patter: particle algorithms for animal tracking in R and Julia

Abstract

O_LIIn 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. C_LIO_LIHere, 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. C_LIO_LIIn 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 individuals possible location probabilistically as a weighted series of samples ( particles). In our illustration, we resolve an individuals (unobserved) location every two minutes during one month in minutes and use particles to visualise movements, map space use and quantify residency (Example Two). C_LIO_LIpatter 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. C_LI