Bat Echolocation Researc h - Bat Conservation International

Figure 10: Fast Fourier transform power spectra for
the calls shown in Fig. 9 (a, b, c, d, respectively)
reveal the artificial harmonics (h) generated by “clipping”
or saturation (arrow), shown in more detail in
expanded time-amplitude displays of calls a and d.
Kapteyn (1993), Limpens and Roschen (1995), Zbinden
(1989), and Limpens (2001). Changes in frequencies
dominating echolocation calls during feeding buzzes
(Fig. 6) influence the detectability of bats by narrowband
detectors. But in many species, individuals change
frequencies between calls (e.g., Habersetzer 1981; Barclay
1983; Kalko 1995; Obrist 1995; Fenton et al. 1998).
Bats producing high-intensity echolocation calls often
use harmonics or overtones to broaden the bandwidth of
any call (Simmons and Stein 1980). In Fig. 7, there are 7
echolocation pulses with most energy between 40 and 50
kHz, but many are flanked above and below by other
apparent calls which may be harmonics. Closer examination
suggests that calls in the 40-50 kHz range alternate
in frequencies (Fig. 8). To ensure that apparent harmonics
are not artifacts of sampling (see below), one can
examine an expanded time-amplitude display and associated
patterns of frequency change over time (Fig. 8).
Using this process, it is clear that the recordings are not
saturated (see below) and that alternate calls differ in frequency
(Fig. 8). Observation suggested that a single bat
was responsible for the calls recorded here (Fig. 8).
In studies designed to assess variation in echolocation
calls, larger samples of data from calls of different individuals
and species will be required. Such details will be
essential for exploring cases of cryptic species, distinguishable
by their echolocation calls. The example of Pipistrellus
pipistrellus and Pipistrellus pygmaeus (Barratt et al. 1997)
demonstrates the identification of species by their calls.
IDENTIFICATION OF SPECIES BY THEIR CALLS
The focus of analysis is now the features of individual
calls. Being able to examine the time-amplitude display as
well as the associated spectrogram allows an observer to
assess the impact of signal strength (time-amplitude) on
spectrogram (Fig. 9). In Fig. 9, there are 4 calls, two (b and
Section 4: Resources, Research and Study
d) that are stronger than the others (a and
c). Some workers have speculated that
variation in alternate calls from the same
individual reflect head movements
(Siemers et al. 2001). When the signals are
compared in more detail (Fig. 10), it is evident
from the power spectra that the
weaker calls (Fig. 10a, c) lack harmonics
(h) present in the stronger ones (Fig. 10b,
d). A closer examination (Fig. 10a, d)
reveals “clipping” (arrows), times when the
signal saturated (overloaded) the system.
Clipping flattens sine waves, turning them
into square waves and generating harmonics
(h) that are artifacts. Unlike the situation
in Fig. 8, the harmonics in Fig. 10 are artifacts of
recording. In some cases, changes in time-frequency displays
make it obvious that the calls of two species have
been recorded, while the time-amplitude display reveals
the quality of the recordings (signal-to-noise ratio) and
the incidence of clipping or saturation. Features of time,
interpulse interval, and duration (measured in ms) can be
measured from the time-amplitude displays, informed by
the time-frequency displays. Measurement of interpulse
intervals requires a time-amplitude display of at least two
pulses, while duration of individual calls can most accurately
be measured from the expanded display of one
pulse. Frequency features (lowest frequency, highest frequency,
and frequency with most energy), including the
presence of harmonics, are most accurately measured
from power spectra (Fig. 3). In assessing highest and lowest
frequencies one must report the dB level at which the
frequency reading was obtained. While Fenton et al.
(2002) used -55 dB from 0 dB to determine the highest
and lowest frequencies in a call, Kalko and Schnitzler
(1993) took a different approach. They used -6 dB and -
15 dB (from the peak) readings to establish the highest
and lowest frequencies in a call. The -6 dB marks half of
the energy in the call. Others (e.g., Surlykke and Moss
2000) have followed this model. In a sample of 10 calls (of
Otomops martiensseni), the difference in highest and lowest
frequencies using these two approaches was < 0.5 kHz.
The challenge presented when trying to identify
species by their calls is directly proportional to the number
of species involved and the similarity between their
calls. The situation is almost exactly as it is for identifying
bats in the hand – some distinctions are easy, while
others are more subjective. When published information
provides an indication of the range of variation,
researchers can assess the feasibility of making distinctions
between species on the basis of call features. Often
bat detectors are used in this way to assess the presence
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