Timely poacher detection and localization using sentinel animal movement

Henrik J De Knegt,Frank Van Langevelde,W François Spruyt,Jasper A J Eikelboom,Herbert H T Prins

doi:10.1038/s41598-021-83800-1

Henrik J De Knegt, Frank Van Langevelde + Show 3 more

Open Access

https://doi.org/10.1038/s41598-021-83800-1

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Abstract

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 500 m 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.

Highlights

Aimed at preventing them from being killed, e.g., via early predator detection and escape[8]
We tested the sentinel-based EWS in an African savanna, home to several targeted species that coexist with an assemblage of mammalian prey species that could be potential sentinels
Apart from alterations in the geometry of individual movement trajectories, patterns of collective geometry changed in the vicinity of the experimental intrusions

Summary

Introduction

Aimed at preventing them from being killed, e.g., via early predator detection and escape[8]. We deployed wearable GPS and tri-axial accelerometer sensors on 138 animals over four species (plains zebra, blue wildebeest, common eland and impala) in a 1200 ha fenced, predator-free area inside Welgevonden Game Reserve (WGR), South Africa (Fig. 1). These sensors transmitted data wirelessly via a LoRa network connected to a backhaul. Data collected in the absence of experimental intrusions were used to characterize undisturbed behavior, allowing quantification of the degree of abnormality of movement behavior at any point in time During all these experimental intrusions and matched controls, a median of 47 sensors yielded data for further analyses. We allocated each experimental intrusion or control segment to either the training phase or the evaluation phase, applying a leave-one-group-out cross-validation approach on these segments to make the best use of all data (see “Methods” section for details)

Methods

Results

Conclusion