Variant maps for bat echolocation call identification algorithms

Automated ultrasonic recordings are widely used in basic and applied research to detect the presence of bats. Often, algorithms for the automated identification of species are based on a pre-processing of acoustic information that involves the generation of spectrograms. Even though this approach is technically advanced, recent surveys highlight substantially high failure rates to identify species correctly, which urges for improved processes. Here, we tested an entirely new method, in particular, the transformation of ultrasonic recordings into variant maps. To compare this method with a spectrogram-based method, we used a database consisting of 160 echolocation calls from eight European bat species, including species of the genus Myotis that are inherently difficult to separate based on echolocation calls. For non-Myotis species, both methods led to a 100% correct identification rate, while for Myotis species the use of variant maps led to a lower identification rate of 85.3% compared to 91.1% that was achieved with a spectrogram-based method. However, a combination of both methods could lead to an identification rate of 94.1% for Myotis species. This result suggests combining our approach with spectrogram-based techniques to improve the automated identification of species based on acoustic information.