Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks

TL;DR

This paper analyzes wild boar movement patterns using GPS data, applying Hidden Markov Models to identify behavioral states and constructing behavioral networks, revealing insights into behavioral independence from geography and ecological factors.

Contribution

It introduces a novel combination of HMM-based behavioral segmentation with network analysis for wild boar movement studies.

Findings

Behavioral states are largely independent of location.

Most locations feature multiple behaviors.

Spatial analysis suggests potential ecological repellents.

Abstract

This study examines the complex movement patterns and behavioral characteristics of wild boars using GPS telemetry data collected over a two-month period. Our methodological approach centers on the application of a Hidden Markov Model (HMM) to discern distinct behavioral states embedded within the trajectories. Furthermore, the study aimed to construct behavioral networks, derived from these segmented trajectories. The resultant network structures showed that the hidden behavioral patterns are mostly independent of geographical locations. While most locations have many behaviors occuring in them, our findings also suggest that Finally, the research incorporates a spatial trajectory analysis, complemented by raster data validation, to potentially delineate areas acting as repellents within the ecological context of Hainich National Park in Germany.