Maximum likelihood estimators are ineffective for acoustic detection of rare bat species

Acoustic monitoring is an important tool for determining presence or probable absence of threatened and endangered bats in the United States (US). Federal guidance requires the use of automated identification programs that classify audio files and calculate a Maximum Likelihood Estimator (MLE) for each bat species during each night of a survey. Acoustic presence or absence of species is based on a significant or non-significant MLE, which can have profound regulatory effects, positive or negative. Despite relying on this metric to determine presence of rare species for the past ten years, little is known about the number of files required by available programs to trigger significant MLE or the effect of species ratio on this calculation. We used 1,120 audio files containing echolocation calls from nine northeastern US bat species to simulate survey nights containing variable absolute counts and ratios of species’ audio files. We developed models to estimate the number of audio files that Kaleidoscope Pro (KPro) and SonoBat programs required to establish acoustic presence for each species, and we then applied our best model to a long-term acoustic dataset collected at the Fort Drum Military Installation in New York. Each program required a similar number of files to detect presence for some species, such as Myotis septentrionalis and M. sodalis (8 to 10 files), but differed in file requirements for other species, such as Lasiurus cinereus (KPro = 4; SonoBat = 7) and Perimyotis subflavus (KPro = 10; SonoBat = 6). Both programs performed poorly with determining presence for any species at low species ratio (