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Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape


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We examine the abundance and distribution of Sumatran tigers (Panthera tigris sumatrae) and nine prey species in Bukit Barisan Selatan National Park on Sumatra, Indonesia. Our study is the first to demonstrate that the relative abundance of tigers and their prey, as measured by camera traps, is directly related to independently derived estimates of densities for these species. The tiger population in the park is estimated at 40–43 individuals. Results indicate that illegal hunting of prey and tigers, measured as a function of human density within 10 km of the park, is primarily responsible for observed patterns of abundance, and that habitat loss is an increasingly serious problem. Abundance of tigers, two mouse deer (Tragulus spp.), pigs (Sus scrofa) and Sambar deer (Cervus unicolor) was more than four times higher in areas with low human population density, while densities of red muntjac (Muntiacus muntjac) and pigtail macaques (Macaca nemestrina) were twice as high. Malay tapir (Tapirus indicus) and argus pheasant (Argusianus argus), species infrequently hunted, had higher indices of relative abundance in areas with high human density. Edge effects associated with park boundaries were not a significant factor in abundance of tigers or prey once human density was considered. Tigers in Bukit Barisan Selatan National Park, and probably other protected areas throughout Sumatra, are in imminent danger of extinction unless trends in hunting and deforestation are reversed.
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Crouching tigers, hidden prey: Sumatran tiger and prey
populations in a tropical forest landscape
Tigers (Panthera tigris) are behaviourally flexible and
may adapt to a host of alterations in their landscape
(Sunquist, Karanth & Sunquist, 1999). Tigers range from
sea level to altitudes of 2000 m, and habitats include the
savannas of India, equatorial rainforest of Southeast Asia
and the pine forests of eastern Russia. Sunquist (1981) and
Karanth & Nichols (2000) showed that tigers in Nepal and
India can tolerate close proximity to human settlements.
Their high fecundity allows for rapid recovery from
poaching (Karanth & Stith, 1999) as long as there is
sufficient prey. A broad diet, including ungulates, bovids
and primates, allows tigers to live wherever large
mammalian prey species are available (Seidensticker,
1986; Karanth & Sunquist, 1995; Miquelle et al., 1999;
Ramakrishnan, Coss & Pelkey, 1999). Not surprisingly,
prey depletion is a critical threat to the long-term
persistence of tigers (Seidensticker, 1986; Karanth &
Stith, 1999).
Despite their ecological flexibility, tigers are critically
endangered throughout their range (Seidensticker, Christie
& Jackson, 1999), and three subspecies have been driven
to extinction. Two of these subspecies, the Bali (P. t. balica)
and the Javan (P. t. sondaica) tiger, have been eliminated
from their respective Indonesian islands primarily through
habitat loss. Prey population reduction and direct killing of
tigers, however, may have been the final coup de grâce for
these subspecies (Seidensticker, 1986, 1987). The Sumatran
tiger (P. t. sumatrae), the only remaining Indonesian tiger,
persists in isolated populations across Sumatra. Sumatran
tigers experience many of the threats faced by tigers
throughout their range, including direct poaching, loss of
prey, forest conversion and human–tiger conflicts leading
to authorized removals of ‘problem tigers’ (Tilson et al.,
1994; Dinerstein et al., 1997; Seidensticker et al., 1999).
The degree to which these threats affect Sumatran tigers
may vary (Tilson et al., 1994), but little work has been done
to assess the relative importance of these threats to their
long-term persistence.
Dinerstein et al. (1997) proposed a plan for identifying
high-priority areas for tiger conservation (called Tiger
Conservation Units, or TCUs) and argued that
conservation efforts should target these areas. One
criterion for a high-priority TCU is large size with an
adequate core area (in general, > 2000 km2). Karanth &
Stith (1999) however, point out that tigers may not persist
in TCUs as large as 3000 km2if the prey base is
inadequate. Unfortunately, we have little information on
Timothy G. O’Brien, Margaret F. Kinnaird and Hariyo T. Wibisono
Wildlife Conservation Society – Indonesia Program, PO Box 311, Jl. Pangrango No. 8, Bogor 16003, Indonesia
(Received 7 January 2002; resubmitted 6 September 2002; accepted 4 November 2002)
We examine the abundance and distribution of Sumatran tigers (Panthera tigris sumatrae) and nine prey
species in Bukit Barisan Selatan National Park on Sumatra, Indonesia. Our study is the first to demonstrate
that the relative abundance of tigers and their prey, as measured by camera traps, is directly related to
independently derived estimates of densities for these species. The tiger population in the park is estimated
at 40–43 individuals. Results indicate that illegal hunting of prey and tigers, measured as a function of
human density within 10 km of the park, is primarily responsible for observed patterns of abundance,
and that habitat loss is an increasingly serious problem. Abundance of tigers, two mouse deer (Tragulus
spp.), pigs (Sus scrofa) and Sambar deer (Cervus unicolor) was more than four times higher in areas with
low human population density, while densities of red muntjac (Muntiacus muntjac) and pigtail macaques
(Macaca nemestrina) were twice as high. Malay tapir (Tapirus indicus) and argus pheasant (Argusianus
argus), species infrequently hunted, had higher indices of relative abundance in areas with high human
density. Edge effects associated with park boundaries were not a significant factor in abundance of tigers
or prey once human density was considered. Tigers in Bukit Barisan Selatan National Park, and probably
other protected areas throughout Sumatra, are in imminent danger of extinction unless trends in hunting
and deforestation are reversed.
All correspondence to: T.G. O’Brien. Tel: 62 251 325 664;
Fax: 62 251 357 347; E-mail:
Animal Conservation (2003) 6, 131–139 © 2003 The Zoological Society of London
DOI:10.1017/S1367943003003172 Printed in the United Kingdom
the status of tigers in many of these TCUs, and even less
information on the prey base. This lack of knowledge
hampers tiger conservation effectiveness not only in
Indonesia but also throughout Asia.
In 1994, Indonesia developed a tiger conservation
strategy (Ministry of Forestry, 1994) that mandated
conservation action based on a comprehensive
understanding of tiger ecology, but to date few studies
have addressed the status of wild tiger populations on
Sumatra (Franklin et al., 1999). In the past decade, land
clearing has accelerated dramatically (Holmes, 2001;
Kinnaird et al., 2003), and tiger poaching has increased
(Plowden & Bowles, 1997; H. T. Wibisono, unpubl. data),
but the direct impact on tigers remains unquantified. In
this study, we assess the distribution and abundance of
tigers and their prey in the Bukit Barisan Selatan National
Park (BBSNP), a little-known but high-priority TCU
in southern Sumatra, Indonesia. Specifically, we test
camera trap-based abundance indices proposed by
Carbone et al. (2001: but see Jennelle, Runge &
Mackenzie, 2002 and Carbone et al., 2002) against
independently derived density estimates, and use these
indices to examine the distribution and abundance of
tigers relative to prey availability, park boundaries and
density of human population.
Study area
Bukit Barisan Selatan National Park (BBSNP) is the third-
largest protected area (3568 km2) on the Indonesian island
of Sumatra (Fig. 1). Located in the extreme southwest of
the island (4° 31to 5° 57S and 103° 34to 104° 43E),
the park covers more than 150 km of the Barisan
Mountain Range. BBSNP contains some of the largest
tracts of lowland rainforest remaining on Sumatra and is
the major watershed for southwest Sumatra. The park is
bordered by villages, agriculture and plantation forestry.
The park’s thin shape results in > 700 km of borders where
encroachment for logging and agriculture and illegal
hunting are major problems. Rainfall is seasonal, ranging
from 3000 mm to more than 4000 mm except during
ENSO events when droughts occur. Temperatures
fluctuate from 22 to 35°C.
The Way Canguk Research Station is located in the
southern part of BBSNP (5° 3932S, 104° 2421E;
Fig. 1) at 50 m elevation, in a mosaic of primary forest,
and forest damaged by fire, drought, wind throws and
earthquakes. The associated study area encompasses
900 ha of forest, is bisected by the Canguk River and is
crossed by trails at 200 m intervals. The area is contiguous
to large tracts of undisturbed lowland forest as well as
areas disturbed by illegal logging and agricultural activity.
Camera trapping
We conducted tiger and prey surveys using passive infrared
camera traps (CamTrak South Inc., Watkinsville, GA
30677) with data packs that stamp each photograph with
time and date of the event. Cameras were set in 10 ×2 km
sampling blocks orientated from forest/park boundary
towards park interior in order to detect potential edge effects
on mammals. Sampling blocks were spaced uniformly at
10–15 km intervals south to north except in a narrow
isthmus of heavily degraded forest in the north-central part
of the park (Fig. 1). In the northernmost block, rugged
terrain and Albhizzia plantation encroachment limited use
of the rectangular design and traps were set in a 4 ×5 km
block. Each block was divided into 20 subunits of 1 km2,
and a random UTM coordinate was chosen within each
subunit. We randomized our sample design to reduce bias
in the abundance indices and chose a 1 km2‘mesh’ for our
sample to assure that no tiger home range could fit between
cameras (see Karanth & Nichols, 1998). Single cameras
were placed at ‘optimal’ locations within 100 m of the UTM
coordinate and new coordinates were taken using a Brunto
XLS 1000 GPS unit. ‘Optimal’ locations were usually
animal trails with signs of recent activity. An additional
three to four pairs of camera traps were deployed
opportunistically within the sampling block at points where
tigers were likely to pass, especially on trails below a
ridgeline, trails near water and passages between hills. Each
camera pair was positioned to photograph simultaneously
both flanks of an animal passing by the sensors. These
132 T. G. O’B
Way Canguk
Bukit Barisan Selatan National Park
Camera location
Kota Agung
Indian Ocean
Fig. 1. Location of Bukit Barisan Selatan National Park in
Sumatra, Indonesia. Camera trap survey was conducted in the
numbered sampling blocks. All line transect data and camera
trap abundance index calibration were collected in Way Canguk
research area. Tiger density estimate was based on data collected
in Way Canguk and sampling blocks 7 to 10.
nonrandom camera placements were used to increase the
possibility of photographing tigers and of identifying left-
flank and right-flank photographs taken with single-
set cameras. Cameras were mounted on trees such that
the infrared beam was set at a height of 45 cm. Each unit
was programmed to delay sequential photographs by
45 seconds and operate 24 hours/day or until the film was
fully exposed.
Owing to the roadless nature of most of the park, access
was difficult and we were unable to check the function of
cameras during a sampling period to monitor performance
or change films. Cameras were left in the forest for 30–35
days. Number of trap-days was calculated for each camera
location from the time the camera was mounted until the
camera was retrieved if the film had remaining exposures,
or until the time and date stamped on the final exposure.
After cameras were retrieved, films were developed
and examined for tigers and prey. We identified each photo
of an animal to species, recorded the time and date, and
rated each photo as a dependent or independent event.
We defined an independent event as (1) consecutive
photographs of different individuals of the same or different
species, (2) consecutive photographs of individuals of the
same species taken more than 0.5 hours apart, (3)
nonconsecutive photos of individuals of the same species.
We used number of independent photographs of a species
as an index of species abundance and calculated two
relative-abundance indices (RAI). The number of days
required to acquire a photograph (RAI1) measured effort
and was expected to decrease as density increased (Carbone
et al., 2001). The inverse of RAI1was the number of
photographs acquired per day (RAI2) and increased as
density increased, making it an easily interpreted index.
RAI2was scaled to photographs per 100 trap-days.
The Way Canguk research area was sampled on three
occasions between September 1998 and October 1999 to
compare camera trap abundance indices with line transect
density estimates for the same location. At 6-month
intervals, we deployed camera traps at a density of 1
trap/16 ha throughout the study area. Cameras were left
in place for approximately 30 days and then retrieved.
Capture–recapture estimate of tiger abundance
We followed methods developed by Karanth (1995) and
Karanth & Nichols (1998) to estimate tiger abundance and
density from camera photos using CAPTURE Program
(Otis et al, 1978; White et al., 1982; Rexstad & Burnham,
1991) and data collected in the southern part of the park
at five sites between September 1998 and February 1999.
We used data only from the southern part of the park
because these data were collected during a restricted time
period (6 months) and the southern section is a peninsula
with limited possibilities for immigration and emigration.
Because CAPTURE produces population estimates based
on closed-population assumptions, use of data from the
southern section of the park gave greater confidence in
geographic and demographic closure.
We established capture histories for each tiger identified
in the photographs. Because most camera locations used
only one camera, however, we restricted our analysis to
animals photographed only on the left side. Although this
approach usually results in lower probabilities of capture
(P) compared to using two cameras/location, it is still
possible to use this method for estimation (Karanth, 1995;
J. D. Nichols, pers. comm.). Capture history for a tiger
consisted of a row of zeroes (no photograph) and ones
(photographs) indicating the result of each sampling
interval of trapping for a tiger. A sampling interval was
the result of a single day of trapping at each of the camera
locations (up to a maximum of 34 trapping occasions at
each of five sampling blocks). Following Karanth &
Nichols (1998), we used CAPTURE Model Mhthat
allows variability in capture probability among individuals
but assumes constant capture probability for a given
individual over time.
Line transects
We estimated prey densities for one avian and six
mammal species using line transect sampling (Table 1:
Burnham, Anderson & Laake, 1980; Buckland et al.,
1993; Laake et al., 1993). Each month, from June 1998 to
December 1999, three pairs of observers walked 18
transects in the Way Canguk Research Area over a 3-day
period for a total of 38 km. Transects were walked each
day by observer pairs spaced at 400 m intervals, beginning
at 0600 and ending at approximately 0930. We recorded
total length of transect walked and, for each species,
number of clusters detected. For each cluster, we noted
the number of animals, sighting distance and sighting
angle. We calculated quarterly density estimates by
combining census data for a 3-month period that centred
on the month of camera trapping in the study area. Prey
densities and standard errors were estimated from line
transect data using Fourier Series Estimator (Burnham et
al., 1980; Buckland et al., 1993) which is generally
regarded as a robust density estimator (Krebs, 1989).
Calibrating camera data
For relative abundance indices to be useful, there should
be a monotonic relationship between the index and actual
density (Conroy, 1996). To test this relationship, we
compared the number of days required to obtain single
independent photographs (RAI1; Carbone et al., 2001) of
tiger and six diurnal or crepuscular prey during three
camera trapping periods in Way Canguk with line transect
density estimates of prey species in Way Canguk and the
capture–recapture estimate of tiger density (tigers were
not observed during line transect surveys) for the southern
peninsula of the park where Way Canguk is located.
We analyzed the relationship between the RAI1and
density estimates using linear regression and reduced
major axis regression (Sokal & Rohlf, 1981; Harvey &
Pagel, 1991). Reduced major axis regression minimizes
the sum of the products of horizontal and vertical
deviations from points on the line, allowing for error in
the independent variable. This has the advantage of
accurately estimating the true functional relationship, but
133Sumatran tigers and prey populations
produces residuals that are usually correlated with the
independent variable. Linear regression models always
have shallower slopes than reduced major axis regression
models because they assume no error in the independent
variable, but the difference between the two regressions
is small when the coefficient of determination is high
(Harvey & Pagel, 1991).
Human and edge effects
We calculated the distance from each camera location to
the nearest park boundary and grouped camera locations
into 1 km intervals. To assess the relative abundance of
tigers and prey in areas of differing human density, we
assumed that human density was correlated with village
density. Village populations around BBSNP range from
500 to 7500 with an average of 2530 people/village
(SE = 290, N= 29 villages; WCS-IP, unpubl. data). We
counted number of villages within 10 km of park
boundaries in 10 km increments from south to north. We
then classified each camera location as adjacent to an area
of high or low human density based on number of villages
within 10 km of park boundaries at the approximate latitude
where cameras were placed. Areas of low human density
were defined as having <10 villages (average human
population < 25,000) and areas of high human density were
defined as > 15 villages (average human population >
37,500). We then compared mean photos/100 trap-days
(RAI2) in samples adjacent to high and low human densities
at varying distances from the park boundary using ANOVA
and ANCOVA (Sokal & Rohlf, 1981).
Camera trap performance
We deployed 370 cameras during the survey and calibration
trials. Of these, 84.3% had unexposed film remaining at the
end of the sampling period. Of the remaining cameras, 5.1%
finished the film within the final week of the sampling
period, 3.2% finished the film within 8–14 days of pickup,
and 1.9% finished the film within 15–21 days of pickup.
Only 5.4% of the cameras finished the film or misfired in
the first week of the sampling period, were lost, or were
damaged by elephants rendering them useless. This
translates into an average failure rate of 1.5 cameras/sample.
Given the density of camera placement, we believe that the
low failure rate was unlikely to influence trapping coverage
or bias indices derived from the camera trap data.
Density estimates and calibration of camera data
We identified nine individual tigers from 12 photographs
of left flanks, based on a total of 2873 trap-days at five
sample locations (Table 1). Using CAPTURE model Mh,
we estimated a capture probability (P-hat) of 0.0271 for the
tiger data set. The estimated population size for the sample
is 13 ± 3.66, with a 95% confidence interval of 10–27
individuals. To determine density we assumed the effective
sampling area to be equal to the area defined by the sides
of the sampling blocks, plus a buffer strip
equal to the longest distance between recapture photographs
(4.5 km for this data set). The estimated sample area was
calculated at 836 km2and the average density for the
southern portion of BBSNP was estimated at 1.6 tigers/100
km2(95% CI = 1.2–3.2 tigers/100 km2; Table 2).
134 T. G. O’B
Table 1. Sampling effort for estimating tiger abundance in the southern
part of Bukit Barisan Selatan National Park in 1998–99 for five locations
Site Date Duration # tigers Camera Trap-days
(in days) (recaptures) points
Way Canguk 9/98 21 1(1) 38 700
Pemerihan 10/98 34 1(0) 24 742
Blambangan 11/98 32 1(0) 24 711
Paya 1/99 34 4(2) 22 737
Sukaraja 2/99 31 2(0) 21 650
Table 2. Activity patterns, density estimates (/km2) and percent coefficient of variation for tigers and selected prey species by sampling method.
Date indicates the month in which the sampling was initiated.
Name Method Date Density % CV Activity
Tiger Capture/recap. Nov 98 0.0156 27.56 Diurnal
Panthera tigris
Argus pheasant Line transect Sept 98 1.76 37.5 Diurnal
Argusianus argus Apr 99 0.91 21.9
Sept 99 3.33 60.0
Mouse deer Line transect Sept 98 6.28 36.8 Crepuscular
Tragulus spp. Apr 99 2.74 34.7
Sept 99 2.00 194.0
Red muntjac Line transect Sept 98 3.96 31.5 Crepuscular
Muntjac muntiacus Apr 99 4.44 25.6
Sept 99 1.76 27.8
Sambar deer Line transect Sept 98 0.88 51.1 No pattern
Cervus unicolor Apr 99 1.42 47.9
Sept 99 0.62 79.0
Pigtail macaque Line transect Sept 98 5.04 31.9 Diurnal
Macaca nemistrina Apr 99 2.6 49.6
Sept 99 5.65 33.1
Wild pig Line transect Sept 98 6.06 43.3 Diurnal
Sus scrofa Apr 99 4.4 45.4
Sept 99 4.6 77.4
Prey densities based on line transect estimates varied
widely among species and over time (Table 2). Densities
range from 0.49 individuals/km2for sambar deer to 6.28
individuals/km2for combined mousedeer species. On
average, pigtail macaques are the most common prey
species, followed by mousedeer and red muntjac.
Coefficients of variation ranged from 22% to 194%,
indicating a broad spectrum of precision associated with
density estimates. Much of the variability may be due to
changes in encounter rates over time associated with
different species, and small sample sizes associated with
some estimates (n< 20 for seven of 18 estimates). In
addition, macaque and wild pig group ranges extended
beyond the study area boundaries (9 km2) and
consequently groups could be missing from the study area
during some surveys. Finally, the density estimates also
may underestimate group-living species if undercounts of
groups occurred.
Way Canguk cameras were active for a total of 2904
trap-days during 21, 30 and 30-day sample periods. We
used the natural logarithm of density estimates from line
transects and CAPTURE to develop a regression analysis
of number of trap-days required to photograph an
individual of a single prey species (RAI1) on transformed
density. The linear regression indicated a close negative
relationship between the RAI1and density (RAI1= 106.8
– 59.8 ×Ln(density), F1,14 = 45.71, P< 0.0001, r2= 0.79
(Fig. 2). Reduced major axis regression produced a steeper
slope for the relationship; RAI1= 111.4 – 68.32 ×
Ln(density), but slopes and intercepts of the two
regression lines are not significantly different, indicating
that number of photos provides a reliable density index
for tigers and their prey in BBSNP.
The value of 350 trap-days to photograph a single tiger
appeared to be an outlier. We tested the value using a one-
sided Grubbs’ statistic (Sokal & Rohlf, 1981) for testing
outliers and found that the tiger data point was not a
significant outlier. Second, we compared regressions with
and without the tiger datum. Although the coefficient of
determination (r2) declined when the tiger datum is
removed, the slope and intercept did not change
significantly (RAI1,w/out tiger = 110.5 – 63.7Ln(density)).
We therefore retained the point because it reflects the range
of densities and trapping effort in the study.
Park-wide camera surveys
We conducted ten surveys over 20 months for 6973
camera-trap-days. We exposed 2074 frames and classified
1665 frames as independent photographs. Among the
photographs, we captured seven bird species, 34 mammal
species from a minimum of 19 families (most rodent
families excluded) and one reptile.
We photographed 1684 individuals of nine prey species
(Table 3). Although additional prey species such as jungle
fowl, rodents, moonrats and treeshrews were identified,
we did not include them in this analysis because they were
small (< 1 kg) and were rarely (< 5 photos) photographed.
RAI2values (expressed as photos/100 trap-days) for prey
ranged from 0–1.90 photos/100 trap-days for the
uncommon sambar deer, to 1.85–22.51 photos/100 trap-
days for the group-living pigtail macaques. Tigers were
photographed on 20 occasions (19 single animals, one
pair) at 17 locations in eight of ten surveys. The RAI2for
tigers varied from 0 to 0.95 photos/100 trap-days. Tigers,
argus pheasant and the deer species were each absent from
at least one sampling block. Mean RAI2values are less
than the median for all species, indicating abundance
distributions are skewed towards 0.
The RAI2of many of the prey species co-vary. Mouse
deer RAI2s are significantly correlated with all other
diurnal/crepuscular prey species except argus pheasant
135Sumatran tigers and prey populations
-5 -4 -3 -2 -1 0 1 2 3
Fig. 2. Linear regression (solid line) and reduced major axis
regression (dashed line) of number of days required to take
one photograph as a function of the natural logarithm of
estimated density. Data are for tigers and six prey species
reported in Table 2.
Table 3. Number of independent photos and mean and range of relative
abundance index values (RAI2) for tiger and prey in BBSNP.
Common name # photos Mean Range
Tiger 20 0.29 0–0.95
Panthera tigris sumatrae
Great argus pheasant 193 2.78 0–7.08
Argusianus argus
Pigtail macaque 618 7.95 1.85–22.51
Macaca nemistrina
Common porcupine 200 2.72 0–6.88
Hystrix brachyura
Malay tapir 92 1.25 0.14–4.99
Tapirus indicus
European wild boar 265 3.53 0.13–9.84
Sus scrofa
Greater and lesser mouse deer 87 1.16 0–3.66
Tragulus spp.
Red muntjac 185 2.45 0–6.33
Muntiacus muntjac
Sambar deer 40 0.55 0–1.90
Cervus unicolor
(Table 4). Red muntjac indices are positively correlated
with sambar deer and pigtail macaque indices, and wild
pig indices are significantly correlated with sambar deer
and pigtail macaque indices. Tiger abundance varies with
the abundance of large prey. Tiger RAI2is correlated with
RAI2of pigs (Table 4: P< 0.05) and Sambar (P< 0.1),
the largest primary-prey species. A regression of the RAI2
of tigers on the sum of the RAI2of pigs and sambar is
significant and positive (RAI2,Tiger = 0.109 +
0.043(RAI2,pig+sambar), T= 3.72, P= 0.045).
Human density and edge effects
Number of villages adjacent to the park was higher (16 to
30 villages) for samples 3, 4, 5, 6 and 7 (Fig. 1), compared
to other sampling areas (0–9 villages). We therefore
classified these blocks as high human-density areas. Tiger
RAI2was four times higher in areas of low human-density
compared to high density (Fig. 2). Similarly, the RAI2of
large prey species were 5.6 (sambar) and 9.5 (pig) times
higher in low human-density samples. Among smaller
prey, mouse deer and pigtail macaque were 9.7 and 2
times more common, respectively, in low human-density
samples. Only argus pheasants and Malay tapir were more
common in samples adjacent to high human density.
We used two-way ANCOVA to assess the importance
of edge effects and human density on the relative
abundance of tigers while controlling for the relative
abundance of large prey, and two-way ANOVA to assess
edge and human effects on relative abundance of prey.
Data were summarized for each species as RAI2in 1 km
intervals from the park boundary in for each sample with
high and low human density adjacent to the park. When
controlling for the effect of human density and distance
from the edge, the abundance of large prey did not
significantly affect RAI2of tigers and was dropped from
the model. In general, human density adjacent to the
sampling area had a larger effect on RAI2of tigers and
prey compared to distance from park boundaries. Tiger
RAI2was significantly higher in low human-density areas
(F1,36 = 4.609, P= 0.039), as was pig abundance (F1,36 =
14.495, P< 0.001), sambar deer (F1,36 = 4.765, P= 0.036),
and mouse deer (F1,36 = 9.128, P= 0.005). Human density
had no significant effect on RAI2of red muntjac, pigtail
macaques, porcupines and Malay tapir. Edge effects were
significant only for red muntjac (F7,36 = 2.71, P= 0.023)
and mouse deer (F7,36 = 3.312, P= 0.008); these species
were more abundant at 0–1 km from the park boundary.
The significance of the interaction term for mouse deer
(F7,36 = 3.382, P= 0.007) and the trend for red muntjac
(F7,36 = 2.124, P= 0.06) indicate the boundary effect is
due to high abundance close to the boundary in areas with
low human density.
Photographs from camera traps have been used by
Karanth (1995) and Karanth & Nichols (1998, 2000) in
combination with mark–recapture models to determine
tiger densities in India. More recently, Carbone et al.
(2001) used photographic capture rates from studies of
tigers across their range in conjunction with computer
simulations to show that camera trap abundance indices
may provide good estimates of tiger density. Carbone et
al.’s study, however, was limited by the lack of
independent density estimates using standard
capture–recapture or line transect methods for comparison
and was criticized by Jennelle et al. (2002; but see
Carbone et al., 2002). Our study is the first to demonstrate
that the relative abundance of tigers and their prey, as
measured by camera traps, is directly related to
independently derived estimates of densities for these
species. We believe that the results of the relative
abundance analysis reflect real differences in the
abundance and distribution of tigers and their prey in
We estimated tiger density for the BBSNP peninsula at
1.6 tigers/100 km2. If we apply the regression to
photographic accumulation rate for tigers across the entire
park (RAI1= 349 trap-days/photo), we predict the park
density at 1.7 tigers/100 km2. The density estimates
expand to a park population estimate of 54–59 tigers older
than 1 year of age. This estimate, however, is based on the
unlikely assumption that the entire park is suitable tiger
habitat. Conversion of forest to agriculture within park
boundaries has increased dramatically in the past decade;
Kinnaird et al. (2003) calculate that 28% of the forest
cover has been lost between 1985 and 2000. A more
conservative estimate, therefore, may be 40–43 tigers,
assuming that tigers require forest cover in BBSNP and
136 T. G. O’B
Table 4. Pearson Correlation matrix (N= 10) among relative abundance values (RAI2) of tigers and prey
Tiger Mouse deer Muntjac Sambar Pigtail Pig Argus Porcupine Tapir
Tiger XX
Mouse deer XX
Muntjac 0.772** XX
Sambar 0.578* 0.793*** 0.754** XX
Pigtail 0.818*** 0.632** XX
Pig 0.620** 0.900*** 0.653** 0.692** XX
Argus XX
Porcupine XX
Tapir XX
* 0.05 < P< 0.1
** P 0.05
***P< 0.01
that the deforested agricultural areas represent marginal
or unsuitable habitat.
Tilson et al. (1994), using population viability analysis,
predicted that a population of 68 tigers in BBSNP would
have a high probability of persistence over the next 100
years. This result was based on a poaching rate of one
tiger/year, no habitat loss, no prey depletion and no natural
catastrophes. Doubling of the poaching rate to two
tigers/year would drive the BBSNP tiger population to
extinction within the next 100 years, and possibly within
the next 50 years. Lindzey et al. (1992, 1994), however,
state that large cats may survive hunting mortality of up
to 25% annually, and modelling by Karanth & Stith
(1999) suggest that these figures are relevant to tigers. If
we follow Karanth & Stith’s model, the present BBSNP
tiger population could withstand the elimination of five
to six tigers/year in the absence of habitat loss and
prey depletion.
Available data indicate that poachers in the BBSNP
have killed at least 32 tigers since 1998 (Kompas, 1999;
H. T. Wibisono, unpubl. data), averaging more than eight
tigers/year. Clearly, the high estimated level of tiger
poaching is unsustainable under any condition. We
believe, however, that depletion of prey and habitat are
additional factors accelerating the disappearance of tigers
from BBSNP.
Although we were unable to measure hunting of prey
directly in this study, indirect measures of hunting
including snares, cartridges, discarded batteries, gunshots,
direct observation of hunters and sale of bushmeat and
wildlife parts (Peres, 2000; Wright et al., 2000) were all
observed in BBSNP or adjacent villages and cities.
Additionally, a national sport-hunting club, PERBAKIN,
openly operates in and around BBSNP and their activities
include the commercial sale of bushmeat, primarily wild
pig (Tempo, 2000). A number of studies have linked
human density to declining wildlife populations, often
owing to hunting (Peres, 2000; Woodroffe, 2000;
Harcourt, Parks & Woodroffe, 2001; Parks & Harcourt,
2002). If we assume that the human density on the
perimeter of the park directly reflects per capita hunting
then our results are consistent with the expectation that
hunting by humans has a strong influence on the
distribution and abundance of prey species in the park.
Under this assumption, we would expect the observed
pattern of lowest abundance of wildlife in sample areas
4–7 (Fig. 1) where human density is highest. The ratios of
species abundance in areas of low and high human
densities are severely skewed (Fig. 3); hunted species are
significantly more abundant in the sample areas with low
human density adjacent to the park.
The influence of human density extends to habitat loss.
Kinnaird et al. (2003) show that forest conversion has
been most severe between sample areas 3 and 7 (Fig. 1),
areas of high human density. They conclude that the
amount of secure habitat in BBSNP available for large
mammals such as tigers is shrinking faster than measures
of forest loss indicate, because wildlife is more vulnerable
at the forest edge. Tigers and many prey species that
normally use edges may be subjected to increased
vulnerability to hunting on the park boundaries where
human density is high. Additionally, the park has become
fragmented and BBSNP tigers may now comprise two
small, isolated subpopulations. If true, the tigers of
BBSNP are more vulnerable to stochastic demographic
events (Lande, 1988).
Although forest loss may ultimately eliminate BBSNP
tigers, there is still enough forest in the park today to
protect viable populations of deer with ranges of a few
square kilometers and tigers with ranges of as much as
100–200 km2. Hunting pressure, however, will eliminate
tigers and prey populations much more rapidly than
habitat loss alone. In the short term, park management
should focus on curbing illegal hunting of tigers and their
prey. Solutions should include better enforcement, control
of bushmeat trade and sport-hunting activity, and better
control of domestic trade in tiger parts. Indonesia,
unfortunately, is experiencing a prolonged financial and
political crisis and decentralization of natural-resource
management that makes increasing the commitment to
conservation in general (Jepson et al., 2001), and to tiger
conservation in BBSNP in particular, very difficult.
International conservation groups (Wildlife Conservation
Society, World Wildlife Fund, National Fish and Wildlife
Foundation), private donors and the US Fish and Wildlife
Service are supporting tiger conservation action in
BBSNP. These actions include monitoring of tiger and
prey populations and habitat, support of anti-poaching
patrols, increasing awareness of local communities, and
working with local governments to modify land-use
plans and consider conservation as a planning objective.
The Indonesian government, especially at the provincial
and district level, needs to show willingness to sustain
these conservation activities if the BBSNP tiger
population is to survive into the future. On a larger
scale, failure to act now in BBSNP and other Sumatran
protected areas may result in Indonesia having the
dubious distinction of driving a third subspecies of tiger
to extinction.
137Sumatran tigers and prey populations
Mouse deer
Wild pig
Sambar deer
Red muntjac
Pigtail macaque
Great argus
Malay tapir
0.5:1 1:1 2:1 4:1 6:1 8:1 10:1
RAILow density:RAIHigh density
Fig. 3. Ratio of average relative abundance indices (RAI2) for
tigers and prey in low human-density samples compared to high
human-density samples.
Our research is a collaborative effort by the Wildlife
Conservation Society – Indonesia Program and the
Indonesian Ministry of Forestry’s Department for
Protection and Conservation of Nature. Our research was
funded by the Wildlife Conservation Society, the Save the
Tiger Fund, a special project of the National Fish and
Wildlife Foundation in partnership with the Exxon Mobil
Corporation (Grants 98–093–060 and 99–268–097), the
US Fish and Wildlife Service Rhinoceros and Tiger
Conservation Fund (Grant 1448–98210–98–G173) and
Princeton University Alumni Association. We would like
to thank J. Ginsberg, M. Rao, F. Bagley, J. Seidensticker
and D. Phemister for their support and advice during this
project. We also thank U. Wijayanto, I. Tanjung, Sunarto,
M. Iqbal, N. Winarni and A. Dwiyahreni for assistance in
data collection, and E. Sanderson, G. Woolmer and
Y. Hadiprakarsa for assistance in GIS analysis. The
manuscript benefited greatly from the comments of
J. Reynolds, E. Dinerstein and an anonymous reviewer.
Finally, we thank our colleagues in the Ministry of
Forestry Pelastarian Hutan dan Konservasi Alam
(PHKA), including W. Ramono, S. Kusumahnegara and
S. Legowo for their continued support.
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139Sumatran tigers and prey populations
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... Photographic capture rates have been proven to be significantly correlated to population density estimates. 9 It was calculated as the number of captures of a species divided by the total number of days for which a camera was operational for each station. 46 Habitat measurements Circular vegetation plots of 10-meter diameter were investigated at every camera trap station. ...
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... In order to reduce the autocorrelation of photos of the same species, we rst removed empty photos and unknown photos, and then considered photos or videos of the same species within 30 min as one independent event(O'Brien, Kinnaird et al. 2003). For each independent event, the time of capture, the type of species, the number of species, and the type of animal activity were recorded. ...
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Building wildlife corridors is thought to be a useful strategy for reducing the negative effects of road construction. However, the wildlife corridors currently built are mainly set up for medium-sized or large wild mammals in the wilderness, while there are relatively few constructions for small animal corridors in urban areas. In this study, we assessed the use of two underpasses on the Beiyan Highway of Chongming Island, Shanghai. We recorded 27 species of animals in a total of 385 events, and 8 species successfully passed through the underpasses. The most frequent animal species recorded in the two corridors were weasels and white-breasted waterhens respectively, followed by both rodents. Overall, mammals and aves frequented the underpasses the most, and their time spent there matched their routines. Our study suggests that local wildlife has successfully used the two underpasses in a variety of ways, but longer-term monitoring is still required.
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Southern Mexico’s tropical forests are home to the country’s highest richness of mammal species; La Chinantla region is situated within this area, its name from the indigenous group residing in the area and holding territorial ownership, namely the Chinantecos. In La Chinantla, there are no Protected Areas; instead, there are Areas Destined Voluntarily for Conservation (ADVC) and “Voluntary Conservation Areas” (VCA), that are managed by local inhabitants through social consensus. These ADVC may function as an archipelago reserve, which represents regional diversity, including the social context, through complementarity. To verify its biodiversity, we analyzed the richness, composition, distribution, and conservation of wild mammals in the region. Records were obtained from four sources—primary data collection, databases, scientific literature, and community monitoring—and were organized into four zones based on altitudinal and vegetation gradients. We compared the diversity between zones for three categories of mammals: small (<100 gr.), bats, and medium and large (>100 gr.). 134 species were identified comprising 11 orders, 26 families and 86 genera. The zone with highest elevation presented the greatest species richness for the assemblage of mammals and terrestrial mammals, while the zone with the lowest elevation had the highest richness of bats. For each mammal category, the zone with the most species also registered the highest number of exclusive species. For the assemblage of mammals and for medium and large mammals, the similarity index was highest between the two intermediate zones, while for small mammals and bats, the greatest similarity occurred between the areas of higher altitude. The study region was found to have the second highest richness of mammals in Mexico. Finally, we suggest that the conservation proposals by indigenous people could function as a set of “islands” that promote the conservation of biodiversity, possibly as an Archipelago Reserve.
The Asian small-clawed otter (Aonyx cinereus) is a globally vulnerable mesopredator endemic to South and Southeast Asia. This species is one of the least studied and smallest of all 13 otter species. The species is tolerant of least-aquatic conditions and reported across a wide range of habitats. Its ecology is not fully and methodically deciphered. Recently, new populations were discovered in eastern Bangladesh, the north-western border of the Indo-Burma Biodiversity Hotspot. Based on camera trapping carried out between January and July 2021, we investigated otter occupancy and spatiotemporal coexistence within an anthropized riparian mixed-evergreen forest. We assessed the influence of stream density, the distance between the camera-trap station and the nearest built-up area, and slope on otter habitat use; cumulated precipitation and Julian day on detection probability. Our results revealed a mammalian mesopredator release effect and heavy anthropogenic activities; however, otters and humans exhibited very low spatiotemporal overlap. Detections showed high temporal convergence with the nocturnal-crepuscular carnivore guild. Otter occupancy did not show any pattern when modelled with altitude or human density; detection neither decreased with the progression of the monsoon. Our findings suggest that otters can adapt their habitat use to human pressure in forests dominated by human activities, especially with dense stream networks. We encourage further research to gain a better understanding of the factors that could impact otter habitat preference, such as food abundance, undergrowth vegetation, human-induced forest fires, and fishing or poaching pressure. Understanding otters is a potential tool to step up research investment in anthropized forests.