A moving–resting process with an embedded Brownian motion for animal movements

摘要: Animal movements are of great importance in studying home ranges, migration routes, resource selection, and social interactions. The Global Positioning System provides relatively continuous animal tracking over time long distances. Nevertheless, the trajectory an animal’s movement is usually only observed at discrete points. Brownian bridge models have been used to model between two locations within a reasonably short interval. Assuming that animals perpetual motion, these ignore inactivity such as resting or sleeping. Using latest developments applied probability, we propose moving–resting process where assumed alternate moving state, during which it moves does not move. Theoretical properties studied first step towards more realistic for movements. Analytic expressions derived distribution one increment consecutive increments, validated with simulations. induced conditioning on starting end points compute probability occurrence observation area observation, has wide applications wildlife behavior research.