and 75.7%, and lower rates for Risso’s and bottlenose dolphins (respectively 37.3% and 46.1%). In the same region, poor classification results were obtained for short-beaked common dolphins by Azzolin et al. [16]: 43.1% were assigned a correct identification while 36.1% of whistles were classified as striped dolphin vocalisations. These contrasting results for short-beaked common dolphin in the Mediterranean Sea outline the influence of data set on whistle classification, an aspect directly related to the question of repertoire variability discussed above for dolphins, and also evidenced for larger delphinids in the northern Atlantic/western Mediterranean region [4]. The geographical relevance of whistle classification models remains an important issue for theoretical as well as practical considerations: intraspecific plasticity constraints the large scale applicability of classification tools, and the use of passive acoustics during sea surveys [15] or for fixed on-site long term monitoring. Although physical barriers such as continents, straits or major oceanographical divides provide obvious cues for delimiting the potential useful range of a regional whistle classification model, more subtle characteristic gradients may also affect model validity [12]. Whistle contour studies suggest that a given species repertoire broadly exhibits the same frequency characteristics, with low intraspecific variations, with the exception of widely separated populations [4–6]. On the contrary, average duration and number of inflexions have been shown to be variable contour parameters [7–9]. Atlantic spotted dolphin whistles around São Miguel were longer in average (0.65 sec) than those recorded in four regions of the Atlantic Ocean, but similar to whistles of the offshore western Atlantic [7]. Common dolphin whistle average duration around São Miguel (0.94 sec) was similar to those obtained in the eastern Atlantic and western Mediterranean Sea, but longer than those

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Table 3 Comparison of whistle variables for the short-beaked common dolphin (Dd) and the Atlantic spotted dolphin (Sf). Significantly different means (Mann-Whitney, p < 0.05) are marked with *. Variable

Species

N. of samples

Min

Max

Mean

SD

Duration (sec) *

Dd Sf

134 117

0.276 0.106

1.639 1.701

0.936 0.654

0.320 0.353

Initial frequency (Hz) *

Dd Sf

134 117

4487 3359

27,030 20,830

12,761 10,256

5219 4138

Final frequency (Hz) *

Dd Sf

134 117

4767 4204

20,267 28,156

11,258 14,592

3763 4845

Minimal frequency (Hz) *

Dd Sf

134 117

4312 3359

16,688 12,937

8171 7290

1911 2024

Maximal frequency (Hz)

Dd Sf

134 117

9559 8458

27,030 28,156

16,755 16,254

3661 3574

Frequency range (Hz) *

Dd Sf

134 117

1687 1500

17,661 20,280

8584 9464

3311 3602

Mean frequency (Hz) *

Dd Sf

37 52

7879 7732

16,353 14,279

12,472 11,508

2126 1542

Initial slope (Hz/sec) *

Dd Sf

104 95


98012
57694

99,447 83,772


1292 12,875

43,781 30,410

Global slope (Hz/sec) *

Dd Sf

134 117


17894
8033

12,682 15,922


1502 4335

5901 5696

Number of inflexions

Dd Sf

134 117

0 0

13 17

2.34 2.67

1.92 2.43

Presence of harmonic

Dd Sf

134 117

0 0

1 1

82.1% 82.0%

– –

Table 4 Discriminant analysis variables as selected by Statistica software during an ascending step-by-step procedure. Wilk’s lambda (global) = 0.64342, F-test (4, 181) = 25.08 , p < 0.0000. Variable

k

k

Global slope Duration Minimal frequency Final frequency

0.773 0.721 0.689 0.670

0.833 0.891 0.934 0.960

p

from the Celtic Sea and English Channel (Table 6). Whistle average durations are observed to vary significantly between widely spaced populations [7,9,13], which could reflect structural divergence linked to isolated evolution [11,12,17]. But duration is also subject to high intra-populational variability because they carry informations on individual identity, emotional state, or other contextual elements [6,22]. In our study, duration variabilities, as indicated by standard deviations, were similar for both species (Table 3) and comparable to values found in the literature for the shortbeaked common and the Atlantic spotted dolphins (Table 5 and 6). Similarities between our results and those of previous studies carried out in the eastern or offshore western Atlantic Ocean suggest that whistle average duration is representative of regional repertoires, for both common and spotted dolphins [7,9]. Hence, even if durations are variable at global level, their regional averages could be useful parameters to discrimate species at a regional level. For both species, the number of inflexions was very diverse, but higher in average than for other regions (Tables 5 and 6). This parameter is highly variable in inter-regional and intra-regional studies: most authors argue that variabilities of the number of inflexions suggest behavioral or short-term environmental influences on whistle structure [6,8,10,22]. For common dolphins, we obtained an average number of inflexions about twice that obtained in a previous study in the eastern Atlantic [9]. We could not exclude that high numbers of inflexions observed in our whistles (>2 for both species) was linked to our own boat proximity to the dolphins, some individuals frequently approaching the plat-

F

p

1- R2

R2

36.39 22.02 12.80 7.56

0.000000 0.000005 0.000445 0.006580

0.440 0.960 0.559 0.304

0.560 0.040 0.441 0.696

form or even bow-riding during recording sessions. This aspect of field operations could hardly be controlled given other constraints, such as the need to be close enough (<500 m) in order to properly identify the species or to collect high intensity whistles. The average number of inflexions was found to be very variable when multiple field sessions were carried out on the same population, as for the Indo-Pacific bottlenose dolphin in Japan [22], hence this parameter could be worthless to discriminate populations, or even species. For Atlantic spotted dolphins, the average initial and final frequencies were high compared to other regions, but the average minimal frequency was close to those obtained in all other regions, except waters off Brazil. Furthermore, the average maximal frequency was close to that of dolphins recorded in the W. Atlantic Ocean (Table 5). Hence, spotted dolphin whistles collected around São Miguel were generally similar to those recorded in the offshore western Atlantic. For short-beaked common dolphins the average initial frequency was within the range of values obtained in other regions, and the final frequency was lower in average than those reported for the Atlantic Ocean and W. Mediterranean Sea (Table 6). The minimal and maximal frequencies were very close to those obtained in the eastern Atlantic and western Mediterranean, and distinct to those reported in the Celtic Sea and English Channel. The minimal frequency is usually considered as indicative of a given species or at least of a delphinid size class [2,3], even if regional average values can differ from this pattern, as for Atlantic spotted dolphins in Brazil [33], or common dolphins in the Celtic Sea [13]. By comparison, average maximal frequencies reported in

8

A. Gannier et al. / Applied Acoustics 161 (2020) 107169

Table 5 Comparison of Atlantic spotted dolphin whistle variables. Numbers in brackets are SD (except for Baron et al. 2008, SE). Variable

eastern Azores

Brazil

Bahamas

Canary

W. Atlantic inshore

W. Atlantic offshore

Number of samples Number of schools Duration (sec) Initial frequency (kHz) Final frequency (kHz) Minimal frequency (kHz) Maximal frequency (kHz) Mean frequency (kHz) Frequency range (kHz) Number of inflexions Reference

117 19 0.65 (0.35) 10.2 (4.1) 14.6 (4.8) 7.3 (2.0) 16.2 (3.6) 11.5 (1.5) 9.4 (3.6) 2.7 (2.4) this study

1092 4 0.36 (0.29) 8.8 (3.2) 12.8 (3.8) 8.0 (2.5) 13.6 (3.6) 10.8 (2.6) 5.5 (3.5) 0.7 (1.3) Azevedo et al. 2010

220 17 0.44 (0.30)

84 ? – 9.4 (2.3) 14.6 (2.4) 7.4 (5.2) 17.9 (10.6) – – – Papale et al. 2015

328 24 0.43 (0.02) 9.3 (0.36) 12.3 (0.37) 7.5 (0.21) 14.2 (0.34) 11.2 (0.37) 6.7 (0.38) 1.6 (0.20) Baron et al. 2008

1377 27 0.65 (0.04) 9.1 (0.25) 13.1 (0.35) 7.5 (0.17) 15.8 (0.32) 11.7 (0.27) 8.3 (0.31) 2.2 (0.14)

7.1 (1.5) 14.5 (2.5) 10.9 (2.0) 7.4 (2.9) – Lammers et al. 2003

Table 6 Comparison of whistle variables from short-beaked common dolphins in the eastern Atlantic/Mediterranean region. Number in brackets are SD. Variable

Eastern azores

Celtic sea

Number of samples Number of schools Duration (sec) Initial frequency (kHz) Final frequency (kHz) Minimal frequency (kHz) Maximal frequency (kHz) Mean frequency (kHz) Frequency range (kHz) Number of inflexions Reference

134 24 0.94 (0.32) 12.7 (5.2) 11.2 (3.7) 8.2 (1.9) 16.7 (3.7) 12.5 (2.1) 8.6 (3.3) 2.3 (1.9) this study

1835 435 43 ? 0.65 (
) 0.65 (
) 12.0 (
) 12.6 (
) 12.0 (
) 12.5 (
) 9.5 (
) 9.8 (
) 14.7 (
) 15.8 (-) 11.9 (
) 12.7 (
) 5.2 (
) 6.0 (
) 0.6 (1.9) 0.6 (1.9) Ansmann et al. 2007

the literature for both species are more variable (Tables 5 and 6), potentially reflecting regional repertoire divergences [9]. Interregional differences in average initial and final frequencies are high for both species (Tables 5 and 6), a result also observed for estuarine and bottlenose dolphins [6,12,22], or larger delphinids [4]. Conversely, average mean frequencies seem to be quite consistent for any given species, although they are not always reported in the literature (Tables 5 and 6). Because Atlantic spotted and short-beaked common dolphin species are in the same size class, it could be expected that their whistle frequencies are roughly similar (Table 4). However, for almost every parameter Atlantic spotted and common dolphin whistles were significantly different one from each other (Table 3), the former species showing vocalizations generally shorter in duration and lower in frequency. For example, average initial frequency was 12.8 kHz for D. delphis, compared to 10.2 kHz for S. frontalis. Minimal and maximal frequencies followed the same trend, but noticeably, average final frequencies were in an opposite situation, with a higher value for Atlantic spotted dolphins (14.6 kHz) compared to common dolphins (11.2 kHz). Comparisons of whistle repertoires of quasi-sympatric small-sized delphinids generally show differences for most frequency parameters, as in the case of spinner and pantropical spotted dolphins (S. longirostris and S. attenuata) in the eastern Tropical Pacific [15] or Atlantic spotted and short-beaked common dolphins off the Canary Islands [18]. These small but significant differences in several contour parameters may be due to selection pressures to maintain distinctiveness [4]. For interspecific repertoire comparisons to be robust, whistle of different species should not be biased by contextual elements such as anthropogenic noise, proximity of boats, contact with other species, school structure and activity [5,7,15,16]. Spotted dolphin social structure is described as flexible with potential segregation by age and sex [34], these characters being also found for shortbeaked common dolphins in the eastern Atlantic [35]. During our study, both species were observed with widely variable school sizes, ranging from 20 to 800 individuals (D. delphis) and from 10

Western channel

Eastern atlantic

Western mediterranean

514 29 0.95 (0.38) 13.0 (4.9) 11.8 (4.0) 8.1 (1.8) 16.7 (3.6) – 8.6 (3.4) 1.1 (1.2) Papale et al. (2014)

188 16 0.92 (0.52) 11.9 (4.4) 12.2 (3.8) 8.3 (2.3) 16.1 (3.0) 7.8 (3.3) 2.0 (1.6) Azzolin et al. (2014)

to 400 individuals (S. frontalis), sometimes including juveniles for both species. Estimated school sizes were lower in 2014 than in 2013, with an approximate 2–3 fold decrease for both species. Boat noise produced by dolphin-watch operators might also trigger repertoire elasticity of one or both species [6,12], but our recordings were mostly obtained outside the main dolphin-watch area or when tourism vessels were away from our sighting/recording site. Globally, it is unlikely that our repertoire comparison was biased by such social or anthropogenic factors, because shortbeaked common and Atlantic spotted dolphins were recorded during the same field seasons, in various locations around the Island. Repertoire elasticity is sometimes observed during interspecific encounter between different species, as observed in Costa Rica for estuarine and bottlenose dolphins [19]. In the Azores, shortbeaked common and Atlantic spotted dolphin repertoires may eventually fluctuate when both species form mixed-groups [27]. Such a temporary variation in repertoires was avoided by discarding mixed-group recordings, and by selecting dolphin schools for which certain identification was granted. Around São Miguel, it would be interesting to compare short-beaked common and Atlantic spotted dolphin whistles when both species are observed together. However, to be conclusive, this kind of study would require accurate localisation of individual whistles sources in mixed-species schools, requiring the use of complementary technical tools [36].

7. Conclusion Our study focused on two seasonaly sympatric oceanic dolphin species, with similar physical and social characteristics. We showed that Atlantic spotted and short-beaked common dolphins could be reliably identified from their whistle repertoires. The use of two distinct datasets, one for model calibration the other for validation, reinforced the analysis robustness, even if the correct classification rate, about 80%, was not extremely high. A clear difference in overall frequency pattern, or global slope, is possibly

A. Gannier et al. / Applied Acoustics 161 (2020) 107169

an indication that repertoires evolved to facilitate mutual recognition when all four elementary frequency variables partly overlap. Dolphin vocalisation studies suggest species repertoire can be influenced by the social and behavioural context of recordings, in addition to inter-regional plasticity. Future research could increase classification method reliability by complementing acoustic data with comprehensive behaviour and social metadata. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Marc Fernandez was supported by grant M3.1.2/F/028/2011 from the Fundo Regional para a Ciência e Technologia (Azores Government). Surveys were made with authorizations of the Azores Government (Direção Regional do Ambiente - licences 30/2013/DRA and SAI/DRA/2014/1260). We warmly thank all benevolent observers for their participation in the surveys. Thanks to the editor and both reviewers for comments which contributed to a much improved final version of the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.apacoust.2019.107169. References [1] May-Collado LJ, Agnarsson I, Wartzok D. Phylogenetic review of tonal sound production in whales in relation to sociality. BMC Evol Biol 2007;7:136. [2] Matthews JN, Rendell LE, Gordon JCD, MacDonald DW. A review of frequency and time parameters of cetacean tonal calls. Bioacoustics 1999;10(1):47–71. [3] May-Collado LJ, Agnarsson I, Wartzok D. Reexamining the relationship between body size and tonal signals frequency in whales: a comparative approach using a novel phylogeny. Mar Mam Sci 2007;23(3):524–52. [4] Rendell LE, Matthews JN, Gill A, Gordon JCD, Macdonald DW. Quantitative analysis of tonal calls from five odontocete species, examining interspecific and intraspecific variation. J Zool Lond 1999;249:403–10. [5] Oswald JN, Barlow J, Norris TF. Acoustic identification of nine delphinid species in the eastern tropical Pacific Ocean. Mar Mammal Sci 2003;19(1):20–37. [6] Wang D, Würsig B, Evans WE. Whistles of bottlenose dolphins : comparisons among populations. Aquatic Mammals 1995;21:65–77. [7] Baron SC, Martinez A, Garrisson LP, Keith EO. Differences in acoustic signals from delphinids in the western North Atlantic and northern Gulf of Mexico. Mar Mammal Sci 2008;24(1):42–56. [8] May-Collado LJ, Wartzok D. A comparison of bottlenose dolphin whistles in the Atlantic Ocean: factors promoting whistles variation. J Mammal 2008;89 (5):1229–40. [9] Papale E, Azzolin M, Cascão I, Gannier A, Lammers MO, Martin VM, et al. Macro- and micro-geographic variation of short-beaked common dolphin’s whistles in the mediterranean sea and atlantic ocean. Ethol Ecol Evol 2014;26 (4):13. https://doi.org/10.1080/03949370.2013.851122. [10] Papale E, Azzolin M, Cascao I, Gannier A, Lammers MO, Martin VM, et al. Geographic variability in the acoustic parameters of striped dolphin’s (Stenella coeruleoalba) whistles. J Acoust Soc Am 2013;133(2):1126–34. [11] Azzolin M, Papale E, Lammers MO, Gannier A, Giacoma C. Geographic variation of whistles of the striped dolphin (Stenella coeruleoalba) within the Mediterranean Sea. J Acoust Soc Am 2013;134(1):694–705.

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