Land use in semi-free ranging Tonkean macaques Macaca tonkeana depends on environmental conditions: A geo-graphical information system approach
ABSTRACT Wild animals use their habitat according to ecological pressures such as predation, resource availability or temperature, yet little is known about how individuals use their environment in semi free-ranging conditions. We assessed whether a semi-free ranging group of Tonkean macaques Macaca tonkeana used its wooded parkland in a heterogeneous way. GIS and GPS were used to determine whether individuals adjusted their behaviors according to variation in environmental constraints over time of day and the course of a year. We demonstrated that social and resting activities occurred in high altitude areas and areas with a high density of bushes, whereas the group foraged in areas where the density of bushes and grass was low. In general, the animals used areas exposed to the sun that were not on a slope. Semi-free ranging Tonkean macaques seemed to behave like their wild counterparts in terms of activity budget, land use per activity and thermoregulation [Current Zoology 57 (1): 8–17, 2011].
- SourceAvailable from: Cédric Sueur[Show abstract] [Hide abstract]
ABSTRACT: The influence of particular individuals on others opinions and behaviours has long been studied by social and political scientists, and it is often suggested that certain individuals can act as leaders because they are socially connected, and have more 'influence' over others. However, this idea is difficult to test in a real-world (human or non-human) setting. Here, we present a study that describes the collective movements of two primate species: Macaca tonkeana and Macaca mulatta faced with the decision of when to stop resting and start foraging. We show that individuals that are central to the group's social network elicit stronger follower behaviour and are crucial to the achievement of consensus decisions. This 'embedded' leader-follower dynamic improves the efficiency of the decision-making process, enabling faster decision times. Our data additionally suggest that a behavioural rule-of-thumb 'follow my close affiliate' can result in the most central individual leading decisions by virtue of the scale-free properties of the network. This may allow groups to utilise the knowledge of elder, dominant, or natal individuals (who are often central in social networks) whilst simultaneously maintaining bonds with highly social individuals which may bring indirect fitness benefits itself.Proceedings of the European Conference on Complex Systems 2012, 01/2012: chapter 71: pages 579-584; Springer., ISBN: 978-3-319-00395-5
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ABSTRACT: Animals adapt their movement patterns to their environment in order to maximize their efficiency when searching for food. The Lévy walk and the Brownian walk are two types of random movement found in different species. Studies have shown that these random movements can switch from a Brownian to a Lévy walk according to the size distribution of food patches. However no study to date has analysed how characteristics such as sex, age, dominance or body mass affect the movement patterns of an individual. In this study we used the maximum likelihood method to examine the nature of the distribution of step lengths and waiting times and assessed how these distributions are influenced by the age and the sex of group members in a semi free-ranging group of ten Tonkean macaques. Individuals highly differed in their activity budget and in their movement patterns. We found an effect of age and sex of individuals on the power distribution of their step lengths and of their waiting times. The males and old individuals displayed a higher proportion of longer trajectories than females and young ones. As regards waiting times, females and old individuals displayed higher rates of long stationary periods than males and young individuals. These movement patterns resembling random walks can probably be explained by the animals moving from one location to other known locations. The power distribution of step lengths might be due to a power distribution of food patches in the enclosure while the power distribution of waiting times might be due to the power distribution of the patch sizes.PLoS ONE 01/2011; 6(10):e26788. · 3.53 Impact Factor
Current Zoology 57 (1): 8−17, 2011
Received Apr. 06, 2010 ; accepted June 14, 2010.
∗ Corresponding author. E-mail: email@example.com
© 2011 Current Zoology
Land use in semi-free ranging Tonkean macaques Macaca
tonkeana depends on environmental conditions: A geo-
graphical information system approach
Cédric SUEUR1, 2*, Paul SALZE2, 3, Christiane WEBER3,Odile PETIT2
1 Department of Ecology and Evolutionary Biology, Princeton University, 08544 Princeton, USA
2 Ethologie des Primates; Département d’Ecologie, Physiologie et Ethologie; UMR 7178 CNRS-UdS; 23, rue Becquerel 67087
3 Laboratoire Image et Ville; ERL 7230 CNRS-UdS; 3, rue de l’Argonne 67000 Strasbourg, France
Abstract Wild animals use their habitat according to ecological pressures such as predation, resource availability or tempera-
ture, yet little is known about how individuals use their environment in semi free-ranging conditions. We assessed whether a
semi-free ranging group of Tonkean macaques Macaca tonkeana used its wooded parkland in a heterogeneous way. GIS and GPS
were used to determine whether individuals adjusted their behaviors according to variation in environmental constraints over time
of day and the course of a year. We demonstrated that social and resting activities occurred in high altitude areas and areas with a
high density of bushes, whereas the group foraged in areas where the density of bushes and grass was low. In general, the animals
used areas exposed to the sun that were not on a slope. Semi-free ranging Tonkean macaques seemed to behave like their wild
counterparts in terms of activity budget, land use per activity and thermoregulation [Current Zoology 57 (1): 8–17, 2011].
Key words GPS, GIS, Temperature, Topography, Activity budget, Spatial autocorrelation, Primates
It is commonly accepted that the main factors affect-
ing individual fitness are predation, resource availability,
and environmental and meteorological conditions such
as temperature or wind (Alexander, 1974; Hill et al.,
2004; Wrangham, 1980). Animals are known to selec-
tively use their habitat and change their activity patterns
according to the type and strength of ecological pressure
in order to maximize their fitness (Boinski and Garber,
2000; Hill, 2006). Apes, for example, choose bed or
nesting sites where their preferred food is available
(Ancrez et al., 2004; Riley, 2007). Chacma baboons
Papio hamadryas ursinus and savannah chimpanzees
Pan troglodytes verus are known to use caves on a
thermoregulatory basis, including sleeping in caves be-
cause they are more comfortable than sleeping outside
(Barrett et al., 2003; Pruetz, 2007). Riley (2007) found
that a wild group of Tonkean macaques Macaca
tonkeana in a more disturbed area (altered by human
activity) spent more time foraging and resting, and less
time moving, than a group in a less disturbed area.
Therefore, wild animals display behaviors adapted to
their environmental constraints. The question remains,
how do such animals behave when they live in a captive
The majority of studies that have compared wild and
captive animals have been carried out on activity budget
(Melfi and Feistner, 2002), social behavior (Thierry,
2007) or the emergence of abnormal behaviors (Bashaw
et al, 2003; Kerridge, 2005). Few studies have assessed
the effect has on the foraging patterns of animals (Dier-
enfield and McCann, 1999; Jaman and Huffman, 2008;
Jaman et al., 2010). Ross and Lukas (2006) reported that
captive chimpanzees Pan troglodytes ssp. preferred the
highest tier of an enclosure, and gorillas Gorilla gorilla
gorilla preferred the floor level. Other authors suggest
that ad libitum access to food in captivity decreases
foraging time in favor of social activity (Honess and
Marin, 2006). Despite free access to commercial pri-
mate pellets, semi-free ranging lion-tailed macaques
Macaca silenus and ring-tailed lemurs Lemur catta
regularly foraged for plants and seeds (Dierenfield and
McCann, 1999). Jaman and Huffman (2008) found
similar results: Japanese macaques Macaca fuscata liv-
ing in a vegetated enclosure spent equal amounts of
time foraging on natural vegetation and feeding on pro-
visioned food. Thus, like wild animals, captive animals
SUEUR C et al.: Land use in Tonkean macaques 9
seem to adapt their behavior to their environment, but
this assumption needs to be confirmed.
Here, we observed a semi-free ranging group of
Tonkean macaques in its 0.32 ha wooded parkland
habitat. Given the previously quoted findings and
semi-free ranging conditions present, we expected our
study group to display shorter foraging and moving time
than time spent socializing. In addition, we did not ex-
pect to observe homogeneous land use except on the
sites where food was available. Following previous re-
sults on other captive primates (Ross and Lukas, 2006),
we tested our prediction that this group would show
preferences for some areas with specific environmental
characteristics adequate for foraging or social activities.
1 Material and Methods
1.1 Subjects and environment
The study group was bred under semi-free ranging
conditions at the Strasbourg University Centre of Pri-
matology, France. At the time of the study (November
2005–March 2006), the group consisted of ten individu-
als: one adult male (>5 yr), five adult females (>4 yr)
and four juveniles (1–3 yr), which is comparable to the
composition of wild groups (Pombo et al., 2004; Riley,
2005, 2007; Supriatna et al., 1992). Wild Tonkean ma-
caques are typically found in the primary and secondary
rainforests of Sulawesi (Indonesia) and are mainly
frugivorous (Pombo et al., 2004; Supriatna et al., 1992).
The Tonkean macaques in our study had complete ac-
cess to 0.32 ha of wooded parkland in addition to indoor
housing within the enclosure. The indoor housing
(20 m²) is made of cement and tiles, and animals were
able to climb on top of this indoor housing. The enclo-
sure area was made up of various slopes and uneven
ground. The distribution of vegetation was also hetero-
geneous, and we could distinguish three layers (grass,
trees and bushes) that were unevenly distributed
throughout the enclosure. Commercial primate pellets
and water were available ad libitum in the indoor hous-
ing. Fresh fruit and vegetables were provided once a
week, and were always given at the same location one
hour after the end of the observation session. Thus, be-
havior of the animals was most likely not affected by
this feeding event. Animals were habituated to the
presence of humans in their enclosure.
1.2 Scoring of variables
Observations occurred from 18 November 2005–23
March 2006, for four hours each day between
09:00–13:00 h or between 13:00–17:00 h, with an equal
number of morning and afternoon sessions across the
In order to assess our predictions, we used a geo-
graphical information system (GIS) for data analysis
and global positioning system (GPS) for data collection.
The topography of the ground (coordinates and altitude)
was recorded using GPS at the beginning if the study.
We only retained points with precision inferior to one
meter. A total of 225 points were used to create a Digital
Elevation Model (DEM, resolution: 100 cm²) using
ArcView (Li et al., 2005; Ormsby et al., 2004). In order
to verify the precision of our DEM, we compared it to
satellite and aerial pictures (provided by Image et Villes
laboratory). No correction was necessary.
Direct in situ observations were also used to map the
vegetation in the enclosure. We used a 9 m² quadrants
visual method (Bonham, 1989; Foster et al., 1991;
Meese and Tornich, 1992) to estimate the cover per-
centage of different vegetal striates (grass, bushes, and
trees). This estimation was carried out every 15 days
during the study. Four observers evaluated the cover
percentage of each striate. Each observer estimated the
cover percentage for an individual quadrant, which was
calculated as a mean. In each quadrant, we estimated the
mean cover percentage for each vegetal striate. We used
this estimation with topographic measurements to divide
the enclosure into 22 areas with differing vegetation
types and topography. These different areas were in-
cluded in ArcView. Table 1 summarizes the cover per-
centage for each area. Area 0 was the indoor housing
and area above it.
We also recorded the temperature for each hour of
observation using a handheld weather station (±3 m
from subjects). The temperature ranged from -5.2–
14.5°C, with an average value of 4.11±0.22°C (n=96).
Every 10 minutes, one observer noted the position
of each animal in the enclosure on a map (scale: 1/550;
precision: one meter) along with its activity using the
instantaneous sampling method (Altmann, 1974). The
map contained 77 landmarks measured before the
study allowing the 1 m level of precision. The activity
and position of each individual were then recorded in
Arc View 9.0.1. (Environmental Systems Research
Institute Inc., Redlands, CA) (Li et al., 2005; Ormsby
et al., 2004). The activities we were interested in
(listed below) were defined according to the same cri-
teria used by Riley (2007) and Pombo et al. (2004) for
Tonkean macaques and by other authors for different
species (Melfi and Feistner, 2002; O’Brien and Kin-
Moving locomotion including walking, running,
10 Current Zoology Vol. 57 No. 1
Table 1 Mean cover percentage for three striates (grass, trees, bushes) and topography (mean altitude, mean slope, mean exposure) for the
different areas in the enclosure of the Tonkean macaques group
Number of bushes Mean altitude (m) Mean slope (°) Mean exposure (°)
1 10.00 5.00 5.00 26 183.82 21.50 148.31
2 8.75 5.00 5.00 26 186.22 25.06 149.74
3 5.00 5.00 5.00 18 190.76 22.20 190.15
4 10.00 0.00 10.00 28 188.69 29.86 161.05
5 7.50 5.00 5.00 35 193.91 39.74 134.28
6 5.00 5.00 5.00 26 189.94 9.29 216.91
7 10.00 5.00 5.00 89 185.65 30.11 294.95
8 5.00 0.00 5.00 61 191.39 24.32 156.16
9 10.00 5.00 5.00 36 190.63 19.18 149.03
10 11.25 5.00 5.00 47 191.14 9.10 272.87
11 100.00 0.00 0.00 0 189.43 12.06 98.50
12 98.75 0.00 0.00 0 183.05 11.14 171.33
13 10.00 5.00 0.00 0 185.06 15.25 121.20
14 10.00 5.00 0.00 0 187.45 7.50 178.34
15 20.00 5.00 0.00 0 188.68 20.78 159.69
16 90.00 0.00 0.00 0 187.87 11.31 163.40
17 10.00 5.00 0.00 0 189.49 15.56 175.23
18 0.00 5.00 0.00 0 186.33 35.50 335.21
19 11.25 0.00 0.00 0 185.16 41.64 307.73
20 10.00 0.00 0.00 0 177.72 2.74 165.97
21 0.00 5.00 0.00 0 184.36 37.36 285.94
Area 0 is not indicated because it corresponds to indoor housing.
climbing and jumping;
Foraging reaching for, picking, manipulating, mas-
ticating, or placing food in mouth, as well as manipu-
lating the contents of a cheek pouch (if an individual
walked while masticating, the activity was classified as
Resting body stationary, usually sitting or lying
Social playing, grooming, sexual and aggressive
We only retained scans where the position and the
activity of all individuals could be noted. At the end of
the study, we obtained 24 days of observation, 558
scans and 5580 recordings of individual positions in the
1.3 Data analysis
1.3.1 Parameters First, we studied whether the
group was cohesive by analyzing the distance between
group members. In this study we defined the group as
cohesive if the distance separating individuals was less
than the theoretical distance under the independence
hypothesis (where individuals are independent). For
each scan, we calculated the distance separating two
individuals, which was 45 distances per scan. We then
calculated the mean distance among individuals (n=45
by 558 scans =25110). We calculated the theoretical
SUEUR C et al.: Land use in Tonkean macaques 11
mean distance under the hypothesis that individuals are
independent by generating a random matrix in Ucinet
6.0 (Borgatti et al., 2002), with 0 meter as minimum
distance and 40 meters (park length divided per two) as
We estimated the activity budget of each individual
by determining the percentage of time spent on each
activity (number of scans spent on an activity divided
by the total number of scans).
The same calculation was used for the percentage of
time animals spent in trees.
Animals want to be in trees during the leafing period
so they can eat buds and young leaves. The beginning of
the leafing period was set at 15 February, according to
the leafing period of the principal tree species in the
enclosure (Acer pseudo-Platanus L., Fraxinus excelsior
L., Robinia pseudacacia L.; herbierfrance.free.fr/,
www.tela-botanica.org/). This condition was approved
by field observations.
The time spent in each square meter area for indi-
vidual activities and in total was calculated in order to
determine whether the animals carried out a specific
activity in certain areas. Our main dependent variables
were total duration of all activities and duration of each
individual activity expressed in minutes per square me-
ter. For analyses and graphs, values were truncated to
two decimal places.
Using the DEM, we calculated topographic vari-
ables such as altitude, slope and exposure. We used
the cover percentage of grassland, bushes and trees
and the number of bushes as independent variables
We checked if high altitude gave the animals better
visibility of their surroundings. For the group under
investigation, the most crucial area of the enclosure was
the entrance, as negative enforcement (vets and keepers)
and positive ones (food) arrived from this location. The
areas from which animals could see this entrance were
determined using a viewshed analysis in ArcView.
Sun exposure was evaluated by scoring the cardinal
direction of the slope (south facing slopes are exposed
to the sun, north facing slopes are not exposed) and the
cover percentage of bushes and trees. We obtained
sunlight exposure values from 0 (northern exposure,
cover 100%) to 881 (southern exposure, cover 0%).
This index was calculated using Zonal Statistics in Arc-
1.3.2 Spatial autocorrelation When studying bio-
geographic patterns such as the duration of land use by
animals, it is important to check for spatial autocorrela-
tion (Fortin and Dale, 2005; Legendre et al., 2002). Spa-
tial autocorrelation is the probability that neighboring
spatial units (adjacent areas) are more likely to have
similar patterns, than by chance alone. This probability
may lead to false-positive results in correlations (Fortin
and Dale, 2005; Legendre et al., 2002). In our study we
analyzed patterns for the duration of activity. Spatial
autocorrelation was therefore checked using a Dietz
R-test that tested correlation between two matrices.
The first matrix was the distance in meters between the
cores of each pair of areas. The second matrix was the
differences of activity duration between each area. We
computed one matrix for the duration of socializing
activity, and another for the duration of foraging activity.
We then carried out matrix correlations using Socprog
2.3 (Whitehead, 1997), setting the number of permuta-
tions at 10 000 for each matrix correlation test
(Whitehead, 1997). Results showed that there was no
evidence of spatial autocorrelation, neither for the du-
ration of socializing activity (n=22, r=-0.08, P=0.779)
nor for the duration of foraging activity (n =22, r=
-0.10, P=0.829), making controls for spatial autocor-
We also tested for collinearity of topographic and
vegetation variables. No two variables were collinear in
our study (collinearity diagnostics, VIF≤2.957); we
could therefore consider all topographic and vegetation
variables as independent (Pallant, 2007).
1.3.3 Statistics We compared the observed mean
distance between individuals and the theoretical dis-
tance using a Mann-Whitney test.
The possible influence of leaves/buds on trees on the
number of observations where animals where present in
trees per day was assessed using Spearman rank corre-
lation test. A Mann-Whitney test was then carried out to
assess if the mean number of scans in trees was different
between the non-leafing period (n=11 days) and leafing
period (n=13 days).
We tested total duration differences between areas
using the Kruskall-Wallis test with the Monte Carlo
option using comparisons with 10 000 random tables. A
Kruskall-Wallis test followed by a Dunn’s multiple
comparisons test established which area differed from
the others in term of land use. The duration of land use
was expressed in min/m2 (see above) in order to com-
pare areas of different size. Duration of land use is rep-
resented in Fig. 1. Maps were obtained using ArcView.
Each area was then compared using a Spearman rank
correlation test to establish if they were used in the same
way for both socializing and foraging. Path analysis was
12 Current Zoology Vol. 57 No. 1
used to assess the influence of independent variables
(topography and vegetation) on the duration of foraging
and socializing activity of different locations. This
analysis was made using AMOS 5 software (AMOS
Development Corporation, Spring House, PA, USA.) with
maximum likelihood estimations (for non-parametrical
Sun exposure for each individual’s position was cor-
related to temperatures using the Spearman rank corre-
Except for matrix correlations, analyses were per-
formed using SPSS 10 (SPSS Inc., Chicago, USA).
Tests were significant for α=0.05. Means are ±SE (stan-
2.1 Activity budget
The mean observed distance between individuals was
4.17±0.05 m. The mean theoretical distance was 29.47±
1.69 m. These results showed that individuals were
seven times closer to each other than under the inde-
pendent hypothesis (Mann-Whitney, n=45, U=1469,
P<0.00001), and were therefore cohesive. Indeed, 54%
of the total data (25110 calculated distances) involved
individuals in contact with or close to another individual
(≤ 1 m).
The group spent most time resting (36.7%), followed
by socializing (28%), moving (19.8%) and foraging
2.2 Locomotion substrate
We investigated whether the studied animals spent
more time in trees or on the ground. From the total of all
observations, we found that they spent 11.5% of their
time in trees. Given the distribution of trees in the park,
if the stratum use is random then animals should have
theoretically only spent 3.3% of their time in trees. The
observed duration was approximately 3.5 times longer
than expected, showing that animals seemed to appreci-
ate being in trees. Time spent in trees significantly var-
ied over time (Spearman rank correlation, r=0.534,
n=24, P=0.007). Animals spent more time in trees when
they were covered with leaves and buds than when they
were not (Mann-Whitney, U=26.5, nleafing=13, nnon-leafing
=11, P=0.008, mleafing=15.2±2.5%, mnon-leafing= 6.8±3.4%).
Moreover, animals spend more time foraging outside
when there was an increase in leaves and buds on trees
(Spearman rank correlation, rs=0.41, n=24, P=0.046).
2.3 Use of parkland
The analysis of overall group activity on the ground
showed that the group used the enclosure in a heteroge-
neous way (Kruskall-Wallis test, H=533.108, df=508,
n=509, based on the number of 1 m² squares, P<0.00001,
Fig. 1A). Two zones were found to be the core areas for
all activities. The first area was inside and just above the
indoor housing (180–960 min/m², area 0) and its vicin-
ity (40–160 min/m², areas 3, 6, 15, 16, 17). The second
was located in the southwestern part of the enclosure,
where the group stayed between 10 to 40 minutes per
square meter (areas 8 and 10). The land use of area 0
was significantly different to the of all other areas
(Kruskall-Wallis test: H=400.377,
P<0.0001; Dunn’s multiple comparisons test: P<0.0001
for all comparisons). Land use did not significantly dif-
fer between areas 3, 6, 8, 10, 15, 16, and 17 (Dunn’s
multiple comparisons test: P>0.05 for all comparisons)
but it did however differ from all other areas (Dunn’s
multiple comparisons test: P< 0.05 for all comparisons).
Thus, we can regroup adjacent areas into two different
zones. We mapped the entire enclosure for land use du-
ration for resting and socializing (Fig. 1B) and for for-
aging (Fig. 1C), and consequently found that the areas
used for resting or socializing were not the same as
those used for foraging (Spearman rank correlation,
r=0.282, n=17, P=0.272). Figure 1 shows that resting
and socializing activities (Fig. 1B) were located in the
same core zones previously determined for general land
use (Fig. 1A). In contrast, the foraging areas were more
widespread throughout the enclosure. The animals spent
36.3% of foraging time finding seeds and leaves and
66.7% foraging within the indoor housing.
2.4 Characteristics of used areas
We wanted to assess whether preferentially used ar-
eas had any specific topographical or vegetative charac-
teristics. The results showed that only altitude and sun
exposure significantly influenced the dependent variable
(Path analysis: df=3;
R²exposure=0.36, P<0.0001; Fig. 2A and Fig. 2B). For for-
aging activity, slope and sun exposure significantly in-
fluenced activity duration (df=3; R²slope=0.61, P <
0.0001; R²exposure=0.09, P=0.005, Fig. 2C and Fig. 2D).
As far as the influence of the vegetation on resting and
social activities is concerned, only the cover percentage
of bushes and trees significantly influenced the duration
of socializing activity (df=6; R²bushes=0.28, P<0.0001;
R²trees=0.07, P=0.022; Fig. 2E and Fig. 2F). However,
the analysis of foraging activity showed that only the
number of bushes and the cover percentage of grass
significantly influenced the dependent variable (df=6;
R²grass=0.08, P=0.001; R²bushes=0.92, P<0.0001; Fig. 2G
and Fig. 2H).
SUEUR C et al.: Land use in Tonkean macaques 13
Fig. 1 Maps of land use duration in the enclosure (in minutes per square meter, for the total number of observations) for
(A) all activities, (B) resting and socializing, (C) foraging
Numbers on the maps indicate the different areas, from 0 to 21.
14 Current Zoology Vol. 57 No. 1
Fig. 2 Influence of topography and vegetation on land use duration for socializing-resting and for foraging activities
A. Influence of altitude on land use duration for socializing-resting activities. B. Influence of sunlight exposure on land use duration for socializ-
ing-resting activities. C. Influence of slope intensity on land use duration for foraging activity. D. Influence of sunlight exposure on land use dura-
tion for foraging activity. E. Influence of bush cover percentage on land use duration for socializing-resting activities. F. Influence of tree cover
percentage on land use duration for socializing-resting activities. G. Influence of grass cover percentage on land use duration for foraging activity.
H. Influence of number of bushes per square meter on land-use duration for foraging activity.
2.5 Altitude and viewshed analysis
The resulting map (Fig. 3) clearly showed that higher
altitude areas (areas 0, 3, 5 and 8) were the only ones from
which the entrance could be seen by Tonkean macaques
(Spearman rank correlation, r=0.587, n=18, P=0.010).
2.6 Influence of temperature on enclosure use
A Spearman rank correlation test showed that tem-
peratures were correlated with the exposure to sunlight
(r=-0.248, n=5580, P<0.0001). Animals seemed to
choose their areas according to the temperature: ex-
posed areas with sunlight when temperature was low-
est, non-exposed areas when temperature was highest
(in the temperature range observed during observa-
SUEUR C et al.: Land use in Tonkean macaques 15
Fig. 3 Map of the enclosure with zones where the entrance is visible (grey) or not visible (white)
This map was obtained using a surface analysis in ArcView. Entry in grey and entrace visible in black.
By comparing observed distances between individu-
als to the theoretical distances under the hypothesis that
animals are independent, we showed that the study
group of Tonkean macaques seemed to be cohesive by
their proximities. They spent more than half their time
in body contact or very close proximity. Previous results
showed that they are also cohesive in their coordination
(Sueur and Petit, 2008; Sueur et al., 2009). Tonkean
macaques also used their land in a heterogeneous way
according to the type of activity, temperature and
topographic variables. Social and resting activities
occurred in the highest altitude areas with high density
of bushes. On the other hand, the group foraged in areas
where the density of bushes and grass were low. In
general, the areas animals used most were exposed to
the sun and not sloped. Our results could not have been
obtained so accurately without the use of GIS metho-
The group exhibited a shorter foraging time than time
spent socializing or resting as predicted. There seemed
to be greater difference between the activity budget for
two wild groups of Tonkean macaques studied by
Pombo et al. (2004) than between our study group and
these wild groups. Similarly, Melfi and Feistner (2002)
found through direct comparison that there were greater
differences between two captive groups and between
two wild groups of crested black macaques Macaca
nigra than for comparisons between captive and wild
groups. In support of other studies on wild Tonkean
macaques (Riley, 2007, 2008), we suggest that resting is
the most prominent activity both in the wild and captivity,
whilst foraging is not. The most striking differences
between wild and captive groups are in socializing and
moving activities. Social activity is a good substitute for
boredom in captivity, and when monkeys are allowed to
express this activity properly their suffering is reduced.
Despite ad libitum access to commercial primate
pellets, the group still foraged for leaves and seeds in
the enclosure. Similar results were found by Jaman and
Huffman (2008) and Jaman et al. (2010) in a group of
Japanese macaques living in a vegetated enclosure. Ac-
cording to Dierenfeld and McCann (1999) and Jaman et
al. (2010), this natural foraging could be due to a high
level of fibers and proteins in these plants. Analysis of
the nutrient composition of the selected plants species is
necessary to validate this hypothesis.
The study group appeared to spend more time on the
ground than in trees, even though they spent more time
in trees than expected. Previous reports stated that
Tonkean macaques spent most of their time in trees
(Pombo et al., 2004; Riley, 2007, 2008). We showed that
animals tended to spend more time in trees during the
leafing season (spring) and were therefore sensitive to
seasonal changes. Similarly, wild Tonkean macaques
spend more time in trees with fruit availability (Riley,
2008). Animals seem to adapt their behavior according
16 Current Zoology Vol. 57 No. 1
to leafing or fruiting periods, because buds and fruits are
rich in proteins. These results showed that natural vege-
tation in enclosures benefits animals as they can en-
hance their foraging activities and consequently their
In our study the Tonkean macaque group used its en-
closure in a heterogeneous way with a preference for
certain areas. The group foraged in areas where the den-
sity of bushes and grass was low. This allowed indi-
viduals to have easy access to plants, seeds and leaves,
and also to the ground where seeds and invertebrates are
buried. Tonkean macaques are mainly frugivorous, but
invertebrates also seem to be an important part of their
diet (Pombo et al., 2004; Supriatna et al., 1992). Social
activities occurred in different areas, but most often in
areas with high altitude and high density of bushes.
These results may be explained in terms of vigilance
and visibility (Boinski and Garber, 2000; Stanford,
2002). These areas allowed the animals to monitor the
enclosure entrance where vet staff and keepers enter. We
showed that animals did not use sloping areas, where
walking seems to be more difficult. In the same way,
African savannah elephants Loxodonta Africana seemed
to avoid costly mountaineering (Wall et al., 2006). The
temperature measured in the park was very different
from temperatures in the natural environment of
Tonkean macaques (Pombo et al., 2004; Riley, 2007;
Supriatna et al., 1992). This difference may explain why
captive animals spend a lot of time in area 0, which was
inside or close to their indoor housing. Animals seemed
to choose areas on a thermoregulatory basis, as shown
in other species (Boinski and Garber, 2000; Hill et al.,
2004; Pruetz, 2007). They exposed themselves to
sunlight when the temperature was low, and avoided
exposed areas when the temperature was high. This be-
havior may also show the animals’ adaptation to a
non-natural environment. A park with heterogeneous
vegetation and hilly landscape seems to lead to a greater
variety and prevalence of animal activities. Specifically,
some areas with higher altitude in our study seemed to
allow Tonkean macaques to better observe their
environments and enhance their well-being.
To conclude, the GIS methodology allowed us to
note, visualize and analyze several parameters that
would have been difficult to measure with classical
methodology. The behavior of the primates seemed to
be constrained by factors similar to those affecting wild
populations (Hill, 2006; Iwata and Ando, 2007; Pruetz,
2007; Riley, 2007; Ross and Lukas, 2006). A heteroge-
neous environment in terms of vegetation, topography
and meteorological variations appeared to affect group
activity, and the captive animals seemed to behave in
the same way as their wild counterparts, as found in
previous studies: they are cohesive and their movements
are coordinated, and they adapted their behavior to their
environment (Jaman and Huffman, 2008; Ross and Lu-
kas, 2006; Sueur et al., 2009). What is usually missing
in captivity is the variability between time and space
(too much time and limited space). Animals need space
which gives them chances to properly manage social
interactions, mainly sexual and agonistic. Nevertheless,
this appears to not have been a problem for our study
group. This type of study is crucial to attain a better
understanding of captive animal behavior, and to assess
whether they behave like their wild counterparts. Fol-
lowing on from our study, we could proceed to an accu-
rate survey that would assure their welfare (Jaman and
Huffman, 2008; Ross and Lukas, 2006). Our results
could be used to design appropriate enclosures for pri-
mates bred in zoological parks.
Acknowledgments The authors are grateful to J. Dubosq, T.
Polowsky for technical assistance, to M. Dindo, R. Knowles
and J. Lignot for language advice and to N. Poulin, biostatisti-
cian of the DEPE, for help with statistics. This work was sup-
ported by the European Doctoral College of Strasbourg Uni-
versities and the French Research Ministry.
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