Accounting for both automated recording unit detection space and signal recognition performance in acoustic surveys: A protocol applied to the cryptic and critically endangered Night Parrot (Pezoporus occidentalis)

Research into the suitability of autonomous recording units (ARUs) when surveying for vocal species is increasing. Simultaneously, there has been extensive research into methods for efficiently extracting signals of interest from the acoustic data sets that accrue from the deployment of ARUs. For some species, bioacoustic monitoring supported by computerised signal detection offers the only effective and efficient method for widespread survey. In these circumstances, the detection space of both the ARU and the performance of the signal detection process must be considered concurrently, but typically, these two elements have been considered separately. Here, using the Night Parrot (Pezoporus occidentalis) as a case study, we consider both ARU detection space and the signal detection process to develop a robust and repeatable survey protocol for the species. After developing a call recogniser for the Night Parrot, we test its performance on a data set of Night Parrot calls given at a known distance from an array of ARUs. Having established a relationship between ARU type, recogniser performance and distance, we determine the sampling radius of an ARU for a given recogniser score cut-off, and the associated probability of detecting a Night Parrot that calls within that sampling radius. Using these data, we outline how to develop a robust and repeatable survey protocol for the Night Parrot, with a defined probability of detection. This protocol could be adapted for other scenarios where deployment of ARUs is necessary to determine a species' status and distribution.