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Recent advances in low-cost automated recording unit (ARU) technology have made large-scale bat monitoring projects more practical, but several key features of ARUs (e.g., microphone quality, triggering thresholds) can influence their ability to detect and record bats. As such, it is important to quantify and report variation in ARU performance as new recording systems become available. We used the automated classification software SonoBat to compare the number of call files, number of echolocation pulses, and number of species recorded by a commonly used, full-spectrum bat detector—the Song Meter SM4BAT-FS—and a less expensive, open-source ARU that can detect ultrasound—the AudioMoth. We deployed paired ARUs across several forest types in Louisiana during breeding (June–August) and non-breeding (December–February) periods in 2020 and 2021. Weatherproof cases were unavailable for AudioMoths at the time of our study. Thus, we used disposable plastic bags and plastic boxes recommended by the manufacturer and other AudioMoth users to house our monitors. We lost several AudioMoths to water damage using both methods and subsequently placed these monitors in waterproof smartphone bags for the remainder of our study. We compared data collected by AudioMoths in the three enclosures and found no differences in the number of call files identified to species or species richness. We found that SM4BATs recorded more call files identifiable to species, call files with high-frequency bat calls, echolocation pulses, and higher species richness than AudioMoths. Our results likely reflected differences in microphone sensitivities, recording specifications, and enclosures between the ARUs. We recommend caution when comparing data collected by different ARUs, especially through time as firmware updates and new enclosures become available, and additional research to examine variation in monitor performance across a wide range of environmental conditions.

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