Bat echolocation calls: adaptation
and convergent evolution
Gareth Jones*and Marc W. Holderied
School of Biological Sciences, University of Bristol, Woodland Road, Bristol BS8 1UG, UK
Bat echolocation calls provide remarkable examples of ‘good design’ through evolution by natural
selection. Theory developed from acoustics and sonar engineering permits a strong predictive basis for
understanding echolocation performance. Call features, such as frequency, bandwidth, duration and pulse
interval are all related to ecological niche. Recent technological breakthroughs have aided our
understanding of adaptive aspects of call design in free-living bats. Stereo videogrammetry, laser scanning
of habitat features and acoustic flight path tracking permit reconstruction of the flight paths of echolocating
bats relative to obstacles and prey in nature. These methods show that echolocation calls are among the
most intense airborne vocalizations produced by animals. Acoustic tracking has clarified how and why bats
vary call structure in relation to flight speed. Bats using broadband echolocation calls adjust call design in a
range-dependent manner so that nearby obstacles are localized accurately. Recent phylogenetic analyses
based on gene sequences show that particular types of echolocation signals have evolved independently in
several lineages of bats. Call design is often influenced more by perceptual challenges imposed by the
environment than by phylogeny, and provides excellent examples of convergent evolution. Now that whole
genome sequences of bats are imminent, understanding the functional genomics of echolocation will
become a major challenge.
Keywords: echolocation; bats; adaptation; convergent evolution
In ‘The Blind Watchmaker’, Dawkins (1986) uses bat
echolocation to illustrate features of ‘good design’ through
evolution by natural selection. Since the design of the
echolocation calls determines the type and quality of
information contained in returning echoes, bats have
evolved different signals to meet a diversity of sensory
demands. Dawkins poses problems that echolocating bats
experience, considers solutions that sensible engineers
might consider and then arrives at solutions that bats have
achieved. Often the solutions adopted by bats, for
example, the use of broadband chirps for ranging, and
the exploitation of Doppler shifts to calculate relative
velocity, have parallels with methods that engineers
have adopted in commercial and military uses of sonar
and radar. Bat echolocation provides rich examples of
good design because echolocation performance can be
predicted from theory developed in acoustics and in sonar
(and radar) engineering. For example, Rayleigh scattering
calculations have been used to predict the sizes of insects
detectable by different frequencies of sound used by
echolocating bats (Houston et al. 2004). Ambiguity
analysis used by radar engineers (Woodward 1953;
Kelly & Wishner 1965) is useful in determining the
Doppler tolerance and range resolution of signals (Altes &
Titlebaum 1970; Boonman et al. 2003).
In this paper, we will show how recent advances in
bat echolocation. We focus on field studies. We discuss how
call features, such as frequency, intensity, duration and
intervals between pulses are all shaped by the perceptual
challenges faced by the bat in its natural environment.
Specifically, we will show how bats using broadband signals
with distantly related taxa evolving similar signal designs
independently in the face of similar environmental chal-
lenges. We will conclude by anticipating how recent
advances in genomics might increase our understanding of
the genetic basis for the evolution of echolocation. We
suggest that engineers can learn from breakthroughs in our
understanding of adaptive design in echolocation signals
used by bats. For example, autonomously guided vehicles
could use speed-dependent sonar signal designs that
minimize localization errors, offering advances over the
simple signals used in many current robotics applications
(e.g. Gao & Hinders 2006).
2. METHODOLOGICAL ADVANCES
To understand how bats use echolocation in the field, it is
necessary to quantify the bat’s position in relation to
surrounding obstacles and insect prey. Signal design can
then be related to the proximity and nature of obstacles,
and changes in signal structure can be monitored as
targets are approached. Biologists have used multiflash
photography to reconstruct the flight paths of wild
bats in three dimensions for some time using stereo
Proc. R. Soc. B (2007) 274, 905–912
Published online 16 January 2007
*Author for correspondence (firstname.lastname@example.org).
Received 20 October 2006
Accepted 18 December 2006
This journal is q 2007 The Royal Society
photogrammetry (e.g. Jones & Rayner 1988; Kalko &
Schnitzler 1993). More recently, two infrared video
cameras have been used in conjunction with measuring
microphones. Since the bat’s position can be quantified
accurately in three dimensions, its distance and position
relative to a calibrated microphone can be reconstructed,
thus allowing investigation of changes in call design and
intensity during flight (Holderied et al. 2005). Flight paths
can also be reconstructed in three dimensions using
acoustic, rather than optical methods. Time-of-arrival
differences of calls recorded at microphone arrays
allow the bat’s position at the time of calling to be
measured accurately (e.g. Holderied & von Helversen
2003). Bats couple call production with wing flapping to
minimize the costs of echolocation during flight
(Speakman & Racey 1991). Bats flap and call at high
rates in flight (often 5–20 Hz), thus allowing frequent
updates of positional data recorded at microphone arrays,
which string together in a pearl-chain-like manner to trace
the bat’s flight trajectory. Holderied & von Helversen
(2003) used two arrays of four microphones, with each
arranged in a symmetrical star, to reconstruct flight paths
accurately, with a direction-dependent location accuracy
of between 0.2% and 2% of the distance (Aubauer 1994).
Depending on the species under study, the tracking range
is up to 35 m.
The range of frequencies exploited by echolocating bats
makes perfect sense from an acoustics perspective. Bat
echolocation calls vary in their dominant frequency
approximately between 11 kHz (e.g. Euderma maculatum;
Fullard & Dawson 1997) and 212 kHz (Cloeotis percivali;
Fenton & Bell 1981). Most insectivorous bats call
with dominant frequencies between 20 kHz and 60 kHz
(Fenton et al. 1998). Lower frequencies are avoided
because echoes from insect-sized targets are weak when
the wavelength is longer than the insect wing length
(Houston et al. 2004). For example, target strength (the
ratio between incident and echo sound pressure) is
reduced by approximately 25 dB at 1 m when the ratio
of target wing length/sound wavelength drops from 1 to
0.2 (Houston et al. 2004). High frequencies are therefore
necessary to detect small targets such as aerial insects.
However, atmospheric attenuation is frequency-depen-
dent, and limits the effective range of echolocation at high
frequencies (Lawrence & Simmons 1982).
Call frequency in closely related bat species may also
diverge to facilitate intraspecific communication, rather
than to facilitate resource partitioning by allowing the
species to forage more effectivelyondifferent size classes of
prey related to call wavelength differences. Cryptic species
of bats are morphologically similar in appearance, but
oftendiffer in echolocation call frequency(Jones & Barlow
2004). The wavelength differences between species are,
however, too small to influence differences in target
strengths from insects within the size range of the prey
taken (Jones & Barlow 2004), and acoustic divergence
probably evolved instead so that each species has its own
‘private bandwidth’ with which it can communicate
effectively with conspecifics (Heller & von Helversen
1989; Kingston et al. 2000; Thabah et al. 2006).
Some species of horseshoe bats have nevertheless
changed call frequency radically during evolution using
different harmonics in a harmonic series of frequencies
(Kingston & Rossiter 2004). Such ‘harmonic hopping’
may allow bats to exploit different sizes of insect prey and
may provide a mechanism for sympatric speciation
(Kingston & Rossiter 2004).
More recently, attention on the adaptive significance of
call frequencyin bats hasfocused onthe importance of call
bandwidth. Bat species that emit calls with broad
bandwidths are most successful in capturing insect prey
close to clutter (clutter is defined as echoes other than the
target of interest; Siemers & Schnitzler 2004). For
example, the calls of Myotis nattereri sweep from 135
kHz to 16 kHz and hence span wavelengths from
approximately 22 mm to 2.6 mm. Such calls will ensonify
many reflecting surfaces, including prey and vegetation
objects, simultaneously, thus allowing the bat to develop a
detailed acoustic snapshot that allows it to separate prey
from background clutter (Siemers & Schnitzler 2004).
4. CALL INTENSITY
Results from acoustic tracking and stereo videogrammetry
have given measurements of the intensities of calls emitted
by a range of echolocating bat species in the wild (Jensen &
Miller 1999; Holderied & von Helversen 2003; Holderied
et al. 2005). Call intensities greater than 125 dB peak
equivalent sound pressure level (peSPL) at 10 cm have
been recorded from aerial feeding species searching for
insects, and measurements of 133 dB peSPL at 10 cm have
been measured for species that fly fast in open spaces
(Holderied & von Helversen 2003). Such values are among
the highest intensities documented for airborne vocaliza-
tions by any animal. Interestingly, the maximum sound
pressure levels of all aerial feeding species studied so far are
very similar. Whereas the largest species weighing 80 g can
reach133 dB,evenabatspeciesweighingonly4 gcanreach
128 dB. Other species that forage in flight, but listen for
walking noises of prey on the ground, such as Hemprich’s
have source levels of no more than 93 dB peSPL (average
82 dB; M.W. Holderied &C. Corine, unpublished results),
which is why such bats have been called whispering bats. In
this manner they reduce the masking effect of their calls
while listening for the faint rustling noises of their prey.
calculating potential detection ranges for insect targets, and
this has helped in the refinement of hypotheses about how
bats time the emission of calls (see §5).
5. DURATION AND PULSE INTERVAL
Bats that call at low duty cycles (i.e. the proportion of time
that the signal is ‘on’ is low) reduce call duration as they
approach targets because they cannot tolerate overlap
between the outgoing signal and the returning echo.
Intense vocalizations mask any faint echo that returns
during or soon after call emission. Moreover, some bat
species contract muscles in their middle ear to avoid
deafening themselveswhile calling (Suga & Jen 1975), and
also employ neural attenuation mechanisms (Suga &
Schlegel 1972) that make the detection of faint echoes
impossible. The zone around the bat in which target
echoes overlap with the emitted call is termed the signal
overlap zone (SOZ; Kalko & Schnitzler 1993). Since
906G. Jones & M. W.Holderied
Review. Bat echolocation calls
Proc. R. Soc. B (2007)
sound travels at 340 m sK1and because the bat has to wait
for sound to reach and return from a target, each 1 ms of
signal adds 17 cm to the SOZ. Bats therefore typically
reduce call duration as they approach targets so that the
SOZ is equal to or less than the distance to the target, and
hence overlap between pulse and echo (forward masking)
is avoided. Nevertheless, some overlap may exist during
the final phases of prey capture (e.g. Britton et al. 1997) or
in species that use exceptionally broad bandwidth calls
close to clutter (Siemers & Schnitzler 2000).
If echoes from background clutter arrive soon after prey
echoes, and interfere with neural activity evoked by the
prey echoes, then backward masking may occur (Kalko &
Schnitzler 1993). Bats are therefore predicted to use signal
durations that result in echoes returning in an overlap-free
window, in which both forward and backward maskings
are avoided (Kalko & Schnitzler 1993).
The interval between pulses (pulse interval) is also
shaped by natural selection so that echolocation is
efficient. This can be shown for two different situations.
First, during insect pursuit, bats produce the next call
immediately after hearing the echo from the prey. Because
echo delay decreases while the bat approaches its prey, the
bat calls at ever shorter intervals. Just before capture, this
ends in a characteristic rapid ‘feeding buzz’ with up to
approximately 200 pulses produced per second (e.g.
Kalko & Schnitzler 1998).
Second, bats flying in open spaces not only use longer
pulses than those flying in clutter, but they also have
longer intervals between pulses, presumably to grant that
all echoes arriving from the preceding call will be received
before the emission of the next call. This would be
adaptive because late echoes from the first pulse, which
arrive after the following pulse, might cause confusion.
Potential errors in call–echo assignation either mean that
distant objects are perceived at close range, potentially
triggering unnecessary evasive manoeuvres, or, in the
more relevant case, that obstacles might erroneously be
perceived at a safe distance, while actually they are
dangerously close. Indeed, calculated maximum detection
ranges (which depend on assumptions regarding the bat’s
echo detection thresholds) are linked to preferred pulse
intervals: during search phase, i.e. when no prey target has
been detected yet, all vespertilionid bats studied so far
produce their next call only when even echoes from flying
objects at the outer limit of their echolocation range
would have returned to them (Holderied & von Helversen
2003; Holderied et al. 2005). In this manner they
maximize calling rate without risking call–echo assignation
problems. This is particularly intriguing, as bats also
couple their call emission to their wing beat cycle in order
to greatly reduce energy expenditure for call production
(Speakman & Racey 1991). Bats that fly in open spaces
achieve optimal timing of their pulse intervals as well as
energetically cheap call production by matching their
detection range for flying objects with their wing beat
period (Holderied & von Helversen 2003; Holderied
et al. 2005).
In addition, certain large structures, such as walls or
rock faces, can be detected at a considerably longer
distance. To achieve correct call–echo assignation also in
this case, bats flying fast in open spaces sometimes skip
calling for one or more wing beats, seemingly to await such
late echoes from distant targets (Holderied & von
Helversen 2003). However, there rests one major dis-
advantage in waiting for the return of all echoes: for bats
such that the acoustic image is updated only at an
unfavourably low rate. Neotropical emballonurid bats,
like other species such as the noctule, found a solution for
this problem—they can call more frequently because they
frontal distance (m)
lateral distance (m)
lateral distance (m)
ranging error (cm)
ranging error (cm)
cross section through hedge
with bat positions and DOFs
Figure 1. (a,c) Two calls of whiskered bats and (b,d) their respective horizontal flight-induced ranging error distributions. Zero
ranging error is marked by a solid line labelled ‘focus’. a: Call no.1 is short and very broadband. b: This call design has a distance
offocus (DOF) of 5 cm. c: Call no.2 is long and relatively narrowband. d: It has a DOFof 111 cm. Intermediate call designs have
intermediate DOFs. e: Cross-section perpendicular through a flight corridor that follows a hedge. Symbols: Twenty-two
individual bats’ positions with respect to the hedge. Stars indicate bats using calls in a and c, respectively. Circles indicate DOF
for the calls emitted closest to the cross-sectional plain (modified from Holderied et al. 2006).
Review. Bat echolocation calls
G. Jones & M. W.Holderied907
Proc. R. Soc. B (2007)
be correctly assigned, because all echoes carry their
respective call’s spectral signature (Jung et al. in press).
6. CALL ‘SHAPE’ AND THE DISTANCE OF FOCUS
Besides the following of general rules, such as long
narrowband calls for long-range detection in contrast to
short broadband calls for accurate localization and
extraction of object features (e.g. Schnitzler et al. 2003),
bats show a remarkable range of call designs (figure 1) that
change in a gradual manner. We are only beginning to
understand and quantify the instantaneous adaptive value
of such slight changes in call bandwidth, let alone in the
curvature of a call during its frequency modulation. One
new and promising approach builds on the fact that
echolocation in flight bears two inevitable sources of
error. The so-called ‘distance of focus’ (DOF) theory
interprets the slight changes in signal design as a way to
control flight-induced errors in distance measurements
(Boonman et al. 2003; Holderied et al. 2006). First,
in-flight ranging accuracy is disturbed because bats call at
one place and hear the echo at a slightly different position
along their flight trajectory. Second, all echoes will be
affected by Doppler shifts, i.e. they are compressed in time
and their pitch increases, which also perturbs distance
measurements considerably. These two ranging errors
together have a very interesting spatial distribution relative
to the flying bat; the overall flight-induced ranging error
will be 0 for all objects on one particular hemisphere with
the bat in its centre. The farther the objects are from this
hemisphere, the larger will be the flight-induced ranging
error. Under natural conditions, such ranging errors can
reach several centimetres. Thus, these errors may
significantly increase collision risk in close-range naviga-
tion and impede capture success. What makes DOF
theory so interesting is that every call design has its specific
hemisphere radius, which depends on sweep rate and
curvature (Boonman et al. 2003). By adjusting call design,
bats can actively increase and decrease the radius of the
hemisphere. It would be clearly adaptive to match the
hemisphere to their target of interest in order to minimize
flight-induced ranging errors. The name ‘distance of
focus’ for the hemisphere radius has been chosen because
this mechanism has similarities to focusing (i.e. accom-
modation) in vision.
In a field study with whiskered bats commuting in a
narrow flight corridor along the surface of a hedgerow,
Holderied et al. (2006) found distance-dependent signal
design that supports the DOF theory: (i) DOFs were in a
reasonable range from 1 cm to 110 cm, (ii) DOFs were
shorter in more confined situations, and (iii) DOF in the
confined situation was adjusted to the instantaneous
distance to the hedge in a distance-dependent manner.
Further studies will reveal whether other bat species in
different ecological situations make similar use of plasticity
in signal design.
7. PHYLOGENETIC CONTEXT
How bat echolocation calls are shaped by environmental
challenges reveals exquisite examples of adaptation, and
raises the question whether distantly related bat species
that forage in similar habitats have independently evolved
similar call designs to solve similar perceptual challenges.
Phylogenetic analyses of genetic sequence data have
revolutionized our understanding of evolutionary relation-
ships among bat taxa. On the basis of morphology and
echolocation characteristics, bats were previously classi-
fied into two suborders, the Megachiroptera (one family,
the Old World fruit bats Pteropodidae) and the Micro-
chiroptera (remaining 17 families; Gunnel & Simmons
2005). Only microchiropteran bats use laryngeal echo-
location (calls produced in the larynx) and produce tonal
signals in echolocation. No megachiropteran bats are
known to echolocate, with the exception of bats in the
genus Rousettus, which use tongue-clicking for orientation
in caves. Traditionally, the brief clicks emitted by Rousettus
were believed to offer poor orientation performance, yet
the bats can perform as well as some microchiropteran
bats in obstacle negotiation tasks. It has recently been
proposed that the clicks emitted by Rousettus resemble
Gabor functions, and hence minimize bandwidths at very
short durations, focusing energy at frequencies that the
bats hear best (Holland et al. 2004). Although clicks are
also used by all other echolocating animals (toothed
whales, swiftlets, oilbirds and insectivores), it is unlikely
thatthey represent an ancestral state in the evolutionof bat
echolocation—it is more likely that echolocation evolved
secondarily in bats in the genus Rousettus so that the bats
can orient in dark caves (Springer et al. 2001; Jones &
New molecular phylogenies based on large concate-
nated datasets from a series of nuclear and mitochondrial
genes support the hypothesis that the Microchiroptera are
paraphyletic (Teeling et al. 2000, 2005; Springer et al.
2001). Microbat paraphyly is supported by DNA–DNA
hybridization (Hutcheon & Kirsch 2004), rare genomic
events (Teeling et al. 2000, 2005), and by examination of
extensive nuclear (Eick et al. 2005) and mitochondrial
(Van Den Bussche & Hoofer 2004) datasets. In the new
molecular phylogenies (figure 2), a deep divergence occurs
between the Pteropodidae, Rhinolophidae, Hipposider-
idae, Megadermatidae, Craseonycteridae and Rhinopo-
Yinpterochiroptera), and the remaining 12 families of
bats classified in the redefined Yangochiroptera. The
implications of microbat paraphyly are profound; horse-
shoe bats (family Rhinolophidae) have the most sophis-
ticated echolocation of any animals. They combine
broadband and constant frequency components in calls
that allow excellent performance for the key tasks
of detection, localization and classification of targets
(Schnitzler et al. 2003). The constant frequency facilitates
detection (Schnitzler et al. 2003) and classification (von
der Emde & Schnitzler 1990) of insect prey by facilitating
the perception of ‘glints’ (small changes in the amplitude
and frequency content of echoes caused by insect wing
beats), while a terminal broadband sweep improves the
localization of targets (Tian & Schnitzler 1997). However,
horseshoe bats are in a clade that is sister to the
Pteropodidae, bats which do not use laryngeal echoloca-
tion. Studies that use a molecular scaffold alongside
morphological analyses of fossil data support the
hypothesis that echolocation was present in the common
ancestor of all bats, and was lost in the Pteropodidae only
to evolve secondarily by tongue-clicking in cave-dwelling
bats in the genus Rousettus (Springer et al. 2001). In
contrast, one recent study has suggested that echolocation
908G. Jones & M. W.Holderied
Review. Bat echolocation calls
Proc. R. Soc. B (2007)
may have evolved twice in bats, once in the common
ancestor of the Yangochiroptera, and again in the
Yinpterochiroptera in the common ancestor of bats in
all families in this clade that excludes the Pteropodidae
(Eick et al. 2005). Resolution of the debate about how
many times echolocation evolved in bats remains an
The new molecular phylogeny of bats also allows a
better understanding of adaptive radiation and convergent
evolution in echolocation. Bats in both the Yinpterochir-
optera and the Yangochiroptera use a diverse range of
echolocation calls, illustrating adaptive radiation, and call
plasticity can be remarkably flexible even within species
(e.g. Siemers et al. 2001). Regarding convergence, high
duty cycle echolocation has appeared twice. This most
sophisticated form of biosonar—the use of long constant
frequency signals terminated by broadband sweeps—
evolved independently in the horseshoe bats (Rhinolo-
phidae: Yinpterochiroptera) and in the moustached bat
Pteronotus parnellii in the Mormoopidae (Yangochiroptera;
figure 2). These bats compensate for Doppler shifts in a
speed-dependent manner during flight. They lower the
frequency of the call the faster they fly (Schnitzler 1972),
so that echoes return consistently at the frequency of best
hearing, the ‘acoustic fovea’ (Schuller & Pollak 1979).
Although the echolocation signals and some aspects of
auditory physiology show remarkable convergence in
horseshoe bats and P . parnellii, substantial differences
occur in the organization of the auditory cortex (O’Neill
1995), and in the route of sound transmission—horseshoe
bats emit calls through their nostrils, P. parnellii calls are
emitted orally (Jones & Teeling 2006).
A similar convergence can be seen in bats in the families
Megadermatidae (Yinpterochiroptera) and Nycteridae
(Yangochiroptera; figure 2). Bats in these families have
large ears, used for listening for prey-generated sounds in
cluttered situations (Fenton et al. 1983; Marimuthu &
Neuweiler 1987) where echolocation can be ineffective
(Arlettaz et al. 2001). The bats emit brief, faint,
multiharmonic broadband signals and have elaborate
noseleaves (Jones & Teeling 2006). Despite their
morphological similarities, the bats in these families are
not closely related to one another, and their morphology
and their echolocation behaviour show remarkable
examples of convergent evolution. In fact, earlier phylo-
genies based on morphology placed the two families as
sister taxa(see Gunnel & Simmons 2005). Convergence in
call design is also seen between the neotropical insectivor-
ous bat Myotis nigricans, which forages frequently in edge-
and-gap and open habitats, and pipistrelle bats, which
exploit similar ecological niches. Both M. nigricans and
pipistrelles typically emit narrowband signals when
searching for prey in open habitats, switching to more
broadband calls with a narrowband tail in edge habitats
(Siemers et al. 2001).
Extensive convergence in signal design makes recon-
struction of the ancestral state difficult (Jones & Teeling
time (ms)20 ms
Figure 2. (a) The new molecular phylogeny of bat families. Associations that were not strongly supported by at least one
independent molecular study are indicated by hatched lines. The position of the newly proposed family Miniopteridae is
indicated by an asterisk. The tree unites the pteropodids, which do not use laryngeal echolocation, with the echolocating
superfamily Rhinolophoidea (e.g. horseshoe bats) in the clade Yinpterochiroptera. All other bats that use laryngeal echolocation
are united in the clade Yangochiroptera. Modified from Jones & Teeling (2006). (b,c) Some examples of convergent evolution in
signal design. In (b), schematics of signals from Pteronotus parnellii (Yangochiroptera: Mormoopidae; left call) and Rhinolophus
pearsonii (Yinpterochiroptera: Rhinolophidae; right call) are shown alongside one another. Both species emit long, constant
frequency calls of similar frequency, with the constant frequency portion initiated and terminated by brief broadband sweeps. In
both the cases, most energy is in the second harmonic of the call. In (c), a call of Nycteris thebaica (Yangochiroptera: Nycteridae;
left call) is shown next to a call from Megaderma lyra (Yinpterochiroptera: Megadermatidae; right call). Both bats emit brief,
broadband multiharmonic signals and also listen for prey-generated sounds to detect and localize prey. Call reconstructions are
from illustrations in Jones & Teeling (2006), from Taylor (2000) and from unpublished recordings by G. Jones (R. pearsonii).
Review. Bat echolocation calls
G. Jones & M. W.Holderied 909
Proc. R. Soc. B (2007)
evolved secondarily and do not represent an ancestral
state. High duty cycle echolocation with Doppler shift
compensation as used by horseshoe bats and P . parnellii is
also highly derived. The prevalence of multiharmonic
signals in many clades suggests that strong harmonic
filtering (e.g. as used by species such as many vesper bats
where the fundamental harmonic is dominant) may also be
derived. Low duty cycle, multiharmonic calls are likely to
have been presentthroughmuchofthe evolutionary history
of bats (Jones & Teeling 2006).
8. FUTURE CHALLENGES
These examples of convergent evolution in echolocation
in shaping bat sonar signals. Bats that feed in similar
situations evolve similar designs of echolocation signals
despite being distantly related to one another. Physical
the effect of clutter on bandwidth, the impact of target
proximity on pulse duration and the pulse interval
all influence the design of bat echolocation signals in
ways that can often override phylogenetic constraints.
Understanding the genetic factors that underpin the
diversity in bat echolocation behaviour has become a
are becoming available (see http://www.genome.gov/
11007951). Comparisons of genes that may be associated
with audition in bats with those in other mammals may be
which convergence in echolocation strategies is achieved. It
is also likely that new understandings in bat echolocation
may assist navigationmechanismsin autonomously moving
vehicles. The ways in which bats that use broadband signals
make subtle adjustments to call design to compensate for
Doppler effects may have implications for signal design in
vehicles that are able to navigate in darkness (Reynolds
1999). Advances in laser imaging are making three-
dimensional reconstructions of the foraging habitats of
bats more precise (figure 3), allowing the perceptual
challenges faced by bats to be quantified in more realistic
struction of the bats’ flight routes in the same habitats,
and perceptual challenges and behavioural strategies
(Aschoff et al. in press).
Figure 3. Cross-section through laser scan data over a forest track together with positions at which individual bats were flying.
The section is perpendicular to the direction of the forest track and the bats’ flight direction. Thirty-one flight paths are colour-
coded for different species. All fly centrally but most species prefer the more open lower part while Daubenton’s bats
(Myotis daubentonii) also fly higher in the more confined situation (from Aschoff et al. in press).
910G. Jones & M. W.Holderied
Review. Bat echolocation calls
Proc. R. Soc. B (2007)
Altes, R. A. & Titlebaum, E. L. 1970 Bat signals as optimally
Doppler tolerant waveforms. J. Acoust. Soc. Am. 48,
Arlettaz, R., Jones, G. & Racey, P. A. 2001 Effect of acoustic
clutter on prey detection by bats. Nature 414, 742–745.
Aschoff, T., Spieker, H. & Holderied M. W. In press.
Untersuchung der Jagdlebensra ¨ume von Flederma ¨usen
in Wa ¨ldern mit Hilfe von Laserscannern und akustischen
Aubauer, R. 1994. Dreidimensionale Flugbahnverfolgung von
Flederma ¨usen Fortschritte der Akustik—DAGA 94. Bad
Boonman, A. M., Parsons, S. & Jones, G. 2003 The influence
of flight speed on the ranging performance of bats using
frequency modulated echolocation pulses. J. Acoust. Soc.
Am. 113, 617–628. (doi:10.1121/1.1528175)
Britton, A. R. C., Jones, G., Rayner, J. M. V., Boonman,
A. M. & Verboom, B. 1997 Flight performance,
echolocation and foraging behaviour in pond bats, Myotis
dasycneme (Chiroptera: Vespertilionidae). J. Zool. Lond.
Dawkins, R. 1986 The blind watchmaker. Essex, UK: Long-
man Scientific and Technical.
Eick, G. N., Jacobs, D. S. & Matthee, C. A. 2005 A nuclear
DNA phylogenetic perspective on the evolution of
echolocation and historical biogeography of extant bats
(Chiroptera). Mol. Biol. Evol. 22, 1869–1886. (doi:10.
Fenton, M. B. & Bell, G. P. 1981 Recognition of species of
insectivorous bats by their echolocation calls. J. Mammal.
62, 233–243. (doi:10.2307/1380701)
Fenton, M. B., Gaudet, C. L. & Leonard, M. L. 1983
Feeding behaviour of the bats Nycteris grandis and Nycteris
thebaica (Nycteridae) in captivity. J. Zool. Lond. 200,
Fenton, M. B., Portfors, C. V., Rautenbach, I. L. &
Waterman, J. M. 1998 Compromises: sound frequencies
used in echolocation by aerial-feeding bats. Can. J. Zool.
76, 1174–1182. (doi:10.1139/cjz-76-6-1174)
Fullard, J. H. & Dawson, J. W. 1997 The echolocation calls of
the spotted bat Euderma maculatum are relatively inaudible
to moths. J. Exp. Biol. 200, 129–137.
Gao, W. & Hinders, M. 2006 Mobile robot sonar backscatter
algorithm for automatically distinguishing walls, fences
and hedges. Int. J. Robot. Res. 25, 135–145. (doi:10.1177/
Gunnel, G. F. & Simmons, N. B. 2005 Fossil evidence and
the origin of bats. J. Mamm. Evol. 12, 209–246. (doi:10.
Heller, K.-G. & von Helversen, O. 1989 Resource partition-
ing of sonar frequency bands in rhinolophoid bats.
Oecologia 80, 178–186.
Holderied, M. W. & von Helversen, O. 2003 Echolocation
range and wingbeat period match in aerial-hawking bats.
Proc. R. Soc. B 270, 2293–2299. (doi:10.1098/rspb.2003.
Holderied, M. W., Korine, C., Fenton, M. B., Parsons, S.,
Robson, S. & Jones, G. 2005 Echolocation call intensity in
the aerial hawking bat Eptesicus bottae (Vespertilionidae)
studied using stereo videogrammetry. J. Exp. Biol. 208,
Holderied, M. W., Jones, G. & von Helversen, O. 2006
Flight and echolocation behaviour of whiskered bats
commuting along a hedgerow: range-dependent sonar
signal design, Doppler tolerance and evidence for
‘acoustic focussing’. J. Exp. Biol. 209, 1816–1826.
Holland, R. A., Waters, D. A. & Rayner, J. M. V. 2004
Echolocation signal structure in the megachiropteran bat
Rousettus aegyptiacus Geoffroy 1810. J. Exp. Biol. 207,
Houston, R. D., Boonman, A. M. & Jones, G. 2004
Do echolocation signal parameters restrict bats’ choice
of prey? In Echolocation in bats and dolphins (eds J. A.
Thomas, C. F. Moss & M. Vater), pp. 339–345. Chicago,
IL: University of Chicago Press.
Hutcheon, J. M. & Kirsch, J. A. W. 2004 Camping in a
different tree: results of molecular systematics of bats using
DNA–DNA hybridization. J. Mamm. Evol. 11, 17–47.
Jensen, M. E. & Miller, L. A. 1999 Echolocation signals of
the bat Eptesicus serotinus recorded using a vertical
microphone array: effect of flight altitude on searching
signals. Behav. Ecol. Sociobiol. 47, 60–69. (doi:10.1007/
Jones, G. & Barlow, K. E. 2004 Cryptic species of
echolocating bats. In Echolocation in bats and dolphins
(eds J. A. Thomas, C. F. Moss & M. Vater), pp. 345–349.
Chicago, IL: University of Chicago Press.
Jones, G. & Rayner, J. M. V. 1988 Flight performance,
foraging tactics and echolocation in free-living Dauben-
ton’s bats Myotis daubentoni (Chiroptera: Vespertilioni-
dae). J. Zool. Lond. 215, 113–132.
Jones, G. & Teeling, E. C. 2006 The evolution of
echolocation in bats. Trends Ecol. Evol. 21, 149–156.
Jung, K., Kalko, E. K. V., & von Helversen, O. In press.
Echolocation calls in Central American emballonurid
bats: signal design and call frequency alternation. J. Zool.
Kalko, E. K. V. & Schnitzler, H.-U. 1993 Plasticity in
echolocation signals of European pipistrelle bats in search
flight - implications for habitat use and prey detection.
Kalko, E. K. V. & Schnitzler, H. U. 1998 How echolocating
bats approach and acquire food. In Bat biology and
pp. 197–204. Washington, DC; London, UK: Smithso-
nian Institution Press.
Kelly, E. J. & Wishner, R. P. 1965 Matched-filter theory for
high-velocity, accelerating targets. IEEE Trans. Military
Elect. 9, 56–69.
Kingston, T. & Rossiter, S. J. 2004 Harmonic-hopping in
Wallacea’s bats. Nature 429, 654–657. (doi:10.1038/
Kingston, T., Jones, G., Zubaid, A. & Kunz, T. H. 2000
Resource partitioning in rhinolophoid bats revisited.
Oecologia 124, 332–342. (doi:10.1007/PL00008866)
Kingston, T., Jones, G., Zubaid, A. & Kunz, T. H. 2003
Alternation of echolocation calls in five species of
Lawrence, B. D. & Simmons, J. A. 1982 Measurements of
atmospheric attenuation at ultrasonic frequencies and the
significance for echolocation by bats. J. Acoust. Soc. Am.
71, 585–590. (doi:10.1121/1.387529)
Marimuthu, G. & Neuweiler, G. 1987 The use of acoustical
cues for prey detection by the Indian false vampire bat,
Megaderma lyra. J. Comp. Physiol. A 160, 509–515.
O’Neill, W. E. 1995 The bat auditory cortex. In Hearing by
bats (eds A. N. Popper & R. R. Fay), pp. 416–480. New
York, NY: Springer.
Review. Bat echolocation calls
G. Jones & M. W.Holderied911
Proc. R. Soc. B (2007)
Reynolds, C. W. 1999 Steering behaviors for autonomous
characters.In Conf.Proc. 1999 Game Developers Conference,
Schnitzler, H.-U. 1972 Control of Doppler shift compen-
sation in the greater horseshoe bat, Rhinolophus ferrume-
quinum. J. Comp. Physiol. 82, 79–82. (doi:10.1007/
Schnitzler, H.-U., Moss, C. F. & Denzinger, A. 2003 From
spatial orientation to food acquisition in echolocating bats.
Trends Ecol. Evol. 18, 386–394. (doi:10.1016/S0169-
Schuller, G. & Pollak, G. 1979 Disproportionate frequency
representation in the inferior colliculus of Doppler
compensating greater horseshoe bats. Evidence for an
acoustic fovea. J. Comp. Physiol. 132, 47–54. (doi:10.1007/
Siemers, B. M. & Schnitzler, H.-U. 2000 Natterer’s bat
(Myotis nattereri Kuhl, 1818) hawks for prey close to
vegetation using echolocation signals of very broad
bandwidth. Behav. Ecol. Sociobiol. 47, 400–412. (doi:10.
Siemers, B. M. & Schnitzler, H.-U. 2004 Echolocation
signals reflect niche differentiation in five sympatric
congeneric bat species. Nature 429, 657–661. (doi:10.
Siemers, B. M., Kalko, E. K. V. & Schnitzler, H.-U. 2001
Echolocation behavior and signal plasticity in the Neo-
tropical bat Myotis nigricans (Schinz, 1821) (Vespertilio-
nidae): a convergent case with European species of
Pipistrellus? Behav. Ecol. Sociobiol. 50, 317–328. (doi:10.
Speakman, J. R. & Racey, P. A. 1991 No cost of echolocation
for bats in flight. Nature 350, 421–423. (doi:10.1038/
Springer, M. S., Teeling, E. C., Madsen, O., Stanhope, M. J.
& de Jong, W. W. 2001 Integrated fossil and molecular
data reconstruct bat echolocation. Proc. Natl Acad. Sci.
USA 98, 6241–6246. (doi:10.1073/pnas.111551998)
Suga, N. & Jen, P. H.-S. 1975 Peripheral control of acoustic
signals in the auditory system of echolocating bats. J. Exp.
Biol. 69, 277–311.
Suga, N. & Schlegel, P. 1972 Neural attenuation of responses
to emitted sounds in echolocating bats. Science 177,
Taylor, P. J. 2000 Bats of southern Africa. Pietermaritzburg,
South Africa: University of Natal Press.
Teeling, E. C., Scally, M., Kao, D. J., Romagnoli, M. L.,
Springer, M. S. & Stanhope, M. J. 2000 Molecular
evidence regarding the origin of echolocation and flight in
bats. Nature 403, 188–192. (doi:10.1038/35003188)
Teeling, E. C., Springer, M. S., Madsen, O., Bates, P.,
O’Brien, S. J. & Murphy, W. J. 2005 A molecular
phylogeny for bats illuminates biogeography and the fossil
Thabah, A., Rossiter, S. J., Kingston, T., Zhang, S., Parsons,
S., Mya My, K., Zubaid, A. & Jones, G. 2006 Genetic
divergence and echolocation call frequency in cryptic
species of Hipposideros larvatus sensu lato (Chiroptera:
Hipposideridae) from the Indo-Malayan region. Biol.
J. Linn. Soc. 88, 119–130. (doi:10.1111/j.1095-8312.
Tian, B. & Schnitzler, H. U. 1997 Echolocation signals of the
greater horseshoe bat (Rhinolophus ferrumequinum) in
transfer flight and during landing. J. Acoust. Soc. Am.
101, 2347–2364. (doi:10.1121/1.418272)
Van Den Bussche, R. A. & Hoofer, S. R. 2004 Phylogenetic
relationships among recent chiropteran families and the
importance of choosing appropriate out-group taxa.
von der Emde, G. & Schnitzler, H.-U. 1990 Classification of
insects by echolocating greater horseshoe bats. J. Comp.
Physiol. A 167, 423–430.
Woodward, P. M. 1953 Probability and information theory, with
applications to radar. New York, NY: McGraw-Hill.
912G. Jones & M. W.Holderied
Review. Bat echolocation calls
Proc. R. Soc. B (2007)