Characterizing the suckling behavior by video and 3D-accelerometry in humpback whale calves on a breeding ground - Sorbonne Université Access content directly
Journal Articles (Review Article) PeerJ Year : 2022

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

Chloé Huetz
  • Function : Author
Aristide Andrianarimisa
  • Function : Author
Joy Reidenberg
  • Function : Author
Anjara Saloma
  • Function : Author
Olivier Adam
Isabelle Charrier
  • Function : Author

Abstract

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 in humpback whales and the baleen whale species.

Dates and versions

hal-03950899 , version 1 (22-01-2023)

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Maevatiana Ratsimbazafindranahaka, Chloé Huetz, Aristide Andrianarimisa, Joy Reidenberg, Anjara Saloma, et al.. Characterizing the suckling behavior by video and 3D-accelerometry in humpback whale calves on a breeding ground. PeerJ, 2022, 10, pp.e12945. ⟨10.7717/peerj.12945⟩. ⟨hal-03950899⟩
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