The Handbook of Acoustic Bat Detection

An accessible and comprehensive guide to all things acoustic bat detection. This highly illustrated handbook provides an in-depth understanding of acoustic detection principles, study planning, data handling, properties of bat calls, manual identification of species, automatic species recognition, analysis of results, quality assurance and the background physics of sound. No other method of detecting bats is so popular and widespread in the context of environmental assessment and voluntary work as acoustic detection, and its increased use has driven the development of a large number of sophisticated devices and analytical methods. Acoustic detection has become a standard approach for establishing the presence of bats, carrying out species identification and monitoring levels of activity. The resolution, accuracy and scale with which these tasks can be done has risen dramatically with the availability of automated real-time recording. But anyone interested in acoustic recording will quickly recognise that there are still quite a few open questions about the limits and possibilities of acoustic detection. Clear definitions of how to handle the data are usually missing, for example, and there are no clearly described activity indices. In response to the lack of thorough information on the underlying science of acoustic detection, the authors present this handbook.

Fewer bat passes are detected during small, commercial drone flights

Advances in technological capabilities, operational simplicity and cost efficiency have promoted the rapid integration of unmanned aerial vehicles (UAVs) into ecological research, providing access to study taxa that are otherwise difficult to survey, such as bats. Many bat species are currently at risk, but accurately surveying populations is challenging for species that do not roost in large aggregations. Acoustic recorders attached to UAVs provide an opportunity to survey bats in challenging habitats. However, UAVs may alter bat behaviour, leading to avoidance of the UAV, reduced detection rates and inaccurate surveys. We evaluated the number of bat passes detected with and without the presence of a small, commercial UAV in open habitats. Only 22% of bat passes were recorded in the presence of the UAV (0.23 ± 0.09 passes/min) compared to control periods without the UAV (1.03 ± 0.17 passes/min), but the effect was smaller on the big brown bat/silver-haired bat (Eptesicus fuscus/Lasionycteris noctivagans) acoustic complex. Noise interference from the UAV also reduced on-board bat detection rates. We conclude that acoustic records attached to UAVs may inaccurately survey bat populations due to low and variable detection rates by such recorders.