ArticlePDF Available

Livestock losses and hotspots of attack from tigers and leopards in Kanha Tiger Reserve, Central India


Abstract and Figures

Carnivore attacks on livestock are a primary driver of human–carnivore conflict and carnivore decline globally. Livestock depredation is particularly threatening to carnivore conservation in Central India, a priority landscape and stronghold for the endangered tiger. To strengthen the effectiveness of conflict mitigation strategies, we examined the spatial and temporal patterns and physical characteristics of livestock depredation in Kanha Tiger Reserve. We combined livestock compensation historical records (2001–2009) with ground surveys (2011–2012) and carnivore scat to identify when and where livestock species were most vulnerable. Between 400 and 600 livestock were reported for financial compensation each year, and most (91–95 %) were successfully reimbursed. Tigers and leopards were responsible for nearly all livestock losses and most often killed in the afternoon and early evening. Cattle and buffalo were most at risk in dense forests away from villages and roads, whereas goats were most often killed in open vegetation near villages. A spatial predation risk model for cattle revealed high-risk hotspots around the core zone boundary, confirming the significant risks to livestock grazing illegally in the core. Such ecological insights on carnivore–livestock interactions may help improve species-specific livestock husbandry for minimizing livestock losses and enabling coexistence between people and carnivores.
Content may be subject to copyright.
Reg Environ Change (2016) 16 (Suppl 1):S17S29
DOI 10.1007/s10113-015-0871-5
Spatial and temporal patterns of livestock losses and hotspots of attack from tigers and
leopards in Kanha Tiger Reserve, central India
Jennifer R. B. Miller1,2*, Yadvendradev V. Jhala2 and Jyotirmay Jena3
1School of Forestry & Environmental Studies, Yale University, New Haven, Connecticut, 06511.
Current affiliation: Panthera, Lion and Leopard Programs, 8 West 40th Street, 18th Floor, New
York, NY 10018, USA
2Wildlife Institute of India, Dehradun, Uttarakhand, India, 248001
3Satpuda Maikal Landscape Programme, WWF-India, Mandla, Madhya Pradesh, India, 481661
*Corresponding author:
Jennifer R. B. Miller, Panthera, Lion and Leopard Programs, 8 West 40th Street, 18th Floor,
New York, NY 10018, USA.
Carnivore attacks on livestock are a primary driver of human-carnivore conflict and carnivore
decline globally. Livestock depredation is particularly threatening to carnivore conservation in
central India, a priority landscape and stronghold for the endangered tiger. To strengthen the
effectiveness of conflict mitigation strategies, we examined the spatial and temporal patterns and
physical characteristics of livestock depredation in Kanha Tiger Reserve. We combined livestock
compensation historical records (2001-2009) with ground surveys (2011-2012) and carnivore
scat to identify when and where livestock species were most vulnerable. Between 400-600
livestock were reported for financial compensation each year and most (91-95%) were
successfully reimbursed. Tigers and leopards were responsible for nearly all livestock losses and
most often killed in the afternoon and early evening. Cattle and buffalo were most at risk in
dense forests away from villages and roads, whereas goats were most often killed in open
vegetation near villages. A spatial predation risk model for cattle revealed high-risk hotspots
around the core zone boundary, confirming the significant risks to livestock grazing illegally in
the core. Such ecological insights on carnivore-livestock interactions may help improve species-
specific livestock husbandry for minimizing livestock losses and enabling coexistence between
people and carnivores.
Carnivore conservation; hotspot predation risk map; human-carnivore conflict; kill site; livestock
depredation; livestock compensation
Large carnivore populations worldwide are rapidly declining, in part due to retaliatory killing by
livestock owners following attacks on domestic animals (Woodroffe et al. 2005; Ripple et al.
2014). Much of this human-carnivore conflict occurs at the edges of protected areas where
carnivores, livestock, and people overlap (Woodroffe and Ginsberg 1998; Nyhus and Tilson
2004). Many non-lethal techniques exist to help reduce livestock and livelihood losses, including
livestock husbandry strategies, physical deterrents, and financial incentives for communities
(Treves and Karanth 2003; Shivik 2006). Yet effective implementation of these tools requires
detailed knowledge of when and where carnivores attack livestock and how risk differs between
livestock species. Understanding carnivore-livestock interactions is a crucial step towards
mitigating human-carnivore conflict and ultimately enabling coexistence between people and
carnivores (Treves and Karanth 2003; Goodrich 2010).
Ecological insights on the environmental factors and animal behaviors that lead to carnivore
depredation on particular livestock are particularly useful for strengthening livestock husbandry
techniques (Wikramanayake et al. 1998; Miller 2015). Many previous studies on human-
carnivore conflict have focused on depredation by a single carnivore species (usually a high-
priority species of conservation concern) on all livestock species generally, which can obscure
unique risk gradients for individual livestock species from specific carnivores (Treves et al.
2011; Lichtenfeld et al. 2014; Athreya et al. 2014; Miller et al. 2015). Differences between the
body sizes, anti-predator defenses, and grazing requirements of livestock species result in distinct
levels of vulnerability to wild carnivores (Seidensticker 1976; Sinclair et al. 2003). For instance,
in many areas smaller large carnivores like leopards, hyenas and wild dogs primarily kill
smaller-bodied livestock such as calves, sheep, and goats, whereas the largest carnivores like
tigers target larger-bodied livestock such as adult cattle, buffalo, and horses (Sangay and Vernes
2008). Likewise, large carnivore species employ unique hunting strategies and segregation
tactics to avoid interspecific competition that results in risks for livestock at different times and
locations (Laundré et al. 2009). For example, tigers and leopards often segregate temporally or
spatially to minimize interference competition (Odden et al. 2010; Harihar et al. 2011; Lovari et
al. 2013), with tigers mostly attacking livestock at night and in forest while leopards attacked in
open agricultural areas in mid-day (Katel et al. 2014; Malviya and Ramesh 2015). Understanding
the temporal and spatial patterns of interactions between different livestock and carnivores
species is necessary for developing ecologically informed strategies for conflict mitigation.
We focused our study in Kanha Tiger Reserve, a protected area in Madhya Pradesh, India
where 18% of households lose livestock to wild carnivores, primarily tigers (Panthera tigris) and
leopards (Panthera pardus; Karanth et al. 2013). Kanha also serves as a source site for tiger and
leopard populations throughout the central Indian landscape (Dutta et al. 2013; Sharma et al.
2013) and is thus a priority region for minimizing human-carnivore conflict. In Kanha, tigers and
leopards mainly kill cattle (Bos indicus), buffalo (Bubalus bubalis), and goats (Captra aegagrus
hircus; Kanha Tiger Reserve Forest Department 2012). The Forest Department permits people to
graze livestock in the multiple-use buffer zone but bans grazing inside the interior core zone of
the park, except by livestock from several villages located in the core.
Local livestock owners implement distinct grazing regimes depending on the livestock
species and season, which reflect tradeoffs between livestock vulnerability to carnivores, herder
costs, and other environmental factors. Cattle and buffalo, which often graze side-by-side in
groups, are allowed to free-graze without a herder in the winter and summer months (November-
June). In the monsoon (July-October), herders accompany cattle and buffalo to prevent livestock
from consuming crops. In contrast, herders accompany goats year-round because goats can
browse on the low-quality forage around villages, are more vulnerable to wild carnivores, and
tend to wander off if unsupervised. These different temporal and spatial patterns of grazing
suggest that cattle and buffalo may experience different threats from carnivores than goats.
However, few studies have assessed how risk varies between domestic prey species, and this
information is not available for central India despite its importance as a Tiger Conservation
Landscape for the protection of the endangered tiger (Wikramanayake et al. 1998).
Our objective was to understand the temporal and spatial patterns of risk for different
livestock species and develop ecology-based insights for reducing livestock losses. Using cases
from the livestock compensation program, we examined historical records from 2001-2009 for
past trends in livestock losses. We obtained more detailed insight on the temporal and spatial
distribution and the physical characteristics of depredated livestock by conducting ground
surveys of livestock killed in 2011-2012. We also investigated the location and prey contents of
tiger and leopard scat to better understand the movement of the carnivores consuming livestock.
Through combining multiple data sources, we provide an ecological perspective on carnivore-
livestock interactions and develop ecologically informed recommendations for minimizing
livestock vulnerability to carnivores.
Materials and methods
The study was conducted in Kanha Tiger Reserve, Madhya Pradesh in central India. This 2,074
km2 protected area consists of a 940 km2 interior core zone, where human activities are
restricted, surrounded by a 1,134 km2 buffer zone, where human residences and activities such as
livestock grazing are permitted. The reserve supports stable populations of 70 tigers and 100
leopards and growing populations of 59,000 cattle, 22,000 buffalo, and 11,000 goats, which are
regularly attacked by wild carnivores (Kanha Tiger Reserve Forest Department 2012; Jhala et al.
2014b). In an effort to prevent livestock owners from retaliating against carnivores, the Indian
Forest Department financially compensates owners for domestic animals killed by wild
carnivores. To receive compensation, a livestock owner must locate and report the livestock
carcass to the Forest Department within 48 hours, after which an officer visits the site to record
evidence of the death. Although not all livestock owners choose to report lost livestock (Karanth
et al. 2012), many people living within the tiger reserve do, and between 400-600 livestock are
reported for compensation each year (Kanha Tiger Reserve Forest Department 2012).
Data collection at livestock kill sites
We utilized the compensation program to investigate patterns of livestock depredation. We
analyzed the Kanha Tiger Reserve Forest Department historical records of compensation cases
from January 2001 through December 2009 to assess long-term trends in livestock losses. These
records provided information on the incident date, livestock species, carnivore, and
compensation amount. To obtain more detailed spatial, temporal, and demographic data on the
livestock killed, we conducted ground surveys of freshly killed livestock reported for
compensation from December 2011 through August 2012. Sampling methods are described in
detail in Miller et al. (2015) and overviewed here.
At each kill site, we recorded the incident date and time (if known by the owner), livestock
species and age, percent of carcass remaining, and GPS coordinates. We differentiated the kill
site (where the animal was killed) from the cache site (where the animal was dragged and
consumed) by trails of scuffmarks, blood, and hair. The death of each animal was attributed to a
specific carnivore based on fresh signs within 50 m of the kill and cache site. Researchers were
trained to identify differences in the size and shape of signs for each carnivore species following
the National Tiger Conservation Authority protocol (Jhala et al. 2009). We identified carnivore
signs conservatively and omitted from analysis any kill sites with ambiguous carnivore signs. A
total of 90% of all ‘confirmed’ kills were identified using direct sightings of the carnivore (25%
of kills), pugmarks (64% of kills), and/or scrapes (2% of kills), which can be clearly
distinguished between tigers and leopards (Karanth and Sunquist 1995). Because the methods
used to identify predators were unknown for historical records, we analyzed only ground survey
data when calculating carnivore-specific trends. Finally, we recorded the compensation amount
and the day payment was issued to livestock owners.
Carnivore scat
To study tiger and leopard diet and movement, we examined the prey contents and spatial
location of carnivore feces. We collected tiger and leopard scat opportunistically along roadsides
and foot trails, features which individuals use often for hunting and general movement (Smith et
al. 1989; Karanth and Sunquist 2000). Tiger and leopard scat are distinct in appearance from the
scat of other carnivores in our study area (Karanth and Sunquist 2000) but can be difficult to
distinguish between the two species. Scat was identified to carnivore species using genetic
analysis conducted by the Wildlife Institute of India (Yumnam et al. 2014). However, because
only a few scat samples (18%) contained viable genetic material, we did not associate carnivore
identity with scat for our final analysis. We identified the prey in feces by drying scat, sampling
hair contents, and microscopically comparing hair width, medullary structure, and other
characteristics to prey reference slides at the Wildlife Institute of India (Mukherjee et al. 1994;
Bahuguna et al. 2010). We mapped the GPS coordinates of each scat to examine where
carnivores moved after consuming livestock.
Landscape attributes
We examined the landscape characteristics associated with kills by sampling environmental and
anthropogenic variables known to influence livestock depredation by large Felidae predators
(Seidensticker 1976; Shrader et al. 2008; Valeix et al. 2009; Kissling et al. 2009; Karanth et al.
2012; Zarco-González et al. 2013; Soh et al. 2014). For land-use, we utilized the Forest Survey
of India State of the Forests 2009 map of land cover, which included non-forest (i.e. agricultural
fields), water, scrubland, open forest, moderately dense forest, and very dense forest. Since the
land-use map did not distinguish villages, we used heads-up digitization with Google Earth
satellite imagery from 2007-2013 to outline village areas. We also quantified human presence
using roads digitized from Survey of India topographic maps and the boundary of the reserve
core zone provided by the Kanha Forest Department. Landscape variables were converted to
raster format at a 20-m spatial resolution using the Spatial Analyst toolset in ArcGIS (v.10.1,
ESRI, CA, USA). We then calculated the Euclidean distance between the center of each kill site
pixel to the center of the nearest pixel with the landscape attribute. We limited our study area to
within 4 km of village centers in the reserve since no livestock were killed beyond this distance
(Miller et al. 2015).
To contextualize kills against the available landscape, we also sampled the range of
landscape attribute values at randomly selected sites across the study area (Johnson et al., 2006;
Manly et al., 2002). The locations of these sites were determined by generating random points
stratified across a 200-m grid in ArcGIS, with one point per pixel separated by at least 200 m so
as not to repeatedly sample the same area. While ground surveying these random sites for
another study (Miller et al. 2015), no wild or domestic prey carcasses were observed.
We examined linkages between precipitation and livestock kills by comparing daily and
monthly rainfall measured by the Kanha Tiger Reserve Forest Department in 2011-2012 to kill
Statistical analysis and modeling
Since the 2011-2012 data on livestock characteristics and landscape characteristics were not
normally distributed, we used Mann-Whitney U tests to compare groups. For livestock species
with adequate sample sizes (cattle, goat, and buffalo but not pig since npig = 2), we investigated
differences by month in the historical frequencies of livestock kills using two-way ANOVAs.
We explored associations between cache distance and livestock age, and between the timing of
kills and daily or monthly rainfall, using linear regression models.
Using surveyed kill sites and landscape attribute data, we built a multivariate logistic
regression model to predict and map the probability of carnivore predation of cattle (Miller
2015). We focused on cattle exclusively because they are the most depredated livestock species
in Kanha, and because the numbers of buffalo and goats killed in 2011-2012 were not adequate
to build a validated model. We modeled combined risk from tigers and leopards for cattle
because we felt the output risk map would be most helpful to managers and owners for
understanding and mitigating risk to cattle generally. Following a use-availability design
(Johnson et al. 2006), the response variable in the model featured binary values, with 0 for
random sites and 1 for kill sites. Incidences where a carnivore killed multiple livestock during
the same predation event (n = 36) were treated as single kill sites to focus the spatial models on
units of kill sites rather than individual animals and avoid pseudoreplication. We generated
univariate linear regression models to examine which landscape attributes were strong predictors
of kill probability. Following standard methods (Garamszegi 2010; Treves et al. 2011), we ran
Spearman correlations between variables and built global models for livestock species that
included the variables that were significant in the univariate regression and not correlated with
more significant variables (rs < 0.6). These requirements excluded the variable distance to non-
forest, which was correlated with distance to village (rs = 0.7) and distance to very dense forest
(rs = 0.7). Based on field observations we suspected that roads, villages, scrubland, and very
dense forest would have a threshold relationship with kill risk such that effects might decrease in
a nonlinear direction at some distance. We found that including the quadratic structural form of
these variables lowered the global model AIC by 2 (Draper and Smith 1993; Burnham and
Anderson 2002). The global model were used to generate and rank models with all combinations
of the eligible variables based on the Corrected Akaike’s Information Criterion (AICc) to account
for small sample size (Burnham and Anderson 2002). Since no one top model emerged (AICc
2), we averaged models to produce a final model.
We used the model to investigate the effect of each landscape attribute on the kill probability
by predicting risk while varying the attribute of interest and holding all other variables constant
at their means. We then mapped the model in ArcGIS to observe hotspots in carnivore kill risk
across the study area. Finally, we validated whether the model could predict future kills by
conducting a randomization test against an independent dataset of kills (detailed methods in
Appendix S1 and Figure S1). Statistical analyses were conducted using R (v.2.15.3, R Project
Development Team, with the MASS, MuMIN, and R DAAG packages.
Historical records from 2001-2009 contained 4,561 livestock reported for compensation,
consisting of 72% cattle, 16% goats, 10% buffalo, 2% pigs, and <1% horses. All cases were
attributed to a specific carnivore: 64% were attributed to tiger attacks, 34% to leopard, 1% to
unknown carnivores, and <1% to wild dog and wolf. However, since the methods used to
identify carnivores is unknown and 30% of ground-surveyed kills in 2011-2012 did not contain
conclusive evidence about the carnivore, these data should be interpreted with care. Between
December 2011-August 2012, we ground surveyed 449 livestock carcasses, which totaled 92%
of all reported kills in Kanha during the study period. Livestock consisted of 76% cattle, 14%
goats, 9% buffalo, and <1% pigs. Based on carnivore signs, we were able to confidently identify
the predator at 71% of kills, of which we attributed 57% to tiger and 43% to leopard.
Ground surveys indicated that tigers and leopards selected different size classes for livestock
(U430 = 7357, P < 0.001; Fig. 1a-c). Leopards killed more young cattle (aged 1-4 years) and
buffalo (3 years) than tigers, whereas tigers killed more older cattle (5-11 years) and buffalo (6-
10 years). Only leopards killed goats, which ranged from 1-6 years. The ages of depredated pigs
could not be identified.
Historically, 95% of livestock were accepted for compensation (the Forest Department does
not record cases that did not meet program requirements, such as injured livestock). The Forest
Department paid a total of INR 81,46,842 in compensation over the eight-year period, ranging
INR 8,78,471-16,28,150 per year. In 2011-2012 the Forest Department similarly paid INR
21,42,650 for 91% of reported kills, which it distributed to owners within an average of 17.4 ±
0.9 days (mean ± SE; based on 120 cases with data on payment date).
Fig. 1. Age (left, a-c) and attack time (right, d-f) of livestock killed by tigers (black) and leopards
(gray) for (a, d) cattle, (b, e) buffalo, and (c) goats (c, f).
Tiger and leopard hunting behavior
Most livestock carcasses were cached away from the site where they were killed. Tigers cached
71% of cattle kills and leopards cached 63%, and both carnivores dragged carcasses similar
distances (mean ± SD for tigers was 50 ± 54 m and leopards was 51 ± 94 m; U155 = 2324, P =
0.154). Tigers and leopards cached 61% and 50% of buffalo kills, respectively, and varied more
in the length of their drags (average distance of 36 ± 26 m for tigers and 116 m ± 86 m for
leopards) but these differences were not statistically significant (U19 = 38, P = 0.203). Leopards
cached 52% of goat carcasses, dragging them an average of 192 ± 229 m, and cached both pig
kills (n = 2), moving one carcass 8 m and the other 470 m. For each carnivore, smaller-bodied
livestock species were cached farther from kill sites, and overall cache distance was significantly
but weakly correlated with livestock age (R2 = 0.066, F1,269 = 18.86, P < 0.001). In only 45% of
cases were carnivores able to consume more than half the carcass before the Forest Department
burned the body (Fig. S2).
We collected 133 tiger and leopard scat distributed across the reserve (Fig. 2), 69 (52%) in
the core zone and 64 (48%) in the buffer zone. The majority of scat (67%) contained only wild
prey animals, 29% contained only domestic animals (cattle and buffalo), and 4% contained both
wild and domestic prey (Fig. S3). Since hair from wild and domestic pig appear identical under
the microscope, we conservatively categorized all pig hairs as wild prey for our analysis. Out of
the 44 scat containing livestock, only eight were found in the park core zone, five of which were
located close (< 2 km) to the core-buffer boundary. Genetic analysis confirmed that two of the
five near the boundary were tiger and one was leopard. Three scats containing livestock were
found deep within the core interior (3.5-7.2 km from the boundary).
Fig. 2. Locations of tiger and leopard scat collected across Kanha Tiger Reserve, showing the
distribution of domestic and wild prey contents with respect to the reserve core and buffer zones.
Villages are primarily located in the buffer zone.
Temporal patterns
From 2001-2012, the frequency of livestock depredations varied substantially by year but did not
consistently increase or decrease over time (Fig. 3). The number of kills did not significantly
differ between month for cattle (F1,106 = 0.106, P = 0.746), buffalo (F1,106 = 0.039, P = 0.845),
goat (F1,106 = 1.664, P = 0.200), pig (F1,106 = 1.492, P = 0.225), or horse (F1,106 = 0.716, P =
0.399). A distinct peak in compensated livestock occurred each year sometime between July-
September during the monsoon (Fig. 3). However, the number of kills was not associated with
monthly (R2 = 0.137, F1,7 = 1.112, P = 0.327) or daily (R2 = 0.002, F1,210 = 0.392, P = 0.532)
Most livestock were killed in the afternoon and evening between 12:00-20:00 h (Fig. 1d-f).
Both tigers and leopards attacked cattle during this period and tigers also frequently killed cattle
throughout the morning (05:00-12:00 h). Leopards killed goats throughout the day and especially
in the early evening (16:00-20:00 h).
Fig. 3. Number of livestock killed each month in Kanha Tiger Reserve from 2001-2009. No
records were available for 2010.
Spatial patterns of livestock kills and attack risk
The majority of livestock were killed in the buffer zone of the reserve (82%), where most
villages and livestock grazing occur. The remaining 18% were killed in the core zone and were
concentrated around the villages in the core zone or close to the core-buffer boundary.
Comparisons between kill and random sites revealed that carnivores tended to kill cattle and
buffalo closer to forests and farther from non-forest (agricultural fields) and villages than random
(Table 1). In contrast, goats were killed closer to fields and villages. Both cattle and goats (but
not buffalo) were killed at farther distances from water and the park core boundary than random
sites. Carnivores killed cattle (but not goats or buffalo) farther from roads.
Table 1 Mann-Whitney U test comparisons between mean values ± standard error of landscape
attribute variables at sites where tigers and leopards killed livestock (kill site) and random sites
in the study area in Kanha Tiger Reserve. P-values in bold are significant (P < 0.05).
Cattle (n = 193)
Goat (n = 39)
Buffalo (n = 32)
Kill sites
Kill sites
Kill sites
Distance to core
2.0 ± 0.2
1.1 ± 0.2
2.1 ± 0.6
Distance to road
0.7 ± 0.4
0.5 ± 0.7
0.7 ± 0.9
Distance to village
956 ± 47
501 ± 79
975 ± 84
Distance to water
2.9 ± 0.1
3.9 ± 0.2
3.0 ± 0.3
Distance to non-
forest (m)
362 ± 23
83 ± 20
511 ± 80
Distance to
scrubland (km)
6.9 ± 0.2
5.8 ± 0.5
7.4 ± 0.7
Distance to open
forest (m)
289 ± 19
285 ± 39
301 ± 58
Distance to
moderately dense
forest (m)
68 ± 8
197 ± 35
55 ± 11
Distance to very
dense forest (m)
173 ± 39
532 ± 101
104 ± 29
We built a predation risk model using 435 random sites and 193 cattle kills with confirmed
predators. The model predicted the probability of a tiger or leopard killing a cattle given an
encounter, ranging from 0 (low risk) to 0.93 (high risk). The contribution of each variable to
predictions of predation risk was measured by its relative importance in the model. Most
variables played a strong role in predicting risk (importance > 0.70), including all human
presence and dense forest variables (Table 2). Randomization tests revealed that model
predictions performed better than random (Fig. S4). The model accurately identified 69% of
validation sites (88 out of 128 known kill sites) as kills, which is greater than would be expected
by random chance (P < 0.001).
The risk to cattle was greatest in moderate and very dense forests and at intermediate
distances from roads, villages, and scrubland (Fig. 4). Risk did not substantially change with
increasing distance from water or the core zone boundary. Kill probability showed a negative
quadratic relationship to the distance to road, village, and scrubland, with cattle vulnerability
increasing at farther distances up to a threshold point and thereafter decreasing. Cattle were most
vulnerable to carnivores 1.2 km from roads, 1.0 km from villages, 6.1 km from scrubland (Fig.
4a, 4c, 4e). The distance to very dense forest showed a negative quadratic relationship, with the
greatest risk directly within (0 km) or far from (> 4 km) very dense forests (Fig. 4i). The
predation risk map revealed the highest risk levels in forest patches adjacent to the park core
boundary and the lowest levels in agricultural areas near villages and roads (Fig. 5).
Table 2 Statistics from the predation risk model for cattle, showing the relative importance,
coefficient (β), and standard error (SE) of variables in the final averaged model. Relative
importance values range from 0-1, with a value of 1 indicating a strong contribution to the
distance to very dense forest2
distance to very dense forest
distance to road
distance to road2
distance to scrub2
distance to village2
distance to moderately dense
distance to scrub
distance to village
distance to open forest
distance to core
distance to water
Fig. 4. The probability of carnivore depredation on cattle with increasing distances to landscape
attributes as predicted by the predation risk model. The 95% confidence intervals are shown in
Predation risk
Fig. 5. Distribution of tiger and leopard predation risk for cattle in Kanha Tiger Reserve. Values
represent the kill probability given an encounter between a carnivore and cattle. Low-risk areas
primarily occur in agricultural fields and village areas whereas high-risk hotspots occur in dense
forest away from human activity. Notable villages are shown for perspective (not all villages are
marked). The study area was designated within 4 km of village centers (see methods for details).
Kanha has one of the highest rates of livestock depredation from tigers and leopards in India
(Kala and Kothari 2013; Karanth et al. 2013; Singh et al. 2015) yet is also renowned as one of
the most successful and stable sites of tiger conservation (Post and Pandav 2013; Jhala et al.
2014a). The low frequency of retaliations against depredating carnivores in Kanha is largely due
to the Forest Department’s prompt livestock compensation program, which in 2011-2012
distributed payment on average about 2.5 weeks after livestock were attacked. This is
considerably faster than other reported compensation timeframes from India (Madhusudan 2003)
and on par with championed programs (Nyhus et al. 2005). The compensation program also
offers tractable long-term data that can be used for assessing the temporal and spatial patterns
and physical characteristics of livestock losses. Kill data offer exclusive fine-scale spatial
information about the sites where livestock are vulnerable to livestock, offering a unique
perspective not otherwise captured by household surveys, which have been the basis of many
previous studies on human-carnivore conflict (e.g. Wang and Macdonald 2006; Nugraha and
Sugardjito 2009; Karanth et al. 2012; Katel et al. 2014; Bhattarai and Fischer 2014).
Our study confirmed that tigers and leopards were the primary depredating carnivores, with
tigers responsible for killing slightly more livestock than leopards. This contrasts with reports
from Corbett Tiger Reserve in northern India and Bhutan, where leopards kill substantially more
livestock than tigers (Wang and Macdonald 2006; Sangay and Vernes 2008; Malviya and
Ramesh 2015). We suspect this difference may be related to the lower availability of free-
grazing adult cattle in Corbett (where ~30% of households stall-feed livestock; Malviya and
Ramesh 2015) and the lower density of tigers in Bhutan (Sanderson et al. 2006). We did not find
evidence of attacks from other carnivores, which the Forest Department had reported in previous
years, and we urge authorities to train field staff to make accurate predator identifications in
order to prevent false perceptions about threats from other carnivores (Dickman 2010;
Suryawanshi et al. 2013). Cattle were killed most frequently, followed by goats, buffalo, and
pigs, respectively. Compensation cases are likely biased against small-bodied livestock (goats
and pigs) because these animals are more often cached further and more completely eaten, and
thus more difficult for livestock owners to locate and report. These strong associations between
certain sized livestock and certain carnivore species suggests that segregating livestock by body
size and age, and grazing cohorts in habitats less conducive to attacks by their main predator
(e.g. open vegetation for tigers and dense forests for leopards), might discourage depredation
(Wang and Macdonald 2006; Goodrich 2010).
Temporal patterns
Tigers and leopards killed livestock at all hours of the day, particularly in the afternoon and
evening. Our analysis found a high number of attacks by tigers in the afternoon (12:00-16:00 h),
likely because this was when livestock were farthest from the village and in dense vegetation
where tigers often attack livestock (Soh et al. 2014; Malviya and Ramesh 2015; Miller et al.
2015). Furthermore, tigers (and leopards) are most active and hunting in the early morning, late
afternoon, evening and night and avoid much activity mid-day (Karanth and Sunquist 2000). Our
results did not reveal temporal separation between tiger and leopard or a tendency towards
nocturnal hunting in either predator as found in previous studies (Seidensticker 1976; Karanth
and Sunquist 2000; Malviya and Ramesh 2015), probably due to the nature of our dataset, which
depended on people witnessing attacks.
We observed a spike in livestock losses each year during the monsoon, which echoes similar
findings from other protected areas in South Asia (e.g. Bhadauria and Singh 1994; Sangay and
Vernes 2008; Singh et al. 2015). However, the number of livestock kills did not explicitly relate
to monthly rainfall as reported from Africa (Kolowski and Holekamp 2006). This may indicate
that the rise in depredations during monsoon is due to human-induced changes, such as herders
leading cattle and buffalo away from crops to graze in denser forests with greater predation risk.
If so, monsoon may be an ideal season in which to implement alternative grazing strategies since
humans have more control over livestock movement.
Spatial patterns
Results revealed distinct risk distributions by livestock species, which to our knowledge has not
been examined for tiger and leopard depredation. The predation risk model found that threats
from both carnivores combined were highest for cattle near dense forests and lowest near
agricultural fields, villages, and roads. These results are comparable to previous conflict studies
on tigers that likewise observed most attacks on livestock in forest and away from roads (Wang
and Macdonald 2006; Soh et al. 2014). The risk map for cattle closely resembles general tiger
risk for all livestock (Miller et al. 2015), likely because cattle are the most frequent species killed
by tiger and thus most strongly represented in the tiger risk model. Risk hotspots occurred 1-2
km from the core zone boundary in both the buffer zone and the interior of the core zone,
corroborating previous findings from central India that livestock depredation increases with close
proximity to protected areas (Karanth et al. 2013). The high kill probabilities inside the core zone
reiterates the need for strict enforcing to eliminate grazing livestock in the core, which has been
prioritized for reducing human-tiger in the past (Goodrich 2010). To reduce livestock losses,
livestock owners could minimize cattle presence in dense forests and favor grazing routes close
to open vegetation and human areas. If grazing routes are adapted to reduce risk, carnivores
should also be initially monitored for behavioral feedbacks to ensure that they are not drawn into
closer contact with people (Miller 2015).
Similarities in the landscape attributes associated with cattle and buffalo kill sites suggest
that buffalo may experience comparable distributions of predation risk as cattle. However, results
showed opposite trends for goats, which were more vulnerable in open vegetation and village
areas. These distinctions may be related to grazing patterns since herders may restrict goats to the
open vegetation and village areas that are most convenient for human access, whereas cattle and
buffalo graze unrestrained farther from villages for most of the year. Our data does not enable us
to discern whether these risk distributions are shaped more by carnivores or by livestock and
people but we encourage future studies to directly pursue the mechanisms behind depredation.
It is currently unknown whether the individual tigers and leopards predating on livestock are
resident or dispersing but our scat results offer some insight into their movement. Though scat
contents indicated that tiger and leopard diets primarily consisted of wild prey, 33% of all scat
(buffer and core zone) and 42% of scat found in the buffer contained domestic livestock, which
is a surprisingly large proportion considering the high abundance of wild prey available in the
core zone (Jhala et al. 2014b). Twelve percent of scat found in the core zone contained livestock
remains, and 10% was found 3-7 km from the core-buffer boundary in the interior of the core.
Although these scat may have been deposited by young tigers or leopards dispersing through the
reserve, it is also possible that resident carnivores may visit the buffer zone to supplement their
diet with livestock. Considering the extensive home ranges of tigers (~10-100 km2; Sharma et al.
2010) and leopards (~10-60 km2; Odden et al. 2014), livestock depredation may not be restricted
to transient individuals as commonly believed. Furthermore, if resident individuals are regularly
attracted out of the park core zone to kill livestock, they may be susceptible to human threats in
the buffer zone and non-protected areas (Balme et al. 2010). However, our limited sample size
and opportunistic (rather than systematic) sampling of scat limits the scope of our conclusions.
Greater efforts in the future must be dedicated to identifying which individual carnivores kill
livestock, especially since this answer may help more fully elucidate the drivers behind livestock
Implications for human-carnivore conflict mitigation
The call to reduce human-carnivore conflict by avoiding predator hotspots has been sounded
before (Wang and Macdonald 2006; Goodrich 2010), and our study contributes insight to help
identify when and where different livestock species are most vulnerable to tigers and leopards.
Middle-aged cattle (4-8 years) were the most vulnerable to tigers and leopards and were attacked
primarily in the afternoon and early evening (12:00-20:00 h) near dense forests and at moderate
distances from road, village and scrub forests. Buffalo were mostly killed by tigers, which tended
to attack middle-aged individuals (6-8 years) during the afternoon and early evening (12:00-
20:00 h) in dense forests and away from open habitat and villages. To reduce losses we
recommend the use of herders year-round, instead of only during the monsoon, to enable greater
control over cattle and buffalo routes and timing to minimize high-risk grazing in forests.
Middle-aged goats (2-6 years) were most at risk from leopards during the early evening (16:00-
20:00 h) in open vegetation and village areas. Rather than shift goat grazing routes to denser
forest to reduce risk, which might increase threats from tigers, owners could consider protecting
goats earlier in the day (before 16:00 h) in reinforced, leopard-proof enclosures. Furthermore, to
decrease losses with all livestock species, owners living in high-risk areas could consider
implementing additional mitigation techniques such as trained guard dogs, predator-proof
enclosures and fencing, deterrents, and sensory stimulants, especially during the highest-risk
season (monsoon), to further reduce attacks from carnivores (Shivik 2006). Previous research in
central India (Karanth et al. 2013) and in east Africa (Kolowski and Holekamp 2006) found that
guard animals and fencing were especially useful in mitigating attacks. Finally, we encourage
managers worldwide to regularly update predation risk models and maps to monitor conflict, and
to develop results into relevant education and outreach materials to assist livestock owners in
understanding risks near their villages (Miller 2015).
This paper demonstrates that livestock compensation programs generate data that can be
useful for understanding and preventing conflict. Financial compensation systems play a
particularly important role in supporting livestock owners that live in the ‘diffuse edge’ buffer
zones of protected areas where the majority of human-carnivore conflict often occurs (this study;
Nyhus and Tilson 2004). This is the case in Kanha, one of the few tiger reserves in India with a
functional buffer zone, the livestock compensation program is key to minimizing retaliations
against carnivores. Compensation programs also present opportunities for villagers to develop
stronger relationships with the Forest Department, which can impact human-carnivore conflict
given that local trust in authority is directly linked to attitudes towards conservation (Treves et al.
2006; Dickman 2010; Carter et al. 2012). To build trust and local engagement, it is important
that authorities ensure that local people understand and can meet the regulations related to
livestock compensation (Nyhus et al. 2005). Most surveyed livestock losses (91%) in Kanha
from 2011-2012 were compensated within several weeks if basic requirements were met. These
levels greatly differ from the lower success rates (29%) reported just outside the buffer of Kanha
(Karanth et al. 2012), where compensation is mandated but often overlooked by governing
authorities in the absence of the high-profile tiger. This discrepancy has sparked confusion and
intolerance in some livestock owners (Karanth et al. 2013), who may be more prone to retaliate
against wild carnivores that depredate livestock. We encourage managers to maintain
consistency and generously award compensation whenever possible (while taking care to
avoiding false claims, corruption and perverse incentives; Nyhus et al. 2005). This is important
both within and outside of protected areas, especially since carnivore dispersal outside of parks is
essential for maintaining resilient populations (Yumnam et al. 2014).
The results of this paper face several potential limitations. First, compensation data may not
have evenly represented the spatial distribution of livestock depredations if village remoteness or
villager-authority relationships biased the reporting of kills. Because Forest Department beat
camps are evenly distributed across the Kanha buffer and core, and because we were not aware
of negative social tensions during nine months of extensive field visits, we do not expect that
results were significantly biased. Second, the presence of herders grazing cattle during the
monsoon months may change the distribution of predation risk from the rest of the year, yet our
cattle risk models did not address season-wise differences. While our goal in modeling cattle risk
was to understand year-round predation risk to offer managers simple guidance for decision-
making, we recognize that risk will shift with different grazing practices and resource
distribution and encourage future studies to more closely examine such short-term shifts. Finally,
the cattle risk model portrays combined risks for tigers and leopards, which offers managers and
owners a tool for strategizing protection for cattle but may limit inferences on the spatial
distribution of risk from each species.
The first increase in the tiger population recently reported from India (Jhala et al. 2014a) offers
hope that collective efforts worldwide can reverse carnivore declines. Yet even if carnivore
populations stabilize, expanding human development guarantees that natural resource managers
and livestock owners will continue to face challenges in mitigating human-carnivore conflict
(Treves and Karanth 2003; Ripple et al. 2014). Understanding the temporal and spatial factors
that underlie ecological interactions between specific carnivore and livestock species will be
essential for developing strong mitigating methods that ultimately make coexistence possible.
We thank Rajesh Gopal and the National Tiger Conservation Authority for permissions and
facilitation for carrying out this research. We acknowledge the Madhya Pradesh Principal Chief
Conservator of Forests, H. S. Pabla, for granting research permission and the Wildlife Institute of
India for institutional support. We are very grateful to the Kanha Forest Department for
providing historical records and field support, especially Field Director J. S. Chauhan, Research
Officer Rakesh Shukla, and the wireless controllers, forest guards, and chowkidars who helped
us survey sites. We thank Naseem Khan, Arvind Thakur, Ashish Bais, Amol Khumbar, and
Ashish Prasad for assisting with data collection. This manuscript benefited from discussions with
Oswald Schmitz, Anne Trainor, and Meghna Agarwala as well as feedback from several
anonymous reviewers. Maya Lim assisted with graphic design. We thank Ruth DeFries, Trishna
Dutta, and Sandeep Sharma for coordinating and editing this special issue. Funding was provided
by the American Institute for Indian Studies; American Philosophical Society Lewis and Clark;
Association of Zoos & Aquariums; John Ball Zoo Society; Yale Tropical Resources Institute;
and the National Science Foundation GRFP.
Electronic supplementary material
The online version of this article contains supplementary material, which is available to
authorized users.
Athreya VR, Odden M, Linnell JDC et al (2014) A cat among the dogs: leopard Panthera pardus
diet in a human-dominated landscape in western Maharashtra, India. Oryx 1–7. doi:
Bahuguna A, Sahajpal V, Goyal SP et al (2010) Species Identification from Guard Hair of
Selected Indian Mammals. Wildlife Institute of India, Dehradun
Balme GA, Slotow R, Hunter LTB (2010) Edge effects and the impact of non-protected areas in
carnivore conservation: Leopards in the Phinda-Mkhuze Complex, South Africa. Anim
Conserv 13:315–323. doi: 10.1111/j.1469-1795.2009.00342.x
Bhadauria RS, Singh AN (1994) Cyclic pattern of predation on domestic livestock by the tigers
of Corbett National Park, U.P., India. TigerPaper 11:5–7.
Bhattarai BR, Fischer K (2014) Human–tiger Panthera tigris conflict and its perception in Bardia
National Park, Nepal. Oryx 1–7. doi: 10.1017/S0030605313000483
Burnham KP, Anderson DR (2002) Model Selection and Multimodel Inference: Practical
Information-Theoretic Approach, 2nd edn. Springer Science+Business Media, Inc., New
Carter NH, Riley SJ, Liu J (2012) Utility of a psychological framework for carnivore
conservation. Oryx 46:525–535. doi: 10.1017/S0030605312000245
Dickman AJ (2010) Complexities of conflict: the importance of considering social factors for
effectively resolving human-wildlife conflict. Anim Conserv 13:458–466. doi:
Draper N, Smith H (1993) Applied Regression Analysis. John Wiley & Sons, Inc., New York
Dutta T, Sharma S, Maldonado JE et al (2013) Gene flow and demographic history of leopards
(Panthera pardus) in the central Indian highlands. Evol Appl 6:949–959. doi:
Garamszegi LZ (2010) Information-theoretic approaches to statistical analysis in behavioural
ecology: an introduction. Behav Ecol Sociobiol 65:1–11. doi: 10.1007/s00265-010-1028-7
Goodrich JM (2010) Human-tiger conflict: a review and call for comprehensive plans. Integr
Zool 5:300–12. doi: 10.1111/j.1749-4877.2010.00218.x
Harihar A, Pandav B, Goyal SP (2011) Responses of leopard Panthera pardus to the recovery of
a tiger Panthera tigris population. J Appl Ecol 48:801-814. doi: 10.1111/j.1365-
Jhala Y V., Qureshi Q, Gopal R (2014a) The status of tigers in India 2014. National Tiger
Conservation Authority, New Delhi and Wildlife Institute of India, Dehradun
Jhala Y V., Qureshi Q, Gopal R, Amin R (2009) Monitoring tigers, co-predators, prey and their
habitats. National Tiger Consertion Authority, New Delhi and Wildlife Institute of India,
Jhala Y V., Qureshi Q, Vettakevan J et al (2014b) Spatial and population ecology of tiger co-
predator and their prey in Kanha tiger reserve. Progress report 2005-2013. Wildlife Institute
of India, Dehradun, National Tiger Conservation Authority, New Delhi and Kanha Tiger
Reserve, Madhya Pradesh
Johnson CJ, Nielsen SE, Merrill EH et al (2006) Resource selection functions based on use-
availability data: theoretical motivation and evaluation methods. J Wildl Manage 70:347–
Kala C, Kothari K (2013) Livestock predation by common leopard in Binsar Wildlife Sanctuary,
India: human-wildlife conflicts and conservation issues. Human-Wildlife Interact 7:325–
Kanha Tiger Reserve Forest Department (2012) Tiger Conservation Plan for Kanha Tiger
Reserve (2012-2022). Mandla
Karanth KK, Gopalaswamy AM, DeFries RS, Ballal N (2012) Assessing patterns of human-
wildlife conflicts and compensation around a central Indian protected area. PLoS One
7:e50433. doi: 10.1371/journal.pone.0050433
Karanth KK, Naughton-Treves L, DeFries RS, Gopalaswamy AM (2013) Living with wildlife
and mitigating conflicts around three Indian protected areas. Environ Manage 52:1320–
1332. doi: 10.1007/s00267-013-0162-1
Karanth KU, Sunquist ME (1995) Prey selection by tiger, leopard and dhole in tropical forests. J
Anim Ecol 64:439–450.
Karanth KU, Sunquist ME (2000) Behavioural correlates of predation by tiger (Panthera tigris),
leopard (Panthera pardus) and dhole (Cuon alpinus) in Nagarahole, India. J Zool 250:255–
265. doi: 10.1017/S0952836900002119
Katel ON, Pradha S, Schmit-Vogt D (2014) A survey of livestock losses caused by Asiatic wild
dogs, leopards and tigers, and of the impact of predation on the livelihood of farmers in
Bhutan. Wildl Res 41:300–310.
Kissling WD, Fernández N, Paruelo JM (2009) Spatial risk assessment of livestock exposure to
pumas in Patagonia, Argentina. Ecography 32:807–817. doi: 10.1111/j.1600-
Kolowski J, Holekamp KEK (2006) Spatial, temporal, and physical characteristics of livestock
depredations by large carnivores along a Kenyan reserve border. Biol Conserv 128:529–
541. doi: 10.1016/j.biocon.2005.10.021
Laundré JW, Calderas JMM, Hernandez L (2009) Foraging in the landscape of fear, the
predator’s dilemma: where should I hunt? Open Ecol J 2:1–6. doi:
Lichtenfeld LL, Trout C, Kisimir EL (2014) Evidence-based conservation: predator-proof bomas
protect livestock and lions. Biodivers Conserv 24: 483-491. doi: 10.1007/s10531-014-0828-
Lovari S, Ventimiglia M, Minder I (2013) Food habits of two leopard species, competition,
climate change and upper treeline: a way to the decrease of an endangered species? Ethol
Ecol Evol 1–14. doi: 10.1080/03949370.2013.806362
Madhusudan MD (2003) Living amidst large wildlife: livestock and crop depredation by large
mammals in the interior villages of Bhadra Tiger Reserve, South India. Environ Manage
31:466–75. doi: 10.1007/s00267-002-2790-8
Malviya M, Ramesh K (2015) Human-felid conflict in corridor habitats: implications for tiger
and leopard conservation in Terai Arc Landscape, India. Human-Wildlife Interact 9:48–57.
Miller JRB (2015) Mapping attack hotspots to mitigate human-carnivore conflict: approaches
and applications of spatial predation risk modeling. Biodivers. Conserv in press.
Miller JRB, Jhala Y V., Jena J, Schmitz OJ (2015) Landscape-scale accessibility of livestock to
tigers: implications of spatial grain for modeling predation risk to mitigate human-carnivore
conflict. Ecol Evol 5:1354–1367. doi: 10.1002/ece3.1440
Mukherjee S, Goyal SP, Chellam R (1994) Standardization of scat analysis techniques for
leopard (Panthera pardus) in Gir National Park, Western India. Mammalia 58:139–143.
Nugraha RT, Sugardjito J (2009) Assessment and management options of human-tiger conflicts
in Kerinci Seblat National Park, Sumatra, Indonesia. Mammal Study 34:141–154. doi:
Nyhus PJ, Osofsky SA, Ferraro PJ et al (2005) Bearing the costs of human-wildlife conflict: the
challenges of compensation schemes. In: Woodroffe R, Thirgood S, Rabinowitz A (eds)
People Wildl. Confl. or Coexistence? Cambridge University Press, Cambridge, UK, pp
Nyhus PJ, Tilson R (2004) Characterizing human-tiger conflict in Sumatra, Indonesia:
implications for conservation. Oryx 38:68–74. doi: 10.1017/S0030605304000110
Odden M, Athreya VR, Rattan S, Linnell JDC (2014) Adaptable neighbours: movement patterns
of GPS-collared leopards in human dominated landscapes in India. PLoS One 9:e112044.
doi: 10.1371/journal.pone.0112044
Odden M, Wegge P, Fredriksen T (2010) Do tigers displace leopards? If so, why? Ecol Res
25:875–881. doi: 10.1007/s11284-010-0723-1
Post G, Pandav B (2013) Comparative evaluation of tiger reserves in India. Biodivers Conserv.
doi: 10.1007/s10531-013-0554-9
Ripple WJ, Estes JA, Beschta RL, et al. (2014) Status and ecological effects of the world’s
largest carnivores. Science 343:1–11. doi: 10.1126/science.1241484
Sanderson EW, Forrest J, Loucks C et al (2006) Setting priorities for the conservation and
recovery of wild tigers: 2005-2015. The technical assessment. WCS, WWF, Smithsonian,
and NFWF-STF, New York and Washington, DC
Sangay T, Vernes K (2008) Human–wildlife conflict in the Kingdom of Bhutan: Patterns of
livestock predation by large mammalian carnivores. Biol Conserv 141:1272–1282. doi:
Seidensticker J (1976) On the ecological separation between tigrs and leopards. Biotropica
8:225–234. doi: 10.2307/2989714
Sharma RK, Jhala Y V., Qureshi Q et al (2010) Evaluating capture-recapture population and
density estimation of tigers in a population with known parameters. Anim Conserv 13:94–
103. doi: 10.1111/j.1469-1795.2009.00305.x
Sharma S, Dutta T, Maldonado JE et al (2013) Forest corridors maintain historical gene flow in a
tiger metapopulation in the highlands of central India. Proc R Soc B 280:20131506. doi:
Shivik JA (2006) Tools for the edge: what’s new for conserving carnivores. Bioscience 56:253–
Shrader AM, Brown JS, Kerley GIH, Kotler BP (2008) Do free-ranging domestic goats show
“landscapes of fear”? Patch use in response to habitat features and predator cues. J Arid
Environ 72:1811–1819. doi: 10.1016/j.jaridenv.2008.05.004
Sinclair ARE, Mduma S, Brashares JS (2003) Patterns of predation in a diverse predator-prey
system. Nature 425:288–290. doi: 10.1038/nature01977.1.
Singh R, Nigam P, Qureshi Q et al (2015) Characterizing human – tiger conflict in and around
Ranthambhore Tiger Reserve , western India. Eur J Wildl Res. doi: 10.1007/s10344-014-
Smith J, McDougal C, Miquelle DG (1989) Scent marking in free ranging tigers, Panthera tigris.
Anim Behav 37:1–10.
Soh YH, Carrasco LR, Miquelle DG et al (2014) Spatial correlates of livestock depredation by
Amur tigers in Hunchun, China: relevance of prey density and implications for protected
area management. Biol Conserv 169:117–127. doi: 10.1016/j.biocon.2013.10.011
Suryawanshi KR, Bhatnagar Y V, Redpath S, Mishra C (2013) People, predators and
perceptions: patterns of livestock depredation by snow leopards and wolves. J Appl Ecol
50:550–560. doi: 10.1111/1365-2664.12061
Treves A, Karanth KU (2003) Human-carnivore conflict and perspectives on carnivore
management worldwide. Conserv Biol 17:1491–1499. doi: 10.1111/j.1523-
Treves A, Martin KA, Wydeven AP, Wiedenhoeft JE (2011) Forecasting environmental hazards
and the application of risk maps to predator attacks on livestock. Bioscience 61:451–458.
doi: 10.1525/bio.2011.61.6.7
Treves A, Wallace RB, Morales A (2006) Co-managing human-wildlife conflicts: a review. Hum
Dimens Wildl 11:383–396. doi: 10.1080/10871200600984265
Valeix M, Fritz H, Loveridge A et al (2009) Does the risk of encountering lions influence
African herbivore behaviour at waterholes? Behav Ecol Sociobiol 63:1483–1494. doi:
Wang SW, Macdonald DW (2006) Livestock predation by carnivores in Jigme Singye
Wangchuck National Park, Bhutan. Biol Conserv 129:558-565. doi:
Wikramanayake ED, Dinerstein E, Robinson JG, et al. (1998) An ecology-based method for
defining priorities for large mammal conservation: the tiger as case study. Conserv Biol
Woodroffe R, Ginsberg JR (1998) Edge effects and the extinction of populations inside protected
areas. Science 280:2126–2128. doi: 10.1126/science.280.5372.2126
Woodroffe R, Thirgood S, Rabinowitz A (2005) People and Wildlife: Conflict or Coexistence?
Cambridge University Press, Cambridge
Yumnam B, Jhala Y V., Qureshi Q, et al. (2014) Prioritizing iger conservation through landscape
genetics and habitat linkages. PLoS One 9:e111207. doi: 10.5061/dryad.c7v41.Funding
Zarco-González MM, Monroy-Vilchis O, Alaníz J (2013) Spatial model of livestock predation
by jaguar and puma in Mexico: conservation planning. Biol Conserv 159:80–87. doi:
... Many households are also involved in raising livestock, collection of non-timber forest products, small-scale industries such as mining and brickwork, subsistence fishing, and the farming of commercial fish in natural and artificial lakes. people in this forest corridor face livestock loss and crop damage due to tigers, leopards, dholes, wild pigs, langurs, spotted deer, and other wildlife (Karanth et al. 2012;Miller et al., 2016;Puri et al. 2020;Srivathsa et al. 2019). ...
... Higher encounter frequencies can lead to higher chances of negative interactions, thus we predicted that those who encountered otters more frequently would have lower attitude scores. Since people living near protected areas (PAs) tend to experience more conflict with wildlife (Karanth et al. 2012;Miller et al. 2016), we expected that those living near Kanha would have lower attitude scores for otters and wildlife in general. We tested this using a multiple linear regression model in which the response variable was the combined attitude score. ...
... They are also more likely to have conflicts with protected area management, leading to more negative perceptions of conservation agencies and wildlife (Dickman 2010). In our study area, people living near Kanha National Park have high chances of facing livestock loss due to tigers and leopards, with 400-600 livestock being reported for financial compensation each year (Karanth et al. 2012;Miller et al., 2016). ...
Full-text available
Otter populations are declining across Asia, especially outside protected areas. In landscapes shared by humans and otters, it is vital to understand the factors affecting human-otter coexistence. We conducted 551 questionnaire surveys and used mixed methods to evaluate human-otter interactions and people’s attitudes towards otters across a 3,300 km² forest-agriculture mosaic near Kanha National Park in Central India. Otter presence was reported from 94% of grid cells, and most (65.7%) respondents reported encountering otters at least once. The majority of interactions (74.9%) were neutral with no impacts on people, 19.4% were negative due to otter-caused damage, and 7.3% were positive. Using a multinomial logistic regression, we found that fishers, especially those who earned a higher percentage of their total income from fishing, were more likely to report negative interactions. Annually, the mean reported monetary loss due to otters (INR 13,145/USD 165) was ~ 10% of the mean income from fishing. Using a multiple linear regression, we found that higher education levels, frequent encounters with otters, and increased distance from protected areas were associated with positive attitudes, while fishing was associated with negative attitudes. Our thematic analysis showed that among those who faced negative impacts due to otters, 20.6% had high levels of tolerance, 29.9% had low levels of tolerance, and the remaining held neutral or ambivalent views. Though we found positive attitudes even amongst those who faced losses, our results highlight the need for targeted mitigation strategies and education programs to to reduce negative impacts and improve coexistence in shared landscapes.
... In the absence of suitable prey rich habitats or availability of easy prey, such interactions often turn negative (Treves and Karanth, 2003;Goodrich, 2010). Large carnivores residing or moving through human-dominated landscapes are widely known to predate on livestock (Madhusudan, 2003;Wilson et al., 2005;Valeix et al., 2012;Miller et al., 2016;Carter and Linnell, 2016), and, to a lesser degree, cause human fatalities and injuries (Gurung et al., 2008;Goodrich, 2010;Dhanwatey et al., 2013;Packer et al., 2019). When left unmanaged, such scenarios may lead to enhanced antagonism between local people and carnivores (Treves and Bruskotter, 2014;Zimmermann et al., 2020), which can lead to retaliatory killings (Woodroffe and Ginsberg, 1998;Inskip and Zimmermann, 2009) and poaching for illegal wildlife trade (Tilson et al., 2010;Moreto, 2019). ...
... Nevertheless, within landscapes where humans and large carnivores have co-occurred for extended periods, more proximate factors may also drive such conflicts (Treves et. al. 2004;Miller et al. 2016). Various environmental factors, such as human density, habitat fragmentation, and availability of prey, water and other resources can all be associated with conflicts (Wilson et al., 2005;Atwood and Breck, 2012;Valeix et al., 2012;Dhanwatey et al., 2013;Packer et al., 2019). ...
Full-text available
Negative interactions between humans and large carnivores like tigers ( Panthera tigris ) and leopards ( Panthera pardus ) are of concern for the conservation of these carnivores, as well as for the health and wellbeing of people who experience such interactions routinely. Such interactions not only lead to human deaths, injuries and loss of domestic animals, but also can result in retaliatory persecution of carnivores and cause their population declines. The Dudhwa-Pilibhit landscape in the northern Indian state of Uttar Pradesh is popularly regarded to be the second most affected hotspot of human-tiger conflicts in the country. Yet, very little information is published on human-tiger conflicts in this landscape. In this study, we recorded 7.4 ± 5.8 (range: 1-20) negative interactions per year between people and tigers that led directly to human deaths and injuries. Although there appeared to be peaks in negative interactions in more recent years, these were found to be statistically non-significant. The peaks in the numbers of cases in 2009-10 and 2016-17 can be attributed to higher numbers of human casualties cause by individual ‘problem’ tigers, and not due to an increase in the number of tigers engaging in conflicts. We used binomial generalized linear modelling to model the risk to humans from being attacked by tigers depending on landscape characteristics. This approach demonstrated that the mosaics of forests and human settlements, especially the presence of agriculture, forest patches and waterbodies, were the predominant factors at play. Notably, higher risks were not mediated by the presence of larger cattle population. Proximity of villages to forests or natural vegetation patches was the most significantly contributing factor identified by the model output. This suggested that despite the prevalent perception of attacks on humans occurring in human-dominated areas of this landscape, areas with forests or other natural vegetation entailed higher risks. This was corroborated by field observations, with most attacks occurring within, or close to, forests or dense vegetation. Based on these findings, we recommend that restricting human movements and taking precautionary measures in high-risk areas can significantly reduce negative interactions with tigers in our study area.
... ± 9.9 per year) than that of a study in Corbett Tiger Reserve, Uttarakhand, India [48]. The reason for the low predation rate in Nepali protected areas can be attributed to the prevalent system of restricted grazing inside the core area, different from the free grazing system in India [46,49,50]. In addition, livestock rearing in communities near the park boundary is related to subsistence livelihood, restricting the number of livestock per household. ...
Full-text available
Human-wildlife conflict is a challenging issue that requires the attention of conservationists worldwide. Habitat fragmentation and encroachment reduce the abundance of prey species, and an increase in the number of predators leads to a higher risk of conflict with large cats such as leopards, jeopardizing conservation efforts. This study explored the spatio-temporal pattern of the human-leopard conflict in Bardia National Park, Nepal, from 2000 to 2020. To analyze the conflict with leopards, we used data (compensation cases filed in the park) from the buffer zone management office, the National Trust for Nature Conservation (NTNC), and the Department of National Park and Wildlife Conservation (DNPWC). Leopard attacks on livestock are increasing exponentially, with 3335 livestock killed in 2652 attacks occurring during the study period. Although livestock depredation by leopards occurred all over the park, the southern cluster has most documented livestock damage (64.01%). The eastern and northern clusters reported fluctuating and dispersed predation events, respectively. Our spatial analysis indicated no effect of topography (slope) on livestock depredation by leopards. We recorded the highest number of leopard attacks and predation during the dry winter season when the nights are longer and livestock remain in their sheds. This carnivore mostly limited its prey to small-sized livestock (95.77%) such as goats, sheep, and pigs, whereas attacks on large-sized (cow and buffalo) livestock were least frequent. Among small-sized livestock, goats are the most predated (66.92%), followed by pigs (20.30%), in all seasons. The escalating human-leopard conflict in BNP is thus a severe threat to conservation efforts as the park has already invested a substantial amount of money (approx. USD 80,000) compensating for livestock lost in leopard attacks over the last two decades. Improving habitat conditions to reduce competition inside the park, developing an insurance scheme for livestock and humans, providing support for upgraded sheds, and the development of practical and feasible strategies that focus on specific animals and clusters of the national park are needed to reduce conflicts to maintain the coexistence between wildlife and human beings.
... The loss of wildlife corridors can lead to mass extinction and escalate the human-wildlife conflict. Some wildlife corridors are invariably important for the long-term survival of wildlife these corridors are "Critical Wildlife corridors" [Miller et al., 2016]. ...
Full-text available
To the rampant rise in urban settlements and human population, the national parks, wildlife sanctuaries, and national tiger reserves of middle and southern India have been intruded upon by human settlements regularly. These are adjoined by paths called as ‘wildlife corridors’ especially the tiger and elephant corridors which are used as a means for migration. Bandipur and Pench-Satpuda national parks have one such essential pocket of wildlife corridors the Bandipur corridor interconnects the population of 8000 plus elephants between Mysuru and Wayanad in southern India whereas the Pench-Satpuda corridor sustains 120 plus tigers between Pench-Satpuda Tiger Reserve in middle India. To assess this it’s imperative to assess the pattern of wildlife movements, changes in the animal habitats in terms of habitat cluster zones, land-use changes, the onset of human settlements and anthropogenic activities are to be monitored. For this, land use land cover (LULC) changes for these corridors were analyzed across two decades using geospatial and remote sensing technique. The study finds a organized deprivation of dense forests and open forests respectively, thus indicating large-scale destruction. The study also found the net area changes of dense forests and open forests which were diverted for agriculture activities indicating extensive encroachment of forest land for human settlement. The classification was monitored for water bodies that have reduced, indicating shrinkage during the duration under research. The existence of substantial coal deposits in the wildlife corridor and operational coal mining in the proximity of the wildlife corridor is a matter of grave concern which has been highlighted in the research. We examine to identify long-term sustenance and protection of such corridors for preserving the natural habitat. Thus, with such suitability in wildlife monitoring, we can mark an increasing need for adaptable, tenable, and secure wildlife management as illustrated under the Sustainable Development Goals (SDG 15 mentioned under United Nations).
... Most of the livestock predation was recorded within a range of 1km from the sanctuary. Miller et al. (2016) also found that the closer a village is to a forest, the greater the likelihood of conflict. ...
... Most of the livestock predation was recorded within a range of 1km from the sanctuary. Miller et al. (2016) also found that the closer a village is to a forest, the greater the likelihood of conflict. ...
Full-text available
The recovery of wild tigers in India and Nepal is a remarkable conservation achievement, but it sets the stage for increased human-wildlife conflict where parks are limited in size and where tigers reside outside reserves. We deployed an innovative technology, the TrailGuard AI camera-alert system, which runs on-the-edge artificial intelligence algorithms to detect tigers and poachers and transmit real-time images to designated authorities responsible for managing prominent tiger landscapes in India. We successfully captured and transmitted the first images of tigers using cameras with embedded AI and detected poachers. Notifications of tiger images were received in real time, approximately 30 seconds from camera trigger to appearing in a smart phone app. We review use cases of this AI-based real-time alert system for managers and local communities and suggest how the system could help monitor tigers and other endangered species, detect poaching, and provide early warnings for human-wildlife conflict.
Full-text available
The study is devoted to the analysis of collections of the felid family , which are housed in natural history museums of Ukraine, two academic and eight university. The basics of the modern taxonomy of felids are considered with the vernacular names of tribes, gen-era and their type species, which is important for the unification of exhibition and catalogue information. For each of the considered museums, four blocks of data are presented: a general summary for the collection, presentation of particularly valuable or unique specimens, and description of the exhibition and scientific collections. The most complete felid collections (in terms of the number of specimens) are housed in the Museum of Nature of Kharkiv University (109 specimens), in the National Museum of Natural History (152 specimens), and in the Zoological Museum of Odesa University (66 specimens); in other collections the amount of material is much smaller (7-36 specimens). The total volume of collections of this family reaches 460 specimens of 21 species of 12 genera (out of 14 living). The examples of materials important for the dissemination of knowledge about this group are given, from folklore to important and interesting scientific facts for the general public. For all species, there is an organised catalogue, classified by tribe, genus, and species (all alphabetically), in which key information about the available specimens is given for each museum. Such information for each species is arranged according to the scheme 'museum, location , number of samples, collection numbers and types of specimens , date, and collector or preparator. ' The present study is the first such generalisation of zoological collections of Ukraine, the experience of which can be extended to other groups of animals. The development of this study showed a high interest of colleagues in such generalisations, which clearly activate the attention of museum workers, researchers of variability and diversity, potential museum visitors and society to the topic of the selected focus groups and activation of exhibiting and research work in relation to those groups. key words felids, taxonomy and nomenclature, collection catalogues, natural history museums, knowledge dissemination.
Full-text available
Carnivore population declines are a time-sensitive global challenge in which mitigating decreasing populations requires alignment of applied practice and research priorities. However, large carnivore conservation is hindered by gaps among research, conservation practice and policy formation. One potential driver of this research–implementation gap is research bias towards charismatic species. Using depredation of livestock by large carnivores in sub-Saharan Africa as a case study, we examined whether taxonomic bias could be detected and explored the potential effects of such a bias on the research–implementation gap. Via a literature review, we compared the central large carnivore species in research to the species identified as the primary livestock depredator. We detected a substantial misalignment between these factors for two species. Spotted hyaenas Crocuta crocuta were the most common depredator of livestock (58.5% of studies), but were described as a central species among only 20.7% of the studies. In comparison, African lions Panthera leo were the most common central species (45% of studies) but were the primary depredator in just 24.4% of studies. Such patterns suggest that taxonomic bias is prevalent within this research. Although spotted hyaenas may depredate livestock most often, their low charisma in comparison to sympatric species such as the African lion and leopard Panthera pardus may be limiting research-informed conservation efforts for them. Efforts to mitigate human-carnivore conflict designed for one species may not be applicable to another co-occurring species, and thus, taxonomic bias could undermine the efficacy of interventions built to reduce livestock depredation by carnivores.
Full-text available
We use the Rajaji-Corbett corridor in the Terai Arc Landscape (TAL) in India to examine the pattern of human-felid conflict in wildlife corridors and its implications for the long-term persistence of tigers (Panthera tigris) and leopards (Panthera pardus) in the landscape. We administerd a questionnaire survey of people residing in and around the corridor and also examined forest department records. Results revealed that leopards caused more frequent losses, whereas tigers caused greater economic losses. Local communities perceived leopards as a bigger threat than tigers, due to the intrusive nature of leopards (i.e., entering villages and houses and carrying off livestock and, in some cases, children). Although people currently are tolerant of wild felids, they are likely to become hostile to them in the future; we discuss specific strategies to resolve the conflicts.
Full-text available
A major challenge in carnivore conservation worldwide is identifying priority human–carnivore conflict sites where mitigation efforts would be most effective. Spatial predation risk modeling recently emerged as a tool for predicting and mapping hotspots of livestock depredation using locations where carnivores attacked livestock in the past. This literature review evaluates the approaches and applications of spatial risk modeling for reducing human–carnivore conflict and presents a workflow to help conservation practitioners use this tool. Over the past decade 18 studies were published, most which examined canid and felid (10 and 8 studies on each group, respectively) depredation on cattle (14) and sheep (12). Studies employed correlation modeling, spatial association and/or spatial interpolation to identify high-risk landscape features, and many (but not all) validated models with independent data. The landscape features associated with carnivore attacks related to the species (carnivore and prey), environment, human infrastructure and management interventions. Risk maps from most studies (14) were used to help livestock owners and managers identify top-priority areas for implementing carnivore deterrents, with some efforts achieving >90 % reductions in attacks. Only one study affected policy, highlighting a gap where risk maps could be useful for more clearly communicating information to assist policymakers with large-scale decisions on conflict. Studies were used to develop a six-step workflow on integrating risk modeling into conservation. This review reveals a need for future predation risk modeling to focus more on validating models, accounting for feedbacks and impacting conflict-related policy in order to reliably improve the mitigation of human–carnivore conflict globally.
Full-text available
Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human–carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator–prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human–carnivore conflict.
Full-text available
Human–wildlife conflict is a significant problem that often results in retaliatory killing of predators. Such conflict is particularly pronounced between humans and tigers Panthera tigris because of fatal attacks by tigers on humans. We investigated the incidence and perception of human–tiger conflict in the buffer zone of Bardia National Park, Nepal, by interviewing 273 local householders and 27 key persons (e.g. representatives of local communities, Park officials). Further information was compiled from the Park's archives. The annual loss of livestock attributable to tigers was 0.26 animals per household, amounting to an annual loss of 2% of livestock. Livestock predation rates were particularly high in areas with low abundance of natural prey. During 1994–2007 12 people were killed and a further four injured in tiger attacks. Nevertheless, local people generally had a positive attitude towards tiger conservation and were willing to tolerate some loss of livestock but not human casualties. This positive attitude indicates the potential for implementation of appropriate conservation measures and we propose mitigation strategies such as education, monetary compensation and monitoring of tigers.
Full-text available
Human–carnivore interactions often influence carnivore conservation and result in mitigating conflicts. We studied human–tiger (Panthera tigris) conflicts in pastoral villages adjacent to Ranthambhore Tiger Reserve (RTR), Rajasthan, India for 6 years (2005–2011) and characterized and examined the causes of conflicts. We recorded 113 human–tiger conflicts. Most of the conflicts between humans and tigers were from attacks on domestic livestock (88.5 %) and humans (11.5 %). Among livestock, cows, bulls, and calves accounted for 31.6, 21.1, and 16.7 %, respectively, of tiger kills followed by buffalos (19.3 %) and goats (11.4 %). Locations of depredations on livestock occurred inside villages (53.4 %), agriculture fields (44.5 %), and forests (1.9 %). We recorded 13 attacks on humans: nine were nonlethal, but four resulted in death. Attacks on humans occurred in agriculture fields (n = 6), forests (n = 5), and within <500 m of villages (n = 2). Attacks on humans and livestock varied seasonally, with the highest conflicts in summer (n = 36) and during the monsoon (n = 42). Factors that may have caused human–tiger conflicts include tiger movements, fragmentation of corridors, and human disturbance. Some of the insurance from compensation for deaths and injury could be used to mitigate conflicts, as has been done with other larger cats to minimize conflicts.
Behavioural factors that are likely to contribute to the coexistence of tiger Panthera tigris, leopard P. pardus and dhole Cuon alpinus, were investigated in the tropical forests of Nagarahole, southern India, during 1986-1992. Examination of predator scats and kills were combined with radiotracking of four tigers, three leopards, and visual observations of a pack of dhole. The three predators selectively killed different prey types in terms of species, size and age-sex classes, facilitating their coexistence through ecological separation. There was no temporal separation of predatory activities between tigers and leopards. Hunting activities of dholes were temporally separated from those of the two felids to some extent. Rate of movement per unit time was higher for leopards compared to tigers during day and night. In general, the activity patterns of predators appeared to be largely related to the activities of their principal prey, rather than to mutual avoidance. The three predator species used the same areas and hunted in similar habitats, although tigers attacked their prey in slightly denser cover than leopards. Both cats attacked their prey close to habitat features that attracted ungulates. There was no evidence for interspecific spatial exclusion among predators, resulting either from habitat specificity or social dominance behaviours. Our results suggest that ecological factors, such as adequate availability of appropriate-sized prey, dense cover and high tree densities may be the primary factors in structuring the predator communities of tropical forests. Behavioural factors such as differential habitat selection or inter-specific social dominance, which are of crucial importance in savanna habitats, might play a relatively minor role in shaping the predator communities of tropical forests.
1. Ecological factors influencing prey selection by tiger Panthera tigris, leopard Panthera pardus and dhole Cuon alpinus were investigated in an intact assemblage of large mammals in the tropical forests of Nagarahole, southern India, between 1986 and 1990. 2. Densities of large herbivores were estimated using line transects, and population structures from area counts. Carnivore diets were determined from analyses of scats (faeces) and kills. Selectivity for prey species was inferred from likelihood ratio tests comparing observed counts of scats to hypothesized scat frequencies generated from prey density estimates using parametric bootstrap simulations. Predator selectivity for size, age, sex and physical condition of prey was estimated using selection indices. 3. Ungulate and primate prey attained a density of 91 animals km-2 and comprised 89-98% of the biomass killed. Predators showed significant (P
For carnivore species, spatial avoidance is one of the evolutionary solutions to coexist in an area, especially if food habits overlap and body sizes tend to coincide. We reviewed the diets of two large cats of similar sizes, the endangered snow leopard (Panthera uncia, 16 studies) and the near-threatened common leopard (Panthera par-dus, 11 studies), in Asia. These cats share ca 10,000 km 2 of their mountainous range, although snow leopards tend to occur at a significantly higher altitude than common leopards, the former being a cold-adapted species of open habitats, whereas the latter is an ecologically flexible one, with a preference for woodland. The spectrum of prey of common leopards was 2.5 times greater than that of snow leopards, with wild prey being the staple for both species. Livestock rarely contributed much to the diet. When the breadth of trophic niches was compared, overlap ranged from 0.83 (weight categories) to one (main food categories). As these leopard species have approximately the same size and comparable food habits, one can predict that competition will arise when they live in sympatry. On mountains, climate change has been elevating the upper forest limit, where both leopard species occur. This means a habitat increase for common leopards and a substantial habitat reduction for snow leopards, whose range is going to be squeezed between the forest and the barren rocky altitudes, with medium-to long-term undesirable effects on the conservation of this endangered cat.