Localized Mutual Information Monitoring of Pairwise Associations in Animal Movement

TL;DR

This paper introduces a localized mutual information measure to monitor and detect changes in pairwise associations in animal movement data, aiding ecological analysis.

Contribution

It proposes a novel LMI measure for analyzing dynamic correlations in animal trajectories, with analytical assessment and an improved version to address limitations.

Findings

LMI effectively detects shifts in animal movement correlations.

The measure captures seasonal and phase-related correlation structures.

Analytical and simulation results validate the measure's utility.

Abstract

Advances in satellite imaging and GPS tracking devices have given rise to a new era of remote sensing and geospatial analysis. In environmental science and conservation ecology, biotelemetric data is often high-dimensional, spatially and/or temporally, and functional in nature, meaning that there is an underlying continuity to the biological process of interest. GPS-tracking of animal movement is commonly characterized by irregular time-recording of animal position, and the movement relationships between animals are prone to sudden change. In this paper, I propose a measure of localized mutual information (LMI) to derive a correlation function for monitoring changes in the pairwise association between animal movement trajectories. The properties of the LMI measure are assessed analytically and by simulation under a variety of circumstances. Advantages and disadvantages of the LMI measure are assessed and an alternate measure of LMI is proposed to handle potential disadvantages. The measure of LMI is shown to be an effective tool for detecting shifts in the correlation of animal movements, and seasonal/phasal correlatory structure.