Behavioural Processes 68 (2005) 145–163
Recognition of partially concealed leopards by
wild bonnet macaques (Macaca radiata)
The role of the spotted coat
Richard G. Cossa,∗ , Uma Ramakrishnanb , Jeffrey Schanka
a
b
Department of Psychology, University of California, Davis, CA 95616, USA
The Connecticut Agricultural Experiment Station, New Haven, CT 06504, USA
Received 24 September 2004; accepted 16 December 2004
Abstract
Wild bonnet macaques (Macaca radiata) have been shown to recognize models of leopards (Panthera pardus), based on their
configuration and spotted yellow coat. This study examined whether bonnet macaques could recognize the spotted and dark
melanic morph when partially concealed by vegetation. Seven troops were studied at two sites in southern India, the Mudumalai
Wildlife Sanctuary and the Kalakad-Mundanthurai Tiger Reserve. The forequarters and hindquarters of the two leopard morphs
were presented from behind thick vegetation to individuals at feeding stations 25 m away. Flight reaction times and frequency of
flight were obtained from video for only those individuals who oriented towards the models prior to hearing alarm calls. Bonnet
macaques exhibited faster reaction times and greater frequency of flight after looking at the spotted morph’s forequarter than
after looking at either its spotted hindquarter or the dark morph’s forequarter. The hindquarter of the dark morph was ignored
completely. Artificial neural network modeling examined the perceptual aspects of leopard face recognition and the role of spots
as camouflage. When spots were integrated into the pattern recognition process via network training, these spots contributed to
leopard face recognition. When networks were not trained with spots, spots did not act as camouflage by disrupting facial features.
© 2004 Elsevier B.V. All rights reserved.
Keywords: Antipredator behavior; Leopards; Bonnet macaques; Artificial neural network; Pattern recognition
1. Introduction
Research on the recognition of solitary felid predators by mammalian prey has been hampered by
∗ Corresponding author. Tel.: +1 530 752 1626;
fax: +1 530 752 2087.
E-mail address: [email protected] (R.G. Coss).
0376-6357/$ – see front matter © 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.beproc.2004.12.004
the rarity of encounters, their reclusiveness, and the
propensity of these predators to hunt at night. The use of
vegetation as cover during the stalk is essential for the
successful daytime hunting of large cursorial prey. In
some contexts in which alarm calls announce the presence of a predator, such as the tiger (Panthera tigris),
the uncertainty of predator whereabouts does not deter hunting of large ungulate prey if grass and bushes
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R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
provide sufficient cover to continue the stalk undetected
(Thapar, 1999). This tactic is less successful for hunting primates that typically forage on the ground near
trees (Stacey, 1986) or can maintain visual surveillance
of the predator’s location from their arboreal refuge
(Zuberbühler et al., 1999). This necessity to continuously monitor a predator’s location has engendered antipredator tactics in which some ungulates and primates
approach felid predators to keep them in view (Bailey,
1993; Boesch, 1991; Gandini and Baldwin, 1978). For
African ungulates, vision plays an essential role for detecting and monitoring the activity of stealthy predators
at a distance, especially when they are partly concealed
by vegetation or elevated terrain (Baenninger et al.,
1977; Caro, 1994; Stanley and Aspey, 1984).
2. Leopard recognition
The present study of wild bonnet macaques
(Macaca radiata) in southern India examined which
perceptual features of leopards (P. pardus) were important for predator recognition in microhabitats with
thick vegetation that afforded some concealment. Observations of bonnet macaques responding to leopards
(Ali, 1981; Ramakrishnan and Coss, 2000) characterize the diversity of daytime and nighttime microhabitats in which leopards are encountered; these are not
unlike the circumstances in which other primates are
hunted by leopards (e.g., Boesch, 1991; Busse, 1980;
Cowlishaw, 1994; Isbell, 1990). Bonnet macaques react
quickly by fleeing up trees when they detect leopards
and evidence from leopard scat (Ramakrishnan et al.,
1999) suggests that leopards are much less successful
hunters of bonnet macaques than they are of sympatric
Nilgiri langurs (Trachypithecus johnii) and Hanuman
langurs (Semnopithecus entellus).
Our initial research on leopard recognition (Coss
and Ramakrishnan, 2000) examined whether wild bonnet macaques differentiated the common spotted yellow morph from the rare dark melanic morph as revealed by their alarm calling and flight responses. A
second facet of this research examined whether leopard body configuration and coat texture afforded distinct recognition cues by presenting the two leopard
morphs in upright and inverted positions. Model presentation employed a pop-up procedure which, for the
upright presentations, simulated the appearance of a
leopard standing, freezing briefly while looking at the
macaques, and then disappearing from view. Results
of this study revealed that the spotted upright model
was the most provocative to bonnet macaques living
in forests where leopards are common and in an urban site where leopards are absent. Comparisons of the
upright and inverted positions of each morph provided
evidence that spots on a yellow coat were still provocative to bonnet macaques despite their appearance on the
inverted felid form.
Consistent with the finding that the spotted yellow coat was provocative, follow-up exploratory study
at one forest and one urban site examined the effects of crouching leopard models constructed of lightbrown towels with a leopard print or bluish-yellow
towels with a flower print. Only the towel model
with the leopard print elicited alarm calling by bonnet macaques, prompting troop members in the forest
to abandon their sleeping tree and mobbing by members of the urban troop on the top of a building. Although shaped the same as the spotted towel model, the
towel leopard with the flower print was ignored by both
troops. Coupled with the finding that the inverted leopard model with spots was still provocative (Coss and
Ramakrishnan, 2000), these observations prompted the
theoretical conjecture that natural selection might have
operated on the ability of bonnet macaques to detect
leopards via visible patches of spots unobstructed by
vegetation or rocks.
The ability to recognize leopards via their coat color
and texture is not restricted to bonnet macaques. Anecdotal observations provide some evidence that common chimpanzees (Pan troglodytes) will mob or alarm
call after detecting realistic leopard models partly obscured by vegetation (Kortlandt, 1967; Zuberbühler,
2000). Hunters of vervet monkeys in the thick forest of
Cameroon locate their prey by capitalizing on vervet
alarm calling to the hunters’ ovoid headgear painted
to resemble the leopard’s spotted yellow coat (McRae,
1997).
Study of the visual features used by prey to
identify leopards is complex due to the interplay of
leopard configuration and habitat features, especially
when the leopard is partly obscured by foreground
vegetation. For primates with trichromatic vision,
even partial exposure of specific predator features,
such as the coupling of face, coat texture, and color,
might have acquired significance in the evolutionary
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
time frame as leopard-recognition cues if detection of
these features afforded survival. On the other hand,
yellowish vegetation during the dry season blends well
with leopard coloration, a property which might force
reliance on the detection of the spotted coat texture. In
South American primates in which only heterozygous
females are trichromats (Smith et al., 2003), the
spotted coat texture alone might provide a sufficient
recognition cue for identifying partially occluded felids. For example, Herzog and Hopf (1986) found that
cinematic presentations of spotted textures with yellow
backgrounds moving briefly across a rectangular frame
elicited alarm calling in captive squirrel monkeys
(Saimiri sciureus). Such alarm calling might characterize recognition of the spotted coats of felid predators
typified by Felis tigrina, F. geoffroyi, and F. jaguarundi.
Similar to our research using a model of the dark
leopard morph, Brown et al. (1992) presented a moving
leopard silhouette to captive vervet monkeys (Chlorocebus aethiops), eliciting alarm calling among several
members of the colony. However, the high rate of alarm
calling typical of individuals encountering leopards in
natural settings (cf. Cheney and Seyfarth, 1990; Isbell,
1990) was emitted by the only wild-caught and presumably experienced individual in the colony. Lack of
alarm calling by inexperienced monkeys might simply
reflect the impoverish properties of the leopard silhouette possibly coupled with the effects of developmental
deprivation due to captive rearing (see Stell and Riesen,
1987; Struble and Riesen, 1978).
3. Experimental questions and predictions
Our previous research showing that the spotted yellow morph was more provocative to bonnet macaques
than the dark morph prompted further field research on
the effects of partial leopard concealment. The current
study of wild bonnet macaques addressed two experimental questions involving the recognition of leopards
partly exposed from behind vegetation: (1) Does detection of the spotted morph constitute a greater perceived
threat than the dark morph? (2) Does perception of a
leopard’s forequarter with its face turned toward the
viewer engender greater alarm than perception of its
hindquarter?
Since in full view, the upright spotted yellow morph
was shown to be more evocative than the upright dark
147
morph (Coss and Ramakrishnan, 2000), we predicted
that partial body concealment by vegetation would not
alter this difference in the two morphs. Our predictions
for the effects of forequarter and hindquarter views
were less confident. We knew from our field research
that bonnet macaques are sensitive to the facing orientation of other troop members (Coss et al., 2002)
and that the ability to recognize two facing eyes is an
evolved trait in a variety of vertebrate taxa (Coss, 1978,
1979a; Coss and Goldthwaite, 1995; Emery, 2000). Because ambush predators typically use concealment afforded by biotic and abiotic substrates while maintaining their visual fixation on prey, natural selection is
more likely to have shaped the recognition of two facing eyes by prey under the stochastic circumstances in
which the two eyes of the predator were still exposed.
As such, two facing eyes might retain their provocative
properties without surrounding facial features, an effect
known to occur in some mammals (Aiken, 1998; Coss,
1970, 1978, 1979b; Hess, 1975; Topál and Csányi,
1994). From this perspective, we predicted that forequarter views showing the face would be more provocative than hindquarters views. Yet for the spotted yellow
morph, the spots and flecks would likely reduce the
contrast of facial features and might disrupt face recognition, possibly yielding more equivalent provocative
effects for forequarter and hindquarter views.
To explore this possibility further from a theoretical perspective, we created an artificial neural network
(ANN) as a simulation tool to investigate whether spots
on the leopard’s face have camouflaging properties and
whether dark pelage would hinder leopard recognition
by masking facial features. Thus, ANN modeling of
perceptual processes was expected to have heuristic
value in pinpointing specific facial–feature relationships that might prompt further field studies of predator
recognition and mechanistic studies of its neurophysiological underpinnings.
4. Field research on recognition of partially
concealed leopards
4.1. Methods
4.1.1. Study sites
The experiments were carried out between April
and October 1997, at two study sites in southern India.
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Table 1
Number of individuals in each troop and demographic category
Troops
Habitat
Adult male
Adult female
Subadult male
Subadult female
Juvenile
Infant
Unclassified
Total
Mundanthurai
Kariyar
Maylar
Theppakadu
Bandipur
Kargudi
Kakkanala
Forest
Forest
Forest
Forest
Forest
Forest
Forest
8
7
5
6
5
5
7
8
9
6
10
7
9
6
5
6
3
4
4
2
4
3
6
3
5
6
4
5
7
3
4
3
3
6
3
3
3
5
7
5
2
2
0
3
2
0
0
0
4
34
37
28
35
30
28
31
The Mudumalai Wildlife Sanctuary is located between
11◦ 32 to 11◦ 43 N latitude and 76◦ 22 to 76◦ 45 E longitude and covers an area of 321 km2 . Four troops
(Bandipur, Kargudi, Kakkanala, Theppakadu) were selected for the study from this site. The second study
site, the Kalakad-Mundanthurai Tiger Reserve, is located between 8◦ 25 to 8◦ 53 N latitude and 77◦ 10 to
77◦ 35 E longitude, and covers an area of 817 km2 .
Three troops (Kariyar, Maylar, Mundanthurai) were
selected for study from this site. The spotted yellow
leopard morph is frequently seen at the two forest sites
while the dark melanic leopard morph is present at these
sites, but rarely seen. All troops in this study were
habituated to humans and could be studied at close
range.
Individuals from the seven study troops (Table 1)
were identified and classified into one of six sex
and age (demographic) categories based on size: infants (unweaned animals that were less than 1 year
of age); juveniles (weaned animals 1–2 years of age);
subadult females (2–4 years of age, smaller than
adult females and larger than juveniles); subadult
males (same size as adult females, smaller than adult
males); adult females (females older than 4 years of
age with at least one offspring); adult males (older
than 5 years of age, larger than adult females).
The responses of infants were not examined in this
study.
4.1.2. Leopard models
Further study of the perceptual aspects of the
common spotted yellow morph and rare dark melanic
morph recognized by bonnet macaques (Coss and
Ramakrishnan, 2000) was accomplished by presenting
the forequarters and hindquarters of these models
from behind bushes (Fig. 1). Unobstructed features
visible to troop members comprised the following:
(1) forequarter of the spotted morph, exposing its
face, shoulder, and one foreleg; (2) hindquarter of the
spotted morph, exposing its tail and one hind leg; (3)
forequarter of the dark morph, exposing its face, shoulder and one foreleg; and finally, (4) hindquarter of the
dark morph, exposing its tail and one hind leg. Model
head and body length was 1.21 m with the following
dimensions: shoulder height: 63 cm; height at pelvis:
61 cm, facial height: 29 cm, and maximum head width:
23 cm. Total model length including tail was 1.5 m. The
model was constructed of Masonite hardboard covered
with cloth and assembled in three sections. Without
the cloth, the dark-brown Masonite provided the background color for the dark melanic morph. For the spotted morph, the cloth was painted to resemble a leopard
in full sun. The following model colors are based on
the 1963 Munsell Book of Color, Neighboring Hues
Edition Matte Surface Samples: Spotted morph; yellow
background body color, 5Y7/4, yellow body shading
and shadows: range 5Y6–7/4, black rosettes, lips, and
eyelids, golden rosette centres and irises: 10YR7/8,
and tongue: 7.5R6/6; dark melanic morph, dark-brown
color, 5YR3/4, with the same colors used for the spotted model to paint the dark morph’s lips, eyelids, and
irises.
4.1.3. Experimental layout
To create a similar motivational context for presenting the experimental treatments (e.g., Hanson and Coss,
1997), feeding stations were set up and food (split peas)
was scattered in a ∼1 m radius, which caused bonnet
macaques to aggregate for video recording. All troops
were fed aperiodically throughout the study period to
preclude any reliable association of food with the experimental treatments. A Panasonic AG-185U VHS
camcorder was used for video taping behavioral and
auditory responses from a 20-m distance to the center
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
149
Fig. 1. Partially exposed leopard models presented to bonnet macaques. Top left: forequarter of spotted yellow leopard morph. Top right:
forequarter of dark melanic leopard morph. Bottom left: hindquarter of spotted yellow leopard morph. Bottom right: hindquarter of dark melanic
leopard morph that was ignored completely by all subjects.
of the feeding station. Camera field of view encompassed the entire feeding area.
Four of the seven troops were exposed to more than
one leopard view with Mundanthurai and Bandipur
troops exposed to three views. For these troops, the
four partly exposed leopard morphs were presented in
a random order, with minimum and maximum intervals
between presentations of 4 and 12 days, respectively.
Experiments were conducted between 06:00 a.m. and
10:00 a.m. and between 03:00 p.m. and 05:00 p.m., corresponding to the peak foraging periods of this species.
Video recording was initiated after the animals arrived
at the feeding station. After 2 min of video recording,
the forequarter or hindquarter of one of the two leopard
models was presented at a distance of approximately
25 m to monkeys gathered at a feeding station. For
these presentations, the assistant, hidden behind thick
vegetation, removed the green cloth envelope used for
transporting the model. On cue, the assistant moved
the front or rear section of the model forward into view
and withdrew the model after the monkeys responded
by flight and alarm calling or after a 1-min interval
if there was no response. This procedure simulated a
leopard emerging from behind a bush into partial view
of the monkeys, freezing and then retreating from view
after being detected (Fig. 1). Video recording continued for 3 min after the each model was no longer in
view.
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4.1.4. Behavioral measures and statistical
analyses
A flight reaction time measure was calculated as the
interval between lifting or turning the head in the direction of the model and initiation of flight. Reaction times
could not be obtained from individuals already looking
in the direction of the models when they were moved
into view. These individuals were not included in this
and the subsequent analyses. Rather than exhibiting a
normal distribution, reaction times are typically skewed
to the right due to physiological limitations on the assessment and recognition of visual information (Rogal
et al., 1985). Therefore, nonparametric tests were applied to the data. We used survival analyses (Gail et al.,
1980; Gehan, 1975) with log-ranked tests on pairwise
comparisons to measure differences in flight reaction
times after individuals detected the models. Individuals were censored if they did not flee within the 1-min
sampling period. We employed multinomial log–linear
analyses (Agresti, 1990) to examine the interaction of
leopard models and the proportion of individuals that
either fled or stayed within camera view.
4.1.5. Monte Carlo simulations of larger subject
pool
A basic problem faced by field research in animal
behavior concerns the exact identification of animals.
In some cases, we have exact information with which
to identify individuals, but in many other cases we only
have partial information. In this study, we could not exactly identify individuals across leopard-model conditions. However, we did know the troop and the sex/age
classes and this allowed us to use Monte Carlo simulation of synthetic data sets based on the troop and
sex/age class of each individual observed to estimate
the frequency range of possible resampling as well as a
probability distribution for this range. In addition, with
the synthetic data sets thus generated, it was possible
to do a robustness analysis of the application of the
Kruskal–Wallis nonparametric analysis of variance test
generalized to survival analysis data using the Mantel
(1967) method for ranking the data (Lee, 1980).
attended to the model. Although this model might have
been looked at briefly via glances undetected on video,
the absence of relevant antipredator behavior precluded
statistical comparisons of the hindquarter of the dark
morph with the other leopard views. As a consequence,
the statistical null hypothesis (not the theoretical hypothesis) was that animals do not respond differentially
to the three remaining leopard models.
4.2.1. Analysis of the synthetic data set
To determine both the range and probability of resampling across test conditions and to determine the
consequences of resampling on the probability of a
Type I error, Monte Carlo simulation was used to construct synthetic data sets based on the original data set.
This was accomplished as follows. For all 42 observations, we had information on the troop and sex/age
class. From Table 1, we knew how many individuals belonged to each troop and age/sex class within troops.
Thus, within each of the three leopard model conditions, individuals were drawn randomly from the corresponding troop and age/class without replacement.
Between leopard model conditions, individuals were
drawn with replacement. Thus, resampling of individuals was possible between conditions.
For each individual, we assumed that it was a responder (and have a measured latency to leave) or a
nonresponder (indicated by a censored data point). The
probability of responding was 19/42 based on the frequency of responding in the actual data set. Because
responding was treated as a probability, any given data
set could have more or less than 19 responders, but over
4.2. Results
Presentation of the hindquarter of the dark morph
for the maximum of 1 min failed to elicit changes in
head or body posture to indicate that individuals had
Fig. 2. A comparison of the empirical and theoretical distributions
used for animals that were assumed to respond to leopard models
(based on 100,000 simulations).
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
many replications, the frequency of responders (or nonresponders) was normally distributed about 19/42 (by
the central limit theorem).
The data collected were either uncensored (“responded to a leopard model”) or censored (“did not
respond to a leopard model”). For those animals that
responded, the distribution of latencies to respond was
modeled by a truncated normal distribution of latencies because it was likely more similar to the actual
distribution than, for example, a Poisson distribution.
If a randomly generated latency fell in the truncated
region, a new random normal latency was generated.
It was assumed that the fastest latency was 0.08 s. The
151
truncated normal distribution was generated with an
initial mean of 55 and standard deviation of 85, which
produced a truncated normal distribution with a mean
of 94.4, very close to the data mean of 94.1 as illustrated
in Fig. 2.
The simulation program used was written in Codewarrior 6.0 for the Macintosh computer using ANSI C.
Synthetic data sets were constructed as just described
for each replication and the frequency of resampling
was calculated for the data set. Ten million simulated
synthetic data sets were generated yielding the frequency distribution of probable resampling frequencies
illustrated in Fig. 3A. As illustrated, the most likely
Fig. 3. (A) Frequency distribution of possible resampling frequencies during multiple model presentations. The double arrow indicates the
range of possible resampling across conditions, with the most likely resampling frequency at about 14%. (B) Plots of 0.05 and 0.01 ␣-levels for
10,000,000 Monte Carlo generated synthetic data sets and survival analyses. As can be seen clearly, the effect of resampling is to lower the Type
I error rate due to the increasing homogeneity of individuals across test conditions.
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frequency of resampling was about 14% with a range
of 5–40%.
With these synthetic data sets, survival analysis
could be applied to determine the effects of resampling
on Type I errors. Using the Kruskal–Wallis ANOVA
generalized to survival data by the Mantel scoring procedure (Lee, 1980), we plotted the P-values for synthetic data sets for ␣-levels of 0.05 and 0.01. We found
that resampling did have an effect on Type I error
rate, a property that decreased as resampling increased
(Fig. 3B). This implies that resampling in this study did
not increase the likelihood of a Type I error; rather, it
likely decreased it.
4.2.2. Analysis of the actual data set
Analysis of the interval between model detection
and the onset of flight, using the Kaplan–Meier estimate of the survivor function (Fig. 4A), showed that
the three views of the spotted and dark morph differed at a statistically significant level (χ2 = 18.146;
Fig. 4. (A) Latency to flee after looking at the models as the proportion of individuals that have not fled at a specific time. Individuals were
censored if they did not flee within the 1-min sampling period, truncated graphically at 26 s. (B) Percentage of individuals fleeing after observing
the models.
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
d.f. = 2; P < 0.0005). The forequarter of the spotted
morph (N = 11) elicited a flight reaction time that
was significantly faster than those elicited by its
spotted hindquarter (N = 16, log-ranked test = 3.265;
P < 0.0025), and the dark morph’ forequarter (N = 15,
log-ranked test = 3.653; P < 0.0005). Unlike the spotted forequarter, the flight reaction time after seeing the
spotted hindquarter was not significantly different than
that after seeing the forequarter of the dark morph (logranked test = 0.429; P = 0.668).
Multinomial log–linear analyses (Fig. 4B) were employed to examine the proportions of individuals that
fled after looking at the three views of the two morphs
(Table 2). The interaction between models and the frequency of flight was statistically significant (likelihood
ratio χ2 = 13.869; d.f. = 2; P < 0.001). Differences in
the proportion of individuals fleeing from each model
followed the same trend as differences in flight reaction
times, with the largest proportion of bonnet macaques
fleeing after they looked at the forequarter of the spotted morph. As such, statistically significant interactions with very large standardized effect sizes appeared
for the proportion of individuals that fled after they
looked at the spotted forequarter and the proportions
of individuals that fled after they looked at the spotted hindquarter (likelihood ratio χ2 = 10.519; d.f. = 1;
P = 0.001, d = 1.65) and dark morph’ forequarter (likelihood ratio χ2 = 11.790; d.f. = 1; P < 0.001, d = 1.89).
The proportion of individuals fleeing after looking at
the spotted hindquarter was not significantly different from the proportion fleeing after looking at the
dark morph’s forequarter (likelihood ratio χ2 = 0.079;
d.f. = 1; P > 0.5).
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4.2.3. Post hoc comparisons of partially and fully
exposed leopard models
The reaction times of individuals after they oriented
toward the partially exposed leopard models clearly
revealed differences in the excitatory effects of model
assessment. However, the generality of these finding
is limited to this particular experimental context. An
interesting question then is whether bonnet macaques
react to partially exposed leopards in a manner similar to when they detect leopards in full view. Such a
comparison can be made post hoc because, in order to
determine the onset of model exposure for measuring
reaction time, individuals were only sampled if they
had looked up or turned their heads in the direction of
the models after the models had been positioned into
view. Thus, the marked differences in the dynamics of
presenting pop-up models in full view for 10 s (Coss
and Ramakrishnan, 2000) and the method of presenting
the forequarters and hindquarters of the models from
behind a bush in the present study were irrelevant for
determining individual reaction times (see Table 2).
Differences in the time interval between the two experiments and composition of troops further reduced the
likelihood of resampling.
Pairwise comparison of the full view and forequarter
view of each morph revealed that the prepotent properties of these views did not differ appreciably (Fig. 5).
Sight of the spotted morph in full view (N = 11) elicited
flight reaction times that were significantly similar
(e.g., reliably identical) to the flight reaction times of
individuals (N = 11) who looked at the spotted forequarter (log-ranked test = 0.034; P = 0.973). Although
the dark morph was less provocative overall, the flight
Table 2
Number of individuals who looked at partially concealed leopard models after the models were positioned into view
Leopard views
Flight
Adult male
Adult female
Subadult male
Subadult female
Juvenile
Unclassified
Total
Spotted forequarter
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
3
1
0
7
1
5
5
0
2
5
3
0
3
4
2
4
4
0
5
1
0
0
1
0
0
0
0
0
1
0
2
0
1
0
0
1
1
0
0
0
2
0
0
0
1
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
10
1
5
11
4
11
11
0
8
6
Spotted hindquarter
Dark forequarter
Spotted in full viewa
Dark in full viewa
The hindquarter of the dark morph was not looked at by troop members in video view. Total proportions were examined by log–linear analyses.
a Leopard models that popped up into full view in Coss and Ramakrishnan (2000) for post hoc comparisons of reaction times.
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were first exposed, events which typically triggered
immediate flight without pauses to scan for the source
of the disturbance (cf. Coss and Ramakrishnan, 2000;
Ramakrishnan and Coss, 2000). As such, reaction time
data were typically restricted to a small number of
individuals in each troop who were the first to detect
the models (Table 2).
Fig. 5. Latency to flee after looking at models of the forequarters of
the spotted and dark morphs compared will fully exposed leopard
models (from Coss and Ramakrishnan, 2000). The forequarter and
fully exposed spotted leopard models elicited significantly similar
reaction times. Graph is truncated at 26 s.
reaction times after individuals saw its full view
(N = 14) and forequarter (N = 15) were not significantly different (log-ranked test = 1.398; P = 0.162).
On the whole, these findings suggest that the complete
body and forequarter views of the same leopard
morphs are similarly provocative.
4.3. Discussion
Models of the spotted yellow and dark melanic leopard morphs were presented briefly to bonnet macaques
as partly exposed views of the leopard’s forequarter
and hindquarter. Such comparisons provided insight
into which perceptual features of leopards were important for predator recognition in microhabitats with
thick vegetation that afforded some concealment. The
frequency of flight of individuals in different troops
that looked at the these models provided evidence that
the forequarter view of the spotted yellow morph was
perceived as much more threatening than either its
hindquarter or the forequarter view of the dark morph.
Despite this difference in flight elicitation, it must be
noted that at least one individual alarm called from
arboreal refuge in every troop exposed to these three
leopard views. With exception of the hindquarter of
the dark morph that was ignored completely, detection
of motionless models by individuals in video view
was hindered by the early emission of alarm calls
from individuals already in trees or by the sight of
neighboring monkeys running on the ground who
had detected the models by their motion as they
4.3.1. Role of the forequarter and face in leopard
recognition
It is reasonable to assert that the primary features
for leopard recognition are exhibited by the leopard’s
anterior portion and spotted yellow coat. The most predominant finding supporting this supposition was the
much faster reaction time and higher frequency of flight
elicited by the forequarter of the spotted morph compared with its hindquarter. Only one individual from all
troops (Table 2) failed to flee after looking at the forequarter of the spotted morph, a frequency of flight that
approaches the 100% flight engendered by the spotted
morph in full view (Coss and Ramakrishnan, 2000).
Also, the reaction times elicited by the spotted forequarter were nearly identical to those engendered by
the spotted morph in full view (Fig. 5). The lack of response to the hindquarter of the dark morph, compared
with its forequarter, also supports the argument that the
essential information for assessing the leopard’s predatory threat is exhibited by the perceptual features of the
leopard’s forequarter, which includes the face. While
bonnet macaques exhibited a complete lack of attention and vigilance directed at the hindquarter of the
dark morph, the presence of the spotted yellow coat
changed this apparently irrelevant hindquarter configuration into a relevant one, equivalent to that of the
forequarter of the dark morph. This parity in responsiveness might also reflect the neurophysiological evidence showing that macaques are most sensitive to the
color yellow (Yoshioka and Dow, 1996; Yoshioka and
Vautin, 1996), a property which might contribute to the
underlying neurological processes of leopard recognition. Thus for recognizing partly exposed leopards,
bonnet macaques would not need to engage in the rapid
perceptual operations of surface completion using similar surface fragments or edge interpolation to reconstruct the entire image hypothesized for humans when
they recognize partly occluded patterns in learned experimental tasks (Sekular and Palmer, 1992; Yin et al.,
1997).
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
5. Artificial neural network modeling of
leopard face recognition
Reliance on specific morphological features for
leopard recognition, rather than the necessity of seeing
the whole image before recognition occurs, is consistent with the properties of the innate perceptual systems
of other species (Coss, 1991, 1999; Curio, 1975, 1993).
This reliance on specific cues is best demonstrated by
the perceptual aspect of two facing eyes, which have
been available historically as a visual cue that the perceiver is being watched (Coss and Goldthwaite, 1995).
The ability to recognize two facing eyes operates independent of other facial features (Coss, 1970, 1978;
Perrett et al., 1982), in part, because the primary source
of selection shaping this ability centers on the success
of assessing the direction of gaze of both conspecifics
and predators. As mentioned above, stealthy predators
using visual obstruction to approach prey have to expose their eyes to monitor prey activity from cover,
thereby revealing this reliable visual cue that prey are
being watched. Two facing eyes are also provocative
under low-contrast conditions if there is sufficient light
to detect the eyes and surrounding eyerings (Coss,
1978) or under nighttime conditions, as suggested by
experimental simulation (Topál and Csányi, 1994), if
the eyes shine via moonlight reflectance on the tapetum.
Consistent with our experimental manipulations in
the field, our ANN simulations addressed the issue
of whether the dark face of the rare melanic leopard morph truly compromises leopard-face recognition by macaques experienced with the spotted morph.
These simulations also addressed the theoretical issue
of whether spots adjacent to the leopard’s eyes camouflage the leopard’s face by either masking the eye region or disrupting eye-schema recognition (cf. Gavish
and Gavish, 1981; Ortolani, 1999) for prey historically
naı̈ve to leopards, but not to large felids, such as female
Asiatic lions (P. leo).
5.1. Methods
Extensive networks of clustered neurons, which
exhibit partially redundant processing of shared
information, are ubiquitous in mammalian neocortex
(Fujita et al., 1992; Lund et al., 1993). In the visual
stream of domestic cats and macaques, the species
that are the primary subjects of electrophysiological
and neuroanatomical studies, the functional effects
155
of distributed inputs and connectivity from adjacent
and nearby neural columns are amenable to computer
simulations (see Miikkulainen et al., 1998). Thus in a
theoretical domain, ANN simulations can complement
the aforementioned empirical research on bonnet
macaque responses to the spotted and dark leopard
morphs by examining whether facial spots and flecks
disrupt leopard face recognition.
The ANN simulator (tlearn) developed by Plunkett
and Elman (1997) was used to construct a neural
network and train it using back-error propagation for
pattern classification tasks. In the backpropagation
procedure, network learning is accomplished by
numerous iterations of forwards and backwards propagation steps (Rumelhart et al., 1986). During each
forwards step, the input pattern generates an output
pattern, which is compared with a desired target pattern. In the backwards step, error computations from
this output-target comparison are propagated down
through the network to adjust connection weights;
the global discrepancy between these forwards and
backwards activations is presented as a single root
mean square (RMS) error (Plunkett and Elman, 1997).
In the current set of experiments, the number of
activation sweeps selected to train the network was
determined by the number of patterns in the training
set and the desire to obtain RMS errors that approached
zero in the output-target pattern comparison.
The network’s ability to distinguish the target pattern from novel patterns outside the training set was
determined by testing each pattern separately using
a single forwards step of output-target comparison to
generate its RMS error. This RMS error was compared
with the target pattern’s RMS error to infer the degree
of pattern generalization (see Basheer and Hajmeer,
2000). It is important to note that this nonlearning testing procedure simulated the recognition process of a
biological neural network attuned to a specific input,
a property that occurs in innate predator recognition
(Coss, 1999). Thus for this state of adaptation, the underlying biological neural network awaits the context
for its specific activation when the appropriate schema
is encountered (see Coss, 1993). However, unlike biological neural networks, which exhibit multiple functional states subserving a wide range of pattern recognition tasks, the ANN described herein exhibited a single awaiting state that characterized its specific training
regime. Also analogous to biological neural networks
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R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
from this state perspective, the global RMS error can
be viewed as the emergent property of the entire neural network’s image processing, reflecting the arrangement of network connectivity and connection weights
(Gochin, 1996).
5.1.1. Artificial neural network architecture
The ANN architecture consisted of 1210 nodes, organized as three hidden layers sandwiched between
input and output layers (Fig. 6). These hidden layers
consisted of a 13 × 13 array of nodes, each with a nonlinear response property characterized by a “squashing” sigmoidal slope in activation function. Computational descriptions of the network activation function
and weight adjustment with backpropagation appear in
Plunkett and Elman (1997). The first two hidden layers
received pattern input from a 14 × 14 lattice of pixels
with luminance values ranging for 0 to 0.9. Each node
in these hidden layers received luminance inputs from
two adjacent pixels in the input lattice, as dominoes ar-
Fig. 6. Neural network architecture of a portion of the tlearn simulator is shown for three overlapping input vectors centered in the input array.
For the whole network, feedforward connectivity with adjustable weights (solid lines) is provided by 703 nodes. Internodal connectivity (dashed
lines) within each hidden layer is accomplished by 506 nonlearning copy-back nodes (not shown).
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
ranged in either the vertical plane (input to hidden layer
1) or horizontal plane (input to hidden layer 2). This
pairing of luminance inputs to each node yielded small
vertical or horizontal receptive fields. To enhance edge
contrast, each node in the first and second hidden layers
received input from adjacent nodes via a single “copyback” node with nonadjustable connections (Plunkett
and Elman, 1997). In this arrangement, each copy-back
node acted as a linear filter, restricting luminance information from surrounding nodes to 33% of the total
luminance input to each node, a property approximating the percentage of suppression of texture surrounds
on receptive field activity in area V1 of the macaque visual cortex (Knierim and van Essen, 1992; Nothdurft et
al., 1999). Nodes in the first and second hidden layers
projected to their topographic counterparts in hidden
layer 3 and their adjacent nodes. The dispersion of 4–9
feedforward connections to hidden layer 3 from each
node in hidden layers 1 and 2, yielded a tessellation
of vertical and horizontal receptive fields, providing a
biomimetic analog to the visual stream described for
cat striate cortex (Gilbert and Wiesel, 1989; Ts’o et al.,
1986) and macaque visual cortex (Lund et al., 1995).
Within hidden layer 3, each node exhibited, via distinct copy-back nodes, feedforward connections with
adjacent nodes in the horizontal plane and with nonadjacent nodes in a vertical radial pattern (Fig. 6), roughly
emulating the topography of intercolumnar connectivity within macaque inferotemporal and prefrontal cortex (Fujita and Fujita, 1996; Levitt et al., 1993). For
nodes in hidden layer 3, the combination of collateral
inputs and those from the hidden layers 1 and 2 yielded
large receptive fields, spanning up to 9 pixels in the input lattice. This dispersion of luminance information
in hidden layer 3 was analogous to the spread of partial pattern information among adjacent neural columns
in macaque inferotemporal cortex (Fujita et al., 1992;
Tanaka, 1996). Finally, these hidden layer 3 nodes projected to their topographic counterparts in a 14 × 14
output layer, with the exception of nodes in the top and
right side of hidden layer 3 which projected to an additional output node in the top and right side of the output
layer. This output layer thus afforded comparison of the
output and target patterns.
5.1.2. Artificial neural network training
To address the questions of leopard-face recognition and the effects of facial camouflage, a grayscale
157
Fig. 7. Tests of neural network learning for two vector sets used in
network training. Note the low root mean square (RMS) error for the
target pattern compared with the other training patterns within each
respective vector set.
target pattern was developed depicting a generic spotted leopard face (with pixel luminance values ranging
in 0.1 increments from 0.0 for black spots, black nostrils, and black eyerings, to 0.7 for whitish eye and
muzzle patches). The background framing the muzzle was 0.9 luminance value. A second target pattern
exhibited the same pixel configuration and luminance
values except that all facial spots were replaced by pixels with 0.5 luminance values. This schematic image
resembled that of a female lion. Two vector sets were
constructed (Fig. 7) in which the outputs of these spotted and unspotted target patterns were compared during
eight 1000 sweep epochs with the outputs of their respective spotted and unspotted patterns exhibiting two
schematic eyes in vertical and diagonal planes. The
choice of these contrasting patterns for training eyeschema recognition as a component of leopard face
recognition was based on empirical research on humans
and other mammals showing that patterns with two
vertically and diagonally positioned eyes were much
less provocative than patterns with two facing eyes in
the horizontal plane (Aiken, 1998; Coss, 1970, 1978,
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R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
1979b; Topál and Csányi, 1994). Also, at the level of
single units in neural columns in macaque inferotemporal cortex, facial configurations engendering maximum
neural responsiveness were determined by empirical
simplification of their provocative properties (Tanaka,
1996). This process of simplification yielded an optimal schematic face pattern with two facing eyes in the
horizontal plane remarkably similar to those used in
behavioral research (cf. Altbäcker and Csányi, 1990;
Coss, 1978, 1979b; Topál and Csányi, 1994).
For network training using the tlearn simulator,
teacher forcing was employed, with learning rate and
momentum = 0.3 and 0.9, respectively, and initial seeding = 1.0, with random training without replacement.
After this procedure, network learning was revealed by
presenting each pattern in the vector set separately to
the network input lattice for one nontraining test sweep
and examining its RMS error. Testing of the patterns
in their respective training vector sets (Fig. 7) showed
that the outputs of spotted and unspotted faces with two
horizontally positioned eyes yielded the lowest RMS
errors. These low RMS errors for the target patterns
reflect the specificity of successful ANN learning.
While testing patterns in the vectors sets characterized target-pattern learning, the examination of targetpattern generalization to related novel patterns could
be accomplished by developing a series of spotted
faces, unspotted faces, and dark faces depicting linear changes in the number of eyes. Again, similar patterns varying in the number of eyes have been used
in behavioral studies of eye-schema recognition (cf.
Altbäcker and Csányi, 1990; Coss, 1978, 1979b; Topál
and Csányi, 1994).
The perceptual differences of the spotted and dark
melanic morphs could be evaluated in simulations using the network trained on the spotted target pattern
with two horizontally positioned eyes. Comparison of
these spotted and dark faces is roughly analogous to the
aforementioned experimental presentations of the spotted and dark leopard forequarters to bonnet macaques
in the field. The role of dark spots in leopard face
recognition could be evaluated by comparing the effects of spot removal using the network trained on
the spotted target face. Conversely, the question of
whether dark spots camouflage the face could be evaluated using the network trained on the unspotted target
face and presenting the series of novel faces with dark
spots.
6. Results
6.1. Comparison of faces of spotted and dark
leopard morphs
This simulation experiment compared the effectiveness of the network in recognizing its target pattern, the
spotted face with two facing eyes, with that engendered
by the novel spotted and dark faces with different numbers of eyes. The lowest and next to the lowest RMS errors for the two V-shaped generalization gradients are
centered, respectively, over the spotted target pattern
(spotted leopard face) and the novel face of the dark
morph sharing the schema of two facing eyes (Fig. 8).
It is important to note that the RMS error for the face
of the dark morph was nearly double that of the spotted target pattern. Nevertheless, the RMS error of the
dark morph’s face was still substantially less than the
RMS errors generated by the spotted and dark patterns
Fig. 8. Generalization gradient of RMS errors when the network
was trained to recognized the spotted target pattern with two facing
eyes and tested on this pattern and a continuum of novel spotted
(crosshatched bars) and dark patterns (dark bars) with different combinations of eyes. The novel dark pattern with two facing eyes, characterizing the dark leopard morph, produced an RMS error nearly
double of that of the spotted target pattern with two facing eyes.
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
159
with other eye arrangements. That is, darkening of the
face by 0.4 luminance to reduce spot conspicuousness
reduced, but did not disrupt face recognition. On the
whole, the generalization gradient is most symmetrical
for the spotted series and appears remarkably similar
to that generated from empirical study of adaptive eyeschema recognition (cf. Coss, 1978, 1979a).
6.2. Comparison of spotted and unspotted faces
The second simulation experiment tested the effects
of removal of the dark spots on leopard-face recognition using the network trained on the spotted face. Because of the physiognomic similarity of faces within
the genus Panthera, this simulation could be viewed
as analogous to leopard-experienced macaques detecting the novel face of a female lion. Removal of the
dark spots on the novel face with two facing eyes produced an RMS error substantially larger than that of the
trained spotted target face and nearly equivalent to that
generated by the spotted face with one eye (Fig. 9). Despite this difference, there were similarities in the RMS
errors of the spotted and unspotted series of patterns,
notably the relatively symmetrical V-shaped generalization gradients with the lowest RMS error centered
for each gradient on the face with two horizontally positioned eyes. The reduction of network recognition with
the removal of darks spots demonstrates the integration
of the dark spots and two facing eyes when these spots
are part of the network training regime.
6.3. Spots as facial camouflage
The pattern of dark spots on the face with two facing eyes (spotted leopard face) was presented as a novel
pattern to the input lattice of the ANN trained on the
unspotted target pattern (lion-like face). This simulation is analogous to testing whether dark spots disrupt
the face-recognition abilities of prey species historically attuned to the provocative qualities of the two
facing eyes of unspotted carnivores. Contrary to our
expectations based on the aforementioned effects of
facial darkening, the addition of dark spots produced
only a slight elevation in the RMS error above that of
the target pattern, indicating that these spots had virtually no camouflaging properties (Fig. 10). Further
comparisons of the entire series of spotted and unspotted patterns with different numbers of eyes revealed V-
Fig. 9. Generalization gradient of RMS errors when the network
was trained to recognized the spotted target pattern with two facing
eyes and tested on this pattern and a continuum of novel spotted and
unspotted patterns. Removal of spots on the novel pattern with two
facing eyes produced a substantial increase in the RMS error, indicating a marked diminution of network recognition of this pattern.
shaped generalization gradients that were remarkably
similar, also indicating that spots had little effect on
pattern recognition. For the ANN architecture developed herein, contrasting spots became important only
if the network was trained to “expect” them as an integral facial feature, whereas the addition of spots added
little additional information if the network was trained
to recognize two facing eyes on a backdrop of moderately contrasting facial features. A similar process
might occur in biological systems if natural selection
has operated consistently on the prey’s ability to distinguish the two facing eyes of ambush predators from
partially obscuring foreground vegetation.
7. Discussion
On the whole, our ANN modeling of leopard face
recognition yielded simulations that afforded some insights for interpreting experimental presentations of the
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R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
Fig. 10. Generalization gradient of RMS errors when the network
was trained to recognize the unspotted target pattern with two facing
eyes and tested on this pattern and a continuum of novel patterns with
and without dark spots. The addition of spots to the novel test pattern
with two facing eyes produced only a slight increase in the RMS error,
indicating strong network generalization in pattern recognition.
spotted and dark morph’s forequarters to wild bonnet
macaques. For example, when the networks was trained
on the spotted target pattern with two facing eyes, darkening of the face produced a moderate mismatch which
nearly doubled the RMS error, but not to the extent of
that of novel patterns with fewer or larger numbers of
eyes (Fig. 8). This mismatch can be best explained by
the loss of conspicuousness of the black spots and eyerings on the dark schematic face. Further evidence that
contrasting spots and eyerings were cohesively linked
as critical features for network recognition was apparent when the black spots were replaced by pixels of a
medium luminance value (Fig. 9), thus characterizing
the novel schematic face of a female lion. The absence
of black spots increased the RMS error substantially, indicating a reduction of overall pattern coherence; albeit,
this absence of spots did not compromise eye-schema
recognition completely as evinced by the lowest RMS
error in the V-shaped generalization gradient centered
on the unspotted face with two facing eyes.
Training the ANN on the unspotted target image
yielded unexpected results when, during testing, novel
spots as black pixels replaced those of medium luminance value. Under this training regime, the ANN
maintained the ability to distinguish spotted patterns
that differed in number of eyes nearly as well as that
elicited by the unspotted patterns (Fig. 10). This finding contrasts with the effects of facial darkening which
did impact face recognition, but likely characterizes the
most prevalent adaptive context in which two facing
eyes connote the exhibitor’s interest in the perceiver
essential for risk assessment (Coss, 1978; Coss and
Goldthwaite, 1995; Emery, 2000). From this simulation, it is reasonable to consider that dark spots smaller
than the eyes and dark eyerings would still permit the
emergence of face recognition even though spots reduce the contrast of facial features markedly. In humans, for example, face-recognition performance is not
degraded until the contrast of facial images drops below 90% (Avidan et al., 2002). However, disruption of
face recognition is likely to occur when larger patches
are paired bilaterally as in the dark muzzles patches of
some carnivores. These patches have perceptual qualities like the additional pair of eyes used in the aforementioned test patterns with four eyes and may indeed
have camouflaging properties (Ortolani, 1999) similar
to the effects of beards that can disguise familiar human
faces (Patterson and Baddeley, 1977; Terry, 1994).
8. General discussion
Our ANN modeling showed that facial spots became relevant only when the ANN was trained to expect them. When the network was not trained to expect
facial spots, the addition of spots did not disrupt leopard
face recognition because it added no new information
that altered the relationships of the perceptual schema.
Such expectation might operate similarly in biological
systems in which the visual system has been shaped by
natural selection to await exposure to specific visual
schemata. For example, the face of a lion in dappled
light with leaf shadows or behind a spotty veil of vegetation might still be recognized by prey if the critical
features of the lion’s face are not obstructed.
Since previous exposure to leopards is not required for leopard recognition (Coss and Ramakrishnan, 2000), the findings of this study suggests that the
R.G. Coss et al. / Behavioural Processes 68 (2005) 145–163
visual system of bonnet macaques is attuned to the invariant perceptual features of the leopard’s head region
and its spotted yellow coat, both of which might facilitate the detection and recognition of leopards partly
concealed by vegetation. For prey species like bonnet
macaques, any initial historical benefits to leopards of
camouflage provided by rosettes, spots, and flecks have
now been circumvented by the evolution of perceptual
systems attuned to these patterns. Conversely, the recessive allele for the dark melanic coat (Sleeper, 1995)
might be sustained in low frequency in leopard populations simply because the absence of the spotted yellow
coat fosters faster prey habituation to leopards that remain still in ambush mode for long periods (see Rice,
1986).
Acknowledgements
This research was supported by Faculty Research
grant D-922 to R.G. Coss and by the Foundation
for Ecological Research, Advocacy and Learning,
Pondicherry, India, to U. Ramakrishnan. We thank
the Forest Department of Tamil Nadu for permission to conduct research in the Kalakad-Mundanthurai
Tiger Reserve and Mudumalai Wildlife Sanctuary and
their staff for facilitating our research. We also thank
our field assistants, Anil Kumar, M. Siddhan, and V.
Yashoda for their contribution in data collection and
A. Dharawat and M. Park for their assistance in quantifying video recordings.
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