# Behavioural context of call production in humpback whale calves: identification of potential begging calls in a mysticetes species

Maevatiana Nokoloina Ratsimbazafindranahaka, Chloé Huetz, Anjara Saloma, Aristide Andrianarimisa, Olivier Adam, Isabelle Charrier

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Maevatiana Nokoloina Ratsimbazafindranahaka, Chloé Huetz, Anjara Saloma, Aristide Andrianarimisa, Olivier Adam, et al. Behavioural context of call production in humpback whale calves: identification of potential begging calls in a mysticetes species. Proceedings of the Royal Society B: Biological Sciences, 2024, 291 (2037), pp.20242048. 10.1098/rspb.2024.2048. hal-05356724

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1 **Behavioural context of call production in humpback**
2 **whale calves: Identification of potential begging calls in a**
3 **Mysticete species**

Maevatiana Nokoloina Ratsimbazafindranahaka¹,²,³, Chloé Huetz¹, Anjara Saloma³, Aristide Andrianarimisa²,

Olivier Adam¹,⁴, Isabelle Charrier¹

¹ Institut des Neurosciences Paris-Saclay, Université Paris-Saclay, CNRS, Saclay, France

² Département de Zoologie et Biodiversité Animale, Université d'Antananarivo, Antananarivo, Madagascar

³ Association Cétamada, Ambodiforaha Sainte Marie, Madagascar

⁴ Institut Jean Le Rond d'Alembert, Sorbonne Université, Paris, France

10 Corresponding Author

Maevatiana Nokoloina Ratsimbazafindranahaka

Institut des Neurosciences Paris-Saclay (NeuroPSI), UMR 9197 CNRS

Campus CEA Saclay, 151 route de la Rotonde, 91400 Saclay, FRANCE

Email address: maevatiana.ratsimbazafindranahaka@universite-paris-saclay.fr

ORCID: 0000-0001-8916-237X

16 Acknowledgements

We would like to thank the Cétamada team and interns since 2013, who contributed to the data collection. We also
18 warmly thank the anonymous referees for their valuable and constructive comments on the manuscript.

19 **Funding sources**

This work was supported by the IDEX Paris-Saclay [ANR-11-IDEX-0003-02], the CNRS, the Cétamada association, the WIOMSA [MARG-I-2021-CO-20], the SMM [2021 SMM Research Grant], the ATBC [ATBC seed grant], the CeSigma company, and donors who contributed to the KissKissBankBank crowdfunding for M. N. R.'s research.

23
## 24 Abstract

Baleen whale calves vocalize, but the behavioural context and role of their social calls in mother-calf interactions are yet to be documented further. We investigated the context of call production in humpback whale (*Megaptera novaeangliae*) calves using camera-equipped animal-borne multi-sensor tags. Behavioural states, including suckling sessions, were identified using accelerometer, depth, and video data. Call types were categorized through clustering techniques. We found that call types and rates predict the occurrence of a given state. Milling, resting, and traveling were associated with a median call rate of 0 calls min⁻¹, while surface play, tagging responses, and suckling were associated with higher call rates, averaging up to a median of 0.5 calls min⁻¹ for suckling. Suckling sessions were associated with three sets of low-frequency calls corresponding to previously described burping, barking, and snorting sounds. Surface play sessions featured mid-frequency calls with whoop-like sounds and other call types.

These results address the significance of vocal signalling in mother-calf communication and the calf's development, including the first identification of potential begging calls. Overall, this study offers new insights into baleen whale behaviour, underscores the importance of social calls in mother-calf interactions, and enhances our understanding of communication systems in aquatic mammalian mother-young pairs.

**KEYWORDS:** begging, classification, marine mammals, mother-offspring, vocalizations

## 39 Background

Mammals mainly rely on their mother during their early life stage to survive. Their mother provides critical care such as food provisioning, protection from predators, warmth, teaching, etc. [1,2]. For such care to be optimal, the mother and offspring must communicate with each other. For instance, the offspring must communicate its needs to its mother [3-5]. In mammals that use the 'following' anti-predation strategy, the mother must encourage following when necessary to ensure that the offspring stays in proximity so she can protect it [4]. More generally, the mother and the offspring need to communicate their identity to each other so that the mother avoids spending resources on non-filial young (targeted investment) and the offspring avoids going towards potentially aggressive conspecifics [3,4,6]. Various modalities can be used, such as olfactory, visual, tactile, and acoustic cues [3,4,6].

In aquatic environments, the use of acoustic signalling is one of the best strategies to communicate, as acoustic signals are efficient at both short and long ranges and are possible to detect and localize given an adequate auditory
system [7], in contrast to visual and chemical cues that do not provide such advantages. As such, cetaceans or
whales, a group of fully aquatic mammals, evolved a communication system mainly based on sounds, vocally
produced or not [8]. In baleen whales (mysticetes), non-vocal sounds include percussive sounds from surface
activities such as tail slaps, pectoral slaps, breaching, etc., and vocal sounds encompass songs [8–12] and social calls
[8,12–14] thought to be produced by circulating air from the lungs to the laryngeal sac or vice versa to vibrate the U-
fold [15–17]. Songs are vocalizations organized in a repeated manner and typically in long bouts, primarily
associated with reproduction and only produced by males. In contrast, social calls are vocalizations produced by all
individual categories (males, females, juveniles, calves) in various contexts, variable through time, interrupted by
silent periods, apparently unpredictable, and not showing a stereotyped rhythmic, consistent, and continuous
temporal pattern of the same level as songs [14,18].

The behavioural context of social calls among baleen whales has been studied for years. Studies have notably been
conducted on species such as the blue whale (*Balaenoptera musculus*) [19,20], humpback whale (*Megaptera
novaeangliae*) [14,21–26], right whale (*Eubalaena glacialis*) [27], and even the minke whale (*B. bonaerensis*) [28].
However, our knowledge remains somewhat limited regarding the specific case of mother-calf pairs. It is understood
that these pairs produce vocalizations, albeit at a typically lower rate than other social groups [29–33]. Nonetheless,
the significance of their calls in mother-calf interactions and for the calf itself is still not well understood.

In humpback whales, given the relatively extensive vocal repertoire of mother-calf pairs [30], it is anticipated that
social calls serve various purposes/correspond to various categories, such as contact calls, alarm calls, distress calls,
and begging calls. For instance, contact calls are vocalizations used to keep in touch and transmit information about
the caller [34]. These calls are especially crucial in mammalian mother-young pairs, where staying close spatially
and socially is essential for the young's survival [35,36]. Alarm and distress calls are vocalizations used to signal
danger or distress, alerting other individuals to potential environmental threats [37–39]. For mother-young pairs,
alarm and distress calls are critical for the young's survival; a mother might use alarm calls to alert her young to the
presence of predators or other dangers, and the young might use distress calls in distressing situations to elicit
responses from the mother. Begging calls are vocalizations typically produced by an individual to express their
nutritional needs to their caregiver, and the caregiver uses them to regulate and adapt their provisioning rate [40–42].
In the case of mammalian mother-young interactions during the first stage of a young's life, begging for milk is the
primary form of begging, helping the mother know the calf's need for milk and respond accordingly. This communication is vital for the young's growth and well-being.

The context and function of calls in mother-calf interactions have been particularly studied in humpback whales among baleen whales due to their relatively accessible nature. For instance, Zoidis et al. [24] noted that certain social calls seemed to prompt the mother's approach and were interpreted as the calf's isolation or alarm calls to alert or call the mother. Videsen et al. [25] discovered that nursing initiation was not based on acoustic cues but rather on tactile signals, which was unexpected. They suggested that social calls serve more of a cohesive function in maintaining contact during active dives. Indeck et al. [26] found that during resting periods, associated with more frequent separations, the vocal activity in mother-calf pairs increased, supporting the hypothesis that some calls function as contact calls. Data from Ratsimbazafindranahaka [43] further suggested that the calf is primarily responsible for acoustic contact. One limitation of several of these studies in understanding further the role of social calls was the focus on overall vocal activities (the occurrence of any call) rather than specific call types.

Several challenges make it difficult to study the behavioural context of social call production in baleen whale calves. First, the existence of graded calls makes it challenging to objectively fully separate social calls into distinct call labels (types) with sufficient reproducibility to associate them with behaviours [30,44]. Nevertheless, unsupervised learning or clustering [45], i.e., automatically finding clusters from unlabelled data, can be a starting point to identify call categories if a set of closely related calls is explicitly used in a given context.

Another challenge concerns the data acquisition itself. Animal-borne multi-sensor tags are now routinely used to study whales' behaviour and vocalizations [20,25,26,28,46] as they allow to accurately track and describe the underwater activities of whales and focus an individual. Unfortunately, when the studied individual is a mother or a calf, the identification of the caller is problematic as a tag placed on a mother may also records calls from the calf and vice-versa. As a result, tag-based studies attempting to associate vocalizations to behaviours in mother-calf groups thus often did not distinguish between the calls from the mother and the calf and assumed that the studied calls represent the total vocal output of the mother-calf pair [25]. Recent results based on data from simultaneous deployments on mother and calf, however, suggest that most calls (90%) recorded by a tag placed on a calf are more likely from the calf than from the mother, at least for low-frequency calls (which represent the majority of calls produced in mother-calf pairs) [43]. This conclusion was supported by a comparison of the acoustic features of calls
recorded simultaneously on both the calf and mother's tags with calls recorded exclusively on the calf's tag versus those recorded only on the mother's tag.

In the present study, we investigated the behavioural context of social calls recorded by tags equipped with a video camera placed on humpback whale calves off Sainte Marie Island, Madagascar, South Western Indian Ocean, with the assumption that calls recorded by a tag placed on a calf mainly record the calls from the calf as suggested by the data from Ratsimbazafindranahaka [43]. We aimed to gain insights into the relationship between activities and calf vocalizations in baleen whales and understand the potential roles of the various call types they produce.

111

# Methods

112

## Dataset

We used data from CATS cam tags, sets of sensors including a depth sensor, a 3-axis accelerometer, a hydrophone, and a video camera, placed on the back of eight young humpback whale calves (< 3-month-old given the season – see below – and their relatively small size, i.e., not more than half the size of their mother), for which the amplitude of the sound recorded by the hydrophone was not saturated (usually due to very high flow noise, which is related to tag placement and orientation). The average data duration per calf was 4.1±2.5 hours (range = 1–8.2 hours), and the total was 32.6 hours. The data were collected off Sainte Marie Island, Madagascar, South Western Indian Ocean, during the winter (breeding/calving season) of 2022 under the Malagasy national research permit #54/22-MPEB/SG/DGPA delivered by Ministry of Fisheries Resources, Madagascar, and are part of the data presented by Ratsimbazafindranahaka et al. [47,48]. The data collection complies with the European Union Directive on the Protection of Animals Used for Scientific Purposes (EU Directive 2010/63/EU). Detailed tagging procedures and tag specifications have been presented elsewhere [30,47,49,50].

124

## Call analysis

We analysed the acoustic recording using Avisoft SASLab Pro version 5.3.01 (Avisoft Bioacoustics, Nordbahn, Germany). We produced spectrograms of the acoustic recordings using a 1024-point Fast Fourier Transform, 75% overlap, and Hamming window. We inspected the audio files aurally and visually to isolate clearly distinguishable social 'calls', i.e., sounds vocally produced and excluding non-vocally produced social sounds such as surface percussive sounds – breaches, slaps –, apparent bubble sounds, rubbing sounds, etc. [24,30]. We distinguished the social calls from the male songs in the background based on the temporal pattern and overlaps [30]. For each

130
distinguishable social call, we measured seven acoustic features: the duration (Dur, in s), the peak frequency (Fmax, in Hz), the first, second and third energy quartile frequency (Q25, Q50, Q75 respectively, in Hz), the frequency bandwidth within which the total energy fell within 12 dB of Fmax (Bdw, in Hz), and the fundamental frequency (F0, in Hz). The duration of the call was measured on the oscillogram. The spectral characteristics were measured on the averaged spectrum (frequency ranges: 30-1000 Hz for low-frequency calls with peak frequency < 160 Hz, and 100-6000 Hz for mid- and high-frequency calls with peak frequency from 160 to 700 Hz and > 700 Hz, respectively). All measurements of spectral characteristics were performed automatically except for the F0, which was measured manually using the harmonic cursor available in Avisoft SASLab Pro to find harmonic structures on the averaged spectrum.

## 140 Behavioural state definition

The data from each calf were broken down into sequences constituted by six possible behavioural states: suckling session (*Suckl.*), surface play session (*Play*), traveling (*Travel*), milling (*Mil.*), response to tag deployment (*Tg. res.*), and resting (*Rest*). These states were defined by a classification algorithm using a combination of depth, 3D accelerometer, sound, and video data. First, the data were globally broken down into four base states based on the calf's relative speed (obtained by z-scoring the flow noise recorded by the hydrophone within the 66-94 Hz frequency band [51]), using a first-order Hidden Markov Model trained on multi-sensor tag data collected since 2013 in the study site: Lv0, Lv1, Lv2, Lv3, from the one with the lowest speed to the one with the highest, respectively (see [48]). Concurrently, suckling events were visually identified on the video data [47], except during periods of uncertainty due to tag movement (camera not directed forward). During those exceptions (12% of the data), the suckling events were identified automatically based on the 3D accelerometry data and depth data using a supervised automatic detection method (see [47]). Subsequently, (1) any state during which a suckling event occurred plus the state directly before was considered as a suckling session (*Suckl.*), (2) any Lv2 and Lv3 states during which the calf was observed displaying surface active behaviours (breaches, head slaps, pectoral slaps, etc.) was considered as surface active playing session (*Play*), (3) any Lv2 and Lv3 states, except those occurring immediately after the tagging, were considered as traveling (*Travel*) if the mother was swimming alongside the calf at approximately the same pace, (4) any Lv2 and Lv3 states during which the mother was resting or was not swimming alongside the calf was considered as milling (*Mil.*), and (5) any state directly immediately after the tag deployment was considered as
158 response to tagging (*Tg. res.*). Finally, the remaining Lv0 and Lv1 states were associated with Resting (*Rest*) and
159 Milling (*Mil*.), respectively.

160 Statistical analyses

161 *Cluster analysis of the social calls*

162 We used cluster analysis to objectively identify relatively homogeneous groups of calls, referred to as call types in
163 the present study. The relevant number of clusters was determined using the NbClust package [52] in R (R core team,
164 https://www.r-project.org/). The Calinsky and Harabasz (CH) index was used as it is among the indexes with the best
165 performance in determining the number of clusters [53]. We set the minimum cluster number to 6 to avoid capturing
166 just the main categories of calls (Low-frequency, Mid-frequency, High-frequency, Pulsed, and Amplitude-modulated
167 [24,30,44,54,55]). Once the relevant number of clusters was determined, we used a Hierarchical Clustering on
168 Principal Components (HCPC) to group the social calls using the FactoMineR R package [56]. The principal
169 components (i.e., PC scores) were computed using the seven measured acoustic features as input variables in a
170 Principal Component Analysis (PCA).

171 *Analysis of the behavioural context of call production*

172 For each continuous period corresponding to one state (referred to as a 'segment'), we calculated the call rate
173 (count/minute) and noted the presence/absence of each identified call type. To investigate how the produced calls
174 and behavioural states related to each other, we tested whether the information on the rate of calls and the type of
175 calls produced allowed the prediction of the behavioural state using a balanced random forest classification. We used
176 the R package rfPermute [57], which allows the estimation of the statistical significance of the variable importance
177 metrics (here, the importance of the call rate and the presence/absence of each call type) in the prediction by
178 permuting the response variable (here the behavioural state). In our model, the number of variables randomly
179 selected at each split was set at 3, and the number of trees grown was set at 1000.

180 Results

181 A total of 508 social calls were detected and labelled in the data from eight deployments on calves. Each deployment
182 has been subdivided into 88±66 segments on average (range: 18–212 segments, total: 700 segments) based on
183 automatic classification of the behavioural state. The average duration of the segments was 2.8±3.9 min (range =
184 0.3–36). Most of the segments corresponded to milling (Mil., 60%), followed by resting (Rest, 27%), traveling (Travel, 7%), surface playing (Play, 2%), suckling session (Suckl., 2%), and finally tagging response (Tg. res., 1%).

186 Main call types recorded

187 The Calinsky and Harabasz (CH) index suggested the occurrence of 13 main call types that were then successfully identified using a Hierarchical Clustering on Principal Components (HCPC) (Type #1 to #13, figure 1). Examples of 188 calls from each type are presented in Media S1 (supplemental information). The first two principal components 189 (PC1–PC2) explained 76% of the sample variance (figure S1, supplemental information). PC1 was primarily 190 positively correlated to the spectral characteristics, whereas PC2 was positively correlated to the call duration (Dur). 191 The Type #1–3 calls were constituted by low-frequency sounds. Type #1 calls were long grunting sounds – about 192 2±0.6 s on average; Type #3 were relatively short barking/snorting sounds – about 0.3±0.1 s on average, and Type #2 193 calls were in between these two (figure 1, table S1, supplemental information). Type #4 and #5 calls showed similar 194 spectral characteristics and time duration (overlapped on the PC1 and PC2) but were of higher frequencies than Type 195 #1–3 calls (figure 1, table S1, supplemental information) with generally a characteristic whooping sound. They 196 mainly differed in their bandwidth (Bdw) and fundamental frequency (F0) (The average F0 values of Type #4 calls 197 was more than twice those of F0 of the Type #5 calls while the average Bdw of Type #4 was less than 1/3 of the Bdw 198 of Type #5 calls) (table S1, supplemental information). Type #6–9 calls overlapped along the PC1 but spread more 199 on PC2. They were mainly whooping, whining, squeaking, or bubbling-like sounds. Among these four call types, 200 Type #7 appeared to have the narrowest bandwidth, the highest F0 (434±41 Hz on average), and the longest duration 201 (1±0.6 s on average, figure 1, table S1, supplemental information). Type #8 had the lowest F0 (65±53 Hz on average, 202 table S1, supplemental information). Type #6 and #9 had similar F0 values, but Type #9 clearly had a broader 203 bandwidth (about four times the Bdw of Type #6) and had, in fact, the broadest bandwidth among Type #6–9 (figure 204 1, table S1, supplemental information). Type #10 and #13, both heek-like sounds, had more energy in high 205 frequencies (average Fmax > 1300 Hz) than all other main call types. Type #10 and #11 (also heek-like sound) had 206 broader bandwidth and were shorter compared to Type #12 (squeaking sound) and #13 (also a heek-like sound) 207 (average Bdw almost five times or more than Type #12's and #13's average Bdw and average Dur not more than 208 two-thirds of Type #12's and #13's Dur), but #10 had higher Fmax (about four times) and with narrower bandwidth 209 (about the half) than Type #11 (figure 1, table S1, supplemental information). Type #12 and #13 were represented by 210 only a single call each and were high-frequency calls with narrow bandwidth. The Type #12 call had much higher F0 211
212 values (eight times) compared to Type #13 (figure 1). Type #3 was the most abundant main type (59%) and was
213 common to all calves, followed by Type #2, which was also relatively abundant (16%) and found in all calves except
214 one. Each remaining call type only constituted < 1% to 9% of all calls detected and was at most common to only half
215 of the studied calves (figure S2, supplemental information).

216 Behavioural state and call production

217 Milling (*Mil*.), Resting (*Rest*), and Travelling (*Travel*) states were mostly associated with silence (median call rate =
218 0 call min⁻¹), although on rare occasions, they were associated with a high level of vocal production (call rate > 1 call
219 min⁻¹ in 21/423 *Mil*., in 3/192 *Rest*, and in 2/50 *Travel*; range, *Mil*.: 0–11 calls min⁻¹; *Rest*.: 0–2 calls min⁻¹; *Travel*.: 0–
220 3 calls min⁻¹, Figure 2A). In contrast, Surface play session (*Play*), response to tagging (*Tg. res*.), and Suckling
221 session (*Suckl*. ) states tended to be associated with a call production at an average rate (median) of 0.1 calls min⁻¹
222 (range = 0–8 calls min⁻¹), 0.1 calls min⁻¹ (range = 0–2 calls min⁻¹), and 0.5 calls min⁻¹ (range = 0–2 calls min⁻¹),
223 respectively.

224 The random forest analysis correctly predicted 29% of the behavioural state based solely on the presence/absence of
225 each of the 13 main call types and the call rate (table 1). This percentage of correct prediction was statistically
226 significantly greater than the result expected by chance, which was about 16.7% (*P* < 0.001). The random forest
227 analysis was able to predict the states corresponding to *Rest* and *Suckl*. at rates that were statistically significantly
228 greater than the values expected by chance (table 1). However, it failed to predict the states corresponding to *Play*
229 and *Tg. res*. at rates statistically significantly greater than the values expected by chance but close to the significance
230 level for *Play* (table 1). The correct prediction rate was zero for *Mil*. and *Travel*. *Mil*. was primarily confused with
231 *Rest*, but sometimes with *Tg. res.*, *Play*, and *Suckl*. *Travel* was also primarily confused with *Rest*.

232 The variables contributing the most (statistically significant contribution) to the prediction of *Suckl*. were, by
233 decreasing order of importance, the presence of Type #3 and #2 calls (barking/snorting sounds with slightly different
234 duration), the high call rate, and, to a lesser extent, the presence of Type #8, #5, and #6 calls (whining, whooping,
235 and bubbling-like sounds). For *Play*, the variables contributing the most to its prediction were the presence of Type
236 #4 and #6 calls (whooping and bubbling-like sounds). Regarding *Rest*, the information on the presence/absence of
237 several call types and the call rate were important in its prediction as it was mainly associated with silence (thus the
238 absence of calls in general) (figure 2).
239 **Discussion**

240 Our initial objective was to identify the behavioural context of call production by young calves in a baleen whale
241 species, the humpback whale, using data from animal-borne tags. We assumed that the calls recorded by the tags
242 placed on calves were from the calves. Indeed, this is supported by the findings in Ratsimbazafindranahaka [43],
243 showing that most calls recorded by a tag placed on the calf were likely from the calf than from the mother (about
244 90% of the calls). The study compared calls recorded from simultaneous tag deployments on both the mother and
245 calf and found that the acoustic characteristics of calls detected only on the calf's tag (i.e., not detected on the
246 mother's tag at the corresponding time) were distinct from those detected only on the mother's tag (i.e., not detected
247 on the calf's tag at the corresponding time), and that four-fifths of the remaining calls – for instance those heard
248 simultaneously on both tags – shared acoustic characteristics with those detected solely on the calf's tag and not the
249 mother's tag. Due to the potential existence of graded calls in humpback whales and especially in calves [30,44], we
250 used an unsupervised approach to group social calls with similar acoustic characteristics. Our result suggested that
251 the call rate and the production (or non-production) of one or several specific call types are related to the calf's
252 behaviour. Indeed, we found that these parameters can be sufficient to predict the occurrence of a specific
253 behavioural state in a given data segment. It is especially true for two main behaviours – the suckling behaviour and
254 the resting behaviour – and likely the play behaviour.

255 Suckling sessions, on average, were associated with a higher call rate than any other behavioural state and were
256 mainly associated with Type #3 and Type #2 calls. Qualitatively speaking, these calls corresponded mainly to low-
257 frequency calls such as burps, barks, wops, thwops, snorts, basses, and grunts presented in the literature
258 [23,24,30,31,44,45,54,58,59]. Most of these calls have been identified as very common in humpback whale vocal
259 repertoire and were suggested to be calls with important social functions [30]. Given that their occurrence predicts
260 the occurrence of suckling sessions, we suggest that one biological function is related to the calf's feeding behaviour.
261 More specifically, evidence suggests they serve as begging calls in mother-calf pairs to initiate nursing, given that
262 they occurred before or during suckling sessions. They are unlikely a by-product of the act of suckling itself as
263 calves are not known to produce sound while locked on a teat [47,50,60]. Previous studies on the potential stimuli
264 calves use to initiate nursing suggested, however, that mechanical stimuli rather than acoustic signals are involved
265 [25]. These modalities are not necessarily exclusive, considering that calves may use various sensory modalities to
266 communicate with their mothers in a given context (interchangeably or in combination). For nursing, while
mechanical stimuli can also be used [25], the use of acoustic signals may prevail given that they are of more
extended range compared to tactile or even visual signals in a low light condition. Acoustic signals may allow calves
to communicate to their mother their nutritional needs (or that they intend to suckle) even when they are not near
their mother, particularly during vertical separation as the calf surfaces to breathe while the mother stays at depth
[47,49,61].

Nevertheless, it is important to consider the methodologies used to investigate the signals involved in nursing.
Videsen et al. [25] defined nursing primarily based on activity levels measured with the minimum specific
acceleration (MSA), using a threshold based on apparent nursing events observed from the surface. This approach
can potentially capture periods of rest for the calf, which also exhibit low MSA, and not necessarily nursing sessions.
In contrast, our study used direct calf point-of-view visual identifications (i.e., video from tags deployed on calves),
providing a different perspective.

Surface active playing sessions and periods directly following tagging were associated with either a high call rate or
with silence. Various call types were associated with the playing state, but only the presence of Type #4 and Type #6
had a statistically significant importance in its prediction. In particular, Type #4 qualitatively corresponded mainly to
the whoops presented in the literature [30], and they may be the expression of a positive and high arousal state
expressed during a playful state. Indeed, humpback whale's social calls have the potential to encode motivational
information [62].

Additionally, the production of diverse call types during play with no clear association pattern for the majority may
indicate the calf is also playing with its voice (vocalizing without immediate practical goal or benefit) and thus
emitting calls randomly, or it might be a by-product of high physical activity. For instance, in rats (Long Evans rats),
the production of some call types is, for example, closely tied to breathing [63]. In mysticetes, surface active play
involves rapid vertical swimming and breaching, which could cause involuntary air circulation within the respiratory
system that also serves for sound production (air circulation from the lungs to the laryngeal sac or vice versa) or air
release in response to changing ambient pressure and the calf's attempts to play with its buoyancy by actively
changing its internal pressure [15,16]. This may result in sounds being produced involuntarily. On a side note, low-
frequency calls produced by the calf during surface playing sessions were associated with bubble production
(personal observations) and Type #6 calls usually featured bubbling sound, supporting that some calls may be tied to
294 air movements within the respiratory system while playing at the surface. In summary, the observed call patterns
295 during play sessions could be influenced by a combination of intentional vocalization, physical exertion, and
296 respiratory mechanics.

297 Resting was mostly associated with silence, which is expected since resting corresponds to periods of low activity
298 during which both the mother and calf are usually in the same behavioural state [48,49,61]. The absence of a clear
299 pattern in the calls associated with traveling suggests that the calf does not need to vocalize to synchronize travelling
300 movements with its mother. The side-by-side swimming configuration may be sustained based mainly on vision and
301 touch (direct physical contact or by sensing surrounding water displacement), and the mother may adjust her pace
302 voluntarily without the need for the calf to signal her acoustically. In ungulate species using the ‘following’ strategy,
303 such maternal behaviour has been referred to as ‘pacing’, a behaviour proposed to induce a maximal following
304 response by the offspring [4]. In these species, it is known that the young instinctively follow a nearby moving object
305 of an appropriate size [4].

306 Our study provides an advanced assessment of the behavioural context of call production in humpback whale calves.
307 It proposes potential biological functions of some call types, ultimately shedding light on their communication
308 patterns and behaviour. Notably, the potential begging calls of humpback whale calves have been identified for the
309 first time. Such results help us understand the underlying mechanism of mother-young acoustic communication in
310 baleen whales and the significance of acoustic signalling for calves’ development and survival. Begging calls have
311 been described in many vertebrate species, such as songbirds [41,64], seabirds [65,66], and mammals [67,68]. In
312 cetaceans, begging calls have not been clearly evidenced but suggested in bottlenose dolphins (*Tursiops truncatus*)
313 [69] and Araguaian river dolphin (*Inia araguaiaensis*) [70].

314 Our results provide new data helping in interpreting baleen whales' behaviours, highlight the importance of social
315 calls in mother-calf interactions, and is thus decisive for regulating noise-inducing human activities that may perturb
316 vital behaviour such as nursing. The existence of context-dependent vocal behaviour suggests an adaptive strategy to
317 balance the need to communicate acoustically and the cost of communication in mother-young pairs, especially in an
318 environment where eavesdropping by predators or unsolicited conspecifics may occur [33,71]. Further investigations
319 are encouraged, and playback experiments on mothers/females could be carried out to assess the biological function
320 of some calves' calls identified in the present study in a controlled setup now that we have more information on their
behavioural context and hypotheses regarding their usage and importance. They must, of course, be designed carefully and have to be done by experts to minimize negative impacts on the whales, and all relevant regulations about approach and respect of cetaceans should be strictly followed. While playback experiments have already been conducted on humpback whales in breeding and calving grounds, including mother-calf pairs [72,73], particular attention should be paid to the potential impacts on mother-calf pairs. Avoidance behavior, for example, may occur in response to playback stimuli [73]. It is essential to assess how these potential responses could affect the calf, disrupt the mother-calf bond, or compromise their overall welfare. Any decision regarding playback experiments should carefully consider these factors and any exposition, especially for the calf, should be kept to the strict minimum.

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500

501
502 **Data accessibility**

503 The code and data used for this study are publicly available on Dryad

504 ([https://doi.org/10.5061/dryad.kh18932hb][74]).
505

Tables

506

**Table 1.** Result of a random forest (confusion matrix) analysis in predicting the behavioural state corresponding to each data segment based on the presence/absence of the 13 main call types identified and the call rate. The numbers in the table's true class vs. predicted class section indicate the count of segments. *Mil.*: milling, *Play*: surface active playing session, *Tg. res.*: response to tagging, *Rest*: resting, *Suck.*: suckling session, *Travel*: traveling. Grey cells along the primary diagonal indicate agreement between the true class (behavioural state) and the predicted class (predicted behavioural state). Bold *P*-values indicate statistically significant differences between the correct prediction rate and the rate expected by chance.

507

508

509

510

<table><thead><tr><th rowspan="2"></th><th colspan="6">Predicted class</th><th rowspan="2">% correct</th><th rowspan="2">% chance</th><th rowspan="2">P</th></tr><tr><th>Mil.</th><th>Play</th><th>Tg. res.</th><th>Rest</th><th>Suckl.</th><th>Travel</th></tr></thead><tbody><tr><th>True class</th><td>Mil.</td><td>0</td><td>8</td><td>21</td><td>371</td><td>23</td><td>0</td><td>0</td><td>17</td><td>1</td></tr><tr><th></th><td>Play</td><td>0</td><td>4</td><td>2</td><td>7</td><td>2</td><td>0</td><td>27</td><td>17</td><td>0.09</td></tr><tr><th></th><td>Tg. res.</td><td>0</td><td>0</td><td>2</td><td>4</td><td>2</td><td>0</td><td>25</td><td>17</td><td>0.135</td></tr><tr><th></th><td>Rest</td><td>0</td><td>1</td><td>2</td><td>187</td><td>2</td><td>0</td><td>97</td><td>17</td><td>0</td></tr><tr><th></th><td>Suckl.</td><td>0</td><td>0</td><td>4</td><td>3</td><td>5</td><td>0</td><td>42</td><td>17</td><td>0.008</td></tr><tr><th></th><td>Travel</td><td>0</td><td>1</td><td>3</td><td>42</td><td>4</td><td>0</td><td>0</td><td>17</td><td>1</td></tr><tr><th></th><td>Overall</td><td></td><td></td><td></td><td></td><td></td><td></td><td>28</td><td>17</td><td>0</td></tr></tbody></table>
512 **Figure captions**

513 **Figure 1.** Hierarchical clustering on principal components analysis (HCPC) showing the identified main call types (delimited by polygons) and calls within each of them (small symbols). Cluster centres are indicated by bigger symbols. Thirteen call types were identified. The calls are dispersed along the PC1 axis, which is positively correlated to spectral characteristics (F0, Fmax, Q25, Q50, and Q75 and Bdw), and dispersed along PC2, which is highly correlated to duration (Dur).

518 **Figure 2.** Relation between the behavioural states *Mil.*, *Play*, *Tg. res.*, *Rest*, *Suckl.*, and *Travel*, and call type production. *Mil.*: milling, *Play*: surface active playing session, *Tg. res.*: response to tagging, *Rest*: resting, *Suckl.*: suckling session, *Travel*: traveling. (A) Call rates by behavioural state. *Suckl.*, *Play*, and *Tg. res.* were associated with higher call rates than *Mil.*, *Rest.*, and *Travel*, which were mainly associated with silence. (B) Relation between the call type produced, numbered from #1 to #13, and the behavioural states. Each count represents the number of segments per behavioural state during which the specified call type was detected. The number of segments per behavioural state during which no call was detected are presented in the bottom row (silence). (C) Ranked importance of the presence/absence of each call type and the call rate in predicting the behavioural state in a random forest analysis. The heat map presents importance scores scaled by ranked importance. The diamonds indicate statistically significant scores. The predictors are sorted by Mean Decrease Accuracy. By descending order of importance, the most significant variables contributing to the prediction of *Suckl.* were the presence/absence of type #3 and #2 calls, the call rate, and the presence/absence of Type #8, #5, and #6 calls. In the case of *Play*, the variables with the greatest contribution to its prediction were the presence/absence of Type #4 and #6 calls. As for *Tg. res.*, the variables that played a crucial role in its prediction were the presence/absence of Type #3 calls, the call rate, and the presence/absence of Type #2 calls. In predicting *Rest*, the presence/absence of several call types, along with the call rate, were important since *Rest* is predominantly associated with the absence of most calls in general.
<figure><img src="image_3.png" /></figure>

Hierarchical clustering on principal components analysis (HCPC) showing the identified main call types (delimited by polygons) and calls within each of them (small symbols). Cluster centres are indicated by bigger symbols. Thirteen call types were identified. The calls are dispersed along the PC1 axis, which is positively correlated to spectral characteristics (F0, Fmax, Q25, Q50, and Q75 and BdW), and dispersed along PC2, which is highly correlated to duration (Dur).

266x228mm (118 x 118 DPI)
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

Relation between the behavioural states Mil., Play, Tg. res., Rest, Suckl., and Travel, and call type production. Mil.: milling, Play: surface active playing session, Tg. res.: response to tagging, Rest: resting, Suckl.: suckling session, Travel: traveling. (A) Call rates by behavioural state. Suckl., Play, and Tg. res. were associated with higher call rates than Mil., Rest., and Travel, which were mainly associated with silence. (B) Relation between the call type produced, numbered from #1 to #13, and the behavioural states. Each count represents the number of segments per behavioural state during which the specified call type was detected. The number of segments per behavioural state during which no call was detected are presented in the bottom row (silence). (C) Ranked importance of the presence/absence of each call type and the call rate in predicting the behavioural state in a random forest analysis. The heat map presents importance scores scaled by ranked importance. The diamonds indicate statistically significant scores. The predictors are sorted by Mean Decrease Accuracy. By descending order of importance, the most significant variables contributing to the prediction of Suckl. were the presence/absence of type #3 and #2 calls, the call rate, and the presence/absence of Type #8, #5, and #6 calls. In the case of Play, the variables with the greatest contribution to its prediction were the presence/absence of Type #4 and #6 calls. As for Tg. res., the variables that played a crucial role in its prediction were the presence/absence of Type #3 calls, the call rate, and the presence/absence of Type #2 calls. In predicting Rest, the presence/absence of several call types, along with the call rate, were important since Rest is predominantly associated with the absence of most calls in general.