Comparing field performance of ultrasonic microphones to facilitate analysis of long‐term acoustic bat monitoring data

Passive acoustic monitoring is a standard technique for studying bat ecology and behavior. However, an issue that has received little attention is how to appropriately analyze data within a long‐term acoustic monitoring dataset when the equipment has been replaced and updated. Equipment changes are often inevitable, especially for microphones, which need to be replaced regularly due to extended weather exposure and associated reductions in recording quality. We compared 2 ultrasonic microphone models (Wildlife Acoustics SMM‐U1 and SMM‐U2) by deploying them side‐by‐side with the same acoustic detector unit. We tested 9 or 10 microphones per model in field deployments lasting an average of 9 nights. We compared triggering frequency, species classification, detection rates, and echolocation call parameters (as indicators of signal quality) from both microphones. The vast majority (97%) of our 25,949 paired recordings were captured simultaneously by both microphones. Yet, the SMM‐U2 outperformed the SMM‐U1 in terms of proportion of files classifiable to the species level (70% versus 61%), rate of bat detections per night (1–6.5 more detections per night depending on species), and recording quality. Based on our results, we propose a correction factor to facilitate direct comparison of datasets collected with these 2 different microphones. Our study will assist bat researchers in selecting appropriate equipment and accounting for potential biases in long‐term acoustic monitoring programs. Long‐term bat acoustic monitoring necessitates replacing microphones due to damage or decreased sensitivity, but changing equipment can introduce bias. We tested two microphone models side‐by‐side to compare their performance and found the newer model produced higher quality audio recordings that were more readily classified to the species level, but species richness and triggering rate did not differ. We propose species‐specific correction factors to reduce bias when analyzing datasets that include both types of microphones.