Bat Echolocation Researc h - Bat Conservation International

The sensitivity of the microphone used, and its relative
position to the bat emitting the echolocation calls, may
mean that the calls are not sufficiently loud enough to
elicit a response from the microphone. Recording techniques
and situations can also affect the quality of signal
recorded, and therefore our ability to identify the
species emitting a particular call (Parsons and Obrist
2003; Parsons et al. 2000; Pye 1993).
Bats exhibit flexibility in the design of echolocation
calls. Call structure varies with degree of acoustic clutter
(e.g., Jensen and Miller 1999; Kalko and Schnitzler
1989, 1993; Rydell 1990; Simmons and Stein 1980), and
at different stages in a bat’s approach to objects such as
prey items and roosts (Kalko 1995; Masters et al. 1991;
Parsons et al. 1997; Simmons et al. 1979). Age also has
an effect on the echolocation calls of bats, independent
of morphology (Jones and Kokurewicz 1994; Jones et al.
1992; Masters et al. 1995). Morphology also influences
call design and can cause convergence in call design
between morphologically similar species (Bogdanowicz
et al. 1999; Jones 1996, 1999).
Species identification systems must be able to cope
with substantial intra- and interspecific variation in call
design. They must also be able to separate species that
produce seemingly identical calls. This is an enormously
difficult task. Central to the development of such a system
is the identification of call parameters that best separate
species. Once identified, these parameters must be
measured in a repeatable, quantitative manner to ensure
the reliability of the results.
To classify calls to species level using a single analysis,
given the number of species in this study and the
number of variables measured per call, is not a trivial
task, especially given the highly variable nature of the
data. We hypothesized that the use of a hierarchical
classification system, in which calls were classified to
genus and species level by separate functions or networks,
would improve the ability of the automated neural
networks to correctly classify species by spreading
the complexity of the task over several analyses. Ultimately,
the success of such an approach is reflected in
increased correct identification rates at the species level.
At every level, (all species together, genus only, within
genera containing multiple species) the automated neural
networks outperformed their equivalent discriminant
function analysis. Analysis uses a series of functions that
best separate the groups and then classifies each data
point in turn. However, the neural network we
employed in this study used an error back-propagation
algorithm (Rumelhart et al. 1986) based on the errorcorrection
learning rule. Error back-propagation learning
consists of two passes through the different layers of
the network: a forward pass and a backward pass. In the
forward pass, inputs are presented to the network and a
signal passes through the various layers resulting in a set
of actual response from the network. During the forward
pass, the synaptic weights of the neurons in the network
are fixed. During the backward pass, these synaptic
weights are adjusted to make the actual response of the
network match the desired response. The use of a network
with hidden neurons i.e., neurons not part of the
input or output layers, means that the network can learn
complex tasks by extracting progressively more meaningful
features from the input data. Therefore, given the
complexity of species identification, it is not surprising
that the artificial neural networks outperformed discriminant
function analysis.
CONCLUSIONS
The accuracy and success of a species-identification
system relies on the quality of the data presented to it
and the conditions under which the recording was made
(e.g., equipment used, degree of acoustic clutter).
Parameters measured from calls must be chosen for low
intraspecific and high interspecific variability. If possible,
the entire echolocation repertoire of the bats should
also be recorded. Using appropriate signal-processing
techniques, parameters can be measured in a quantitative
and repeatable manner. Classification systems must be
flexible but also able to make subtle decisions when separating
calls from different species. Artificial neural networks,
because of the error-minimization routines, outperform
traditional statistical methods.
LITERATURE CITED
Fig. 5—Correct identification rates for the artificial neural networks
trained to identify calls to genus level.
Section 3: Ultrasound Species Identification
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