Diving Behavior of Mirounga leonina: A Functional Data Analysis Approach

• Published in: Frontiers in Marine Science
• Main Authors: Morgan Godard, Claude Manté, Christophe Guinet, Baptiste Picard, David Nerini
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2020
• Subjects: elephant seals; foraging behavior; functional data analysis; capture events; dive; curves; Science; Q; General. Including nature conservation; geographical distribution; QH1-199.5; Elephant Seals; Mirounga leonina; Southern Elephant Seals
• Online Access: https://doi.org/10.3389/fmars.2020.00595; https://doaj.org/article/db6ed893d13b4b83a23050aaeaa2165f

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.