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Acoustic Bat Survey - Terrestrial Species Stressor Monitoring [ds2825]

Published by California Department of Fish and Wildlife | State of California | Metadata Last Checked: July 28, 2025 | Last Modified: 2023-11-29T18:33:44.082Z
Automated acoustic recorders were deployed at 331 sites across the Mojave Desert ecoregion and 263 sites across the Great Valley ecoregion between March and July of 2016 and 2017. At each survey location, an SM3-BAT bioacoustic recorder was deployed with both an SMM-A1 acoustic microphone and an SMM-U1 ultrasonic microphone (Wildlife Acoustics, Inc., Maynard, MA, USA, hereafter termed ARU). Automated recorders were cable locked to securely-placed T-posts 2 meters above the ground, and were located within 10 meters of the site center at a relatively open spot not occupied by thick vegetation. The ARUs were programmed to record three 5-minute sessions each morning during the survey period, with the first session at 30 minutes before sunrise, the second at sunrise, and the third at 30 minutes after sunrise. The ARUs were also programmed to alternate between 7.5-minute audible acoustic recordings and 22.5-minute triggered, full-spectrum ultrasonic recordings from 30 minutes before sunset until 0400 the following morning, at which time the ultrasonic recordings ceased; the 7.5-minute acoustic recordings continued every 30 minutes until the 5-minute dawn chorus recordings began at 30 minutes before sunrise. Deployments lasted for seven consecutive days, on average. Following the survey, Kaleidoscope Pro version 4.3.2 with the KPro classifier (Wildlife Acoustics, Inc., Maynard, MA, USA) was used to auto-classify file recordings to the species level for bats. Using the probabilistic output from the classifier, files were filtered to retain only those determined to have a misidentification probability less than 0.05. All of the remaining files were then manually reviewed (i.e., examination of diagnostics and spectrograms) to confirm or reject the auto-classified species identification. The automated and post-validated datasets were analyzed using five occupancy modeling frameworks, which differed in how they did or did not address false positive detections.

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