Results
Detected species
We found 100 thermal detections of seven taxa in point counts, 2009
detections of six taxa in automated ultrasonic recordings, and captured
83 bats from seven species in mist nets (Fig. 3). We excluded eight
point count detections where neither ultrasound nor near-infrared
pictures were recorded. We found one BFM, three CF, and three FM-QCF
sonotypes (Table S1, Figure S1). We identified the FM sonotype to genus
in automated ultrasound recordings, but found its putative identity from
mist-netting data (Kerivoula pellucida ). We identified all CF
sonotypes to species (Hipposideros kunzi, H. orbiculus,
Rhinolophus sedulus ), and none was found using mist nets. All FM-QCF
sonotypes were found using bat point counts and automated ultrasound
recordings. One FM-QCF was identified to species using acoustic data
(Pipistrellus stenopterus ,
(Kingston, 2013)) and
was not found in mist nets. Using relative measurements from
near-infrared imagery, one FM-QCF sonotype consisting of two candidate
species was resolved to species-level in point counts (Scotophilus
kuhlii ); it was the only species detected by all methods. The third
FM-QCF sonotype was a complex of six candidate species and was reduced
to three candidate species using near-infrared imagery. It was
putatively identified with mist-netting (undescribed Myotis sp.1sensu (Huang et
al., 2014)). One pteropodid genus (Cynopterus sp.) was detected
in bat point counts and resolved to three distinct species in the
mist-netting dataset (Cynopterus sphinx, C. brachyotis, C.
minutus ). Mist nets detected one pteropodid species from another genus
(Macroglossus minimus ) and conversely, aEonycteris/Rousettus genera complex was detected in bat point
counts.
Rarefaction and extrapolation sampling
curves
At 95 % sampling coverage, with incidence-based data, bat point counts
detected a higher mean projected species richness, earlier than other
methods; with abundance-based data, bat point counts reached a higher
mean projected species richness, but at higher numbers of individuals
(Fig. 2). At low numbers of sampling hours (≲ 2.5) and of sampled
individuals (≲ 20), bat point counts and automated recordings allowed to
detect significantly more species than mist-netting. The abundance-based
extrapolation curves saturated more quickly for ultrasound recording and
mist netting than for bat point counts, indicating that bat point counts
had a higher probability to detect new species with increasing numbers
of individuals. We provide an in-depth analysis of
rarefaction-extrapolation sampling curves (Text S3, Fig S2).
Acoustic and thermal detection
spaces
Bat point counts swept a large thermal detection area that encompassed a
larger area than our mist nets, and their ultrasound detection spaces
were larger and more narrow than those of the automated ultrasound
recorders (Fig. 1). Ultrasound detection ranges of bat point counts
(where the microphone was fitted with a horn) were almost three times
larger than the unattended ultrasound recorders’ ranges (without horn)
in the direction the microphone was pointing to (bat point counts: 450
m; automated ultrasound recorders: 164 m), and to some degree also to
the side (Figure S3). The thermal scope had a range of 48 m on average,
with a minimum of 19 m to a maximum of 84 m; its range was usually
limited by obstacles such as oil palms or terrain irregularities. The
mist nets approximately delimited an area of 150 m2when a quadrilateral was drawn across their outer corners.