Résumé
Animals most often move in a non-homogeneous way as a long movement path through a heterogeneous landscape that corresponds to a sequence of various behavioural states. Hence, a large majority of movement analyses make the assumption that long movements combine typical behaviours like intensive search or resting which are separated by sharp transitions. This study aimed at providing an alternative method for identifying intensive search areas using sharp as well as more continuous (smooth) transitions. I proposed analyzing movement data over temporal and spatial scales by the use of the wavelet analysis and drew inferences about the behaviours that shape movements. I computed a synthetic index built with wavelet time-spectra of turning angle and speed parameters, this method offered a robust and automatic way to characterize movement transitions. The first step was to work on simulated movements to define the confidence levels of detection. The second was to illustrate the use of wavelet analysis on the movements of wandering albatrosses. As a result, this study outlined two fundamental areas of interest in animal movement analysis: i) it is relevant to select behavioural modes with continuous transitions between them along the animal’s movement, as it is done with usual segmentation methods; ii) to suppose that every behaviour and every transition between them is intrinsically multiscale (i.e. with a scaling property) appeared to be an interesting approach to identify and characterize them. The mathematical robustness and predictive ability of wavelet analysis make it a promising road towards multiscale movement ecology that fuses insights from the study of animal behaviour and environmental properties.