Materials and methods
Study site and design
We surveyed bats in three different sites in a closed-canopy oil palm
plantation using bat point counts, mist nets, and automated ultrasound
recorders. Our sampling sites are inside the Humusindo Makmur Sejati
(01.95° S and 103.25° E, 47 ± 11 m a.s.l.) company estate, near Bungku
village in the lowlands of Jambi province, Sumatra, Indonesia. We set
the center of each site within 10 m of a stream (2-4 m wide) and an
unpaved road (4-5 m wide) in order to maximise potential species
detections, as it is widely known that bats use trails and streams for
commuting and hunting
(Voigt & Kingston,
2016). The sites were bordering the same river and separated by at
least 600 m to allow independent captures. We sampled all three sites
simultaneously with rotating methods on three consecutive nights with
one field team, and we repeated this twice, obtaining three sampling
nights for each method and site in total (Fig 1). The surveys occurred
during nine nights from 21 to 31 May 2019. Due to our selection of sites
with identical surrounding habitats, the temporally rotating design of
the methods, the simultaneous comparison of methods in equivalent sites,
and the short sampling period, any effects of weather conditions, moon
phase, and fluctuating food resource availability was minimized and
should not bias our results.
Bat point counts
We conducted bat point counts for one hour per night in each site. We
used a sampling rig with sensors for ultrasound, thermal, and infrared
waves; we provide full technical details of the rig and the observation
workflow in (K. Darras
et al., 2021). Each point count scanned a 120° field of view, directed
either towards the road, river, or oil palm. We determined the detection
area of the thermal scope and the full-spectrum microphone - fitted with
a horn to amplify sounds from the front - with a chirp emitter at 40 kHz
(K. Darras et al.,
2021). The first three point counts took place in the first hour after
the survey started, and the next three point counts happened in the
second hour. Between the two hours, the bat point counts team assisted
bat extraction at the mist netting site that was permanently attended by
a third person. Thus, it was not possible to sample bats with point
counts for the same duration as with the other methods.
Mist netting
We mist-netted bats for 4 hours per night for a total of 576 net-hours
(m✕h) in each site. Mist netting was our reference trapping method for
sampling bats: we did not use harp traps as they were ineffective in
previous assessments in oil palm plantations in our region (Darras,
unpublished data). We opened four 3.2 m high × 12 m long nets 1 to 1.5 m
above the ground for four hours starting at sunset (Ultra Thin Series M
Mist Net, 20mm mesh, Ecotone). Nets were installed in presumed flight
ways, delimited a quadrilateral, and their position relative to the
river, oil palm, or road was approximately the same in all plots for
each survey set. Most of the below-canopy flying space was covered with
our nets. Mist nets were checked every 15 minutes from sunset to two
hours afterwards, then every 30 minutes until four hours after sunset.
Captured bats were kept in tissue pouches until the nets were closed.
Bat morphology was measured to identify bats according to
(Huang et al., 2014)
directly in the field. There were no particular regulations or ethical
guidelines for research on live bats in Indonesia at the time of the
study, but we wore protective equipment (masks and gloves) to handle
them.
Ultrasound recordings
We made continuous ultrasoundscape recordings (i.e., without triggers)
with sound recorders for 4 hours per night in each site. One recorder
(SM2Bat+, Wildlife Acoustics) were set up with one microphone (Parus
open-source model, (K.
Darras et al., 2018)) parallel to the ground, sampling at 384 kHz at 2
m height, starting at sunset and lasting four hours in each site. We
measured the ultrasound detection space covered by the recorders
(K. Darras et al.,
2016): similarly to bat point counts, we pointed an ultrasound
calibrator (Wildlife Acoustics) to the recorder, and recorded its 40 kHz
chirps emitted from 2 m height at distances of 4, 8, 16, and 32 m, in
three directions (to the river, the road, and the oil palm plantation)
to derive the site’s sound transmission profiles (Fig S3).
Data analysis
Species identification
Ultrasound recordings from bat point counts and automated ultrasound
recorders were uploaded on the open-source platform BioSounds
(K. Darras et al.,
2020) to annotate the spectrograms with identitfied bat detections; we
included both acoustic as well as thermal-only detections (detections
without ultrasound that were vocally mentioned by the observer)
(K. Darras et al.,
2021). All bat calls were identified using our reference collection of
bat calls obtained from captured bats (Chiroptera reference collection
in BioSounds (K.
Darras et al., 2020)) and literature data
(Hughes et al., 2011;
Kingston, 2013; Zhu et al., 2012). We distinguished broadband
frequency-modulated (BFM), constant-frequency (CF), and
frequency-modulated, quasi-constant frequency (FM-QCF) calls. We
measured calls for each bat call type within each recording, but only if
the bat pass was recorded clearly (to avoid biased call parameters from
distant calls), using the three strongest, not saturated calls: We
measured peak frequency (Fmax, frequency with maximum energy), start
frequency, end frequency, call duration, and inter-call interval (from
the start of one call to the onset of the next). Frequency-modulated
quasi-constant frequency (FM-QCF) calls were split in three sonotypes
based on their end frequency: around 33 kHz, between 38 and 42 kHz, and
around 48 kHz.
We matched ultrasound recordings and near-infrared photographs from bat
point counts to their respective detections using a conservative
workflow that discarded uncertain matches
(K. Darras et al.,
2021). We used data from one additional, incomplete survey night during
which our infrared lamp power supply failed to aid with the taxonomic
identification. Bat species identification usually relies on direct,
external and internal body part measurements and categorical features of
caught specimens. However, absolute measurements from pictures are
inaccurate due to our large depth of field of approximately four meters:
a 10 cm bat at a distance of 8 m would appear as large as a 15 cm bat at
12 m. Thus, we rely on categorical features as well as relative
measurements of external, readily recognisable body parts - as they do
not depend on the bats’ distance - for identifying bat point count
detections. We used pixel-measuring software tools on photos where the
measured body parts were parallel to the camera focus plane to avoid
underestimates. We only used near-infrared images to identify
thermal-only detections. We confirmed or determined the identity of bat
sonotypes in bulk by using clear near-infrared pictures of selected
detections. We devised a new identification key for South-East Asian
bats found in oil palm based on
(Huang et al., 2014)
to determine bat identity from near-infrared pictures and ultrasound
calls (Box 1).
Rarefaction and extrapolation sampling
curves
We compared the species richness sampling effectiveness of bat point
counts against mist netting and automated ultrasonic recording by
comparing the size of the species pools sampled by each method using
rarefaction and extrapolation sampling curves
(Chao et al., 2014).
We pooled all three sampling sites to represent our oil palm
plantation’s bat community. We calculated a conservative estimate of
each species’ abundance in sound recordings by usingthe maximum number
of simultaneously recorded bats per night. We computed taxon
presence/absence at each sampling hour and method as well as abundance
matrices for each method, by summing the species abundances over the
three sites. We only used the taxonomic identities yielded by each
sampling method, independently of the insights gained from the other
methods. We generated raw incidence as well as abundance-based
rarefaction and extrapolation sampling curves and compared the number of
species at a 95% sampling coverage for a robust estimation of diversity
(Hsieh et al., 2016).
We assessed the statistical significance of differences in species
richness between sampling methods with 83% confidence intervals
(Krzywinski & Altman, 2013).
Acoustic and thermal detection
ranges
Thermal detection ranges of bat
point counts were obtained for each direction by measuring the maximal
distance at which the hand of a field assistant was detectable in the
thermal scope. Acoustic detection ranges for point counts (with
ultrasonic horn) and automated ultrasound recordings (without horn) were
obtained for each direction from the intersection of the chirp emitter’s
sound level curve against distance with the ambient sound level
(K. Darras et al.,
2016). Sampling spaces of mist nets are not determinable per se, but we
assumed that they cover at least the inner area delimited by their
border.