Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks
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arXiv
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| Hauptverfasser: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916813082722304 |
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| author | Basilone, Riccardo Bergamin, Eleonora Fanelli, Federica Kotov, Egor Morelle, Kevin Klamm, Alisa Nhili, Manal Rosen, Joshua Schendl, Andrew Holubowska, Olena Renninger, Andrew Smolak, Kamil |
| author_facet | Basilone, Riccardo Bergamin, Eleonora Fanelli, Federica Kotov, Egor Morelle, Kevin Klamm, Alisa Nhili, Manal Rosen, Joshua Schendl, Andrew Holubowska, Olena Renninger, Andrew Smolak, Kamil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks Basilone, Riccardo Bergamin, Eleonora Fanelli, Federica Kotov, Egor Morelle, Kevin Klamm, Alisa Nhili, Manal Rosen, Joshua Schendl, Andrew Holubowska, Olena Renninger, Andrew Smolak, Kamil Physics and Society 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. |
| title | Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2506.22138 |