Alghamdi, Omar Ahmad2025-10-092025-10-092025-10-07https://hdl.handle.net/10222/85466Understanding marine animal behaviour is essential for ecological studies and conservation. Traditional models assume animals switch between a few fixed behavioural states, but behaviour often changes gradually, requiring more flexible methods. This thesis introduces a time segmentation framework for estimating time-varying behavioural parameters from high-resolution northern fur seal tracking data. The approach divides trajectories into short, overlapping windows and applies simple statistical models to estimate movement persistence and variability, enabling dynamic behavioural classification without predefined states. Three studies demonstrate its flexibility: a state-space model with a Kalman Filter for horizontal movement, autoregressive spectral analysis for vertical diving patterns, and a three-dimensional continuous-time model incorporating ocean drift. The framework identifies multiple behavioural modes while avoiding the complexity of switching models, providing interpretable, time-resolved insights into animal movement and offering a practical foundation for future research in movement ecology.enState-space modelsAnimal movementTime segmentationNorthern fur seals