Particle algorithms for animal movement modelling in autonomous receiver networks

Abstract

1. Particle filters and smoothers are powerful sequential Monte Carlo algorithms used to fit non-linear, non-Gaussian state-space models. These algorithms are well placed to fit process-orientated models to animal-tracking data, especially in autonomous receiver networks, but to date they have received limited attention in the ecological literature. 2. Here, we introduce a Bayesian filtering-smoothing algorithm that reconstructs individual movements and patterns of space use from animal-tracking data, with a focus on passive acoustic telemetry systems. Within a sound probabilistic framework, the methodology uniquely integrates the movement process and the observation processes of disparate datasets, while correctly representing uncertainty. In a comprehensive simulation-based analysis, we compare the performance of our algorithm to the prevailing, heuristic methods used in passive acoustic telemetry systems and analyse algorithm sensitivity. 3. We find the particle smoothing methodology outperforms heuristic methods across the board. Particle-based maps consistently represent simulated movements more accurately, even in dense receiver networks, and are better suited to analyses of home ranges, residency and habitat preferences. 4. This study sets a new state-of-the-art for movement modelling in autonomous receiver networks. Particle algorithms provide a flexible and intuitive modelling framework with potential applications in many ecological settings.