Data and scripts underlying the publication: Timely poacher detection and localization using sentinel animal movement

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.

野生动物犯罪是全球范围内获利最为丰厚的非法产业之一。当前旨在遏制此类犯罪的举措收效甚微，未能阻止诸多濒危物种种群数量下滑，因此亟需创新的反盗猎解决方案。本研究提出并测试了一套基于非目标哨兵动物（sentinel animal）运动响应的盗猎者早期预警系统：这类动物会通过逃离和改变种群结构的方式自然响应威胁。我们借助穿戴式传感器、无线数据传输与机器学习算法构建的物联网架构，分析了135头隶属于四个不同物种的草原植食性哺乳动物的避人运动模式。研究表明，通过算法识别哨兵动物运动的特征变化，可精准检测（准确率达86.1%）并定位人类入侵者（在54.2%的模拟入侵实验中，定位误差小于500米）。此类行为特征包括但不限于：运动速度、能量消耗、身体加速度、方向持续性与种群凝聚力提升，以及所选栖息地适宜性下降。成功识别这些特征的关键，在于识别动物在季节、时段与栖息地等相似环境下与常规行为的系统性偏离。本研究同时表明，盗猎引发的间接代价不仅限于警戒行为，还包括：1）长距离高速奔逃；2）能耗高昂的奔逃路径；3）奔逃过程中栖息地选择的次优性。无线生物记录（biologging）、预测分析与哨兵动物行为学的结合，不仅可通过早期盗猎检测助力野生动物保护，还能解决监测、安全与健康相关的诸多挑战。