An autonomous network of acoustic detectors to map tiger risk by eavesdropping on prey alarm calls

Abstract Tiger ( Panthera tigris ) attacks are a frequent source of injuries and fatalities among villagers in Nepal, where many communities make extensive use of dense forests for foraging and grazing of livestock. As conservation efforts have boosted the tiger population in the country, a conflict exists between maintaining traditional practises whilst ensuring human safety and protecting endangered predators. Hence, there is a need for cost‐effective management strategies that do not reduce habitat use by humans or wildlife. Passive acoustic monitoring (PAM) offers a promising approach to mapping tiger presence in real‐time and providing a warning system for villagers. Although tigers vocalize infrequently, their presence triggers alarm calls from prey species, meaning these alarm calls could potentially act as a proxy for detecting tigers. To explore the potential for tracking tigers and other dangerous predators such as leopards using these alarm calls, we designed and tested a PAM system in the Terai region of southern Nepal. We implemented a TinyML low‐memory convolutional neural network (~1000 parameters) for chital deer ( Axis axis ) automatic detection—a species that reliably produce loud predator‐specific alarm calls—and deployed a distributed network of 10 autonomous interconnected sensors for continuous operation over 3 months. The network transmits chital deer alarm call events via a cellular‐connected gateway to a remote base station to generate a heatmap of predator risk. Incidences of high predator risk can be used to alert local forest rangers, who can then inform nearby villagers of areas with a higher likelihood of predator presence. The neural net achieved an F1 score of 0.91 in training and 0.72 in the field. We suggest that this proof of concept indicates that automated PAM could be an effective tool for detecting and tracking tigers and other predators and a potentially valuable tool for facilitating human‐wildlife co‐existence.