Is pre-breeding prospecting behaviour affected by snow cover in the irruptive snowy owl? A test using state-space modelling and environmental data annotated via Movebank

• Published in: Movement Ecology
• Main Authors: Therrien, Jean-François, Pinaud, David, Gauthier, Gilles, Lecomte, Nicolas, Bildstein, Keith L, Bety, Joël
• Other Authors: Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2015
• Subjects: Env-DATA; Dispersal; Snowy owl; Pre-breeding movements; Snow; State-space model; Movebank; [SDE]Environmental Sciences; Arctic; snowy owl
• Online Access: https://hal.archives-ouvertes.fr/hal-01207123; https://doi.org/10.1186/s40462-015-0028-7

International audience Background: Tracking individual animals using satellite telemetry has improved our understanding of animalmovements considerably. Nonetheless, thorough statistical treatment of Argos datasets is often jeopardized by theircoarse temporal resolution. State-space modelling can circumvent some of the inherent limitations of Argos datasets,such as the limited temporal resolution of locations and the lack of information pertaining to the behavioural state ofthe tracked individuals at each location. We coupled state-space modelling with environmental characterisation ofmodelled locations on a 3-year Argos dataset of 9 breeding snowy owls to assess whether searching behaviour forbreeding sites was affected by snow cover and depth in an arctic predator that shows a lack of breeding site fidelity.Results: The state-space modelling approach allowed the discrimination of two behavioural states (searching andmoving) during pre-breeding movements. Tracked snowy owls constantly switched from moving to searching behaviourduring pre-breeding movements from mid-March to early June. Searching events were more likely where snow coverand depth was low. This suggests that snowy owls adapt their searching effort to environmental conditions encounteredalong their path.Conclusions: This modelling technique increases our understanding of movement ecology and behavioural decisions ofindividual animals both locally and globally according to environmental variables.