DOI: 10.5061/dryad.wdbrv15s8
title: The soundscape of swarming: Proof of concept for a non-invasive acoustic species identification of swarming Myotis bats

description: Bats emit echolocation calls to orientate in their predominantly dark
 environment. Recording of species-specific calls can facilitate species
 identification, especially when mist-netting is not feasible. However,
 some taxa, such as Myotis bats are hard to distinguish acoustically. In
 crowded situations where calls of many individuals overlap the subtle
 differences between species are additionally attenuated. Here we sought to
 non-invasively study the phenology of Myotis bats during autumn swarming
 at a prominent hibernaculum. To do so we recorded sequences of overlapping
 echolocation calls (N=564) during nights of high swarming activity and
 extracted spectral parameters (peak frequency, start frequency, spectral
 centroid) and Linear Frequency Cepstral Coefficients (LFCCs) which
 additionally encompass the timbre (vocal ‘colour’) of calls. We used this
 parameter combination in a stepwise discriminant function analysis (DFA)
 to classify the call sequences to species level. A set of previously
 identified call sequences of single flying Myotis daubentonii and Myotis
 nattereri, the most common species at our study site, functioned as a
 training set for the DFA. 90.2% of the call sequences could be assigned to
 either M. daubentonii or M. nattereri, indicating the predominantly
 swarming species at the time of recording. We verified our results by
 correctly classifying a second set of previously identified call sequences
 with an accuracy of 100%. In addition, our acoustic species classification
 corresponds well to the existing knowledge on swarming phenology at the
 hibernaculum. Moreover, we successfully classified call sequences from a
 different hibernaculum to species level and verified our classification
 results by capturing swarming bats while we recorded them. Our findings
 provide the basis for a new non-invasive acoustic monitoring technique
 that analyses “swarming soundscapes” by combining classical acoustic
 parameters and LFCCs, instead of analysing single calls. Our approach for
 species identification is especially beneficial in situations with
 multiple calling individuals, such as autumn swarming.
description: We employed a total of three
 data sets consisting of echolocation call sequences for the analyses. The
 first data set (test data A and B) contained recordings of overlapping
 echolocation call sequences of swarming bats in front of the Kalkberg cave
 (A) and a second site in Northern Germany (B). Our goal was to identify
 the predominantly echolocating species in these recordings. Therefore, the
 reference data set was used as training data in a discriminant function
 analysis to classify recordings from the test data and the control
 data.  <strong>Test data
 A:</strong> Sound recordings of the test data set A were conducted
 on nights with high swarming activity of <em>Myotis</em> bats
 during the autumn swarming seasons in 2018 and 2019 at both entrances of
 the Kalkberg cave in Bad Segeberg, Germany. Recordings were made whenever
 a high number of bats was swarming simultaneously using a high-quality ultrasonic microphone (Avisoft USG
 116Hm with condenser
 microphone CM16; frequency range 1‑200 kHz) connected to a small computer (Dell Venue
 8) running the software Avisoft Recorder (v4.2.05, R. Specht, Avisoft
 Bioacoustics, Glienicke, Germany). For the subsequent
 acoustic analysis, 564 echolocation call sequences (mean: 11.3 sequences
 per night; range: 1-29) with a length of four seconds each were selected
 based on the quality of the sound recordings and the presence of a high
 number of echolocation calls without interfering social
 vocalizations. <strong>Test data B: </strong>The
 swarming bats within this data set were recorded at another autumn
 swarming site in Northern Germany (Lüneburg) during one night
 (22.09.2021). Recordings were made and selected as described
 above. Bats of this data set were identified by simultaneous
 mist-netting.  <strong>Reference
 data:</strong> To classify the recorded echolocation
 call sequences from the swarming situation, identified echolocation call
 sequences of <em>M</em>. <em>daubentonii</em> and
 <em>M</em>. <em>nattereri</em> were used as a
 reference (i.e. as training set in a discriminant function analysis).
 These echolocation call sequences came
 from singly flying individuals and were recorded at ten
 underground sites with a Batcorder (ecoObs GmbH, Nürnberg, Germany) using
 a sampling rate of 500 kHz and a trigger threshold of -36 dB (quality
 26-28). The calling species were identified via photos from synchronized
 camera trap images (Wimmer and Kugelschaft (2015): Akustische Erfassung
 von Fledermäusen in unterirdischen Quartieren: GRIN Verlag). 
 <strong>Control
 data: </strong>To validate our statistical
 classification of the test data sets A and B, we classified an additional
 data set as a control using the same reference data. The echolocation call
 sequences in the control data set were recorded using a Petterson D980
 (Pettersson Elektronik AB, Sweden) in time expansion mode (Skiba (2009):
 Europäische Fledermäuse: Kennzeichen, Echoortung und Detektoranwendung,
 2nd ed.: Westarp Wissenschaften-Verlagsgesellschaft mbH).