Analysis of temporal patterns in animal movement networks

We implemented our method in R. We provide the codes and the bumblebee and black kite datasets in Dryad Digital Repository https://doi.org/10.5061/dryad.47d7wm390 (Pasquaretta et al., 2015). The roe deer dataset was obtained from MOVEBANK (Wikelski & Kays, 2020). Animal Identifier: Sandro (M06), from Cagnacci et al. (2011) (https://www.movebank.org/). The wolf dataset was obtained from MOVEBANK (Wikelski & Kays, 2020), Animal identifier: Zimzik, from Kaczensky et al. (2006) (https://www.movebank.org/).We implemented our method in R. We provide the codes and the bumblebee and black kite datasets in Dryad Digital Repository https://doi.org/10.5061/dryad.47d7wm390 (Pasquaretta et al., 2015). The roe deer dataset was obtained from MOVEBANK (Wikelski & Kays, 2020). Animal Identifier: Sandro (M06), from Cagnacci et al. (2011) (https://www.movebank.org/). The wolf dataset was obtained from MOVEBANK (Wikelski & Kays, 2020), Animal identifier: Zimzik, from Kaczensky et al. (2006) (https://www.movebank.org/).International audience1. Understanding how animal movements change across space and time is a fundamental question in ecology. While classical analyses of trajectories give insightful descriptors of spatial patterns, a satisfying method for assessing the temporal succession of such patterns is lacking.2. Network analyses are increasingly used to capture properties of complex animal trajectories in simple graphical metrics. Here, building on this approach, we introduce a method that incorporates time into movement network analyses based on temporal sequences of network motifs.3. We illustrate our method using four example trajectories (bumblebee, black kite, roe deer, wolf) collected with different technologies (harmonic radar, platform terminal transmitter, global positioning system). First, we transformed each trajectory into a spatial network by defining the animal's coordinates as nodes and movements in between as edges. Second, we extracted temporal sequences of network motifs from each movement network and compared the resulting behavioural profiles to topological features of the original trajectory. Finally, we compared each sequence of motifs with simulated Brownian and Lévy random motions to statistically determine differences between trajectories and classical movement models.4. Our analysis of the temporal sequences of network motifs in individual movement networks revealed successions of spatial patterns corresponding to changes in behavioural modes that can be attributed to specific spatio‐temporal events of each animal trajectory. Future applications of our method to multi‐layered movement and social network analysis yield considerable promises for extending the study of complex movement patterns at the population level