Characterizing the suckling behavior by video and 3D-accelerometry in humpback whale calves on a breeding ground

• Published in: PeerJ
• Main Authors: Ratsimbazafindranahaka, Maevatiana, Huetz, Chloé, Andrianarimisa, Aristide, Reidenberg, Joy, Saloma, Anjara, Adam, Olivier, Charrier, Isabelle
• Other Authors: Institut des Neurosciences Paris-Saclay (NeuroPSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Département de Zoologie et Biodiversité Animale, Université d'Antananarivo, Center for Anatomy and Functional Morphology, Association Cétamada, Institut Jean Le Rond d'Alembert (DALEMBERT), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: Automatic identification; Breeding area; Mother-calf interaction; Multi-sensor tag; Nursing; Suckling; [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology; [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior; [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences; Indian; Humpback Whale; Megaptera novaeangliae
• Online Access: https://hal.science/hal-03617037; https://hal.science/hal-03617037/document; https://hal.science/hal-03617037/file/Ratsimbazafindranahaka_et_al_peerJ_2022.pdf; https://doi.org/10.7717/peerj.12945

International audience Getting maternal milk through nursing is vital for all newborn mammals. Despite its importance, nursing has been poorly documented in humpback whales ( Megaptera novaeangliae ). Nursing is difficult to observe underwater without disturbing the whales and is usually impossible to observe from a ship. We attempted to observe nursing from the calf’s perspective by placing CATS cam tags on three humpback whale calves in the Sainte Marie channel, Madagascar, Indian Ocean, during the breeding seasons. CATS cam tags are animal-borne multi-sensor tags equipped with a video camera, a hydrophone, and several auxiliary sensors (including a 3-axis accelerometer, a 3-axis magnetometer, and a depth sensor). The use of multi-sensor tags minimized potential disturbance from human presence. A total of 10.52 h of video recordings were collected with the corresponding auxiliary data. Video recordings were manually analyzed and correlated with the auxiliary data, allowing us to extract different kinematic features including the depth rate, speed, Fluke Stroke Rate (FSR), Overall Body Dynamic Acceleration (ODBA), pitch, roll, and roll rate. We found that suckling events lasted 18.8 ± 8.8 s on average ( N = 34) and were performed mostly during dives. Suckling events represented 1.7% of the total observation time. During suckling, the calves were visually estimated to be at a 30–45° pitch angle relative to the midline of their mother’s body and were always observed rolling either to the right or to the left. In our auxiliary dataset, we confirmed that suckling behavior was primarily characterized by a high average absolute roll and additionally we also found that it was likely characterized by a high average FSR and a low average speed. Kinematic features were used for supervised machine learning in order to subsequently detect suckling behavior automatically. Our study is a proof of method on which future investigations can build upon. It opens new opportunities for further investigation of suckling behavior ...