Classification of anomalous diffusion in animal movement data using power spectral analysis

TL;DR

This paper uses power spectral density analysis to classify and understand different movement behaviors in animal tracking data, revealing novel $1/f$ noise patterns in movement that were previously unreported.

Contribution

It introduces a spectral analysis approach to classify movement modes and identify $1/f$ noise in animal trajectories, providing new insights into movement ecology.

Findings

Detection of $1/f$ noise in animal movement data

Segmentation and clustering of movement behaviors

Comparison of PSD exponents with theoretical models

Abstract

The field of movement ecology has seen a rapid increase in high-resolution data in recent years, leading to the development of numerous statistical and numerical methods to analyse relocation trajectories. Data are often collected at the level of the individual and for long periods that may encompass a range of behaviours. Here, we use the power spectral density (PSD) to characterise the random movement patterns of a black-winged kite (Elanus caeruleus) and a white stork (Ciconia ciconia). The tracks are first segmented and clustered into different behaviours (movement modes), and for each mode we measure the PSD and the ageing properties of the process. For the foraging kite we find $1/f$ noise, previously reported in ecological systems mainly in the context of population dynamics, but not for movement data. We further suggest plausible models for each of the behavioural modes by comparing both the measured PSD exponents and the distribution of the single-trajectory PSD to known theoretical results and simulations.