Keywords: breeding area, cetacean, multi-sensor tags, ontogeny, parent-offspring interactions Baleen whales, swimming behavior, buoyancy, depth, ascent, descent, biologging, mother-young, aquatic habitat, respiratory system baleen whale, breeding ground, maternal care, parent-offspring interaction, social call, sound production mother-offspring, begging, baleen whales, classification

Marine mammals have been proposed to have a passively changing buoyancy depending on the depth due to the collapse/expansion of their lungs as the ambient pressure changes. Consequently, their vertical movements are characterized by either active swimming or gliding, depending on the depth. Mysticetes have been described to have the same passive mechanism for buoyancy change without considering that their unique respiratory system, including two major air-containing spaces, can potentially provide the ability to change their buoyancy to enhance their vertical movements actively. Here, we present evidence supporting the hypothesis that mysticetes actively change buoyancy during shallow dives through analysis of diving data from multi-sensor tags placed on humpback whale mother-calf pairs. We show that adult female humpback whales displayed low effort to swim downward and upward regardless of the depth. In addition, they were able to stay stationary at varying depths. Calves displayed depth-dependent swimming effort and could only remain stationary with external help or at a specific depth.

i

Extended summary

Mammals exhibit particularly sophisticated forms of parental care, mainly provided by the female (maternal care). From the birth of the young until they become independent, the mother provides them with essential nutrition in the form of maternal milk, ensures their protection, and assists in their development. Maternal strategies, and thus the characteristics of mother-offspring relationships, vary significantly from one species to another and are related to biological traits and environmental characteristics. Various aspects, such as nursing, movements, and communication, differ depending on the maternal strategy used.

Among mammals, cetaceans (baleen whales and toothed whales) have the unique feature of living exclusively in aquatic environments (obligate swimmers). They have retained several mammalian traits inherited from their terrestrial ancestors; however, they have also evolved several unique traits related to the challenges they have had to adapt to in the context of life underwater. The aspects of maternal care in cetaceans have long interested researchers. Studies have been conducted to understand nursing behavior, swimming, and acoustic communication in females and their calves. However, these studies were limited by the technical and logistical means available at the time, which did not allow for comprehensive observation and tracking of cetaceans in their natural habitat. Thus, most studies were limited to captive animals or partial observations at the surface or subsurface.

In this thesis, I took the recent opportunity offered by the development of animalborne multi-sensor tags to understand better mother-offspring interactions in freeranging aquatic mammals. I focused on the humpback whale (Megaptera novaeangliae), one of the most popular, extensively studied, and accessible baleen whale species. I used data from tags equipped with or without a camera (CATS cam and Acousonde tags) placed on young calves (<3 months old) and their mothers off Sainte Marie, Madagascar.

I examined nursing behavior in the first part (Chapters 1 and 2). In Chapter 1, I developed a method to study nursing behavior even with data lacking videos and presented an initial detailed description. Starting from Chapter 1, which allowed the combination of data with and without videos to create a larger dataset, details about the nursing pattern in humpback whales were obtained (Chapter 2). In these first two chapters, I showed that nursing behavior is highly stereotyped, and nursing sessions are segmented into several successive series occurring approximately every two hours. Furthermore, I documented that nursing can occur at night. I suggested that some characteristics, such as the nursing intensity and the depth of occurrence of nursing, change as the calf grows.

In the second part (Chapters 3 and 4), I focused on the swimming behavior of mothercalf pairs. I demonstrated that the mother-calf pairs' swimming activities change with the calf's age, particularly the time engaged in slow swimming (milling) and that the mother mainly drives some of the activities. In contrast, other activities are initiated by the calf (Chapter 3). I concluded that the time budget, especially the time spent resting and milling, is a balance between the mother's need to conserve energy for the upcoming migration and caring for the calf (humpback whales being capital breeders) and the calf's need to optimize its growth and rapidly improve its swimming performance. In Chapter 4, I demonstrated for the first time that adult humpback whales can control their buoyancy and exploit it to optimize swimming efforts during vertical movements. In contrast, calves cannot do so and rely on their mothers, especially to remain stationary at a given depth.

In the third part (Chapters 5 and 6), I studied the context of social calls in mother-calf pairs to understand their biological functions better. I began by finding a method to identify and separate the mother's calls from those of the calf, a significant challenge in studying cetacean vocalizations from animal-borne tags for several years. With this problem solved, I found that the calf is primarily responsible for maintaining acoustic contact during vertical separations (e.g. when the calf goes to the surface to breathe), and the mother rarely responds vocally. I also showed that the acoustic characteristics of calf calls differ significantly from those of adults and thus could facilitate maternal responses. In Chapter 6, I explored the relationships between calf behaviors and the calls. I found that some behaviors, such as suckling, are associated with a high call rate and a homogeneous set of calls (calls of more or less the same type). In contrast, others, like synchronized traveling of the mother and calf and resting, are associated with silence.

These findings provide new and crucial insights for a better understanding of the maternal care dynamics, the development of young whales, and the importance of acoustic signals in mother-young communication in a relatively challenging aquatic environment. They constitute a novel knowledge that could motivate and guide conservation measures and serve as the foundation for further studies.

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Résumé étendu

Les mammifères présentent des formes de soins parentaux particulièrement sophistiquées qui sont principalement dispensées par la femelle (soin maternel). La mère, à partir de la naissance des jeunes et jusqu'à leur indépendance, approvisionne obligatoirement les jeunes en nourriture sous forme de lait maternel, assure leur protection, et les assiste dans leur développement. Les stratégies maternelles, et donc les caractéristiques des relations mères-jeune, sont très diversifiées et en relation avec les traits biologiques et les caractéristiques de l'environnement. Selon la stratégie maternelle utilisée, divers aspects tels que l'allaitement, le déplacement, et la communication vont être différents.

Parmi les mammifères, les cétacés (baleines à fanons et baleines à dents) ont la particularité de vivre exclusivement en milieu aquatique (nageurs obligés). Ils ont conservé plusieurs caractéristiques mammaliennes héritées de leurs ancêtres terrestres, mais ils ont également acquis plusieurs traits uniques liés aux défis auxquels ils ont dû s'adapter dans le contexte de la vie sous l'eau. Les aspects du soin maternel chez les cétacés ont intéressé les chercheurs depuis longtemps déjà. Des études ont été conduites pour comprendre le comportement d'allaitement, la nage et la communication acoustique chez les femelles et leurs petits. Ces études ont toutefois été limitées par les moyens techniques et logistiques disponibles à l'époque qui ne permettaient pas d'observer et de suivre intégralement les cétacés dans leur milieu naturel. Ainsi, la plupart des études se sont limitées à des animaux en captivité ou à des observations partielles en surface ou en subsurface.

Dans cette thèse, j'ai saisi l'opportunité récente offerte par le développement de balises multi-capteurs embarquées pour mieux comprendre les interactions mère-jeune des mammifères aquatiques dans leur milieu naturel. Je me suis focalisé sur la baleine à bosse (Megaptera novaeangliae), une des espèces de baleine à fanons les plus populaires, les plus étudiées et les plus accessibles. J'ai utilisé des données issues de balises équipées ou non de caméra (balises CATS cam et Acousonde) posées sur des jeunes baleineaux (<3 mois) et leurs mères dans les eaux côtières de Sainte Marie, Madagascar.

Dans la première partie (Chapitres 1 et 2), je me suis consacré à l'étude du comportement d'allaitement. Dans le Chapitre 1, j'ai développé une méthode pour étudier le comportement d'allaitement même avec des données sans vidéos et en présente une première description détaillée. A partir du Chapitre 1 qui a permis la combinaison de données avec et sans vidéos pour avoir un jeu de données plus grand, des détails sur le schéma d'allaitement chez la baleine à bosse ont été obtenus (Chapitre 2). J'ai montré, dans ces deux premiers chapitres, que le comportement d'allaitement est très stéréotypé et que les sessions d'allaitement sont segmentées en plusieurs séries et se font toutes les deux heures environ. De plus, j'ai établi que l'allaitement peut également se faire la nuit, et suggéré que certaines caractéristiques telles que l'intensité d'allaitement et la profondeur à laquelle l'allaitement se passe changent à mesure que le baleineau grandit.

Dans la deuxième partie (Chapitres 3 et 4), je me suis focalisé sur la nage des couples mère baleineau. J'ai montré que les activités de nage des couples mère baleineau changent avec l'âge du baleineau (notamment le temps passé à faire des nages lentes) et que certaines activités sont surtout imposées par la mère et d'autres sont entreprises par le baleineau (Chapitre 3). J'ai conclu que le budget temps, notamment le temps passé à se reposer et à nager de manière erratique, est une balance entre le besoin pour la mère de conserver de l'énergie pour la migration à venir et pour prendre soin du baleineau (la baleine à bosse étant un reproducteur capital) et le besoin du baleineau à optimiser sa croissance et à améliorer rapidement sa performance de nage. Dans le Chapitre 4, j'ai démontré pour la première fois que les baleines à bosse adultes sont capables de contrôler leur flottabilité et l'exploiter pour optimiser les efforts de nage lors des mouvements verticaux, alors que les baleineaux n'en sont pas capables et se font alors aider par leur mère, notamment pour se maintenir à une profondeur donnée.

Dans la troisième partie (Chapitres 5 et 6), j'ai étudié le contexte des cris sociaux des couples mère-baleineau pour mieux comprendre leurs fonctions biologiques. J'ai commencé par trouver un moyen pour identifier et séparer les cris de la mère de ceux du baleineau, un problème majeur depuis plusieurs années dans l'étude des vocalisations des cétacés à partir de balises embarquées. Ce problème résolu, j'ai pu trouver que le baleineau est le principal responsable du contact acoustique lors des séparations verticales (e.g., quand le baleineau part respirer à la surface) et que la mère ne répond vocalement que rarement. J'ai aussi montré que les caractéristiques acoustiques des cris des baleineaux diffèrent suffisamment de celles des adultes et pourraient faciliter les réponses maternelles. Dans le Chapitre 6, j'ai exploré les relations entre les comportements du baleineau et les cris qu'il utilise. J'ai trouvé que certains comportements comme l'allaitement sont associés à un taux de vocalisation élevé et sont associés à un lot homogène de cris (cris plus ou moins du même type), alors que d'autres comportements comme les déplacements synchronisés de la mère et le baleineau et le repos sont associés à un silence.

v Ces résultats constituent des éléments nouveaux et déterminants pour une meilleure compréhension de la dynamique des soins maternels, du développement des jeunes, et de l'importance des signaux acoustiques dans le contexte de la communication mère-jeune dans un environnement aquatique relativement contraignant. Ils constituent une base de connaissance inédite qui pourrait motiver et guider la mise en place de mesures de conservation et qui pourrait servir de fondement pour des études complémentaires -----⁂ -----

Parental care in mammals

From an organismal point of view, each organism aims to maximize its fitness and, in the long term, to preserve its genes, in particular through its descendants [START_REF] Gould | The structure of evolutionary theory[END_REF]. In this sense, [START_REF] Smiseth | What is parental care[END_REF] defined parental care as any parental trait that improves the fitness of the descendants and favors the preservation of the parental genes. In most animal species, parental care is mainly limited to choosing a safe and adequate place for the eggs and providing eggs with a reserve to feed the young until they can live independently. In other species, like mammals and birds, the parents will go as far as providing care even after birth/hatching. Such post-natal/post-hatching care may be provided exclusively by one parent (mother or father) or both (Balshine, 2012). Since parental care represents a form of reproductive investment, it usually involves targeted allocations to prevent spending resources on non-filial offspring and investing in other's reproductive success (Clutton-Brock, 1991).

In most mammals, only the mother usually provides post-natal care (Gubernick & Klopfer, 1981;Maestripieri & Mateo, 2009;Nowak et al., 2000;[START_REF] Rosenblatt | Maternal behavior among the nonprimate mammals[END_REF]. Biparental and cooperative care (where several adults of a group provide care to the young) are uncommon [START_REF] Kleiman | The evolution of male parental investment in mammals[END_REF][START_REF] König | Cooperative care of young in mammals[END_REF][START_REF] Russell | Mammals: comparisons and contrasts[END_REF][START_REF] West | Male care and life history traits in mammals[END_REF]. The most striking form of maternal care is food provisioning, water provisioning, and immunity support through maternal milk (Nowak et al., 2000;[START_REF] Oftedal | The evolution of milk secretion and its ancient origins[END_REF]. Maternal milk is a nutritive substance produced by mammary glands, a unique feature of mammals from which their name was derived [START_REF] Gregory | The orders of mammals[END_REF]. As maternal milk is the only food source for young mammals during the first stage of their life, at least a minimum of maternal attendance is compulsory in mammals. Milk production by male mammals remains rare and mostly anecdotal [START_REF] Daly | Why don't male mammals lactate[END_REF][START_REF] Kunz | Male lactation: why, why not and is it care?[END_REF].

Along with providing food to their young, female mammals can provide them warmth (thermal assistance), protect them from predators, assist their locomotion, etc. Thermal assistance can be a direct action, such as embracing the young (e.g., in bats; [START_REF] Sano | Postnatal growth and development of thermoregulative ability in the Japanese greater horseshoe bat, Rhinolophus ferrumequinum nippon, related to maternal care[END_REF] or providing them with an adequately chosen 'nest' (or shelter/burrow/den). Protection against predators is achieved through behaviors such as discouraging predators, assisting escape, or creating barriers/hides to prevent predators from reaching the young [START_REF] Ford | Fight or flight: antipredator strategies of baleen whales[END_REF][START_REF] Hamel | Maternal defensive behavior of mountain goats against predation by golden eagles[END_REF][START_REF] Pitman | Whale killers: prevalence and ecological implications of killer whale predation on humpback whale calves off Western Australia[END_REF][START_REF] Smith | Maternal defense in Columbian white-tailed deer: when is it worth it?[END_REF]. Regarding locomotory assistance, a classic example is the case of primates carrying their young [START_REF] Ross | Park or ride? Evolution of infant carrying in primates[END_REF].

Pattern of maternal care in mammals

How maternal care is provided in mammals varies from one species to another, depending on its life history and environment. Among the notable correlates are the neonates' developmental status and the litter's size. In species that give birth to underdeveloped young (limited sensory, locomotor, and thermal regulation capacities at birth -'altricial' species) and in large litters, the mother uses a nest where all motheryoung interactions happen. These species include canids, felids, several rodents, and lagomorphs [START_REF] Benedek | Exploring the genetic background of the differences in nest-building behavior in European rabbit[END_REF][START_REF] Denenberg | Maternal behavior in the rat: an investigation and quantification of nest building[END_REF][START_REF] Fernández | The use of breeding dens and kitten development in the Iberian lynx (Lynx pardinus)[END_REF][START_REF] Naaktgeboren | Some aspects of parturition in wild and domestic Canidae[END_REF]Nowak et al., 2000). Conversely, 'precocial' species (as opposed to altricial species) that produce only small litter, like most ungulates, typically do not nest (Fisher et al., 2002;Lent, 1974;Nowak et al., 2000). On the other hand, intermediate species (relatively well-developed senses but with insufficient locomotory and thermoregulatory abilities at birth), such as primates, may nest or not, mainly depending on the litter size [START_REF] Ross | Park or ride? Evolution of infant carrying in primates[END_REF].

The mother has to look after several young gathered and isolated in the same place in the case of nesting species. The nest provides litter both protection from cold and predators. The remaining main task of the mother is to provide food. Non-nesting species, on the other hand, have the particularity of being delivered at birth to the environment where they will spend their adult life. The young are directly exposed to environmental hazards and challenges, including predators and potentially aggressive conspecifics. In addition to providing food, the mother must thus immediately provide warmth if needed, defend their offspring from predators and other conspecifics, and eventually assist them in their locomotion.

Along with young developmental status and litter size, a notable correlate related to the maternal care pattern concerns habitat structure. This aspect has been particularly well documented in terrestrial ungulates. Terrestrial ungulates are generally precocial, produce only one young at a time (with a few exceptions), and are non-nesters exposed to predation. However, depending on the structure of their habitat, a variation in the pattern of maternal care can be observed. For those living in closed habitats, the young remain hidden in the vegetation, safe from predators, while the mother engages in various vital activities, such as foraging. This strategy is commonly referred to as the 'hiding' anti-predator strategy (Fisher et al., 2002;Lent, 1974). The difference with nester species is that the hiding place can change regularly, and, in many cases, the young choose the exact hiding spot [START_REF] Blank | Antipredator tactics are largely maternally controlled in goitered gazelle, a hider ungulate[END_REF][START_REF] Grovenburg | Bed site selection by neonate deer in grassland habitats on the northern Great Plains[END_REF]Lent, 1974). The young choosing the hiding spot is made possible by its great autonomy soon after birth.

For terrestrial ungulates living in open environments, the young follow the mother wherever she goes. Indeed, the environment does not offer any cover for the young as it does for the hiders; thus, the young must rely on continuous protection from their mother. This strategy is known as the 'following' anti-predator strategy (Fisher et al., 2002;Lent, 1974) and may also be favored by the mother's size (Fisher et al., 2002). Large females are naturally better able to protect an offspring that is constantly next to them and are less vulnerable to predators that may take advantage of the reduced mobility and ease of detection caused by the presence of a youngster.

Various aspects of mother-offspring interactions are closely linked to the patterns above. The following sections will introduce three essential components: the nursing behavior, the movements, and the communication.

Nursing and suckling behavior

It is essential to clarify some terminologies used to discuss nursing behavior first. The literature often confuses different vocabularies, notably between lactation, suckling, sucking, and nursing [START_REF] Hall | Terminology for use in investigations of nursing and suckling[END_REF]. Lactation refers to the physiological state associated with milk production or, more generally, the period during which an individual produces milk. Suckling refers to the behavior of the young to obtain milk from a lactating individual. Generally speaking, nursing and suckling correspond to the same mother-young behavior, the transfer of milk from a lactating individual to the young, but from two different perspectives: from the lactating individual's perspective (the mother) and the young's perspective, respectively. The term suckling is to be distinguished from sucking, a component of suckling. Sucking refers specifically to the action of exerting negative pressure on the nipples or teats to obtain the milk.

In 'monotremes' (platypuses and echidnas), milk beads from the milk patch on the mother's abdomen [START_REF] Oftedal | The mammary gland and its origin during synapsid evolution[END_REF][START_REF] Oftedal | The evolution of milk secretion and its ancient origins[END_REF][START_REF] Stead | Of mammals and milk: how maternal stress affects nursing offspring[END_REF]. Their nursing is, therefore, relatively simple. The mother lets the young lick the milk patch to feed them. In 'metatherians' (represented by marsupials) and 'eutherians' ('modern' mammals), the milk is expelled via nipples or teats [START_REF] Oftedal | The evolution of milk secretion and its ancient origins[END_REF][START_REF] Stead | Of mammals and milk: how maternal stress affects nursing offspring[END_REF]. In metatherians, the young directly crawl towards the nipple at birth and attach themselves to it continuously to obtain milk during the first stage of their development [START_REF] Hunsaker | Behavior of new world marsupials[END_REF][START_REF] Stead | Of mammals and milk: how maternal stress affects nursing offspring[END_REF]. After that, suckling becomes intermittent. For eutherians, nursing is intermittent rather than continuous [START_REF] Stead | Of mammals and milk: how maternal stress affects nursing offspring[END_REF].

The nursing pattern throughout the day is closely linked to the maternal strategy employed by the mother-offspring group. This link has been well explored in terrestrial ungulates, where there is a marked contrast between the nursing pattern of the hider and follower species [START_REF] Gloneková | Giraffe nursing behaviour reflects environmental conditions[END_REF]Lent, 1974;[START_REF] Rosenblatt | Maternal behavior among the nonprimate mammals[END_REF]. In nesting species, nursing is reduced to infrequent periods corresponding to when the mother returns to the nest (Nowak et al., 2000;[START_REF] Zarrow | Rabbit: frequency of suckling in the pup[END_REF]. In hider species, a similar pattern has been observed. Nursing is limited to a few moments during the day when the mother-offspring pair meets and is often prolonged. With follower species, nursing is frequent but in short sessions, as the pair is always together [START_REF] Gloneková | Giraffe nursing behaviour reflects environmental conditions[END_REF]Lent, 1974;[START_REF] Rosenblatt | Maternal behavior among the nonprimate mammals[END_REF].

Nursing is, by nature, costly because producing sufficiently rich milk requires a lot of energy from mothers (Braithwaite et al., 2015;[START_REF] Oftedal | The behavior, physiology, and anatomy of lactation in the pinnipedia[END_REF][START_REF] Speakman | The physiological costs of reproduction in small mammals[END_REF]. However, depending on whether it is performed in a nest or not, it may also incur additional costs for the mother. For example, the mother and the young may need to stop their movements or at least slow down to facilitate suckling, which, in turn, may inadvertently facilitate predator attacks.

The duration of lactation varies significantly from one species to another. It can last several years in some species, such as great apes [START_REF] Carvajal | Learning and skill development in wild primates: toward a better understanding of cognitive evolution[END_REF], elephants [START_REF] Langbauer | The meanings of weaning: growth, lactation, and life history[END_REF], some pinniped species [START_REF] Riedman | The pinnipeds: seals, sea lions, and walruses[END_REF], and toothed whales [START_REF] Chivers | Cetacean life history[END_REF]. In others, it can last just a few days. The most striking example is the hooded seal, whose lactation lasts only four days [START_REF] Bowen | Birth to weaning in 4 days: remarkable growth in the hooded seal, Cystophora cristata[END_REF]. The lactation length is linked to different constraints and needs in different species [START_REF] Hayssen | Empirical and theoretical constraints on the evolution of lactation[END_REF][START_REF] Langbauer | The meanings of weaning: growth, lactation, and life history[END_REF]. For instance, more prolonged lactation may be associated with sophisticated social learning requirements for the young [START_REF] Brodie | Duration of lactation in Cetacea: an indicator of required learning[END_REF]. Short lactation may be linked to constraints pushing the mother to leave her young rapidly. An example is the need for the mother to feed after fasting in hooded seals [START_REF] Oftedal | The adaptation of milk secretion to the constraints of fasting in bears, seals, and baleen whales[END_REF].

The nursing intensity varies from birth to weaning [START_REF] Fiialkovskyi | Evidence of three distinct lactation stages in nursing gazelles: Implications on maternal behaviour assessment[END_REF]Nowak et al., 2000;[START_REF] Skok | Suckling strategies in terrestrial ungulates (Artiodactyla, Perissodactyla) and the factors influencing them: The offspring perspective[END_REF]. In several species, neonates suckle intensively during the first period following birth. This first milk is crucial because, at this stage, the milk contains immunoglobulins essential for building initial immunity ('colostrum'). Afterward, suckling intensity decreases and eventually stabilizes until the weaning period approaches. As the weaning period nears, suckling intensity rapidly declines, and eventually, nursing ceases. The decrease in nursing intensity may be associated with a reduction in the milk requirement of the young as they begin to eat solid food, either on their own or by eating what their mother brings. Increased mother-offspring conflict may also explain this decrease. As the young grow, their energy demands increase, exceeding what the mother can offer without severely affecting her future reproductive success (Trivers, 1974). As a result, the mother starts to restrict milk access.

Mother-offspring locomotion and movements

The provision of maternal care implies the mother's presence at the offspring's side, at least for nursing. However, the mother must also ensure her needs are met to survive. She must thus move from one place to another, for example, to forage and sustain herself. In nesting species, the young have no locomotory ability to leave the nest; thus, the mother forages outside the nest unaccompanied. There is no notion of mother and young moving together to join another place. She may only move with her young when forced to because of a disturbance or increased predation risk [START_REF] Boonstra | Natal nest location and small mammal tracking with a spool and line technique[END_REF][START_REF] Galef | The ecology of weaning: parasitism and the achievement of independence by altricial mammals[END_REF][START_REF] Laack | Reproductive life history of ocelots Leopardus pardalis in southern Texas[END_REF]. For instance, in such forced situations, the mother simply carries the young to the new nest using her mouth. Otariids can also be compared to nesters. Although otariid pups are sensorially and thermally autonomous on land, their fur is not adapted to water yet, and they have limited swimming ability [START_REF] Donohue | The ontogeny of metabolic rate and thermoregulatory capabilities of northern fur seal, Callorhinus ursinus, pups in air and water[END_REF]. Thus, otariid pups stay in one place (on the beach) while their mother feeds at sea. Hider species also show similar characteristics to nesting species. The mother moves alone while the young wait in their hiding place (Fisher et al., 2002;Lent, 1974).

Other species, such as marsupials and several primates, are constantly moving with their young; however, the young do not need to deploy any locomotion effort. For marsupials, the young remain in the mother's ventral pouch for most of their early life stage [START_REF] Hunsaker | Behavior of new world marsupials[END_REF]. In the case of primates, the young cling to their mother's abdomen or back [START_REF] Ross | Park or ride? Evolution of infant carrying in primates[END_REF]. These strategies imply that the mother is not restricted to staying in or returning to the same place and can freely explore her environment to meet her needs.

For follower species, the young move alongside their mother without being carried. As with mothers who carry their young, the following strategy enables the mother to continue her vital activities and the young to benefit from continuous maternal care (Fisher et al., 2002;Lent, 1974). However, it presents specific challenges, such as the fact that the offspring, even if very precocious, may have more limited locomotor skills than the mother (Noren et al., 2006). The offspring must manage to keep up with the mother, and the mother must adjust her movements to allow the offspring to follow her. The offspring must, for instance, quickly improve their locomotor performance. In the case of terrestrial ungulates, this means significantly practicing running and walking. As for the mother, she must accommodate her pace to her offspring's performance to reduce the risk of separation (Huetz et al., 2022;Lent, 1974;Szabo & Duffus, 2008). Such accommodation includes allocating sufficient time for resting. She can also occasionally provide direct assistance, like pushing or dragging the young [START_REF] Ford | Fight or flight: antipredator strategies of baleen whales[END_REF][START_REF] Noren | Hitching a ride: the formation locomotion strategy of dolphin calves[END_REF]Noren & Edwards, 2011;[START_REF] Pitman | Whale killers: prevalence and ecological implications of killer whale predation on humpback whale calves off Western Australia[END_REF]. As both the mother and the young are independent in their movements, there must be some coordination, including communicating (when and where to move) and maintaining track of each other's position. The aspect of communication will be developed in the next section.

Mother-offspring communication

Animal communication refers to the transmission of information or signals, whether acoustic, visual, chemical, or tactile, between individuals of the same or different species [START_REF] Bradbury | Principles of animal communication[END_REF]. It involves at least two protagonists: the sender, who transmits coded information in a given channel, and the receiver, who decodes and interprets it. Communication implies signals and cues. Signals are intentional and benefit both the sender and the receiver. In contrast, cues are sent unintentionally by a sender to a receiver and benefit only the receiver. Both give some information that can be interpreted by the receiver to adjust its behavioral state and adapt its behavior. A typical example of the use of cues is the case of predators attentive to information transmitted by their prey [START_REF] Roberts | Consequences of complex signaling: predator detection of multimodal cues[END_REF].

The mother-young relationships usually involve a two-way information exchange between the young and the mother. From the young's point of view, maternal care is a matter of individual survival (Trivers, 1974). Therefore, they must ensure they get the maximum benefit from maternal care. As a result, they seek to influence their mother's behavior, notably by sending signals to her about their needs (Gubernick & Klopfer, 1981;Lent, 1974;Nowak et al., 2000). Returning to the earlier points, young animals may, for example, communicate with their mother to beg for milk. They may also call the mother in the event of separation. Since maternal care is a way for the mother to secure her reproductive success, she remains attentive to these signals from her young. She responds accordingly, as long as the cost does not negatively affect her long-term reproductive success (Trivers, 1974).

Another vital point that must involve some form of information exchange concerns the identification of the young by the mother to ensure that the care is only provided to filial offspring and the identification of the mother by the young for cases where other potentially aggressive conspecifics may be present. For nesting mammals, the mother only needs to recognize her litter identity and(or) remember her nest location. Individual recognition of the young is thus optional (Nowak et al., 2000); hence, at most, only signals containing litter identity from the young are needed. The young's individual recognition of the mother is not necessary either, as no other individual than the mother is expected to enter the nest (Nowak et al., 2000). Thus, the mother does not necessarily have to transmit information about her identity. For non-nesting mammals, there is a strong need for mother-young individual recognition (mutual or unidirectional). Indeed, the mother may be surrounded by offspring from other females, and the young must avoid aggressive conspecifics (Clutton-Brock, 1991;Lent, 1974;Martin et al., 2022;Nowak et al., 2000).

Depending on the information to be transmitted and the conditions in the environment, the mother and her young may employ different communication modalities or combine several [START_REF] Higham | An introduction to multimodal communication[END_REF] to ensure the information reaches the receiver reliably. Indeed, the environmental conditions and the type of signal impose various transmission constraints. Acoustic signals, for example, can be effective over long distances and for rapid communication [START_REF] Mccomb | Long-distance communication of acoustic cues to social identity in African elephants[END_REF][START_REF] Rosenthal | Visual and acoustic communication in nonhuman animals: a comparison[END_REF][START_REF] Zuberbühler | Diana monkey long-distance calls: messages for conspecifics and predators[END_REF], enabling mother and young to communicate at a distance. However, their propagation can be altered by physical obstacles or noise. For instance, in dense forests, the vegetation can attenuate or distort sound [START_REF] Michelsen | Strategies for acoustic communication in complex environments[END_REF][START_REF] Wells | The effect of vegetation on the propagation of calls in the neotropical frog Centrolenella fleischmanni[END_REF]. Visual signals can be effective at short and medium distances and also allow immediate communication but largely depend on light or weather conditions restricting visibility [START_REF] Rosenthal | Visual and acoustic communication in nonhuman animals: a comparison[END_REF]. Chemical signals can be especially effective at short distances but are highly sensitive to local conditions such as wind direction, wind speed, humidity, etc. [START_REF] Alberts | Constraints on the design of chemical communication systems in terrestrial vertebrates[END_REF]. Finally, tactile communication only works at very short distances [START_REF] Langbauer | The meanings of weaning: growth, lactation, and life history[END_REF].

Mother-offspring interactions in cetaceans

Cetaceans (whales, including dolphins and porpoises), along with sirenians, are the only mammal groups that are entirely aquatic or obligate swimmers [START_REF] Ballance | Cetacean ecology[END_REF][START_REF] Jefferson | Marine mammals of the world[END_REF][START_REF] Thewissen | From land to water: the origin of whales, dolphins, and porpoises[END_REF]. The vast majority are exclusively marine. Cetaceans are divided into two distinct groups: baleen whales (mysticetes) and toothed whales (odontocetes). They descended from a terrestrial mammalian ancestor they share with all modern ungulates [START_REF] Fordyce | Cetacean evolution[END_REF][START_REF] Thewissen | From land to water: the origin of whales, dolphins, and porpoises[END_REF]. While they have retained most of the main features of mammals, including the fact that they breathe air and that the females lactate and take care of the young, they have undergone several physiological, anatomical, and behavioral transformations in the course of evolution to adapt to this exclusively aquatic life. Such transformations include their exceptional diving ability, the evolution of unique sound production systems, the switch to a mainly sound-based lifestyle, the disappearance of the hind limbs, the transformation of the tail into a flipper, etc. [START_REF] Thewissen | From land to water: the origin of whales, dolphins, and porpoises[END_REF].

Cetaceans are similar to their terrestrial relatives in that they give birth to unique and highly precocial offspring and are often compared to follower species concerning the mother-calf spatial relationships (Huetz et al., 2022;[START_REF] Rendell | Causes and consequences of female centrality in cetacean societies[END_REF]Szabo & Duffus, 2008;Taber & Thomas, 1982;[START_REF] Thomas | toothed whales[END_REF]Tyson et al., 2012). Cetacean mother-calf pairs, however, face distinct challenges related to the unique nature of the aquatic environment. Firstly, cetaceans move in a three-dimensional space. Moreover, there is a separation between where they spend most of their life (underwater) and where they get oxygen (at the surface). In the case of mother-calf pairs, both the mother and the calf must, therefore, manage their movements in this three-dimensional environment, balancing the need to breathe at the surface -which may differ between adults and calves (Cartwright & Sullivan, 2009;Huetz et al., 2022;Szabo & Duffus, 2008) -and the need to stay underwater, ideally together.

Secondly, cetaceans live in an environment where sounds propagate very well [START_REF] Au | Principles of marine bioacoustics[END_REF][START_REF] Ladich | Acoustic communication in terrestrial and aquatic vertebrates[END_REF]. It is not surprising, therefore, if this is their primary means of communication (Dudzinski et al., 2009). Unfortunately, while sound efficiency in an underwater context can be advantageous for optimizing maternal care (long-range and rapid communication, good acoustic vigilance), it can also be a disadvantage. Indeed, the background sound is highly variable under water and diverse (surface vs. deep, anthropized vs. non-anthropized areas, presence of singing males, etc.) [START_REF] Hildebrand | Sources of anthropogenic sound in the marine environment[END_REF][START_REF] Richardson | Marine mammals and noise[END_REF], making it potentially challenging to extract information from acoustic signals, and predators or unsolicited conspecifics can also easily exploit sound to detect communicating mother-calf pairs [START_REF] Dabelsteen | Public, private or anonymous? Facilitating and countering eavesdropping[END_REF][START_REF] Haskell | Experimental evidence that nestling begging behaviour incurs a cost due to nest predation[END_REF].

Cetaceans also generally rely on resources extending to vast areas, meaning the mother and their calf must cover substantial distances. Such need for extensive travel is especially pronounced among most baleen whale species, which rely on a seasonal food resource in a well-defined geographical area, different and far from the habitat favorable to breeding and calving (Bannister, 2018;Corkeron & Connor, 1999;Lockyer, 1984). Baleen whale mothers thus often rely on a limited energy reserve to care for the calf, and the calf must be prepared to undertake the travel to join the area where the food resource is abundant.

Although the aquatic environment is very different from the terrestrial environment, other environmental conditions experienced by cetaceans are also comparable and potentially homologous to some of the conditions experienced by terrestrial and semiaquatic mammals. For example, cetaceans live in an environment that can be considered open and are exposed to predators, similar to terrestrial mammals that use the following strategy (Szabo & Duffus, 2008;Tyson et al., 2012). Like some seals, baleen whales rely on their reserves to reproduce (capital breeders) [START_REF] Oftedal | The adaptation of milk secretion to the constraints of fasting in bears, seals, and baleen whales[END_REF].

Nursing and suckling behavior

Toothed whale calves generally remain with their mothers for approximately two years, while baleen whale calves depend on their mothers for only one year [START_REF] Chivers | Cetacean life history[END_REF]. The frequency of nursing during these periods of dependence and details on how the calves suckle remain poorly documented. Nursing has been mainly studied in captive toothed whales. It has been shown that captive toothed whale calves suckle by remaining attached to their mother's teats for only a brief period (less than 10 s) but repetitively (Clark & Odell, 1999a; J. M. [START_REF] Russell | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF][START_REF] Triossi | The development of suckling behavior in two captive-born calves of bottlenose dolphins (Tursiops truncatus)[END_REF]. The intensity of nursing peaks at birth and then decreases as the calf grows (Clark & Odell, 1999a, 1999b;[START_REF] Russell | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF][START_REF] Triossi | The development of suckling behavior in two captive-born calves of bottlenose dolphins (Tursiops truncatus)[END_REF]. After a few months, however, there may be an upturn in nursing intensity, but the reason for this is unclear [START_REF] Triossi | The development of suckling behavior in two captive-born calves of bottlenose dolphins (Tursiops truncatus)[END_REF]. In all cases, the approaching weaning was suggested to be always associated with a progressive decline in nursing intensity (Clark & Odell, 1999a, 1999b; J. M. [START_REF] Russell | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF][START_REF] Triossi | The development of suckling behavior in two captive-born calves of bottlenose dolphins (Tursiops truncatus)[END_REF]. In natural environments, the most accurate descriptions of cetacean nursing behavior have been obtained from underwater videos recorded by divers (e.g., [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF][START_REF] Zoidis | A note on suckling behavior and laterality in nursing humpback whale calves from underwater observations[END_REF] or by placing cameras on calves [START_REF] Tackaberry | From a calf's perspective: humpback whale nursing behavior on two US feeding grounds[END_REF]. These studies (on sperm and humpback whales) have highlighted two crucial points: the calves remain continuously attached to their mother's teat for an average of 20-40 s, and suckling occurs at depth, not at the surface.

Mother-calf swimming behavior

The cetacean mother-calf pair's swimming behavior is highly synchronized (Fellner et al., 2013;Huetz et al., 2022;[START_REF] Noren | Hitching a ride: the formation locomotion strategy of dolphin calves[END_REF]Noren & Edwards, 2011;[START_REF] Rendell | Causes and consequences of female centrality in cetacean societies[END_REF][START_REF] Smultea | Cetacean mother-calf behavior observed from a small aircraft off Southern California[END_REF]Szabo & Duffus, 2008;Taber & Thomas, 1982;[START_REF] Thomas | toothed whales[END_REF]Tyson et al., 2012). The pair usually adopts an elaborate swimming configuration known for providing energetic benefits for the calf to accommodate its limited swimming capabilities (Noren, 2008;[START_REF] Noren | Hitching a ride: the formation locomotion strategy of dolphin calves[END_REF]Noren & Edwards, 2011;[START_REF] Ratsimbazafindranahaka | Dépendance maternelle chez les baleineaux à bosse (Megaptera novaeangliae) du canal de Sainte Marie, Madagascar : regard sur la morphométrie et la dynamique de la nage[END_REF][START_REF] Smultea | Cetacean mother-calf behavior observed from a small aircraft off Southern California[END_REF]. These energetic advantages have been particularly well quantified in dolphins (Noren, 2008;[START_REF] Noren | Hitching a ride: the formation locomotion strategy of dolphin calves[END_REF]Noren & Edwards, 2011;[START_REF] Weihs | The hydrodynamics of dolphin drafting[END_REF]. By moving in echelon relative to its mother, the calf is pushed towards the mother, and the effort needed to swim is considerably reduced. Mothers also tend to display modified swimming behavior adapted to the calf's performance. In humpback whales, for example, it has been shown that mothers reduce their dive time (Szabo & Duffus, 2008). These behavioral adaptations are likely one of several strategies mother-calf pairs employ to facilitate the calf's ability to stay close to the mother.

Mother-calf acoustic communication

Acoustic recordings in the wild have shown that like most cetacean social groups, cetacean mother-calf pairs produce diverse sounds (in addition to echolocation clicks for toothed whales), commonly called 'social' sounds [START_REF] Ames | Evidence of stereotyped contact call use in narwhal (Monodon monoceros) mother-calf communication[END_REF]Cusano et al., 2022;[START_REF] Hill | Cooooooommmmmmmeeeeeeeee heeeerrrrrreeeee.... Momma dolphin has something to say[END_REF]Indeck et al., 2021Indeck et al., , 2022;;[START_REF] King | Maternal signature whistle use aids mother-calf reunions in a bottlenose dolphin, Tursiops truncatus[END_REF][START_REF] Nielsen | Acoustic crypsis in southern right whale mother-calf pairs: infrequent, low-output calls to avoid predation[END_REF][START_REF] Parks | Acoustic crypsis in communication by North Atlantic right whale mother-calf pairs on the calving grounds[END_REF]Saloma et al., 2022;[START_REF] Sayigh | Bottlenose dolphin mothers modify signature whistles in the presence of their own calves[END_REF]Videsen et al., 2017;Zoidis et al., 2008). These sounds include non-vocal sounds, such as the percussive sounds produced by hitting the sea surface with either the caudal, pectoral fins, or the head, and vocal sounds produced by specialized vocal apparatus (Dudzinski et al., 2009;Edds-Walton, 1997). The functions of these social sounds, however, remain to be determined. In dolphins, studies have suggested that signature whistles facilitate the reunion and mutual recognition of mother-calf pairs (S. L. [START_REF] King | Maternal signature whistle use aids mother-calf reunions in a bottlenose dolphin, Tursiops truncatus[END_REF]. In baleen whales, vocalizations have mainly been associated with mother-calf contact (Indeck et al., 2022;Videsen et al., 2017;Zoidis et al., 2008). To date, no sound type has been clearly associated with a particular mother-calf behavior. More recently, several studies focused on how mother-calf vocalizations adapt to the presence of predators. In particular, cryptic communication has been highlighted: mothers and calves communicate discreetly (i.e., low amplitude level and low call rate) to avoid predators and unsolicited males who may seek to mate with the mother (Indeck et al., 2022;[START_REF] Nielsen | Acoustic crypsis in southern right whale mother-calf pairs: infrequent, low-output calls to avoid predation[END_REF][START_REF] Parks | Acoustic crypsis in communication by North Atlantic right whale mother-calf pairs on the calving grounds[END_REF]Videsen et al., 2017).

Why our knowledge of the mother-offspring interactions in cetaceans remained very limited?

The above information depicts a relatively limited knowledge of mother-young interactions in cetaceans. Observations from animals in captivity (mainly toothed whales) form much of the current knowledge. The extent of their validity in the natural environment remains uncertain since life in the aquarium is very different from natural conditions and, therefore, far from the natural challenges faced by animals. The focus on captive animals was mainly motivated by the fact that, by nature, cetaceans are challenging to access in their natural environment. Some species are very cryptic and difficult to detect, others do not specifically stay in a given area, and as already stated above, they all spend most of their time underwater [START_REF] Ballance | Cetacean ecology[END_REF]. Studies of larger species in their natural habitat, particularly baleen whales, have begun to develop, but most studies have been based on surface and subsurface observations. The main drawbacks of these observations are that they do not allow precise identification of behaviors and only represent cetacean's life at the surface. The most relevant example of this is the description of nursing behavior, which is almost impossible to confirm from the surface [START_REF] Tackaberry | From a calf's perspective: humpback whale nursing behavior on two US feeding grounds[END_REF]. Regarding acoustic communication studies in the wild, the available technology has also limited many observations. Many studies have relied on moored, towed, or diver-carried hydrophones and, thus, have been limited to recordings at a fixed or relatively immobile point. They were unable to follow the whales as they moved or dove.

The development of animal-borne multi-sensor tags has opened up a new way of overcoming these problems. Their integrated pressure sensor, triaxial accelerometer, and integrated hydrophone can remotely record whale behavior and vocalizations accurately and at a fine scale (Goldbogen et al., 2006;Johnson & Tyack, 2003). More recent models are even equipped with a camera, enabling further characterizations [START_REF] Goldbogen | Using digital tags with integrated video and inertial sensors to study moving morphology and associated function in large aquatic vertebrates[END_REF][START_REF] Tackaberry | From a calf's perspective: humpback whale nursing behavior on two US feeding grounds[END_REF]. Unfortunately, many studies focusing on these new techniques have suffered from the difficulty in placing the tags on the whales, which has largely restricted the sample size. Furthermore, in attempts to associate behaviors with vocalizations, the interpretations remained limited in the case of mother-calf pair studies because of the uncertainty in the identity of the emitters. Indeed, a tag placed on an individual may record both the vocalizations of the tagged individual and any surrounding individual and the methods proposed to separate them are still debated (Goldbogen et al., 2014;[START_REF] Saddler | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF].

Towards understanding mother-offspring interactions in an exclusively aquatic environment: the humpback whale as a model

Understanding the mother-offspring interactions in a given environmental context can provide insight into how the environmental and social challenges have shaped the maternal strategies mammals use. It also helps comprehend the role of maternal care in multiple aspects of young mammal development, such as locomotor, cognitive abilities, social, and communication skills development. In this scope, the humpback whale, a mysticete species, can be a good model for investigating mother-calf interaction in cetaceans in their natural environment and, on a larger scale, understanding mother-calf interactions in the complex context of the aquatic environment.

The humpback whale is one of the most popular and most studied cetaceans in its natural environment (Clapham, 2018). This abundant species can be found in all the world's oceans, often along the coasts. Like most mysticetes, most humpback whales undertake long seasonal migrations. The summer is spent feeding in high latitudes' rich, productive, cold waters. In winter, the majority move to warmer tropical waters and stop feeding. More adequate for reproduction, they stay there to mate and give birth. Humpback whales mostly occupy shallow coastal waters and continental shelves [START_REF] Dawbin | The seasonal migratory cycle of humpback whales. Whales, Dolphins and Porpoises[END_REF].

The species is polygamous, i.e., it does not form fixed couples [START_REF] Clapham | The social and reproductive biology of humpback whales: an ecological perspective[END_REF][START_REF] Clapham | Reproduction and recruitment of individually identified humpback whales, Megaptera novaeangliae, observed in Massachusetts Bay, 1979-1985[END_REF][START_REF] Riedman | The pinnipeds: seals, sea lions, and walruses[END_REF][START_REF] Glockner-Ferrari | Individual identification, behavior, reproduction, and distribution of humpback whales, Megaptera novaeangliae, in Hawaii[END_REF]. During the same season, the males may mate with several females, and the females may mate with several males. The breeding season is usually characterized by intense competition between males for access to the females. This intense competition gives rise to behavioral displays visible from the surface, such as breaching, pectoral slaps, caudal slaps, etc. (Silber, 1986). Males are also known to sing during the breeding season, probably to attract females (Dudzinski et al., 2009;Edds-Walton, 1997;Payne & McVay, 1971;Tyack, 1981Tyack, , 1998Tyack, , 1999)). Adults are about 14-15 m long, and females are usually longer than males. They can weigh up to 40 tons. Sexual maturity is reached around five years old, but males generally do not mate until later, around seven years old, as they often cannot compete with older males [START_REF] Chittleborough | Puberty, physical maturity, and relative growth of the female humpback whale, Megaptera nodosa (Bonnaterre), on the Western Australian coast[END_REF][START_REF] Clapham | Age at attainment of sexual maturity in humpback whales, Megaptera novaeangliae[END_REF]Clapham, , 2018)). Females give birth to their first calf at around five to eight years old and produce a calf on average every two years [START_REF] Chittleborough | Puberty, physical maturity, and relative growth of the female humpback whale, Megaptera nodosa (Bonnaterre), on the Western Australian coast[END_REF][START_REF] Chittleborough | The breeding cycle of the female humpback whale, Megaptera nodosa (Bonnaterre)[END_REF]Clapham, 2018).

Gestation lasts about 10 to 12 months and is well synchronized with the migration timing [START_REF] Chittleborough | The breeding cycle of the female humpback whale, Megaptera nodosa (Bonnaterre)[END_REF]Clapham, 2018). The calf measures 4-4.5 m long at birth [START_REF] Chittleborough | Dynamics of two populations of the humpback whale, Megaptera novaeangliae (Borowski)[END_REF]Clapham, 2018). It stays with its mother until it is one year old [START_REF] Chittleborough | The breeding cycle of the female humpback whale, Megaptera nodosa (Bonnaterre)[END_REF]Clapham, 2018). The first three months of the calf's life are spent in the breeding area. Then, the calf leaves the breeding area alongside its mother to reach the feeding area. The calf feeds on prey for the first time in the feeding area at around six or seven months old (Clapham, 2018). Weaning occurs during the trip back to the breeding area or while still in the feeding area [START_REF] Baraff | Separation of humpback whale mothers and calves on a feeding ground in early autumn[END_REF][START_REF] Steiger | Reproductive rates of humpback whales off California[END_REF].

With all the studies on the species, the location of humpback whales at any given time and their behaviors can be easily anticipated. This predictability helps streamline the planning of data acquisition campaigns and facilitates the allocation of resources for research purposes. Moreover, since they are easy to find and spot because of their remarkable abundance and their visually conspicuous surface active behaviors, challenges associated with locating them are minimized. The advanced knowledge of their ecology and life history also means that observations can be better contextualized for interpreting mother-calf interactions within a broader ecological and behavioral framework.

Research objectives

Motivated by the compelling attributes of the species, I investigated in the present thesis the mother-calf interactions in free-ranging humpback whales using innovative methods to acquire novel perspectives for understanding mammalian mother-calf interactions in an aquatic environment.

I focused on three facets of the mother-young interactions in mother-calf pairs with calves less than three months old: the nursing/suckling behavior, the swimming behavior, and the acoustic communication. The aim was to answer the following questions:

 How, where, and when is the nursing performed?

 Does the nursing pattern change as the calf grows?

 How are the swimming and resting activities of mother-calf pairs organized in time?

 Are there swimming skills that the adult has but the calf does not? If any, how do mother-calf pairs cope with it?

 In which context do the mother and the calf use vocalizations, and what functions do the vocalizations potentially have?

In order to answer these questions, I conducted various studies using existing data from animal-borne multi-sensor tags with or without a camera (collected from 2013 to 2019) as well as new data that I collected during the period of my thesis. I have organized the presentation of the studies into three parts, corresponding to the three main aspects of interest of the dissertation:

Part I: On the suckling and nursing behavior of humpback whales

In this first part, in two chapters, I present two studies that have addressed the first two questions of my thesis regarding the nursing and suckling behavior of the humpback whale in a breeding area. Nursing is challenging to observe, and acquiring animalborne tag data is complex, time-consuming, and expensive. Two central problems had thus to be solved first: (1) the practical limit of animal-borne tags equipped with a camera in some instances (inappropriate camera orientation, limited field of view, insufficient light, water turbidity, video data gaps, etc.), and ( 2) the limited sample size of available data collected from animal-borne tags equipped with a camera. Thus, through the study presented in Chapter 1, I developed a method to address these issues. Thanks to this method, I have been able to describe the frequency, duration, posture, depth, and temporal distribution of the nursing behavior with satisfactory data constituting the largest sample collected to date to study nursing/suckling behavior in baleen whales (Chapter 2). Chapters 1 and 2 provide the foundation for understanding the nursing behavior of humpback whales, one of the most crucial and decisive components of mother-calf interactions.

Part II: Keeping up with mom: the swimming behavior of humpback whale mother-calf pairs

The second part dealt with my thesis's third and fourth questions on the movements and locomotion of mother-calf pairs. In Chapter 3, I present a study in which I established the time budget for the mother-calf pair's activities to understand better the extent of time spent moving and how they are organized. Then, in Chapter 4, I introduce a study addressing the often-forgotten yet critical topic in most aquatic animals: buoyancy control. I showed that mysticete species can manage their buoyancy and investigated the ontogeny of such ability. For the calf, an inability to control its buoyancy has implications at the behavioral level (spatial configuration of the mothercalf pair, depth preference when engaging in certain activities, required learning) and at the energetic level for vertical movements (diving and breaching). This Part II delivers essential elements to understand the strategies used by mother-calf pairs to evolve in their aquatic environment. Each chapter in the present work corresponds to an article published, submitted, or targeted for publication in a scientific journal. The formats have been preserved as in the published, submitted, or prepared versions, allowing them to be read independently. The chapters serve as self-contained contributions to the broader interest of the dissertation.

-----⁂ -----As the present work has been organized into chapters corresponding to an article, each chapter already contains all the details concerning the corresponding materials and methods. The present section focuses only on an overview to provide a global perspective without going into the specific details already addressed in each chapter.

Study site and population

The animal-borne multi-sensor tag data used for the present dissertation were collected off Sainte Marie, Madagascar, during southern humpback whales' breeding seasons (southern winter) in 2013-2019 and 2021-2022. The Sainte Marie Island is located in the northeast of the mainland of Madagascar, less than 30 km away from the coast, between latitudes 17° 19' and 16° 42' South and longitudes 49° 48' and 50° 01' East (Figure 1). The coastal waters around Sainte Marie are relatively shallow. They are known to be an important calving ground where several female humpback whales with their calves can be encountered between July and September [START_REF] Trudelle | Influence of environmental parameters on movements and habitat utilization of humpback whales (Megaptera novaeangliae) in the Madagascar breeding ground[END_REF](Trudelle et al., , 2018)). The humpback whales in the zone are part of the southwestern Indian Ocean humpback whale population. They feed in Antarctica, mainly between longitudes 10° and 70° East [START_REF] Fossette | Humpback whale (Megaptera novaeangliae) post breeding dispersal and southward migration in the western Indian Ocean[END_REF].

Using animal-borne multi-sensor tags to study humpback whale mother-calf pairs

As highlighted, animal-borne multi-sensor tags can be a powerful tool for studying whales in their natural environment. Indeed, they enable continuous recording of data on the individual, even when they are at depth. Tags can also be used to collect data on the whale's environment. Moreover, the tags enable researchers to stay outside the vicinity of the studied individuals, which can significantly limit disturbances. Therefore, it is evident that multi-sensor tags are particularly useful for studying groups susceptible to disturbance, such as mother-calf pairs, and especially for investigating cryptic behaviors, such as nursing underwater.

For my thesis, I used Acousonde tags (Acousonde 3B) and CATS cam tags attached to whales via four suction cups (Figure 2). Suction-cup tags have the advantage of being minimally invasive regarding their attachment system. They are also very lightweight and small, minimizing the impact on animal behaviors. Their weight in the air represents less than 0.1% of that of a newborn humpback whale calf. The Acousonde tag measures 23 cm long, 8 cm wide, and 3 cm high, with a weight in air of around 400 g, and the CATS cam tag measures 25 cm long, 13 cm wide, and 4 cm high, with a weight of around 500 g. The Acousonde and CATS cam tags are fairly comparable: both feature a 3-axis accelerometer, a 3-axis magnetometer, a pressure sensor, a temperature sensor, a light sensor (auxiliary sensors), and a hydrophone. The major difference is that the CATS cam tag has a camera, a significant asset for the present work. When used at sea, a VHF emitter was attached to the tags to enable them to be tracked and found. Sensor sampling frequencies can be set as required. For my thesis, the sampling frequencies chosen were a compromise between power savings, the need for sufficiently high-resolution data, storage management, and anticipation of data processing speed. Thus, from year to year, settings may vary somewhat. However, I have always ensured that the data were uniform for each analysis. An important step when working with multi-sensor tags is to produce 'intuitively interpretable' measurements. For my purposes, I mainly focused on the computation of the following parameters from the raw data: depth, depth rate, posture, fluke strokes, forward speed, and overall dynamic body acceleration (ODBA).

The depth (in meters) was obtained from the pressure data. It represents the depth at which the animal is located, although strictly speaking, it measures the depth at which the tag is located. The depth was the primary data for almost all the studies presented in this manuscript, as it allows the establishment of the whale's dive profile. The depth rate (in meters/second) was obtained from the depth data and allowed the investigation of the speed at which the animal ascends or descends and whether the animal remains at a given depth.

Postures represented by the pitch and roll (and heading, although not used in the present study; in degrees) (Johnson & Tyack, 2003) and fluke strokes (pitch corresponding to the dorsoventral body undulations; in degree) [START_REF] López | Tracking the kinematics of caudal-oscillatory swimming: A comparison of two on-animal sensing methods[END_REF] were obtained from the acceleration data. Indeed, the accelerometer measures the dynamic acceleration and the acceleration of gravity in the three axes of the tag, making it possible to deduce the tag's orientation relative to the gravity vector and changes in tag velocity. By correcting this orientation to take account of the orientation of the tag relative to the animal (the axis of the tag and the animal do not generally coincide), the animal's orientation is obtained (Figure 3; Johnson & Tyack, 2003). A filter is then applied to isolate posture (frequencies <0.2 Hz) from rapid, cyclical movements linked to the body undulations and rapid movements (frequencies 0.2-1 Hz) (Simon et al., 2012). The postures were particularly valuable for identifying, describing, and understanding the suckling/nursing behaviors (Chapters 1 and 2) and deriving other measures (see below, Chapters 1 and4). The fluke stroke data was useful for quantifying the swimming effort (Chapters 1 and 4). The forward speed (often simply referred to as speed, in meters/second) represents the speed at which the animal moves in its 3D environment. As the tags I used did not contain a speed sensor, I had to rely on various speed estimation techniques based on the available data. Each had its advantages and limitations, which I detailed at each mention of their use in the chapters of this thesis. First was the orientation corrected depth rate (OCDR), the speed estimated from the pitch and vertical speed (depth rate) with the formula OCDR = Δdepth/sin(pitch) (Cade et al., 2021;Miller et al., 2004; Chapters 1 and 4). It was only calculated when the pitch was sufficiently high (>30° or <-30°) because the estimation error is significant at a lower pitch (Narazaki et al., 2018).

In order to obtain the speed for the rest of the data, the technique consisted of regressing the OCDR on the flow noise recorded by the hydrophone or the vibration of the tag (tag jiggle) recorded by the accelerometer (if the latter samples at frequency >100 Hz) and using the regression to predict the speed (Cade et al., 2017). Indeed, the animal's speed is correlated to the flow noise and vibration of the tag (Cade et al., 2017;[START_REF] Finger | Measurements of low-velocity flow noise on pressure and pressure gradient hydrophones[END_REF][START_REF] Izadi | Night-life of Bryde's whales: ecological implications of resting in a baleen whale[END_REF]. I have named these speed estimations the absolute speed, as they are estimated directly in meters/second. Second was the relative speed, e.g., relative to the mean or the min and max, estimated from the flow noise recorded by the hydrophone. This technique has been particularly useful in cases where some deployments did not have sufficient data points for regressing the OCDR on the flow noise or tag jiggle (Chapters 2, 3, 4, and 6).

The ODBA represents the animal's effort and complements the fluke stroke data (Chapters 1 and 2). It was calculated as the sum of the absolute dynamic accelerations on the three axes. This measurement can be particularly sensitive to variations in tag placement, especially from one individual to another. So, in some experimental designs, I had to normalize the measurements by dividing the ODBA by the median calculated for each individual (Chapter 2).

The tagging

It goes without saying that, to obtain the data mentioned above, one must first succeed in placing the tags on the whales. The tags were deployed from a 6.40 m rigid boat using a long carbon-fiber pole. The whole process required experimented operators because it was necessary to know both the animals' behavior to get close to them and the safety measures for the security of the participants and the animals. The crew on the boat was always comprised of at least three persons and did not exceed five. One person was in charge of the tagging, one operated the boat, and one or two persons were in charge of the note-taking and the photo identification.

For every deployment, we followed the same strict approach. Once we had detected a mother-calf group, we took the time to observe them closely, keeping a parallel distance of 200 m between the boat and the group. This observation was performed to ensure that the group was new (based on the shape of the dorsal fin, the pigmentation on the fluke, the markings on the body, etc., and with the help of any photos already taken) and avoid double sampling. It was also performed to assess the group's behavior and decide whether an approach could be attempted (only relatively calm groups with consistent behavior were targeted). The first observation phase also allowed the note taker to record information about the group, in particular, the presence of escort(s) (accompanying male(s) trying to access the female) and the relative age of the calf: C1, C2, or C3. C1 refers to newborn calves (neonates). C1 calves have noticeable folds and scars, possibly caused during birth by the barnacles near the mother's genital slit, have predominantly light grey dorsal and white ventral skin coloration, and have a dorsal fin furl angle of approximately 45° or less. C2 describes calves that are still relatively young but no longer considered neonates. They display a dorsal fin furl angle of more than 45° but less than approximately 70°. C3 refers to older calves under three months old with an unfurled dorsal fin, typically exceeding an angle of approximately 70° (Cartwright & Sullivan, 2009;Faria et al., 2013;Huetz et al., 2022).

The tagging approach was initiated after all needed data on the individuals had been taken, if and only if the behavior was judged appropriate. The group was approached from three-quarters rear (Figure 4). The tagger always aimed the back in the case of CATS cam tags for a better camera view and the flank for Acousonde tags to minimize periods during which the tag is out of the water (for optimized sound recordings, see Chapter 5). Depending on the opportunity, the targeted individual can be the mother or the calf, except for the CATS cam tags, which were prioritized for the calf. The two were targeted one after another whenever the opportunity arose to tag them sequentially. The whales were never followed after tagging to avoid further disturbance. The tags were recovered by tracking the VHF signal emitted by the transmitter using a radio antenna. The retrievals were initiated after confirming that the tags already detached.

A periodic loss of the VHF signal generally indicated that the tag was still attached to the animal. Indeed, the VHF signal can only be received when the VHF antenna is out of the water, which only happens when the animal surfaces (causing the signal to be

A B
received periodically) or when the tag is no longer attached to the animal (continuous signal). The tags usually detached from the animals after 4-6 hours, but in some cases, they remained for up to 20 hours. The tag recovery is imperative; otherwise, the recorded data are lost.

In total, a multi-sensor tag dataset from 50 mother-calf pairs was considered in the present study. The dataset consisted of data from 31 single tag deployments on calves (12 CATS cam tag and 19 Acousonde tag deployments), 14 single deployments on mothers (all with Acousonde tags), and six simultaneous deployments (i.e., both the mother and the calf were equipped with a tag -Acousonde tags only) (Table 1). This dataset was the culmination of a decade-long sampling effort in Sainte Marie, representing up to 4 to 8 hours of outings per day spanning 10 to 30 days per two to three months of fieldwork campaign per year.

Data analysis

With the depth, depth rate, posture, fluke stroke, speed, ODBA, sound, and video data, I conducted various analyses and statistical tests to answer my different research questions. However, before getting to this stage, I had to identify and define the different behaviors of interest. One of the challenges was, of course, to identify nursing events when there were no images to identify them visually. I developed and adopted an approach based on supervised machine learning (AdaBoostM1) (Chapters 1 and 2). I used visually confirmed nursing features to train the machine learning model to automatically detect nursing in data lacking video. I have dedicated an entire chapter to discussing this method's various stages and testing its performance (Chapter 1). To identify the sequences of behavioral states, such as resting and traveling, I used an unsupervised machine learning technique that considers the temporal sequence and switching patterns of behaviors: the hidden Markov model (HMM) (Chapters 3 and 6).

Concerning the acoustic analyses, it was necessary to identify the types of social calls and their sources (i.e., the caller identity) (Chapters 5 and 6). To identify the call types, I used two approaches. The first approach was based on qualitatively comparing the calls to those from a catalog we established for the study population beforehand (Appendix I) and relied purely on aural and visual identification (Chapter 5). The second approach used an unsupervised automatic classification method (Hierarchical Clustering on Principal Components, HCPC) to separate the calls into relatively homogeneous clusters (Chapter 6). To identify the emitter, I used the data from simultaneous deployments and a machine learning technique (Random Forest algorithm -RF) (Chapter 5). I compared the calls recorded only on the mother's tag with those recorded only on the calf's tag. The comparison allowed me to perform automated classification to assign an identity to each call. I then used the result of Chapter 5 to conclude which individual emitted the calls studied in Chapter 6.

I used classic statistical analyses such as linear mixed-effects models (LMMs) followed by Tukey's post-hoc test, for example, to compare the means of several groups of data or to analyze the relationship between several variables while controlling for individual variations. For instance, I used these approaches to compare nursing and non-nursing periods (Chapters 1 and 2), to test the variation of nursing characteristics and time budget with age (Chapters 2, 3), or to investigate the relationship between swimming behavior, depth, and age (Chapter 4). When I used statistical models, I always made sure that the model's assumptions were respected (homoscedasticity, independence, and normality of the residuals) using a graphical approach. To compare observation frequencies, I used tests like the χ² and the exact multinomial test (Chapters 2 and 5). I used a threshold α = 0.05 for statistical significance.

As traditional statistical analyses often rely on very specific and restrictive assumptions about the data to be analyzed, their application on complex and heterogeneous data can sometimes be difficult or even impossible. So, in some instances, I adopted analysis techniques based on machine learning instead to extract biological knowledge from the data. In particular, I used this approach to analyze the link between vocalizations and the involved activity (Chapter 6). In this example, the analysis question was formulated as follows: To what extent can the recorded vocalizations predict the behavioral state in which the animal is involved or will be involved?

-----⁂ -----

Synthesis

How, where, and when is the nursing performed? Preliminary results from the calf's perspective and test of a new detection method

Methods

The data from the first three successful CATS cam tag deployments on calves (2018)(2019) were used to obtain a preliminary detailed characterization of the suckling behavior and to identify and test the appropriate supervised machine learning algorithm for automatically detecting suckling events based on acceleration and depth-derived data. The suckling events were visually identified on the videos, and their characteristics were extracted from the accelerometer and depth sensor data and then compared to the characteristics of non-suckling periods using LMMs. The summary statistics of several measured characteristics were used as features fed to supervised machine learning candidate algorithms in a design where data from two calves were used as a training set for detecting the suckling events of the remaining calf.

Findings

Suckling events are relatively short (less than 30 s) and primarily occur during dives.

During suckling, the calf maintains a pitch angle of about 30-45° relative to the midline of its mother's body, always rolls either the right or left depending on the suckled teat, and uses its tongue. For her part, the mother leans slightly downward. The calf usually performs successive 2-6 suckling events and alternates between the two teats.

The suckling events have very distinctive kinematic signatures. They are associated with a higher average absolute roll, often a higher effort level (high average fluke stroke rate), and a lower average speed than non-suckling periods. When the data are divided into non-overlapping 2 s blocks, the AdaBoostM1, an ensemble classifier that performed the best among several candidate algorithms, can find at least about 40% of the blocks corresponding to suckling with a detection precision of at least 60% based solely on the acceleration and depth-derived data. For Calf3, the tag detached at 521 min. However, only the first 479 min (92%) of the data was analyzed due to a lack of visibility on the video recording as the evening approached. There was no evidence of trends with suckling frequency increasing later in the deployments. The periods towards the end of the deployments were not necessarily associated with more frequent suckling events

Synthesis

How, where, and when is the nursing performed? Further results from a larger sample Does the nursing pattern change as the calf grows?

Methods

Thirty-seven deployments on calves were used, including 11 CATS cam tag deployments with usable video, one CATS cam tag deployment without usable video, and 25 Acousonde tag deployments. Suckling events in data with video were identified visually. When no usable video was available, the method developed in Chapter 1 was used to automatically identify suckling events using the 11 deployments with video as a training set. When combined with a post-processing step, the detection method achieves a performance of around 80% in sensitivity and 75% in precision. Time spent suckling, laterality, spatial and behavioral context, and suckling rhythm were quantified. Their variation with age was then analyzed, notably using LMMs.

Findings

Suckling events are short (<30 s on average), converging to the results presented in Chapter 1. They represent less than 2% of the calf's time in total. Duration does not change with age. However, the total time spent suckling increases potentially as the calf grows. The calf shows no preference between left and right teats. Similar to what has been found in Chapter 1, it alternates between the two.

As pointed out in Chapter 1, the calf suckles mainly at depth, during the descent or bottom phases of dives. On average, suckling takes place at around 15±4 m for C1, 17±6 m for C2, and 18±8 m for C3 calves. Suckling occurs in sessions that contain bouts of up to six successive events. Sessions occur approximately every two hours, including at night. For the exclusion of features, we set a simple cut-off that is a compromise between excluding a sufficient number of high Gini importance features to considerably reduce the inter-individual differences and keeping as many informative features as possible for identifying suckling blocks. We thus chose to exclude the first six features: the maximum, mean, and minimum values for both depth and relative speed. When these features were not included in a second RF analysis, the out-of-the-bag error rate when classifying individuals increased to 36.54%, showing that the inter-individual difference dropped significantly without these features. Compared to non-suckling segments, the visually confirmed suckling events had a mean average depth rate closer to zero. They also had a mean average pitch closer to the horizontal (0°) even during the descent and ascent phases. In some cases, the average pitch during suckling took an extremely positive value (i.e., calf directed upward at almost 50° relative to the horizontal). However, it never took an extremely negative value (all recorded negative average pitch were >-30°, i.e., calf only slightly directed downward at most when suckling). Compared to non-suckling segments, the mean average relative speed during suckling was slightly lower during the surfacing and descent phases and slightly higher during the bottom and ascent phases. Unlike non-suckling, the average relative speed was always between -1 and 1 when the calf was suckling. The mean average normalized ODBA (nODBA), and the mean average roll rate during suckling were slightly higher than when not suckling. The average nODBA and roll rate for suckling events rarely took very high value compared to non-suckling. Finally, the mean absolute average roll was exceptionally high during suckling events compared to non-suckling segments.

The absolute average roll during suckling reached more than 45°. The automatically detected suckling events followed the same trend observed in the visually suckling confirmed events regarding the characteristics. Table S3. Starting side preferences when performing bouts of suckling events (events spaced less than a minute apart) in humpback whale calves tagged with Acousonde or CATS cam tags. The counts include visually confirmed suckling events and automatically detected suckling events. Deployments in which no suckling events have been detected are not shown. Note that isolated suckling events (i.e., events that are individually more than a minute apart from any other suckling events) are also included in the counts

Findings

Calves switch between resting, milling, moderate swimming, and fast swimming more often than mothers. The proportion of resting time is similar from C1-C2 to Adults (mothers). The proportion of time milling, however, decreases. The time spent resting is potentially lower in mothers accompanied by younger calves. The mother and the calf are often found in synchrony up to 50% of the time with respect to their behavior. Generally speaking, the mother and the calf can both be responsible for the behavioral synchrony, but one is more likely to take responsibility than the other, depending on the context. For instance, resting and fast swimming are scheduled by the mother.

Introduction

Mammals, like birds in general, are known for their highly elaborate forms of parental care. Most often provided mainly by the mother in mammals, parental care takes various forms, such as providing the nutrients required for embryo development, protection, feeding, learning, etc. (Balshine, 2012;Clutton-Brock, 1991). The purpose of parental care is to improve the fitness of the offspring, and it is a costly investment that can reduce the survival chances of the parents themselves or limit their future reproduction (Balshine, 2012;Clutton-Brock, 1991). For female mammals, food provisioning in the form of maternal milk represents one of the most critical and costly aspects of this care (Braithwaite et al., 2015;[START_REF] Gittleman | Energy allocation in mammalian reproduction[END_REF][START_REF] Oftedal | Use of maternal reserves as a lactation strategy in large mammals[END_REF].

In species producing young that are locomotory, sensorially, and thermoregulatory independent relatively early after birth, the maternal care strategy generally falls into either the 'hider' or 'follower' category (Fisher et al., 2002;Lent, 1974). In the case of 'hiding' species, the mother and young are not permanently together. The young remain in a 'hiding place' while the mother is foraging. The pair only reunites from time to time, mainly for nursing. In the 'follower' species, the young follow its mother wherever she goes, and the two individuals show a fairly marked synchronization in their behavior [START_REF] Green | Social Effects of Maternal Age and Experience in Bison: Pre-and Post-weaning Contact Maintenance with Daughters[END_REF]Lent, 1974). These two strategies are known to be linked to predation pressure, habitat structure, and life history (Fisher et al., 2002).

The mother and the offspring's behavior changes with the evolving offspring's independence and energetic needs (Trivers, 1974). Early after birth, the mother usually displays more behaviors directed at establishing and strengthening the motheroffspring bond than when the offspring is older (Lent, 1974;Nowak et al., 2000). Such behaviors involve intense contact and interactions (tactile, visual, acoustic, etc.) and highly defensive responses of the mother against approaching conspecifics (Lent, 1974). In addition, the mother is more vigilant because the risk of predation on a very young individual is high [START_REF] Blank | Antipredator strategy of female goitered gazelles (Gazella subgutturosa Guld., 1780) with hiding fawn[END_REF]. Moreover, she can adjust her behavior to match what the young can or cannot do (Huetz et al., 2022;Szabo & Duffus, 2008). Indeed, even when an offspring can rapidly move around on its own, regulate its temperature, and has already functional senses, its physical performance remains limited at first [START_REF] Herrel | Ontogeny of performance in vertebrates[END_REF]Huetz et al., 2022;Noren et al., 2006), and it still has to learn and develop various skills and traits crucial for its survival [START_REF] Bekoff | The development of social interaction, play, and metacommunication in mammals: an ethological perspective[END_REF].

Among marine mammals, mysticetes fall into the category known as capital breeders.

They build up a reserve of energy in the feeding area and use it to reproduce later in the breeding area (Bannister, 2018;Lockyer, 1984). In this sense, mothers have a limited energy reserve to care for their calf during at least the first few months of its life. They must minimize their energy consumption and save energy for the upcoming migration while producing milk for their unique calf (Bejder et al., 2019;Braithwaite et al., 2015;Clapham, 2018). As for the calf, it must optimize the energy allocated to its growth and to various activities essential for its cognitive and social development (Braithwaite et al., 2015). The maternal strategy of mysticetes has been described as equivalent to the 'follower' strategy observed in terrestrial ungulates (Huetz et al., 2022;Ratsimbazafindranahaka et al., 2023;Szabo & Duffus, 2008;Tyson et al., 2012).

Humpback whales (Megaptera novaeangliae), one of the most common and wellstudied baleen whale species, undertake extensive migrations between the feeding and breeding areas (Clapham, 2018). Humpback whale calves are born in the winter in the breeding area and stay with their mother for about one year (Clapham, 2018). Their first three months of life are spent in the breeding area, a period during which they build up a blubber layer and rapidly grow in preparation for the migration towards the feeding ground (Corkeron & Connor, 1999). They only start to feed on prey when they reach about seven months old (Clapham, 2018). The time budget of each individual of the mother-young pair can inform on the energy allocation, on the development of the calves, as well as on the level of dependence of the calves on their mother during the critical phase of the calf's development in the breeding ground. Thus, in the present study, we investigated the time budget of mother-calf pairs and evaluated the synchrony of their activities. More specifically, we assessed whether the time budget changes with the calves' age and whether those of the mothers and the calves are similar. We also quantified the level of synchronization of their activities and identified which individual of the dyad leads the activities.

Materials and methods

Dataset

We used an animal-borne multi-sensor tag dataset from 47 mother-calf pairs off Sainte Marie Island, Madagascar, South Western Indian Ocean, during the winter (July-September) of 2013-2019 and 2021-2022 tagged either with Acousonde 3B or CATS cam (only for calves). The dataset consisted of data from 31 single tag deployments on calves, 10 single deployments on mothers, and six simultaneous deployments (i.e., the mother and the calves were both tagged, and their data overlapped in time). They included pressure data sampled at 10 Hz (sampled initially at 20 Hz for some individuals but then downsampled for consistency) and sound data sampled at 12 kHz (sampled initially at 24kHz, 24.453 kHz, or 48 kHz but then downsampled for consistency). Other data were available but irrelevant to the present study (3D acceleration, compass data, etc.). Detailed tagging procedures and specifications have been presented elsewhere (Huetz et al., 2022;Ratsimbazafindranahaka et al., 2022Ratsimbazafindranahaka et al., , 2023;;Saloma et al., 2022).

For each individual, we computed the depth (in meters) and the whale's relative forward speed (relative speed hereafter) with a sampling frequency of 10 Hz. The depth was obtained from the pressure data. It was smoothed with a 0.5 s running median filter.

The relative speed was calculated as the z-scored flow noise (per individual) extracted from the sound data (66-94 Hz frequency band, Cade et al., 2017). We associated each deployment (each individual) to an age class (C1, C2, or C3 for the calves -see below, or A -Adult -for the mothers). Mothers were further categorized into A-C1, A-C2, or A-C3, depending on the age of their dependent calf.

The relative age of the calves was estimated based on skin coloration, skinfolds, and the angle of unfurling of their dorsal fin (Cartwright & Sullivan, 2009;Faria et al., 2013;Huetz et al., 2022;Ratsimbazafindranahaka et al., 2023;Saloma et al., 2022): C1 (neonate) for calves presenting some folds, scars, and skin color that tends to be light grey dorsally and white ventrally and with less than ~45° dorsal fin furl; C2 for very young but non-neonate calves having more than ~45° but less than about 70° dorsal fin furl; and C3 for older calves (but <3 months old) that have unfurled dorsal fin (approximately >70°).

Behavioral states modeling

We used the hidden Markov model (HMM) to model the time organization of the activities of the mothers and the calves. For this purpose, we computed the average relative speed of each whale on a non-overlapping 20 s sliding windows basis as the input time series data (i.e., the sampling unit for the modeling corresponded to one 20 s windows). HMMs are commonly used for modeling and analyzing behavioral time series in cetaceans [START_REF] Deruiter | A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure[END_REF][START_REF] Tennessen | Hidden Markov models reveal temporal patterns and sex differences in killer whale behavior[END_REF]. We modeled the activities as a first-order four-state Markov chain that gives rise to the observed swimming speed. In the model, we assumed that all state transitions were possible. We constructed the HMM using the depmixS4 package [START_REF] Visser | depmixS4: An R package for hidden markov References Adam[END_REF] in R (R Core Team, https://www.r-project.org/). We included the relative speed (assumed to have a Gaussian distribution) as the response. The model was fitted by maximum likelihood estimation using the expectation-maximization (EM) algorithm in the depmixS4 package following the framework described by [START_REF] Tennessen | Hidden Markov models reveal temporal patterns and sex differences in killer whale behavior[END_REF]. Finally, the Viterbi algorithm was applied to find the most likely state sequence that best translates to the observed behavior (the relative speed).

The model choice was motivated by the observation in the field and from animal-borne camera tags (Ratsimbazafindranahaka MN, unpublished data) of four distinct behavioral states associated with a typical swimming speed each for both the mother and the calf: resting, milling, directed moderate swimming, and directed fast swimming.

The resting state corresponds to the whale being static or quasi-static (speed less than one knot). The milling state is when the whale wanders/is lolling around (e.g., a calf circling its mother) at a speed likely corresponding to their global average speed, thus less than their average traveling speed (around 2.8-3.5 knots, [START_REF] Chittleborough | Aerial observations on the humpback whale, Megaptera nodosa (Bonnaterre), with notes on other species[END_REF]. In contrast to resting and milling, the states qualified as directed swimming refers to activities corresponding to a swimming speed higher than the average: the first to the usual travel speed (directed moderate swimming), and the second to a more hasted traveling (directed fast swimming, e.g., flight behavior). Although we refer to them as directed here, they do not necessarily imply a straight movement. The term instead refers to an apparent motivation of the whale to intentionally move from one point to another. We chose to use a model in its simplest possible form as the goal was only to capture the general activity pattern of the individuals in mother-calf pairs.

Data analysis

Time budget and ontogeny

With the state sequences from the HMM, we established the time budget for each individual. In other words, we calculated the proportion (in %) of time spent in each behavioral state for each whale. Then we tested whether the proportion of time spent in a given state varies with age class using linear models in R, followed by Tukey's posthoc multiple comparisons test with adjusted P-values using the emmeans package (Lenth et al., 2018) when a statistically significant effect was detected. We also tested whether the proportions vary depending on the category of the mothers. For the analysis, we transformed each proportion of interest into a log-ratio of proportion using one of the states as a reference, i.e.:

log-ratio state of interest = log (proportion state of interest / proportion reference )

Such transformation is required in compositional data analysis for the standard statistical approaches to be valid [START_REF] Aitchison | The Statistical Analysis of Compositional Data[END_REF]. The choice of the reference for the transformation is arbitrary because of the permutation invariability property of the logratio approach [START_REF] Aitchison | The Statistical Analysis of Compositional Data[END_REF]. In our analyses, we used the proportion of time spent in directed swimming (i.e., directed moderate swimming + directed fast swimming) as a reference. The grouping of directed moderate swimming and directed fast swimming into one broad class was because the directed fast swimming state contained zero proportion, preventing us from calculating a log-ratio with the directed swimming state either as a reference or as a state of interest. All proportions of time spent in each state per whale were assumed to be independent of the deployment length. Exploratory analyses supported this assumption.

Behavioral synchrony

We analyzed the behavioral synchrony between the mother and the calf using the data from simultaneous deployments (N = 6). We time-aligned the calf's predicted state sequence with the mother's predicted state sequence (only overlapping periods were considered). As the internal clock of the tag units sometimes had several seconds of delay (time drift), simultaneous mother-calf data had to be manually aligned. For the process, we used sound cues detected in both units to align the mothers' and calves' data (crew voices recorded between the tagging time and the moment the tags were turned on, impact noises recorded prior to tagging, or even distant male songs) (Huetz et al., 2022).

We calculated the proportion of synchronized behavior for each aligned pair, the behavioral independence scores (calculated for each state), and Hinde's association index [START_REF] Hinde | Assessing the roles of social partners in maintaining mutual proximity, as exemplified by mother-infant relations in rhesus monkeys[END_REF]. The proportion of synchronized behavior was calculated as the time corresponding to the mother and calf being in the same state relative to the total simultaneous data length. The behavioral independence score for each state was calculated as the ratio of the total time spent in the state to the time spent in that state at the same time as the other individual of the pair. The Hinde's index was obtained with the following formula that takes as argument the number of times the mother and the calf initiated or terminated a bout of synchronized state (respectively, MI and MT for the mother and CI and CT for the calf):

Hinde's index = (M I / (M I + C I )) -(M T / (M T + C T ))

The Hinde's index varies between -1 and +1, corresponding to the calf or the mother being exclusively responsible for the synchrony. For the calculation, an individual was considered as having initiated a bout of synchronized state when it changed its behavioral state to synchronize with the other individual. Conversely, it was considered as having terminated the synchronized bout when it changed its behavior state and broke the synchrony (Fellner et al., 2013). It is worth noting that in our framework, synchrony does not necessarily assume spatial proximity and spatial synchrony.

Results

A total of 202.63 h of data were analyzed from all 53 individual deployments (41 single deployments + 6 simultaneous deployments). The analyzed time per deployment was 3.82 h on average (range = 0.39 -17.36 h). For the behavioral synchrony study, 13.65 h of simultaneous mother-calf data from the six simultaneous deployments were used.

The analyzed time per simultaneous deployment was 2.28 h on average (range = 0.48-7.77 h). Among the 37 calves, two were C1 calves, 14 were C2 calves, and 21 were C3 calves. Due to the low number of C1 calves, we pooled them with the C2 calves for the statistical tests (C1-C2 calves). Among the 16 mothers, one was with C1 calf (A-C1), three were with C2 calf (A-C2), and 12 were with C3 calf (A-C2). As we only had one A-C1, we pooled her with the A-C2 for the statistical tests (A-C1/C2).

Our The two C1 calves spent 24% and 30% of time resting, respectively, 40% and 47% of time milling, 11% and 20% in directed moderate swimming, and 10% and 20% in directed fast swimming. The C2 calves spent on average 22% of time resting (range = 9-31%), 43% of time milling (range = 23-77%), 27% of the time in directed moderate swimming (range = 12-51%), and 10% of the time in directed fast swimming (range: 0-16%). The C3 calves spent on average 22% of time resting (range = 13-38%), 39% of time milling (range = 14-62%), 31% of the time in directed moderate swimming (range = 12-51%), and 9% of the time in directional fast swimming (range: 3-16%). The mothers spent on average 26% of the time resting (range = 15-41%), 31% of time milling (range = 14-57%), 29% of the time in directed moderate swimming (range = 10-43%), and 14% of the time in directed fast swimming (range: 3-25%) (Figure 1). We found no statistically significant effect of age on the proportion of time resting (linear model with log-ratio transformed response: F2, 50 = 1.29, P = 0.283). However, we detected a statistically significant effect of age on the proportion of time milling (linear model with a log-ratio transformed response: F2, 50 = 3.76, P = 0.03). The linear model revealed a gradual decrease in the proportion of time milling from C1-C2 to A (mothers). The proportion of milling for mothers was statistically different compared to C1-C2 calves (Tukey's post-hoc test, A vs. C1-C2, β = -0.58, P = 0.023) but not statistically different compared to C3 calves (Tukey's post-hoc test, A vs. C3, β = -0.332, P = 0.227). In addition, the difference between C1-C2 and the C3 calves was not statistically significant (Tukey's post-hoc test, A vs. C3, β = 0.246, P = 0.435).

The A-C1/C2 mothers spent on average 20% of time resting (range = 16-26%), 40% of time milling (range = 37-43%), 27% of the time in directed moderate swimming (range = 17-33%), and 13% of the time in directed fast swimming (range: 8-20%). With respect to the A-C3 mothers, on average, the time spent resting was 28% (range = 15-41%), the time spent milling was 28% (range = 14-57%), the time in directed moderate swimming was 30% of (range = 10-43%), and the time in directed fast swimming was 14% of (range: 3-25%) (Figure 1). Although the time spent resting was lower in A-C1/C2 mothers and the time spent milling was higher, we failed to find a statistically significant difference with calf's age class (linear model with log-ratio transformed response, A-C1/C2 vs. A-C3, resting: F1, 14 = 2, P = 0.179; Milling: F1, 14 = 2.07, P = 0.172). Complete results on the behavioral synchrony in six mother-calf pairs are presented in Table 1. An example of paired mother-calf data (pair G28) showing the behavioral synchrony between the mother and the calf is presented in Figure 2. The proportion of synchronized behavior per pair, relative to the total simultaneous data, ranged from 34 to 79% (median = 50%). The behavioral independence ranged from 1.04 to 6.6 in calves (median = 1.11) and from 1.11 to 2.16 in mothers (median = 1.55) for the resting state, from 1.33 to 5.32 in calves (median = 3.39) and from 1.03 to 4.05 in mothers (median = 1.66) for the milling state, from 1.29 to 2.08 in calves (median = 1.53) and from 1 to 5.12 in mothers (median = 1.9) for the directed moderate swimming state, and from 1.01 to 1.68 in calves (median = 1.01) and from 1.01 to 7.45 in mothers (median = 1.78) for the directed fast swimming. The Hinde's index of association ranged from -0.3 to 0.7 (median = 0.12). showing the behavioral synchrony between the two. Re: resting, Mi: milling, MS: directional moderate swimming, and HS: directional fast swimming. Seventy-nine percent of the total simultaneous data presented a synchronized behavioral state (i.e., the calf and the mother were in the same state). Green and red dots represent each time the individual initiated and terminated a bout of synchronized state, respectively. The number of times the mother and the calf initiated and terminated a bout of synchronized state gave a Hinde's index of -0.3. The index suggested that while the calf tends to match its behavior to its mother's behavior more often compared to its mother (negative value), the mother and the calf were almost equally responsible for maintaining the two's behavioral synchrony (value close to zero)

Discussion

Our objective was to assess the time allocation patterns of humpback whale mothercalf pairs and estimate the level of synchrony in their activities. We aimed to compare the time budgets of the mothers and calves, investigate any changes in the quantified time budget as the calves age, quantify the degree of synchronization between their activities, and determine which individual within the pair is the leader. Although we based our study on the use of suction cup tags, which only partially inform on the daily life of mothers and their calves, our study allowed us to gain additional knowledge on the behavioral dynamics and interactions within humpback whale mother-calf pairs. We observed that mothers and calves allocated a substantial portion of their time to resting (up to 41% of the time in mothers). Additionally, we found that the time spent milling, a behavior associated with slow wandering movement, was highest in young calves and tended to decrease as they aged. Although the calves displayed some degree of independence in their behaviors, they also frequently engaged in activities similar to their mothers (50% of the time on average). Interestingly, our result suggested that the initiation of specific activities within the pair could be driven by either the calf or the mother, depending on the nature of the activity.

Our result on the time spent resting is in accordance with previous results suggesting that mothers and calves rest frequently to minimize their energy consumption (Bejder et al., 2019). For the mothers, energy saving is crucial as they do not feed for a prolonged period while having to produce milk for their calf (Braithwaite et al., 2015;Clapham, 2018). Optimizing the time spent resting minimizes the decline in body condition and thus ensures sufficient milk production to nurse their calf and secure enough energy reserve left to migrate back to the feeding ground. For the calves, a low activity level helps maximize energy allocation into growth (Braithwaite et al., 2015).

Our data suggested that the mothers with younger calves tend to spend less time resting and more time milling compared to mothers with older calves, although further data are needed as our test with a small sample failed to detect a statistically significant difference. Depending on the age of the calf, a mother may have to adjust her activity budget because of the specific needs of her calf at various development stages. The need for maternal vigilance can be, for example, at its highest shortly after the calf's birth [START_REF] Blank | Antipredator strategy of female goitered gazelles (Gazella subgutturosa Guld., 1780) with hiding fawn[END_REF]. As the calf grows, its awareness of dangers improves, it becomes increasingly mobile, and it has improved locomotory autonomy. Thus, the level of vigilance of the mother can gradually decrease. Similarly, the need for maternal stimulations for the imprinting and to strengthen the mother-young bond, especially for species that use the following strategy, is maximal during the early post-partum phase (Lent, 1974). As such, the humpback whale mothers may be constrained to rest less often at first. Furthermore, as the calf grows, its milk consumption typically increases, resulting in higher energy demands for the mother (Trivers, 1974). Consequently, the mother with an older calf may require more rest to save energy and meet the needs of her growing offspring and for the upcoming migration. Further investigations on the potential difference in the time budget of the mothers in relation to their maternal experience should be undertaken as we may expect a more optimized time budget in experimented females (primiparous vs multiparous females). However, it is important to acknowledge the inherent challenges associated with such research, particularly the challenge of re-sighting the same whale over multiple years.

Regarding the time spent milling in calves, past studies suggested that young cetaceans tend to swim continuously to train swimming and develop locomotor muscles (Cartwright & Sullivan, 2009;[START_REF] Thomas | toothed whales[END_REF]. Calves spending much time milling may also be just a display of a more playful nature of young individuals, which is also crucial for cognitive and social development [START_REF] Bekoff | The development of social interaction, play, and metacommunication in mammals: an ethological perspective[END_REF][START_REF] Burghardt | The genesis of animal play: Testing the limits[END_REF].

Calves often engaged in the same activity as their mother, as expected for species adopting a 'following' anti-predator strategy. For instance, in American bison, experimented mothers and their calves engage in the same activity type 52% of the time on average. For novice mothers, the level of synchrony is about 35% [START_REF] Green | Social Effects of Maternal Age and Experience in Bison: Pre-and Post-weaning Contact Maintenance with Daughters[END_REF].

Mother-young behavioral synchrony may serve as a way to strengthen bonds, establish social relationships, and mediate social interactions [START_REF] Ham | Synchrony in parent-offspring social interactions across development: A cross-species review of rodents and humans[END_REF]. The behavioral synchrony described here must be distinguished from dive and spatial synchrony, which are expected to be even higher, as suggested by previous studies on humpback whales (Huetz et al., 2022;Tyson et al., 2012). It is likely that the calf, even when engaging in a different activity than its mother, stays mainly close to her.

The Hinde's index of association we found displayed high variability. It was close to zero for most of the pairs. Taken globally, the mother and the calf appear thus almost equally responsible for maintaining the two's behavioral synchrony. However, depending on the context, the individual who takes the lead in engaging in a given activity can be, in fact, the calf or the mother. The mothers were more likely to engage in resting and directed fast swimming alone than the calves, which suggests that the resting activities and the directed fast swimming are scheduled/imposed by the mothers. In contrast, the calves are responsible for synchrony (i.e., they rest mostly when their mother rests or swim fast when their mother does so). The mother chooses when or where to rest and when to change pace because she is more aware of the surrounding dangers. This may explain why Huetz et al. (2022) found that the mothers tend to take the lead in the context of dive initiation. Both the mother and the calf could engage in milling independently of the other individual's milling activity. However, we found that the calves were way more likely to engage in milling alone. Such results show the importance of milling for the development of calves. To some extent, both the mother and the calf could also engage in directed moderate swimming independently of the other individual's directed moderate swimming. In addition to side-by-side traveling during which the pair would engage in directed moderate swimming with a common goal (to travel), directed moderate swimming can also be associated with various individual contexts/goals. For instance, the two individuals have different breathing constraints, meaning they may come up at the surface for air (thus potentially engage in directed moderate swimming to reach the surface) at different times. In addition, in case of separation, one individual may swim at a moderate speed to reunite with the other. models. Journal of Statistical Software, 36(7), 1-21. https://doi.org/10.18637/jss.v036.i07 -----⁂ -----

Synthesis

Are there swimming skills that the adult has but the calf does not? If any, how do mother-calf pairs cope with it?

Methods

Tag dataset from 47 mother-calf pairs was used (Acousonde and CATS cam tag data, as in Chapter 3). The relation between the swimming vertical direction, the swimming effort (ratio of fluke stroke rate to absolute speed), the depth, and the age were investigated using LMM. The depths of occurrence of stationary periods were also compared by age, and the spatial configuration of the mother-calf pair was checked for data from CATS cam tags.

Findings

The calves are unable to control their buoyancy, but the adults do. Unlike adults (mothers), the calves put more effort to swim downward than upward when close to the surface and more effort to swim upward than downward when at greater depth. In other words, the calf's swimming effort when vertically moving depends on the depth, relating to the ambient pressure change affecting buoyancy. The calves can remain stationary only at a specific depth (around 18 m for C3 calves). Otherwise, they must be helped by their mother by staying below her, pressed against her ventral part if above this specific depth (i.e., at shallow depths and thus with positive buoyancy), or resting on the seafloor if below this specific depth (i.e., when with a negative buoyancy).

Introduction

Aquatic animals evolved various adaptations to move vertically within the water column (Alexander, 1982;Pelster, 2009). For example, many shark and squid species rely on the angle of attack of their fins to go up or down and continuously swim to maintain a given depth or to ascend because their bodies are denser than the water. Most teleost fish regulate body density relative to water density by changing the volume of gas in their swim bladder. This facilitates nearly effortless vertical movements or enables maintaining neutral buoyancy. Marine mammals are thought to use the passive collapse/expansion of their lungs (due to changes in ambient pressure with depth) to aid descent/ascent (Miller et al., 2004;[START_REF] Skrovan | The diving physiology of bottlenose dolphins (Tursiops truncatus). II. Biomechanics and changes in buoyancy at depth[END_REF][START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF]. Archimedes' principle states that the upward buoyant force is equal to the weight of the displaced fluid. This force pushes the body up to the sea surface in shallow depths, but below a specific depth, Dneutral, this force decreases and allows the body to sink. Marine mammals modulate the energetics of swimming to accommodate these forces at various depths. At a depth above Dneutral, they actively swim to descend but effortlessly glide on ascent. The effort to descend decreases as their gas-filled organs (mainly the lungs) collapse, making their bodies less buoyant and denser. In contrast, at a depth below Dneutral, they effortlessly glide on the descent but must actively swim to ascend. The effort to ascend decreases as their gas-filled organs expand, making them more buoyant and less dense. This relationship between changes in hydrostatic pressure, buoyancy, and swimming kinematics has been initially demonstrated in pinnipeds (mainly seals and sea lions) and odontocetes (mainly small toothed whales such as dolphins) (Miller et al., 2004;[START_REF] Skrovan | The diving physiology of bottlenose dolphins (Tursiops truncatus). II. Biomechanics and changes in buoyancy at depth[END_REF][START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF], but it has been less studied in mysticetes (baleen whales).

Mysticetes are often compared to odontocetes with respect to their buoyancy and are expected to share the same swimming pattern during dives (Goldbogen et al., 2006;[START_REF] Nowacek | Buoyant balaenids: the ups and downs of buoyancy in right whales[END_REF][START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF]. However, past studies do not take into account the unique anatomy of the respiratory system of mysticetes that may provide some control over their buoyancy (i.e., serving as an active ballast) (Adam et al., 2013;Gandilhon et al., 2015;[START_REF] Reidenberg | Where does the air go? Anatomy and functions of the respiratory tract in the humpback whale (Megaptera novaeangliae)[END_REF]Reidenberg, , 2022). The respiratory system of mysticetes consists of paired nasal passageways, pharynx, larynx, trachea, paired lungs, and laryngeal sac -an organ not found in any other marine mammal species [START_REF] Reidenberg | Where does the air go? Anatomy and functions of the respiratory tract in the humpback whale (Megaptera novaeangliae)[END_REF](Reidenberg, , 2022;;[START_REF] Reidenberg | Discovery of a low frequency sound source in Mysticeti (baleen whales): anatomical establishment of a vocal fold homolog[END_REF]. In this system, the air can go between the lungs and laryngeal sac, linked together at the rostral end of the trachea through the paired arytenoid cartilages that support the U-fold (vocal folds homolog) of the larynx [START_REF] Reidenberg | Where does the air go? Anatomy and functions of the respiratory tract in the humpback whale (Megaptera novaeangliae)[END_REF](Reidenberg, , 2022;;[START_REF] Reidenberg | Discovery of a low frequency sound source in Mysticeti (baleen whales): anatomical establishment of a vocal fold homolog[END_REF]. It has been proposed that baleen whales can circulate air between the lungs and the laryngeal sac to increase the duration of apnea, produce sounds, and adjust buoyancy (Adam et al., 2013;Gandilhon et al., 2015;[START_REF] Reidenberg | Where does the air go? Anatomy and functions of the respiratory tract in the humpback whale (Megaptera novaeangliae)[END_REF]Reidenberg, , 2022)).

In the present paper, we tested the hypothesis that mysticetes can change their buoyancy to facilitate positioning and movements at different depths, using the humpback whale mother-calf pairs in their breeding ground as a model. Humpback whales are migratory, spending the summer in mid or high-latitudes (peri-polar) to feed and the winter in low-latitudes (peri-equatorial) to breed and give birth without feeding (Clapham, 2018). In this context, lactating females rely entirely on their energetic reserve to produce milk while sustaining themselves, making it even more important to optimize the energy allocated to other activities (Bejder et al., 2019;Braithwaite et al., 2015). By using diving data from animal-borne multi-sensor tags placed on mothers and calves, we analyzed the changes in swimming efforts at various depths during upward (ascent) and downward (descent) swimming movements and determined the depth at which the whales were able to stay stationary (neutral buoyancy).

Methods

Dataset

We used data from animal-borne multi-sensor tags deployed on 47 mother-calf pairs off Sainte Marie Island, Madagascar, South Western Indian Ocean, either on the back of the calf (31 mother-calf pairs), the mother (10 mother-calf pairs), or simultaneously on both (6 pairs) collected during the winter of 2013-2019 and 2021-2022. The multisensor tag used on most whales was the Acousonde 3B, except for 12 calves tagged with CATS cam. The Acousonde tags include a pressure sensor, a temperature sensor, a hydrophone, a 3D accelerometer, and a 3D magnetometer. The CATS cam includes the same sensors plus a video camera. Detailed tagging procedures and tag specifications have been published previously (Huetz et al., 2022;Ratsimbazafindranahaka et al., 2022Ratsimbazafindranahaka et al., , 2023;;Saloma et al., 2022). Each deployment (individual) was initially associated with an age class (Ratsimbazafindranahaka et al., 2023): C1 (N = 2) -neonate, C2 (N = 14) -very young but non-neonate, C3 (N = 21)older calves but < 3 months old (calves of the year), or Adult (N = 16) -the mothers. Due to the limited sample, C1 calves were grouped with C2 calves into one category, C1-C2.

For each deployment, we extracted the depth (in meters), the depth rate (in meters/second), the vertical acceleration (in meters/second 2 ), the body posture (body pitch and roll, in degree), and the fluke stroke signals (in degree) (Ratsimbazafindranahaka et al., 2022;Simon et al., 2012) at a sampling rate of 10 Hz. Stroking was identified when the fluke stroke signals exceeded a threshold set for each individual (mean = 1.5±0.8°, range = 0.5-5°) by visual inspection as there are evident differences in stroking and non-stroking periods [START_REF] López | Gait switches in deep-diving beaked whales: Biomechanical strategies for long-duration dives[END_REF]. From the identified fluke strokes, we calculated the Fluke Stroke Rate (FSR, in Hz) on the basis of half-strokes [START_REF] López | Gait switches in deep-diving beaked whales: Biomechanical strategies for long-duration dives[END_REF]Ratsimbazafindranahaka et al., 2022). Finally, we computed two proxies of the forward speed: (1) the relative speed based on the flow noise recorded by the tag's hydrophone (66-94 Hz frequency band) (Cade et al., 2017) and ( 2) the absolute speed as the orientation-corrected depth rate, Δdepth/sin(pitch) (Cade et al., 2021;Miller et al., 2004). The flow noise was rescaled to represent a relative speed ranging from 0 to 1, corresponding to the minimum and the maximum detected speed, respectively. The absolute speed was only calculated when |pitch| exceeded 30° (Narazaki et al., 2018).

Data analysis

Comparison of the swimming effort during downward and upward swimming

We subdivided all continuous periods with available absolute speed into 5-second duration segments, which were then used as the units of analysis. We categorized each segment as either downward (pitch < -30°) or upward (pitch > 30°) swimming and calculated the ratio of FSR to absolute speed as a proxy for the swimming effort (Noren et al., 2006). In addition, we calculated the average depth. We then modeled the relationships between the swimming effort (log-transformed response), depth, age, and swimming vertical direction (fixed effects) using a linear mixed-effects model (LMM), estimated in R (R core team, https://www.r-project.org/) with the lme4 package [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF], followed by a Type II ANOVA test. We included the interaction between the fixed effects and added individuals as random effects. Compliance with the model assumptions was checked graphically. The LMM was followed by Tukey's post-hoc tests (R package emmeans) (Lenth et al., 2018) to compare the swimming effort between swimming directions at 5, 15, 30, and 45 m depth for each age class.

Chapter 4 133

Segments with an absolute speed of 0.5 m s -1 or less were excluded from the analysis as considered stationary or quasi-stationary.

Description of the stationary behaviour

For each deployment, we located all periods during which the individual was stationary and calculated the corresponding average depth. A stationary period was defined as a >10 s continuous period during which the relative speed was <0.2 (i.e., speed less than 20% of the maximum speed recorded for each individual), the depth rate was <0.05 m s -1 , and the vertical acceleration was <0.05 m s -2 . For each calf tagged with a CATS cam (thus with video data available), we audited the video corresponding to each stationary period using BORIS v.7.9.22 [START_REF] Friard | BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations[END_REF] to note the calf's position relative to its mother at that time.

Results

Adult female humpback whales exert similar effort to swim downward and upward at any given depth, but calves do not A total of 2072 downward (428 from C1-C2 calves, 1388 from C3 calves, and 256 from adults) and 1549 upward (308 from C1-C2 calves, 1107 from C3 calves, and 134 from adults) segments were extracted for the analysis of the swimming effort. We found a statistically significant interaction effect of depth, age, and swimming vertical direction on the swimming effort (Type II ANOVA, χ² = 18.88, df = 2, P < 0.001). For both C1-C2 and C3 calves, the swimming effort during upward swimming was lower compared to downward swimming when at shallow depth. This discrepancy narrowed with increasing depth until reaching a depth at which the relationship reversed (around 35 m for the C1-C2 calves and 18 m for C3 calves, yellow stars in Figure 1), and the effort during upward swimming became higher than downward swimming with widening discrepancy with increasing depth (Figure 1). For the adult females, the swimming effort during upward and downward swimming was similar regardless of the depth (Figure 1). There was a statistically significant difference in effort between downward and upward swimming for the C1-C2 calves at 5 and 15 m and a statistically non-significant difference at 25, 35, and 45 m (Table 1). For the C3 calves, the post-hoc tests indicated a statistically significant difference at all tested depths (Table 1). For the mother, no statistical difference was found at any of the tested depths (Table 1).

Figure 1.

Relation between the swimming effort, depth, and direction for C1-C2 calves, C3 calves, and Adult females. Each point on the graph represents a 5 s swimming segment, either upward (red dots) with a pitch greater than 30° or downward (blue dots) with a pitch less than -30°. The lines represent the regression from a linear mixedeffects model (LMM): solid red lines for upward swimming and dashed blue lines for downward swimming. For C1-C2 and C3 calves, the effort exerted during upward swimming was lower compared to downward swimming when swimming at shallow depths. However, as the depth increased, the swimming effort during upward and downward swimming became more similar until reaching a certain depth where a transition occurred (intersections between the solid red line and the dashed red line, indicated by yellow stars). Beyond this depth, the effort exerted during upward swimming became higher. On the other hand, for the adult females, the swimming effort during upward and downward swimming remained similar regardless of the depth, as indicated by the quasi-superposition of the solid red line and the dashed blue line Table 1. Comparison of the swimming effort between downward (Down) and upward (Up) swimming at 5, 15, 25, 35, and Humpback whales are able to stay stationary at varying depths Most adult females and a significant proportion of the C3 calves could be found stationary at varying depths (Figure 2A): the range was at least 5 m in 1/7 C1-C2 calves, 7/13 C3 calves, and 10/16 mothers. By inspecting the video data when available, we found that the C1-C2 calves were always below their mother, pressed against her when stationary (circle symbols in Figure 2A, Figure 2B, 36 stationary periods from four calves). On average, the stationary periods occurred at 5.7±6 m depth (range = 1.2-17.4 m). The C3 calves were observed stationary and mostly close to their mother, but without touching her (diamond symbols in Figure 2A, Figure 2C, 22/23 stationary periods from three calves) at 18±1.5 m depth (range = 16.1-23 m). During the stationary period at 23 m (the observed maximum depth at which a calf was observed stationary), the calf rested on the seafloor (Figure 2D, Video S1, supplemental information). We observed a C3 calf staying stationary below the mother, pressed against her only on one out of 23 occasions, at 2.1 m depth. These periods corresponded to depths above the depth at which the calves displayed similar effort to go up and down (around 35 m for the C1-C2 calves and 18 m for C3 calves; see also Figure 1). Diamond symbols indicate periods during which the calf was visually confirmed (from video data, thus only for CATS cam data) to be close to its mother but not touching her and concerned only C3 calves. These periods corresponded mostly to depths at which the C3 calves displayed similar effort to go up and down (18 m). (B) A C1-C2 calf observed staying below its mother and pressed against her at 7.

Discussion

The generally described vertical swimming patterns of marine mammals (Miller et al., 2004;[START_REF] Skrovan | The diving physiology of bottlenose dolphins (Tursiops truncatus). II. Biomechanics and changes in buoyancy at depth[END_REF][START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF] were not observed in our results on adult female humpback whales. Instead, we found that adult female humpback whales exerted similar effort to swim downward and upward at any depth. However, the described patterns align well with our results on humpback whale calves. The calves exerted more effort to swim downward than upward in shallow depths and more effort to swim upward than downward at greater depths. This observation suggests that a calf's swimming effort depends on the depth, which is related to the ambient pressure change. However, our results on adult female humpback whales suggest a different mechanism. Adult female humpback whales have some control over their buoyancy that allows them to optimize their swimming efficiency regardless of the depth and swimming direction by reducing buoyancy to descend or increasing it to ascend. This control appears absent in calves < 3 months old because it is still developing. This precise control likely needs a learning process, probably obtained during breaching activities, which are also frequently observed in calves in the breeding area (calves born in the season). Our statements are also corroborated by the observed stationary behaviour. We found that both the adult females and the calves can stay stationary with no movements at various depths. However, for calves, unless at a depth where they are neutrally buoyant due to the ambient pressure (estimated to be around 18 m for C3 calves and 35 m for C1-C2 calves, see Figure 1), external help (mainly from their mother) appears to be needed. Within the range where they would require more effort to go downward than upward (thus positive buoyancy), they were always found positioned below their mother, pressed against her, likely to prevent accidental surfacing. Conversely, within the range where they would require more effort to go upward than downward (thus negative buoyancy), they may rest on the seafloor.

We argue that the ability to control the buoyancy is a unique feature of mysticetes among mammals, as one of the potential underlying mechanisms lies in the particular characteristics and specific structure of their respiratory system (Adam et al., 2013;Gandilhon et al., 2015;[START_REF] Reidenberg | Where does the air go? Anatomy and functions of the respiratory tract in the humpback whale (Megaptera novaeangliae)[END_REF]Reidenberg, , 2022;;[START_REF] Reidenberg | Discovery of a low frequency sound source in Mysticeti (baleen whales): anatomical establishment of a vocal fold homolog[END_REF]. The tension of the surrounding tissues and several anatomical elements of the mysticete respiratory tract can potentially isolate the system from the effects of ambient pressure. Most importantly, mysticetes may be able to control the volume of the inhaled air, especially by changing the volume of the laryngeal sac, and thus modulate their buoyancy depending on the situation (Adam et al., 2013;Gandilhon et al., 2015;[START_REF] Reidenberg | Where does the air go? Anatomy and functions of the respiratory tract in the humpback whale (Megaptera novaeangliae)[END_REF]Reidenberg, , 2022;;[START_REF] Reidenberg | Discovery of a low frequency sound source in Mysticeti (baleen whales): anatomical establishment of a vocal fold homolog[END_REF]. There are several valves that may isolate portions of the respiratory tract, including a) the blowholes that are tightly closed at rest, b) the epiglottis that isolates the nasal cavities from the larynx, c) the arytenoid cartilages that, when opposed with a large thick cushion on the cricoid cartilage, fully isolates the laryngeal sac from the trachea and the lungs. In addition, the surrounding bones, cartilage, and strong muscles help stabilize the volumes of these chambers. Regarding this last point, the nasal cavities are positioned between the bones of the skull and mesorostral and alar cartilages; the trachea is reinforced by complete circular cartilaginous rings on all its length; and the laryngeal sac is surrounded by a thick layer of skeletal muscles [START_REF] Reidenberg | Discovery of a low frequency sound source in Mysticeti (baleen whales): anatomical establishment of a vocal fold homolog[END_REF]. [START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF] found that adult blue whales become negatively buoyant at around 18 m, a conflicting result compared to our findings. However, the sample they used (three dives from one individual) likely failed to capture the general swimming pattern associated with the buoyancy control. Interestingly, only the dive profile of the blue whale showed a significant depth overlap between the descent's terminal glide and the ascent's terminal glide among the presented dive profiles from different species (Figure 1 in [START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF]. It may be because the whale gained momentum from the active swimming phase, but it may also indicate the absence of a fixed depth at which the buoyancy switches from positive to negative in mysticetes due to the active control of their respiratory system. Alternatively, the buoyancy control may only have evolved in humpback whales, thus explaining the differential swimming behaviour reported in other mysticete species between descent and ascent (e.g., blue whale, [START_REF] Williams | Sink or swim: strategies for cost-efficient diving by marine mammals[END_REF]right whale, Nowacek et al., 2001). Dive depth should also be considered. While mysticetes may be able to control their buoyancy, it may only apply to shallow dives (e.g., dives within the range we observed at least -0-60 m depth) as the pressure may be too strong at significantly greater depth. Future work should focus on the analysis of data recorded during deep dives (up to 616 m for humpback whales, [START_REF] Derville | Horizontal and vertical movements of humpback whales inform the use of critical pelagic habitats in the western South Pacific[END_REF]. In those cases, the whale may suffer respiratory system collapse and decreased buoyancy, enabling long descent glides as in [START_REF] Nowacek | Buoyant balaenids: the ups and downs of buoyancy in right whales[END_REF]. Nevertheless, the potential ability to control buoyancy should be investigated further. Even if buoyancy control only occurs at relatively shallow depths, it may have implications for energetic costs in mysticetes, particularly adult females who may be conserving energy while swimming with or nursing calves near the surface. In a recent study where the same model was used for baleen and toothed whales for calculating the energetic cost of breaches, a particularly high expense was returned [START_REF] Segre | Energetic and physical limitations on the breaching performance of large whales[END_REF]. Since mysticetes generally live on their energetic reserve during the breeding season, engaging in such an energy-expensive activity in the long term, especially for lactating females, is counter-intuitive. The hypothesis of a buoyancy control may solve this issue, as a voluntary change to positive buoyancy at depth may significantly reduce the required effort to accelerate toward the surface and breach.

Methods

Depth and sound data from six mother-calf simultaneous Acousonde deployments were used. The two tracks (mother and calf sound data) were audited simultaneously as a stereo recording to label social calls depending on whether it was heard on both channels (unknown emitter) or only on one of them (thus very likely to be from the mother or the calf if only on the mother or the calf channel, respectively) and according to their type based on previously established catalog. An RF analysis was then used to compare the acoustic characteristics of the calls likely from the mother and the calls likely from the calf and to automatically identify the caller identity for the calls with an unknown emitter. The vertical distance at which the calls were emitted relative to the other individual was compared for the calls from the mother and the calf. The occurrence of vocal exchange (one individual replying vocally to another) was also analyzed.

Findings

Humpback whale mother-calf pairs produce social calls at a rate of about 11 calls/hour, and they mainly use low-frequency calls. A higher fundamental frequency characterizes the calves' calls compared to those of the mothers, and the RF model can predict the emitter identity. The calf is responsible for the acoustic contact during periods of vertical separation, and the mother rarely vocally responds to the calf's calls. A particularly relevant finding is that most calls recorded by a tag placed on an individual in mother-calf pairs are likely from the tagged individual.

Introduction

The survival of young mammals and, thus implicitly, the reproductive success of females depends on successful maternal care (Clutton-Brock, 1991). In this context, the young must communicate their need to their mother to influence their mother's behaviour and ensure they benefit from maternal care (Gubernick & Klopfer, 1981;Lent, 1974;Nowak et al., 2000). Conversely, mothers must communicate with their offspring, for example, to stimulate them and to encourage them to follow (Lent, 1974) and to express their needs in maternal care (food, warmth, protection against predators) (Gubernick & Klopfer, 1981). Successful maternal care usually includes targeted investment to avoid spending effort on non-filial offspring and maximize the chance of perpetuating the mother's genetic material (Clutton-Brock, 1991). In species with precocial offspring (locomotory, sensorially, and thermoregulatory independent young very soon after birth) or those living in large groups or colonies (e.g., ungulates, otariids, etc.), this involves, for instance, the ability of the mother and the offspring to mutually recognize each other through chemical, acoustic, or visual communication [START_REF] Charrier | Mother-pup recognition mechanisms in Australia sea lion (Neophoca cinerea) using uni-and multi-modal approaches[END_REF]Lent, 1974;Martin et al., 2022).

In an aquatic environment, acoustic signalling is the best communication strategy as acoustic signals are efficient at short and long ranges and easy to detect, identify, and localize, given an appropriately adapted auditory system. In contrast, visual and chemical cues do not provide such advantages. Cetaceans (baleen and toothed whales) are very well known to use acoustic signals in all crucial biological functions: navigation, foraging, breeding, predator avoidance, social contact, and care of the young (for review see [START_REF] Clark | Baleen Whale Acoustic Ethology[END_REF][START_REF] Tyack | Communication by sound and by visual, tactile, and chemical sensing[END_REF].

The humpback whale, Megaptera novaeangliae, is one of the most studied baleen whale species as it occurs in all oceans and is easily observable along coastlines during its annual migration (Clapham, 2018). Previous studies on the acoustic communication of humpback whales have primarily focused on males' song behaviour. The use and function of non-song vocalizations are yet to be investigated in depth.

Non-song vocalizations are variable through time, interrupted by silent periods, apparently unpredictable, and do not show the rhythmic, consistent, and continuous repetitive temporal pattern of songs by males (Silber, 1986;Tyack, 1981) and are commonly considered as social 'calls.' Only recent studies have shown that humpback whale mother-calf pairs use acoustic signalling, particularly social calls (Cusano et al., 2022;Indeck, Girola, et al., 2021;Indeck et al., 2022;Saloma et al., 2022;Videsen et al., 2017;Zoidis et al., 2008). However, most of these calls' behavioural context and biological function still need to be better understood. Social calls are expected to play a significant role in the mother-calf pair's synchronization and social bond since vocal communication can convey various information such as individual identity (Martin et al., 2022) and physical attributes [START_REF] Briefer | Indicators of age, body size and sex in goat kid calls revealed using the source-filter theory[END_REF]Reby & McComb, 2003).

As in other species, some social calls in mother-offspring groups may correspond to contact, distress, and begging calls [START_REF] Carlson | Individual vocal recognition across taxa: a review of the literature and a look into the future[END_REF]. Humpback whale calves' social calls appeared to elicit the mother's approach and were assumed to be potentially isolation or "alarm" calls by the calf to alert and(or) call the mother (Zoidis et al., 2008). Furthermore, certain calls linked to active dives of the calf were attributed to a cohesive function aimed at maintaining contact (Videsen et al., 2017). On another note, Indeck et al. (2022) found that during resting, when mothers and calves are more likely to not be in close contact, mother-calf pairs exhibited increased vocal activity, suggesting the role of the social calls as contact calls. An even relatively undocumented aspect of mother-calf communication in humpback whales and in baleen whale species in general concerns the existence of vocal exchanges, i.e., whether one individual responds vocally to the other's vocalisations. Vocal exchanges can be particularly useful in the context of mother-calf communication as they may serve for mutual recognition, contact maintenance, and reassurance [START_REF] Pika | Taking turns: bridging the gap between human and animal communication[END_REF].

Animal-borne acoustic multi-sensor tags (referred to as tags hereafter) enable the remote, fine-scale description of the behaviour of whales along with their acoustic activities (Johnson & Tyack, 2003), and as such, they became a powerful tool for studying acoustic communication in humpback whale mother-calf pairs (Cusano et al., 2022;Indeck et al., 2022;Indeck, Girola, et al., 2021;Indeck, Noad, et al., 2021;Saloma et al., 2022;Videsen et al., 2017). One remaining big challenge in using such tags concerns, however, the identification of the sound emitter. Even if tags enable more comprehensive insights thanks to their integrated hydrophone, assigning the recorded vocalizations to an individual remains debatable (Goldbogen et al., 2014;[START_REF] Saddler | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF]. Indeed, the tags may record all surrounding sounds, including both the vocalizations from the tagged individual and nearby conspecifics. For groups with individuals spatially close to each other, like mother-calf pairs, it is considered that most vocalizations from the two individuals can be recorded by one tag placed either on the mother or on the calf. In other words, it is considered that the calls recorded by one tag represent the combined vocal outputs of the two individuals. Matching between accelerometer and sound signals has been suggested for assigning a call to a tagged whale (Goldbogen et al., 2014). However, this has only been applied to very low-frequency sounds from solitary species such as blue whales. A later study showed that in the case of non-solitary animals, vocalizations from nearby individuals could also be recorded in the accelerometer signals of the tagged animal [START_REF] Saddler | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF]. Indeck et al. (2021) proposed the use of an unsupervised machine learning algorithm to globally cluster social calls from tags placed on mothers into two groups (one assigned to the mothers and the other to the calves). While interesting, such an approach is limited because an unsupervised machine learning algorithm only splits the data into a defined number of clusters so that the intra-cluster variation is small and the inter-cluster variation is large. Since social calls can also be classified into wellmarked types (regardless of who produced it, for instance, barks, snorts, basses, etc.), the algorithm may report clusters based solely on the acoustic characteristics of the call types and not based on individual vocal differences of the emitters. As a result of such challenges, it remained difficult to clearly investigate various aspects of mothercalf acoustic communication by direct comparisons. Are calls emitted by adults and calves in humpback whales acoustically different and potentially encoding physical attributes (e.g., young vs. adult or small individual vs. large individual)? How can such calls reach the intended receiver in mother-calf pairs? What is the context in which each individual emits these calls, and is there evidence of an acoustic dialogue between the mother and the calf? In addition to these unanswered questions, the estimated overall call rate of mother-calf pairs remains uncertain, primarily because most call rate estimations were based on single deployments, typically on the mother (e.g., Cusano et al., 2022;Indeck et al., 2022). Such estimations overlook that it is improbable for the vocalizations of two animals, even closely associated, to be equally likely recorded by a tag placed on one of them.

In the present study, we investigated whether social calls potentially encode physical attributes and examined the vocal behaviours in adult female humpback whales and their young. More specifically, we tested whether there is an acoustic signature attributed to calves and mothers (young vs. adults), whether the vocal activities of the mothers and the calves differ depending on the spatial context (separation vs. reunion), and whether a pattern in vocal exchanges occurs. In addition, we quantified the vocal activity level of mother-calf pairs (i.e., call rate). In order to achieve our goals, we proposed an alternative method for identifying the sound emitter identity using tags deployed simultaneously on mothers and their calves.

Materials and Methods

Field site

We collected the data off Sainte Marie island, Madagascar, South Western Indian Ocean (between latitudes 17° 19′ and 16° 42′ South, and longitudes 49° 48′ and 50° 01′ East) during the southern winter (July-September) of 2016 and 2017. The area around Sainte Marie island is among the most important breeding and calving areas for humpback whales in the South Western Indian Ocean (Trudelle et al., 2018). The present study is part of an ongoing study on humpback whale mother-calf interactions conducted since 2013 in the area.

Tag specifications

We used two units of Acousonde 3B for our study. Acousonde tags are small, lightweight tags (<400 g) attached to whales via four suction cups. They include a hydrophone and five primary auxiliary sensors (3-axis accelerometer, 3-axis magnetometer, pressure/depth sensor, temperature sensor, and light sensor). Each Acousonde unit was coupled with a VHF transmitter used for tag retrieval. For both units, the hydrophone recorded sounds at a 24.453 kHz sampling rate (16-bit resolution). The hydrophone's sensitivity/clip level was -187 dB re 1 V/µPa. The sampling frequency for one of the Acousonde units was set at 10 Hz for all auxiliary sensors. For the other one, the sampling frequency of the auxiliary sensors was set at 20 Hz, except for the accelerometer, which was set at 400 Hz. Although the two units recorded auxiliary data at different sampling frequencies, all data were downsampled to 10 Hz for consistency in subsequent analyses.

Tagging procedures

We deployed the tags on the mother and the calf in mother-calf pairs from a 6.40 m rigid motorboat using a 5 m rigid handheld carbon fiber pole. The tagging order for the two individuals depended on the opportunity. The approaches we used are detailed in Huetz et al. (2022) and Saloma et al. (2022). Tags were placed on the top lateral side of the animals (Supplemental Figure S1). Laterally positioned tags minimize periods during which the tag is out of the water and thus allow optimized sound recordings. The pairs were not followed after tagging to avoid disturbance, and the tags were retrieved after a few hours or the next day when they detached from the animals (usually due to rubbing against the other individual, surface active behaviour, etc.). As we did not follow the tagged animals, we did not have information on the extent to which the tags may have changed location on the animals throughout the deployments. We assumed that similar to the CATS cam tag (another model of suctioncup tags but with a camera), location changes are, if any, usually relatively small. For instance, within a deployment of about 8 hours, the tag displacement is approximately less than half a meter toward the animal's tail before the tag detachment (personal observation). As in previous studies (Indeck et al., 2022;Indeck, Girola, et al., 2021;Videsen et al., 2017), we conducted all the analyses described hereafter without considering the potential impact of varying tag locations, which is expected to be small.

Data processing

The sounds from the hydrophone and the data from the auxiliary sensors were downloaded as MT files and imported into MATLAB (Mathworks). The sound files were then downsampled to 12 kHz. The auxiliary data were calibrated using dedicated scripts adapted from the CATS Matlab toolkit (https://github.com/wgough/CATS-Methods-Materials, Cade et al., 2021). Deployments with data that could not be calibrated following the routine were discarded. This included for example, deployments with a high axial imbalance in the accelerometry and magnetometer data. As mentioned above, for consistency, all auxiliary data were downsampled to obtain a common sampling rate of 10 Hz across all sensors. The depth data (in meters) were then smoothed with a 0.5 s running median filter.

The internal clocks of the two units deployed on mother-calf pairs simultaneously were not always well synchronized (there may be several seconds of delay). Thus, we used sound cues detected in both units to align and synchronize the mothers' and calves' data (i.e., crew voices on the boat board after the tag setups, impact noises recorded prior to tagging, or even distant male songs). Only overlapping mother-calf data (sound and auxiliary data) were included in the study (i.e., data from the tagging time of the second individual to the moment one of the tags detached).

Acoustic analysis

We analysed simultaneously the sounds recorded by the calf's tag and those recorded by the mother's tag as a stereo recording (the calf as the first channel -calf channel, and the mother as the second -mother channel) using Avisoft SASLab Pro version 5.3.01 (Avisoft Bioacoustics,Nordbahn,Germany). We produced spectrograms of the stereo 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 presumed vocally produced social 'calls' (i.e., not including social sounds such as surface percussive sounds -breaches, slaps -, apparent bubble sounds, rubbing sounds, etc.). We distinguished social calls from the surrounding male songs based on temporal patterns and overlaps (Saloma et al., 2022). Social calls do not show rhythmic, consistent, and continuous temporal repetitive patterns like songs (Silber, 1986;Tyack, 1981).

For each clearly audible and distinguishable social call, we labelled it as 'Calf' if it was audible and visually distinguishable in the calf channel only, as 'Mother' if audible and distinguishable in the mother channel only, and 'Both' if audible and visually distinguishable in both the calf and the mother channels (in all cases, the time of occurrence and the fundamental frequency matched). If a call was audible and visually distinguishable on the calf channel, but there was a loud noise recorded on the mother channel at the corresponding time (e.g., surfacing noise), the call was labelled as 'Calf -Unsure.' Similarly, if a call was audible and visually distinguishable on the mother channel, but there was a loud noise recorded on the calf channel at the corresponding time, the call was labelled as 'Mother -Unsure.' We assumed that social calls detected only on the calf channels, thus labelled 'Calf,' are likely to be social calls produced by calves; calls detected only on the mother channels, thus labelled as 'Mother,' are likely to be produced by mothers; and calls detected on both calf and mother channels, thus 'Both' are either produced by the calves or by the mothers. Such assumptions relied on a general premise in tag-based studies that the vocalisations by an individual would be at least recorded by the tag directly placed on it.

We also categorized the call based on the peak frequency observed on the spectrogram as either a low-frequency call (LF, peak frequency ≤160 Hz), midfrequency harmonic call (MF, 160 <peak frequency ≤700 Hz), high-frequency harmonic call (HF, peak frequency >700 Hz), amplitude modulated call (AM, a combination of long harmonic and amplitude modulated components with peak frequency ranging from 20 to 300 Hz), or pulsed call (PS, low-frequency sounds repeated rhythmically) (Saloma et al., 2022). Furthermore, a type (name) was attributed by aurally and visually comparing the social call with spectrograms, descriptions, and examples from previous descriptions of social call types found in the same area (see the catalogue in Saloma et al., 2022). The robustness of such aural-visual classification (77% agreement when tested with an automatic classifier), as demonstrated by Saloma et al. (2022), provided enough accuracy for the scope of our study. Sounds that might be recorded during brief and rare periods where the tag was out of the water were not included in the present study. Out-of-the-water periods were identified as periods at the surface corresponding to a sudden change in ambient noise and directly preceding and following a splashing sound associated with the transition from water to air (usually when the whale emerges to breathe).

For each labelled call, we used the data from the pressure sensor to calculate the corresponding vertical distance between the mother and her calf (depth difference between the two individuals, in meters). In addition, we measured nine acoustic features for calls with SNR >6 dB (on the clearest signal and with SNR >6 dB of the two channels if the call was detected on both channels): 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 energy below 100 Hz for LF and PS calls, below 300 Hz for MF and AM calls, and below 900 Hz for HF calls (ebelow, in %), 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 spectral characteristics were measured on the averaged spectrum (frequency ranges: 30-1000 Hz for LF and PS calls, 100-6000 Hz for MF and AM calls, and 500-6000 Hz for HF calls). All spectral characteristics' measurements were performed automatically except for the F0, which was measured manually using the harmonic cursor available in Avisoft SASLab Pro for finding harmonic structures. Frequencies were measured on a linear scale as we were equally interested in all frequencies within the already restricted bandwidth of analysis (0-6 kHz), from the F0 (usually of very low frequency) to the highest harmonics. The signal-to-noise ratio (SNR, in dB) was calculated as the ratio between the measured call received level (RL) and ambient noise level. The RL (in dB SPL re 1 μPa Root Mean Square) was measured using a 0.125 s window covering the loudest section of the call (Videsen et al., 2017). The ambient noise level (in dB SPL re 1 μPa Root Mean Square) was taken as the lowest of the noise power measurements among 0.5 s intervals throughout the 10 s period prior to each call [START_REF] Parks | Individual right whales call louder in increased environmental noise[END_REF].

The RL and SNR were only used for assessing the quality of the recorded signals for further acoustic measurements and not for assigning an emitter to each call (see below). Indeed, the comparison of RL and SNR between the mother and calf channels to assign an emitter to each call is very limited as these values are highly dependent on the noises recorded by the tag at a given time, which can be completely different for the two individuals due to difference in activities (difference in swimming speed, location -surface vs. at depth, etc.), physical attributes, and tag placement.

Data analysis

To assess the occurrence of acoustic signatures attributed to calves or mothers, we first examined the frequency distribution of each call type (determined qualitatively) per labels ('Calf,' 'Mother,' 'Both,' and 'Unsure'). Then we compared the temporal and spectral characteristics of calls labelled as 'Calf' and those labelled as 'Mother' in a multivariate analysis, using a Random Forest (RF) algorithm for classification. Specifically, we trained a balanced RF model in R (R core team, https://www.rproject.org/) with the package randomForest [START_REF] Liaw | Classification and regression by randomForest[END_REF] to classify the calls into either 'Calf' or 'Mother' based on the measured temporal and spectral characteristics (Dur, Fmax, Q25, Q50, Q75, ebelow, Bdw, and F0). The balanced RF design maintained equal sample sizes for each class ('Calf' vs. 'Mother') in the classification and thus prevented over-representation of the most represented class [START_REF] Chen | Using random forest to learn imbalanced data[END_REF]. Only calls for which all acoustic parameters have been measured were included. The number of variables randomly selected at each split was set to 2.

The number of trees grown was set to 500. The misclassification rate was calculated as the out-of-bag (OOB) error rate [START_REF] Liaw | Classification and regression by randomForest[END_REF]. The accuracy of the classification (calculated as 1 -OBB error rate) was compared to a prediction by chance (0.5 or 50% since there are two balanced classes) to evaluate how much the prediction by RF is better than a random allocation of class. In addition, multidimensional scaling (MDS) was applied to visualize the dissimilarities in the calls across the two classes using the proximity matrix from the RF algorithm [START_REF] Hennelly | Howl variation across Himalayan, North African, Indian, and Holarctic wolf clades: tracing divergence in the world's oldest wolf lineages using acoustics[END_REF]. The Gini index, which gives the importance of the variables used for the classification, was used to identify the most informative acoustic characteristic(s), allowing the discrimination of the calls labelled as 'Calf' and those labelled as 'Mother.' As the separation between the main call categories (LF, MF, HF, etc.) in humpback whales is relatively pronounced (Dunlop et al., 2008;Saloma et al., 2022), we deemed it reasonable to consider them separately (i.e., different RF models). However, only LF calls were well-represented (see Results). Thus, we only conducted an RF analysis for LF calls, these ones being the most common calls in humpback whale mother-calf pairs (Saloma et al., 2022).

After checking for the occurrence of acoustic signatures attributed to calves and mothers, we used the trained RF model to assign an emitter to calls recorded simultaneously on both channels or uncertain (i.e., those labelled as 'Both' or 'Unsure') and thus obtain a presumed emitter for all of these calls. Then, to assess whether there was a difference in the distance at which each protagonist (calf or mother) emitted calls relative to the other one, we compared the vertical distance between the pair at the time of call production for the two presumed emitters using a Mann-Whitney U test in R. Furthermore, to get an insight into the vocal exchanges between the calf and its mother, we analysed the calves' and mothers' call temporal patterns. All calls (the LF calls previously used in the RF analysis and the remaining calls) were included to consider the pairs' global vocal activities. The emitter identity of the MF and HF calls recorded simultaneously on both channels was kept as 'undefined' since the trained RF model was only for LF calls. The emitter identity of all LF calls for which some acoustic parameters could not be measured was also kept as 'undefined.' We grouped the calls into sequences with breaks based on the calf's diving/surfacing behaviour. For instance, we considered all calls within the same dive (any submergence to a depth of >10 m, Huetz et al., 2022;Ratsimbazafindranahaka et al., 2022;[START_REF] Stimpert | Tagging young humpback whale calves: methodology and diving behavior[END_REF] or surface activity period (the period between two successive dives) as part of one sequence. For each sequence, we characterized the succession pattern of the mother-calf calls. Using a pairwise exact multinomial test with FDR-adjusted P, we compared the occurrence of sequences with exchanges between the calf and the mother, the occurrence of sequences composed only of calls from the calf, and the occurrence of sequences composed only of calls from the mother. Furthermore, we characterized the context of the sequences. The context included whether the calf's activity was synchronized with the mother's activity (the calf and mother diving or being at the surface together) or whether the individuals of the pair were vertically separated. If the behaviour corresponding to the sequence was a dive, we determined whether the descent was synchronized (both the calf and the mother initiated a dive, and the mother was still at a ≤10 m depth when the calf left the surface), corresponded to reunion dive (the mother was already at >10 m depth when the calf left the surface), or was a lone dive (the mother was still at the surface depth when the calf reached 10 m depth). In addition, we determined whether the ascent was synchronized (both the calf and the mother went back to the surface, and the mother was at a ≤10 m depth when the calf reached the surface) or separated (the calf went back to the surface, but the mother was still at >10 m depth when the calf reached the surface, either because the calf went up rapidly compared to the mother or because the mother stayed at depth). When the behaviour corresponding to the sequence is a surface activity, we determined whether the activity was synchronized (calf's surface activity between successive synchronized ascent and synchronized descent), partially synchronized (partial overlap of the calf's surface activity with its mother's surface activity, because the previous ascent from dive and/or the subsequent descent was/were not synchronized), or corresponding to the calf being alone at the surface (the mother stayed at depth).

Where applicable, Standard Deviations (SDs) are presented along with the mean in the format mean±SD. Non-parametric statistical tests were used in our analyses because of the small sample size and the non-normality of the data. The statistical significance level was set to α = 0.05.

Results

Over the two fieldwork seasons, we performed 13 simultaneous mother-calf deployments (i.e., 13 mother-calf pairs). However, only six were considered in the present study as the others showed insufficient overlapping time (less than half an hour) or were not possible to calibrate following the chosen calibration routine. The analysed simultaneous deployments data lasted 2.3±2.8 hours on average (range = 0.5-7.8 hours, total = 13.7 hours) and corresponded mainly to daytime. All the pairs included in the study were not accompanied by any escort at the time of the tagging except one (dep04).

A total of 113 distinguishable social calls were detected. LF calls were the most represented (98/113 calls), followed by MF calls (14/113 calls), and finally by HF calls (1/113 calls). About 11 calls per hour were produced on average by a mother-calf pair (range = 5-15 calls/hour/pair, N = 6). Although no PS calls were reported, we must note that we found some low-frequency sounds rhythmically repeated that may be considered pulsed sounds. However, these sounds were very mechanical and percussive. While we do not exclude the possibility of mysticetes being able to produce such sounds vocally, we chose not to consider these sounds as vocalizations due to uncertainty.

A total of 28 calls were detected only in the mother channels (i.e., labelled as 'Mother'), and 47 were detected only in the calf channels (i.e., labelled as 'Calf'). A total of 33 calls were simultaneously detected in both calf and mother channels (i.e., labelled as 'Both').

The five remaining calls were labelled as 'Calf -Unsure' as they were seen on calf channels but unconfirmed on the corresponding mother channel due to the presence of loud noise (mostly surfacing time). The distribution of the detected calls per type per category is shown in Figure 1, and a representative spectrogram of each of them is presented in Supplemental Figure S2. The most common calls could be attributed to 'bark,' 'bass,' 'snort' (LF calls), and 'groan' (MF calls). 'Barks' were either detected only on the calf channels (although some were labelled as 'Calf -Unsure') or simultaneously on both mother and calf channels. No 'barks' were detected in the mother channels alone. 'Basses' were detected mostly on the calf channels alone. 'Snorts' and 'groans' were detected in calf or mother channels alone or both channels. The other calls such as 'burp,' 'gru,' 'thwop,' and 'wop,' 'heek,' 'whoop,' 'wiper,' and 'HF groan' were less common and were never detected at the same time in both channels, except for 'thwop' (one case). The 'HF groan' is a new name we attributed to a type of call that did not fall within the social calls previously described in the same area. The name relates to its similarities with the groans but with higher frequencies (>700 Hz). High-frequency calls. 'Calf': calls detected in the calf channels only. 'Mother': calls detected in the mother channels only. 'Both': calls detected in both calf and mother channels at the same time. 'Calf -Unsure': calls detected on the calf channels but with uncertain categorization as 'Calf' or 'Both' due to a loud noise recorded on the mother channel at the corresponding time (e.g., surfacing noise). 'Mother -Unsure' (not shown as no call fell within this label): similar to 'Calf -Unsure' but concerning the calls detected on the mother channels

The calls labelled as 'Mother' and those labelled as 'Calf -Unsure' were produced mainly when the calf and the mother were vertically close to each other, i.e., when approximately at the same depth (calf-mother vertical distance, 'Mother': mean = 1±0.9 m, range = 0 -4.3 m, N = 28; 'Calf -Unsure': mean = 1.5±1.2 m, range = 0.2 -2.9 m, N = 5). Calls labelled as 'Calf' and those labelled as 'Both' were, however, produced at more variable vertical distances, from short to relatively long (vertical) distances (calf-mother vertical distance, 'Calf': mean = 4.7±6 m, range = 0 -25.8 m, N = 47; 'Both': mean = 6±5.6 m, range = 0.1 -23.9 m, N = 33). An example of dive profiles of one mother-calf pair plotted along with the occurrence of calls is shown in Figure 2.

The eight acoustic features (Dur, Fmax, Q25, Q50, Q75, ebelow 100 Hz, Bdw, and F0) measured for 62 LF calls (28 'Calf,' 5 'Calf -Unsure,' 17 'Mother,' 12 'Both') are shown in Figure 3 and in Supplemental Table S1. For the classification of the calls labelled as 'Calf' and 'Mother' based on their acoustic features, the trained random forest showed a global accuracy of prediction of 80% (OOB error rate = 20%, Figure 4). This accuracy was greater than the accuracy expected by chance in a balanced dataset (50%).

Although there was some overlapping in the multidimensional scaling (MDS) plot, most of the LF calls originally labelled as 'Calf' and those originally labelled as 'Mother' appeared to occupy distinct regions (Figure 4). The acoustic variable showing the highest importance for the classification was F0 (Gini index: 4.11) and was followed by Q25, ebelow 100Hz, Fmax, Q75, Q50, Bdw, and then Duration (Gini index: 2.15, 2.06, 1.96, 1.85, 1.82, 1.53, 1.51, respectively). Ten LF calls detected simultaneously in both mother and calf channels ('Both') were attributed to calves by the trained RF model. The two remaining calls were attributed to the mothers. For the LF calls detected on the calf channels but with a loud noise recorded on the mother channels at the corresponding time ('Calf -Unsure'), four were attributed to calves. The remaining one was attributed to a mother. In total, 42 LF calls were thus attributed to calves and 20 to mothers.

The examination of the distribution of the vocalizations with respect to the vertical distance between the calf and mother indicated that the LF calls from the mothers were mainly produced when the pair was vertically close to each other. In contrast, the LF calls presumably from calves were produced when the calves were either close or far from their mother (from around 10 m below the mother to 25 m above the mother, Figure 5). The absolute vertical distance between the pair at the time of call production was 4±4.7 m on average (range = 0.1-23.9 m; N = 42) for calls presumably from calves and 1.2±1.1 m (0-4.2 m; N = 20) for calls presumably from mothers. The difference in absolute vertical distance at the time of call production for the mother as the emitter versus the calf as the emitter was statistically significant (Mann-Whitney U test, W = 581, P = 0.015). All calls labelled as 'Both' from calves were produced when the calf was above the mother or approximatively at the same level as the mother in the water column (Figure 5). calls by the two presumed emitters: mothers and calves. A negative value indicates that the calf was above the level of the mother, and inversely, a positive value indicates that the calf was below the level of the mother. LF calls originally labelled as 'Mother' (red), i.e., only detected in the mother channels, were assumed to be calls from mothers, and conversely, LF calls originally labelled as 'Calf' (blue), i.e., only detected in the calf channels, were assumed to be calls from calves. A Random Forest (RF) model was used to predict the emitter when the LF calls were detected simultaneously in the two channels ('Both,' yellow) or when the LF calls were detected on the calf channels and there was a loud noise recorded on the mother channels at the corresponding time ('Calf -Unsure,' green) With all 113 distinguishable calls considered, we identified 41 mother-calf call sequences (Table S2, Supplemental information). Although we called it sequence in the present study, sometimes a 'sequence' was composed of one isolated call (15/41 sequences). Ten sequences contained one or several calls with undefined emitter identity and thus were not considered as they did not allow meaningful conclusions. Sequences consisting only of calls from the calf (21 sequences) were statistically more frequent than sequences consisting of an exchange between the mother and the calf (five sequences, exact multinomial test, P = 0.004) and sequences consisting only of calls from the mother (also five sequences, exact multinomial test, P = 0.004). The sequences consisting of an exchange between the mother and the calf were encountered as often as the sequences consisting only of calls from the mother (exact multinomial test, P = 1). In all of the five sequences with vocal exchange between the mother and calf, the mother was the one to initiate the dialogue. The vocal exchanges between the mother and the calf appeared to occur mostly when the mother and calf were both at the surface (Table S2, Supplemental information). The vocal sequences consisting only of calls from the calf occurred in various contexts, from synchronous activities to cases of vertical separation. The sequences consisting only of calls from the mother were mostly when both were at the surface.

Discussion

Our study investigated the vocal behaviour of adult female humpback whales and their young calves. We present a new estimation of the global vocal activities of mother-calf pairs. Furthermore, we identified the potential differences in vocal attributes between the mothers and their calves, which is critical to future studies dealing with mother-calf vocal communication and interactions and allowed us to draw the conclusion that social calls in mother-calf pairs potentially encode physical attributes. More importantly, our findings allowed us to show how mother-calf pairs use low-frequency calls (LF) in the context of vertical spatial separation and to understand which individual initiates vocal exchanges. The method we used for identifying the emitter was based on the use of mother-calf simultaneous tag deployments paired with a machine learning technique (Random Forest or RF).

The call rate in mother-calf pairs (i.e., combined vocal activities of mother and calf) was estimated to be about 11 calls/hour on average. This new estimation from simultaneous mother-calf tag deployments likely better captures the combined vocal activities of humpback whale mother and calf compared to estimation from single deployments. Indeed, our results show that calls emitted by one individual are not necessarily recorded on the other individual's tag in mother-calf pairs. Most calls detected on the calf channel (i.e., calls only detected on the calf channel as well as calls detected simultaneously on the calf and mother channels), at least for LF calls, are likely from the calf only. Similarly, most vocalizations detected and marked as 'Mother' on the mother channel are likely from the mother only, although at a lower proportion compared to calves, as a great proportion of the calls recorded on both the mother and the calf channels were categorized as calls emitted by calves. As we may expect then, our estimated call rate was higher than those reported in the literature from single deployments on mothers (Cusano et al., 2022;Indeck et al., 2022). Cusano et al. (2022) reported an average of 5 calls/hour, and Indeck et al. ( 2022) reported an average call rate likely way less than 10 calls/hour if the calls supposedly from calves and supposedly from mothers are combined. The call rate we reported remains, however, lower than those reported for mother-calf pairs accompanied by an escort and other social groups without calves (Cusano et al., 2022;[START_REF] Rekdahl | Non-song social call bouts of migrating humpback whales[END_REF]. Our result thus still supports the idea of mother-calf pairs maintaining a low level of acoustic activity to reduce the probability of predators' and males' detection that may impact them (Cusano et al., 2022;Indeck et al., 2022;Videsen et al., 2017).

Although a high diversity of calls has been found, a few specific call types were more common and frequent than others, such as LF calls. These common and frequent LF calls included call types such as 'barks' and 'snorts' also found in mother-calf pairs from other study areas (Cusano et al., 2022;Dunlop et al., 2008;Indeck, Girola, et al., 2021). Such call types may be associated with the main activities of the mother-calf pairs (nursing, when the calf wanders around its resting mother, etc.). The less common ones may be associated with less common and highly specific situations (e.g., distress). Our results suggested that several call types were shared by the mothers and the calves and indicated that the mothers and calves may have, to some extent, a common call repertoire. Yet, our Random Forest (RF) highlighted a significant difference in acoustic characteristics between the calls from the mothers and from the calves. The most discriminative parameter was the fundamental frequency (F0). These results thus support that the LF calls used by mothers and calves, regardless of the type, potentially encode physical attributes of the individuals (relatively small individuals, the calves, and relatively larger individuals, the mothers). In several terrestrial mammal species, it has been observed that smaller or younger individuals produce vocalizations with higher fundamental frequency compared to older ones [START_REF] Ey | Do age-and sex-related variations reliably reflect body size in non-human primate vocalizations? A review[END_REF][START_REF] Martin | Vocal repertoire, microgeographical variation and within-species acoustic partitioning in a highly colonial pinniped, the Cape fur seal[END_REF][START_REF] Taylor | The contribution of source-filter theory to mammal vocal communication research[END_REF]. This trend is thought to be related to changes in the anatomy of the vocal tract as individuals age/grow, which generate honest information for receivers [START_REF] Ey | Do age-and sex-related variations reliably reflect body size in non-human primate vocalizations? A review[END_REF][START_REF] Fitch | Unpacking "honesty": vertebrate vocal production and the evolution of acoustic signals[END_REF][START_REF] Taylor | The contribution of source-filter theory to mammal vocal communication research[END_REF]. Although the vocal apparatus of baleen whales differs from all other mammal species (Adam et al., 2013;Reidenberg, 2022), we can also expect that there are growth-related anatomical changes in the components of the vocal production system (volume of the laryngeal sac, diameter of resonant tract, etc.). Such honest acoustic signals (i.e., signals conveying information that is a true indicator of some characteristics of the sender) may facilitate the rapid identification of signals from young individuals by the mothers, which then triggers maternal responses. The occurrence of differences between mothers' and calves' call characteristics is very encouraging as it may be exploited to discriminate the two in acoustic recordings from single animal-borne tags using supervised machine learning methods (e.g., RF). Of course, the possibility of calls from surrounding individuals outside the mother-calf pairs (e.g., escort non-song vocalisations) being recorded by the tag and thus mistakenly considered as calls from the mothers or the calves cannot be completely ruled out. However, such cases are expected to be rare, considering that the surrounding individuals are unlikely closer to the tagged individual than the other individual of a pair, as it has been suggested by video data from camera-equipped animal-borne tag (CATS cam) placed on calves in the same area (MN Ratsimbazafindranahaka, unpublished data). These surrounding individuals should have their calls even less likely to be recorded by the tag than the other individual of the pair. It should be noted that our study did not take into account the potential impact of varying tag locations on the animals (between different individuals and throughout a given deployment). We assumed that such impact was minor, but as noted by [START_REF] Clayton | Acoustic behavior of gray whales tagged with biologging devices on foraging grounds[END_REF], the variation of the recorded acoustic parameters as a result of such tag slips is yet to be investigated.

Results from our dataset composed of LF calls suggested that the mother's calls are mostly produced when vertically close to her calf. On the other hand, only the calf emitted calls at a long vertical distance from its mother. Given such context, these calls probably serve as contact calls, i.e., calls used by the calf to communicate its identity and position to the mother and/or to call the mother. As already shown above, these calls are signals that may transmit honest signals, at least regarding whether the sender is a young individual who needs care. Since the mother essentially stays approximatively at the same place or moves very slowly most of the time (resting) (Bejder et al., 2019), she does not necessarily have to indicate her position to her calf.

Conversely, the calf being, in general, more mobile, wandering around (milling) (Bejder et al., 2019) while the mother is resting, it needs to communicate its position and maintain acoustic contact with its mother to prevent separation. Further studies are needed to confirm whether, in addition to transmitting information on the age/size, the calls from calves also encode precise individual signatures (Martin et al., 2022). Such a pattern is expected for species with some risk of misdirected maternal care (Lent, 1974;Martin et al., 2022;Nowak et al., 2000). Regarding the vocal exchanges between the mother and the calf, we found that vocal sequences heard in mother-calf pairs were predominantly composed of calls from the calf, which is in line with the hypothesis of the calf being the driver of the acoustic contact, although, most were unsuccessful in eliciting a vocal response from their mothers. The vocal exchanges we detected occurred mostly when the dyad was close to each other (vertically speaking) and may correspond to instances of vocal interactions to reinforce the social bond. As suggested by [START_REF] Chereskin | Allied male dolphins use vocal exchanges to "bond at a distance[END_REF] and [START_REF] Pika | Taking turns: bridging the gap between human and animal communication[END_REF], vocal exchanges can indeed function as a mechanism for social bonding. As our dataset only allowed spatial analysis on the vertical plane, we cannot rule out that the mother and calf were further away from each other in the horizontal plane during these vocal exchanges. It is, however, known in other baleen whale species observed from above that, horizontally speaking, the calves are mostly very close to their mother 90% of the time (within 1/4 of the mother's body length) (Taber & Thomas, 1982). It is possible that the main instances of separation are vertical, which are more related to the calf's breathing capacity (Huetz et al., 2022;Tyson et al., 2012). Datasets from tags equipped with a camera with a large field of view may help in addressing such aspects in future studies. Due to a limited sample, we could not specifically target the other main call categories (mid-frequency, highfrequency, and pulsed calls), and thus, further investigations and simultaneous deployments are needed to better understand the context and source of these other vocalizations.

As already stated above, the method we used in the present study was based on mother-calf simultaneous tag deployments. The combined and time-aligned acoustic data from the calves' tags and mothers' tags allowed us to highlight that in a pair, the calf's tag does not record several calls from the mother, and conversely, the mother's tag does not record several calls from the calf. The fact that one individual's tag does not record several calls from the other individual does not imply that several vocal signals emitted by the calf do not reach the mother and vice versa in the mother-calf pairs. Such circumstances would not benefit the pairs in the context of mother-calf interactions where the calf needs to communicate to its mother to influence her behaviour and vice-versa (Gubernick & Klopfer, 1981;Lent, 1974;Nowak et al., 2000) and where the pair needs to minimize vocal activities to avoid eavesdropping predators or males that may try to mate with the mother (Cusano et al., 2022;Indeck et al., 2022;Videsen et al., 2017). Several possible explanations exist for the calls from one whale not always being recorded by a tag placed on a nearby individual, and all of them imply constraints and challenges in the context of mother-calf acoustic interactions. One explanation is that one whale (let us call it the receiver) creates a body mass acoustic screen that attenuates the call when the other vocalizing individual (let us call it the emitter) is on the opposite side of its tag (i.e., generally below the receiver since the tags tended to be dorsally placed). The screening effect of the body has been, for example, evidenced in king penguins [START_REF] Aubin | Cocktail-party effect in king penguin colonies[END_REF]. For whales, further investigations are needed to evaluate the extent to which their body can attenuate the sound transmission in an underwater setup. Although constituted of water in a significant proportion, the large body of whales comprises tissues of different densities and non-negligible volumes (bones, fat, air-filled organs, etc.). The degradation of sounds during propagation in the natural environment should not be neglected. The body mass screening hypothesis can explain why the calls, presumably from calves, are recorded on both tags, mostly when calves are above the level of their mother and not below and reciprocally for the mothers. The receiver's body mass screening effect may also be coupled with specific spatial radiation of sounds produced by the emitter. Indeed, we may expect a larger ventral radiation of vocalizations in baleen whales, given the ventral position of their vocal apparatus (Adam et al., 2013;Reidenberg, 2022). The effect of the distance between individuals of the pair can be excluded, as there was no apparent correlation between the decreasing vertical distance between mother-calf and the detection of calls on both tags. Another explanation for the calls only sometimes being detected in both channels is the difference in the mother's and calf's activity at a given time. Higher swimming speed for one individual or a change in tag position may, for example, induce more flow noise in its tag, thus reducing the detectability of the calls. These explanations on how the signals may propagate to reach the intended receiver have important implications for the communication and behaviour of the mother-calf pairs. First, the presence of individuals (e.g., escorts) between the mother and the calf may thus disturb the mother-calf acoustic interactions and may increase the risk of separations. Second, the ventral radiation implies a more efficient acoustic transmission of information from the calf during vertical separation where the calf is above the mother (the most common configuration as the mother usually stays at the bottom while the calf goes to the surface more often, Huetz et al., 2022), but less efficient from the mother unless she changes her posture.

Conclusion

Our study explored the vocal behaviour of adult female humpback whales and their calves, with a primary focus on potential differences in vocal attributes and vocal interactions. Using simultaneous tagging of both the mother and her calf in six pairs and a machine learning approach, we were able to better capture the vocal activities of mother-calf pairs and examine which individual vocalizes in the pair. We estimated a higher vocal activity in mother-calf pairs than previously reported, although the results still aligned with the hypothesis that mother-calf pairs maintain a low level of acoustic activity to avoid being detected by predators or males. Furthermore, we showed that the calf is the main responsible for acoustic contact during periods of vertical separation, and usually, the calls from the calf do not elicit vocal responses from the mother. LF calls were the main calls used by mother-calf pairs, which gave an insight into the calls potentially associated with routine situations (e.g., contact calls), and our study suggested that differences in acoustic characteristics, especially fundamental frequency (F0), between adults and calves, exists and such differences may aid in rapid identification of the signals from the young by the mother, which could play a role in encouraging maternal responses. Our research contributes to the understanding of the underlying mechanism of mother-offspring interactions in fully aquatic large mammals and emphasizes the vital role of calves' social calls in maintaining contact with the mother. Reunion dive (the mother was already at depth) and synchronous ascent C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Synchronous surface activity C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Synchronous descent and separated ascent (the mother stayed at depth)

The call occurred before the vertical separation Dep02 (total = 16) M-M-M-M (4) Tagging of the mother, Synchronous surface activity C-C [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Synchronous surface activity M-M-M-C-C [START_REF] Chen | Using random forest to learn imbalanced data[END_REF] Synchronous surface activity M-C-M-C-M [START_REF] Chen | Using random forest to learn imbalanced data[END_REF] Partially synchronous surface activity (the calf was at the surface before the mother)

The call sequence occurred after the reunion Dep03 (total = 19) C-C-C-C (4) Tagging of the calf, Partially synchronous surface activity (the calf was at the surface before the mother)

The call sequence occurred before the reunion The mother was already joining the surface before the call sequence C-C-C-C (4)

Partially synchronous surface activity (the calf was at the surface before the mother)

The mother started to go to the surface after the second call The call sequence occurred before the reunion C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Calf at the surface (The mother stayed at depth) C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Synchronous descent and separated ascent (the mother stayed at depth) The call occurred before the vertical separation C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Reunion dive (the mother was already at depth) and synchronous ascent The call occurred upon reunion at depth C-C [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Lone dive (the calf was joined by the mother right after) and separated ascent (the calf arrived at the surface first) C-C [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Synchronous descent and separated ascent (the mother stayed at depth) The calls occurred before the vertical separation C-C-C -C (4) Synchronous descent and separated ascent (the calf arrived at the surface first)

The calls occurred before the vertical separation Dep04 (total = 11) M-M-M-M-?-?-?-M-M (9) Tagging of the mother, Synchronous surface activity The call sequence occurred immediately after the tagging C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Synchronous surface activity C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Synchronous descent and ascent The call occurred before the ascent Dep05 (total = 8) M-C ( 2) Synchronous surface activity ?-? [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Partially synchronous surface activity (the calf was at the surface before the mother)

The call sequence occurred before the reunion The mother already started to go to the surface before the call sequence C-C-?-? (4)

Partially synchronous surface activity (The calf was at the surface before the mother)

The call sequence occurred after the reunion Dep06 (total = 55) C ( 1) Tagging of the calf, Synchronous surface activity The call occurred immediately after the tagging M-M [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Partially synchronous surface activity (the calf was at the surface before the mother)

The call sequence occurred after the reunion M-M [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Synchronous surface activity ?-? [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Partially synchronous surface activity (the mother dove before the calf)

The call sequence occurred before the separation

Synthesis

In which context do the mother and the calf use vocalizations, and what functions do the vocalizations potentially have? On the behavioral context of vocal productions in calves

Methods

Data from eight calf CATS cam deployments were used. The acoustic characteristics of the recorded social calls were measured and used in an HCPC to objectively identify relatively homogeneous groups of calls that were then referred to as call types. Given the result in Chapter 5, the calls were assumed to be from the calf. Each deployment was broken down into sequences consisting of suckling sessions, surface play sessions, traveling (in synchrony with the mother), milling, response to tagging, and resting using the results of Chapter 2 and Chapter 3. For each continuous period corresponding to each behavioral state, the call rate and the presence/absence of each identified call type were noted. I then tested with an RF analysis whether the recorded vocalizations and the behavioral state were related.

Findings

Milling, resting, and traveling are mainly associated with silence. Surface play sessions, response to tagging, and suckling sessions are associated with call rates of about 0.1, 0.1, and 0.5 calls/min, respectively. According to the RF analysis, the suckling sessions are mainly associated with two sets of low-frequency calls that are quite similar: one with an average fundamental frequency of 40 Hz and an average duration of 0.7 s and another with a fundamental frequency of 45 Hz and an average duration of 0.3 s. Surface play sessions are more associated with sets of mid-frequency calls with whooplike sounds. However, these sessions are also usually associated with other call types without a consistent association pattern.

Introduction

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. (Balshine, 2012;Clutton-Brock, 1991). 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 (Gubernick & Klopfer, 1981;Lent, 1974;Nowak et al., 2000). In the case of mammals that use the 'following' antipredation strategy, the mother needs to encourage following when necessary to ensure that the offspring stays in proximity so she can protect it (Lent, 1974). More generally, both 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 avoid going towards potentially aggressive conspecifics (Lent, 1974;Nowak et al., 2000;[START_REF] Okabe | The importance of motherinfant communication for social bond formation in mammals[END_REF]. Various modalities are used, such as olfactory, visual, tactile, and acoustic communication (Lent, 1974;Nowak et al., 2000;[START_REF] Okabe | The importance of motherinfant communication for social bond formation in mammals[END_REF].

In an aquatic environment, the use of acoustic signaling is one of the best strategies to communicate as acoustic signals are efficient at both short and long-range and easy to detect and localize, whereas visual and chemical cues do not provide such advantages. As such, the cetaceans or whales, a group of fully aquatic mammals, evolved a communication system that is mainly based on sounds that are vocally produced or not (Dudzinski et al., 2009). Non-vocal sounds produced by baleen whales include percussive sounds from surface activities such as tail slaps, pectoral slaps, breaching, etc. The vocally produced sounds encompass songs (Dudzinski et al., 2009;Edds-Walton, 1997;Payne & McVay, 1971;Tyack, 1998Tyack, , 1999) ) and social calls (Dudzinski et al., 2009;[START_REF] Payne | Behavior and vocalizations of humpback whales (Megaptera sp.). Report on a Workshop on Problems Related to Humpback Whales[END_REF]Silber, 1986;Tyack, 1998). They are produced by circulating air from the lungs to the laryngeal sac or vice versa to vibrate the U-fold (Adam et al., 2013;Gandilhon et al., 2015). Songs are vocalizations organized in a repeated manner and in long bouts, while social calls are 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 (Silber, 1986;Tyack, 1981).

Humpback whales produce social calls in various contexts. Unlike songs that are produced primarily by males and mainly associated to reproduction (Dudzinski et al., 2009;Edds-Walton, 1997;Payne & McVay, 1971;Tyack, 1998Tyack, , 1999)), Social calls are used by all categories of individuals (female, male, calf), whether they are in small groups or in large groups, in the breeding ground, in the feeding ground or during the migration [START_REF] Cerchio | Variation in feeding vocalizations of humpback whales Megaptera novaeangliae from southeast Alaska[END_REF][START_REF] Vincent | Vocalization and coordinated feeding behavior of the humpback whale in southeastern Alaska[END_REF][START_REF] Dunlop | The social vocalization repertoire of east Australian migrating humpback whales (Megaptera novaeangliae)[END_REF]Dunlop et al., , 2008;;[START_REF] Dunlop | Changes in vocal parameters with social context in humpback whales: considering the effect of bystanders[END_REF][START_REF] Dunlop | Potential motivational information encoded within humpback whale non-song vocal sounds[END_REF]Epp, Fournet, & Davoren, 2021;[START_REF] Epp | Allopatric humpback whales of differing generations share call types between foraging and wintering grounds[END_REF][START_REF] Fournet | Repertoire and classification of non-song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae)[END_REF]Indeck et al., 2021;[START_REF] Rekdahl | Common nonsong social calls of humpback whales (Megaptera novaeangliae) recorded off northern Angola, southern Africa[END_REF]Rekdahl et al., 2013;Saloma et al., 2022;Silber, 1986;[START_REF] Stimpert | Non-song sound production and its behavioral context in humpback whales (Megaptera novaeangliae)[END_REF][START_REF] Stimpert | Common humpback whale (Megaptera novaeangliae) sound types for passive acoustic monitoring[END_REF]Zoidis et al., 2008). As highly vocal animals with relatively large vocal repertoire, it is expected that social calls are used for various purposes in humpback whale mother-calf pairs, such as to maintain contact, to send alarms, and to initiate critical behaviors such as nursing, etc.

Only a few studies attempted to understand whether humpback whale calves use social calls to communicate with their mothers and whether social calls are associated with a particular context. Zoidis et al. (2008) observed that some social calls appear to elicit the mother's approach and are isolation or "alarm" calls by the calf to alert and/or call the mother. Videsen et al. (2017) suggested that suckling initiation is not based on acoustic cues but rather tactile ones, which was unexpected. Additionally, some calls associated with active dives were attributed to a cohesive function to maintain contact (Videsen et al., 2017). Interestingly, Indeck et al. (2022) found that during resting, an activity associated with more frequent separation, the vocal activity in mother-calf pairs was more intense, supporting the hypothesis that some calls function as contact calls. [START_REF] Ratsimbazafindranahaka | Who's talking? Vocal communication in humpback whale mother-calf pairs and identification of the caller. Under Review in Animal Behavior Ratsimbazafindranahaka[END_REF] suggested that the calf is the main responsible for acoustic contact during periods of separation. One of the main limitations of the various studies on the function of social calls in calves was the fact that the context was mostly associated with the vocal activities (call rates) and not specific call types.

Several challenges make it difficult to study the behavioral context of social call production in whale calves. First, the existence of graded calls, in addition to discrete calls, makes it challenging to objectively separate social calls into distinct call types (Epp, Fournet, & Davoren, 2021;Saloma et al., 2022). The gradation of calls makes it difficult to establish sufficiently reproducible call labels (call types) that can be specifically associated with behaviors. Nevertheless, unsupervised classification [START_REF] Fournet | Repertoire and classification of non-song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae)[END_REF] can be an alternative, or a starting point, to group calls into relatively homogeneous clusters and identify if a set of closely related calls is specifically used in a given context. Another challenge concerns the data acquisition itself. Animal-borne multi-sensor tags are now routinely used to study the behaviors and the vocalizations of whales (e.g., Indeck et al., 2021Indeck et al., , 2022;;[START_REF] Ratsimbazafindranahaka | Who's talking? Vocal communication in humpback whale mother-calf pairs and identification of the caller. Under Review in Animal Behavior Ratsimbazafindranahaka[END_REF]Videsen et al., 2017) as they allow to track and accurately 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 it has been suggested that the calls recorded by a tag placed on a mother would also record calls from the calf and vice-versa. Studies attempting to associate vocalizations to behaviors in mother-calf groups thus did not make a distinction between the calls from the mother and from the calf and assumed that the studied calls represent the total vocal output of the mother-calf pair. Recent results based on data from simultaneous deployments on mother and calf, however, suggested that most calls recorded by a tag placed on a calf are likely from the calf, at least for low-frequency calls (which represent the majority of calls produced in mother-calf pairs) [START_REF] Ratsimbazafindranahaka | Who's talking? Vocal communication in humpback whale mother-calf pairs and identification of the caller. Under Review in Animal Behavior Ratsimbazafindranahaka[END_REF].

In the present study, we investigated the behavioral context of social calls recorded by tags equipped with a camera placed on calves off Sainte Marie Island, Madagascar, South Western Indian Ocean, with the assumption that a tag placed on a calf mainly records the calls from the calf. Our objective was to gain insights into the relationship between activities and calf vocalizations and understand the potential roles of the various call types they use.

Methods

Dataset

We used data from the CATS cam tag, a set of sensors including a depth sensor, a 3axis accelerometer, a hydrophone, and a video camera, placed on the back of eight humpback whale calves, for which 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 of 2022 and are part of the data presented in Ratsimbazafindranahaka et al. (2023a[START_REF] Ratsimbazafindranahaka | Who's talking? Vocal communication in humpback whale mother-calf pairs and identification of the caller. Under Review in Animal Behavior Ratsimbazafindranahaka[END_REF]. Detailed tagging procedures and tag specifications have been presented elsewhere (Huetz et al., 2022;Ratsimbazafindranahaka et al., 2022Ratsimbazafindranahaka et al., , 2023a;;Saloma et al., 2022).

Call analysis

We analyzed the vocalizations recorded by the tags 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. We distinguished the social calls from the male songs in the background based on the temporal pattern and overlaps (Saloma et al., 2022). For each 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 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, respectively). All spectral characteristics' measurements were performed automatically except for the F0, which was measured manually using the harmonic cursor available in Avisoft SASLab Pro for finding harmonic structures on the averaged spectrum.

Behavioral state definition

The data from each calf were broken down into sequences constituted by six possible behavioral states: suckling session (Su), surface play session (Pl), traveling (T), milling (Mi), response to tag deployment (Tg), and resting (Re). 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 -two directly corresponding to Re and Mi and two corresponding to two general trends, moderate directed swimming and fast directed swimming -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, Cade et al., 2017), using a first-order Hidden Markov Model trained on multi-sensor tag data collected since 2013 (see [START_REF] Ratsimbazafindranahaka | Who's talking? Vocal communication in humpback whale mother-calf pairs and identification of the caller. Under Review in Animal Behavior Ratsimbazafindranahaka[END_REF]. Concurrently, suckling events were visually identified on the video data (Ratsimbazafindranahaka et al., 2023a), except during periods of uncertainty due to tag movement (camera not directed forward). During those exceptions (12.2% of the data), the suckling events were identified automatically based on the 3D accelerometry data and depth data using the supervised automatic detection method described in Ratsimbazafindranahaka et al. (2023a). Subsequently, (1) any state during which a suckling event occurred was considered as a suckling session (Su), and any state directly before it was included as part of the session, (2) any directed swimming state during which the calf is observed displaying surface active behaviors (breaches, head slaps, pectoral slaps, etc.) was considered as surface active playing session (Pl), (3) any directed swimming state (moderate or fast), except those occurring immediately after the tagging, were considered as traveling (T) if the mother was swimming alongside the calf at approximately the same pace, (4) any directed swimming state (moderate or fast) during which the mother was resting or was not swimming alongside the calf was considered as also milling (Mi), and ( 5) any state immediately after the tagging was considered as response to tagging (Tg).

Statistical analyses

Cluster analysis of the social calls

We used a cluster analysis to objectively identify relatively homogeneous groups of calls, referred to as call types. The relevant number of clusters was determined using the NbClust package [START_REF] Charrad | NbClust: an R package for determining the relevant number of clusters in a data set[END_REF] in R (R core team, https://www.rproject.org/). The Calinsky and Harabasz (CH) index was used as it has been shown to be among the indexes with the best performance in determining the number of clusters [START_REF] Milligan | An examination of procedures for determining the number of clusters in a data set[END_REF]. We set the minimum cluster number to 6 to avoid capturing just the main categories of calls (Low-frequency, Mid-frequency, High-frequency, Pulsed, and Amplitude-modulated; [START_REF] Dunlop | The social vocalization repertoire of east Australian migrating humpback whales (Megaptera novaeangliae)[END_REF]Epp, Fournet, & Davoren, 2021;[START_REF] Fournet | Some things never change: multi-decadal stability in humpback whale calling repertoire on Southeast Alaskan foraging grounds[END_REF]Saloma et al., 2022;Zoidis et al., 2008). Once the relevant number of clusters was determined, we used a Hierarchical Clustering on Principal Components (HCPC) to group the social calls using the FactoMineR R package [START_REF] Lê | FactoMineR: an R package for multivariate analysis[END_REF]. The principal components (i.e., PC scores) were computed using the seven measured acoustic features as input variables in a principal component analysis (PCA).

Analysis of the behavioral context of call production

For each continuous period corresponding to a state, we calculated the call rate (count/minute) and noted the presence/absence of each identified call type. To investigate how the produced calls and behavioral states relate to each other, we tested whether the information on the rate of production of calls and the type of calls produced allowed the prediction of the behavioral states using a balanced random forest classification. We used the R package rfPermute [START_REF] Archer | Estimate Permutation p-values for Random Forest Importance Metrics[END_REF], which allows the estimation of the statistical significance of the variable importance metrics (here, the importance of the call rate and the presence/absence of each call type) in the prediction by permuting the response variable (here the behavioral states). In our model, the number of variables to be randomly selected at each split was set to 3, and the number of trees grown was set at 1000.

Results

A total of 508 social calls were detected and labelled in the data from eight deployments on calves. Each deployment has been subdivided into 88±66 segments on average (range: 18-212 segments, total: 700 segments) based on automatic classification of the behavioral states. The average duration of the segments was 2.8±3.9 min (range = 0.3-36 min). Most of the segments corresponded to milling (Mi, 63%), followed by resting (Re, 27%), traveling (T, 7%), surface playing (Pl, 2%), suckling session (Su, 2%), and finally tagging response (Tg, 1%).

Main call types recorded

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) (call type #1 to #13, Figure 1). Examples of calls from each type are presented in Audio S1 (Supplemental information). The first two principal components (Dim1-Dim2) explained 76% of the sample variance (Figure S1, Supplemental information). Dim1 was primarily positively correlated to the spectral characteristics, whereas Dim2 was positively correlated to the call duration (Dur). The type #1-3 calls were constituted by low-frequency sounds. Type #1 calls were longabout 2±0.6 s on average, and type #3 were relatively short -about 0.3±0.1 s on average, and type #2 calls were in between these two (Figure 1, Table S1, Supplemental information). Type #4 and #5 calls showed similar spectral characteristics and time duration (overlap on the Dim1 and Dim2) but were of higher frequencies compared to type #1-3 calls (Figure 1, Table S1, Supplemental information). They mainly differed in their bandwidth (Bdw) and fundamental frequency (F0) (The average F0 of type #4 calls was more than twice the average F0 of the type #5 calls, while the average Bdw of type #4 was less than 1/3 of the average Bdw of type #5 calls) (Table S1, Supplemental information). Type #6-9 calls overlapped along Dim1 but spread more on Dim2. Among these four call types, type #7 appeared to have the narrowest bandwidth, the highest F0 (434±41 Hz on average), and the longest duration (1±0.6 s on average, Figure 1, Table S1, Supplemental information). Type #8 had the lowest F0 (65±53 Hz on average, Table S1, Supplemental information). Type #6 and #9 had similar F0, but type #9 had a clearly broader band (about four times the Bdw of type #6) and had, in fact, the broadest band among type #6-9 (Figure 1, Table S1, Supplemental information). The remaining types, type #10 and #13, had more energy in high frequencies (average Fmax >1300 Hz) compared to all other main call types. Type #10 and #11 calls had broader bands and were shorter compared to type #12 and #13 (average Bdw almost five times or more than type #12's and #13's average Bdw and average Dur not more than two-thirds of type #12's and #13's Dur), but type #10 had higher Fmax (about four times) and with a narrower band (about the half) than type #11 (Figure 1, Table S1, Supplemental information). Type #12 and #13 were represented by only a single call each and were high frequency calls with narrow bandwidth. The type #12 call had a much higher F0 (eight times) compared to type #13 (Figure 1). Type #3 was the most abundant main type (59%) and was common to all calves, followed by type #2, that were also relatively abundant (16%) and found in all calves except one. Each remaining call type only constituted <1% to 9% of all calls detected and were at most common to only half of the studied calves (Figure S2, Supplemental information). with a high level of vocal production (call rate >1 calls min -1 in 21/423 Mi, in 3/192 Re, and in 2/50 T; range, Mi: 0-11 calls min -1 ; Re: 0-2 calls min -1 ; T: 0-3 calls min -1 , Figure 2A). In contrast, Surface play session (Pl), reaction to tagging (Tg), and Suckling session (Su) states tended to be associated with a call production at an average rate (median) of 0.1 calls min -1 (range = 0-8 calls min -1 ), 0.1 calls min -1 (range = 0-2 calls min -1 ), and 0.5 calls min -1 (range = suckling, 0-2 calls min -1 ), respectively.

The random forest analysis correctly predicted 29% of the behavioral states based solely on the presence/absence of each of the 13 main call types and the call rate (Table 1). This percentage of correct prediction was statistically significantly greater than the result expected by chance, which is about 17% (P <0.001). The random forest analysis was able to correctly predict the states corresponding to Re and Su at rates that were statistically significantly greater than the values expected by chance (Table 1).

However, it failed to correctly predict the states corresponding to Pl and Tg at rates statistically significantly greater than the values expected by chance but close to the significant level for Pl (Table 1). The correct prediction rate was zero for Mi, and T. Mi was primarily confused with Re but sometimes with Tg, Pl, and Su. T was also primarily confused with Re and, on some occasions, with Tg and Pl, but never with Su.

The variables contributing the most (statistically significant contribution) to the prediction of Su were, by decreasing order of importance, the presence of type #3 and #2 calls, the high call rate, and, to a lesser extent, the presence of type #8, #5, and #6 calls (Figure 2). For Pl, the variables contributing the most to its prediction were the presence of type #4 and #6 calls (Figure 2). Regarding Re, the information on the presence/absence of several call types and the call rate were important in its prediction as it was mainly associated with silence (thus the absence of most calls in general, Figure 2). Decrease Accuracy. The most significant variables contributing to the prediction of Su, by descending order of importance, 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 Pl, the variables with the greatest contribution to its prediction were the presence/absence of type #4 and #6 calls. As for Tg, 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 Re, the presence/absence of several call types, along with the call rate, were important since Re is predominantly associated with the absence of most calls in general

Discussion

Our initial objective was to identify the context of call production by the calves using data from animal-borne tags. We assumed that the calls recorded by the tags (placed on calves) were from the calves, based on the results in Ratsimbazafindranahaka et al. (2023) on low-frequency calls that most calls recorded by a tag placed on the calf are mainly from the calf (about 90% of the calls). Due to the potential existence of graded calls in humpback whales and especially in calves (Epp, Fournet, & Davoren, 2021;Saloma et al., 2022), we used an unsupervised approach to group social calls with similar acoustic characteristics. Our result suggested that the call rate and the production (or non-production) of one or several specific call types may be related to the behavior of the calf. Indeed, we found that these parameters can be sufficient to predict the occurrence of a specific behavioral state in a given data segment. It is especially true for two main behaviors: the suckling behavior and the resting behavior, and likely the play behavior.

Suckling sessions were associated with a higher call rate than any other behavioral states on average and were mainly associated with type #3 and type #2 calls. Qualitatively speaking, these calls corresponded mainly to low frequency calls such as burps, barks, wops, thwops, snorts, basses, and grunts presented in the literature [START_REF] Dunlop | The social vocalization repertoire of east Australian migrating humpback whales (Megaptera novaeangliae)[END_REF](Dunlop et al., , 2008;;Epp, Fournet, & Davoren, 2021;[START_REF] Fournet | Repertoire and classification of non-song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae)[END_REF]Indeck et al., 2021;Rekdahl et al., 2013;Saloma et al., 2022;[START_REF] Stimpert | Common humpback whale (Megaptera novaeangliae) sound types for passive acoustic monitoring[END_REF]Zoidis et al., 2008). Most of these calls have been identified as very common in humpback whale repertoire and were suggested to be calls with important social functions (Saloma et al., 2022). Here, our results suggest that one such function is related to the neonate feeding behavior. They may serve as begging calls in mother-calf pairs to initiate nursing.

Both surface active playing sessions and tagging responses were also associated with a high call rate but could also be associated with silence. For the playing state, various call types could be associated with it, but only the presence of type #4 and #6 calls had statistically significant importance in its prediction. These calls qualitatively corresponded mainly to the whoops presented in the literature (Saloma et al., 2022) and may be associated with a positive emotion expressed during a playful state. In addition, the production of diverse call types during play may be associated with the calf playing with its own voice and practicing its vocal repertoire randomly, or it can also be a byproduct of high physical activity. In rats, the production of some call types is, for example, potentially closely tied to breathing [START_REF] Winkler | Play vocalisations and human laughter: a comparative review[END_REF]. In mysticete whales, as the surface active play involves rapid vertical swimming and breaching, the air circulation within the respiratory system (from the lungs to the laryngeal sac or vice versa) in response to the changing ambient pressure and the calf's attempt to play with its buoyancy (Adam et al., 2013;Gandilhon et al., 2015) may result in similar phenomena where sounds are produced involuntarily. On a side note, in some cases, the low-frequency calls produced by the calf during surface playing sessions were associated with the production of bubbles (Ratsimbazafindranahaka MN, personal observation), corroborating the hypothesis of some calls being tied to breathing while playing at the surface. Resting was mostly associated with silence, which is expected since resting corresponds to periods of low activity during which both the mother and calf are usually in the same behavioral state (Huetz et al., 2022;[START_REF] Ratsimbazafindranahaka | Who's talking? Vocal communication in humpback whale mother-calf pairs and identification of the caller. Under Review in Animal Behavior Ratsimbazafindranahaka[END_REF]Tyson et al., 2012). The absence of a clear pattern in the calls associated with traveling suggests that the calf does not need to vocalize to synchronize movements with its mother. The side-by-side swimming configuration may be sustained based mainly on vision, and the mother may adjust her pace automatically without the need for the calf to signal her. In terrestrial ungulate species using the 'following' strategy, such maternal behavior has been referred to as 'pacing,' a behavior that has been proposed to induce a maximal following response by the offspring (Lent, 1974). In these species, it is known that the young instinctively follow a moving object of an appropriate size (Lent, 1974).

Our study provides a first assessment of the context of call production in humpback whale calves and suggests the potential function of some of the call types, ultimately shedding light on their communication patterns and behavior. For the first time, the potential begging calls of humpback whale calves have been identified. Such results help us understand the underlying mechanism of mother-young acoustic communication in exclusively aquatic mammals. Further investigations are needed on a larger sample to assess the biological functions of other call types, and playback experiments could be carried out to investigate the function of some calls identified in the present study in a controlled setup.

Figure S2.

Frequency of the calls from CATS tag deployments on calves by type identified using a Hierarchical Clustering on Principal Components (HCPC) and by individuals. Type #3 calls, which accounted for 59% of the main types, were the most abundant and present in all calves. Type #2 calls, on the other hand, were relatively abundant (16%) and found in all calves except one. The remaining call types comprised less than 1 to 9% of all detected calls and were only common to a maximum of half of the total calves examined

Summary of the work performed and findings

Throughout this thesis, I presented novel perspectives on three aspects of mother-calf interactions in humpback whales -the suckling/nursing behavior, the swimming behavior, and the acoustic communication -during the first three months of life of the calf using animal-borne multi-sensor acoustic tag data collected since 2013 off Sainte Marie Island, Madagascar. In Part I, Chapters 1 and 2, for the first time, thanks to an automatic behavioral identification method I developed during the thesis, robust details on how, where, and when the suckling/nursing occurs in humpback whales have been obtained, answering my first research question. From these results, I reached the conclusion that the humpback whales' suckling/nursing behavior is very stereotyped.

The calf leans to one side to better reach one of the two mammary teats of its mother and access the milk, and with a rhythmic movement of its tongue, it stimulates the milk ejection or limits milk loss into the water. As for the mother, she leans slightly downwards, i.e., nose down, as if to present her breasts to the calf. The mother-calf pair usually keeps moving while nursing, but the speed is reduced, and in general, the calf exhibits a relatively high swimming effort to maintain the suckling posture. The results also led me to conclude that humpback whale mothers nurse mainly at depth, with a tendency to get deeper as the calf grows. The on-teat periods, or suckling events, are relatively short, lasting less than 30 s, but are performed repeatedly, alternating between the right and left teats (or vice versa), organized in bouts occurring less than 10 minutes apart and grouped into sessions occurring every two hours or so, including at night. Furthermore, the time spent suckling by the calves represents less than 2% of their time, and there is potentially an increase in the suckling frequency with age.

In Part II, Chapter 3, a close examination was conducted on the activities of mothercalf pairs to understand their activities on a fine scale and to address the question of how the swimming behaviors of mother-calf pairs are organized. In Part II, Chapter 4, one specific aspect of the difference in swimming abilities between adults and calves, the ability to control buoyancy, was explored. The findings suggested that resting takes a considerable amount of time in the time budget for both the mother and the calf. However, calves, particularly the very young ones, often go into a milling state, moving slowly, probably wandering around the mother. Additionally, mothers with very young calves tend to rest less than those with older ones. The results also pointed out that the mother and the calf are often synchronous in their activities and suggested that there is a behavioral adjustment by both the mother and the calf with respect to the other's behavior. Depending on the context, either the calf or the mother can take the lead in initiating a given activity. The data supported that the mother is the one who imposes resting periods and fast swimming. On the other hand, each individual can be milling independently, but the calf more often initiates it than the mother. Similarly, routine swimming can be initiated solo by either the mother or the calf. Regarding buoyancy, the behavioral data has shown for the first time that the humpback whale can change its buoyancy at will to optimize its swimming efficiency and that such ability is absent in calves. Calves thus need to acquire this ability, probably through learning and organ maturation, but until then, they have to rely on their mother to assist them in managing their buoyancy at different depths. Down to a certain depth, they have to wedge themselves under their mother to remain stationary, and they have to put more swimming effort when descending.

In the final part, Part III, the question on the context of vocalizations (social calls) in mother-calf pairs was addressed in two steps. First, in Chapter 5, simultaneous mothercalf tag deployments were used to understand which individual usually vocalizes and to comprehend the relationship between vertical separation and vocal production. The results supported that social calls are produced more frequently than previously reported for mother-calf pairs, and the social calls are predominantly emitted by the calf, especially in the event of vertical separation. Moreover, a slightly higher pitch characterizes the social calls from the calf. Surprisingly, the mother appeared to rarely vocally respond to the calf. The mother mainly vocalizes when she is at the same depth as her calf, and it is also mainly during these moments that vocal exchanges take place. Through Chapter 5, I concluded that a tag placed on the calf can be sufficient for studying the calf's vocalizations because, in the end, vocalizations from the mother are rarely recorded by the calf's tag. This conclusion led to Chapter 6, where the results of Part I and II for the camera-equipped tag data from calves were combined and analyzed along with the sound data to take a closer look at the behavioral context of calf social calls. With the analyses conducted in Chapter 6, I found that nursing sessions are associated with an increased call rate. The associated calls are not just any call types but a somewhat defined set of social calls corresponding to low-frequency calls of short duration. I also showed that periods of surface playing activity are associated with high vocal activity (high call rate), but the produced social calls varied. Surprisingly, I found no social calls associated with traveling. It seems that traveling, as well as resting, are mainly associated with silence. It thus suggests that calves use a specific set of social calls to initiate nursing, produce several call types when playing, and don't vocalize much with the mother when travelling or resting.

While the nursing, swimming, and vocal behavior have already been addressed to some extent in humpback whales and other related marine species, my results represent new and critical elements for a better understanding of the dynamics of maternal care, the development of skills crucial to the survival of young whales, and the importance of acoustic signals for maintaining mother-calf contact, optimizing maternal care, and maintaining social bonds in a marine environment. Some studies have for example already investigated the nursing behavior in dolphins (Clark & Odell, 1999a, 1999b;[START_REF] Triossi | The development of suckling behavior in two captive-born calves of bottlenose dolphins (Tursiops truncatus)[END_REF], beluga whales (J. M. [START_REF] Russell | Characterizing chilean blue whale vocalizations with DTAGs: A test of using tag accelerometers for caller identification[END_REF], sperm whales [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF], and humpback whales [START_REF] Tackaberry | From a calf's perspective: humpback whale nursing behavior on two US feeding grounds[END_REF]Videsen et al., 2017;[START_REF] Zoidis | A note on suckling behavior and laterality in nursing humpback whale calves from underwater observations[END_REF]. Also, several studies have investigated the mother-calf swimming behavior and energy management in baleen whales (e.g., Bejder et al., 2019;Huetz et al., 2022;[START_REF] Ratsimbazafindranahaka | Dépendance maternelle chez les baleineaux à bosse (Megaptera novaeangliae) du canal de Sainte Marie, Madagascar : regard sur la morphométrie et la dynamique de la nage[END_REF][START_REF] Smultea | Cetacean mother-calf behavior observed from a small aircraft off Southern California[END_REF][START_REF] Thomas | toothed whales[END_REF] The end goal of maternal care is to maximize the young's chance of survival and thus contribute to the mother's reproductive success and the perpetuation of her genes (Balshine, 2012;Clutton-Brock, 1991). Globally speaking, maternal care touches on various aspects such as food supply, protection against environmental hazards, locomotor assistance, and learning facilitation (Balshine, 2012;Clutton-Brock, 1991). In order to provide care to their young, the humpback whale mothers, being aquatic, face unique challenges linked to the characteristics of their environment compared to terrestrial mammals. In this regard, my work, as discussed below, highlights that the humpback whale displays a complex and multi-faceted form of maternal care that has adapted to cope with its various environmental constraints.

Taking care of a young in an exclusively aquatic environment

First of all, in terms of nursing behavior, humpback whales have developed (or kept) behavior that is comparable to what has been observed in terrestrial mammals living in open environments with predators, the following strategy (Fisher et al., 2002;Lent, 1974). However, the nursing behavior of humpback whales must take into account the fact that the calf has to go to the surface to breathe (Cartwright & Sullivan, 2009;Huetz et al., 2022;Szabo & Duffus, 2008). Thus, it is expected that the calf is nursed frequently, but the sessions are fragmented. My results matched this expectation as I found that nursing sessions are divided into several bouts between which the calf can go to the surface to breathe. The calf is also nursed at depth, potentially to avoid surface noises associated with waves and wind and thus enabling the mother to be more alert to predators (Videsen et al., 2017), to avoid boats, or to facilitate calf's attachment to nipples and ease suckling configuration by exploiting depths where the calf is neutrally buoyant (Videsen et al., 2017). Nursing is increasingly intense as the calf grows during its early life stage (<3 months old), likely to optimize its growth speed [START_REF] Oftedal | The behavior, physiology, and anatomy of lactation in the pinnipedia[END_REF] in preparation for the upcoming long migration to the feeding ground and to enhance its chances of avoiding predation. Nursing in humpback whales likely diminishes in intensity only when the calf finally begins to feed on prey (at around seven months old; Clapham, 2018). While nursing in other species is not only for feeding purposes but also to comfort the young [START_REF] Hall | Suckling isn't feeding, or is it? A search for developmental continuities[END_REF]Lent, 1974;[START_REF] Wolff | Sucking patterns of infant mammals[END_REF], in humpback whales, it might be that nursing is only to feed the calf or maximized to be always nutritive, due to the constraints related to aquatic life. Indeed, suckling in an aquatic environment requires a certain level of locomotor activity to maintain a good functional posture for successful milk transfer (Chapters 1 & 2; [START_REF] Tackaberry | From a calf's perspective: humpback whale nursing behavior on two US feeding grounds[END_REF]. Performing it without any energetic gain (nutritive value), given its potential energetic cost, may be disadvantageous.

One particular aspect that I have highlighted is the buoyancy control. Buoyancy control is essential for species living in an aquatic environment and exploiting various depths (Alexander, 1982;Pelster, 2009). In humpback whales, as the neonate is not yet able to control its buoyancy, the physical contact between the mother and the young has taken on a function that does not exist at all in land mammals and that has never been documented in other groups of marine mammals: the mother acts as a physical support for the calf to prevent it from popping up involuntarily to the surface. This is a remarkable adaptation illustrating the emergence of a new form of maternal care in response to a specific context imposed by the environment. In other species, this type of configuration may only be used as a form of tactile communication, to hide the young from predators, or to promote heat exchange.

Acquiring new skills beside mother

One of the advantages for young mammals of growing up alongside their mother is that they have the time and opportunities to develop complex behaviors that are useful later for survival and that can be plastic, either through self or social learning, but under the mother's supervision [START_REF] Alonso-Alvarez | Benefits and costs of parental care[END_REF][START_REF] Caro | Effects of the mother, object play, and adult experience on predation in cats[END_REF][START_REF] Hoppitt | Lessons from animal teaching[END_REF][START_REF] King | Social information transfer in monkeys, apes, and hominids[END_REF]. In the case of the humpback whale, several aspects of the calf's behavioral development are made possible by the unique interactions with its mother. First, the mother adapts her behavior to the age of the calf. The mother is more vigilant, more active, and probably interacts more often with the calf at birth and during its first weeks of life. This likely corresponds to an early stimulation by the mother for the cognitive and locomotor development of the calf [START_REF] Davis | Exposure to unpredictable maternal sensory signals influences cognitive development across species[END_REF][START_REF] Liu | Maternal care, hippocampal synaptogenesis and cognitive development in rats[END_REF]. Since the mother is at her maximum vigilance when the calf is still very young, the calf can start 'experimenting' with its environment and aptitudes. As has been shown for right whales and dolphins, young calves swim continuously to develop their muscles and quickly grasp how to move around in their environment (Cartwright & Sullivan, 2009;[START_REF] Thomas | toothed whales[END_REF]. As mentioned above, suckling occurs at greater depths as the calf grows, so it is conceivable that the mother gradually imposes diving constraints on the calf to swim at greater depths to enable it to develop its diving and buoyancy control skills. Similar schemes concerning the dive duration have been suggested (Huetz et al., 2022;Szabo & Duffus, 2008). The mother gradually lengthens her dives so that the calf improves its apnea. As pointed out previously, mother humpback whales appear to impose behaviors such as resting or fast swimming. These instances are also likely important for the development of the calf. Through them, the calf can learn to identify resting areas, dangers, and how to avoid them.

Globally speaking, humpback whale mother-calf pairs, based on their time budget, employ a strategy that appears to be a compromise between minimizing energy expenditure and engaging in activities related to the calf's locomotor development, as previously suggested in past studies (Bejder et al., 2019;[START_REF] Ejrnaes | Ontogenetic changes in energy expenditure and resting behaviour of humpback whale mother-calf pairs examined using unmanned aerial vehicles[END_REF]. For the mothers, such a strategy ensures a sufficient reserve for maternal care (in particular milk production) and for migration. For the calves, it ensures optimal growth, securing, as already stated above, the ability to undertake the upcoming migration and enhancing the chances of avoiding predation.

Communicating with mother

My work on acoustic communication highlighted the importance of the use of sound by mother-calf pairs to communicate as expected, given that it is one of the most effective means of communication in the aquatic environment [START_REF] Au | Principles of marine bioacoustics[END_REF][START_REF] Ladich | Acoustic communication in terrestrial and aquatic vertebrates[END_REF]. In the water and in the context of mother-calf interactions, the main modality of communication should not rely on vision, should propagate over a reasonable distance, and must be fast. It should not rely on vision because the visibility underwater can be affected by water turbidity, depth, etc. It should also propagate over a reasonable distance because, although the mother and the calf are in close proximity most of the time, there are frequent vertical separations as the mother often remains at depth while the calf swims to the surface to breathe. Finally, it must be fast because rapid response by the other individual is usually crucial. Acoustic communication meets these requirements and, as such, is used by mothercalf pairs to ensure permanent contact and prevent premature separation, potentially to maintain strong social bonds, and to quickly send messages to coordinate vital activities such as nursing.

The richness of the vocal repertoire of mother-calf pairs has the potential to convey an extensive range of messages. One might expect acoustic communication to be used in many aspects of the life of mother-calf pairs and thus features patterns where certain types of calls are associated with specific behaviors or social interactions. However, from what I have found, this is not totally the case. For example, social calls are used sparingly during traveling. In these cases, other potentially non-acoustic signals, such as visual and tactile, are probably used. The use of visual and tactile signals for activities associated with close spatial relationships would not be surprising because both can work for short-range communication underwater. Among the extant terrestrial relatives of whales that also use the 'following' strategy, the coordination of movements, for example, tends to be based on 'pacing,' where the mother adjusts her speed to allow the calf to follow her and keep her in sight, and the calf would just tend to follow the movement of the individual it sees moving beside it (which should be the mother) (Lent, 1974). Humpback whale mother-calf pairs can probably do the same as long as there is sufficient visibility for the calf to distinguish the mother's silhouette.

Limitations, future directions, and concluding remarks

The present work provides, for the first time, a detailed description of a little-known aspect of baleen whale suckling/nursing behavior and explores in greater depth points concerning the activities of mother-calf pairs and their acoustic communication. The results undoubtedly contribute to a better understanding of the close bond between mother and calf and its implication in the survival and development of the calf. However, the work is limited to a portion of a population of humpback whales in the breeding area, thus also restricted to dependent calves less than three months old. In addition, it is restricted to a geographical area corresponding to a relatively shallow sea of about 35 m depth on average [START_REF] Trudelle | Influence of environmental parameters on movements and habitat utilization of humpback whales (Megaptera novaeangliae) in the Madagascar breeding ground[END_REF]). Yet, humpback whale calves stay with their mother until they are one year old [START_REF] Chittleborough | The breeding cycle of the female humpback whale, Megaptera nodosa (Bonnaterre)[END_REF]Clapham, 2018). During their dependent period, they also pass through the migratory route, then the feeding area [START_REF] Baraff | Separation of humpback whale mothers and calves on a feeding ground in early autumn[END_REF][START_REF] Chittleborough | The breeding cycle of the female humpback whale, Megaptera nodosa (Bonnaterre)[END_REF]Clapham, 2018;[START_REF] Steiger | Reproductive rates of humpback whales off California[END_REF]. The calves and their mothers, even during the breeding and calving periods, may occupy areas with water much deeper than 35 m depth. For example, humpback whale mother-calf pairs off West Maui, Hawaii, occupy waters up to about 120 m deep [START_REF] Pack | Diurnal increases in depths of humpback whale (Megaptera novaeangliae) mother-calf pods off West Maui, Hawaiʻi: A response to vessels?[END_REF]. It would thus be interesting to extend the studies presented in my thesis to other whale populations in other geographic areas and contexts (migration and feeding) to generalize the results better and capture the calf's development until weaning and the evolution of its relationship with its mother.

For instance, such an extension would help pinpoint the stage at which conflicts between the mother and the calf emerge as the cost of caring for the calf becomes detrimental to the mother's future reproduction (Trivers, 1974).

Another limitation is the data being primarily restricted to daytime data. How mothercalf interactions occur at night remains mostly unclear. Nonetheless, the data I exploited have already given a first glimpse of this interaction. For instance, I showed that nursing also occurs at night. Deploying tags later in the day could help better fill in the gaps. Developing new tag attachment systems so that the tags stay on the whales longer would also be beneficial. Darts-based attachment systems have existed for a long time and can provide longer deployment duration [START_REF] Szesciorka | Testing tag attachments to increase the attachment duration of archival tags on baleen whales[END_REF]. However, they may not be of interest at present, as one of the main objectives is to be as non-invasive as possible during data collection, both for animal welfare reasons and to ensure good quality data.

On another note, the challenges specific to my studies in terms of data collection may have implications for my interpretations of the results. My entire thesis is based on placing tags on whales. However, the tagging process is a complicated and potentially hazardous challenge for the data collection operators as it requires a very close approach to the whales. As such, although the sampling is assumed to be random, the field team and I have often been driven to focus on resting individuals. Moreover, some of the whales we chose to approach may have been easier to approach than others depending on their arousal state, their personality, their age or maternal experience (primiparous vs multiparous females), and even their previous experience with humans. How this could have induced a bias in the database remains undefined. For example, did the way we sampled the whales induce a pseudo-selection to the extent that my results only represent the behaviors of individuals with a particular personality [START_REF] Biro | Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling[END_REF][START_REF] Biro | Sampling bias resulting from animal personality[END_REF]? Studies on the personality of whales and tests to compare tagging approach success according to personality profiles could answer such questions.

Although the data I present constitute one of the most extensive datasets from multisensor tags for studying mother-calf pairs (57 deployments from 2013 to 2022), we could have done more with more funding and more time in the field. Our main limitation was mostly for the simultaneous mother-calf tag data. As placing a tag on one individual usually puts the mother-calf pair in a more alert state in the coming minutes, it is challenging to sequentially place a tag on the mother and then on the calf or vice versa. In the current context, the only way to collect more tag data would be to multiply sampling efforts to maximize the chances of tagging success. It has to be said, however, that there has been a significant improvement in our approach technique since the very first tag was placed back in 2013. Thanks to such improvements, I expect future data collection successes to be multiplied.

As the whale searches and pre-tagging observations were carried out from a boat, there was a significant gap in the data concerning each studied individual, limiting the analyses I was able to conduct. There was a lack of precision when estimating the age of the calves, and I had no reliable data on the sex of the individuals. The estimation of the age in my studies was based solely on the angle of inclination of the calf's dorsal fin (Cartwright & Sullivan, 2009;Faria et al., 2013;Huetz et al., 2022), a non-invasive approach already used routinely, and which, in all cases, enabled me to address to some extent the questions I was interested into. Unfortunately, this approach only gives a relative age, whereas a more accurate estimate would be beneficial. An alternative method I have planned to estimate age was to use a photogrammetry technique I developed [START_REF] Ratsimbazafindranahaka | Morphometric study of humpback whale mothercalf pairs in the Sainte Marie channel, Madagascar, using a simple drone-based photogrammetric method[END_REF] Appendix II) to estimate size and relate it to age since the two are closely linked. However, the management of the drone launches and the drone batteries in parallel with the approaches for the tagging was very challenging, and on several occasions, we did not have drones available in the field. Moreover, the photogrammetric approach would still only give a rough age estimation. With respect to the sex, from the boat, it was difficult to get visual access to the calf's genitals. An approach based on genetic analysis, using biopsy samples, would provide the sex of the individuals [START_REF] Bérubé | Identification of sex in cetaceans by multiplexing with three ZFX and ZFY specific primers[END_REF]. A common technique to biopsy whales involves crossbows with purpose-designed arrows [START_REF] Gauthier | Behavioral response of four species of balaenopterid whales to biopsy sampling[END_REF]. Although such equipment was available in the field, we preferred not to use it on the individuals we were interested in because shooting before tagging reduces the chances of successful tagging approaches, and trying to biopsy after tagging could corrupt the quality of the data recorded by the tags because the biopsy approaches induce more disturbance. Finding solutions to collect both data on age and sex would be beneficial as it would give more temporal precision on the behavioral development of the calf and help understand whether there is an ontogenic divergence between males and females. For instance, a particularly relevant question concerns whether there is a link between baleen whale dimorphism (females being larger than males) and suckling intensity. Additionally, it would be interesting to compare acoustic communication between male and female calves to shed light on the ontogeny of songs. Data on the mother's age would also be beneficial, as it can be used to show how maternal care and potential breeding success vary with maternal experience. Developing tagging poles that allow both the collection of tissue samples and tagging could be the way forward for studies on these topics.

The communication components of my thesis were exclusively focused on the acoustic channel and social calls. Aspects concerning non-vocal and non-acoustic signals, such as tactile or visual signals, were not explored. Vocal communication is an essential point in the current context where there is more and more marine noise pollution [START_REF] Chahouri | Recent progress in marine noise pollution: A thorough review[END_REF] and because vocalizations are predominant in cetaceans. However, a more comprehensive analysis could explore other communication modalities. In my studies, for example, I found that although humpback whales potentially use social calls to initiate nursing, there are cases where nursing occurs without the calf vocalizing. A closer look at the behavioral sequences of the mother and calf prior to nursing, using data from simultaneous mother-calf tag deployments, could shed light on other modes of communication in humpback whales. These analyses were planned but still need to be completed. I aim to finish them in a future project.

An important point to consider that I did not address and would require quantification in the future is the impact of tagging on the observed behaviors of humpback whale mother-calf pairs. In my thesis, this impact was considered minimal on the scale of all the data I analyzed. The impact of tagging on humpback whale mother-calf pairs is considered to occur approximately mainly in the first 10 minutes of the deployment (Bejder et al., 2019;[START_REF] Stimpert | Tagging young humpback whale calves: methodology and diving behavior[END_REF]. In my data, I noted that mother-calf pairs usually stop any hasted swimming in response to tagging within less than 10 minutes, which corresponds to what has been reported by Bejder et al. (2019) and Stimpert et al. (2012). I, however, acknowledge that these observations are not sufficient for quantifying the real extent of the tagging impact because of the lack of a baseline for what is really a 'normal' behavior. Pre-and post-tagging tracking and observations would provide a basis for comparison and would allow us to quantify the impact of the tagging more precisely.

Despite various logistic and technical challenges, my studies have provided new answers concerning multiple facets of mother-calf relationships in humpback whales. I particularly highlighted the aspects of nursing behavior, swimming behavior, and acoustic communication. This thesis is the first to precisely describe an entirely aquatic species' suckling/nursing pattern in its natural environment. It also offers new insights into the control of buoyancy and its ontogeny. Finally, it offers a new perspective on the context of vocalizations in humpback whale mother-calf pairs and thus provides a better understanding of the various roles of social calls. Generally speaking, this thesis provides valuable insights into how the mother-calf behaviors adapted to the unique challenges of the marine environment and how the close relationship between the mother and the calf contributes to the development and survival of the calf. It also proposes several new approaches that can be entirely or partially applied to studying cetaceans in general in their natural environment. Additionally, it highlights several technical challenges that need to be overcome, pointing to several research directions.

Beyond what has been mentioned above, it would be really interesting to extend the present work to other species of entirely aquatic mammals and other marine mammals.

Comparative studies on various species of marine mammals would indeed provide insights into the universality or uniqueness of the behaviors I described here. Over and above its usefulness as a collection of knowledge that could guide conservation measures and future studies, I hope the present work will awaken fascination and curiosity, raising awareness and motivating species protection and conservation.

-----⁂ ----- Abstract : Mammals exhibit sophisticated forms of maternal care that are essential for the survival of their young. This thesis aims to better understand mother-calf interactions in aquatic environments by studying humpback whales (Megaptera novaeangliae) mother-calf pairs off Sainte Marie, Madagascar. The method used was based on animalborne multi-sensor tags.

In the first part, a methodology was developed to study suckling behavior, revealing a stereotyped suckling pattern adjusted to the calf's need to breathe at the surface and its growth and occurring in regular sessions throughout the day. The second part explored the swimming behavior of the calves and their mothers, revealing how their activities unwind with age and how the mothers help the calves. The final part examined the vocalizations and their biological functions. Calf calls have been identified as a means of acoustic contact with the mother during vertical separations and are also involved in initiating behaviors such as suckling.

This thesis provides crucial information on mothercalf interactions in humpback whales, offering a unique insight into maternal care, calf development, and the use of acoustic signals for communication in a context somewhat different from the terrestrial one. These results have the potential to inform conservation measures and stimulate future research.

  My thesis's third and last part is dedicated to my last research question and includes two chapters: Chapters 5 and 6. In Chapter 5, I present how I explored a new possibility of differentiating the calls emitted by the mother and the calf and gave results on the spatial context of call production and the occurrence of vocal exchanges. The results presented in Chapter 5 served as a basis for the investigations developed in Chapter 6 on the behavioral context of the calf's calls. Part III highlights the use of specific acoustic signals to maintain social bonds and to coordinate mother-calf pairs' activities.

Figure 1 .

 1 Figure 1. Map of the study site. Red points indicate the various boat departure locations between 2013 and 2022

Figure 2 .

 2 Figure 2. Acousonde 3B tag (A) and CATS cam tag (B)

  So, I have, on several occasions, downsampled the data to match the sampling frequencies. I created MATLAB (Mathworks) code routines, which also use open-source codes widely used by the scientific community working on multi-sensor tags (CATS Matlab toolkit, https://github.com/wgough/CATS-Methods-Materials; Cade et al., 2021; Animal Tag toolbox, http://www.animaltags.org), to standardize the Acousonde and CATS cam data.

Figure 3 .

 3 Figure 3. Diagram showing the frame of references for computing the whale orientation from 3-axis accelerometer data. (A) whale frame, (B) tag frame, (C) earth frame. Blue, red, and yellow correspond to the x, y, and z axes. Pitch, roll, and heading correspond to the rotation around the y, x, and z-axis, respectively, as represented in (A) for the whale

Figure 4 .

 4 Figure 4. Approach for tagging an individual in a mother-calf pair (A) and example of successful CATS cam tag deployment on a calf (B)

Figure S2 .Figure S3 .

 S2S3 Figure S2. Depth, pitch, roll, speed, FSR, and ODBA during and around suckling events for each calf. Blue, red, and green areas in the depth profile correspond to the dive's descent, bottom, and ascent phases, respectively. Uncolored areas in the depth profile correspond to surface phases. Suckling events are identified in red in the depth profile and by the yellow boxes in the raw pitch(blue)/roll(yellow) profiles. The yellow boxes on top indicate that the calf was observed rolling to the right side on the corresponding

Figure S1 .

 S1 Figure S1. Gini importance of different features in classifying suckling blocks by individuals using a Random Forest algorithm. Max.: maximum. Min.: Minimum. rel.: relative. nODBA: normalized Overall Dynamic Body Acceleration. The out-of-the-bag error rate was 23.99%

Figure S2 .

 S2 Figure S2. Characteristics of the automatically detected suckling events (31.4 s average duration) and visually confirmed suckling events (18.5 s average duration) found in Acousonde and CATS cam deployments on calves. The data are broken down by activity phase. Suckl. (Auto.): automatically detected suckling events, Suckl. (Manual): visually confirmed suckling events, Avg.: average, DR: depth rate, rel.: relative, nODBA: normalized Overall Dynamic Body Acceleration. Suckling data are plotted alongside randomly selected non-suckling data (20 s segments) for comparison (N = 214). The mean and median are indicated by red diamond marks and bold black horizontal lines, respectively

Figure S3 .

 S3 Figure S3. Schematic representation of the expected vertical distance between the humpback whale mother and calf considering various possible nursing configurations. The animal-borne tags used to record the behaviors are illustrated in orange. The calf is represented suckling on the right teat (thus rolling to the right). The vertical distances between the mother and the calf (strictly speaking, the vertical distance between the two tags) are represented by double arrows. (a) The extreme case where the mother is almost vertical, and the calf is almost horizontal in the water column. (b) The expected typical configuration. (c) The extreme case where the mother is almost horizontal, and the calf is almost vertical. The configurations are based on the known nursing configuration reported in the literature. Considering the average dimensions and body proportions of mothers and calves in the studied population (Appendix II), the vertical distance is expected to be about 1-2 m, 2-3 m, and 5-6 m in (a), (b) and (c) respectively

  HMM model successfully captured the general activity pattern of the whales. The corresponding transition probabilities are presented in Figure S1. The HMM captured (i) a state with a mean average speed of about one standard deviation below the global average swimming speed (resting: mean average relative speed = -1.1±0.5, range = -3.8-0.3; N = 8594), (ii) a state with a mean average speed coinciding with the global average swimming speed (milling: mean average relative speed = -0.1±0.3, range = -1.1-1.3, N = 13254), (iii) a state with a mean average speed of about half a standard deviation above the global average swimming speed (directed moderate swimming: mean average relative speed = 0.6±0.3, range = -0.3-1.5, N = 10747), and (iv) a state with a mean average speed of more than one standard deviation above the global average swimming speed (directed fast swimming: mean average relative speed = 1.3±0.4, range = -0.2-3.6, N = 3733). On average, the C1-C2, the C3, and the mothers (A) switched from one state to another 22±9, 20±7 and 17±8 times per hour, respectively (range, C1-C2: 9-41 switches per hour, C3: 7-36 switches per hour, A: 9-38 switches per hour).

Figure 1 .

 1 Figure 1. Behavioral time budget of 47 calves and 16 mothers in a breeding area off Sainte Marie, Madagascar. The behaviors were modeled using a hidden Markov model (HMM) based on the relative swimming speed. Re: resting, Mi: milling, MS: directional moderate swimming, and HS: directional fast swimming. C1, C2, and C3 represent the relative age of the calves estimated based on skin coloration, skinfolds, and the angle of unfurling of their dorsal fin. A corresponds to Adults (mothers), further categorized according to the age of their dependent calf. Age (C1-C2 vs. C3 vs. A) had no statistically significant effect on the proportion of time resting (linear model with logratio transformed response: F2, 50 = 1.29, P = 0.283). However, it had a statistically significant effect on the proportion of time milling (linear model with a log-ratio transformed response: F2, 50 = 3.76, P = 0.03)

Figure 2 .

 2 Figure 2. Dive profile of a simultaneously tagged mother (top) and calf (bottom)showing the behavioral synchrony between the two. Re: resting, Mi: milling, MS: directional moderate swimming, and HS: directional fast swimming. Seventy-nine percent of the total simultaneous data presented a synchronized behavioral state (i.e., the calf and the mother were in the same state). Green and red dots represent each time the individual initiated and terminated a bout of synchronized state, respectively. The number of times the mother and the calf initiated and terminated a bout of synchronized state gave a Hinde's index of -0.3. The index suggested that while the calf tends to match its behavior to its mother's behavior more often compared to its mother (negative value), the mother and the calf were almost equally responsible for maintaining the two's behavioral synchrony (value close to zero)

Figure 2 .

 2 Figure 2. Stationary behaviour of humpback whale calves and adult females. (A) Depths at which a stationary period >10 s occurred for seven C1-C2 calves (red), 13 C3 calves (green), and 16 adult females (blue). Several individuals, and in particular the adult females, stayed stationary at varying depths. Cross symbols indicate data without video (either data from Acousonde tags or data during misdirected camera from CATS cam tags). Circle symbols indicate periods during which the calf was visually confirmed(from video data, thus only for CATS cam data) to be below its mother and pressed against her and concerned only C1-C2 calves, except one period in one C3 calf (dep09). These periods corresponded to depths above the depth at which the calves displayed similar effort to go up and down (around 35 m for the C1-C2 calves and 18 m for C3 calves; see also Figure1). Diamond symbols indicate periods during which the calf was visually confirmed (from video data, thus only for CATS cam data) to be close to its mother but not touching her and concerned only C3 calves. These periods corresponded mostly to depths at which the C3 calves displayed similar effort to go up and down (18 m). (B) A C1-C2 calf observed staying below its mother and pressed against her at 7.1 m depth. (C) A C3 calf staying stationary at 17.3 m depth next to its mother without touching her. (D) A C3 calf observed staying stationary at 23 m depth, resting on the seafloor. See also Video S1, Supplemental information. (B-D) Red and blue arrows indicate the longitudinal axis of each individual in the images and point toward the snout

  Figure 2. Stationary behaviour of humpback whale calves and adult females. (A) Depths at which a stationary period >10 s occurred for seven C1-C2 calves (red), 13 C3 calves (green), and 16 adult females (blue). Several individuals, and in particular the adult females, stayed stationary at varying depths. Cross symbols indicate data without video (either data from Acousonde tags or data during misdirected camera from CATS cam tags). Circle symbols indicate periods during which the calf was visually confirmed(from video data, thus only for CATS cam data) to be below its mother and pressed against her and concerned only C1-C2 calves, except one period in one C3 calf (dep09). These periods corresponded to depths above the depth at which the calves displayed similar effort to go up and down (around 35 m for the C1-C2 calves and 18 m for C3 calves; see also Figure1). Diamond symbols indicate periods during which the calf was visually confirmed (from video data, thus only for CATS cam data) to be close to its mother but not touching her and concerned only C3 calves. These periods corresponded mostly to depths at which the C3 calves displayed similar effort to go up and down (18 m). (B) A C1-C2 calf observed staying below its mother and pressed against her at 7.1 m depth. (C) A C3 calf staying stationary at 17.3 m depth next to its mother without touching her. (D) A C3 calf observed staying stationary at 23 m depth, resting on the seafloor. See also Video S1, Supplemental information. (B-D) Red and blue arrows indicate the longitudinal axis of each individual in the images and point toward the snout

  do the mother and the calf use vocalizations, and what functions do the vocalizations potentially have? On the spatial context and the difference in vocal behavior and attributes

Figure 1 .

 1 Figure 1. Distribution of the social calls per category and per type recorded in mothercalf simultaneous deployments. LF: Low-frequency calls. MF: Mid-frequency calls. HF:High-frequency calls. 'Calf': calls detected in the calf channels only. 'Mother': calls detected in the mother channels only. 'Both': calls detected in both calf and mother channels at the same time. 'Calf -Unsure': calls detected on the calf channels but with uncertain categorization as 'Calf' or 'Both' due to a loud noise recorded on the mother channel at the corresponding time (e.g., surfacing noise). 'Mother -Unsure' (not shown as no call fell within this label): similar to 'Calf -Unsure' but concerning the calls detected on the mother channels

Figure 2 .Figure 3 .Figure 4 .

 234 Figure 2. Dive profiles of a female humpback whale and her calf tagged simultaneously with Acousonde tags. Red and blue curves correspond to the mother and the calf's dive profile, respectively. The vertical lines correspond to the occurrence of social calls detected simultaneously in the mother and calf channels (yellow, labelled as 'Both'), detected only in the mother channel (red, labelled as 'Mother'), heard only in the calf channel (blue, labelled as 'Calf'), and heard in the calf channel but unconfirmed on the corresponding mother channel due to the presence of loud noise (green, labelled as 'Calf -Unsure')

Figure 5 .

 5 Figure5. Calf-mother vertical distances at the production time of Low-frequency (LF) calls by the two presumed emitters: mothers and calves. A negative value indicates that the calf was above the level of the mother, and inversely, a positive value indicates that the calf was below the level of the mother. LF calls originally labelled as 'Mother' (red), i.e., only detected in the mother channels, were assumed to be calls from mothers, and conversely, LF calls originally labelled as 'Calf' (blue), i.e., only detected in the calf channels, were assumed to be calls from calves. A Random Forest (RF) model was used to predict the emitter when the LF calls were detected simultaneously in the two channels ('Both,' yellow) or when the LF calls were detected on the calf channels and there was a loud noise recorded on the mother channels at the corresponding time ('Calf -Unsure,' green)

Figure S2 .

 S2 Figure S2. Representative spectrograms of the 12 social call types recorded in six simultaneous mother-calf Acousonde tag deployments off Sainte Marie, Madagascar. Low-frequency (LF) calls: (A) bark, (B) bass, (C) burp, (D) gru, (E) snort, (F) thwop, (G) wop. Mid-frequency (MF) calls: (H) heek, (I) whoop, (J) Wiper, (K) Groan. High-frequency (HF) call: (L) HF groan. These call types have already been described in detail in a previous study of the mother-calf pairs' vocal repertoire of the same population by Saloma et al. (2022), except for the HF groan. Spectrogram parameters: Hamming window, FFT window size: 1024 pts, 90% overlap. Generated using the Seewave package in R

Figure 1 .

 1 Figure 1. Hierarchical Clustering on Principal Components (HCPC) showing the identified main call types (delimited by polygons) and calls within each of them (small symbols). Cluster centers are indicated by bigger symbols. Thirteen call types were identified. The calls are dispersed along Dim1 axis, which is positively correlated to spectral characteristics (F0, Fmax, Q25, Q50, and Q75 and Bdw), and dispersed along Dim2, which is highly correlated to duration (Dur)

Figure 2 .

 2 Figure 2. Relation between the behavioral states Mi, Pl, Tg, Re, Su, and T, and the produced calls. Mi: milling, Pl: surface active playing session, Tg: response to tagging, Re: resting, Su: suckling session, T: traveling. (A) Call rates by behavioral state. Su, Pl, and Tg were associated with higher call rates compared to Mi, Re, and T, which were mainly associated with silence. (B) Relation between the call type produced, numbered from #1 to #13, and the behavioral states. Each count represents the number of segments per behavioral state during which the specified call type was detected. The number of segment per behavioral 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 behavioral 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

  Dans la première partie, une méthodologie a été développée pour étudier le comportement d'allaitement, révélant un schéma d'allaitement stéréotypé s'adaptant au besoin du baleineau à aller respirer en surface et à sa croissance, et se faisant sur une base de sessions régulières dans la journée. Dans la deuxième partie, la nage des baleineaux et de leur mère a été explorée, montrant comment leurs activités évoluent avec l'âge et comment les mères aident les baleineaux. La dernière partie s'est penchée sur les vocalisations et leurs fonctions biologiques. Les cris des baleineaux ont été identifiés comme étant un moyen de contact acoustique avec la mère lors des séparations verticales et sont aussi impliqués dans l'initiation de comportements tel que l'allaitement.Cette thèse fournit des informations cruciales sur les interactions mère-baleineau chez la baleine à bosse, offrant un aperçu unique des soins maternels, du développement des jeunes, et de l'utilisation des signaux acoustiques pour la communication dans un contexte assez différent du contexte terrestre. Ces résultats ont le potentiel d'informer les mesures de conservation et de stimuler des recherches futures.Title : Humpback whale mother-calf interactions: investigating nursing, swimming, and vocal behaviors Keywords : Mother-offspring interactions, Cetaceans, Marine habitat, Biologging, Ontogeny

Table 1 .

 1 Summary of the deployments used for studying the mother-calf interactions in humpback whales off Sainte Marie, Madagascar

Raw deployment ID Pair ID Age (a) Category Tag on date (local) Tag off date (local) Duration (h): all data Duration (h): data with 'good' video Tag model (b)

	dep3_09aout2016_Mother dep13-28.08.2017_Mother	G018 G035	A A	Mother Mother	09-Aug-2016 13:08 28-Aug-2017 12:48	09-Aug-2016 14:56 28-Aug-2017 16:48	1.79 4	0	Acousonde Acousonde
	dep4_10aout2016_Mother dep14-01.09.2017_Calf	G019 G036	A C3	Mother Calf	10-Aug-2016 13:26 01-Sep-2017 09:15	10-Aug-2016 14:58 01-Sep-2017 11:42	1.53 2.45	0	Acousonde Acousonde
	dep5_11aout2016_Mother dep14-01.09.2017_Mother	G020 G037	A A	Mother Mother	11-Aug-2016 08:58 01-Sep-2017 10:57	11-Aug-2016 14:35 01-Sep-2017 14:12	5.61 3.24	0	Acousonde Acousonde
	dep1_5aout2013_Mother dep6_13aout2016_Mother dep15-03.09.2017_Calf	G001 G021 G038	A A C2	Mother Mother Calf	05-Aug-2013 12:17 13-Aug-2016 14:38 03-Sep-2017 10:30	05-Aug-2013 17:12 13-Aug-2016 21:26 03-Sep-2017 10:59	4.91 6.81 0.48	0	Acousonde Acousonde Acousonde
	dep2_7aout2013_Mother dep7_17aout2016_Calf dep15-03.09.2017_Mother	G002 G022 G038	A C3 A	Mother Calf Mother	07-Aug-2013 12:12 17-Aug-2016 11:20 03-Sep-2017 10:08	07-Aug-2013 16:13 17-Aug-2016 13:52 03-Sep-2017 12:36	4.03 2.53 2.47	0	Acousonde Acousonde Acousonde
	dep5_5sept2013_Mother dep7_17aout2016_Mother Orange_01Sept2022_Calf	G003 G022 G039	A A C2	Mother Mother Calf	05-Sep-2013 08:37 17-Aug-2016 11:28 01-Sep-2022 09:56	05-Sep-2013 10:02 17-Aug-2016 18:01 01-Sep-2022 18:09	1.42 6.55 8.21	6.7	Acousonde Acousonde CATS cam
	dep6_9sept2013_Mother dep8_18aout2016_Calf Orange_05Sept2022_Calf	G004 G023 G040	A C1 C2	Mother Calf Calf	09-Sep-2013 10:02 18-Aug-2016 13:13 05-Sep-2022 09:02	09-Sep-2013 10:36 18-Aug-2016 14:19 05-Sep-2022 15:14	0.56 1.1 6.19	5.62	Acousonde Acousonde CATS cam
	dep7_12sept2013_Calf dep8_18aout2016_Mother Orange_07Aout2022_Calf	G005 G023 G041	C3 A C2	Calf Mother Calf	12-Sep-2013 09:44 18-Aug-2016 13:44 07-Aug-2022 10:57	12-Sep-2013 12:56 18-Aug-2016 23:58 07-Aug-2022 14:14	3.21 10.24 3.27	2.88	Acousonde Acousonde CATS cam
	dep1_20aout2014_Calf dep9_05sept2016_Calf Orange_13Sept2022_Calf	G006 G024 G042	C2 C3 C2	Calf Calf Calf	20-Aug-2014 11:08 05-Sep-2016 13:54 13-Sep-2022 13:42	20-Aug-2014 11:52 05-Sep-2016 22:54 13-Sep-2022 16:15	0.73 9.01 2.56	2.56	Acousonde Acousonde CATS cam
	dep2_22aout2014_Calf dep9_05sept2016_Mother Orange_15Aout2022_Calf	G007 G024 G043	C3 A C2	Calf Mother Calf	22-Aug-2014 09:52 05-Sep-2016 13:53 15-Aug-2022 11:00	22-Aug-2014 14:53 05-Sep-2016 21:40 15-Aug-2022 11:57	5.01 7.78 0.95	0.95	Acousonde Acousonde CATS cam
	dep3_24aout2014_Mother dep2-04.08.2017_Calf Orange_19Aout2022_Calf	G008 G025 G044	A C3 C3	Mother Calf Calf	24-Aug-2014 11:29 04-Aug-2017 14:04 19-Aug-2022 09:35	24-Aug-2014 14:04 04-Aug-2017 15:02 19-Aug-2022 15:29	2.57 0.96 5.88	5.88	Acousonde Acousonde CATS cam
	dep4_26aout2014_Mother dep4-10.08.2017_Calf Orange_26Aout2022_Calf	G009 G026 G045	A C3 C3	Mother Calf Calf	26-Aug-2014 08:26 10-Aug-2017 12:30 26-Aug-2022 09:26	26-Aug-2014 22:48 10-Aug-2017 13:48 26-Aug-2022 11:12	14.37 1.3 1.78	1.07	Acousonde Acousonde CATS cam
	dep5_29aout2014_Calf dep4-10.08.2017_Mother Orange_30Aout2022_Calf	G010 G026 G046	C3 A C2	Calf Mother Calf	29-Aug-2014 07:57 10-Aug-2017 12:26 30-Aug-2022 14:48	29-Aug-2014 08:24 10-Aug-2017 13:44 30-Aug-2022 18:34	0.45 1.31 3.77	2.97	Acousonde Acousonde CATS cam
	dep6_08sept2014_Calf dep5-11.08.2017_Calf Orange_31July2022_Calf	G011 G027 G047	C3 C3 C2	Calf Calf Calf	08-Sep-2014 09:29 11-Aug-2017 11:39 31-Jul-2022 10:52	08-Sep-2014 12:50 11-Aug-2017 16:47 31-Jul-2022 13:02	3.34 5.13 2.15	0	Acousonde Acousonde CATS cam
	dep7_09sept2014_Calf dep6-13.08.2017_Calf Jaune_06aout2019_Calf	G012 G028 G048	C3 C2 C3	Calf Calf Calf	09-Sep-2014 14:40 13-Aug-2017 11:52 06-Aug-2019 10:56	09-Sep-2014 19:32 13-Aug-2017 14:39 06-Aug-2019 12:34	4.87 2.78 1.61	1.61	Acousonde Acousonde CATS cam
	dep8_10sept2014_Calf dep7-15.08.2017_Calf Jaune_14sept2018_Calf	G013 G029 G049	C1 C2 C3	Calf Calf Calf	10-Sep-2014 13:51 15-Aug-2017 15:22 14-Sep-2018 11:34	10-Sep-2014 15:44 15-Aug-2017 15:46 14-Sep-2018 14:18	1.89 0.39 2.48	2.48	Acousonde Acousonde CATS cam
	dep9_11sept2014_Calf dep7-15.08.2017_Mother Orange_09aout2019_Calf	G014 G029 G050	C2 A C3	Calf Mother Calf	11-Sep-2014 12:22 15-Aug-2017 16:00 09-Aug-2019 10:03	11-Sep-2014 12:54 15-Aug-2017 16:29 09-Aug-2019 18:44	0.54 0.48 7.95	6.42	Acousonde Acousonde CATS cam
	dep11_17sept2014_Calf dep8-16.08.2017_Calf	G015 G030	C3 C2	Calf Calf	17-Sep-2014 12:28 16-Aug-2017 09:03	17-Sep-2014 12:56 16-Aug-2017 16:18	0.46 7.25		Acousonde Acousonde
	dep1_01aout2016_Calf dep9-21.08.2017_Calf	G016 G031	C3 C2	Calf Calf	01-Aug-2016 09:40 21-Aug-2017 12:13	01-Aug-2016 10:09 21-Aug-2017 15:28	0.48 3.26		Acousonde Acousonde
	dep2_02aout2016_Calf dep10-22.08.2017_Mother	G017 G032	C3 A	Calf Mother	02-Aug-2016 14:37 22-Aug-2017 12:58	02-Aug-2016 16:51 22-Aug-2017 16:22	2.24 3.41		Acousonde Acousonde
	dep2_02aout2016_Mother dep12-26.08.2017_Calf	G017 G033	A C3	Mother Calf	02-Aug-2016 14:40 26-Aug-2017 14:57	02-Aug-2016 15:51 27-Aug-2017 08:19	1.18 17.36		Acousonde Acousonde
	dep13-28.08.2017_Calf	G034	C3	Calf	28-Aug-2017 10:25	28-Aug-2017 15:53	5.46		Acousonde

(a) relative age. For the calves, the relative age was estimated based on the coloration, skinfolds, and angle of furling of the dorsal fin. C1: neonate, C2: very young but non-neonate, C3: older calves (but <3 months old), A: adult. (b) Acousonde: animal-borne multi-sensor tag not equipped with a camera. CATS cam: animal-borne multi-sensor tag equipped with a camera.

Part I:

On the suckling and nursing behavior of humpback whales Chapter 1. Characterizing the suckling behavior by video and 3D-accelerometry in humpback whale calves on a breeding ground

	PeerJ (2022) 10:e12945
	DOI: 10.7717/peerj.12945
	Received: 08 August 2021 / Accepted: 25 January 2022 / Published: 17 February 2022

Maevatiana N. Ratsimbazafindranahaka, Chloé Huetz, Aristide Andrianarimisa, Joy S. Reidenberg, Anjara Saloma, Olivier Adam, Isabelle Charrier

Table S1 .

 S1 Comparison of the number of non-suckling dives during which the calf was observed staying in close proximity beneath the mother for at least five consecutive seconds and the number of suckling dives

		Number
	Individuals	Non-suckling dives	Suckling dives	Total dives
	Calf

observed staying in close proximity beneath the mother for at least 5 s during the dive Calf never observed staying in close proximity beneath the mother for at least 5 s during the dive

	Calf1	17	18	2	37
	Calf2	5	2	3	10
	Calf3	20	21	9	50

Table S2 .

 S2 Summary table of the mixed-effect models for the characteristics of suckling events. The models included the suckling status and activity phase (descent, bottom, ascent, or surface) as fixed effects and individuals as random effects (reference level = bottom and non-suckling, i.e., bottom non-suckling). The data used in the analysis consisted of individual suckling events (18.8 s average duration) and random assortments of non-suckling segments (20 s duration). Significant P (<0.05 in this study) are marked in bold. CI: Confidence interval. SE: Standard Error of estimate

	Response	Fixed effect	Effect estimate (β)	95% CI	SE	t(96)	P
	Average depth rate	(Intercept)	0.06	[-0.05, 0.17]	-0.45	1.07	0.285
		Suckling	-0.01	[-0.15, 0.13]	-0.04	-0.19	0.850
		Descent	0.60	[0.48, 0.72]	1.62	9.54	<0.001
		Surface	-0.02	[-0.2, 0.17]	-0.05	-0.19	0.849
		Suckling*Descent	-0.44	[-0.67, -0.22]	-1.20	-3.82	<0.001
		Suckling*Surface	0.03	[-0.27, 0.32]	0.07	0.17	0.863
	Average speed	(Intercept)	1.62	[1.28, 1.97]	0.05	9.23	<0.001
		Suckling	-0.10	[-0.3, 0.11]	-0.21	-0.91	0.363
		Descent	0.22	[0.04, 0.4]	0.49	2.42	0.016
		Surface	0.30	[0.02, 0.57]	0.66	2.13	0.033
		Suckling*Descent	-0.21	[-0.54, 0.12]	-0.46	-1.23	0.220
		Suckling*Surface	-0.38	[-0.82, 0.05]	-0.85	-1.74	0.082
	Average FSR	(Intercept)	0.09	[0.05, 0.13]	-0.63	4.22	<0.001
		Suckling	0.16	[0.09, 0.22]	1.27	4.95	<0.001
		Descent	0.06	[0, 0.11]	0.46	2.06	0.040
		Surface	0.10	[0.02, 0.18]	0.79	2.41	0.016
		Suckling*Descent	-0.06	[-0.16, 0.04]	-0.47	-1.13	0.258
		Suckling*Surface	-0.12	[-0.25, 0.01]	-0.97	-1.80	0.073
	Average ODBA	(Intercept)	0.43	[0.18, 0.67]	-0.04	3.40	0.001
		Suckling	0.05	[-0.11, 0.22]	0.17	0.64	0.522
		Descent	0.16	[0.02, 0.3]	0.49	2.19	0.028
		Surface	0.10	[-0.12, 0.31]	0.30	0.88	0.377
		Suckling*Descent	-0.20	[-0.46, 0.06]	-0.62	-1.51	0.132
		Suckling*Surface	-0.10	[-0.44, 0.24]	-0.32	-0.60	0.551
	Average pitch	(Intercept)	0.57	[-8.51, 9.66]	0.25	0.12	0.901
		Suckling	6.36	[0.18, 12.53]	0.42	2.02	0.044
		Descent	-18.26	[-23.58, -12.94]	-1.21	-6.73	<0.001
		Surface	-5.05	[-13.15, 3.06]	-0.34	-1.22	0.222
		Suckling*Descent	12.77	[2.93, 22.61]	0.85	2.54	0.011
		Suckling*Surface	1.41	[-11.45, 14.28]	0.09	0.22	0.829
	|Average roll|	(Intercept)	8.98	[4.22, 13.74]	-0.58	3.70	<0.001
		Suckling	38.46	[31.79, 45.13]	1.83 11.30	<0.001
		Descent	-0.44	[-6.29, 5.41]	-0.02	-0.15	0.882
		Surface	0.64	[-7.98, 9.25]	0.03	0.14	0.885
		Suckling*Descent	-0.25	[-11.06, 10.57]	-0.01	-0.04	0.965
		Suckling*Surface	-11.56	[-25.65, 2.52]	-0.55	-1.61	0.108
	Average roll rate	(Intercept)	3.00	[2.06, 3.94]	-0.34	6.25	<0.001
		Suckling	1.17	[-0.05, 2.39]	0.52	1.88	0.060
		Descent	0.32	[-0.75, 1.38]	0.14	0.58	0.559
		Surface	0.28	[-1.3, 1.87]	0.13	0.35	0.726
		Suckling*Descent	0.40	[-1.57, 2.37]	0.18	0.40	0.692
		Suckling*Surface	1.51	[-1.06, 4.08]	0.67	1.15	0.249

Table S3 .

 S3 Comparison of suckling events (18.8 s average duration) and non-suckling segments (20 s duration) with respect to activity phase using Tukey's post-hoc multiple comparison test. Comparisons were computed using the R package emmeans. Significant P (<0.05 in this study) are marked in bold. SE: Standard Error of estimate

	Responses	Contrast	Estimate (β)	SE	df	t	P
	Average	Descent suckling -Descent non-suckling	-0.46	0.1 98	4.8	<0.001
	depth rate	Bottom suckling -Bottom non-suckling	-0.01	0.07 98	0.18	1
		Surface suckling -Surface non-suckling	0.01	0.13 96	-0.09	1
	Average	Descent suckling -Descent non-suckling	-0.30	0.14 96	2.21	0.243
	speed	Bottom suckling -Bottom non-suckling	-0.10	0.11 97	0.91	0.944
		Surface suckling -Surface non-suckling	-0.48	0.19 96	2.48	0.141
	Average FSR	Descent suckling -Descent non-suckling	0.10	0.04 98	-2.32	0.197
		Bottom suckling -Bottom non-suckling	0.16	0.03 94	-4.7	<0.001
		Surface suckling -Surface non-suckling	0.04	0.06 96	-0.61	0.99
	Average	Descent suckling -Descent non-suckling	-0.15	0.11 96	1.36	0.752
	ODBA	Bottom suckling -Bottom non-suckling	0.05	0.08 97	-0.64	0.988
		Surface suckling -Surface non-suckling	-0.05	0.15 96	0.33	0.999
	Average	Descent suckling -Descent non-suckling	19.13	4.08 96	-4.68	<0.001
	pitch	Bottom suckling -Bottom non-suckling	6.36	3.17 97	-2.01	0.345
		Surface suckling -Surface non-suckling	7.77	5.76 96	-1.35	0.756
	Average	Descent suckling -Descent non-suckling	38.21	4.54 98	-8.42	<0.001
	absolute roll	Bottom suckling -Bottom non-suckling	38.46	3.56 96 -10.82	<0.001
		Surface suckling -Surface non-suckling	26.89	6.33 96	-4.25	0.001
	Average roll	Descent suckling -Descent non-suckling	1.57	0.82 98	-1.9	0.406
	rate	Bottom suckling -Bottom non-suckling	1.17	0.64 97	-1.82	0.458
		Surface suckling -Surface non-suckling	2.68	1.15 96	-2.32	0.194

Table S4 .

 S4 Results

of the machine learning optimization procedure for automatically identifying suckling in non-labeled data. A Bayesian optimization procedure was run per model type (Ensemble, KNN, Decision tree, SVM) 30 times in order to select the best model (by seeking to minimize classification error). Values are presented following the format mean±SD. n represents how often each model was selected by the optimization procedure for each type of classifier over the 30 runs. FPR: False positive

rate Classifier Type Models n Sensitivity FPR (%) Precision Specificity F-score Accuracy (%)

	Chapter 2.
	Ensemble	AdaBoostM1	17 0.71±0.04 0.38±0.09 0.89±0.02	1 0.79±0.02	98.48±0.13
	(30 runs)	Bag (Random forest) 4	0.58±0.05 0.27±0.1 0.9±0.04	1 0.71±0.04	98.08±0.26
		GentleBoost	9	0.64±0.14 0.46±0.27 0.84±0.12	1 0.72±0.13	98.11±0.75
	K-Nearest
	Neighbors (KNN)	Cityblock	15 0.66±0.05 0.51±0.24 0.85±0.05	0.99 0.74±0.03	98.14±0.21
	(30 runs)	Cosine	1	0.6	0.47	0.84	1	0.7	97.99
		Euclidean	2	0.55±0.06 0.63±0.28 0.79±0.06	0.99 0.65±0.02	97.6±0.05
		Minkowski	12 0.69±0.05 0.44±0.12 0.87±0.03	1 0.76±0.03	98.32±0.21
	Decision tree:	Deviance	19 0.54±0.11 0.99±0.51 0.71±0.08 0.99±0.01 0.6±0.06	97.21±0.29
	(30 runs)	Gdi	11 0.54±0.05 0.84±0.37 0.74±0.06	0.99 0.62±0.03	97.36±0.22
	Support Vector
	Machine (SVM)	Gaussian	26 0.58±0.21 0.55±0.32 0.82±0.07	0.99 0.64±0.23	97.78±0.7
	(30 runs)	Linear	2	0.52±0.01 0.72±0.16 0.75±0.04	0.99 0.61±0.01	97.41±0.14
		Polynomial	2	0.66±0.02 1.76±0.56 0.61±0.07 0.98±0.01 0.63±0.03	96.93±0.45

Humpback whale suckling behavior: an insight into the mother-offspring strategy in mysticetes

  Maevatiana N. Ratsimbazafindranahaka, Chloé Huetz, Joy S. Reidenberg, Anjara Saloma, Aristide Andrianarimisa, Isabelle Charrier, Olivier Adam

	Behavioral Ecology and Sociobiology (2023) 77:96
	DOI: 10.1007/s00265-023-03369-9
	Received: 23 April 2023 / Accepted: 25 July 2023 / Published: 12 August 2023

Table S1 .

 S1 Summary of the deployments used for studying the suckling behavior in humpback whale

	Deployment ID	Pair ID	Category Age a	Tag on date (local) dd-mmm

-yyyy hh:mm Tag off date (local) dd-mmm-yyyy hh:mm

  relative age. For the calves, the relative age was estimated based on the coloration, skinfolds, and angle of furling of the dorsal fin. C1: neonate, C2: very young but non-neonate, C3: older calves (but <3 months old), A: adult. b Acousonde: animal-borne multi-sensor tag not equipped with a camera. CATS cam: animal-borne multi-sensor tag equipped with a camera. Calf deployment with available nighttime data (i.e., with data between 1800 and 0500 d+1 local time)

	dep9_05sept2016_Calf*	G024	Calf	C3	05-Sep-2016 13:54	05-Sep-2016 22:54	9.01	0	Acousonde
	dep9_05sept2016_Mother	G024	Mother	A	05-Sep-2016 13:53	05-Sep-2016 21:40	7.78	0	Acousonde
	Jaune_06aout2019_Calf Jaune_14sept2018_Calf dep8_10sept2014_Calf Orange_09aout2019_Calf* dep8_18aout2016_Calf Orange_19Aout2022_Calf dep8_18aout2016_Mother Orange_26Aout2022_Calf	G048 G049 G013 G050 G023 G044 G023 G045	Calf Calf Calf Calf Calf Calf Mother Calf	C3 C3 C1 C3 C1 C3 A C3	06-Aug-2019 10:56 14-Sep-2018 11:34 10-Sep-2014 13:51 09-Aug-2019 10:03 18-Aug-2016 13:13 19-Aug-2022 09:35 18-Aug-2016 13:44 26-Aug-2022 09:26	06-Aug-2019 12:34 14-Sep-2018 14:18 10-Sep-2014 15:44 09-Aug-2019 18:44 18-Aug-2016 14:19 19-Aug-2022 15:29 18-Aug-2016 23:58 26-Aug-2022 11:12	Duration (h): all data 1.61 2.48 1.89 7.95 1.10 5.88 10.24 1.78	1.61 Duration (h): 2.48 data with 'good' video 0. 6.42 5.88 1.07	CATS cam Tag model b CATS cam CATS cam Acousonde CATS cam Acousonde Acousonde CATS cam
	dep1_20aout2014_Calf	G006	Calf	C2	20-Aug-2014 11:08	20-Aug-2014 11:52	0.73		Acousonde
	dep15-03.09.2017_Calf	G038	Calf	C2	03-Sep-2017 10:30	03-Sep-2017 11:33	0.48		Acousonde
	dep15-03.09.2017_Mother	G038	Mother	A	03-Sep-2017 10:08	03-Sep-2017 12:36	2.47		Acousonde
	dep6-13.08.2017_Calf	G028	Calf	C2	13-Aug-2017 11:52	13-Aug-2017 14:39	2.78		Acousonde
	dep7-15.08.2017_Calf	G029	Calf	C2	15-Aug-2017 15:22	15-Aug-2017 15:46	0.39		Acousonde
	dep8-16.08.2017_Calf	G030	Calf	C2	16-Aug-2017 09:03	16-Aug-2017 16:18	7.25		Acousonde
	dep9_11sept2014_Calf	G014	Calf	C2	11-Sep-2014 12:22	11-Sep-2014 12:54	0.54		Acousonde
	dep9-21.08.2017_Calf	G031	Calf	C2	21-Aug-2017 12:13	21-Aug-2017 15:28	3.26		Acousonde
	Orange_01Sept2022_Calf*	G039	Calf	C2	01-Sep-2022 09:56	01-Sep-2022 18:09	8.21	6.70	CATS cam
	Orange_05Sept2022_Calf	G040	Calf	C2	05-Sep-2022 09:02	05-Sep-2022 15:14	6.19	5.62	CATS cam
	Orange_07Aout2022_Calf	G041	Calf	C2	07-Aug-2022 10:57	07-Aug-2022 14:14	3.27	2.88	CATS cam
	Orange_13Sept2022_Calf	G042	Calf	C2	13-Sep-2022 13:42	13-Sep-2022 16:15	2.56	2.56	CATS cam
	Orange_15Aout2022_Calf	G043	Calf	C2	15-Aug-2022 11:00	15-Aug-2022 11:57	0.95	0.95	CATS cam
	Orange_30Aout2022_Calf*	G046	Calf	C2	30-Aug-2022 14:48	30-Aug-2022 18:34	3.77	2.97	CATS cam
	Orange_31July2022_Calf	G047	Calf	C2	31-Jul-2022 10:52	31-Jul-2022 13:02	2.15		CATS cam
	dep1_01aout2016_Calf	G016	Calf	C3	01-Aug-2016 09:40	01-Aug-2016 10:09	0.48		Acousonde
	dep11_17sept2014_Calf	G015	Calf	C3	17-Sep-2014 12:28	17-Sep-2014 12:56	0.46		Acousonde
	dep12-26.08.2017_Calf*	G033	Calf	C3	26-Aug-2017 14:57	27-Aug-2017 08:19	17.36		Acousonde
	dep13-28.08.2017_Calf	G034	Calf	C3	28-Aug-2017 10:25	28-Aug-2017 15:53	5.46		Acousonde
	dep14-01.09.2017_Calf	G036	Calf	C3	01-Sep-2017 09:15	01-Sep-2017 11:42	2.45		Acousonde
	dep2_02aout2016_Calf	G017	Calf	C3	02-Aug-2016 14:37	02-Aug-2016 16:51	2.24		Acousonde
	dep2_02aout2016_Mother	G017	Mother	A	02-Aug-2016 14:40	02-Aug-2016 15:51	1.18		Acousonde
	dep2_22aout2014_Calf	G007	Calf	C3	22-Aug-2014 09:52	22-Aug-2014 14:53	5.01		Acousonde
	dep2-04.08.2017_Calf	G025	Calf	C3	04-Aug-2017 14:04	04-Aug-2017 15:02	0.96		Acousonde
	dep4-10.08.2017_Calf	G026	Calf	C3	10-Aug-2017 12:30	10-Aug-2017 13:48	1.30		Acousonde
	dep4-10.08.2017_Mother	G026	Mother	A	10-Aug-2017 12:26	10-Aug-2017 13:44	1.31		Acousonde
	dep5_29aout2014_Calf	G010	Calf	C3	29-Aug-2014 07:57	29-Aug-2014 08:24	0.45		Acousonde
	dep5-11.08.2017_Calf	G027	Calf	C3	11-Aug-2017 11:39	11-Aug-2017 16:47	5.13		Acousonde
	dep6_08sept2014_Calf	G011	Calf	C3	08-Sep-2014 09:29	08-Sep-2014 12:50	3.34		Acousonde
	dep7_09sept2014_Calf*	G012	Calf	C3	09-Sep-2014 14:40	09-Sep-2014 19:32	4.87		Acousonde
	dep7_12sept2013_Calf	G005	Calf	C3	12-Sep-2013 09:44	12-Sep-2013 12:56	3.21		Acousonde
	dep7_17aout2016_Calf	G022	Calf	C3	17-Aug-2016 11:20	17-Aug-2016 13:52	2.53		Acousonde
	dep7_17aout2016_Mother	G022	Mother	A	17-Aug-2016 11:28	17-Aug-2016 18:01	6.55		Acousonde

a *

Table S2 .

 S2 Suckling side preferences in humpback whale calves tagged with Acousonde or CATS cam tags. The counts include visually confirmed suckling events and automatically detected suckling events. Deployments in which no suckling events have been detected are not shown

		Suckling event counts
	Deployment/Calf ID	Left (Negative roll)	Right (Positive roll)
	dep12-26.08.2017_Calf	20	16
	dep13-28.08.2017_Calf	6
	dep14-01.09.2017_Calf	2
	dep2-04.08.2017_Calf	1
	dep2_02aout2016_Calf	3
	dep2_22aout2014_Calf	4
	dep4-10.08.2017_Calf	3
	dep5-11.08.2017_Calf	4
	dep6_08sept2014_Calf	0
	dep7-15.08.2017_Calf	0
	dep7_09sept2014_Calf	2
	dep7_12sept2013_Calf	4
	dep7_17aout2016_Calf	0
	dep8-16.08.2017_Calf	10	13
	dep8_10sept2014_Calf	1
	dep8_18aout2016_Calf	4
	dep9-21.08.2017_Calf	0
	dep9_05sept2016_Calf	6
	Jaune_06aout2019_Calf	1
	Jaune_14sept2018_Calf	2
	Orange_01Sept2022_Calf	3
	Orange_05Sept2022_Calf	1
	Orange_07Aout2022_Calf	5
	Orange_09aout2019_Calf	14	13
	Orange_13Sept2022_Calf	5
	Orange_19Aout2022_Calf	5
	Orange_26Aout2022_Calf	2
	Orange_30Aout2022_Calf	6
	Orange_31July2022_Calf	1
	Total	115	129

Keeping up with mom: the swimming behavior of humpback whale mother-calf pairs Chapter 3. Time budget and behavioral synchrony of humpback whale mother-calf pairs

			Chapter 3
	Synthesis
	How
		Suckling series counts
	Starting side: Maevatiana N. Ratsimbazafindranahaka, Chloé Huetz, Anjara Saloma, Aristide Deployment/Calf ID Left (Negative roll) Right (Positive roll) dep12-26.08.2017_Calf 16 16 Andrianarimisa, Isabelle Charrier, Olivier Adam
	dep13-28.08.2017_Calf	4	3
	dep14-01.09.2017_Calf	2	1
	dep2-04.08.2017_Calf	1	1
	dep2_02aout2016_Calf	2	4
	dep2_22aout2014_Calf	2	2
	dep4-10.08.2017_Calf	2	1
	dep5-11.08.2017_Calf	1	3
	dep6_08sept2014_Calf	0	1
	dep7-15.08.2017_Calf	0	1
	dep7_09sept2014_Calf	2	1
	dep7_12sept2013_Calf	1	1
	dep7_17aout2016_Calf	0	2
	dep8-16.08.2017_Calf	9	12
	dep8_10sept2014_Calf	1	3
	dep8_18aout2016_Calf	3	1
	dep9-21.08.2017_Calf	0	1
	dep9_05sept2016_Calf	4	5
	Jaune_06aout2019_Calf	1	2
	Jaune_14sept2018_Calf	1	1
	Orange_01Sept2022_Calf	3	2
	Orange_05Sept2022_Calf	1	2
	Orange_07Aout2022_Calf	2	2
	Orange_09aout2019_Calf	9	2
	Orange_13Sept2022_Calf	1	2
	Orange_19Aout2022_Calf	3	1
	Orange_26Aout2022_Calf	0	1
	Orange_30Aout2022_Calf	4	2
	Orange_31July2022_Calf	1	2
	Total	76	78
		Submitted to Marine Mammal Science
			107

https://doi.org/10.1007/s00265-023-03369-9 Data S1. Dataset generated and analyzed during the study Part II:

are the swimming and resting activities of mother-calf pairs organized in time? Methods Tag

  dataset from 47 mother-calf pairs was used: data from 31 single deployments on calves, 10 single deployments on mothers, and six simultaneous deployments (thus, 53 individuals). A HMM was used to model the time organization of the activities of the mothers and the calves based on their swimming speed. The variation of this time organization with age was then investigated using LMM. With the simultaneous deployments, the behavioral synchrony was analyzed by calculating the proportion of synchronized behavior, the behavioral independence score, and Hinde's association index.

Table 1 .

 1 Synchronized behaviors of six mother-calf pairs off Sainte Marie, Madagascar. HS: directional fast swimming, MS: directional moderate swimming, Mi: milling, and Re: resting. Async.: no period of synchrony has been observed

	Behavioral independence scores	Initiation	Termination

  45 m using Tukey's post-hoc multiple comparisons tests following a linear mixed-effects model (LMM) for three age classes C1-C2 calves, C3 calves, and Adult females. Bold P-values indicate statistically significant differences

		C1-C2		C3		Adult
	Depth (m)	Down/Up	P	Down/Up	P	Down/Up	P
	5	1.438	<0.001	1.387	<0.001	1.031	0.995
	15	1.277	<0.001	1.087	<0.001	1.009	1
	25	1.134	0.649	0.853	<0.001	0.988	1
	35	1.007	1	0.668	<0.001	1.967	>0.999
	45	0.894	0.989	0.524	<0.001	1.947	>0.999

Table S2 .

 S2 Mother-calf call succession patterns and their behavioral context. The total number of calls is provided next to the deployment ID for each deployment (mothercalf pair). The separation of the calls into sequences was based on the calf's diving/surfacing behaviours

	Deployment ID	Sequence (count)	Context
	Dep01 (total = 4)	C-C (2)

Table 1 .

 1 Result of a random forest analysis (confusion matrix) in predicting the behavioral states based on the presence/absence of the 13 main call types identified

and the call rate. Mi: milling, Pl: surface active playing session, Tg: response to tagging, Re: resting, Su: suckling session, T: traveling. Bold P indicates a statistically significant difference between the correct prediction rate and the rate expected by chance

Predicted class Mi Pl Tg Re Su T % correct % chance P True class

	Mi	0	8	21	371 23 0 0	17	1
	Pl	0	4	2	7	2	0 27	17	0.09
	Tg	0	0	2	4	2	0 25	17	0.135
	Re	0	1	2	187 2	0 97	17	0
	Su	0	0	4	3	5	0 42	17	0.008
	T	0	1	3	42	4	0 0	17	1
	Overall						28	17	0

Table S1 .

 S1 Characteristics of the 13 call types (#1-13) from eight calves tagged with a CATS cam tag identified using a Hierarchical Clustering on Principal Components (HCPC). The values are presented in the form mean±SD(min-max)

	Type #	N	Dur (s)	F0 (Hz)	Fmax (Hz)	Q25 (Hz)	Q50 (Hz)	Q75 (Hz)	Bdw (Hz)
	1	14	2±0.6 (1.4-3.3)	40±15 (24-82)	46±20 (30-86)	61±21 (37-112)	128±57 (67-278)	297±156 (115-627)	87±51 (8-158)
	2	81	0.7±0.2 (0.5-1.2) 40±17 (16-141)	56±34 (28-187)	68±36 (33-297)	126±72 (39-523)	261±137 (95-768)	88±59 (4-304)
	3	300	0.3±0.1 (0.1-0.5) 45±22 (22-137)	57±38 (0-290)	68±28 (33-150)	122±56 (36-348)	241±113 (42-741)	128±110 (5-659)
	4	43	0.3±0.1 (0.1-0.5) 204±49 (120-334) 217±63 (106-416)	215±48 (143-316)	363±134 (190-782)	1153±549 (275-2431)	189±135 (14-550)
	5	16	0.3±0.1 (0.1-0.6) 79±56 (26-186)	190±68 (64-370)	285±133 (131-647)	539±208 (246-896)	1039±502 (375-2405)	686±147 (483-943)
	6	19	0.3±0.1 (0.2-0.8) 186±83 (30-292)	216±80 (134-445)	366±133 (175-644)	1099±423 (603-2498)	2909±581 (1916-4341) 371±284 (29-833)
	7	5	1±0.6 (0.3-1.5)	434±41 (396-497) 588±338 (405-1190)	487±212 (345-863)	657±308 (444-1190)	1811±499 (931-2144)	172±212 (11-436)
	8	9	0.2±0.1 (0.1-0.5) 65±53 (38-205)	717±74 (667-910)	644±162 (287-800)	889±131 (708-1126)	1721±950 (981-3826)	901±331 (505-1470)
	9	12	0.4±0.2 (0.1-0.9) 186±91 (55-313)	263±141 (142-670)	634±137 (436-968)	1145±278 (755-1494)	2013±408 (1347-2458) 1510±291 (1136-2018)
	10	4	0.2±0.1 (0.1-0.3) 210±79 (105-296) 1388±165 (1154-1532) 852±217 (626-1083)	1401±211 (1136-1640) 2217±488 (1722-2824) 1656±420 (1042-1939)
	11	3	0.3±0.1 (0.3-0.4) 161±80 (69-210)	324±105 (210-416)	943±545 (407-1497)	2216±209 (2053-2452) 3451±353 (3055-3735) 3009±1074 (2000-4138)
	12	1	0.6	993	988	963	1675	4111	363
	13	1	0.5	123	2099	2024	2280	3398	301

https://doi.org/10.5281/zenodo.8362299 Audio S1. Example of calls from each identified cluster

Acknowledgments

Before starting, I would like to express my deepest gratitude to the wonderful people who made this thesis possible, gave me the strength to finish, and enriched my academic journey. Their support and friendship have been instrumental in making all

Acknowledgements

We warmly thank the Cétamada team members who contributed to the data collection. We also thank Eric Ward and Jennifer Tennessen (NOAA), who provided the R scripts for implementing the hidden Markov models used in our study. This study was supported and funded by the Centre National de la Recherche Scientifique (CNRS), the Cétamada association, the Western Indian Ocean Marine Science Association (MARG Program), the Society for Marine Mammalogy (2021 Research Grant), the Association for Tropical Biology and Conservation (ATBC seed grant), the CeSigma company, and donors who supported the study via the KissKissBankBank crowdfunding platform for MNR. MNR received a cotutelle Ph.D. scholarship from the ADI2020 project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02. The data collections were conducted under the national research permits #44/13-MPRH/SG/DGPRH, #43/14-MRHP/SG/DGRHP, #46/15-MRHP/SG/DGRHP, #28/16-MRHP/SG/DGRHP, #26/17-MRHP/SG/DGRHP and #28/18-MRHP/SG/DGRHP, #36/19-MAEP/SG/DGPA, #30/21-MAEP/SG/DGPA, and #54/22-MPEB/SG/DGPA and complied with the European Union Directive on the Protection of Animals Used for Scientific Purposes (EU Directive 2010/63/EU).

Acknowledgements

We warmly thank the Cétamada team, who contributed to the data collection. 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 MNR's research.

https://doi.org/10.7717/peerj.12945 Data S1. Raw data containing characteristics of suckling events (18.8 s average duration) and non-suckling periods (20 s duration) obtained from CATS cam tags deployed on three humpback whale calves in the Sainte Marie channel, Madagascar. Each line corresponds to either a suckling event or a 20-s non-suckling period selected randomly. The corresponding metadata is provided https://doi.org/10.7717/peerj.12945 Data S2. Raw data from CATS cam tags deployed on three humpback whale calves in the Sainte Marie channel, Madagascar, used for testing the automatic identification of suckling behavior using supervised machine learning. Lines correspond to 2 s nonoverlapping sliding blocks (windows). The corresponding metadata is provided https://doi.org/10.7717/peerj.12945 Code S1. R scripts for analyzing the behavioral signatures of suckling events and interindividual differences between suckling blocks https://doi.org/10.7717/peerj.12945 Code S2. MATLAB scripts for tuning and training supervised machine learning algorithms for automatic identification of suckling behavior

Supplementary information

Figure S1. Supervised machine learning workflow for automatically identifying suckling behavior in free-ranging humpback whale calves using CATS cam tag data. The data derived from the 3-axis accelerometer and the depth sensor of the tags, sampled at 10 Hz, were split into 2 s non-overlapping blocks (windows), and each block was labeled either as 'suckling' or 'non-suckling' depending on which behavioral period it fell under (suckling period or any non-suckling period). The data underwent segmentation to remove noise and reduce the class imbalance partially. The thresholds we used (<1.5 m depth and >2 m s -1 speed) were based on the known characteristics of the nursing/suckling behavior of humpback whales, and their validity in our dataset was checked to ensure that no suckling events were removed partially or entirely following the segmentation. The pie charts represent the class distribution (suckling versus non-suckling). N = 18331 and N = 7827 before and after segmentation, respectively

Supplementary information

Abstract

In capital breeders, like most mysticetes whales, the mothers rely on a limited energy reserve to care for their young while sustaining themselves. Understanding the activities and time budget can inform how the energy is allocated in mother-young pairs and its relation to the development of the young. In the present study, we compared the time budgets of the mothers and the calves in humpback whales, investigated the changes in time budgets as the calves age, and assessed the mothercalf behavioral synchrony using suction cup tag data collected in Sainte Marie, Madagascar. We found that mothers and calves allocated much of their time to resting. We also found that young calves tended to engage more in milling than older calves. The calves displayed some degree of independence in their behaviors but were also frequently observed synchronizing their activities with their mothers (50% of the time on average). Depending on the nature of the activity, the initiation of an activity by the pair could be driven by either the calf or the mother. Our study provided additional knowledge for understanding the behavioral dynamics and interactions within mothercalf pairs in aquatic capital breeders. The small red area corresponds to the targeted spot, and the light red area (including the small red area) corresponds to the area within which the tags were typically successfully placed ?-? (2) Calf at the surface (the mother stayed at the bottom) C-C-C -C (4)

Calf at the surface (the mother stayed at the bottom) ? [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Partially synchronous surface activity (the calf was at the surface before the mother)

The call occurred after the reunion ?-C-C-C -C [START_REF] Chen | Using random forest to learn imbalanced data[END_REF] Partially synchronous surface activity (The calf was at the surface before the mother)

The first call occurred before the reunion The remaining calls occurred after the reunion C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Partially synchronous surface activity (the calf was at the surface before the mother, and the mother dove before the calf) C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Reunion dive (mother at depth) and separated ascent (the calf arrived at the surface before the mother)

Partially synchronous surface activity (The calf was at the surface before the mother, and the mother dove before the calf)

The first call occurred before the reunion The remaining calls occurred after the reunion, before the separation C-C [START_REF] Reidenberg | Anatomy of Sound Production and Reception[END_REF] Reunion dive (mother at depth) and separated ascent (calf arrived at the surface first)

The calls occurred before the vertical separation M-M-M-C (4)

Partially synchronous surface activity (the calf was at the surface before the mother)

The call sequence occurred after the reunion M [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Synchronous descent and separated ascent (the calf was at the surface before the mother) M-C-M-M-M-C ( 6)

Partially synchronous surface activity (the calf was at the surface before the mother)

The call sequence occurred after the reunion M [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Partially synchronous surface activity (The calf was at the surface before the mother)

The call occurred after the reunion C [START_REF] Sarano | Nursing behavior in sperm whales (Physeter macrocephalus)[END_REF] Calf at the surface (The mother stayed at the bottom) C-?-C-? (4)

Calf at the surface (The mother stayed at the bottom) ?-C-?-C-C-?-? (7)

Calf at the surface (The mother stayed at the bottom) C ( 1