Information on individual calling behaviour and source levels are important for understanding acoustically mediated social interactions of marine mammals, for which visual observations are difficult to obtain. Our study, conducted in the Stellwagen Bank National Marine Sanctuary (SBNMS), located in the Gulf of Maine, USA, used passive acoustic arrays to track North Atlantic minke whales and assess the sound production behaviour of individuals. A total of 18 minke whales were acoustically tracked in this study. Individual calling rates were variable, with a median intercall interval (ICI) of 60.3 s. Average source levels (SLrms) for minke whales pulse trains ranged between 164 and 168 dB re 1 μPa, resulting in a minimum detection range of 0.4–10.2 km for these calls in this urban, coastal environment. All tracked animals were actively swimming at a speed of 5.0 ± 1.2 km/h, which matches swimming speeds of migrating minke whales from other areas and confirms SBNMS as part of the migration route of this species in the Western North Atlantic. Tracked minke whales produced 7 discrete call types belonging to 3 main categories, yet no individual produced all call types. Instead, minke whales produced 2 multisyllabic call sequences (A and B) by combining 3–4 different call types in a non-random order. While 7 of the tracked individuals produced calling pattern A, 10 whales used calling pattern B, and only 1 animal combined call types differently. Animals producing different call sequences were in acoustic range of one another on several occasions, suggesting they may use these sequences for mediating social interactions. The fact that the same calling patterns were shared by several individuals suggests that these patterns may contain information related to sex, age or behavioural context.
Understanding the behavioural context of individual calling behaviour and the temporal patterns of call production is an important aspect of studying animal communication systems. Several taxa arrange different calls or syllables into larger units of sound. Such combinations allow for syntactical rule building, and increase information transfer over monosyllable communication. Songbirds, in particular, recombine simple calls to form a variety of higher-order songs that function primarily in a reproductive context (Nowicki & Searcy, 2004; Catchpole & Slater, 2008). In addition, multi-syllable phrases may convey information about group membership, as well as the size and threat of predators (Templeton et al., 2005; Briefer et al., 2013). In mammals, males of the Brazilian free-tailed bat produce songs that share several structural and functional traits with bird song and vary based on social context (Bohn et al., 2013). Several non-human primate species also produce higher-order call combinations, which may carry meaning related to predator presence and type or food source (Clarke et al., 2006; Ouattara et al., 2009; Clay & Zuberbühler, 2011).
In a marine context, many cetacean species exhibit highly advanced vocal systems, some of which have been studied extensively. In an environment, where light is attenuated quickly, behaviours such as the advertisement of breeding condition, coordination of group movements or the maintenance of social bonds are often mediated through sound (e.g., Tyack & Clark, 2000). However, due to the fact that most marine mammals spend only little time at the surface, and underwater observations are often infeasible, the visual quantification of behaviour and identification of individuals at sea is severely limited. Thus, there is a lack of knowledge on individual calling behaviour and the behavioural context of vocalizations.
Call sequences and their behavioural correlates have mainly been studied in odontocetes. For example, bottlenose dolphin signature whistles, which encode individual identity, are often produced in sequence both by individual animals, as well as groups (e.g., Quick & Janik, 2012; Janik & King, 2013). Similarly, short-finned pilot whales and killer whales produce non-random sequences of stereotyped call types, which most likely function in individual recognition and to maintain group cohesion (e.g., Ford et al., 1989; Sayigh et al., 2013).
Several species of baleen whales combine individual sound units to form songs that, similar to bird song, represent a series of notes arranged in a recognizable temporal pattern (Payne & McVay, 1971). These patterned sequences have been termed songs, based on the definition by Broughton (1963) that song is: ‘…a series of notes, generally of more than one type, uttered in succession and so related as to form a recognizable sequence or pattern in time’. In particular, humpback whales (Megaptera novaeangliae) produce complex, hierarchically structured songs (Payne & McVay, 1971; Cholewiak et al., 2013). Similarly, bowhead whales (Balaena mysticetus) sing elaborate songs (Stafford et al., 2008; Tervo et al., 2011), while blue (Balaenoptera musculus) and fin (Balaenoptera physalus) whales produce high intensity song units at very low frequencies (approx. 15–30 Hz) (McDonald et al., 2001; Croll et al., 2002). In humpback, fin, and blue whales it has been shown that only males produce songs (Glockner, 1983; Croll et al., 2002; Oleson et al., 2007a). In these species, songs function as male advertisement or to mediate interactions between competing males (Tyack, 1981; Tyack & Whitehead, 1983; Oleson et al., 2007a). Recent acoustic recording efforts of marine mammals are beginning to show that song occurs not only during the traditional breeding season but also on feeding grounds and during migration (Stafford et al., 2007; Simon et al., 2010; Vu et al., 2012). And alternative functions of songs, such as navigation and prey detection, have also been suggested (Clark & Ellison, 2004). In addition to song production, in most species of baleen whales, both sexes produce a range of different call types in various contexts. Several species produce feeding-associated vocalizations that may be repeated in monosyllabic sequences (Cerchio & Dahlheim, 2001; Oleson et al., 2007b; Širović et al., 2013). In addition, sequences of frequency modulated call-counter calls occur in fin, blue and right whales (Eubalaena spp.) and serve as contact calls to maintain group cohesion (Clark, 1982; Oleson et al., 2007b; Širović et al., 2013). A variety of variable social calls have been described for most species (Oleson et al., 2007a; Dunlop et al., 2008; Stafford et al., 2008; Parks et al., 2011; Stimpert et al., 2011); the function of these calls is largely unknown but many of the calls are stable over several years suggesting an important role in mediating social interactions (Rekdahl et al., 2013).
Sounds produced by North Atlantic minke whales have only recently been described in more detail. Mellinger et al. (2000) described low-frequency pulse trains with a varying interpulse interval (IPI) structure; and a recent study in the Gulf of Maine described 7 distinct pulse train types, which fall into 3 main categories and occur with varying frequency (Risch et al., 2013). However, the behavioural significance of these vocalizations and whether they are specific to sex, age, recording site or season is unknown.
Given the identification of several stereotypic call types in the minke whale vocal repertoire, the main objective of the current study was to investigate whether individual minke whales use the full vocal repertoire, whether they combine pulse trains in predictable vocal sequences and how individuals use these sounds when engaging in vocal exchanges with conspecifics. Despite the reliance on primarily passive acoustic data and the lack of visually observed context, answers to these questions will allow the development of testable theories with respect to the behavioural function of minke whale pulse trains.
In addition, passive acoustic localization can also elucidate other, non-vocal aspects of behaviour, such as swimming speeds and movement behaviour (e.g., Stanistreet et al., 2013). Thus, a secondary goal of this study was to use acoustic data to quantify fine-scale movements of minke whales in the Gulf of Maine study area, where little is known about the general behaviour of this species.
Lastly, basic data on individual vocal behaviour, source level and detection range, as obtained by acoustic localization, is necessary for passive acoustic monitoring (PAM) applications. North Atlantic minke whales are still exploited commercially. Thus, although the species is currently listed as ‘a species of least concern’ in the IUCN Red List (Reilly et al., 2008), accurate monitoring of population size and structure is essential for its conservation. Most current monitoring and abundance estimates for minke whales are based on visual sightings data (Skaug et al., 2004; de Boer, 2010; Bartha et al., 2011). However, new methods such as PAM, coupled with new analytical approaches (Marques et al., 2013), offer an opportunity to significantly improve abundance estimates for this cryptic species (Oswald et al., 2011; Martin et al., 2013). Since PAM depends on the detection of vocalizing animals, it is critical to understand how individual calling behaviours influence calling rates, as well as any sex, season or site specificity of different call types. Only a few studies have collected such vocalization data for baleen whales using either acoustic recording tags or passive acoustic array configurations (Matthews et al., 2001; Parks et al., 2011, 2012; Stanistreet et al., 2013). For North Atlantic right whales, considerable variability in individual calling rate patterns, related to behaviour, age, sex and season has been documented (Parks & Tyack, 2005; Parks et al., 2005, 2011; Van Parijs et al., 2009). These results reinforce the importance of describing and considering such variability when interpreting passive acoustic data.
2. Material and methods
2.1. Acoustic data collection
During 2 October–30 November 2009 and 17 August–11 October 2011, acoustic data were continuously recorded in the Stellwagen Bank National Marine Sanctuary (SBNMS) located in the Southern Gulf of Maine, Northwest Atlantic Ocean (Figure 1). Arrays of 10 and 11 (2009 and 2011, respectively) marine acoustic recording units (MARUs) (Calupca et al., 2000) were deployed at depths ranging from 25 to 114 m, and anchored 1–2 m above the sea floor. Units were spaced approx. 11 and 5 km apart in 2009 and 2011, respectively (Figure 1). Each MARU was equipped with a HTI-94-SSQ hydrophone (sensitivity −168 dB re 1 V/μPa), connected to a pre-amplifier and A/D converter, resulting in an effective system sensitivity of −151.7 dB re 1 V/μPa. All units sampled at 2000 Hz and 12 bit resolution, yielding an effective analysis bandwidth of 10–1000 Hz, with a flat frequency response (±1 dB) between 55 and 585 Hz. Recordings from individual units were time-aligned using calibration signals recorded at the beginning and end of the deployments and compiled into multi-channel data files.
2.2. Individual calling behaviour
All acoustic data were examined manually for the presence of minke whale pulse trains by generating multi-channel spectrograms using the sound analysis software XBAT (Figueroa & Robbins, 2008; FFT size 1024, 85% overlap, Hanning window). Pulse train types were assigned to one of seven categories within three main groups, based on interpulse interval (IPI) structure, as described in (Risch et al., 2013). These main groups were slow-down (sd), constant (c) and speed-up (sp) pulse trains (Figure 2). All pulse trains that were not stereotypic, or of too low quality for categorization, were placed in a variable (v) group. Calling rates for each animal were calculated as the total number of calls/min, and intercall interval (ICI) was calculated as the difference between the start times of two consecutive pulse trains produced by the same individual. All temporal measurements were carried out in XBAT based on manually delineated event boxes.
To determine whether transitions between call types were random, transition frequencies were analysed as a first-order Markov chain, where a succeeding event is only dependent on the immediately preceding event. R package msm (Jackson, 2011) was used to arrange the continuous call data into a 2-way contingency table of preceding and following pulse train types and a transition probability matrix was estimated based on maximum likelihood. Observed and expected transition matrices were compared using a goodness-of-fit test and a 2-tailed Z-test for proportions (Fleiss, 1981) was used to compare each observed transition to its corresponding expected transition.
The transition frequencies for each call type combination were used to calculate an index of association (Ford, 1989). This index, based on Dice’s coefficient of association, normalizes the data to account for call type abundance:
where i and j are consecutive pulse trains, N is the number of transitions from one pulse train to the next, and S is the total number of transitions of a particular type. Hierarchical clustering of this association matrix was performed by applying the UPGMA method, using function hclust in the R stats package (R Core Team, 2013). The cluster results were then plotted as a dendrogram.
2.3. Localization and movement
Series of pulse trains recorded on three or more channels and occurring for a period of at least 20 min, with gaps not exceeding 10 min, were chosen for localization. Individual pulse trains were localized using a correlation sum estimation algorithm (CSE), applied in XBAT (Cortopassi & Fristrup, 2005). This localization method differs from techniques based on time differences of arrival (TDOA) and hyperbolic fixing as it does not rely on the selection of waveform cross-correlation peaks to estimate locations. Instead, it calculates accumulated cross-correlation sums for all channel pairs across a grid of spatial points and chooses the point that maximizes the correlation sum as the most likely location. Due to this process, the method is considered to be more robust to background noise (Cortopassi & Fristrup, 2005). Each localized signal was verified visually in multi-channel spectrograms to ensure that the same pulse train was picked on all channels and the candidate location, as determined by the CSE algorithm, agreed with the observed TDOAs. Pulse trains for which reliable and repeatable location estimates could not be obtained using CSE, were removed from all further movement analyses. However, if the visually observed TDOA estimates of these calls agreed with the general pattern of the tracked animal, they were still included in temporal calling pattern analyses (see next paragraph). After manual review of each localization, animal tracks were defined as the time-ordered collection of locations from a single source connected by a straight line (Turchin, 1998). Tracks were smoothed with a 5-point moving average (MA) to reduce the influence of localization error. Statistical simulation tests using the CSE algorithm and comparable array geometry show, that localization accuracy depends on the position of the source relative to the array and increases with distance from the centre of the array (Urazghildiiev & Clark, 2013). Thus, movement characteristics were calculated only for tracks within 5 km of the array boundaries to reduce the impact of increasing error outside of the array. R package adehabitat (Calenge, 2006) was used to calculate track statistics, including track duration (h), net displacement (km), total distance (km) and average speed (km/h). A straightness index (SI), defined as the quotient of net displacement and total distance (1 = straight line path, 0 = meandering path), was calculated to assess directness of movement. All analyses using R were performed using version 2.15.3 (R Core Team, 2013). Location error for the arrays was determined by conducting calibration experiments on 22 October 2009 and on 9 October 2011 at two and five sites within the array, respectively. A series of 5 to 10 frequency modulated sweep tones were played at each site. The source location of each playback sweep was estimated using the CSE algorithm. Location error in meters was then quantified by subtracting the estimated position for each locatable sweep from the known speaker location. Differences between known and estimated source locations were averaged over all sweeps and transmission sites.
2.4. Source level estimation
To estimate pulse train source levels (SL), received levels (RL) were measured for a subset of the 2011 data, based on several detection criteria that included (a) a high signal-to-noise ratio (SNR > 10 dB); (b) could be reliably located; and (c) did not overlap with other sounds. The signals were bandpass filtered between 50 and 250 Hz. RL measurements were carried out in Raven Pro version 1.5 (Bioacoustics Research Program, 2013) for every fifth individual pulse and for the entire pulse train. Minimum and maximum frequencies of the measured signals were defined as the −10 dB end points relative to the signal peak in the power spectrum. Measurements included peak-to-peak (RLpp) and root-mean-square (RLrms) sound pressure levels (dB re 1 μPa) for every pulse, and RLrms for the whole pulse train. RLrms was measured over a time window encompassing 90% of the total signal energy in the selection window (Madsen & Wahlberg, 2007). Following these measurements, SL was calculated from RL by compensating for transmission loss (TL). Under the assumption of mainly spherical spreading, TL equals , where R is the range of the whale from the receiver (Urick, 1983). In shallow water environments refraction and reflections from the sea bottom or surface will considerably affect TL, making the cylindrical spreading law () more appropriate, while in many environments an intermediate term is most appropriate. Due to empirical measurements showing that is a reasonable approximation for TL in the study area (unpublished data), we chose this term for all SL estimations. SL results were averaged by pulse train and reported in terms of peak-to-peak (SLpp, dB re 1 μPa), root-mean-square (SLrms, dB re 1 μPa) and energy flux density (SLefd, dB re 1 μPa2s).
Assuming source and receiver depths of 20 m and using averaged minke whale SLs from our study, signal propagation in the SBNMS was then modelled using the Acoustic Integration Model (Hatch et al., 2012). Hourly ambient noise level (NL) values were calculated for the week of 4–10 October 2009. NLs were averaged over the frequency band containing the most pulse train energy (56.2–355 Hz) and summarized as 5th, 25th, 50th 75th and 95th percentiles. In the absence of hearing and detection threshold data for minke whales, these NL values were compared with the signal propagation curve, and the range at which SNR = 0 was determined.
3.1. Individual calling behaviour
Since whales could only be successfully tracked to a certain range (< 8 km) outside the array boundaries, the data analysed were effectively censored; that is, start and end times of tracks did not necessarily mark the beginning or end of a calling bout. In addition, in order to reliably track individual animals and minimize the possibility of switching individuals, we only analysed tracks for which gaps in calling did not exceed 10 min (see Methods). Thus, all calling rate parameters are based on the time period during which an animal could be reliably tracked, given the acoustic range of the hydrophone arrays and limitations set by the analysis approach. Calling rates for individual whales ranged from a minimum of 8.7 to a high of 133.3 pulse trains/h. Figure 4 illustrates the distribution of observed intercall intervals (ICI). Tracked minke whales tended to call at a regular rate, with a median ICI of about 60.3 s (Figure 4, Table 1). The average call rate was 48.6 ± 27.5 calls/h, and the maximum silence between tracked calls extended to an average of 392.6 ± 292.1 s, with a maximum of about 6.5 min (Figure 4).
Summary statistics of movements and calling rate parameters for individual minke whales recorded in the Stellwagen Bank National Marine Sanctuary (SBNMS) during autumn 2009 and 2011.
The 18 individual minke whales tracked in this study produced all major pulse train categories defined in a larger scale study from the same area (Risch et al., 2013): slow-down pulse trains (sd1–sd3), constant pulse trains (c1–c3), and speed-up pulse trains (sp) (Figure 2). However, pulse train type transitions were not random. The results of the Markov chain analysis showed that some types were highly likely to occur before or after other pulse train types (Goodness-of-fit test, , df = 56, , Figure 5). This pattern was not evenly distributed between pulse train type transitions. While 15 out of 64 transitions were positively correlated, 17 were negatively correlated (Figure 5). The calculated index of association between pulse train types showed strong positive associations between types sd1–c3 (0.39), sd3–c1 (0.29) and sd3–c2 (0.30) (Figures 5 and 6). Hierarchical cluster analysis of the association matrix grouped pulse train types sd1, sd2 and c3 as calling pattern A, while pulse train types sd3, c1, c2 and sp were grouped as calling pattern B (Figures 2 and 6). When Markov chain analyses were run by individual, the resulting probability matrices showed that pulse train type association patterns reflected differential call type usage by individual minke whales. While 39% () of the tracked animals preferably used pattern A, 56% () used pattern B, and only one animal combined call types in a different pattern (pattern C, Figure 7). In our sample, tracked minke whales used calling patterns A and B simultaneously during five occasions, when vocalizing individuals were at an average distance of 4.6 ± 2.5 km, and thus likely within acoustic range of one another.
3.2. Localization and movement
Average localization error as quantified during the calibration experiments (mean ± SD) was 422.7 ± 5.0 m for the 2009 array () and 105.1 ± 64.4 m for the smaller aperture array in 2011 (). A total of 18 individual minke whales were tracked during the study: 3 in 2009 (4.3 h) and 15 (20.1 h) in 2011. Track duration ranged from 0.4 to 3.1 h and lasted on average 1.4 ± 0.8 h (mean ± SD). While all analysed tracks were within 8 km of the array perimeter, only 13 of these, which were less than 5 km outside the array, were further analysed to obtain movement parameters (see summary data in Table 1). Vocalizing animals were generally moving and covered distances between 0.9 and 9.2 km with a mean ± SD distance of 5.3 ± 4.2 km, a net displacement of 3.1 ± 2.4 km, and an average speed of 5.0 ± 1.2 km/h. Movement directionality, as expressed by the straightness index (SI), varied between individuals but was closer to direct path travel (mean ± SD = 0.7 ± 0.2).
3.3. Source level estimation
A large enough sample of non-overlapping high quality calls could only be obtained for pulse trains c2 and sd3 (Figure 2). These pulse trains were measured from three individuals at an average radial distance of 2145.8 ± 845.1 m from the nearest MARU. A total of 57 pulse trains and 993 individual pulses were measured. The results are summarized in Table 2. Peak-to-peak source levels (mean ± SD) were 181.6 ± 6.6 dB re 1 μPa and 176.7 dB re 1 μPa for types c2 and sd3, respectively. Root-mean-square source levels (SLrms) averaged over individual pulses were 168.9 ± 6.6 dB re 1 μPa and 164.0 ± 4.6 dB re 1 μPa for type c2 and sd3, and averaged over the whole pulse train, SLrms were 166.3 ± 3.3 and 161.8 ± 2.5 dB re 1 μPa, respectively (Table 2). Source levels varied by individual (Figure 3) and increased throughout the duration of the pulse train. While the first measured pulses had a mean calculated SLrms of 154.7 ± 4.1 dB re 1 μPa, pulses measured towards the end of the pulse train (e.g., pulse No. 25) were about 12 dB louder, with a mean SLrms of 166.5 ± 1.5 dB re 1 μPa.
Summary statistics of measured source levels.
During the week of 4–10 October 2009, the average hourly ambient noise levels (NL) for the frequency band between 56.2–355 Hz was 101.7 ± 7.0 dB re 1 μPa and ranged from 92.2–115.7 dB re 1 μPa (5th–95th percentile). Given average SLrms values of 168.9 and 164.0 dB re 1 μPa for pulses in pulse train types c2 and sd3, respectively and an assumed source and receiver depth of 20 m, the range over which these signals propagate in the SBNMS environment before SNR equals 0 is 0.7–10.2 km for c2 and 0.4–7.3 km for sd3.
Little is known about vocalizations produced by North Atlantic minke whales and how they use these sounds to mediate behaviour. Our study is the first to use stationary passive acoustic array recordings to acoustically track this species in order to investigate individual calling and movement behaviour. Although passive acoustic tracking is spatially restricted and lacks the behavioural context and demographic information that can be obtained in conjunction with visual observations and acoustic recording tags, it is more feasible and less logistically costly than these other approaches. The successful tracking of 18 individual minke whales in our study demonstrated the feasibility of using long-term passive acoustic arrays for this purpose.
Tracked minke whales produced all major pulse train categories defined by Risch et al. (2013). However, certain call types were more closely associated than others (Figure 6). In particular, most individuals combined pulse trains in either of two call sequences (A and B, Figure 6), and only 1 animal combined call types in a different pattern (C, Figure 7). The structural organization of pulse trains in distinct sequences is an unexpected and interesting finding. Males of several baleen whale species produce hierarchically organized songs associated with reproductive behaviour (Payne & McVay, 1971; Croll et al., 2002; Oleson et al., 2007a). And it has been noted that the ‘star wars’ vocalizations produced by dwarf minke whales wintering on the Great Barrier Reef share characteristics, such as stereotypy and repetitiveness, with these reproductive displays of other species (Gedamke et al., 2001). Since the sex, as well as the context of vocalizing North Atlantic minke whales remain unknown, it is impossible to attribute pulse trains to any particular behaviour. However, based on structural differences between pulse trains recorded in the Gulf of Maine, and those recorded in the Caribbean winter grounds (the latter lasting considerably longer and exhibiting more than twice as many pulses), a reproductive function of these calls has been suggested (Risch et al., 2013). A common feature of baleen whale song is that males vocalizing in the same region and time period typically share the same song (Cerchio et al., 2001; Stafford et al., 2007; Simon et al., 2010). A notable exception to this pattern occurs in bowhead whales where multiple distinct songs occur within a continuous space and time. However, whether these distinct songs are shared between individuals is unknown (Stafford et al., 2008, 2012; Delarue et al., 2009).
Our results show that individual minke whales share the same calling patterns, which thus may reflect different behavioural functions, sex or age of the caller, rather than individual identity. This interpretation is further supported by the fact that both main calling patterns were present in 2009 and 2011, indicating that calling patterns are stable across years. During our study, there were five occasions where minke whales producing different calling patterns were in acoustic range of one another. Independent of the question whether minke whale call sequences serve in a reproductive context, it is likely that the simultaneous production of two different types of calling patterns by two individuals serves a specific function such as maintaining spacing between individuals within a shared acoustic environment (Gedamke, 2004).
Individual calling rates were variable, ranging from 8.7 to 133.3 calls/h (mean ± SD: 48.6 ± 27.5). The median intercall interval (ICI) was about 1 min (mean ± SD: 82.4 ± 87.9 s) and the longest period of silence between two calls was about 6.5 min (Table 1). One of the constraints of tracking individuals using passive acoustic techniques is that the tracked animal needs to vocalize consistently in order to be reliably tracked. The concentration on high quality, relatively long acoustic tracks may have biased our sample to only vocally active animals (and possibly particular types of behaviour), rather than being representative of the overall calling behaviour of North Atlantic minke whales in our study area. Thus, while it is possible to estimate calling rates for vocally active individuals using passive acoustics, it is not possible to accurately assess the time animals spend vocalizing throughout the day using this approach alone. For example, most of the tracks that we analysed were recorded at night. A strong diel pattern has been described for minke whale pulse trains in the SBNMS (Risch et al., 2013). Such diel variation in the occurrence of vocalizations occurs in several other baleen whale species and has often been attributed to a switch from less vocal behaviours such as feeding to more vocal behaviours such as social interactions (Mellinger et al., 2007; Baumgartner & Fratantoni, 2008; Parks et al., 2011). Thus, calling rates as measured in this study have to be evaluated based on the context in which they have been recorded.
Although track parameters varied by individual, all tracked animals were actively moving while vocalizing. Minke whales in Monterey Bay, CA, USA were estimated to have mean swimming speeds between 6.5 and 8.3 km/h (Stern, 1992). Rankin & Barlow (2005) reported a swimming speed of 5.6 km/h during an encounter with a North Pacific minke whale. While the behavioural context of these observations was unclear, feeding minke whales tracked with satellite tags in northern Norway travelled at much lower average daily speeds of 2.2–2.7 km/h (Heide-Jørgensen et al., 2001). A recent study employing satellite tags on minke whales in Icelandic waters found average swimming speeds to be considerably lower in inshore waters where whales are presumably feeding, as compared to offshore waters, where migratory behaviour was evident and where average swimming speeds ranged from 4.6 to 7.3 km/h (Víkingsson & Heide-Jørgensen, 2013). The average swimming speed of minke whales in the SBNMS was 5.0 ± 1.2 km/h (Table 1), similar to that reported for North Pacific minke whales (Stern, 1992; Rankin & Barlow, 2005), and well within the range reported for migrating minke whales around Iceland (Víkingsson & Heide-Jørgensen, 2013). All tracks analysed in our study were recorded during the peak season (September–October) of minke whale pulse train occurrence in the Stellwagen Bank National Marine Sanctuary (SBNMS) (Figure 1) (Risch et al., 2013). Peak minke whale abundance during these months is corroborated in visual sighting records from this area (Murphy, 1995). The summer feeding grounds of minke whales in the North Atlantic generally extend north of SBNMS, starting at around 50°N, and range from Labrador in the Northwest Atlantic to the Barents Sea in the Northeast Atlantic (Horwood, 1990; Andersen et al., 2003). Noting that minke whale pulse trains and visual sightings are mostly absent during the summer feeding season, it has been suggested that SBNMS is part of the migration route of North Atlantic minke whales (Risch et al., 2013), rather than a feeding ground destination. The swimming speed estimates derived for minke whales in our study lend further support to this theory. In addition, although individual tracks were generally too short to explore movement direction in more detail, our observations of generally straight-line, rather than meandering path movements and a net displacement of 3.1 ± 2.4 km/h (Table 1) are also indicative of migratory behaviour.
Source levels for pulse trains produced by minke whales in our study varied by individual, but averaged 161.8 ± 2.5 and 166.3 ± 3.3 dBrms re 1 μPa, for pulse trains sd3 and c2, respectively (Table 2, Figure 3). Source levels for both measured call categories ranged between 160.9 and 168.9 dBrms re 1 μPa (25th–75th percentiles; Table 2). We found an average increase in pulse source level of about 12 dB from the start to the end of the measured pulse trains. Lacking information about the depth of a calling animal, it is not possible to discern whether this apparent increase in source power is produced by the calling animal or is a function of the animal’s position in the water column. Signal propagation is dependent on depth of both sound production and reception. Shallow sources, in particular, may be influenced by the Lloyd mirror effect, in which sound reflected by the surface may cause positive or negative interference on the propagating signal (Jensen et al., 1994). Although transmission loss is generally less dependent on depth in the deeper parts of the water column, it increases substantially closer to the surface, ranging between 10–20 dB in the upper 10 m of the water column (Jensen, 1981). The acoustic recorders in the present study were moored just above the sea floor. Thus, an alternative explanation for the apparent increase in source level over the duration of the pulse trains could be that the animals were actively diving while vocalizing. Assuming a signal directivity of close to 0 dB, if the calling animal was at or close to the surface at the start of the pulse train and continued to dive throughout the production of the call, it is conceivable that a reduction in received level (and thus estimated source level, when depth is not considered) of about 10–15 dB would be observed.
Source level measurements obtained in this study are slightly higher than reported source levels for ‘boing’ sounds recorded from North Pacific minke whales (150 dB re 1 μPa; Thompson & Friedl, 1982) and the ‘star-wars’ vocalization produced by Australian dwarf minke whales (150–165 dB re 1 μPa; Gedamke et al., 2001). Peak-to-peak source levels were 13 dB greater than rms source levels, similar to values reported by Munger et al. (2011) for right whale upcalls. Compared to other baleen whale species, our measured minke whale source levels are most similar to North Atlantic right whale tonal calls (137–162 dBrms re 1 μPa; Parks & Tyack, 2005) and also to individual humpback whale song units (144–169 dBrms re 1 μPa; Au et al., 2006), but about 25–30 dB lower than the high-intensity low-frequency calls produced by blue and fin whales (Thode et al., 2000; Charif et al., 2002; Širović et al., 2007). Given these data, the potential detection radius of minke whale pulse trains in SBNMS is between 0.4 km and 10.2 km. This theoretical detection range will vary based on spatial and seasonal variability in sound propagation and noise levels and is also dependent on the animal’s ability to recognize the signal in background noise (Clark et al., 2009). Yet, our results suggest that the detection radius of these calls in an urbanized coastal area, which is highly impacted by anthropogenic noise (Hatch et al., 2012), is relatively small compared to calls recorded from other baleen whale species and in different environments (e.g., Stafford et al., 2007; Samaran et al., 2010; Širović et al., 2011).
In conclusion, this study showed that North Atlantic minke whales produce at least two distinct call sequences, consisting of 3–4 stereotyped call types. These sequences were stable across years and are shared between individuals. Although their specific behavioural function is currently unknown, these patterns may be important in mediating social interactions between individuals or may reflect age or sex differences. In addition, this study provided data on calling rates and call source levels for North Atlantic minke whale pulse trains, which are useful for developing models of detectability as a basis for acoustic density estimation (Marques et al., 2013; Martin et al., 2013). The documented variability in individual calling behaviour should be considered when monitoring this species using passive acoustics.
This work was supported by an Office of Naval Research grant (number N00014-07-1-1029) awarded by the National Oceanographic Partnership Program. Additional funding was provided by the U.S. Navy N45 Program and the NOAA Ocean Acoustics Program. We thank D. Arch, D. Cholewiak, C. Clark, P. Dugan, H. Figueroa, M. Fowler, L. Hatch, C. Muirhead, A. Murray, S. Mussoline, S. Kibner, W. Krkoska, M. Popescu, J. Stanistreet, C. Tesseglia-Hymes, M. Thompson, C. Tremblay, B. Wallace, J. Walluk, A. Warde, F. Wenzel, D. Wiley and E. Vu for help with various stages of fieldwork and data processing. In addition, many thanks to everyone at the Bioacoustics Research Program, Cornell University Laboratory of Ornithology, and the Stellwagen Bank National Marine Sanctuary for their extensive support over the years. Thanks to M. Simpkins, F. Serchuk and D. Cholewiak for insightful comments that helped improve this manuscript significantly.
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