UTILITY OF AUTOMATED SPECIES RECOGNITION FOR ACOUSTIC MONITORING OF OWLS

Presence or abundance of owls is frequently assessed using call-broadcast surveys to elicit responses and increase detection rates, but can draw owls in from a distance and could affect conclusions about fine-scale habitat associations. Passive acoustic surveys with field personnel or autonomous recording units (ARUs) may be a less biased method for surveying owls. Automated recognition techniques have proven useful to process large volumes of acoustic recordings from ARUs, and we sought to test the utility of automated recognition for three owl species. We built templates or recognizers'' for the territorial calls of the Barred Owl (Strix varia), the Boreal Owl (Aegolius funereus), and the Great Horned Owl (Bubo virginianus). We assessed the performance of each recognizer by evaluating precision, processing time, and false negatives. We used ARUs to survey for owls in northeastern Alberta, Canada, and compared the results from the recognizers to results from researchers listening to a subsample of the recordings. We verified the results to filter out false positives, but verification time was substantially lower than time spent listening. We processed more recordings and obtained a larger dataset of owl detections than would have been possible with either listening to the recordings only or conducting traditional field surveys without ARUs, suggesting a significant benefit of automated recognition. Precision was quite variable, but false negatives were relatively low and did not affect results of owl habitat associations. Given the relatively low detection rates of owls by listening to recordings, an automated recognition approach is likely to be highly useful for monitoring owls.