Time-Frequency Segmentation of Northern Fur Seal Diving Behavior Using Autoregressive Spectral Analysis

Analyzing marine animal movement data is essential for understanding at-sea behavior. This study introduces an autoregressive spectral analysis framework for assessing time-varying movement dynamics in northern fur seals. A time segmentation approach, based on an AR(3) model with Yule-Walker estimation, is employed to estimate movement parameters and characterize behavioral variability over time. The method captures temporal changes in movement persistence and oscillatory patterns, enabling the segmentation of behavioral states such as resting, exploratory diving, and active foraging. Using high-resolution vertical velocity data from eight northern fur seals tagged at the Pribilof Islands, Alaska, the analysis shows that a 26-minute window with 50% overlap achieves stationarity while preserving behavioral transitions. Spectral analysis identifies mid-frequency oscillations associated with active diving, low-frequency signals corresponding to exploratory diving, and spectral shifts indicative of behavioral transitions. Comparisons with non-parametric methods highlight the advantages of AR(3)-based spectral estimation in producing smooth and interpretable frequency-based insights. The framework provides new perspectives on fur seal foraging strategies and behavioral adaptations to environmental conditions. It also offers a computationally efficient alternative to state-space models, with potential for broader application in studying movement patterns of marine predators to support ecological and conservation research.