Abstract
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.
Highlights
In a global warming context, studying and understanding the foraging behavior of predators and their relationships with their surrounding environment is fundamental for conservation and management programs (Runge et al, 2014)
This study demonstrates the potentiality of the FDA method for the characterization and the classification of time-depth dive profiles
Using the shape of dive profiles enables us to obtain, in a very visual way, results that are ecologically consistent and coherent with previous dive classification methods implemented on elephant seal diving data
Summary
In a global warming context, studying and understanding the foraging behavior of predators and their relationships with their surrounding environment is fundamental for conservation and management programs (Runge et al, 2014). Technological advances have led to size and weight reduction of autonomous devices with gains in battery lifetime and memory size as well as new variables monitored. This emergence of new data sampled at high frequency (Block et al, 2011) enables the study of diving behaviors at various scales in time and space (Jonsen et al, 2007; Scheffer et al, 2012; Nowacek et al, 2016). Time Depth Recorders (TDRs) provide a large amount of data that are downloaded from sensors to
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