Edinburgh Napier University

  In the field of movement ecology, state-space models have emerged as a powerful modelling framework that represents individual movements and the processes that connect movements to observations. However, fitting state-space models to animal tracking data is often difficult and computationally expensive.

Here, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state-space models to tracking data, with a focus on data from aquatic animals in autonomous receiver arrays. patter is written in R, with a high-performance Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping.

In two worked examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle-based methods represent an individual’s possible location probabilistically as a weighted series of samples (‘particles’). In our illustration, we resolve an individual’s (unobserved) location every two minutes during one month in minutes and use particles to visualise movements, map space use and quantify residency (Example Two).

Patter facilitates robust, flexible and efficient analyses of animal tracking data. The methods are widely applicable and enable refined analyses of home ranges, residency and habitat preferences.