Automated auditory detection of a rare, secretive marsh bird with infrequent and acoustically indistinct vocalizations

Abstract

Autonomous recording units (ARUs) provide a non‐invasive and efficient method for acoustic detection of elusive species across large temporal and spatial scales. However, species with indistinct vocalization structures can be a considerable challenge for automated signal recognizers. We investigated the performance of ARUs and signal recognizers in identifying the broadband, short‐syllable, pulsed calls of a secretive, threatened marsh bird, the King Rail Rallus elegans. Other sympatric species in the same habitat also have repetitive calls within the same frequency range that can be difficult to distinguish. Following serial ARU deployments at specified sites in known breeding habitat, we conducted standardized callback surveys and nest searches to provide an independent measure of breeder density. To analyse recordings, we developed a signal recognizer based on user‐input training files to detect two common call types, kek and grunt. Detections that remained following manual review of recognizer output revealed a previously undescribed seasonal decline and crepuscular diel pattern in calling rate. The rate of the grunt call also predicted density. These patterns emerged despite the recognizer's low precision and high false‐positive rate, which were largely due to misclassification of other species' calls, although ambient noise and effective detection radius also limited the detectability of King Rail calls. We demonstrate that with informed ARU scheduling, improved ability to manipulate user‐specified parameters within signal detection software, and attention to quality control, even the simplest call structures can be located consistently in a diverse acoustic landscape. Our behavioural findings will inform improvements to auditory surveys and to management of King Rails across their range.