Preprint

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

Basilone, R., Bergamin, E., Fanelli, F., Kotov, E., Morelle, K., Klamm, A., Nhili, M., Rosen, J., Schendl, A., Holubowska, O., Renninger, A., Smolak, K.

arXiv e-prints 2506.22138
23 pages.
arXiv
This work is an output of the Complexity72h workshop (https://complexity72h.com/). submitted on: 27 June 2025 (version 1) (2025), unpublished

DOI:10.48550/arxiv.2506.22138

Open Access

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.