Predicting preferred prey of Sumatran tigers
Panthera tigris sumatrae via spatio-temporal overlap
M A X I M I L I A N L . A L L E N , M A R S Y A C . S I B A R A N I and M I H A K R O F E L
Abstract Encounter rates of carnivores with prey are

dependent on spatial and temporal overlap, and are often
highest with their preferred prey. The Critically Endangered
Sumatran tiger Panthera tigris sumatrae is dependent on
prey populations, but little is known about its prey preferences. We collected camera-trap data for  years (–)in Bukit Barisan Selatan National Park, Sumatra, to investigate spatial and temporal overlap of tigers with potential
prey species. We also developed a novel method to predict
predator–prey encounter rates and potential prey preferencesfrom camera-trap data. We documented at least  individual
tigers, with an overall detection rate of . detections/
trap nights. Tigers exhibited a diurnal activity pattern andhad highest temporal overlap with wild boar Sus scrofa andpig-tailed macaques Macaca nemestrina, but highest spatial
overlap with wild boar and sambar deer Rusa unicolor. We
created a spatial and temporal composite score and three
additional composite scores with adjustments for the spatial
overlap and preferred prey mass. Wild boars ranked highest
for all composite scores, followed by sambar deer, and bothare known as preferred tiger prey in other areas. Spatial andtemporal overlaps are often considered as separate indices,
but a composite score may facilitate better predictions of encounter rates and potential prey preferences. Our findings suggest that prey management efforts in this area should focus
on wild boar and sambar deer, to ensure a robust prey base forthis Critically Endangered tiger population.
Keywords Activity patterns, composite score, Panthera
tigris, prey preference, spatial overlap, Sumatra, temporal
overlap, tiger

Supplementary material for this article is available atdoi.org/./SMAXIMILIAN L. ALLEN (Corresponding author,
orcid.org/0000-0001-8976889X) Illinois Natural History Survey, University of Illinois, 1816S. Oak
Street, Champaign, Illinois 61820, USA. E-mail maxallen@illinois.edu
MARSYA C. SIBARANI Wildlife Conservation Society—Indonesia Program, Bogor,West Java, Indonesia
MIHA KROFEL ( orcid.org/0000-0002-2010-5219) Department of Forestry,
Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
Received  January . Revision requested  April .
Accepted  May . First published online  March .

IntroductionI

nterspecific interactions are important aspects of community ecology, affecting the functional ecology of ecosystems and dictating the ecological niches inhabited by
species (Begon et al., ). Such interactions can be difficult to assess, however, particularly for cryptic species suchas wild carnivores (Allen et al., ; Saggiomo et al., ).
Carnivore–prey encounter rates are dependent on spatial
and temporal overlap, and high encounter rates are often indicative of prey preference (Holling, ; Fortin et al., ).
Data on spatio-temporal overlap of carnivores with potential prey species may thus facilitate inference of prey preferences and patterns of interspecific interactions, providing
insights into ecosystem functions that can inform effective
conservation. Camera trapping is a non-invasive methodthat is increasingly being used to monitor wildlife andprovides data on species richness, behaviour, and spatiotemporal activity (Swanson et al., ; Rich et al., ;Allen et al., ).
The tiger Panthera tigris is categorized as Endangered
throughout its range, with four subspecies probably extinct
in the wild (Seidensticker, ; Goodrich et al., ).
The Sumatran tiger Panthera tigris sumatrae is CriticallyEndangered (Linkie et al., ), as are many other specieson the Indonesian island of Sumatra (O’Brien & Kinnaird,; Pusparini et al., ). Prey abundance can have strong
effects on the abundance and population density of tigers(Karanth et al., ; Barber-Meyer et al., ), and tiger
declines have been linked to declines of prey in Russia andIndia (Miquelle et al., ; Ramakrishnan et al., ). The
prey preferences of tigers are unknown in many areas, butsuch knowledge is important to inform conservation planningand ensure sufficient prey is available in areas critical to tiger
conservation. Information about the Sumatran tiger’s diet islimited (e.g. O’Brien et al., ; Linkie & Ridout, ), anddata on spatial and temporal activity patterns and overlap
between tigers and potential prey could improve our understanding of the subspecies’ prey preferences.
Bukit Barisan Selatan National Park is one of the largest
protected areas on the island of Sumatra, and is critical
for the conservation of the Sumatran tiger and otherspecies of conservation concern, including the CriticallyEndangered Sumatran rhinoceros Dicerorhinus sumatrensis and Sumatran elephant Elephas maximus sumatranus(O’Brien & Kinnaird, ; Pusparini et al., ). The Park
provides relatively abundant tiger habitat, but threat levels
are moderate to high because of inadequate conservation

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/).
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measures (Sanderson et al., ). Given the Park’s importancefor tiger conservation, it is important to understand the species’ ecology in this area. Previous studies of activity patternsof Sumatran tigers in the Park produced conflicting results;
O’Brien et al. () reported tigers had a diurnal activity
pattern, whereas Pusparini et al. () reported a crepuscular
activity pattern with highest activity levels near dawn. The
tigers’ prey preferences and prey abundance in the area arealso unknown.
To inform conservation efforts, we investigated tiger
spatio-temporal overlap with potential prey species, using 
years of camera-trap data from an area of the Park with little
human activity. Our objectives were to () document theminimum number of individual tigers in the area, () determine the temporal and spatial overlap of tigers with six potential prey species, and () create a composite score from theindices of temporal and spatial overlap as a novel method topredict predator–prey encounter rates and determine potential prey preference. In our analyses we included all potential
tiger prey species present in the study area, based on a review
of tiger dietary studies (Hayward et al., ). In line withknown prey preferences of tigers across their range, we expected sambar deer Rusa unicolor and wild boar Sus scrofa
to have the highest composite spatio-temporal score.

FIG. 1 The study site with camera-trap locations of both arrays
within Bukit Barisan Selatan National Park on the island of
Sumatra, Indonesia.

Study area
Our study site is in Bukit Barisan Selatan National Park, in
the South Barisan Range ecosystem on the Indonesian island of Sumatra (Fig. ). The Park is the third largest protected area (, km) on Sumatra (O’Brien & Kinnaird,), spanning two provinces: Lampung and Bengkulu.
Topography ranges from coastal plains and lowland rainforest at sea level in the southern peninsula of the Park
to mountains up to , m in the central and northernparts (Pusparini et al., ). The Park contains montane,
lowland tropical, coastal and mangrove forests. Annual
rainfall is ,–, mm, most of which falls in the monsoon season (November–May), and annual temperaturesare – °C (O’Brien et al., ). The Park contains a highdiversity of wildlife, with tigers and  other species listed
in the CITES Appendices and categorized as Endangeredor Critically Endangered on the IUCN Red List.

designed our survey to monitor multiple species effectively(Rich et al., ). We set two arrays of  camera traps,
using the Network’s protocols (TEAM Network, ), insites chosen for accessibility for long-term repeated surveys,
all in lowland forests at – m altitude. We placed camera
traps in each array at a density of  per  km (Fig. ), andthe arrays covered a total of . km. We deployed thecamera-trap arrays in the dry season (April–July) of eachyear during –, with array  in operation during
April–May and array  during June–July, with the aim tocomplete at least  sampling days for each camera trap.
We positioned camera traps near animal trails and/or
places used regularly by wildlife, to maximize detections.
We placed camera traps – cm off the ground, with norefractory period between images.

Statistical analyses
Methods
Camera trapping
We set camera traps in the Park as part of the Tropical
Ecology and Assessment Monitoring Network, which collects long-term biodiversity data in tropical environments
globally to guide conservation actions. Our goal was tomonitor the terrestrial vertebrate community, and we

To avoid pseudo-replication, we considered consecutive
photo captures of the same species as independent events
only if they occurred after an interval of .  min(Rovero & Zimmermann, ). We calculated the numberof independent captures for each species, but combined
both mouse deer species (greater mouse deer Tragulus
napu and lesser mouse deer Tragulus kanchil) in one category because they share similar characteristics as potential

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Preferred prey of Sumatran tigers

tiger prey and it was difficult to distinguish between them on
camera-trap images (O’Brien et al., ).
We used a relative abundance index as a proxy for tiger
abundance, because this is a more accurate proxy forabundance than occupancy values (Parsons et al., ).
We calculated the index as:
relative= (detection events/trap nights) × 100
abundance index
for each camera trap, to determine detection events per 
trap nights (e.g. Allen et al., ), and averaged the values
from all camera traps to determine an overall mean for the
study area. We used the stripe patterns of individual tigers to
identify the minimum number of individuals, separately forphotographs of the right and left flanks.
We used kernel density estimation to determine activity patterns and quantify overlap among species (Ridout &Linkie, ). We reviewed potential prey species for tigers(Hayward et al., ) and analysed those in our study areawith .  detection events, which included the greater andlesser mouse deer (n = ), Malay tapir Tapirus indicus(n = ), pig-tailed macaque Macaca nemestrina (n = ),red muntjac Muntiacus muntjac (n = ), sambar deer(n = ), and wild boar (n = ). We first converted thetime of each event into a radians measurement for analysis.
We then used the overlap package (Meredith & Ridout, )in R .. (R Core Team, ) to fit the data to a circularkernel density, and estimated the activity level at each time
period from the distribution of the kernel density using aΔ overlap value based on our sample sizes. We used theoverlapEst function to estimate the degree of overlap in activity patterns between tigers and the potential prey species. We
calculated confidence intervals (CI) by bootstrapping ,
estimates of activity for each species, and then using thebootEst and bootCI functions to estimate the % CI for theoverlap between tigers and each potential prey species.
To determine spatial overlap with potential prey species
we used the methods of Ngoprasert et al. (). We first calculated the relative abundance index for each prey species,
as for tigers, and then scaled the index for each prey species at each camera-trap site to continuous probability valuesof – (Ngoprasert et al., ). We then created a logistic
regression for each prey species using data from eachcamera-trap location. In the logistic regression we usedtiger presence as the dependent variable and prey probability values as the independent variable. We then comparedspatial overlap of prey species using the area under the
curve (AUC) of receiver operating characteristic plots(Fielding & Bell, ), and quantified the spatial overlapof tigers with individual prey species as the AUC values,
which range from . (random) to . (perfect fit).
To determine which prey species may be preferred weplotted the spatial and temporal overlap of tigers with eachpotential prey species. The upper right quadrant of the plot(high spatial and temporal overlap) indicates the most encountered and potentially most preferred prey species, whereas the upper left (high temporal but low spatial overlap) andlower right (low temporal but high spatial overlap) quadrants
would indicate potential alternative prey that were encountered opportunistically in space or time. The lower left quadrant (low spatial and temporal overlap) would indicate species
rarely encountered and probably not preferred.
Finally, we calculated the mean of the spatial and temporal overlap values for each prey species to create a spatial and temporal composite score (Song et al., ). This
allowed us to rank the potential prey species, with higherscores indicating higher encounter rate and potentially higher preference.
We expected that this simple composite score could be
improved by giving additional weight to some variables or
including other variables in the score. We therefore calculated three additional composite scores to determine how
different weighting of overlap scores or the inclusion of additional variables affect the preference rankings of potential
prey.
Firstly, we assigned more weight to the spatial overlap
value, because spatial overlap with prey is an important aspect of niche selection and resource partitioning for carnivores(Schoener, ; du Preez et al., ) and may better reflect
prey species being sought out, compared to temporal overlap.
We calculated the spatial adjusted composite score as:spatial adjusted(spatial overlap × 0.6) +=composite score(temporal overlap × 0.4)
Secondly, we considered a composite score that also included prey mass, with a higher mass adjustment value (spatial and temporal composite score × .) for prey within thepreferred size range of tigers (– kg; Hayward et al.,) and a lower value (spatial and temporal composite
score × .) for potential prey outside this range. We obtained prey mass values from Nowak () and used %of the mean weight of adult females to account for young
animals being eaten (Hayward et al., ). We then calculated the mass adjusted composite score as:
mass adjusted(spatial overlap × temporal overlap) ×=composite scoremass adjustment
Thirdly, we calculated a spatial and mass adjusted composite score as:((spatial overlap × 0.6) +spatial and mass
= (temporal overlap × 0.4)) ×
adjusted composite scoremass adjustment
We then ranked potential prey species based on eachcomposite score, with higher scores indicating higher encounter rate and potentially higher preference.

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TABLE 1 The indices of potential prey species of tigers Panthera tigris sumatrae in Bukit Barisan Selatan National Park, Sumatra, including
relative abundance (detection events/ trap nights), temporal overlap, spatial overlap, and composite scores. Higher composite scoresindicate greater encounter rates and potential prey preference. Species are listed in order of their spatial and temporal composite score.
Composite scores
Species
Wild boar Sus scrofa
Sambar Rusa unicolor
Pig-tailed macaque
Macaca nemestrina
Red muntjac
Muntiacus muntjac
Mouse deer1
Tapir Tapirus indicus

Relative
abundance

Temporaloverlap

Spatialoverlap

Spatial &temporalSpatialadjusted

Prey mass
adjusted

Spatial & preymass adjusted

3.15
1.02
4.32

0.80
0.70
0.76

0.71
0.66
0.60

0.76
0.68
0.68

0.77
0.67
0.66

0.83
0.75
0.61

0.84
0.74
0.60

7.10

0.68

0.57

0.63

0.62

0.56

0.56

3.39
0.85

0.62
0.43

0.53
0.52

0.58
0.48

0.57
0.48

0.52
0.52

0.51
0.53



Greater mouse deer Tragulus napu and lesser mouse deer Tragulus kanchil.

Results
We had  camera traps operating during – for a
total of , trap nights. We obtained , photographsof  species, including  mammals. We documented all six
potential tiger prey species in all  years of the study. We
captured photographs of people  times in  study years( = ,  = ,  = ,  = ), and one domestic
dog in .
We recorded a total of  tiger captures ( = ,  = , = ,  = ,  = ,  = ,  = ), with an overall
relative abundance of . ± SE . detections/ trap nights
per camera trap. We identified at least  individual tigers inleft flank photographs and eight individuals in right flank
photographs (Supplementary Material ).
We documented , captures of potential prey species,
with red muntjacs being recorded most frequently, followedby pig-tailed macaques and mouse deer (Table ). Tigers
exhibited a diurnal activity pattern (Fig. ) and had the highest temporal overlap with wild boar, followed by pig-tailed
macaques and sambar (Fig. , Table ). The highest spatial
overlap was also with wild boar, followed by sambar andpig-tailed macaques (Table ).
When plotting the values indicating spatial and temporal
overlap of tigers with potential prey species, wild boar andsambar deer fell in the upper right quadrant, suggesting they
are potentially preferred prey. Tapirs were in the lower left
quadrant, indicating they were probably not preferred, andthe other prey species were in the upper left (high temporal
but low spatial overlap), indicating potential alternative prey(Fig. ).
Tigers had the greatest spatio-temporal overlap with wildboar, with a spatial and temporal composite score of .,which is % higher than the species with the second highest
scores (sambar and pig-tailed macaques, both .; Table ).
The additional composite scores produced a similar
ranking to the spatial and temporal composite score. Wildboar ranked highest for the spatial adjusted composite

score, the prey mass adjusted score and the spatial andprey mass adjusted score, with scores –% higher than
the next species (Table ). As for the spatial and temporal composite score, sambar and pig-tailed macaques alsoranked second and third, respectively, for these additional
composite scores. The spatial adjusted composite scoresfor sambar and pig-tailed macaque were similar (sambar
scored .% higher than pig-tailed macaque), but the difference was greater for the prey mass adjusted score and thespatial and prey mass adjusted score (sambar scored %higher for both of these scores).

Discussion
The tiger is an important flagship species for conservation,
but remains threatened throughout its range (Seidensticker,
; Walston et al., ; Sibarani et al., ). Sumatrantigers are categorized as Critically Endangered (Linkie et al.,) and tiger populations in Bukit Barisan Selatan National Park and other areas are threatened by encroachment and habitat destruction (O’Brien & Kinnaird, ;Pusparini et al., ), and by poaching of tigers and/or
their prey (Linkie et al., , ). Effective conservation
is dependent on collaboration between countries, government agencies, local communities, and scientific organizations. The Tropical Ecology and Assessment Monitoring
Network is focused on open sharing of scientific data andcan be used as a model for data sharing amongst scientists
and other stakeholders for conservation.
We found tigers exhibited diurnal activity patterns, andwe created a composite score of spatial and temporal overlap
with potential prey species to provide insights into tiger prey
preferences, which can inform conservation (e.g. Karanth
et al., ; Barber-Meyer et al., ). Previous studies inthe study area suggested tigers have a diurnal activity pattern (O’Brien et al., ), or a crepuscular pattern withhighest activity near dawn (Pusparini et al., ). The

Oryx, 2021, 55(2), 197–203 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000577

Preferred prey of Sumatran tigers

FIG. 2 The temporal activity(including % confidence
intervals) and overlap of thekernel activity density of tigers
and potential prey species:(a) mouse deer (including
greater mouse deer Tragulus
napu and lesser mouse deer
Tragulus kanchil), (b) pigtailed macaque Macaca
nemestrina, (c) red muntjac
Muntiacus muntjac, (d) sambar
deer Rusa unicolor, (e) tapir
Tapirus indicus, (f) wild boar
Sus scrofa. Tiger activity isrepresented as solid lines andprey activity as dotted lines,
with their temporal overlap
shown as the shaded area.

FIG. 3 The spatial (area under curve; AUC) and temporal (kernel
density) overlap of tigers with potential prey species plotted
together (with axes scaled to the reported values for ease ofcomparison).

sample size of tiger captures in our study was lower than inboth previous studies, but our findings appear to confirm
the diurnal activity pattern observed by O’Brien et al.
(). Reasons for the observed differences could be
different sampling techniques, variation in individual tiger

behaviour, or varying degrees of interactions with humans
or other species in different parts of the Park. For example,
Pusparini et al. () reported high rates of illegal humanactivity (photographic captures of humans with guns) andrelative tiger abundances (relative abundance index = .)that were an order of magnitude higher than in our study(relative abundance index = .). This may have caused tigers to change their activity patterns to avoid threats posedby humans (e.g. Clinchy et al., ). Further research is required to ascertain reasons for these conflicting results fromthe same Park and subpopulation.
A high degree of spatio-temporal overlap does notnecessarily indicate prey preference but it suggests potential for high encounter rates between carnivores andtheir prey, which is a key component of prey preference(Holling, ; Fortin et al., ). Temporal overlap has
been posited as a way of determining prey preferences(Linkie & Ridout, ), but probably provides an incomplete picture if spatial overlap is not included (e.g. O’Brienet al., ). Other factors to be considered include prey
body size and potential avoidance strategies by prey species.
We created four composite index scores that included both
temporal and spatial overlap. Each composite score appears
to accurately rank prey preference of tigers in Bukit Barisan
Selatan National Park, with wild boar and sambar deer
being the most preferred, as predicted based on the findings of Hayward et al. (). The spatial and temporal

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composite score was effective, but the scores including preferred prey mass better separated sambar and pig-tailed
macaques as potential prey species for tigers. We used small(%) adjustments to create the adjusted composite scores,
and future studies in areas with known prey preferences
could conduct sensitivity analyses to determine ideal weighting adjustments for spatial and temporal composite scoresto determine prey preference.
The different composite scores indicated mostly the sameranking amongst potential prey species, with the only exception being tapirs ranking slightly higher than mouse deer inthe spatial and mass adjusted composite score. Wild boar
ranked highest in all cases, followed by sambar deer, andthese two species are frequently the preferred prey of tigers
throughout their range (Seidensticker & McDougal, ;Hayward et al., ; Basak et al., ). The greater overlap
and composite scores for wild boar in this study site could
be a result of wild boar having a relative abundancec. three times greater than sambar deer. Species with higher
abundance are probably more widely distributed acrossthe landscape, which could inflate their spatial overlap
with predators and potentially overestimate prey preference.
Red muntjac, pig-tailed macaque and mouse deer wereindicated as potential alternative prey species with intermediate composite index scores and a position in theupper left quadrant on the spatial and temporal overlap
plot (Fig. ), which indicates high temporal but low spatial
overlap with tigers. Tapirs had low spatial and temporal
overlap with tigers, which corresponds to published dataand suggests they may be non-preferred prey of tigers(Hayward et al., ). Based on these results we suggestthat conservation efforts in the area should be focused onwild boar and sambar deer, to ensure a robust prey basefor this tiger population. We found evidence of illegal snares
set for sambar deer in the Park, suggesting conservation
actions may be necessary.
Prey preference is usually assessed using the ratio of preykilled to prey available, and our study shows a potential alternative method to predict prey encounter rates and preference. Determining the number of prey of different species
killed by tigers, or numbers of prey available, can be costly
and time-intensive compared to using camera traps to collect relative abundance data. Our method of plotting spatial
and temporal overlap between predator and prey species andcreating composite index scores from camera-trap data could
be useful for inferring the encounter rates of carnivores
with prey and appears to have correctly ranked the prey preferences for Sumatran tigers. The inclusion of prey mass appeared to improve upon the spatial and temporal composite
score, and including mass or other variables such as abundance or habitat preference may facilitate more effective
inference in future studies, which could also fine-tune thevalues used in the adjusted composite scores. Further testing of a similar composite score for spatio-temporal overlap

should be conducted in other systems with known prey preferences of carnivores (e.g. from dietary analyses) to further
evaluate the accuracy of this method, assess general applicability of the method, and further interpret the observed
relationships.
Acknowledgements All data used in this study were collected by
the Tropical Ecology Assessment and Monitoring Network, a collaboration between Conservation International, the Missouri Botanical
Garden, the Smithsonian Institution and the Wildlife Conservation
Society. The work was partially funded by these institutions, theGordon and Betty Moore Foundation, the Illinois Natural History
Survey, the Slovenian Research Agency (P4-0059), and other donors.
Monitoring activities were managed by the Wildlife Conservation
Society in collaboration with the Bukit Barisan Selatan National Parkand the Ministry of Environment and Forestry, Republic of Indonesia.
We thank all field staff and forest rangers involved in camera-trap
deployment, and W. Marthy for help in the field and coordination.
Author contributions Study concept: all authors; data collection:
MCS; statistical analyses: MLA; writing: MLA; revisions: all authors.
Conflicts of interest None.
Ethical standards This research abided by the Oryx guidelines on
ethical standards. The research was based on passive monitoring,and did not involve human subjects, experimentation with animals
and/or collection of specimens.

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