S U P P L E M E N T A R T I C L E
Whistle variability of the Mediterranean short beak common
dolphin
Marta Azzolin
1,2|
Alexandre Gannier
3|
Elena Papale
1,4|
Giuseppa Buscaino
4|
Barbara Mussi
5|
Giandomenico Ardizzone
6|
Cristina Giacoma
1|
Daniela Silvia Pace
61
Life Sciences and Systems Biology Department, University of Torino, Via Accademia Albertina 13, 10123 Torino, Italy 2
Gaia Research Institute Onlus, Corso Moncalieri 68, 10133 Turin, Italy 3
Groupe de Recherche sur les Cétacés, Antibes, France
4
BioacousticsLab, IAS Capo Granitola, National Research Council, Via del Mare 3, 91021 Torretta Granitola (TP), Italy 5
Oceanomare Delphis Onlus, Viale delle Rimembranze 14, 47924 Rimini, Italy 6
Department of Environmental Biology, Sapienza University of Rome, Italy
Correspondence
Marta Azzolin, Life Sciences and Systems Biology Department, University of Torino, Via Accademia Albertina 13, 10123. Torino, Italy Email: [email protected]
Abstract
1. The short
‐beaked common dolphin is a highly vocal species, with a wide distribution
in all oceans, including the Mediterranean and the Black Seas. In the Mediterranean
Sea, the short
‐beaked common dolphin inhabits both pelagic and neritic waters.
2. Osteological collections and the literature show that short
‐beaked common
dolphins were widespread and abundant in much of the Mediterranean Sea until
the late 1960s. During recent decades the species has declined in the whole basin,
and, in 2003, it was listed as Endangered in the IUCN Red List.
3. Genetic studies strongly suggest that the Mediterranean and the Eastern North
Atlantic populations are isolated from each other. Genetic differentiation within
the Mediterranean Sea, between the Eastern Mediterranean (Ionian Sea) and
Western Mediterranean populations, is also reported.
4. The aim of this study was to investigate the geographical variation in the
charac-teristics of whistles of free
‐ranging short‐beaked common dolphins living in the
Mediterranean Sea, and to evaluate if whistle acoustic structure is the result of
adaptation to local environment characteristics or of a possible genetic
diversification.
5. Recordings were collected from 1994 to 2012 throughout the basin, employing
multiple platforms. Twenty
‐six independent acoustic detections were made, and
704 whistles were extracted and considered for statistical analysis.
6. Whistle analysis enabled the identification of distinct geographical units of
short
‐beaked common dolphin within the Mediterranean Sea. Genetic isolation
is probably the major cause of the geographic variance of the Mediterranean
short
‐beaked common dolphin whistle structure, which may reflect some
evolutionary adaptations to particular ecological conditions or may be the
by
‐product of morphological evolution.
7. The results of the present study show that intra
‐Mediterranean variability of
whis-tle structure reflects the path of genetic studies, highlighting the possible use of
acoustic data in combination with other sources of data (genetic, morphological,
etc.) to identify geographic areas where discrete management units occur.
K E Y W O R D S
acoustic communication, behaviour, coastal, mammals, ocean
DOI: 10.1002/aqc.3168
1
|I N T R O D U C T I O N
Sound plays a vital role in the ecology and social structures of all ceta-cean species. The vocal repertoire of many odontocetes includes whis-tles, narrowband tonal calls that lie in the bandwidth between 800 Hz (Schultz & Corkeron, 1994) and 28.5 kHz (May‐Collado & Wartzok, 2008) and with durations that range between 100 ms and just over 4 s (Buckstaff, 2004). The whistles of killer whale (Orcinus orca) repre-sents an exception, since they may also be produced in the ultrasonic domain (Andriolo, Reis, Amorim, Sucunza, & de Castro, 2015; Filatova et al., 2012; Samarra et al., 2010; Simonis et al., 2012). The variation of whistle characteristics between species is illustrated by different stud-ies (Azzolin et al., 2014; Gannier, Fuchs, Quebre, & Oswald, 2010; Oswald, Barlow, & Norris, 2003; Oswald, Rankin, & Barlow, 2004; Oswald, Rankin, Barlow, & Lammers, 2007; Rendell, Matthews, Gill, Gordon, & Macdonald, 1999; Schultz & Corkeron, 1994; Steiner, 1981; Wang, Wursig, & Evans, 1995a). There has also been a growing interest in whistle variation within single species. Different studies on the whistle characteristics of bottlenose dolphin highlight that whistles may diverge among localities (Baron, Martinez, Garrison, & Keith, 2007; May‐Collado & Wartzok, 2008; Morisaka, Shinohara, Nakahara, & Akamatsu, 2005; Papale et al., 2014; Wang, Wursig, & Evans, 1995b), among groups within populations (Hawkins & Gartsiden, 2010; Janik, 2000; Janik, Dehnhardt, & Todt, 1994) and among indi-viduals (Sayigh, Esch, Wells, & Janik, 2007; Smolker, Mann, & Smuts, 1993; Tyack, 2000). The geographical variation of whistles is also illus-trated for other wide‐ranging species, such as short‐beaked common dolphin (Delphinus delphis) (Azzolin, 2008; Papale et al., 2013b), spin-ner dolphin (Stenella longirostris) (Bazua‐Duran & Au, 2004), Atlantic spotted dolphin (Stenella frontalis) (Baron et al., 2007), striped dolphin (Stenella coeruleoalba) (Azzolin, 2008; Azzolin, Papale, Lammers, Gannier, & Giacoma, 2013; Papale et al., 2013a), pilot whales (Globicephala spp.) (Baron et al., 2007) and tucuxi dolphin (Sotalia
guianensis) (Azevedo & Van Sluys, 2005; Rossi‐Santos & Podos,
2006). However, little information is available on the geographical var-iation of the whistle structure of the Mediterranean short‐beaked common dolphin (Azzolin, 2008; Papale et al., 2013b).
The short‐beaked common dolphin is a highly vocal species, producing echolocation clicks (peak frequency 23–67 kHz), ‘chirps’ (8–14 kHz), ‘barks’ (below 3 kHz) and whistles (generally 2–18 kHz) (Au, 1993; Caldwell & Caldwell, 1968; Richardson, Greene, Malme, & Thomson, 1995). It is a cosmopolitan species with a wide distribution in all oceans, including the Mediterranean and the Black Seas. In the Mediterranean Sea, the short‐beaked common dolphin inhabits both oceanic and neritic waters (Notarbartolo di Sciara & Birkun, 2010). Osteological collections and the literature show that common dolphins were widespread and abundant in much of the Mediterranean Sea until the late 1960s (Bearzi et al., 2003). During recent decades, how-ever, the species has declined in several areas (Bearzi et al., 2003), with important strongholds remaining only in the Alboran Sea (Cañadas & Hammond, 2008), around the Island of Malta (Vella, 2005) and in the waters along western North Africa (Boisseau et al., 2010). The declining trend in abundance raised conservation concerns,
and in 2003 the Mediterranean short‐beaked common dolphin was listed as Endangered in the IUCN Red List.
Genetic studies highlight that the Mediterranean and Eastern North Atlantic populations are differentiated from each other (Natoli et al., 2008; Tonay et al., 2016), with a directional gene flow that sug-gests the movement of females out of the Mediterranean Sea (Natoli et al., 2008). Genetic differentiation within the Mediterranean, between Eastern Mediterranean (Ionian Sea) and Western Mediterra-nean populations, was also reported by Natoli et al. (2008) and con-firmed by Tonay et al. (2016). Genetic analysis suggests that this population difference evolved recently. The adaptation to different environments and/or foraging strategies may have been the driving factors for this differentiation, and it is likely to have been reinforced by a recent bottleneck (Moura, Natoli, Rogan, & Hoelzel, 2013) that affected the Eastern Mediterranean (Ionian Sea) common dolphins (Bearzi et al., 2003) in the last decades.
At present, data supporting genetic intra‐basin differentiation belong to a small dataset, and long‐term monitoring of Mediterranean short‐beaked common dolphin has been carried out only in a few areas within the basin, giving little and scattered information on occur-rence, distribution and habitat use (Pace et al., 2016). Thus, additional elements are needed to complement the observed genetic intra‐basin differentiation. The analysis of the whistle characteristics of short‐ beaked common dolphin could be a useful tool for understanding the population structure and dynamics of the species within the basin. The aim of this study was to investigate the geographic variation in the characteristics of whistles of free‐ranging short‐beaked common dolphins living in the Mediterranean Sea, and to evaluate if whistle acoustic structure is the result of adaptation to local environment characteristics or a possible genetic diversification. This work describes the geographical differences between short‐beaked com-mon dolphins belonging to the Eastern and Western basins, and to four sub‐basins of the Mediterranean Sea (Alboran Sea; Central Western Mediterranean; Strait of Sicily; Ionian Sea). The variation of whistle parameters in relation to distance from the geographical bar-rier represented by the Strait of Gibraltar is also analysed, as well as whistle parameter variation in relation to environmental (distance from shore, depth) and social (group size) factors.
2
|M E T H O D S
2.1
|Study area
The Mediterranean Sea is divided into two basins at the Sicilian Strait, which are referred to as the Western Mediterranean and Eastern Mediterranean (Figure 1). The Western Mediterranean includes the following sub‐basins: Alboran Sea, Algerian–Provençal basin and Tyrrhenian Sea. The Algerian–Provençal Sea and the Tyrrhenian Sea constitute together the Central Western Mediterranean, an area characterized by a current moving east from Gibraltar (Miller, 1983). The Eastern Mediterranean includes the fol-lowing sub‐basins: Adriatic Sea, Ionian Sea, Aegean Sea and
Levantine basin, and it is influenced by a deep water stream moving west from the Levantine basin (Miller, 1983).
2.2
|Data collection
Acoustic data were gathered between 1997 and 2012, from different areas of the Mediterranean Sea, using multiple platforms. Recordings were collected by the following organizations: Alnitak (Spain), Gaia Research Institute Onlus (Gaia, Italy), Groupe de Recherche sur les Cetaces (GREC, France), IAMC (Institute for Marine and Coastal Environment)–CNR (Italy), International Fund for Animal Welfare (IFAW, UK), Oceanomare Delphis Onlus (Italy), Thalassa Research and Formation (Italy) and the Life Sciences and Systems Biology Department of the University of Torino (Italy).
Visual sightings of the recorded animals enabled the identification of the species. When common dolphins were observed within mixed‐ species schools, the corresponding recordings were discarded from the dataset.
Twenty‐six independent acoustic detections associated with visual sightings were collected (Figure 1). Sound recordings were obtained using a variety of recording equipment, all of which had a flat fre-quency response (±3 dB) up to 22 kHz or more (Table 1). The coordi-nates of each sighting, all referred to the WGS84 system, were collected using a variety of Garmin TM GPS units or an equivalent positioning systems (such as LORAN C). To attribute a depth (m) to each sighting, the navigation charts of the Italian Istituto Idrografico and the UK Hydrographic Office were used.
To calculate the progressive distance from the Atlantic Ocean, the distance of each acoustic detection from the Strait of Gibraltar (the shortest seaway) was measured using ARCGIS 9.0. The same software was employed for calculating the distance from shore.
2.3
|Acoustic analysis procedures
Approximately 12 h of recordings were examined (Table 1). A total of 1176 loud and clear whistles, that did not overlap extensively with other whistles, were extracted for manual measurement based on two criteria: (a) the signal‐to‐noise ratio needed to be sufficiently high so that timing and frequency parameters could be unambiguously discerned from background noise; and (b) whistles that had similar contours were discarded in order to increase the whistle diversity of each acoustic detection, and to minimize the possibility of oversampling the whistles belonging to a particular individual, to a group, or to a specific behavioural context (Azzolin et al., 2013). The examined whistles represent 65% of those recorded.
According to previous studies (Azzolin et al., 2013, 2014; Oswald et al., 2003), 10 scalar parameters were obtained for each whistle using Raven Pro 1.4, through manual measurements of the fundamen-tal component of the whistle contour (Figure 2). These parameters included: (a) duration; (b) beginning frequency; (c) final frequency; (d) minimum frequency; (e) maximum frequency; (f) frequency range (cal-culated by subtracting the values of the maximum and minimum fre-quency); (g) number of inflection points in the frequency contour; (h) number of steps (a discontinuous change in frequency); (i) number of contour minimum; and (j) number of contour maximum.
FIGURE 1 Area of study: black dots represent acoustic detection and simultaneous visual sighting of short‐beaked common dolphins within the Mediterranean Sea
Whistles contours that presented interruptions shorter than 200 ms were considered‘discontinuous’. In order to consider a whistle contour belonging to two different whistles, the duration of a break along the contour had to be 200 ms or greater (Bazua‐Duran & Au, 2002). Since discontinuous whistles could have been collected from animals not ori-ented directly towards the recording system, they were not considered for statistical analysis, because the whistle contour was not always clearly identifiable. Whistles that went off scale were also not included in statistical analysis, because of their missing a portion of their contour. To reduce over‐representation of the most ‘vocal’ dolphins, the maximum number of whistles to be analysed per group was set to four times the number of individuals present in the group (Azevedo & Van Sluys, 2005).
2.4
|Statistical analyses
Statistics were carried out using the software PASW STATISTICS 18.0 (SPSS Institute Inc., Chicago, IL, USA). The normality of the whistle parameters was checked by applying the Kolmogorov–Smirnov test. Owing to the small dataset of the Algerian Provençal basin, the data of this sub‐basin were pooled together with those of the Tyrrhenian Sea into the Central Western Mediterranean Sea dataset. Different statistical methods were employed to document and test the variation of whistle parameters.
A descriptive statistic was generated to define: (a) the mean varia-tion of whistle parameters of the short‐beaked common dolphin inhabiting the Mediterranean sub‐basins; (b) the mean variation of TABLE 1 Details of the data collection.
Area Group
Acoustic
detections Period Equipment for data collection
Alboran Sea Alnitak 1 2001 Omnidirectional hydrophone.
Flat response of ±2 dB from 200 Hz to 30 kHz. Sampling frequency: 48 kHz
IFAW 10 2003, 2004 Omnidirectional hydrophone and towed array with two hydrophones. Flat response: ±1 dB between 1 Hz and 15 kHz, and
of 63 dB between 15 and 30 kHz. Sampling frequency: 48 kHz GREC 1 1999 Mono towed hydrophone.
Flat response of ±2 dB from 200 Hz to 30 kHz. Sampling frequency: 48 kHz
Central Western Mediterranean
GREC 2 1997 Stereo towed hydrophone. Flat response of ±2
dB from 200 Hz to 30 kHz. Sampling frequency: 44 kHz Oceanomare Delphis 6 2005, 2009, 2011,
2012, 2013
Towed array with two hydrophones. Flat response of ±2 dB from 200 Hz to 24 kHz. Sampling frequency: 48 kHz Gaia 1 2005 Omnidirectional hydrophone. Flat response of ±3
dB from 200 Hz to 30 kHz. Sampling frequency: 96 kHz
Strait of Sicily CNR 2 2012 Omnidirectional hydrophone. Flat response of ±2 dB from 0.1 Hz to 30 kHz. Sampling frequency: 300 kHz
Ionian Sea Gaia 3 2009 Omnidirectional hydrophone. Flat response of ±3 dB from 200 Hz to 30 kHz. Sampling frequency: 96 kHz
Total 26
FIGURE 2 Spectrogram of a whistle call showing the variables measured in the study (except frequency range): duration, beginning frequency, final frequency, minimum frequency, maximum frequency, inflection point, step, minimum in the contour and maximum in the contour
whistle parameters of the short‐beaked common dolphin of the Western and Eastern Mediterranean basins; (c) the environmental (dis-tance from shore, depth) and social factors (number of animals) of the whole Mediterranean dataset; and (d) the environmental and social fac-tors of each acoustic detection.
A monovariate non‐parametric analysis (Man–Whitney and Kruskal–Wallis tests) was applied to compare: (a) the acoustic structure of whistles of the Western and Eastern Mediterranean basins; and (b) the acoustic structure of whistles recorded in four Mediterranean sub‐ basins. The P‐values for multiple comparisons were corrected applying the Benjamini–Hochberg procedure.
A multivariate analysis [discriminant function analysis (DFA) – stepwise method] was used to highlight the possibility of correctly classifying whistles to the Western and Eastern Mediterranean basins and to the four investigated sub‐basins. For cross‐validating, the DFA leave‐one‐out procedure was applied.
A Spearman's rho test was performed to investigate the correlation between: (a) whistle parameters and the progressive distance from the Strait of Gibraltar; and (b) whistle parameters and environmental (dis-tance from shore and depth) and social (number of animals) factors. Spearman's Rho test was applied to the whole dataset and replicates for the areas with at least five acoustic detections.
3
|R E S U L T S
After selecting and subsampling the dataset, 704 whistles were con-sidered for statistical analysis. The results of the Kolmogorov–Smirnov test show that all the investigated parameters were not normally distributed.
Table 2 summarizes mean values, standard deviations and coeffi-cients of variation (CVs) of whistle parameters for the whole Mediter-ranean Sea. Duration and all frequency parameters show low variability (CVs <52%), while modulation parameters present a high degree of intra‐species variation (CVs >82%).
3.1
|Variability among Western and Eastern
Mediterranean
In order to investigate whether the Italian Peninsula and the Strait of Sicily represent a geographical barrier for the movement and exchange of acoustic properties of short‐beaked dolphins within the Mediterranean Sea, the whistle characteristics of the distinct Eastern and Western short‐beaked common dolphin sub‐populations were analysed (Table 2).
The whistles belonging to the Western Mediterranean show the highest mean values for duration, final frequency, minimum frequency, number of inflection points, steps and number of minimum points. The whistles belonging to the Eastern Mediterranean show the highest mean values for beginning and maximum frequency, frequency range and number of maximum points (Table 2).
Statistical analysis (Mann–Whitney Test) shows that differences among the two areas are significant for the following parameters: duration, frequency range, number of inflection points, number of steps and number of maximum points (Table 2).
DFA (Wilks’ lambda = 0.992; F = 5.95; d.f. = 700; P < 0.05) cor-rectly assigns whistles to the basin where they were recorded in 65% of the cases, when running the cross‐validated procedure, with a percentage of correct classification significantly greater than that expected by chance. The whistles of the Western Mediterranean are correctly classified in 64% of the cases, while those of the Eastern Mediterranean are correctly classified in 68% of the cases. The same five parameters that are statistically different according to Mann– Whitney test contribute to the discriminant model of the two sub‐ populations: number of maximum points (−0.974), frequency range (−0.732), number of steps (0.653), number of inflection points (0.717) and duration (0.450).
Including the Sicilian whistles within the Western Mediterranean basin dataset, DFA (Wilks’ lambda = 0.969; F = 22.508; d.f. = 700;
P < 0.001), the percentage of correct classification increased to 70%,
with the whistles of the Western Mediterranean correctly classified
TABLE 2 Descriptive statistics for whistle parameters produced by short‐beaked common dolphins inhabiting the Mediterranean Sea and Western and Eastern basins
Whole Mediterranean (N = 704) Western Mediterranean (N = 580) Eastern Mediterranean (N = 124)
Mean Standard deviation CV Mean Standard deviation CV Mean Standard deviation CV Duration (s) 0.893 0.462 51.81 0.91 0.46 50.25 0.81 0.48 59.05 P < 0.005
Beginning frequency (Hz) 11610 4228 36.41 11603 4199 36.19 11647 4380 37.60 Not significant
Final frequency (Hz) 11889 3863 32.49 11962 3829 32.01 11551 4017 34.77 Not significant Minimum frequency (Hz) 8233 2008 24.38 8264 1995 24.14 8089 2070 25.60 Not significant
Maximumfrequency (Hz) 15481 3270 21.12 15393 3268 21.23 15894 3259 20.51 Not significant
Frequency range (Hz) 7253 3262 44.98 7135 3237 45.37 7805 3338 42.77 P < 0.050
Number of inflection points 1.974 1.610 81.52 2.04 1.62 79.50 1.66 1.51 91.11 P < 0.050
Number of steps 2.121 2.532 119.39 2.19 2.50 113.69 1.77 2.68 151.08 P < 0.050
Number of minimum points 1.013 0.909 89.75 1.02 0.89 86.68 0.97 1.01 104.53 Not significant
in 69% of the cases and those of the Eastern Mediterranean correctly classified in 73% of cases. Three parameters contributed to the dis-criminant model of the two sub‐populations: duration (−1.071), num-ber of maximum points (0.732) and frequency range (0.614).
3.2
|Variability among four Mediterranean
sub
‐basins
Table 3 and Figure 3 summarize mean values of whistle parameters for the four investigated Mediterranean sub‐basins: Alboran Sea, Central Western Mediterranean, Sicily Strait and Ionian Sea. The Alboran Sea whistles had the highest mean values for final frequency, mini-mum frequency, maximini-mum frequency, frequency range and number of inflection points. The Central Western Mediterranean whistles present the lowest mean values for maximum frequency, frequency range and the maximum mean values for number of steps and number of minimum points. The Sicilian whistles showed the highest mean values for duration and beginning frequency, and the lowest mean
values for final frequency, number of inflection points, number of steps and number of minimum and maximum points. The Ionian Sea whistles showed the lowest mean value for duration, beginning fre-quency and minimum frefre-quency, and the highest mean value for a number of maximum points.
Figure 4 shows examples of whistles for each investigated sub‐ basin. The analysis of the CVs shows a low variability of all frequency parameters and duration (CV from 17 to 58%), particularly of minimum and maximum frequencies (CV from 17 to 28%), and a high variability of modulation parameters (CV from 64 to 163%) for all the considered Mediterranean sub‐basins (Table 3). Statistical analysis (Kruskal–Wallis test) shows that all the parameters are significantly different among sub‐basins (Table 3).
DFA (Wilks’ lambda = 0.958; F = 10.160; d.f. = 698; P < 0.001) correctly assigns whistles to the sub‐basins where they were recorded in 43% of the cases, when running the cross‐validated procedure, with a percentage of correct classification significantly greater than that expected by chance. The whistles are correctly classified in 45% of the cases for the Alboran Sea, in the 41% for the Central Western
TABLE 3 Descriptive statistics for whistle parameters produced by short‐beaked common dolphins inhabiting distinct sub‐basins of the Medi-terranean Sea Alboran Sea (N = 124) Central Western Mediterranean (N = 456) Sicily Strait (N = 39) Ionian Sea (N = 85) Duration (s) Mean 0.949 0.900 1.104 0.673 P < 0.001 Standard deviation 0.399 0.472 0.646 0.293 CV 41.99 52.47 58.52 43.50
Beginning frequency (Hz) Mean 12099 11467 14000 10567 P < 0.001
Standard deviation 4384 4141 3929 4165
CV 36.24 36.11 28.06 39.42
Final frequency (Hz) Mean 12931 11698 9810 12349 P < 0.001
Standard deviation 3742 3813 2561 4313
CV 28.94 32.60 26.11 34.93
Minimum frequency (Hz) Mean 8652 8158 8324 7980 P < 0.050
Standard deviation 2164 1935 1506 2282
CV 25.01 23.72 18.10 28.60
Maximum frequency (Hz) Mean 16718 15032 16228 15739 P < 0.001
Standard deviation 2861 3281 2885 3421
CV 17.11 21.83 17.78 21.74
Frequency range (Hz) Mean 8066 6881 7903 7759 P < 0.001
Standard deviation 2952 3267 3389 3333
CV 36.60 47.48 42.89 42.96
Number of inflection points Mean 2.500 1.917 1.103 1.918 P < 0.001
Standard deviation 1.615 1.604 1.231 1.568
CV 64.62 83.69 111.65 81.75
Number of steps Mean 1.581 2.362 1.103 2.082 P < 0.001
Standard deviation 2.576 2.449 1.553 3.021
CV 162.97 103.70 140.81 145.06
Number of minimum points Mean 0.968 1.037 0.641 1.118 P < 0.050
Standard deviation 0.883 0.887 0.811 1.062
CV 91.23 85.56 126.47 95.06
Number of maximum points Mean 0.734 0.649 0.641 0.976 P < 0.050
Standard deviation 0.903 0.793 0.628 1.023
Mediterranean, in the 69% for the Sicily Strait and in the 48% for the Ionian Sea (Table 4). Three canonical discriminant functions (Table 5) contribute to the classification model. The first function explains 45% of the variance, the second 36%, and the third 19%. Figure 5 shows the all‐groups scatter plot built employing functions 1 and 2.
3.3
|Traits variability in relation to the distance from
the Strait of Gibraltar
The correlation between whistle parameters and the distance from the Strait of Gibraltar shows that duration, beginning frequency, minimum and maximum frequency, and number of inflection points are negatively correlated with the distance from Gibraltar (Spearman's rho test), while number of steps is positively correlated with this factor (Table 6).
3.4
|Traits variability in relation to environmental
and social factors
The correlation between whistle parameters and depth (m) and dis-tance from shore (km) was investigated for the whole dataset, to
study the influence of environmental factors on whistle structure. Statistical analysis (Spearman's rho test) shows that depth is nega-tively correlated with beginning frequency, number of steps and number of minimum points, and distance from shore is negatively correlated with number of steps (Table 6). Similarly, the correlation between whistle parameters and the number of animals of a recorded group was investigated to study the influence of social fac-tors on whistle structure. Statistical analysis (Spearman's rho test) shows that number of animals is negatively correlated with final fre-quency, maximum frequency and frequency range, and positively correlated with number of steps (Table 6).
To evaluate if the obtained correlation results were consistent among sub‐basins, Spearman's rho test was also run for each dataset with at least five acoustic detections (Tables 7–8). The results of this analysis show that the correlation is slightly different for each consid-ered sub‐basin. For the Alboran Sea, only the correlation between number of animals and frequency range reflects that of the whole Mediterranean dataset (Table 7). For the Central Western Mediterranean Sea, depth reflects the pattern of the whole Mediterra-nean, and distance from shore shares with the Mediterranean dataset the correlation with number of steps (Table 8).
FIGURE 3 Values of whistle parameters produced by short‐beaked common dolphins inhabiting the investigated four sub‐basins of the Mediterranean Sea. (a) Frequency parameters; (b) duration and modulation parameters. Significant differences (Kruskal– Wallis test) among sub‐basins are represented by asterisks (* P < 0.05; ** P < 0.001)
FIGURE 4 Examples of spectrogram of whistles belonging to the four investigated sub‐basins: (a1–a4) Alboran Sea; (b1–b4) Central Western Mediterranean; (c1–c4) Sicily Strait; (d1–d4) Ionian Sea
TABLE 4 Classification results discriminant function analysis (DFA) – stepwise method
Predicted Group Membership
Total Alboran Sea Central Western Mediterranean Sicily Strait Ionian Sea Alboran Sea 45.2 17.7 15.3 21.8 100.0
Central Western Mediterranean 21.1 41.4 18.6 18.9 100.0
Sicily Strait 5.1 10.3 69.2 15.4 100.0
4
|D I S C U S S I O N
The geographical variation of whistles structure of short‐beaked com-mon dolphins has already been analysed within the Atlantic Ocean (Ansmann, Goold, Evans, Simmonds, & Keith, 2007), between the Atlantic Ocean and the Eastern Tropical Pacific (Ansmann, 2005), and between the Atlantic Ocean and the Mediterranean Sea (Papale et al., 2013b). The dataset here presented is much larger than the pre-vious Mediterranean study and it includes geographical areas within the basin that had not previously been investigated (i.e. the Sicily Strait and the Ionian Sea). This may have influenced the variations in the mean values of the acoustic parameters found in this study, highlighting how the short‐beaked common dolphin whistle acoustic
structure varies within the Mediterranean Sea and what factors are acting on the whistles traits.
Past studies have revealed the existence of geographic variations in the whistles of several odontocete species, ascribing differences to geographical separation (limited exchange of individuals), genetic/cultural differentiation, adaptation to different habitats and environmental noise, differences in behavioural sampled patterns, social structure and group size (Ansmann, 2005; Ansmann et al., 2007; Azzolin et al., 2013; Bazua‐Duran, 2004; Morisaka et al., 2005; Rendell et al., 1999; Wang et al., 1995a, 1995b). Genetic studies on short‐beaked common and striped dolphins describe how the Strait of Gibraltar acts as a geographical barrier that reduces the exchange of individuals, leading to genetic differentiation of Atlantic and Mediter-ranean individuals (Garcıa‐Martinez, Moya, Raga, & Latorre, 1999; Natoli et al., 2008; Tonay et al., 2016). Differences in whistles struc-tures between the two basins are also documented for both species (Azzolin, 2008; Papale et al., 2013b, 2013a). Similarly, within the Mediterranean Sea, the Sicily Strait acts as a geographical barrier that separates and genetically differentiates the short‐beaked common and the striped dolphins of the Western and Eastern Mediterranean basins (Garcıa‐Martinez et al., 1999; Gaspari, Azzellino, Airoldi, & Hoelzel, 2007; Natoli et al., 2008; Tonay et al., 2016). Intra‐Mediterranean var-iability of whistles structure, because of geographical separation, is known for striped dolphins (Azzolin, 2008; Azzolin et al., 2013). The analysis here reported seems to highlight a similar path for the short‐beaked common dolphin, where intra‐Mediterranean whistles differentiation may reflect the geographical separation that leads to genetic differences (Natoli et al., 2008; Natoli, Moura, & Hoelzel, 2016; Tonay et al., 2016). Pooling together acoustic data of the Western Mediterranean basin and the Sicily Strait, that genetic analy-ses indicate cluster together (Natoli et al., 2016), leads to an increase in DFA score from 65 to 70%, therefore supporting the hypothesis that acoustic results match the genetic ones.
DFA allows the classification of striped dolphins to Western/ Eastern basins in 64% (Azzolin et al., 2013) and short‐beaked common dolphin in 70% (this work). The higher classification scores for short‐ beaked common dolphins could be a consequence of the bottleneck that this species has faced in recent years (Moura et al., 2013), with a reduction in the number of individuals and exchange among basins, TABLE 5 Standardized canonical discriminant function coefficients
Function 1 2 3 Duration −0.891 0.382 0.389 Final frequency 0.568 0.144 0.336 Minimum frequency −0.362 0.267 0.344 Maximum frequency −0.121 −0.967 0.015
Number of inflection points 0.603 0.371 0.678
Number of steps 0.458 0.784 −0.120
Number of maximum points 0.213 −0.844 −0.338
FIGURE 5 Canonical discriminant function scatter plot of the short‐ beaked common dolphins populations of Alboran Sea (AS), Central Western Mediterranean Sea (CWM), Sicily Strait (SS) and Ionian Sea (IS), from the two functions that accounted for 81% of the observed variance
which could have led to a greater acoustic differentiation compared with that of striped dolphins.
With respect to trait variability, the parameters under strong morphophysiological constraint and genetic influence are usually char-acterized by a lower intra‐specific CV than other parameters (Mousseau & Roff, 1987), which may be affected by social and/or eco-logical factors (May‐Collado, Agnarsson, & Wartzok, 2007). The results of this study show that duration and all frequency parameters show low variability, while modulation parameters present a high degree of intra‐species variation, in agreement with the hypothesis that genetic differences may contribute to the geographic variance of spe-cific whistle traits.
Most of the considered whistle parameters are negatively corre-lated with the distance from Gibraltar. This west–east gradual gradient also supports the genetic explanation, indicating that the differences in short‐beaked common dolphin whistles gradually increase with advance into the Mediterranean Sea, diversifying more and more from the Atlantic population.
Differences in frequency parameters are usually associated with variation between populations, rather than behavioural contexts within populations, as frequency characteristics are generally related to anatomical variables, such as body size (Bazúa‐Durán & Au, 2004; Rendell et al., 1999), or may be associated with environmental factors, such as ambient noise levels, typical of a specific geographical area (Ansmann et al., 2007; Morisaka et al., 2005; Petrella, Martinez, Anderson, & Stockin, 2012). Modulation parameters, which are more freely adjusted by individuals, are usually more variable within popula-tions and considered to carry information about individual identity or behaviour (Bazua‐Duran & Au, 2004; Morisaka et al., 2005; Rendell et al., 1999).
In the present study frequency range, duration and most of the modulation parameters (number of inflection points, number of steps, number of maximum points) were significantly different between the Western and the Eastern Mediterranean, and they allow whistles to be correctly assigned to these two distinct regions. Furthermore, all the parameters change significantly among the four sub‐basins, and three frequency parameters (final, minimum and maximum frequency), duration and most of the modulation parame-ters (number of inflection points, number of steps, number of maxi-mum points) enable the whistles to be properly classifies to the four geographical districts. The variation of frequency parameters can explain the variability between populations connected to genetic dif-ferences, while the variation of modulation parameters could be related to identity and behaviour or could represent an expression of genetic differences, reflecting evolutionary adaptations to particu-lar ecological conditions. Indeed, this study highlights that a correla-tion between whistle modulacorrela-tion parameters and environmental (depth and distance from shore) factors as well as a correlation between frequency parameters and social factor (group size) exists. In deeper and offshore waters, short‐beaked common dolphins sim-plify their whistle contour, similarly to Mediterranean striped dol-phins (Azzolin et al., 2013). Since modulation parameters are those considered to be the more affected by ecological and/or social
TABLE 6 Corr elation betwe en whist le para meters of the whole Me diterra nean Sea a n d dista nce from Gibr altar, dept h, dista nce from shore and number of animals of the acou stic detection (Spearm an's rho test) Whole Mediterranean Sea Duration Beginning frequency Final frequency Minimum frequency Maximum frequency Frequency range Number of inflection points Number of steps Number of minimum points Number of maximum points Distance from Gibraltar (km) Correlation coefficient − 0.150 − 0.104 − 0.014 − 0.094 − 0.084 − 0.050 − 0.087 0.170 0.061 0.061 Significance (two ‐tailed) 0.000 0.006 0.708 0.013 0.026 0.188 0.021 0.000 0.104 0.106 N 702 704 704 704 704 704 704 704 704 704 Depth (m) Correlation coefficient − 0.038 − 0.092 0.018 0.020 0.045 0.031 0.039 − 0.120 − 0.141 − 0.043 Significance (two ‐tailed) 0.311 0.014 0.638 0.589 0.232 0.414 0.301 0.001 0.000 0.259 N 702 704 704 704 704 704 704 704 704 704 Distance from shore (km) Correlation coefficient 0.053 0.041 − 0.037 0.027 0.008 0.003 0.055 − 0.204 − 0.046 − 0.019 Significance (two ‐tailed) 0.160 0.274 0.327 0.469 0.823 0.933 0.144 0.000 0.228 0.622 N 702 704 704 704 704 704 704 704 704 704 Number of animals Correlation coefficient 0.061 − 0.006 − 0.078 − 0.007 − 0.124 − 0.132 − 0.060 0.186 0.061 − 0.031 Significance (two ‐tailed) 0.105 0.872 0.039 0.845 0.000 0.000 0.107 0.000 0.105 0.425 N 702 704 704 704 704 704 704 704 704 704
TABLE 7 Corr elation betwe en whist le para meters of the Alboran Sea and dep th, dista nc e from shore a n d numb er of anima ls of the acoustic det ection (Spearm an's rho test) Alboran Sea Duration Beginning frequency Final frequency Minimum frequency Maximum frequency Frequency range Number of inflection points Number of steps Number of minimum points Number of maximum points Depth (m) Correlation coefficient 0.050 0.111 − 0.035 0.075 − 0.147 − 0.229 0.087 0.061 0.026 − 0.167 Significance (two ‐tailed) 0.584 0.219 0.703 0.408 0.102 0.010 0.338 0.502 0.772 0.064 N 124 124 124 124 124 124 124 124 124 124 Distance from shore (km) Correlation coefficient 0.202 0.203 − 0.164 0.128 − 0.074 − 0.205 0.175 0.079 0.135 − 0.064 Significance (two ‐tailed) 0.025 0.024 0.069 0.156 0.416 0.022 0.051 0.385 0.136 0.477 N 124 124 124 124 124 124 124 124 124 124 Number of animals Correlation coefficient − 0.078 0.000 0.188 0.120 − 0.109 − 0.252 − 0.014 − 0.194 − 0.107 0.029 Significance (two ‐tailed) 0.391 0.997 0.037 0.183 0.229 0.005 0.876 0.031 0.239 0.746 N 124 124 124 124 124 124 124 124 124 124
TABLE 8 Corr elation betwe en whis tle para met ers of the Centr al We stern Me diterr anean and dept h, dista nce from sh ore and numb er of anim als of the acousti c det ection (S pearm an's rho test) . Central Western Mediterranean Duration Beginning frequency Final frequency Minimum frequency Maximum frequency Frequency range Number of inflection points Number of steps Number of minimum points Number of maximum points Depth (m) Correlation coefficient − 0.130 − 0.136 0.004 − 0.006 0.021 0.025 0.023 − 0.140 − 0.170 − 0.020 Significance (two ‐tailed) 0.005 0.004 0.926 0.902 0.655 0.596 0.619 0.003 0.000 0.666 N 454 456 456 456 456 456 456 456 456 456 Distance from shore (km) Correlation coefficient − 0.079 − 0.101 − 0.124 − 0.106 − 0.184 − 0.126 − 0.015 − 0.151 − 0.017 − 0.006 Significance (two ‐tailed) 0.092 0.032 0.008 0.024 0.000 0.007 0.745 0.001 0.718 0.905 N 454 456 456 456 456 456 456 456 456 456 Number of animals Correlation coefficient 0.016 − 0.006 − 0.043 − 0.006 − 0.031 − 0.039 0.031 0.038 0.097 0.047 Significance (two ‐tailed) 0.735 0.894 0.361 0.903 0.509 0.410 0.503 0.420 0.038 0.318 N 454 456 456 456 456 456 456 456 456 456
factors (May‐Collado et al., 2007), a study aiming to investigate the behaviour and ecology of short‐beaked common dolphins in deeper waters would help explain if whistle modulation parameters variabil-ity can be linked to a distinct behavioural context. The correlation between whistle parameters and group size shows that dolphins reduce maximum frequency, and frequency range in larger groups, as Mediterranean striped dolphins do (Azzolin et al., 2013). The results of correlation parameters suggest that environmental and social factors act in a similar way on different species. However, dif-ferent from Mediterranean striped dolphins, short‐beaked common dolphins decrease final frequency and increase their number of steps in larger groups. Nevertheless, these patterns are not consistent within the whole Mediterranean, and each sub‐basin for which the analysis was replicated (Alboran Sea, Central Western Mediterra-nean) presents its own pattern, which may be a consequence of adaptation to different habitats or of distinct sampled behavioural context.
Finally, most of the parameters relevant for geographical classifi-cation do not show any correlation with environmental and social factors or show a low CV, supporting the hypothesis of a genetic influence on whistle structure, and the possibility of using short‐ beaked common dolphin whistles for the spatial identification of management units.
5
|C O N C L U S I O N
Genetic isolation is probably the major cause of the geographic vari-ance of the Mediterranean short‐beaked common dolphin whistle structure, which may reflect some evolutionary adaptations to partic-ular ecological conditions or may be the by‐product of morphological evolution.
The results of this study show that intra‐Mediterranean variability of whistles structure seems to reflect the path of genetic studies, highlighting the possible use of acoustic data in combination with other source of data (e.g. genetic, morphological, etc.) to identify geo-graphic areas where discrete management units occur. Defining man-agement unit boundaries is fundamental for species conservation and the multidisciplinary approach seems to be a promising methodology to define monitoring plans and to outline effective management and protection strategies (Esteban et al., 2016; Sveegaard et al., 2015).
A C K N O W L E D G E M E N T S
This project was carried out thanks to the support of ALNITAK, GAIA, GREC, IFAW, OCEANOMARE DELPHIS and THALASSA which sup-plied the recordings and data collected during their scientific cruises.
O R C I D
Marta Azzolin https://orcid.org/0000-0002-4007-5010
Alexandre Gannier https://orcid.org/0000-0002-8434-9538
Elena Papale https://orcid.org/0000-0002-5150-9240
Barbara Mussi https://orcid.org/0000-0002-8772-1091
Daniela Silvia Pace https://orcid.org/0000-0001-5121-7080
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How to cite this article: Azzolin M, Gannier A, Papale E, et al.
Whistle variability of the Mediterranean short beak common dolphin. Aquatic Conserv: Mar Freshw Ecosyst. 2019;1–15.
