Species detection framework using automated recording units: a case study of the Critically Endangered Jerdon's courser

With the advent of automated recording units, bioacoustic monitoring has become a popular tool for the collection of long-term data across extensive landscapes. Such methods involve two main components: hardware for audio data acquisition and software for analysis. In the acoustic monitoring of threatened species, a species-specific framework is often essential. Jerdon's courser Rhinoptilus bitorquatus is a Critically Endangered nocturnal bird endemic to a small region of the Eastern Ghats of India, last reported in 2008. Here we describe a reproducible and scalable acoustic detection framework for the species, comparing several commonly available hardware and detection methods and using existing software. We tested this protocol by collecting 24,349 h of data during 5 months. We analysed the data with two commercially available sound analysis programmes, following an analysis pipeline created for this species. Although we did not detect vocalizations of Jerdon's courser, this study provides a framework using a combination of hardware and software for future research that other conservation practitioners can implement. Vocal mimicry can aid or confound in detection and we highlight the potential role of mimicry in the detection of such threatened species. This species-specific acoustic detection framework can be scaled and tailored to monitor other species.