10.5061/DRYAD.WDBRV15S8
Bergmann, Anja
0000-0002-5175-1686
Museum für Naturkunde
Burchardt, Lara
Museum für Naturkunde
Wimmer, Bernadette
Naturschutz, Landwirtschaft, Gartenbau, Schifffahrt und
Wasserwirtschaft, Landratsamt Garmisch-Patenkirchen
Kugelschafter, Karl
ChiroTEC - Verhaltenssensorik und Umweltgutachten
Gloza-Rausch, Florian
Museum für Naturkunde
Knoernschild, Mirjam
Museum für Naturkunde
The soundscape of swarming: Proof of concept for a non-invasive acoustic
species identification of swarming Myotis bats
Dryad
dataset
2022
FOS: Biological sciences
echolocation calls
non-invasive acoustic monitoring
non-invasive species identification
linear frequency cepstral coefficients
bioacoustic monitoring
Bioacoustics
Myotis bats
Swarming
Myotis nattereri
Myotis daubentonii
Elsa-Neumann Foundation*
2022-10-31T00:00:00Z
2022-10-31T00:00:00Z
en
1934235104 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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. Test data A:
Sound recordings of the test data set A were conducted on nights with high
swarming activity of Myotis 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. Test data B: 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. Reference data: To classify the recorded echolocation call
sequences from the swarming situation, identified echolocation call
sequences of M. daubentonii and M. nattereri 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).
Control data: 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).