Timely poacher detection and localization using sentinel animal movement

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 real-time 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.

Perspectives in machine learning for wildlife conservation

“…As demonstrated by the rising field of hybrid environmental algorithms (leveraging both DL and bio-physical models 48,49 ) and, more broadly, by theory-guided data science 50 , such hybrid models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than 61 was used to detect Carolina wren vocalizations in more than 35,000 h of passive acoustic monitoring data from Ithaca, New York, allowing researchers to document the gradual recovery of the population following a harsh winter season in 2015. b Machine-learning algorithms were used to analyze movement of savannah herbivores fitted with bio-logging devices in order to identify human threats. The method can localize human intruders to within 500 m, suggesting `sentinel animals' may be a useful tool in the fight against wildlife poaching 148 . c TRex, a new image-based tracking software, can track the movement and posture of hundreds of individually-recognized animals in real-time.…”

Unveiling the intertwined roles of the spatial-temporal environment and behavioural modes in animal movement

“…The GPS sensors collected spatial positions with a frequency ranging between 15 and 2 minutes, depending on the amount of body activity as determined by the accelerometer (see de Knegt et al, 2021).…”

“…The position data were corrected, smoothed and modelled to regular ten-minute resolution trajectories as described in de Knegt et al (2021). Due to sensor failures, we selected from each species only the ten individuals with the most recorded data in the period from September to December 2017 (the first four months of the study period, during which the sensors collected most data).…”

“…Especially the effects of vegetation and topography on the movement of large terrestrial herbivores have received much attention. Vegetation-rich environments often coincide with lower movement speeds (Avgar et al, 2013;de Knegt et al, 2007de Knegt et al, , 2021Frair et al, 2005;Fryxell et al, 2008). Topography can influence movement through energetic costs associated with moving through sloping or rugged terrain (Dailey & Hobbs, 1989;Parker et al, 1984;White & Yousef, 1978).…”

WATLAS: high-throughput and real-time tracking of many small birds in the Dutch Wadden Sea

“…Insights from animal tracking studies are regularly incorporated in policy and conservation management [5,15]. For example, identifying important areas for the protection of migration routes [16,17], detecting wildlife crime [18,19], and quantifying the human-wildlife conflict [20].…”