A Comparison of Bat Calls Recorded by Two Acoustic Monitors

Recent advances in low-cost autonomous recording unit (ARU) technology have made large-scale bat monitoring projects more practical, but several key features of ARUs (e.g., microphone quality and 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 numbers of call files, echolocation pulses, and 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 nonbreeding (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, more call files with high-frequency bat calls, more echolocation pulses, and higher species richness than AudioMoths. Our results likely reflect differences in microphone sensitivities, recording specifications, and enclosures between the ARUs. We recommend caution when comparing data collected by different ARUs, especially over time as firmware updates and new enclosures become available, and additional research is needed to examine variation in monitor performance across a wide range of environmental conditions

Potential effects of traffic noise on anuran call characteristics in Louisiana, USA during winter

Urban environments expose wildlife to levels of anthropogenic noise they would not experience in rural areas (e.g., traffic noise), and research suggests that many species adjust their acoustic signals for optimal transmission in urban soundscapes. However, our understanding of anuran (order Anura) responses to noise pollution in urban environments of the southeastern United States is limited, particularly for species that can breed during winter. Our goal was to examine how vocal anuran advertisement call characteristics during winter varied with increasing distance from roadways in bottomland hardwoods of Louisiana, USA. We deployed acoustic recording units at two sites (i.e., rural and urban) perpendicular to Interstate 10 at 200-, 400-, and 600-m intervals (i.e., close, middle, and far) from November 2019 to January 2020. We detected Cajun Chorus Frogs (Pseudacris fouquettei) and Cricket Frogs (Acris spp.) at our rural site, and only detected Cricket Frogs at our urban site. At the rural site, Cajun Chorus Frogs produced longer duration notes at the far location compared to the middle location. At the urban site, Cricket Frogs produced higher dominant frequency calls at the close location compared to the far and middle locations and longer duration notes at the far location compared to the close location. We were unable to account for additional factors in our models (e.g., temperature, noise levels), but our results generally align with previous research. Our study provides baseline data for future research to examine the potential effects of traffic noise on winter advertisement calls in locations with similar environmental conditions and species

Bat Habitat Use and Activity in Forests of Central Louisiana

In the southeastern U.S., where forests are the primary land cover type and trees are often harvested for production purposes, understanding how forestry practices influence bat distributions is critical for bat conservation and management. It is also important for researchers to quantify and report variation in the performance of automated recordings units (ARUs) used to survey for bats because several key features of ARUs (e.g., microphone sensitivity, triggering thresholds) can influence an ARUs ability to detect bat calls. My goals were (1) to examine the influence of forest management practices on seasonal bat species occurrence and activity in central Louisiana, and (2) to compare the number of bat call files, echolocation pulses, and species recorded by two ultrasonic ARUs (i.e., AudioMoths and Song Meter SM4BAT-FS monitors) and identified using automated classification software (i.e., SonoBat). For (1), I deployed ARUs at sites representing five pine management treatments and bottomland hardwood forests to record bat calls. I also collected environmental data at the landscape and local scales. I detected Eptesicus fuscus, Lasiurus borealis/L. seminolus, Myotis species, Perimyotis subflavus, Tadarida brasiliensis, and Aeorestes cinereus during both seasons, and additionally detected Nycticeius humeralis during the breeding season. I found that activity was higher at group selection harvest, red-cockaded woodpecker, and clearcut treatments and that habitat use was different between periods for some species. I used ARU data that I collected at the study sites described above and at an urban greenspace in Baton Rouge to address (2). I found that SonoBat identified more call files to species, call files with high-frequency bat calls, echolocation pulses, and species from SM4BAT recordings compared to AudioMoth recordings, but that SonoBat identified a similar number of call files with low-frequency bat calls between monitors. My research identifies forest management practices and habitat characteristics that promote bat species diversity and activity. In addition, my research demonstrates that SM4BATs provide more comprehensive data that can be used with automated classification software than the version of AudioMoths I used, which has implications for survey results and comparability across studies