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.

Jean-François Therrien,Nicolas Lecomte,Gilles Gauthier,David Pinaud,Joël Bety,Keith L Bildstein

doi:10.1186/s40462-015-0028-7

Abstract

BackgroundTracking individual animals using satellite telemetry has improved our understanding of animal movements considerably. Nonetheless, thorough statistical treatment of Argos datasets is often jeopardized by their coarse 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 of the tracked individuals at each location. We coupled state-space modelling with environmental characterisation of modelled locations on a 3-year Argos dataset of 9 breeding snowy owls to assess whether searching behaviour for breeding sites was affected by snow cover and depth in an arctic predator that shows a lack of breeding site fidelity.ResultsThe state-space modelling approach allowed the discrimination of two behavioural states (searching and moving) during pre-breeding movements. Tracked snowy owls constantly switched from moving to searching behaviour during pre-breeding movements from mid-March to early June. Searching events were more likely where snow cover and depth was low. This suggests that snowy owls adapt their searching effort to environmental conditions encountered along their path.ConclusionsThis modelling technique increases our understanding of movement ecology and behavioural decisions of individual animals both locally and globally according to environmental variables.

Highlights

Tracking individual animals using satellite telemetry has improved our understanding of animal movements considerably
Our study showed that in snowy owls, there is a strong relation between prospecting behaviour during pre-breeding movements and environmental conditions encountered and snow cover and depth
We categorized critical behaviour that related to key life cycle events of snowy owls

Summary

Introduction

Tracking individual animals using satellite telemetry has improved our understanding of animal movements considerably. The last several decades have nurtured a rise in the number of studies using the Argos system to analyse the movements of individual animals through time [1] Even with those ever-growing datasets of animal locations, we are still limited in our ability to address some broad questions pertaining to movement ecology of organisms. Thorough statistical treatment of Argos datasets and interpretation of specific animal behaviour during movement are hampered by three major limitations. Those datasets have a coarse temporal resolution and often have irregular location estimates because most transmitters use programmed duty cycles and are dependent on the communication strength with moving satellites.

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