Fine-scale foraging movements and energetics in penguins

• Main Author: Sutton, Grace
• Other Authors: Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de La Rochelle, Deakin university (Geelong, Australie), John Arnould, Charles-André Bost
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: HAL CCSD 2021
• Subjects: Energetics; Penguin; Prey; Énergétique; Manchot; Proie; [SDV.SA]Life Sciences [q-bio]/Agricultural sciences; Manchots; Eudyptes chrysolophus; Macaroni penguin
• Online Access: https://theses.hal.science/tel-03719573; https://theses.hal.science/tel-03719573/document; https://theses.hal.science/tel-03719573/file/2021SUTTON181426.pdf

Quantifying predator-prey interactions can be logistically difficult, especially in marine environments. However, it is essential to predict how individuals respond to changes in prey availability, an important factor in assessing the impact of climate change. In comparison to flying seabirds, penguins (Family: Spheniscidae) experience greater constraints when breeding due to restrictions in foraging range. As such, this group of seabirds are considered good indicators of local ecosystem health. Animal-borne video cameras have made it possible to observe behaviour in response to prey field. In the present study, a combination of animal-borne video cameras, accelerometers, dive recorders and GPS were used to determine the factors influencing foraging effort and efficiency in penguins. These were investigated in 3 species: 1) little penguin, Eudyptula minor; 2) African penguin, Spheniscus demersus, 3) Macaroni penguin, Eudyptes chrysolophus. In each species, the immediate prey field dictated the 3-dimensional movement in the water column. Foraging effort in little penguins was influenced by the abundance of prey, not prey type. The mean body acceleration of little penguins was examined as an index of effort and was found to be highly correlated to energy expenditure rates determined from doubly-labelled water. Machine learning was used to detect prey captures which were validated using video cameras in African and Macaroni penguins. It was found that African penguins exhibited pelagic dives and a large proportion of successful benthic dives. Benthic dives were costlier but more successful than pelagic ones, indicating a trade-off between effort and success. Macaroni penguins displayed prey-specific behaviour, diving deep when foraging on subantarctic krill (Euphausia vallentini) and completing shallow dives when targeting juvenile fish.This body of work highlights the effect of prey field and the drivers of variability in foraging behaviour. Quantifier les interactions prédateur-proie peut être difficile sur le ...