Diving Behavior of Mirounga leonina: A Functional Data Analysis Approach

• Published in: Frontiers in Marine Science
• Main Authors: Godard, Morgan, Manté, Claude, Guinet, Christophe, Picard, Baptiste, Nerini, David
• Other Authors: Aix Marseille Université (AMU), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: elephant seals; foraging behavior; functional data analysis; capture events; dive; curves; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; [STAT]Statistics [stat]; Elephant Seals; Mirounga leonina; Southern Elephant Seals
• Online Access: https://hal.archives-ouvertes.fr/hal-02969745; https://hal.archives-ouvertes.fr/hal-02969745/document; https://hal.archives-ouvertes.fr/hal-02969745/file/fmars-07-00595-1.pdf; https://doi.org/10.3389/fmars.2020.00595

International audience The diving behavior of southern elephant seals, Mirounga leonina, is investigated through the analysis of time-depth dive profiles. The originality of this work is to consider dive profiles as continuous curves. For this purpose, a Functional Data Analysis (FDA) approach is proposed for the shape analysis of a collection of dive profiles. Complexity of dive shapes is characterized by a mixture of three main shape variations accounting for about 80% of the entire variability: U or V shape, vertical depth variability during the bottom time, and skewed left or right. Model-based clustering allows the identification of eight dive shape clusters in a quick and automated way. Connection between shape patterns and classical descriptors, as well as the number of prey capture events, is achieved, showing that the clusters are coherent to specific foraging behaviors previously identified in the literature labeled as drift, exploratory and active dives. Finally, FDA is compared to classical methods relying on the computation of discrete dive descriptors. Results show that taking the shape of the dive as a whole is more resilient to corrupted or incomplete sampled data. FDA is, therefore, an efficient tool adapted for processing and comparing dive data with different sampling frequencies and for improving the quality and the accuracy of transmitted data.