Upload
hathu
View
215
Download
0
Embed Size (px)
Citation preview
People, predators and perceptions: patterns of
livestock depredation by snow leopards and wolves
Kulbhushansingh R. Suryawanshi1,2*, Yash Veer Bhatnagar1,2, Stephen Redpath3 and
Charudutt Mishra1,2
1Nature Conservation Foundation, 3076/5, IV Cross Gokulam Park, Mysore 570002, India; 2Snow Leopard Trust,
4649 Sunnyside Av. North, Suite 325, Seattle, WA 98103, USA; and 3Aberdeen Centre for Environmental
Sustainability, Macaulay Institute and University of Aberdeen, Craigiebuckler, Aberdeen AB15 8QH, UK
Summary
1. Livestock depredation by large carnivores is an important conservation and economic con-
cern and conservation management would benefit from a better understanding of spatial vari-
ation and underlying causes of depredation events. Focusing on the endangered snow leopard
Panthera uncia and the wolf Canis lupus, we identify the ecological factors that predispose
areas within a landscape to livestock depredation. We also examine the potential mismatch
between reality and human perceptions of livestock depredation by these carnivores whose
survival is threatened due to persecution by pastoralists.
2. We assessed the distribution of the snow leopard, wolf and wild ungulate prey through field
surveys in the 4000 km2 Upper Spiti Landscape of trans-Himalayan India. We interviewed local
people in all 25 villages to assess the distribution of livestock and peoples’ perceptions of the risk
to livestock from these carnivores. We monitored village-level livestock mortality over a 2-year
period to assess the actual level of livestock depredation. We quantified several possibly influen-
tial independent variables that together captured variation in topography, carnivore abundance
and abundance and other attributes of livestock. We identified the key variables influencing live-
stock depredation using multiple logistic regressions and hierarchical partitioning.
3. Our results revealed notable differences in livestock selectivity and ecological correlates of
livestock depredation – both perceived and actual – by snow leopards and wolves. Stocking
density of large-bodied free-ranging livestock (yaks and horses) best explained people’s threat
perception of livestock depredation by snow leopards, while actual livestock depredation was
explained by the relative abundance of snow leopards and wild prey. In the case of wolves,
peoples’ perception was best explained by abundance of wolves, while actual depredation by
wolves was explained by habitat structure.
4. Synthesis and applications. Our results show that (i) human perceptions can be at odds
with actual patterns of livestock depredation, (ii) increases in wild prey populations will inten-
sify livestock depredation by snow leopards, and prey recovery programmes must be accom-
panied by measures to protect livestock, (iii) compensation or insurance programmes should
target large-bodied livestock in snow leopard habitats and (iv) sustained awareness
programmes are much needed, especially for the wolf.
Key-words: Canis lupus, Capra ibex, human–wildlife conflict, large carnivores, Panthera
uncia, Pseudois nayaur, trans-Himalaya
Introduction
Livestock depredation by large carnivores and their retal-
iatory or preventive killing is a world-wide conservation
concern (Madhusudan & Mishra 2003; Woodroffe,
Thirgood & Rabinowitz 2005; Treves et al. 2006). Perse-
cution of carnivores over livestock depredation, together
with the desire to conserve them, leads to situations
referred to as human–carnivore conflict (Woodroffe, Thir-
good & Rabinowitz 2005). The conflict arises because farm-
ers’ interests are compromised, as are conservation goals.
These conflicts have two important dimensions – the
reality of damage caused by wildlife to humans,
and the perceptions and psyche of humans who suffer*Correspondence author. E-mail: [email protected]
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society
Journal of Applied Ecology 2013, 50, 550–560 doi: 10.1111/1365-2664.12061
wildlife-caused damage. Domestic animals are particu-
larly vulnerable to wild carnivore depredation because a
decreased risk of predation in a human-mediated envi-
ronment has led to a degeneration of their antipredatory
abilities (Zohary, Tchernov & Horwitz 1998; Madhusu-
dan & Mishra 2003). Inadequate management of
livestock is another important cause of livestock depre-
dation (Woodroffe et al. 2007). On the other hand, peo-
ple’s tolerance for large carnivores varies, depending on
several factors, including their religious beliefs, income,
education level, characteristics of carnivores and cultural
factors (Mishra 1997; Liu et al. 2011). Often, livestock
losses attributed to wild carnivores tend to get exagger-
ated, either mistakenly or deliberately (Mishra 1997;
Rigg et al. 2011). These perceptions can have strong
emotional and political consequences, ultimately result-
ing in persecution of carnivores (Kellert et al. 1996). An
understanding of the nature of conflicts along both
these dimensions – actual damage and people’s percep-
tions – is important if conflicts are to be managed
effectively, because together they influence human
responses to these losses.
Conservation and the management of human–wildlife
conflict would therefore benefit from a better understand-
ing of the extent, causes and correlates of actual live-
stock damage caused by wild carnivores, and the threat
of damage that affected people perceive. While actual
levels of livestock depredation are likely to be influenced
by the behavioural ecology of the carnivore and live-
stock-rearing practices, the perceived threat is likely to
be influenced by a suit of individual, family, socio-
economic and cultural factors such as the cultural and
economic value of the livestock killed, the cultural signif-
icance of the carnivore, general awareness of the local
communities, the presence of good conservation models
(unlike livelihood threatening exclusionary conservation
actions), and the physical and behavioural characteristics
of the carnivore.
The wolf Canis lupus and the endangered snow leopard
Panthera uncia occur across the mountain ranges of Cen-
tral Asia where they live alongside large numbers of live-
stock (Mishra et al. 2003). Persecution of these two
carnivores by pastoralists over livestock depredation
threatens their survival across their range (Mishra 1997;
Mishra et al. 2003; Namgail, Bhatnagar & Fox 2007).
Studies report 3–12% annual losses of livestock holdings
to snow leopards and wolves in high conflict areas
(Mishra 1997; Hussain 2000; Mishra et al. 2003; Jackson
& Wangchuk 2004; Namgail, Bhatnagar & Fox 2007).
High losses sometimes create such levels of intolerance
that local communities view carnivore extermination as
the only solution (Oli, Taylor & Rogers 1994). Managing
human–carnivore conflict is of utmost priority for the
continued survival of these carnivores in Central Asia
(McCarthy & Chapron 2003).
Research on human–carnivore conflicts in Central Asia
has focused on documenting the extent of livestock
depredation and peoples’ attitudes in target villages (Oli,
Taylor & Rogers 1994; Mishra 1997; Hussain 2003; Ikeda
2004; Namgail, Bhatnagar & Fox 2007; Jackson et al.
2010). Researchers have recognized the existence of con-
flict ‘hotspots’ or sites predisposed to livestock depreda-
tion, but they have not explored the ecological causes or
correlates of livestock depredation or conflict hotspots.
Studies have relied on interviews of affected people to
understand patterns in livestock depredation. Interviews
are likely to reflect peoples’ perceptions of the conflict,
which may be at odds with the reality of actual livestock
depredation. This potential dichotomy between human
perceptions and the actual levels of livestock depredation
has not been explored. At an even more fundamental level,
many studies have failed to distinguish between livestock
damage caused by snow leopards from that caused by
wolves (e.g. Mishra 1997; Hussain 2003). These carnivores
have different hunting strategies. Being a stalker, the snow
leopard is expected to prefer structurally complex habitat
with adequate ambush cover for hunting, while the pack-
living wolf is expected to hunt in more open habitats
(Caro & Fitzgibbon 1992). The difference in their hunting
strategies is expected to lead to different patterns of live-
stock depredation, which may require different conflict
management approaches. Similarly, people may relate to
these two carnivores differently, which would affect their
perceptions of each species in conflict situations.
What are the factors that predispose certain areas or
villages within a landscape to livestock depredation by
snow leopards or wolves? How similar are the perceptions
of local people compared with the actual patterns of live-
stock depredation? In this study, we focus on identifying
(i) the landscape-level ecological factors influencing live-
stock depredation by snow leopards and wolves and
(ii) the correlates of human perceptions of livestock depre-
dation. Our data come from extensive field surveys of car-
nivore and prey distribution, interviews of local people to
understand conflict perceptions and monitoring actual
livestock damage over a 2-year period in a 4000 km2
trans-Himalayan landscape. To explain the spatial varia-
tion in livestock depredation by each carnivore, we exam-
ine the influence of key explanatory factors that together
capture variation in habitat topography, abundance of
carnivores and wild prey and abundance and other attri-
butes of livestock.
Materials and methods
STUDY AREA
The Upper Spiti Landscape (USL; lat 32°00′–32°42′N; Long
77°37′–78°30′E) in trans-Himalayan India is a high elevation (3500
–6700 m) region. Annual temperatures range from �40 °C in peak
winter to c. 30 °C in summer. Vegetation is classified as ‘Alpine
scrub’ or ‘dry alpine steppe’ (Champion & Seth 1968). Large
mammalian fauna of USL includes bharal Pseudois nayaur, ibex
Capra ibex and their predators, the snow leopard and the wolf.
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
Human–carnivore interactions 551
Agro-pastoralist communities, currently of Buddhist denomina-
tion, have inhabited this region for two to three millennia. Pres-
ently, the population of USL is 4908 people in 25 permanent
villages and a township. The livestock assemblage includes sheep,
goat, donkey, cow, cow–yak hybrid, horse and yak. Livestock
graze in the pastures except during extreme winter when they are
stall-fed. Based on herding practices, livestock can be classified as
(i) Large-bodied free ranging, henceforth referred to as large
stock (yaks and horses), and, (ii) Medium and small-bodied
herded, henceforth referred to as herded stock (cow, donkey, cow
–yak hybrid, goat and sheep). Although large stock of each
village graze in separate pastures with exclusive village rights, two
or more neighbouring villages sometimes share large pastures for
few months. Herded stock is shepherded to the pastures every
morning and brought back to the stocking pens inside the villages
in the evening. Every family has a separate corral. In summer,
the herded stock is kept in well-secured corrals adjoining the
house. These corrals often have a separate section covered with
wire mesh for smaller animals such as sheep and goats. During
winters, all the livestock are penned inside the houses in a sec-
tion on the ground floor. Every family takes turns at shepherd-
ing the entire village’s herded stock along with a designated
village-shepherd. Families mostly own small agricultural land
holdings (1–2 ha). Primary crops are barley Hordeum vulgare,
green pea Pisum sativum and a local variety of green pea called
black pea.
FIELD SURVEYS FOR DISTRIBUTION AND RELATIVE
ABUNDANCE OF CARNIVORES AND PREY
We divided the USL into 32 sampling blocks comprising catch-
ments of smaller valleys formed by the tributaries of Spiti River
(Fig. 1). Block sizes (after excluding areas above 5200 m eleva-
tion, where wildlife populations are absent) ranged from 50 to
160 km2 with the mean around 80 km2. We uniformly sampled
for wildlife occurrence in all the blocks along three 5-km tran-
sects in each block. Each transect was subdivided into five sec-
tions of 1-km. All transects were located along microhabitats
used by carnivores for social marking as well as good vantage
locations such as ridgelines and cliff tops. Ridgelines and cliffs
are common features of all blocks in this landscape which
allowed consistent placement of transects across blocks. Within
each block, we preferred this sampling design focusing on carni-
vore marking habitats over randomly laid transects as it maxi-
mized our chances of detecting carnivore signs. The vantage
locations, offering high visibility, provided extensive coverage of
habitats within each block for locating evidences of prey. Tran-
sects were separated from each other by at least one kilometre.
The surveys were conducted by a team of 10 trained people from
September 2008–January 2009. Sightings of bharal and ibex
groups while surveying the blocks were recorded. The location,
group size and composition were noted. Snow leopard and wolf
signs (pug marks and scrape marks for snow leopard and only
pug marks for wolves) were recorded along every section of each
transect. Snow leopard pug marks and scrape signs were unam-
biguous as this is the only large felid in USL. Wolf signs could
be confused with those of village dogs. Therefore, we cross-
checked wolf pug marks with secondary reports of wolf sightings
in the sampling block within the past month by asking key infor-
mants in the nearest village. A total of 412 key informants were
interviewed to cross-check wolf presence and gather additional
information about presence of bharal and ibex. Of the 96 potential
transects, we could survey 75 transects in 30 blocks. The others
could not be surveyed due to difficulty in accessing the areas.
INTERVIEW SURVEYS FOR PERCEIVED THREAT TO
LIVESTOCK
To identify areas where people perceived snow leopards and/or
wolves to be a threat to livestock, we conducted group interviews
in all the villages (n = 25) between October 2008 and January
2009. Interviewees comprised village elders, village heads and the
youth. We collected village-level information on livestock hold-
ing, land holding and human population. Resource uses such as
the livestock grazing cycle, pastures, location of livestock holding
pens, agricultural fields, roads and streams were mapped and
later digitized at 1:50000 scale (MANIFOLD version 8.0; Manifold
Net Ltd., Carson City, NV, USA).
We asked the interviewees to list the problems they faced in
rearing livestock, without prompting them about livestock damage
by carnivores. Interviewees who reported livestock depredation by
wild carnivores were also questioned about the species (snow leop-
ard and/or wolf). Each village was accordingly scored one or zero
for livestock depredation by snow leopard and by wolf. Unlike
many other parts of the snow leopard’s range, the stocking pens in
our study area are better protected, and all livestock depredation
instances in the last 5 years were reported to occur in the pastures.
There was just one case of killing of over 20 goats in the stocking
pen of a hamlet with just one house. Thus, the explanatory vari-
ables we used in the analysis were related to the pastures where
the livestock of each village grazed.
ACTUAL L IVESTOCK DAMAGE
During the interview surveys of 2008, we requested the profes-
sional herders of each village to maintain a record of all livestock
mortality on a monthly basis. The herders maintained records of
the species and age of livestock, month, cause of mortality and
the total number of livestock in each village throughout January
2009 to December 2010. Identity of the predator was confirmed
through direct sighting or signs around the kill. By cross-checking
their knowledge, the team ensured that the herders keeping
records were experienced at identifying signs of snow leopards
and wolves. A local representative maintained contact with each
herder every 4–6 months and recorded all the livestock mortali-
ties. We expect the herder’s records to be accurate, as they also
had to report any mortality to the livestock owner.
We examined seven explanatory variables that potentially deter-
mined the occurrence of livestock depredation by wolves and snow
leopards (Table 1). They captured variation in topography, relative
use by carnivores and prey, and abundance and other attributes of
livestock. Encounter rates of carnivore signs were used as indices
of relative use of each block. To ensure that our indices were
robust, we compared na€ıve occupancy estimate with estimates
using software PRESENCE 2.0 wherever necessary (Hines 2004).
Stocking density was the number of livestock per square kilometre.
Mean altitude of pastures was estimated using a 90 9 90 m SRTM
digital elevation model (http://srtm.csi.cgiar.org/SELECTION/
inputCoord.asp). Ruggedness was calculated using a moving circu-
lar window of five-pixel radius. Ruggedness of the central pixel of
the window was calculated as the standard deviation of altitude of
all the other pixels within the window. Ruggedness values of all
pixels were then averaged for each pasture.
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
552 K. R. Suryawanshi et al.
We considered encounter rates of the snow leopard and wolf
signs and wild-prey sightings as potential explanatory variables.
Higher encounter rate of carnivore signs was assumed to reflect
relative abundance or greater use of an area by the carnivores.
Encounter rate was calculated for each sampling block as the
proportion of transect sections with carnivore signs. Encounter
rate of wild prey was calculated as the number of animals seen
per kilometre. Higher encounter rate of bharal and ibex sightings
was assumed to reflect greater availability of wild prey. For each
village, encounter rate was calculated using data from all the
blocks over which its livestock ranged.
DATA ANALYSIS
Prey and carnivore distribution
Site occupancy for each species was estimated as the proportion
of blocks in which it was detected. A species was considered pres-
ent if it was detected in any of the transect sections within a
block. For the wolf, the data were used to estimate detection
probability and ‘corrected’ site occupancy using the programme
‘PRESENCE 2.0’ (MacKenzie et al. 2002). This analysis was not
deemed necessary for the snow leopard as the species was
detected in all blocks. Site occupancy for the snow leopard,
bharal and ibex was calculated as the proportion of blocks with
the species presence sign (for snow leopard) or sighting (for bharal
and ibex). We chose to rely on na€ıve site occupancy for the prey
species because their sightings in different transects were not
always independent, owing to high visibility in our study area.
LIVESTOCK SELECTIV ITY
We assessed the selectivity of snow leopard and wolf for various
livestock species following Vanderploeg & Scavia (1979). Electivi-
ty index (E*) was calculated as: Ei* = [Wi � (1/n)]/[Wi + (1/n)],
where n is the total number of livestock species. Wi = (ri/pi)/Σ(ri/pi), where the proportion of the ith livestock species in all the
livestock killed by the carnivore is denoted by ri, and the propor-
tion of the ith livestock species in the total population is denoted
by pi.
VARIABLES INFLUENCING LIVESTOCK DEPREDATION
AND PERCEIVED CONFLICT
We had two Poisson-distributed variables: number of livestock
killed by snow leopard and by wolves. We also had two binary
response variables: villages where people perceived a threat to
livestock from the snow leopard and from the wolf. We used
Moran’s I coefficient, based on straight-line distances between
villages to test whether or not the response variables in village
close together were spatially autocorrelated.
We evaluated 16 multiple logistic regression models and ranked
them using Akaike’s information criterion adjusted for small
samples (AICc; Burnham & Anderson 2002). Although multiple
regressions allow inferences from observational data when there
are several competing hypotheses, ultimately the inferences are
based on the models included in the candidate set. The exclusion
of the best model or inclusion of a meaningless model can mis-
lead the inferences (Johnson & Omland 2004). Also, for inferring
Fig. 1. The distribution of the bharal Pseudios nayaur and ibex Capra sibirica, key snow leopard prey, in the Upper Spiti Landscape,
India. Encounter rates of bharal and ibex was calculated as number of individuals sighted per km along transects.
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
Human–carnivore interactions 553
the relative importance of predictor variables, it is necessary to
examine the entire set of candidate models. We therefore used
two complementary analyses. We calculated cumulative Akaike
weight for each explanatory variable by summing model weights
for all models containing that variable (Burnham & Anderson
2002). The variables with the highest cumulative AICc weight
have the greatest relative influence on the response variable,
allowing the explanatory variables to be ranked from most
important to least important. Secondly, we used hierarchical par-
titioning with Root Mean Square Error as the measure of good-
ness-of-fit to determine the relative importance of each
explanatory variable (Chevan & Sutherland 1991; Mac Nally
2002; Mac Nally & Walsh 2004). This method addresses multicol-
linearity between different explanatory variables (Mac Nally
2000). We followed Mac Nally (2002) to identify statistically sig-
nificant variables by randomizing the data matrix 1000 times. We
used Poisson and logistic regression models in the hier.part pack-
age in the software package R (version 2.13: http://www.r-project.
org) to estimate the independent and joint contribution of each
variable. All statistical analyses were performed using the soft-
ware R (version 2.9: http://www.r-project.org).
Mac Nally (2000) argues that these two methods should agree
for the inferences to be significant. We compared the explanatory
variables identified by both methods to identify the factors having
the largest independent effect on villages affected by livestock
depredation by either carnivore.
Results
DISTRIBUTION OF CARNIVORES AND PREY
Site occupancy was highest for snow leopard with their
signs being detected in all the blocks (Fig. 2). Thus, we do
not expect under-estimation of site occupancy due to
pseudoabsences and we used encounter rates of snow leop-
ard sign as predictor variable in explaining actual and per-
ceived livestock depredation by this carnivore. Bharal site
occupancy was 0�65 (Fig. 1). Bharal presence could be con-
firmed through direct sightings (192 bharal in 26 groups) in
all the blocks identified as bharal areas by key informants.
Two blocks identified as bharal areas by key informants
could not be surveyed due to logistic difficulties. Site occu-
pancy for ibex was 0�36. We could not confirm the ibex
presence through direct sightings in two grid cells identified
as ibex areas by key informants (Fig. 1). We sighted 120
ibex in nine groups during this survey. Only field sign sur-
vey data were used for estimating site occupancy. Cumula-
tive site occupancy for ibex and bharal was 0�94. Only
10% of the blocks were occupied by both bharal and ibex.
Detection probability estimated for the wolf using soft-
ware PRESENCE 2.0 was 0�45 (SE = 0�15). The site occu-
pancy estimated (using PRESENCE) was 0�32 (SE = 0�12).The corrected site occupancy estimate was not signifi-
cantly different from the na€ıve estimate of 0�25 (z-score
0�58; P = 0�56). Na€ıve estimate is the proportion of sites
where wolf presence was actually detected. Wolf distribu-
tion seems to be limited to the blocks where we actually
detected their presence (Fig. 3). Thus, we used encounter
rates as predictor variables in explaining actual and
perceived livestock depredation by the wolf.
PERCEIVED THREAT TO LIVESTOCK
In 13 villages, people did not perceive any threat to live-
stock from either snow leopard or wolf. In five villages,
people perceived both snow leopard and wolf as a threat
to livestock. Only the snow leopard was perceived as a
threat in three villages, and only the wolf in four. Thus,
in eight villages, people perceived livestock damage by
snow leopard as a cause of concern for livestock rearing.
These were not spatially autocorrelated, with the observed
Moran’s I score for them being -0�05 (SE 0�03) and
expected being -0�04 (P = 0�58).The best model, identified as the one with least AICc,
in explaining and positively associated with villages where
snow leopards were perceived as a threat to livestock
Table 1. Explanatory variables considered as potential landscape-
level predictors of the extent of livestock depredation by snow
leopards and wolves
Variable name Variable description
Wild-prey
encounter rate
Encounter rate of wild prey (bharal
and ibex) calculated as number of
individuals sighted per km along
transects
Altitude Mean altitude of pastures grazed by
village livestock
Density of
herded stock
Mean stocking density of small-bodied
herded stock (cow, cow–yak hybrid,
donkey, goat and sheep) weighted by
the number of months that the animals
were stocked at a particular density
Density of
large stock
Mean stocking density of large-bodied
free-ranging livestock (horse and yak)
weighted by the number of months that
the animals are stocked at a particular
density
Ruggedness of
herded stock pasture
Mean ruggedness of pastures grazed by
small and medium-bodied herded stock.
Ruggedness was estimated using a
Digital Elevation Model (MODIS 250
9 250 m). Ruggedness was calculated
using a moving circular window of
radius five pixels. Ruggedness of the
central pixel of the window was
calculated as the standard deviation of
altitude of all the pixels within the
window. Ruggedness values of all pixels
were then averaged for each pasture.
ARC GIS was used to calculate
ruggedness
Ruggedness of large
stock pasture
Mean ruggedness of pastures grazed by
large-bodied free-ranging stock
Wolf encounter rate Encounter rate of wolf signs. Encounter
rate of wolf presence signs was
calculated as the proportion of transect
sections in which wolf signs were
recorded
Snow leopard
encounter rate
Encounter rate of snow leopard presence
signs calculated as in the case of wolf
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
554 K. R. Suryawanshi et al.
included just one parameter: Density of large stock
(R2 = 0�42). Coefficient estimate was 0�22 (SE = 0�08).Burnham & Anderson (2002) suggest considering all mod-
els within two ΔAICc units of the best model. We did not
have any other model within two ΔAICc units of the best
model. The next best model had a ΔAICc of 2�04 and
included two parameters: density of large stock and den-
sity of herded stock (R2 = 0�44; Table 2). Density of large
stock had the highest cumulative Akaike weight (0�99) fol-lowed by density of herded stock (0�25). All other vari-
ables had cumulative AICc of <0�2 (Table 3). Hierarchical
partitioning again showed that density of large stock had
the highest explanatory power for livestock depredation
by snow leopards, with independent, statistically signifi-
cant (P < 0�005) contribution of 82�38% (Fig. 4a).
Although ruggedness of large stock pastures was next in
explanatory power, its independent contribution was only
6�48% and it was not statistically significant (P = 0�9).People in nine villages perceived the wolf as a threat to
livestock rearing. These villages were not spatially
autocorrelated. The observed Moran’s I score for these
villages was �0�07 (SE = 0�03) against the expected score
of �0�04 (P = 0�25). The model with least AICc explaining
perceived threat by wolves included only one parameter:
Wolf encounter rate that was positively related to the
villages where people perceived wolves to be responsible
for livestock depredation (R2 = 0�32; AICc = 26�91)(Table 2). The coefficient estimate was 24�64 (SE = 9�59).The only other model within two ΔAICc units included
the variables wolf encounter rate and wild-prey encounter
rate. The cumulative AICc weight of wolf encounter rate
was 0�91 followed by density of large stock (0�25), densityof herded stock (0�20) and wild-prey encounter rate
(0�20). In the hierarchical partitioning analysis, wolf
encounter rate had significantly high independent explana-
tory power (P < 0�05) with an independent contribution
of 41%. Density of large stock was the next best variable
with independent contribution of 19�86% (z-score = 0�75;P = 0�45), but it was not statistically significant (Fig. 5a).
ACTUAL L IVESTOCK DEPREDATION
Over 2 years of monitoring, livestock damage by snow
leopard was recorded in 14 villages. This included seven
of the eight villages where people in 2008 had reported a
perceived threat from the snow leopard to livestock.
These villages were not autocorrelated in space. The
observed Moran’s I was �0�03 (SE 0�02) against an
expected �0�04 with a nonsignificant P = 0�86. Interest-
ingly, in one village where people had not perceived snow
leopard as a threat to livestock in 2008, 51 goats were
killed by snow leopards in 2010. The other five villages
Secondary report
Fig. 2. Distribution and encounter rate of snow leopard Panthera uncia, and villages where they were perceived as a threat to livestock
(open circles) in the Upper Spiti Landscape, India. Encounter rate of snow leopard signs was calculated as the proportion of transect
sections in which snow leopard signs were recorded. These villages were not spatially autocorrelated with the observed Moran’s I score
being �0�05 (SE 0�03) against the expected score of �0�04 (P = 0�58).
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
Human–carnivore interactions 555
where people had not perceived the snow leopard to be a
threat together recorded snow leopard caused mortality of
12 livestock over 2 years.
Actual livestock damage by wolves was recorded from
eight villages during the study period. This included only
five of the nine villages where people had perceived wolves
to be a threat to livestock at the beginning of our study,
and three additional villages where people had not per-
ceived wolves to be a threat. These villages were not auto-
correlated in space. The observed Moran’s I score was
�0�02 (SE 0�02) against and expected �0�04 with a non-
significant P = 0�39. Two villages where people did not
perceive wolf as a threat to livestock in 2008 recorded
wolf-caused mortality of five sheep and three donkeys
over the next 2 years. A third village where people had
not perceived the wolf to be a threat to livestock in 2008
recorded mortality of an unprecedented 66 sheep, 11 don-
keys and two horses in 12 independent events in 2009.
In total, we recorded 809 livestock mortalities (330
events) in the 25 villages over a period of 2 years
(Table 4). Surprisingly, the biggest cause of mortality was
predation by feral dogs (338 livestock heads). Snow leop-
ards and wolves killed 194 and 173 livestock, respectively.
Disease accounted for 52 animals and another 52 either
went missing or the cause of mortality was uncertain.
Over 90% (170) of the livestock damage by snow leopard
occurred during the 5 months between May and Septem-
ber. Livestock damage by wolves, on the other hand, was
spread throughout the year.
The electivity index (E*) of snow leopards was positive
for horse and yak (0�62 and 0�18, respectively), the two
large-bodied free-ranging livestock types. Of the total yaks
(67) and horses (34) killed by snow leopards, over 90%
and 80%, respectively, were young animals (<1 year old).
The selection of snow leopards for all herded livestock
including cow, yak–cow hybrids, donkey, goat and sheep
was negative in relation to their proportional abundance
(�0�74, �0�96, �0�95, �0�09 and �0�66, respectively).
E* index of wolf was positive for donkey and sheep (0�4and 0�35, respectively). E* indices of wolf for cow, dzo,
goat, horse and yak were �0�43, �0�87, 0�03, �0�18 and
�0�48, respectively. Three of the six yaks killed by wolves
were adult females and the other three were young males.
FACTORS INFLUENCING ACTUAL L IVESTOCK
DEPREDATION
Of the models explaining livestock depredation by snow
leopard, the one with the least AICc score included snow
leopard encounter rate and wild-prey encounter rate
(R2 = 0�56; Table 2). The coefficients of snow leopard
encounter rate and wild-prey encounter rate were 6�6
Fig. 3. Distribution and encounter rate of the wolf Canis lupus, and villages where they were perceived as a threat to livestock (open cir-
cles) in the Upper Spiti Landscape, India. Encounter rate of wolf signs was calculated as the proportion of transect sections in which
wolf signs were recorded. These villages were not spatially autocorrelated with the observed Moran’s I score being �0�07 (SE = 0�03)against the expected score of �0�04 (P = 0�25).
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
556 K. R. Suryawanshi et al.
Table
2.Structure
ofmodelsusedin
amultiple
logisticregressionframew
ork
withΔA
ICcasmodel
selectioncriteria
toidentify
thevariablesofvillages
affectedbyhuman–snow
leopard
and
human–w
olfconflict
Model
Structure
K
Livestock
damagebysnow
leopard
Livestock
damagebywolf
Perceived
threatofsnow
leopard
Perceived
threatofwolf
AIC
c
Residual
deviance
Delta
AIC
c
AIC
c
weight
AIC
c
Residual
deviance
Delta
AIC
c
AIC
c
weight
AIC
c
Residual
deviance
Delta
AIC
c
AIC
c
weight
AIC
c
Residual
deviance
Delta
AIC
c
AIC
c
weight
carnivore.enc
2331�0
271�5
105�5
0�0
557�0
518�1
278�6
0�0
35�6
31�1
12�9
0�0
26�9
22�37
0�0
0�4
den.hs
2407�6
348�1
182�1
0�0
579�3
540�4
300�9
0�0
34�6
30�1
11�9
0�0
35�1
30�52
8�2
0�0
den.ls
2434�6
375�1
209�1
0�0
589�6
550�7
311�2
0�0
22�7
18�16
0�0
0�5
31�6
27�01
4�6
0�0
wp.enc
2329�6
270�1
104�1
0�0
595�2
556�3
316�8
0�0
35�9
31�33
13�2
0�0
36�2
31�65
9�3
0�0
den.hs+den.ls
3410�1
348
184�6
0�0
575�9
534�4
297�5
0�0
24�7
17�55
2�0
0�2
33�0
25�83
6�1
0�0
den.ls+rugg.ls+alt
4372�5
307�5
147�0
0�0
301�6
257�3
23�2
0�0
27�4
17�41
4�7
0�0
36�4
26�35
9�4
0�0
den.hs+rugg.hs+alt
4330�9
265�9
105�4
0�0
278�4
234
0�0
0�9
38�6
28�58
15�9
0�0
39�3
29�28
12�4
0�0
den.hs+den.ls+rugg.hs+rugg.ls
5338�6
270�4
113�0
0�0
283�6
236
5�2
0�1
30�5
17�37
7�8
0�0
39�0
25�81
12�1
0�0
carnivore.enc+wp.enc
3225�5
163�4
0�0
1�0
554�2
512�8
275�8
0�0
38�2
31�09
15�5
0�0
28�4
21�3
1�5
0�2
carnivore.enc+den.ls
3328�1
266
102�6
0�0
559�1
517�6
280�7
0�0
25�3
18�12
2�6
0�1
29�0
21�86
2�1
0�1
carnivore.enc+den.hs
3328�0
265�9
102�5
0�0
559�0
517�5
280�6
0�0
37�2
30�09
14�5
0� 0
29�1
21�99
2�2
0�1
carnivore.enc+den.ls+rugg.ls
4281�4
216�4
55�9
0�0
354�6
310�2
76�2
0�0
26�8
16�77
4�1
0�1
31�8
21�84
4�9
0�0
carnivore.enc+den.hs+rugg.hs
4244�3
179�3
18�8
0�0
310�9
266�6
32�5
0�0
40�0
30�03
17�3
0�0
32�0
21�99
5�1
0�0
rugg.hs+alt
3330�4
268�4
104�9
0�0
289�1
247�7
10�7
0� 0
36�2
29�09
13�5
0�0
37�3
30�12
10�3
0�0
rugg.ls+alt
3375�3
313�2
149�8
0�0
298�8
257�3
20�4
0�0
34�7
27�6
12�0
0�0
36�7
29�52
9�7
0�0
wp.enc+den.ls+den.hs
4333�5
268�6
108�0
0�0
577�9
533�5
299�5
0�0
27�1
17�06
4�4
0�1
33�5
23�49
6�6
0�0
Abbreviationsindicate
snow
leopard
encounterrate
(sl.enc),wolfencounterrate
(w.enc),wild-preyencounterrate
(wp.enc),density
ofherded
stock
(den.hs),density
oflargestock
(den.ls),altitude
(alt),ruggednessofherded
stock
pasture
(rugg.hs)
andruggednessoflargestock
pasture
(rugg.ls).Variablesare
defined
inTable
1.K
isthenumber
ofestimable
parametersincludingtheinter-
cept.AIC
c,Akaike’sinform
ationcriterion.
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
Human–carnivore interactions 557
(SE 0�7) and 7�4 (SE 0�9), respectively. No other model
was within two or even four AICc units of the best
model. Snow leopard encounter rate and wild-prey
encounter rate had a cumulative AICc weight of 1�0.Hierarchical partitioning also showed snow leopard
encounter rate and wild-prey encounter rate as the most
important variables with independent effects of 43�8 and
25�0%, respectively (Fig. 4b). Both the terms were signifi-
cant (P < 0�0005 and P < 0�05, respectively).In the case of the wolf, the model with the least AICc
score included density of herded stock, ruggedness of
herded stock pastures and altitude (R2 = 0�58; Table 2).
The coefficients for density of herded stock, ruggedness of
herded stock pasture and altitude were �0�003 (SE 0�001),�0�05 (SE 0�006) and 0�0003 (SE 0�0008), respectively. No
other model was within two or even four AICc units of
the best model. The cumulative AICc weight of density of
herded stock, ruggedness of herded stock pasture and alti-
tude were 0�99, 1�0 and 0�93, respectively. However, hier-
archical partitioning analysis suggested that only the
ruggedness of herded stock pasture with an independent
contribution of 34% was significant at P < 0�05. Both
density of herded stock and altitude had an independent
effect of only 10�6 and 13�9%, respectively (Fig. 5b).
These contributions were not statistically significant
(P = 0�96 & 0�78, respectively).
Discussion
We recorded notable differences between the ecological
correlates of livestock depredation, both perceived and
actual, by snow leopards and wolves. Although snow
leopards were ubiquitous in our study area, people
Table 3. Cumulative Akaike’s information criterion (AICc)
weight of explanatory variables from all models of actual live-
stock damage by snow leopards and wolves, and the threat to
livestock perceived by local people from snow leopards and
wolves
Variables
Snow
leopard
damage
Wolf
damage
Perceived
threat snow
leopard
Perceived
threat
wolf
Snow leopard
encounter rate
1�00 NA 0�20 NA
Wolf encounter
rate
NA 0�00 NA 0�91
Density herded
stock
0�00 0�99 0�25 0�20
Density large
stock
0�00 0�07 1�00 0�25
Altitude 0�00 0�93 0�05 0�01Ruggedness herded
stock pasture
0�00 1�00 0�01 0�04
Ruggedness large
stock pasture
0�00 0�07 0�12 0�04
Wild-prey
encounter rate
1�00 0�00 0�06 0�20
010
2030
4050 (a)
*
*
sl.enc wp.enc den.hs den.ls alt rugg.hs rugg.ls
020
4060
8010
0
(b)
*
% In
depe
nden
t effe
cts
(%I)
Fig. 4. Independent contribution of the seven explanatory vari-
ables to (a) actual livestock damage by snow leopard, and, (b) vil-
lages where people perceived a threat to livestock from the snow
leopard, determined by hierarchical partitioning. Asterisk denotes
statistical significance. Abbreviations indicate snow leopard
encounter rate (sl.enc), wild-prey encounter rate (wp.enc), density
of herded stock (den.hs), density of large stock (den.ls), altitude
(alt), ruggedness of herded stock pasture (rugg.hs) and ruggedness
of large stock pasture (rugg.ls). Variables are defined in Table 1.
(a)
*
010
2030
40w.enc wp.enc den.hs den.ls alt rugg.hs rugg.ls
010
2030
4050 (b)
*
% In
depe
nden
t effe
cts
(%I)
Fig. 5. Independent contribution of the seven explanatory vari-
ables to (a) actual livestock damage by the wolf, and, (b) the
villages where people perceived a threat to livestock from the
wolf, determined by hierarchical partitioning. Asterisk denotes
statistical significance. Abbreviations indicate wolf encounter rate
(w.enc), wild-prey encounter rate (wp.enc), density of herded
stock (den.hs), density of large stock (den.ls), altitude (alt),
ruggedness of herded stock pasture (rugg.hs), ruggedness of large
stock pasture (rugg.ls). Variables are defined in Table 1.
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
558 K. R. Suryawanshi et al.
perceived them to be a threat to livestock in only one-
third of the villages, especially those where large-bodied,
free-ranging livestock (yaks and horses) were economically
important. In contrast, wolves occurred only in one-thirds
of our study area but were perceived as a threat to livestock
in all the villages in those areas. The occurrence of wolves,
by itself, was the most important predictor of peoples’ per-
ception of the threat posed by this canid to livestock.
Snow leopards preferred horses and yaks over all other
species of livestock. Case studies of snow leopard diet
have also reported snow leopards to attack horses dispro-
portionately compared with their abundance (Mishra
1997; Bagchi & Mishra 2006; Namgail, Bhatnagar & Fox
2007). Although people in only eight villages had per-
ceived snow leopards to be a threat to livestock in the
beginning of our study, many more (14 villages) recorded
instances of livestock depredation by snow leopards over
the following 2 years. In contrast, although people in nine
villages had perceived wolves to be a threat, fewer (eight
villages) actually recorded depredation by the wolf. Our
results show that compared with snow leopards, threat
perceptions are disproportionately biased against the wolf.
Peoples’ perception of a carnivore in conflict is likely to
be influenced by (and in turn influence) their attitude
towards the species in question. A strong cultural bias
against the wolf is reflected in strong negative attitudes and
relatively high persecution compared with other sympatric
carnivores such as the mountain lion Puma concolor in
North America (Kleiven, Bjerke & Kaltenborn 2004). The
attitudes towards a species are reportedly influenced by the
physical and behavioural characteristics of the carnivore,
and their cultural and historical associations (Kellert et al.
1996; Kleiven, Bjerke & Kaltenborn 2004). Although causal
relationships are not clear, factors such as greater visibility,
perceived threat and conspicuous behaviours such as howl-
ing and group living, ease in detection of denning sites etc.,
may accentuate the threat perception and generate greater
negative attitudes towards the wolf (Kellert et al. 1996).
Actual livestock depredation by snow leopards was best
explained by its own encounter rate and of its wild prey.
In the case of wolves, the ruggedness of the pastures used
by herded stock was the most important factors influenc-
ing actual livestock depredation. This relationship
between structural complexity of the habitat and the
extent of livestock depredation by wolves was negative.
This is consistent with the expectation that a cursorial
carnivore would prefer structurally less complex habitat.
Surprisingly, we did not find any association between live-
stock depredation by snow leopards and ruggedness of
the pasture. It appears that structural complexity may be
more important for snow leopards at a finer scale of hunt-
ing site rather than at the scale of a grazing pasture.
IMPL ICATIONS
Our study provides two main insights into human–
carnivore conflicts. First, human perceptions can be at
considerable odds with actual patterns of livestock depre-
dation, and, second, livestock depredation by snow leop-
ards and wolves show rather different patterns in prey
selectivity and ecological determinants. This suggests that
while interviews of local people, which have been com-
monly employed to study livestock depredation conflicts,
could yield accurate information on peoples’ perception of
a conflict situation, the reality of livestock depredation
must be measured additionally and independently.
More importantly, the insights from our study have
implications for conflict management programmes and help
gauge the future of human–carnivore conflicts in Central
Asia. The relationship between livestock depredation by
snow leopards and the relative abundance of wild prey sug-
gests that human–snow leopard conflicts are likely to get
more intense if successful conservation programmes lead to
increases in wild-prey abundance from the low densities
typical of multiple use, livestock-grazed landscapes (Mishra
et al. 2004). This is in contrast to our own earlier work
where we have indicated that livestock depredation by snow
leopards may decline with increase in wild-prey abundance
(Bagchi & Mishra 2006). In fact, this premise has guided an
important aspect of conflict management, viz., facilitating
wild-prey population recovery (Mishra et al. 2003). Our
present study, however, suggests that an increase in wild
prey – a highly desirable conservation outcome considering
their own endangerment and their functional role – would,
in fact, lead to an increase in livestock depredation by snow
leopards, presumably by supporting a greater abundance of
the cat. We therefore hypothesize that the relationship
between snow leopard depredation of livestock and wild-
prey abundance may be bimodal. We expect livestock dep-
redation to increase as wild-prey populations increase or
Table 4. Number of livestock deaths due to various causes between January 2009 and December 2010 in the 25 villages of the Upper
Spiti Landscape, India
Cause of
mortality Cow Donkey Dzo Goat Sheep Horse Yak Total
Snow
leopard
5 1 1 63 23 34 67 194
Wolf 5 41 1 30 88 2 6 173
Feral Dog 6 5 4 120 200 3 0 338
Disease 6 5 2 11 17 4 7 52
Missing 4 2 6 3 7 1 9 32
Unknown 1 2 2 1 13 0 1 20
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
Human–carnivore interactions 559
decline beyond certain thresholds that are influenced by the
carrying capacity of the prey, predator and availability of
other resources such as denning and resting sites. This
means that conservation initiatives aimed at facilitating the
recovery of wild-prey populations must also be accompa-
nied by measures to better protect livestock. Furthermore,
based on livestock preference patterns, we suggest that con-
flict management programs should especially target large-
bodied livestock in snow leopard habitats where they form
an important part of the peoples’ economy.
Our results suggest that the extent of livestock depreda-
tion by wolves may not be similarly affected by changes in
wild-prey abundance. However, the wolf is likely to face
even more intense persecution in the future, considering the
on-going socio-economic changes in Central Asia where the
global demand for cashmere is leading to an increase in
livestock population, and replacement of larger bodied live-
stock with smaller bodied, cashmere-producing goats
(Schaller 1998; Namgail, Bhatnagar & Fox 2007). Increas-
ing abundance of goats, especially in relatively flatter parts
of Central Asia such as the Tibetan Plateau and the north-
ern steppes, will likely intensify human conflicts with wolves
much more compared with snow leopards. Apart from
measures to better protect livestock, sustained education
and awareness towards the importance of the conservation
of these carnivore species to increase the social carrying
capacity will be needed, especially for the wolf.
Acknowledgements
We thank the Forest Department of Himachal Pradesh for financial
support and permissions. BBC Wildlife Fund, Whitley Fund for
Nature, and Conservation Leadership program for funding. We thank
Rashid Raza, Koustubh Sharma, Umesh Srinivasan for comments.
Sushil ‘Tandup Dorje’, Tenzin Thillay, Dorge Tsewang, Chudim,
Rinchen Tobgay, Kalzang Pulzor, Kalzang Gurmet, Palden Rabgay,
Thukten provided valuable contribution to fieldwork. We thank the
412 key informants for invaluable information.
References
Bagchi, S. & Mishra, C. (2006) Living with large carnivores: predation on
livestock by the snow leopard (Uncia uncia). Journal of Zoology, 268,
217–224.Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multi-
Model Inference: A Practical Information-Theoretic Approach, 2nd edn.
Springer Verlag, New York, NY, USA.
Caro, T.M. & Fitzgibbon, C.D. (1992) Large carnivores and their prey:
the quick and the dead. Natural Enemies (eds M.J. Crawley), pp. 117–142. Blackwell Scientific, Malden, MA, USA.
Champion, F.W. & Seth, S.K. (1968) A Revised Survey of the Forest Types
of India. Government of India Press. Nasik, India.
Chevan, A. & Sutherland, M. (1991) Hierarchical partitioning. American
Statistician, 45, 90–96.Hines, J.E. (2004) PRESENCE 2.0. US Geological Survey - Patuxent
Wildlife Research Center. Available: www.mbr-pwrc.usgs.gov/software/
doc/presence/presence.html
Hussain, S. (2000) Protecting the snow leopard and enhancing farmers’
livelihoods. Mountain Research and Development, 20, 226–231.Hussain, S. (2003) The status of snow leopard in Pakistan and its conflict
with local farmers. Oryx, 37, 26–33.Ikeda, N. (2004) Economic impacts of livestock depredation by snow leop-
ard Uncia uncia in the Kanchenjunga conservation area, Nepal Hima-
laya. Environmental Conservation, 31, 322–330.
Jackson, R. & Wangchuk, R. (2004) A community-based approach to mit-
igating livestock depredation by snow leopards. Human Dimensions of
Wildlife, 9, 307–315.Jackson, R., Mishra, C., McCarthy, T. & Ale, S. (2010) Snow leopards:
conflicts and conservation. Biology and Conservation of Wild Felids (eds
D.W. Macdonald & A.J. Loveridge), pp. 417–430. Cambridge Univer-
sity Press, Cambridge.
Johnson, J.B. & Omland, K.S. (2004) Model selection in ecology and evo-
lution. Trends in Ecology and Evolution, 19, 101–108.Kellert, S.R., Black, M., Rush, C.R. & Bath, A.J. (1996) Human culture
and large carnivore conservation in North America. Conservation Biol-
ogy, 10, 977–990.Kleiven, J., Bjerke, T. & Kaltenborn, B.P. (2004) Factors influencing the
social acceptability of large carnivore behaviours. Biodiversity and Con-
servation, 13, 1647–1658.Liu, F., Mcshea, W.J., Garshelis, D.L., Zhu, X., Wang, D. & Shao, L.
(2011) Human-wildlife conflicts influence attitudes but not necessarily
behaviors: factors driving the poaching of bears in China. Biological
Conservation, 144, 538–547.Mac Nally, R. (2000) Regression and model-building in conservation biol-
ogy, biogeography and ecology: the distinction between – and reconcili-
ation of – ‘predictive’ and ‘explanatory’ models. Biodiversity and
Conservation, 9, 655–671.Mac Nally, R. (2002) Multiple regression and inference in ecology and
conservation biology: further comments on identifying important predic-
tor variables. Biodiversity and Conservation, 11, 1397–1401.Mac Nally, R. & Walsh, C.J. (2004) Hierarchical partitioning public-
domain software. Biodiversity and Conservation, 13, 659–660.MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A. &
Langtimm, C.A. (2002) Estimating site occupancy rates when detection
probabilities are less than one. Ecology, 83, 2248–2255.Madhusudan, M. & Mishra, C. (2003) Why big, fierce animals are threa-
tened. Battles Over Nature (ed M. Rangarajan), pp. 31–55. Permanent
Black, New Delhi, India.
McCarthy, T.M. & Chapron, G. (2003) Snow Leopard Survival Strategy.
ISLT and SLN, Seattle, WA, USA.
Mishra, C. (1997) Livestock depredation by large carnivores in the Indian
Trans-Himalaya: conflict perceptions and conservation prospects. Envi-
ronmental Conservation, 24, 338–343.Mishra, C., Allen, P., McCarthy, T., Madhusudan, M.D., Bayarjargal, A.
& Prins, H.H. (2003) The role of incentive programs in conserving the
snow leopard. Conservation Biology, 17, 1512–1520.Mishra, C., Van Wieren, S., Ketner, P., Heitkonig, I.M.A. & Prins,
H.H.T. (2004) Competition between domestic livestock and wild bharal
Pseudois nayaur in the Indian trans-Himalaya. Journal of Applied Ecol-
ogy, 41, 344–354.Namgail, T., Bhatnagar, Y.V. & Fox, J.L. (2007) Carnivore-caused live-
stock mortality in trans-Himalaya. Environmental Management, 20, 1–8.Oli, M.K., Taylor, I.R. & Rogers, E.M. (1994) Snow leopard Panthera
uncia predation of livestock: an assessment of local perception in the
Annapurna conservation area, Nepal. Biological Conservation, 68, 63–68.Rigg, R., Findo, S., Wechselberger, M., Gorman, M.L., Sillero-Zubiri, C.
& Macdonald, D.W. (2011) Mitigating carnivore-livestock conflict in
Europe: lessons from Slovakia. Oryx, 45, 272–280.Schaller, G.B. (1998) Wildlife of the Tibetan Steppe. University of Chicago
Press, Chicago, IL, USA.
Treves, A., Wallace, R., Naughton-Treves, L. & Morales, A. (2006)
Co-managing human–wildlife conflicts: a review. Human Dimensions of
Wildlife, 11, 383–396.Vanderploeg, H.A. & Scavia, D. (1979) Two electivity indices for feeding
with special reference to zooplankton grazing. Journal of Fish Research
Board Canada, 36, 362–365.Woodroffe, R., Thirgood, S.J. & Rabinowitz, A. (2005) People and Wildlife.
Conflict or Co-existence. Cambridge University Press, Cambridge, UK.
Woodroffe, R., Frank, L.G., Lindsay, P.A., Ole Ranah, S.M.K. &
Romanach, S. (2007) Livestock husbandry as a tool for carnivore
conservation in Africa’s community rangelands: a case-control study.
Vertibrate Conservation and Biodiversity, 16, 1245–1260.Zohary, D., Tchernov, E. & Horwitz, L.K. (1998) The role of unconscious
selection in the domestication of sheep and goat. Journal of Zoology,
245, 129–135.
Received 21 August 2012; accepted 29 January 2013
Handling Editor: Nathalie Pettorelli
© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology, 50, 550–560
560 K. R. Suryawanshi et al.