4.6 Article

Do you hear what I hear? Implications of detector selection for acoustic monitoring of bats

Journal

METHODS IN ECOLOGY AND EVOLUTION
Volume 3, Issue 6, Pages 992-998

Publisher

WILEY
DOI: 10.1111/j.2041-210X.2012.00244.x

Keywords

acoustic detector selection; bat activity; bats; echolocation; passive acoustic monitoring; synthetic signals

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The probability of detecting the echolocation calls of bats is affected by the strength of the signal as well as the directionality and frequency response of the acoustic detectors. Regardless of the research question, it is important to quantify variation in recording system performance and its impacts on bat detection results. The purpose of this study was to compare the detection of echolocation calls among five commonly used bat detectors: AnaBat SD2 (Titley Scientific), Avisoft UltraSoundGate 116 CM16/CMPA (Avisoft Bioacoustics), Batcorder 2.0 (ecoObs), Batlogger (Elekon AG) and Song Meter SM2BAT (Wildlife Acoustics). We used playback of synthetic calls to optimize detection settings for each system. We then played synthetic signals at four frequencies (25, 55, 85 and 115 kHz) at 5-m intervals (540 m) and three angles (0 degrees, 45 degrees, 90 degrees) from the detectors. Finally, we recorded free-flying bats (Lasiurus cinereus), comparing the number of calls detected by each detector. Detection was most affected by the frequency dominating the signal and the distance from the source. The effect of angle was less apparent. In the synthetic signal experiment, Avisoft and Batlogger outperformed other detectors, while Batcorder and Song Meter performed similarly. Batlogger performed better than the other detectors at angles off-centre (45 degrees and 90 degrees). AnaBat detected the fewest signals and none at 85 kHz or 115 kHz. Avisoft detected the most signals. In the free-flying bat experiment, Batlogger recorded 93% of calls relative to Avisoft, while AnaBat, Batcorder and Song Meter recorded 4050% of the calls detected by Avisoft. Numerous factors contribute to variation in data sets from acoustic monitoring; our results demonstrate that choice of detector plays a role in this variation. Differences among detectors make it difficult to compare data sets obtained with different systems. Therefore, the choice of detector should be taken into account in designing studies and considering bat activity levels among studies using different detectors.

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