Abstract
Many species are difficult to study either due to their rarity, elusiveness, difficult access to their area of occurrence, or any combination of these. This can be particularly problematic for threatened species. Passive acoustic monitoring (PAM) is a recently developed survey technique that has shown great potential in addressing this problem for species that communicate through vocalizations. However, the large amount of data it generates can be difficult to process manually. Here, we present an entirely automatic workflow to record and detect the vocalizations of a bird species that is both elusive (nocturnal) and restricted to difficult terrain in the most remote rainforests of an oceanic island: the recently discovered Principe Scops-Owl. Specifically, we evaluated (i) the performance of the workflow to monitor the presence of the owl, (ii) we assessed the most suitable time for monitoring it; and (iii) we examined the potential of this species to present detectable vocal individual signatures. For 12 days, we deployed omnidirectional recording stations (AudioMoth devices) in 72 points along 10 transects that were surveyed during one night at the same time by observers in the field. We trained TADARIDA, a machine learning software toolbox, to automatically detect owl calls. Results on the presence of the owl per site were similar for both methods. The automatic workflow showed that the owl is active during the whole night and the PAM recording setting should encompass at least the 21–23 h interval. Possibly, vocalizations had individual signatures—but the small sample size and temporal window prevented a definite conclusion. The automatic workflow developed here is an efficient method to monitor the Principe Scops-Owl and can be easily adapted for other elusive vocal species.