Commensal in conflict: Livestock depredation patterns by free-ranging domestic dogs in the Upper Spiti Landscape, Himachal Pradesh, India

Abstract

In human-populated landscapes worldwide, domestic dogs (Canis lupus familiaris) are the most abundant terrestrial carnivore. Although dogs have been used for the protection of livestock from wild carnivores, they have also been implicated as predators of livestock. We used a combination of methods (field surveys, interview surveys, and data from secondary sources) to examine the patterns and factors driving livestock depredation by free-ranging dogs, as well as economic losses to local communities in a Trans-Himalayan agro-pastoralist landscape in India. Our results show that livestock abundance was a better predictor of depredation in the villages than local dog abundance. Dogs mainly killed small-bodied livestock and sheep were the most selected prey. Dogs were responsible for the majority of livestock losses, with losses being comparable to that by snow leopards. This high level of conflict may disrupt community benefits from conservation programs and potentially undermine the conservation efforts in the region through a range of cascading effects.

Full text

REPORT
Commensal in conflict: Livestock depredation patterns by free-
ranging domestic dogs in the Upper Spiti Landscape, Himachal
Pradesh, India
Chandrima Home , Ranjana Pal, Rishi Kumar Sharma, Kulbhushansingh R. Suryawanshi,
Yash Veer Bhatnagar, Abi Tamim Vanak
Received: 20 May 2016 / Revised: 20 September 2016 / Accepted: 29 November 2016
Abstract In human-populated landscapes worldwide,
domestic dogs (Canis lupus familiaris) are the most
abundant terrestrial carnivore. Although dogs have been
used for the protection of livestock from wild carnivores,
they have also been implicated as predators of livestock.
We used a combination of methods (field surveys,
interview surveys, and data from secondary sources) to
examine the patterns and factors driving livestock
depredation by free-ranging dogs, as well as economic
losses to local communities in a Trans-Himalayan agro-
pastoralist landscape in India. Our results show that
livestock abundance was a better predictor of depredation
in the villages than local dog abundance. Dogs mainly
killed small-bodied livestock and sheep were the most
selected prey. Dogs were responsible for the majority of
livestock losses, with losses being comparable to that by
snow leopards. This high level of conflict may disrupt
community benefits from conservation programs and
potentially undermine the conservation efforts in the
region through a range of cascading effects.
Keywords Canis lupus familiaris Economic loss 
High-altitude desert Human–animal conflict 
Human-subsidized carnivore
INTRODUCTION
Livestock depredation is an important economic and
conservation concern (Clark et al. 1996; Treves and
Karanth 2003). The literature on human–animal conflict
typically focusses on livestock depredation by large car-
nivores such as tigers (Harihar et al. 2014; Miller et al.
2015), wolves (Nie 2001; Karlsson and Sjostrom 2007;
Kaartinen et al. 2009), and bears (Gunther et al. 2004;
Goldstein et al. 2006; Pie
´dallu et al. 2016). However,
misidentification of the carnivores responsible for live-
stock depredation can lead to negative attitudes toward
carnivore conservation and reduce the effectiveness of
conservation programs. The problem is exacerbated when
species such as domestic dogs, which are generally con-
sidered benign (or even beneficial) toward livestock, are
responsible for depredation (e.g., Echegaray and Vila
2010; Caniglia et al. 2013).
Domestic dogs play diverse and complex roles in
human communities (Lescureux and Linnell 2014; Treves
and Bonacic 2016), but there is growing evidence that the
human–dog relationship is not always one of happy
coexistence. Unlike in developed countries, where strict
rules and regulations govern the ownership of dogs, in
most of the developing world, there is only a loose sense
of ownership, and a large proportion of the dog population
is free ranging. Due to their adaptability and key bio-
logical traits such as early sexual maturity, large litter
sizes, and the ability to digest carbohydrates (Moehlman
1989; Axelsson et al. 2013), globally free-ranging dogs
are now the most abundant terrestrial carnivore (Gompper
2014). As mid-sized carnivores, dogs can have varied
impacts on both wild and domestic species that they
interact with. There is now a large body of evidence that
dogs are an important threat to native wildlife globally
(Young et al. 2011; Hughes and Macdonald 2013; Ritchie
et al. 2014; Wierzbowska et al. 2016). However, only a
few studies have assessed their specific role as predators
of livestock (Blair and Townsend 1983; Bouvier and
Electronic supplementary material The online version of this
article (doi:10.1007/s13280-016-0858-6) contains supplementary
material, which is available to authorized users.
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DOI 10.1007/s13280-016-0858-6

Arthur 1995; Bergman and Bender 2009) and their role in
exacerbating human–wildlife conflict (e.g., Echegaray and
Vila 2010).
In this study, we examine the impact of free-ranging
dogs as predators of livestock in the Upper Spiti Landscape
(USL) of the Trans-Himalayan region of India. India has
among the highest population of free-ranging dogs in the
world [ca. 59 million (Gompper 2014)] as well as a large
livestock population [ca. 512 million (www.dahd.nic.in,
accessed on 12–09–2014)]. The study area is a part of the
Tibetan plateau and supports traditional pastoralism and
agro-pastoralism (Handa 1994; Bishop 1998) along with a
unique assemblage of wild herbivores and carnivores
(Mishra 1997). The last two decades have seen a rapid
increase in tourism and related infrastructure in the Spiti
valley. However, in an otherwise resource-poor environ-
ment, the absence of a proper garbage disposal system has
provided a boost of resources for the free-ranging dog
population. A consequential increase in the dog population
has resulted in unwanted interactions with both people and
wildlife (Hennelly et al. 2015; Kumar and Paliwal 2015;
Ghoshal et al. 2016). Due to low resource availability in the
lean tourist season, the dogs have started to prey on live-
stock as well as wildlife. A recent study on human–wildlife
conflict in the same landscape found that free-ranging
domestic dogs killed more livestock than snow leopard
(Panthera uncia) and Tibetan wolf (Canis lupus chanco)
(Suryawanshi et al. 2013).
Our study examines the patterns of livestock depreda-
tion by free-ranging dogs to understand the drivers of this
unique conservation and livelihood challenge. Although
prey abundance plays a crucial role in determining predator
responses (Korpimaki and Norrdahl 1991; Wellenreuther
2002; Karanth et al. 2004), the effect of anthropogenic
influences on predators cannot be neglected (Bino et al.
2010; Rodewald et al. 2011). For domestic dogs, abun-
dance is largely determined by anthropogenic subsidies,
which may be in the form of direct feeding by humans, or
access to garbage or livestock. Because of the close link-
ages between the presence of dogs and humans, the factors
normally associated with livestock depredation by wild
predators may not be as influential for dogs. For example,
rapid urbanization affects predator abundance through
bottom-up forces in the form of anthropogenic subsidies
giving rise to the predation paradox, i.e., predator numbers
increase but predation rates decrease (Fischer et al. 2012).
On the other hand, a study on dog predation patterns in
Poland has shown that predation rates on livestock and
wildlife are correlated with local dog abundance (Wierz-
bowska et al. 2016). Predation rates for predator–naı
¨ve
prey such as livestock may even increase with increasing
predator densities due to the lack of anti-predatory
behavior, unlike wild predator–prey systems (Abrams
1993). Thus, for commensal predators like the domestic
dog, the question remains as to whether depredation rates
are dependent on predator abundance (already determined
by bottom-up forces) or on prey abundance (wildlife and/or
livestock)?
Our objectives were to test two alternate hypotheses
(a) that livestock depredation patterns were governed pri-
marily by the size of the resident dog population (predator
abundance hypothesis) or (b) that depredations were a
numeric effect of the livestock population, irrespective of
the local dog abundance (prey abundance hypothesis). We
also determined the village-wise, seasonal, and species-
wise patterns of livestock depredation by dogs, and the
economic losses suffered by the agro-pastoralist commu-
nity due to depredation.
MATERIALS AND METHODS
Study area
The USL is a subdivision of Lahaul & Spiti district of
Himachal Pradesh (Fig. 1). The area comes under the rain
shadow of the Pir Panjal range of the Himalaya and is
characterized in the winter by extreme cold (*-40 °Cin
peak winter) and dry conditions with precipitation mainly
in the form of snow. Summer temperatures typically range
from 4 to 30 °C, and the predominant vegetation type is
‘alpine scrub’ or ‘dry alpine steppe’ (Champion and Seth
1968). Large mammalian fauna include blue sheep (Pseu-
dois nayaur), ibex (Capra sibirica), and their predators
such as the snow leopard (Panthera uncia) and the Tibetan
wolf (Canis lupus chanco).
The landscape has the lowest densities of humans in the
country (2 person km
-2
)(www.census2011.co.in) and
comprises agro-pastoralists. The livestock assemblage
includes sheep, goat, donkey, cattle, yak, cattle–yak
hybrids (dzo, dzomo), and horse. Livestock are grazed in
pastures except during peak winter, when they are stall-fed.
Based on herding practices, we classified livestock as
large-bodied free-ranging (yaks and horses) and medium-
to small-bodied herded (cattle, donkeys, cow–yak hybrids,
goats, and sheep) (Suryawanshi et al. 2013,2014). The
landscape experienced a major change in its agro-economy
with the introduction of green pea in the early 1980s,
causing a shift from a subsistence/barter-based system to a
market-driven one (Mishra et al. 2003a). Tourism infras-
tructure has expanded over the years and is an important
source of income for the local communities. While tourism
has benefitted local people, the associated increase in
garbage has resulted in large stable populations of dogs in
the town of Kaza and in the largest village of Rangrik (Pal
2013).
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Field methods
Estimating dog populations
We carried out photographic capture–recapture surveys
(Goswami et al. 2007; Sharma and Jhala 2011) to assess
dog population size in 25 villages across the landscape
from April to June 2013 (Pal 2013). We used a three-day
sampling effort for dogs in 23 of 25 villages, and a one-day
time-constrained sampling effort for the remaining two
smaller hamlets (Gete and Tashigang =7 households
each). For all villages, we walked along all the roads and
trails in and around the villages, and photographed every
dog that was seen. For the 23 villages, we repeated this
exercise over the next 2 days as temporal replicates to
determine detection probability in the capture–recapture
analysis framework (Pollock et al. 1990). Each dog was
identified based on their distinct natural color, sex, and
other marks (such as notched ears or scars).
Quantifying predictor variables
To obtain a measure of food availability, we quantified the
number and total area of garbage dumps in each village. At
each dump, we measured the dimensions (length and
breadth) and summed across all dumps in the village to
determine the total area of garbage/village. We calculated
the distance between each village and the two areas of high
dog source populations (Kaza or Rangrik) to determine the
impact of distance on livestock depredation rates. We used
a least-cost method rather than simple Euclidean distance
measures due to the highly rugged terrain. We derived
Terrain Ruggedness Index (Riley et al. 1999) from a
30 m 930 m digital elevation model (Aster Global Digital
Himachal Pradesh
Jammu and Kashmir
Tibe t
Pin Valley National Park
Kinnaur
0’0"E
0’0"E
40’0"N
40’0"N
30’0"N
30’0"N
20’0"N
20’0"N
10’0"N
10’0"N
0’0"N
0 9 18 27 364.5
Kilometer s
Digital Elevation Model
in m
High : 6467
Low : 3433
Villages
Drainage
State Boundary
Lahaul and Spiti
°
°
°
°
°
°
°
°
°
°
°
Fig. 1 Map of the study area showing the sampled villages in the Upper Spiti Landscape. The map inset shows the location of the study area in
the state of Himachal Pradesh, India. Map Courtesy: Ecoinformatics Centre, Ashoka Trust for Research in Ecology and Environment and Nature
Conservation Foundation
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Elevation Model) using Quantum GIS 2.8.2 (QGIS
Development Team 2014). The ruggedness index was used
to prepare cost surfaces and a least-cost distance measure
between villages was calculated using GRASS GIS
(GRASS Development Team 2015). Livestock data for all
the villages (except Kaza) were obtained through targeted
herder surveys. Livestock data for Kaza were obtained
from the Department of Animal Husbandry and data for the
number of households in each village were obtained from
District Magistrate headquarters in Kaza.
Livestock herding and depredation
Interview surveys were conducted in all 25 villages to
collect data on livestock depredation by dogs in the pre-
vious year (2012–2013). Due to logistic and time con-
straints, we performed a convenience sampling (Robinson
2014) where the questionnaire surveys targeted 15–40 % of
the total households in the villages. One adult was inter-
viewed in each household with their consent. The ques-
tionnaire was administered in Hindi (by RP) and/or Spitian
language (by Kesang Chunit, a local field assistant) when
required. Data were collected for each head of livestock
that was reportedly killed by dogs, with specific informa-
tion on kill location (whether corral/agricultural field/pas-
ture/inside village), month, time (morning/evening), sex
and age of livestock, and herder attendance (presence/ab-
sence). We also obtained a detailed account of herding
practices in each village (collected with the help of expe-
rienced herders). At the end of the year (Jan–Feb 2014),
key and prominent herders were interviewed (CH and
Kesang Chunit) to collect information on village-level
livestock mortality for the year 2013. Herders confirmed
predator identities through direct sightings or signs around
or on the kill. Since they needed to report livestock losses
and causes of death to the owner for compensation pay-
ments (if livestock were lost to wild carnivores), we
expected that their records would be fairly accurate. The
study area has also been the focus of intense research and
conservation efforts for the last two decades and the col-
lection of village-level livestock mortality data has been an
annual exercise since 2009. The research field staff also
verified herder accounts based on occasional field visits.
Analytical methods
Dog population estimates
Individual capture history for the identified dogs was
constructed using a standard ‘‘X-matrix format’’ (Otis et al.
1978) and the program CAPTURE was used to analyze the
capture history data. Population closure was tested using
the ‘‘Close Test’’ program (Stanley and Burnham 1999).
CAPTURE produces abundance estimates from seven
different models that differ in their assumptions about
capture probability, where models assume no variability
(M
o
), or assume differences in capture probabilities due to
heterogeneity/individual variation (M
h
), behavior (M
b
), and
time (M
t
). Pairwise combinations of model assumptions
(M
bh
,M
th
,M
tb
) were also generated. Population estimates
of dogs obtained from this analysis were used as a pre-
dictive variable in determining factors of livestock
depredation.
Correlates of livestock depredation
We summarized the information on livestock depredation
by dogs generated from interviews using standard
descriptive statistics. We used compositional analysis to
determine selectivity (use vs available) of livestock prey by
dogs (Aebischer et al. 1993) where
Availability ¼Number of each livestock type/Total livestock
Use ¼Number killed by dogs in each livestock type=
Total livestock killed by dogs:
To identify the influence of potential determinants on
the number of livestock lost to dogs, we fitted generalized
linear models using a Poisson distribution. Our response
variable was the number of livestock lost to dogs in every
village, while the explanatory variables were the number of
households and livestock abundance (for the prey
abundance hypothesis), and garbage area, dog population,
and distance to high-dog population centers (for predator
abundance hypothesis). We considered the total number of
livestock, except for adults of large-bodied livestock, as
potential available prey for dogs.
We tested for multicollinearity in the dataset and found
that dog population estimates, garbage area, and the num-
ber of households were correlated. Among the three, we
opted for dog population estimate and garbage area as
important variables but made sure that these two variables
were not used in combination within the GLM models. As
the overdispersion parameter was greater than one, we
fitted negative binomial models to the data. For each
model, we used additive combinations of the explanatory
variables in a nested manner. To prevent over-parameter-
ization of the model, only meaningful interactions between
explanatory variables were considered. We included an
interaction term between dog population estimate and
distance to high-dog population centers. All the variables
were scaled to mean for GLM analysis. Akaike information
criterion corrected for a small sample size (AIC
c
) was used
to assess model weights and the models were ranked using
DAIC
c
. We used Akaike weights (AIC
c
w
i
) to determine
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the relative support for a model and accounted for model
selection uncertainty by averaging the estimates of the
coefficients of main effect variables across all models
(Burnham and Anderson 2002).
Quantifying economic losses
Finally, we summarized the livestock losses incurred due to
dogs, wild carnivores, and disease for the year 2013–2014
and calculated their associated financial losses. We used
the average annual sale price for the different livestock
types (based on age and sex) obtained from market sources.
Financial loss was quantified in Indian rupees, which was
then converted to USD (@ $1 =68 INR in 2015) to enable
comparison with other studies. All statistical analyses were
performed using the R programming environment (version
3.1.1) (R Core Team 2015).
RESULTS
Dog population estimates
For two hamlets, the time-constrained sampling resulted in
a count of one dog from one village and no dogs for the
other. We obtained sufficient data to conduct a mark–re-
capture analysis for 12 out of the 23 villages sampled. For
the remaining 11 villages, due to the low number of dogs
seen, we used the naı
¨ve estimate (not accounting for
detection probability) of the total number of uniquely
identifiable dogs as the minimum population (Table S1).
We estimated 541 (±59.03 SE) dogs across 12 villages
with the township of Kaza and the largest village (Rangrik)
accounting for 74 % of the total dog population (Table S2).
We recorded an additional 29 dogs in the remaining vil-
lages resulting in a minimum population size of *570
dogs.
Patterns of dog depredation and herding practices
We recorded 238 livestock mortalities by dogs between Jan
2012 and May 2013 across 287 respondents interviewed in
25 villages. Sheep and goats comprised 80 % of the kills.
Most predation events occurred during the day (62 %
compared to evening) and in pastures (40 %) or agricultural
fields (35 %) compared to corrals or inside villages. Fifty-
seven percent of predation events occurred in the absence
of the herders. About 46 % of the livestock losses took
place during autumn followed by spring (24 %), summer
(18 %), and winter (12 %). During spring and summer,
most of the depredation by dogs occurred in the pastures,
while during autumn most of the depredation took place in
the agricultural fields (Fig. 2).
Dogs did not use the available livestock species at
random but selected certain kinds of livestock as prey
(Compositional analysis, k=0.102, P=0.002). Sheep
were the most selected prey, followed by donkeys and
goats based on availability (Fig. 3). Calves and adults of
larger-sized livestock (yak, horses, dzomo, and cow) were
not preferred (losses of large livestock comprised 5 % of
the total losses due to dogs).
Herding patterns in the landscape varied according to
season and agricultural activities (Table 1). Medium- (cow,
cow–yak hybrids, and donkeys) and small-bodied livestock
(sheep, goat) were usually accompanied by herders to the
pastures. They were however left unattended in the fields to
forage on crop residue in autumn. Large-bodied livestock
(yak and horses) were free ranging and were only brought
back to villages during peak winters. In other times of the
year, these animals were brought to villages for short
Fig. 2 The seasonal patterns and location of livestock loss show that
more livestock were killed in autumn compared to the other seasons,
and more livestock were killed in pastures, except in autumn where
they were more targeted in the agricultural fields
Fig. 3 Proportion of livestock available and proportion being used by
dogs (in terms of depredation) in the Upper Spiti Landscape
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durations for specific purposes (i.e., yaks for ploughing the
fields and horses for cultural events).
Determinants of livestock depredation
Our initial analysis (n
vill
=25) indicated that livestock
abundance had a positive influence on the magnitude of
livestock depredation (b
livestock
=0.53, SE =0.28), while
garbage and dog population had a negative influence
(b
garbage
=-1.06, SE =0.54; b
dogpop
=-0.42, SE =0.37).
Distance to high-dog population centers had an indeterminate
influence (b
dist
=0.03, SE =0.3). Garbage area was an
important variable explaining depredation followed by live-
stock abundance (Table 2). However, we decided to exclude
the township of Kaza and the largest village (Rangrik) from
the GLM analysis since they had the largest areas under gar-
bage and, importantly, no small-bodied livestock for the last
10 years.
After excluding Kaza and Rangrik from the analysis, the
multicollinearity between dog population and garbage was
weak (VIF\2) for the remaining 23 villages, and hence we
used these variables in combination with other variables to
predict livestock depredation levels. The best model
explaining livestock depredation by dogs included only live-
stock abundance followed by the model including livestock
and garbage as additive terms. Two closely related models
(within 2 DAIC
c
) included one with only garbage, followed by
a model with local dog population in combination with live-
stock and garbage (Table 2). The model-averaged bestimates
indicated that livestock abundance, local dog population, and
distance to high-dog population centers had a positive influ-
ence on patterns of livestock depredation (b
livestock
=0.58,
SE =0.3; b
dogpop
=0.31, SE =0.29; b
dist
=0.11,
SE =0.32) (Fig. 4a, b, d), while garbage had a negative
influence (b
garbage
=-0.61, SE =0.39) (Fig. 4c). However,
both local dog populations and distance to high-dog popula-
tion center had high variability around the estimates and were
therefore poor predictors.
Financial losses
We recorded a total of 441 cases of livestock losses for the
year 2013 across 29 villages within the landscape. Depre-
dation removed 4.5 % of the total livestock population,
followed by disease (1 %). Depredation by both dogs and
native carnivores accounted for 340 livestock losses (77 %)
followed by disease (18 %) and unknown factors (5 %).
Dogs contributed to the majority of livestock losses
(63.5 %) followed by snow leopards (28.5 %) and wolves
(8 %). The total value of livestock losses reported for one
year due to depredation and diseases was USD 46 662.
Dogs were the main cause of the economic losses (USD
17 522), followed by snow leopards (USD 15 029), disease
(USD 11 846), and wolves (USD 2265). The average
economic loss/household/year was USD 54 and 40 % this
loss could be attributed to dogs alone.
DISCUSSION
Our study demonstrates that dogs can take a heavy toll on
domestic herbivores, potentially having a major impact on
the livelihoods of marginal communities. The majority of
kills by dogs were of small-bodied livestock (sheep and
goat), and sheep was the most selected prey. Dogs also
killed medium-bodied livestock such as donkeys in greater
proportion than their availability. Our analysis provides
support for the prey abundance hypothesis, because of the
Table 1 Livestock herding patterns across seasons in the Upper Spiti Landscape by the resident agro-pastoral community
Jan–Feb–Mar Apr–May–Jun Jul–Aug–Sep Oct–Nov–Dec
Agricultural activity No agriculture
Peak winter
Cropping season Cropping ?harvest (Aug–Sep) Post-harvest grain
processing
Livestock types
Small (sheep
and goat)
Inside corral within
house (Feb–Mar)
In pastures during day. Inside
corral at night outside the house.
One herder ‘‘riyok’’ particularly
for sheep and goat along with
people accompanying from the
village
In pastures till about mid-August.
After harvest in September, feed
on stubble in agricultural fields
for manure input. Herded in
corrals at night outside the
house
Herded in pastures
from Nov till peak
winter but within
ca. 2 km radius of
the village
Medium (donkey,
cow, cow–yak
hybrids)
Inside corral within
house (Feb–Mar)
In pastures during day. Inside
corral at night outside the house.
In pastures and sometimes around
agricultural fields. Herded in
corrals at night outside the
house
In pastures within
ca. 2 km range of
village. Herded in
corral at night
outside the house
Large (yak, horse) Inside corral (Feb–
Mar)
In pastures. Not herded In pastures. Not herded In pastures. Not
herded
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Table 2 Generalized linear models to identify variables influencing livestock depredation by dogs with associated number of parameters (K),
Akaike information criterion values corrected for sample sizes AIC
c
,DAIC
c
, AIC
c
weights (AIC
c
w
i
), cumulative weights, and log likelihood
(LL). Models \4DAIC
c
are shown. In the analyses for 23 villages, the villages of Kaza and Rangrik (having large garbage dumps, but no small-
bodied livestock) have been dropped
Models KAIC
c
DAIC
c
AIC
c
w
i
Cum.wt LL
25 villages
Garbage ?livestock 4 153.43 0.00 0.29 0.29 -71.71
Garbage 3 154.48 1.05 0.17 0.46 -73.67
Dogpop ?livestock ?dist ?dogpop 9dist 6 154.96 1.53 0.13 0.60 -69.15
Livestock 3 155.19 1.76 0.12 0.72 -74.02
Garbage ?livestock ?dist 5 156.58 3.15 0.06 0.78 -71.71
Dogpop 3 157.04 3.61 0.05 0.82 -74.95
Garbage ?dist 4 157.31 3.88 0.04 0.86 -73.65
23 villages
Livestock 3 144.72 0.00 0.22 0.22 -68.73
Livestock ?garbage 4 144.92 0.19 0.20 0.41 -67.35
Garbage 3 146.58 1.85 0.09 0.50 -69.66
Dogpop ?livestock 4 146.58 1.86 0.09 0.58 -68.18
Dogpop ?livestock ?garbage 5 147.28 2.56 0.06 0.64 -66.88
Dogpop 3 147.33 2.61 0.06 0.70 -70.04
Livestock ?dist 4 147.39 2.67 0.06 0.76 -68.59
Dist 3 147.67 2.94 0.05 0.81 -70.20
Livestock ?garbage ?dist 5 148.03 3.30 0.04 0.85 -67.25
Dogpop ?livestock ?dist ?dogpop 9dist 6 148.49 3.77 0.03 0.88 -65.62
garbage area under garbage in each village, livestock abundance of small- and medium-bodied livestock for each village, dogpop dog population
estimate in each village, dist distance to high dog source
Fig. 4 a The number of livestock lost per village has a stronger relationship with the total population of livestock in that village. bThere is,
however, a weak positive relationship with the size of the local dog populations across villages. cThe number of livestock lost per village is
weakly negatively correlated to garbage area. dHowever, there is no relationship between livestock loss per village and distance from high-dog
population area (Kaza or Rangrik)
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stronger predictive power of livestock abundance rather
than predator abundance in explaining predation rates.
These results contrast with Wierzbowska et al. (2016), who
showed that livestock depredation was better predicted by
local dog abundances rather than prey abundance. These
differences in patterns could in part be explained by prey
naivety as well as predator familiarity. Where livestock
depredation by dogs is a recent phenomenon (such as in our
study area), livestock may not display anti-predator
behaviors to dogs as they would to wild predators such as
wolves (Laporte et al. 2010). Even for wolves, the factors
explaining depredation patterns of wild prey versus live-
stock are different. For example, wolf density was a better
predictor of kill rates of wild herbivores (Vucetich et al.
2002), whereas overall livestock losses were correlated
with the size of the flock per farm (Iliopoulos et al. 2009).
Indeed, in some cases other ecological factors such as
ruggedness of terrain used by herded livestock contributed
more to depredation by wolves than herd size of small-
bodied livestock (Suryawanshi et al. 2013). Thus, for naı
¨ve
prey such as livestock, novel predators such as dogs may
not be perceived as a danger, even though the numerical
impact of depredation is similar to wild predators.
Our expectation that distance from high-dog population
centers would influence livestock depredation was not
supported in the analysis. In Kaza and Rangrik, the human-
generated organic material is disposed as waste, whereas in
most of the other villages the daily organic waste generated
is utilized for fodder or composted for agriculture. The
high resources in these large villages have resulted in the
highest local dog abundances. The spillover populations
from Kaza and Rangrik have moved to villages where there
are more small-bodied livestock (Anonymous 2011). The
small populations of dogs in these villages seem to be
maintained by livestock carcasses during peak winters.
Winter is the parturition season for many livestock species
and as per local reports there is very high mortality of
young animals which provides an easy resource for dogs in
this season. Additionally, our mark–recapture surveys
showed that dogs are moving between villages and it is
therefore highly likely that only a subset of the dog pop-
ulation may be responsible for most of the livestock
depredation. If so, this would explain the weak effect of
local dog population size in explaining depredation and the
lack of support for the predator abundance hypothesis.
Small-bodied livestock are most susceptible to depre-
dation by dogs and are killed both in the presence and in
the absence of herders. In the post-harvest (Sept–Oct)
season, small-bodied livestock were killed when left
unattended in agricultural fields, and in spring and summer
(Apr–Aug) most of the depredations occurred in the pas-
tures where they were herded. Although speculative, we
feel that the herding practices of the villages may also
contribute to the patterns of depredation seen here. Our
observations in recent years suggests that the use of
migrant labor, especially children, for herding livestock has
increased in the Trans-Himalaya, which has possibly
caused a decline in the vigilance levels in herding. Local
villagers, as well as key herders, reported observing the
presence of large numbers of dogs in packs (ranged from 8
to 15 dogs) in the pastures intermittently across the sea-
sons. Dogs were also reported to be in the pastures con-
tinuously for 3–5 days, which is when they are likely to
have preyed on livestock. The herders also expressed their
concern for protecting and managing herds in the presence
of large packs of dogs during such events.
Depredation by free-ranging dogs was not only
responsible for a majority of the livestock losses but also
contributed substantially to the average economic
loss/household/year. Economically, losses due to dogs and
snow leopards were almost comparable. This is because
dogs killed more smaller-bodied livestock species, while
snow leopards killed fewer but more expensive large-
bodied livestock (yak and horses). We also believe that
some livestock deaths that were attributed to wolves may
instead be due to dogs as wolves are rare, and there is a
strong negative perception of wolves in the landscape
(Suryawanshi et al. 2013,2014). As a result, it is likely that
actual levels of dog depredation are somewhat higher than
the levels that we estimated from village surveys. These
high rates of depredation by free-ranging dogs can disrupt
community benefits from conservation programs such as
livestock insurance programs. In such programs, a pre-
mium is collected from owners, based on livestock size and
risks associated with loss to wild carnivores, and con-
tributes to a communal fund to offset depredation costs
(Mishra et al. 2003b). Compensation is generally paid only
for insured livestock killed by wild carnivores and not by
dogs. For medium- and lower-income households who are
already suffering a substantial economic burden from dog
attacks, any further loss from wild carnivores is unlikely to
be tolerated, potentially resulting in poor conservation
outcomes.
Patterns of livestock composition have also been
changing across the landscape over time. Many villages
along the Spiti river valley have reduced their small live-
stock holding due to increased access to development as
well as dog depredation over the years (Fig. 5). However,
some of the villages higher up in the mountains (Lalung,
Kibber, Langza, Chicham, Demul, Hikkim) that still
maintain small-bodied livestock are facing continued pre-
dation pressures by dogs. Except for three villages (Demul,
Lalung, and Kibber) where there has been an increase in
small stock in the last 5 years, most villages have witnessed
reduction in their small stock and in one of the villages
(Gete), people have stopped keeping sheep and goats since
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123 ÓRoyal Swedish Academy of Sciences 2016
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2013. Although dogs did not select larger-bodied livestock
as prey in our study, there is more recent evidence that they
are killing calves of larger-bodied livestock (CH personal
observations and communication with herders during Dec
2014–Jan 2015). This trend may increase in the future due
to the reduction in small-bodied livestock population
across the landscape (Anonymous 2011).
Dogs not only targeted livestock but also preyed on
wildlife, as reported widely (Young et al. 2011; Ritchie
et al. 2014). During the winter of 2013 (Jan-March), we
received nine independent eye-witness reports of blue
sheep death by dogs (Pal 2013). Locals also observed dogs
chasing ibex herds and preying on woolly hare (Lepus
oiostolus). The shepherd dogs maintained by the migratory
Gaddi pastoralists were also observed to hunt Himalayan
marmots (Marmota himalayana) in the nearby Chandra Tal
area (high-altitude lake and a Ramsar site). This suggests
that there could be more widespread impacts of domestic
dogs on the alpine ecosystems.
CONCLUSION
The emergence of domestic dogs as one of the main threats
to livestock is an outcome of improved financial opportu-
nities in the landscape through tourism and unplanned
infrastructure development. With dogs being responsible
for much of the livestock losses in the landscape, this could
have a disruptive effect on existing conservation efforts,
primarily the livestock insurance program, especially if
predation on calves and foals increases (Mishra et al.
2003b). Current programs to manage garbage and control
dog populations by the Capture–Neuter–Release method
are likely to take several years of sustained effort before
any significant reduction is seen in the dog population
(Totton et al. 2010). It is therefore imperative that dog
populations be reduced and controlled through a combi-
nation of sustained management regimes such as reducing
food subsidies through garbage management, removing un-
owned dogs, and focusing on responsible dog ownership.
In the interim, considering that it is likely that only a subset
of the dog population engages in livestock depredation, a
targeted and consistent effort should be made to capture
and remove dogs that are known to predate on livestock,
thus providing an immediate relief to livestock owners. In
the long term, improving herding practices for the resident
agro-pastoralist communities could help in mitigating los-
ses not only from dogs, but also from wild predators.
Acknowledgements Financial support for this study was provided
through the International Foundation for Science grant to CH. We
would like to thank the Himachal Pradesh Forest Department, par-
ticularly the Divisional Forest Officer, Kaza, Shri Rajesh Sharma and
Range Officer, Kaza, Shri Devender Singh Chauhan for their logistic
support. We would like to thank the Animal Husbandry Department,
Kaza, for facilitating secondary data collection. CH would like to
thank Charudutt Mishra, NCF for helping in conceptualizing the
paper and Ajay Bijoor, NCF for overall logistic support in field. We
thank Maria Thaker for providing useful comments on the manu-
script. We are thankful to the entire NCF field crew in Kibber: Chunit
Kesang, Tanzin Thinley, Tanzin Thuktan, Rinchen Tobgye, Lobzang
Gyalson, Kalzang Gurmet, Chudim Dorje, Sherup, Lama Tenzing,
Tashi Gonpo, Takpa Tanzin, field crew from Lalung village, and the
reserve guards of Chicham and Lossar village for immense support
during fieldwork. We would like to thank the respondents and herders
Fig. 5 Change in small-bodied livestock and large-bodied livestock for 19 villages from 2010 to 2015 and the cumulative damage caused by
dogs
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for their support and participation in data collection. Finally, we thank
the three anonymous reviewers whose suggestions have improved the
quality of the manuscript.
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AUTHOR BIOGRAPHIES
Chandrima Home (&) is a Doctoral Candidate at the Ashoka Trust
for Research in Ecology and the Environment and Manipal Univer-
sity. She is interested in the ecology of mesocarnivores in human-
altered landscapes and human dimensions of ecology.
Address: Ashoka Trust for Research in Ecology and the Environment,
Royal Enclave, Sriramapura, P.O. Jakkur, Bangalore 560 064, India.
Address: Manipal University, Madhav Nagar, Manipal 576 104,
India.
e-mail: chandrima.home@atree.org
Ranjana Pal is a Researcher in the National Mission for Sustaining
Himalayan Ecosystem (NMSHE) Program at the Wildlife Institute of
India. She is interested in population ecology, landscape ecology, and
human dimensions in wildlife management.
Address: Wildlife Institute of India, Post Box # 18, Chandrabani,
Dehradun 248 001, Uttarakhand, India.
e-mail: ranjana.biocon@gmail.com
Rishi Kumar Sharma is a Conservation Biologist at World Wildlife
Fund (WWF). His research interests include applied ecology and
human dimensions of conservation.
Address: World Wildlife Fund, Pirojsha Godrej Building, 172 B
Lodhi Estate, New Delhi 110 003, India.
e-mail: rishi.eco@gmail.com
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ÓRoyal Swedish Academy of Sciences 2016
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Kulbhushansingh R. Suryawanshi is a Scientist at the Nature
Conservation Foundation, Mysore, and the Snow Leopard Trust,
Seattle. His primary research interests include population ecology and
human–wildlife interactions.
Address: Nature Conservation Foundation, 3076/5 IV Cross, Goku-
lam Park, Mysore 570002, India.
Address: Snow Leopard Trust, 4649 Sunnyside Ave N #325, Seattle,
WA 98103, USA.
e-mail: kulbhushan@ncf-india.org
Yash Veer Bhatnagar is a Senior Scientist at the Nature Conserva-
tion Foundation, Mysore, and the Snow Leopard Trust, Seattle. He is
interested in integrated conservation of mountain ecosystems and is
involved with participatory landscape-level management planning for
conservation of such areas.
Address: Nature Conservation Foundation, 3076/5 IV Cross, Goku-
lam Park, Mysore 570002, India.
Address: Snow Leopard Trust, 4649 Sunnyside Ave N #325, Seattle,
WA 98103, USA.
e-mail: yash@ncf-india.org
Abi Tamim Vanak is an Associate Professor (Fellow) at the Ashoka
Trust for Research in Ecology and the Environment, Bangalore, a
Wellcome Trust/DBT India Alliance Intermediate Fellow, and an
Honorary Research Associate at the School of Life Sciences,
University of KwaZulu-Natal, South Africa. His research interests
include animal movement ecology, disease ecology, and ecology of
human-subsidized carnivores.
Address: Ashoka Trust for Research in Ecology and the Environment,
Royal Enclave, Sriramapura, P.O. Jakkur, Bangalore 560 064, India.
Address: School of Life Sciences, University of KwaZulu-Natal,
Westville, King George V Ave, Durban 4041, South Africa.
e-mail: avanak@atree.org
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123 ÓRoyal Swedish Academy of Sciences 2016
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