A graduate student at the University of Alberta has developed an automated system for detecting owl calls, eliminating the need for researchers to spend nights in the field.
Julia Shonfield's new method, which combines audio recorders with software that can detect and count owl calls, is as accurate as traditional methods-and far more efficient, according to her analysis.
"One of the most common ways that we collect data on owls is through acoustic surveys," explained Shonfield, who is supervised by U of A biologist Erin Bayne. "Studying owls is important because they are components of their ecosystems, and are susceptible to changes in the environment due to their position at the end of the food chain."
Shonfield came up with the idea while searching ways to improve the efficiency of her own work studying the effects of industrial noise on owls in northeastern Alberta.
The traditional method of collecting this data is to do fieldwork at night-which poses a safety concern for researchers. More recently, researchers placed recorders in the field during the day and programmed them to record throughout the night, then played back the audio files and identify any owl calls on the recordings, which is extremely time-consuming.
To improve the efficiency and accuracy of the fieldwork, Shonfield developed the automated system using software that processes recordings to identify and count owl calls, with minimal additional work required.
"Thus far, the new system can detect barred owls, boreal owls and great horned owls," said Shonfield. "Automating this part of my research leaves room for me to gather more data and dig deeper into the analysis."
The paper, "Utility of Automated Species Recognition for Acoustic Monitoring of Owls," was published in the Journal of Raptor Research.