Introduction
Biodiversity sampling is biased towards species that are easily and
directly detectable with our human senses
(Moussy et al.,
2021). Even though remote visible light imagery has been used for
decades (Blackwell et
al., 2006; Cutler & Swann, 1999), newer technologies based on the
detection of the broader electromagnetic energy spectrum are becoming
more accessible and further facilitate detecting and identifying animals
passively and remotely
(Turner et al., 2003;
Vance et al., 2016). The applications in ecology and biodiversity
conservation have great potential for scientists and conservationists
(Pimm et al., 2015),
especially when sampling elusive animals. Here, we focus on the
detection of bats (Chiroptera), a taxon that is notoriously difficult to
sample because they are nocturnal, fast, and silent fliers. This partly
explains the relative lack of knowledge about bats, although they are
the second most diverse order of mammals, provide important,
wide-ranging ecosystem services, and experience acute threats
(Frick et al., 2020;
Kunz et al., 2011).
Bats are typically studied by capture using traps or by roost surveys.
Mist netting and harp-trapping are the most common sampling methods for
bats outside of their roosts. They are valuable for measuring the bats’
morphology precisely, taking physical samples (blood, tissue,
parasites), assessing their physiological status, and estimating bat
abundance directly. However, they are logistically challenging and have
biases: species flying above nets (e.g., large fruit bats) are rarely
caught, nets are avoided by some echolocating bats (e.g. “whispering
bats”), and other bats can learn to avoid them, requiring daily net
moving (Marques et
al., 2013). Harp traps are more effective for some species, but they
have variable performance
(Berry et al., 2004)
and may be more useful in South-east Asia
(Furey et al., 2010).
Furthermore, permits are often needed for catching bats, their handling
comes with potential zoonotic risks
(Wong et al., 2007),
the animals become stressed and more vulnerable to predation
(Rocha-Mendes &
Bianconi, 2009), and can even succumb to this invasive sampling method.
Passive acoustic monitoring is also commonly used for sampling bats,
since most bats vocalise in the ultrasonic range for navigation with
so-called echolocation calls. Passive ultrasound recording relies on
automated devices to record echolocation calls made by bats. Single,
cheap devices can sample large spaces and be programmed to record for
long durations. However, the vast majority of Pteropodidae, occurring in
the Paleotropics and Oceania, do not echolocate (except genusRousettus ), which explains why capture-based methods are
essential there. Still, little is known about bat acoustics in the
tropics, and acoustic methods need to be adopted more widely, especially
in the Paleotropics
(Kingston, 2010).
Also, bats do not necessarily have species-specific echolocation calls,
and calls are variable
(Obrist, 1995). As a
result, many species cannot be distinguished on the basis of ultrasound
alone and are grouped within ”sonotypes” (Walters et al., 2013).
Finally, very high frequency bat calls usually attenuate quickly in air
and are seldom picked up by microphones that have declining sensitivity
with frequency. Some bats also produce narrow ultrasound beams which are
less likely to hit a microphone
(Brinkløv et al.,
2011). Finally, sound detection spaces are species-specific and seldom
accounted for (K.
Darras et al., 2016). Thus, acoustic detection and identification of
bats is challenging, and density estimation is nearly impossible -
especially across species.
Mist-netting and passive acoustic monitoring are now established,
standardized sampling methods for bat biodiversity surveys
(Flaquer et al.,
2007). It is often advised to combine both methods to reduce the
overall sampling bias
(Kuenzi & Morrison,
1998), especially where Pteropodidae occur. However, recently, a
proof-of-concept has been proposed for technologically enhanced point
counts to sample flying bats at night
(K. Darras et al.,
2021). These enhanced bat point counts are an active (i.e., requiring a
human operator) sampling method to detect and identify all flying bats
within a sampling area at night, combining thermal sensing to detect
flying bats, ultrasound sampling to record their echolocation calls, and
near-infrared imagery to capture their morphology. Thermal and
near-infrared imagery have been used before to count bat colonies
directly in caves
(Betke et al., 2008;
Sabol & Hudson, 1995), and thermal imaging has also been combined with
ultrasound recording to detect bats with drones
(Fu et al., 2018) and
at wind farms (Correia
et al., 2013). Near-infrared imaging can also detect pollinating bats
(Frick et al., 2009).
However, these studies surveyed sites with a great density of inactive
bats, or focused on specific sites where a particular interaction
occurs. Near-infrared imaging has not been used yet for identifying
flying bats passively; it remains to be seen whether entire bat
communities can be sampled with this method and how it compares to
established methods.
Here, we showcase bat point counts and demonstrate how they can be used
for ecological studies. We compare them against mist-netting and
ultrasound recording in an agricultural system in the Paleotropics,
where both insectivorous, echolocating bats and frugivorous,
non-echolocating bats are common. We measure the detection spaces of all
three sampling methods, present a novel, morphological-acoustic bat
identification key tailored to our study system to make use of the
acoustic and photographic data, investigate how accurately and
efficiently the species pools are sampled by each method, and compare
diversity patterns using rarefaction and extrapolation sampling curves.
We discuss practical considerations, and we give an outlook as to the
new possibilities offered by bat point counts for the study of bats.