Factors associated with automated detection of Northern Spotted Owl (Strix occidentalis caurina) four-note location calls

Automated signal detection of passive acoustic data produces enormous amounts of data that requires efficient processing. Furthermore, processed data requires assessment to ensure correct categorization of sounds to match field observations. Failure to compare data directly may lead to inaccurate estimates of occupancy state or population status and contribute to sub-optimal management decisions. We evaluated three automated detection methods for Northern Spotted Owl (Strix occidentalis caurina) four-note location calls at two sites representing vegetational and topographic conditions common to Northern Spotted Owl sites in western Oregon, United States. Our results indicated that the detection distance, resulting areal coverage, and occupancy status all varied with site, call broadcast direction, and software used for analysis. A machine learning algorithm (convolutional neural network) built specifically for detection of Northern Spotted Owl performed better at determining occupancy than two commercially developed software packages. At distances less than 250 m, the convolutional neural network correctly identified occupancy in more than 73% of trials and both commercial methods correctly identified occupancy in less than 60% of trials. Areal coverage was a function of distance from source to microphone, location of the source relative to the microphone, and method of call analysis. Calls broadcast toward the microphones were more likely to be detected than calls broadcast away from the microphones. Our results, although limited in scope, suggest that detection distance merits extended evaluation before autonomous recording units are deployed broadly as replacements for human observers.