People, predators and perceptions: patterns of livestock depredation

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.


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.



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



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).



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).



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.



(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.



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



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



Documents

People, predators and perceptions: patterns of livestock depredation