The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/13900106

J.A.J. (Jasper) Eikelboom; Henrik J. de Knegt (2021): Data and scripts underlying the publication: Timely poacher detection and localization using sentinel animal movement. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/13900106.v2

Dataset

x choose version: version 1 - 2021-02-15

• Artificial Intelligence and Image Processing
• Ecology

accelerometer, collective animal movement, GPS, machine learning, movement ecology

view on openstreetmap

Sep. 2017 - Mar. 2018

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by J.A.J. (Jasper) Eikelboom , Henrik J. de Knegt

Wildlife crime is one of the most profitable illegal industries worldwide. Current actions to reduce it are far from effective and fail to prevent population declines of many endangered species, pressing the need for innovative anti-poaching solutions. Here, we propose and test a poacher early warning system that is based on the movement responses of non-targeted sentinel animals, which naturally respond to threats by fleeing and changing herd topology. We analyzed human-evasive movement patterns of 135 mammalian savanna herbivores of four different species, using an internet-of-things architecture with wearable sensors, wireless data transmission and machine learning algorithms. We show that the presence of human intruders can be accurately detected (86.1% accuracy) and localized (less than 500m error in 54.2% of the experimentally staged intrusions) by algorithmically identifying characteristic changes in sentinel movement. These behavioral signatures include, among others, an increase in movement speed, energy expenditure, body acceleration, directional persistence and herd coherence, and a decrease in suitability of selected habitat. The key to successful identification of these signatures lies in identifying systematic deviations from normal behavior under similar conditions, such as season, time of day and habitat. We also show that the indirect costs of predation are not limited to vigilance, but also include 1) long, high-speed flights; 2) energetically costly flight paths; and 3) suboptimal habitat selection during flights. The combination of wireless biologging, predictive analytics and sentinel animal behavior can benefit wildlife conservation via early poacher detection, but also solve challenges related to surveillance, safety and health.

• 2021-02-15 first online

• 2021-02-16 published, posted

4TU.ResearchData

R scripts, R data structures and CSV files

Timely poacher detection and localization using sentinel animal movement

• NWO program “Advanced Instrumentation for Wildlife Protection”

Wageningen University and Research, Wildlife Ecology and Conservation Group