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\begin{document}

\title{Assessing prey selection of snow leopard in Mt. Kangchenjunga, Nepal}



\author[1]{Kamal Thapa}%
\author[2]{Natalie Schmitt}%
\author[3]{Narendra Pradhan}%
\author[4]{Hem  Acharya}%
\author[1]{Santosh  Rayamajhi}%
\affil[1]{Tribhuvan University}%
\affil[2]{McMaster University Department of Biomedical Sciences}%
\affil[3]{Affiliation not available}%
\affil[4]{Government of Nepal Ministry of Forests and Soil Conservation}%


\vspace{-1em}



  
  \date{June 02, 2024}


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\selectlanguage{english}
\begin{abstract}
In spring of 2012, we studied the feeding habits of snow leopard using a
comprehensive approach that combines fecal genetic sampling, macro and
microscopic analysis of snow leopard diets and direct observation of
Naur and livestock in Kangchenjunga Conservation Area of east Nepal. Out
of collected 88 putative snow leopard scat samples from 140 transects
(290 km) in 27 (4*4 km2) sampling grid cells, 83\% were confirmed to be
from snow leopard. The genetic analysis accounted for 19 individual snow
leopards (10 male and 9 female) with a mean population size estimate of
24 (95\% CI: 19- 29), and an average density of 3.9 snow leopards/100
km2 within 609 km2. Total available prey biomass of Naur and Yak was
estimated at 355,236 kg (505 kg yak/km2 and 78 kg Naur/km2). From the
available prey biomass, we estimated snow leopards consumed 7\% annually
which was comprised of wild prey (49\%), domestic livestock (45\%), and
6\% unidentified items. The estimated 47,736 kg Naur biomass gives a
snow leopard-Naur ratio of 1: 59 on a weight basis. The proportion of
young Naur was estimated at 17\%, with an almost double predation rate
at 28\%. Predators such as common leopard and wolf share the same
habitat and might compete with snow leopard for prey which will likely
influence future predator-prey associations in KCA. Along with livestock
insurance scheme improvement, there needs to be a focus on improved
livestock guarding as well as engaging and educating local people to be
citizen scientists on the importance of snow leopard conservation,
involving them in long-term monitoring programs and promotion of
ecotourism.%
\end{abstract}%



\sloppy


\textbf{Assessing prey selection of snow leopard in Mt. Kangchenjunga,
Nepal}

Kamal Thapa, \textsuperscript{1}\emph{Tribhuvan University- Institute of
Forestry, Kritipur, Kathmandu, Nepal}

Natalie Schmitt, \emph{Department of Biochemistry and Biomedical
Research, McMaster University, Canada}

Narendra M. B. Pradhan, \emph{International Union for the Conservation
of Nature (IUCN), Kathmandu, Nepal}

Hem Raj Acharya, \emph{Department of National Parks and Wildlife
Conservation, Babar Mahal, Kathmandu, Nepal}

Santosh Rayamajhi, \emph{Tribhuvan University- Institute of Forestry,
Kritipur, Kathmandu, Nepal}

\textsuperscript{1}Corresponding author's name: Kamal Thapa, Tribhuvan
University- Institute of Forestry, Kritipur, Kathmandu, Nepal. Telephone
(Mobile): 977 9840851551. Email:dreamerhimalaya@gmail.com

\textbf{Abstract} In spring of 2012, we studied the feeding habits of
snow leopard using a comprehensive approach that combines fecal genetic
sampling, macro and microscopic analysis of snow leopard diets and
direct observation of Naur and livestock in Kangchenjunga Conservation
Area of east Nepal. Out of collected 88 putative snow leopard scat
samples from 140 transects (290 km) in 27 (4*4 km\textsuperscript{2})
sampling grid cells, 83\% were confirmed to be from snow leopard. The
genetic analysis accounted for 19 individual snow leopards (10 male and
9 female) with a mean population size estimate of 24 (95\% CI: 19- 29),
and an average density of 3.9 snow leopards/100
km\textsuperscript{2}within 609 km\textsuperscript{2}. Total available
prey biomass of Naur and Yak was estimated at 355,236 kg (505 kg
yak/km\textsuperscript{2}and 78 kg Naur/km\textsuperscript{2}). From the
available prey biomass, we estimated snow leopards consumed 7\% annually
which was comprised of wild prey (49\%), domestic livestock (45\%), and
6\% unidentified items. The estimated 47,736 kg Naur biomass gives a
snow leopard-Naur ratio of 1: 59 on a weight basis. The proportion of
young Naur was estimated at 17\%, with an almost double predation rate
at 28\%. Predators such as common leopard and wolf share the same
habitat and might compete with snow leopard for prey which will likely
influence future predator-prey associations in KCA. Along with livestock
insurance scheme improvement, there needs to be a focus on improved
livestock guarding as well as engaging and educating local people to be
citizen scientists on the importance of snow leopard conservation,
involving them in long-term monitoring programs and promotion of
ecotourism.

\textbf{KEY WORDS} common leopard, fecal, genetic analysis, naur, wolf,
yak

\textbf{INTRODUCTION}

Sustainable snow leopard conservation must necessarily involve the local
people; however, it is becoming increasingly more difficult for humans
and snow leopards to coexist peacefully in an increasingly human altered
ecosystem (Ale et al., 2007; Jackson, 2015). Despite their abundant wild
prey, snow leopard often kill livestock (Khanal et al., 2020; Jackson,
2015) as they are highly detectable and easy to capture (Lovari \&
Mishra, 2016), but are often the primary livelihood source for many
agropastoral communities. As a result, retaliatory killing of snow
leopard by herders are common (Lovari \& Mishra, 2016; Jackson \& Lama,
2016; Ale et al., 2010) and practical solutions are becoming more
challenging (GoN, 2012; Wegge et al., 2012; Ale et al., 2014). A key
contributing factor to these continuing threats may be the difficulty in
implementing conservation strategies (Ale et al., 2016) because of the
lack of reliable information, both scientific and socio-economic
(Jackson \& Lama, 2016). In Nepal or elsewhere, snow leopard is one of
the poorly understood threatened species especially with respect to
their distribution, home range, abundance, and feeding behavior (Wegge
et al., 2012; Jackson et al., 2002). This is largely because snow
leopard is sparsely distributed with low density in remote, highly
rugged terrain (KCA, 2019) which can be logistically challenging and
places constraints on methodology. As such, conservation planning and
strategy in Nepal, especially for snow leopard, has been largely based
on anecdotal evidence (DNPWC, 2017). 30 years ago, Oli (1993) conducted
a detailed study on snow leopard diet, and subsequently by Wegge et al.
(2012), where they predicted a sustainable predator-prey ratio with the
presence of abundant small livestock breeds around Manang in the
Annapurna Conservation Area (ACA) of west Nepal. However, unlike west
Nepal, Lovari et al., 2009 reported that where small livestock breeds
were not present in the Everest region, eastern Nepal, Himalayan tahr
populations were becoming more suppressed as snow leopard were targeting
young individuals preferentially. In Nepal, most studies on snow leopard
status, their prey base and feeding behavior were concentrated around
Dolpa in the west, Annapurna and Manslu in the Centre (Khanal et al.,
2020; Shrestha et al., 2019; Chetri et al., 2019, 2017; Shrestha et al.,
2018; Ale et al., 2014; Wegge et al., 2012; Thapa, 2005; Oli, 1994; Oli
et al., 1993; Schaller, 1973) and around the Mt. Everest region in the
east (Shrestha et al., 2018; Lovari \& Mishra, 2016; Lovari et al.,
2013, 2009; Ale et al., 2007). However, no systematic study on snow
leopard status, prey abundance and feeding behavior has been undertaken
in the Kangchenjunga Conservation Area (KCA) in the far eastern region
of Nepal, a critical snow leopard habitat to maintaining connectivity
across the eastern snow leopard range and stands to suffer the greatest
impact from climate change (MoFSC, 2017). Gurung et al. 2011 reported
that snow leopard numbers had dramatically increased and there were no
reports of retaliatory killing in the far east mainly because of
successful implementation of community-managed livestock insurance.

A comprehensive understanding of the impact of snow leopard predation on
wild ungulates and the role livestock play in their diet, is essential
to devising strategy to reduce human- snow leopard conflicts. In the Mt.
Kangchenjunga region, a human-altered mountain ecosystem in the far east
of Nepal which is transboundary snow leopard habitat with India and
China (KCA, 2019), we combine genetic sampling to estimate snow leopard
abundance, both macro- and microscopic analysis to assess their diet,
and an assessment of prey population dynamics. Based on comprehensive
field data, we discuss status of snow leopard, abundance of wild prey
and feeding behavior of snow leopards and recommend further conservation
implications.

\textbf{MATERIALS AND METHODS}

\textbf{Study area}

KCA is part of the Taplejung District located in the far north-eastern
part of Nepal. Bordering the state of Sikkim in India to the east and
the Tibet Autonomous Region (TAR) of China to the north, the district
(20\textsuperscript{0} 24\textsuperscript{'}-
27\textsuperscript{0}56\textsuperscript{'} N \& 87 \textsuperscript{0}
39\textsuperscript{'}- 88\textsuperscript{0} 12\textsuperscript{'} E)
covers mid-hills to high mountain terrains including Mt. Kangchenjunga
(8,586 m), the world's third highest peak (Fig. 1). On 21 July, 1997, it
was declared a conservation area spanning 2,035 km\textsuperscript{2}.
The climate is alpine and harsh in the upper parts, above 3,000 m, and
below this elevation, mixed temperate and sub-alpine climate is
prevalent. The lower part of the study area receives snow fall for a few
months and the upper range, heavy snow in winter every year. The
topography is steep and rugged with mountainous terrain. The major fauna
in KCA includes snow leopard, blue sheep \emph{Pseudois nayaur} (here
forth Naur), wolf\emph{Canis lupus} , red fox \emph{Vulpes} , pikas
(Ochotona spp.), common leopard \emph{Panthera pardus} , clouded leopard
\emph{Neofelis nebulosa} , red panda \emph{Ailurus fulgens} , musk deer
\emph{Moschus fuscus} , and assamese monkey \emph{Macaca assamensis}
including many others (KCA, 2019; Thapa et al., 2013).

The study area comprises the headwaters of the Tamor River system and is
characterized by rugged topography, with deep river canyons cutting
through the lower reaches. Here rough boulder and scree-dominated
hillsides intersperse alpine grasslands and meadows. Because of the
abundant pastureland in the region, local people of upper Tamor have
been practicing their livestock rearing for more than 150 years (Hooker,
1854). Yak, Dzo and cattle are common livestock kept for rearing in the
area. Unlike western Nepal, domesticated small livestock are almost
non-existence especially within snow leopard habitat.

\textbf{2.2 Survey design for snow leopard DNA sampling}

The study area of this project was selected based on the potential
habitat of snow leopard, as outlined by WWF Nepal (2009). The
altitudinal range of the study was chosen between 3000 and 5400m (using
a 1:50000 topographic map), consistent with the range both snow leopard
and Naur have been detected across the Nepal Himalaya (Jackson \&
Hunter, 1996).

The target study area included all suitable snow leopard habitat within
the KCA. However, due to the immensity of this area, the steep terrain
and numerous cliffs, it was not possible to cover all suitable habitat.
Snow leopards are difficult to detect directly and are a wide-ranging
species with an estimated annual home range of 1031.6
km\textsuperscript{2} for males and 470 km\textsuperscript{2} for female
in the KCA (KCA, 2019). These estimates are significantly higher than
home range estimates in other regions of Nepal such as the Longu valley
and Manang (12 to 39 km\textsuperscript{2}; Oli, 1997; Jackson \&
Ahlborn, 1989) but comparable to Mongolia for females (up to 500
km\textsuperscript{2}; Johansson et al., 2016; McCarthy et al., 2005).
Because of the high variation in home range estimates, numerous sampling
grids were used to divide up (each cell: 4*4 km; 16
km\textsuperscript{2}; Fig. 1) potential snow leopard habitat within the
KCA. A relatively large survey cell was used to allow for areas that
might be inaccessible. Multi-stage sampling (Janecka et al., 2011) was
employed to ensure high interspersion of primary grids and more precise
sampling cells (transects). Transects (following trail segment) were
distributed within an altitudinal range of 4,195m to 4,928m in sites
that were most likely to be visited by snow leopards, especially narrow
ridgelines, cliff bases, passes, and the valley base at or immediately
adjacent to frequently scent-sprayed rocks and scrape sites.

\textbf{2.3 Snow leopard scat collection}

From early April to mid-May (42 days) in 2012, 83 putative snow leopard
scat samples were collected within 27 grids (432 km\textsuperscript{2})
along the 140 transects (290 km). At least two transects each 1- 1.5 km
apart within each sampling grid was used following the technique used by
Thapa, 2007 adopted from Jackson \& Hunter (1996). The length of
transects was dependent on the terrain and human accessibility. To
minimize collection of erroneous samples, scats were collected at snow
leopard scrape sites and further discriminated by size and shape. A
portion (\textasciitilde{}1 CM\textsuperscript{2}; Janecka et al., 2011)
of the collected scat samples were stored in 15 ml vials with silica
desiccant \textbf{(} Janecka et al., 2008), labeled (with grid number,
transect number, date, location, sample ID, GPS location etc.) and kept
at room temperature prior to DNA analyses. Remaining portions of each
sample were sun dried and put into paper envelopes for diet analysis.

\textbf{2.4 Molecular genetic analysis}

The collected samples were processed for identification of species, sex
and individual. Genetic analyses (DNA extraction, species
identification, sex identification, and individual identification) of
putative snow leopard scat samples were conducted following the protocol
developed by Karmacharya et al., 2011, adopted from Janecka et al.
(2008).

\textbf{2.5 Population abundance and density estimation}

A free software program SPACECAP package version 3.0.2 (Gopalaswamy et
al., 2012) was run to estimate snow leopard density from genetic
samples; a user-friendly software package, implementing a Bayesian
Spatially Explicit Capture Recapture (SECR) analysis (Royle et al.,
2009). This software is recommended by Efford et al. (2009) for spatial
CR data originating from different types of non-invasive sources, such
as camera traps, hair snares or fecal DNA samples. SECR models in the
SPACECAP package directly estimates animal density by explicitly using
information on capture histories in combination with spatial locations
of captures under a Bayesian modeling framework.

Following Singh et al. (2010), three types of input files were prepared
to analyze data in the SPACECAP package: I. e. (i) Animal Capture
Details, (ii) Trap Deployment Details and (iii) State-space Details. The
trap deployment file consisted of trap location ID and spatial location
of trap IDs in X and Y- coordinates (Universal Transverse Mercator UTM
projection system) along with information on the occasions when each
individual location was operational during the sample collection
duration. Here the mid-center of grids was used as detectors (location)
and we also reduced the number of sampling occasions by pooling the data
together across three days due to a low encounter probability, as
suggested by Janecka et al., 2011. The trap deployment data were
organized in a two-dimensional matrix of individually identified
locations and sampling occasions in a binary format (1s and 0s),
indicating whether an individual location was or was not operational on
a particular sampling occasion. A mesh of points (potential home range
center) in each cell (0.25 km\textsuperscript{2}) was generated using
the Arc Geographical Information System (GIS) in a sampling array and
1.6 km buffer. The potential home range center file consisted of X and Y
coordinates of all the potential activity centers in the UTM Projection
System, and a habitat suitability indicator (using 1s or 0s)
representing the potential activity centers within suitable or
unsuitable habitat. The analysis was run in SPACECAP v 3.1 under the
program R environment. For MCMC simulation, 60000 iterations were
selected, 1000 burn in values (number of initial values to discard
during the MCMC analysis) and 1 as thinning which was recommended by
Royle et al. (2009). Only iteration numbers defined by the thinning rate
are stored for the analysis (Singh et al., 2010). Since there was
uncertainty about the total number of animals, which was likely to be
larger than the minimum number identified during fecal DNA analysis, the
value needed to be ``augmented''. Here fecal DNA analysis identified 19
individual snow leopards in this survey, thus data augmentation has been
set at 100 as a rule thumb this was 5 times greater (Royle et al.,
2009).

\textbf{2.6 Subsistence prey: naur and livestock}

Based on local knowledge and alleged dispersal barriers such as high
peaks, deep gorges and glaciers, we divided the study area into four
blocks (Fig.2) to survey naur and livestock within potential snow
leopard habitat in the KCA. Each block was selected as follows:

1. Gola block; comprising the western border of Walangchung Gola and
extending up to Tiptala in northern China, to the Nup Himalayan range in
the east.

2. Yagma block; comprising the Nup Himalayan range along with Khangla
along the northern border of China, to Nanggola in the east.

3. Khambachen block; extending from Nanggola to the Jhonu Himalayan
range in the east.

4. Ramjer block; extending from the Jhonu Himalayan range to Ratong La
and Khang La along the eastern border of India.

In spring of 2012, we performed direct counts of Naur from appropriate
vantage points (also known as a fixed-point count) within the four
blocks of the KCA. The total number of individuals in a group was
counted and classified by sex and age. To classify the sex and ages of
Naur, a Snow Leopard Monitoring Guideline (Thapa, 2007) was utilized
(adopted from Wegge, 1979). Observations were made with 8-24x binoculars
and 15-60x spotting scope early in the morning (06.00am -10.00am) and
afternoon (14.00pm-17.30pm) at Nepal Standard Time (NST) when Naur are
known to be most active. Naur densities were estimated by dividing the
total number of animals observed by the total area sampled within four
blocks. Sampled area was obtained by adding up areas of all surveyed
blocks. The area of each block was obtained by demarcating them on a GIS
platform (i.e., Google Earth Program) post survey by the team of
surveyors.

For domestic livestock information, opportunistic counting of livestock
numbers was made in the pasture and consultation with key informants
were used to verify the data obtained during the study.

\textbf{2.7 Food habits of snow leopard}

From known snow leopard scat samples derived from DNA analysis, a total
of 73 collected scat samples were dried in the shade, labeled with a
unique code and stored in an envelope for laboratory analyses. The
samples were washed with tap water through a fine mesh sieve and
oven-dried at a temperature of approximately 60\selectlanguage{ngerman}°C. Each sample was
further cleaned in an ethanol- alcohol mixture (1:1).

Food items from scat samples were identified mainly from the macro- and
microscopic structure of hairs. For this purpose, cuticular scale
patterns of hairs were analyzed and further validated by medullary
structure and cross-section (Koppikar \& Sabnis, 1976; Joslin, 1973). In
order to compare hair samples with reference samples, reference images
of available prey species from the study site were developed using
microscopic images from a model B K Biological Trinocular Microscope
(10x, 100x, 400x and 1000x) with DCM510 digital camera and further
verified with reference samples developed by Oli (1993). For further
accuracy, local reference samples were collected from each study site.
Diet was analyzed through identification of 10 random hairs taken from
each scat sample. Identification was further verified from the remains
of bones, claws and hooves from prey species of interest also found
within the scat samples. Remaining materials such as plants, rock,
bones, and feathers were noted but not included in the analysis of diet.
Results were expressed as the relative frequency of prey species found
in the samples.

\textbf{2.7.1 Conversion of Prey Biomass}

An estimate of biomass consumed by snow leopard was used rather than
occurrence percentage, as diet quantification may be more reliable
(Wegge et al., 2012). Smaller animals, for example, are usually
over-represented in the scat analysis (Floyd et al., 1978). Frequency of
occurrence of individual prey items are converted to biomass and numbers
consumed according to the relationship between scat production and prey
size (Ackerman et al., 1984; Floyd et al., 1978) in the following way: Y
= 1.980 + 0.035 X, where Y is weight of prey consumed per scat, and X is
the live weight of particular prey (in kg).

To quantify the predation rate of a snow leopard, average consumption
rates per day of wild (Naur) and domestic livestock were used following
Oli (1993) and Wegge, et al. (2012) as follows: Naur 34 kg, Yak 150 kg,
horse/cattle 140 kg, goat 25 kg, and sheep 30 kg. Of large prey,
predators did not consume the whole ungulate carcass due to inedible
parts such as skeletal pieces, horns/antlers, rumen content and parts of
the skin (Floyd et al., 1978). It was assumed that snow leopards
consumed the following proportional weights of domestic and wild
ungulate as described by Oli et al. (1993) and Wegge et al\emph{.}
(2012): Naur 75\%, yak 50\%, horse and cattle 60\% and sheep and goat
70\%.

A G-Test was employed to determine if snow leopard have a preference for
certain species according to their availability across the study sites
(Manly et al., 2007). To determine if particular species will be
preferred, less preferred or avoided, a 95\% confidence interval was
created for each category by applying Bonferroni corrections to the
Z-statistic (Neu et al., 1974).

\textbf{Results}

\textbf{3.1} \textbf{Snow Leopard Abundance}

Out of 88 putative samples, 73 (83\%) were confirmed by genetic analyses
to be from snow leopard. Among snow leopard positive samples, 60\% (44)
were successfully genotyped. Fecal DNA analysis identified 19 individual
snow leopards (nine females and ten males) (Table 1; Fig. 2). GPS
locations of individually identified snow leopards were plotted on
digital topographic maps in ArcView 3.1 (Fig. 2) to calculate the survey
area of sample collection. The mean maximum linear distance moved by
individual snow leopards was calculated at 3.3 km (std. error 0.6) with
a buffer strip width of 1.6 km. The program SPACECAP estimated a mean
snow leopard population size of 24 (95\% CI: 19-29; SD: 3.04). The mean
density was 3.9 individuals/100 km\textsuperscript{2} (95\% CI: 3.1-
4.8; SD: 0.8; Table 1) for the survey area of 609 km\textsuperscript{2}.
Khambachen block had a higher number of snow leopards, followed by
Ramjer (eastern part of Khambachen), Yagma and Gola (both blocks lies
north-western part of Khambachen).

The encounter probability was 1.73 (95\% CI: 0.21-1.19). Detection
probability at trap locations was found to be 0.05 and the Psi, the
ratio of the number of animals actually present within the space to the
maximum allowable number, was 0.08. The number of effective alleles
(A\textsubscript{e}) was 2.4 (se±0.18) and observed heterozygosity
(H\textsubscript{o}) was 0.57 (se±0.08) in six polymorphic loci.

\textbf{3.2 Available Biomass of Naur and Livestock}

Within the four blocks of the study area, a total of 45 Naur groups with
1404 individuals were counted and estimated density was 2.3/
km\textsuperscript{2} (Table 2) in 609 km\textsuperscript{2}.
Recruitment was estimated to be 44 young per 100 adult females in the
spring season. The mortality rate of yearlings was estimated to be 45\%,
which could be consistent with a similar recruitment number in the
previous year. Overall, the sex ratio was female-biased (73 males per
100 adult female).

In KCA, yaks (Dzo, Dee, Jhomo) dominated livestock types but small
livestock like goat and sheep are negligible in the snow leopard
habitat. Total estimated available standing biomass of yak was over six
times greater compared to that of Naur (Table 3). The total number of
Yak (2050) and Naur (1404) together accounted for 355,236 kg (Yak:
307,500kg i.e. 505kg/km\textsuperscript{2} and Naur: 47,736kg i.e.
78kg/km\textsuperscript{2}) available standing biomass in the KCA.

\textbf{3.3 Snow leopard food habits}

Out of 73 snow leopard scat samples, micro-histological analysis
revealed that one third of scats consisted of multiple prey and two
thirds of single prey species. The prey consumed included three wild and
four domestic mammals. Wild prey accounted for 49\% whereas domestic
prey accounted for 45\%, with the remainder unidentified, 6\% (SD=11.2;
N=8; Table 4). Naur dominated wild prey in the snow leopard diet (70\%)
(SD=3.1 among total available wild prey species, N=3) and yak was the
dominant domestic prey item, accounting for approximately 43\% of the
total domestic livestock in the snow leopard diet, (SD=7.02, N=4).

The harvested biomass by one snow leopard per day was estimated at 3.7
kg/day (Table 5). This is equivalent to 1343 kg annually. This annual
biomass estimate, in the KCA, would amount to 21 blue sheep, 2 musk
deer, 318 pika, 2 yak, 0.5 cattle, 4 goats, 0.3 sheep and other small
mammals including birds (Table 5). Snow leopard preferred Naur to yak,
goat and sheep whereas they avoided cow and musk deer on prey biomass
availability. Symbols (-, 0, +) denote (Table 6) prey species that are
avoided, less preferred, and more preferred respectively, according to
their availability (based on 95\%, Bonferroni confidence intervals).

\textbf{3.4 Snow leopard-prey availability}

An annual food requirement for one snow leopard is 1343 kg. Standing
available food biomass of both wild and domestic livestock was
calculated at 355, 236 kg. Naur, the snow leopard's staple food in the
KCA, was calculated at 47, 736 kg and domestic yak, 307, 500 kg.
Harvesting rate of the estimated 24 snow leopards was about 7\% per
annum. If a snow leopard killed 21 Naur annually then the extant 24 snow
leopards would require 504 Naur per annum. Without the biomass
contribution of small mammals and livestock, the estimated available
Naur biomass of 47, 736 kg gives a predator- prey ratio of 1:59 in KCA
on a weight basis. Here in the KCA the proportion of young (Naur) is
estimated at 17\%, but predation rate is almost double at 28\%.

\textbf{DISCUSSION}

The study estimated snow leopard density at 3.9/ 100
km\textsuperscript{2} (95\% CI= 3.1- 4.8; SD: 0.8) in the KCA. This
estimate is lower than the densities recorded in the Phu valley by
Wegge, et al., 2012 (6 individuals/125 km\textsuperscript{2}), by
Janecka et al., 2011 in Mongolia (5.9 individuals/100
km\textsuperscript{2}; 95\% CI= 3.4- 8.0) and Oli, 1994 in Manang
4.8-6.7/100 km\textsuperscript{2}. Jackson \& Ahlborn (1989) estimated
5-10 snow leopards/100 km\textsuperscript{2} in the prime habitat of the
Langu valley and then extrapolated this estimate to a country-wide
density of less than 0.1- 0.5 snow leopards/100 km\textsuperscript{2} in
most parts of Nepal. The studies such as Jackson and Ahlborn (1989) and
Oli (1997) which were based on expert opinion, sign evidence, VHF radio
telemetry, and their extrapolations may be less reliable (McCarthy \&
Chapron, 2003). Chetri et al. (2019) however, estimated a lower density
of 1.07 individuals/100 km\textsuperscript{2} (95\% CI: 0.71- 1.62) in
Annapurna and 1.16 individuals/100 km\textsuperscript{2} (95\% CI: 0.73-
1.87) in Manaslu of Nepal by modeling genetic sampling over a larger
area. This lower density was likely due to Chetri et al. (2019)
including the high mountain zone up-to 6000 m as potential snow leopard
habitat in their study rather than the 3000-5400 m range considered
(prime and fair) suitable snow leopard habitat in Nepal (Jackson \&
Hunter, 1996). Recently, Khanal et al., 2020 estimated lower densities
using remote camera traps compared to our estimates i.e., 2.51/100
km\textsuperscript{2} (95\% CI: 1.36= 4.60) in Upper Dolpa and 1.21/100
km\textsuperscript{2} (95\% CI: 0.58-2.54) in Lower Dolpa of SPNP west
Nepal. The estimated lower snow leopard density in western Nepal
compared to the KCA could be a result of camera trap analysis from this
study excluding sub-adult and young in density estimates.

Based on food requirements, 24 snow leopards would need an annual
biomass of 32,232 kg in the KCA. Snow leopard diet in this study
comprised both wild (49\%) and domestic (45\%) prey. Our estimates were
consistent with that of Bagchi \& Mishra (2006) and Wegge et al. (2012),
with 58\% and 42\% livestock in India and Nepal respectively. Chetri et
al., 2017 estimated 57\% of Naur and 31\% livestock in snow leopard diet
within the Annapurna-Manaslu region, similar to the estimates of Lovari
et al. (2013) and Ferretti et al. (2014) in the Mt Everest region (25\%
livestock and 56\% Himalayan tahr). Within the Dolpa region of western
Nepal however, Devkota et al., 2013 found that snow leopard diet
consisted of only 30\% Naur where marmot (buffer prey) and small
livestock are plentiful. Oli (1994) stressed that snow leopards killed
livestock because they are abundant and easy to kill compared to wild
prey, not because they were a preferred food source. These findings are
consistent with the results of our study in the KCA, particularly with
the standing biomass of yak representing approximately 2/3 of the total
volume (with Naur occupying the other 1/3).

Based on the estimate of Jackson \& Ahlborn (1989), that an adult snow
leopard requires 20-30 Naur annually to survive, 24 snow leopards would
need to consume 480-720 Naur per year. Jackson and Ahlborn (1989)
hypothesized that a single snow leopard requires an estimated population
of 150- 230 Naur to achieve this (possibly less in areas where other
prey species are easily available). In the KCA snow leopards were
estimated to harvest 28\% of the Naur population annually, nearly twice
the amount estimated (15.1\%) by Wegge et al. (2012) in the Phu valley,
however the proportion of young Naur was found to be similar between the
KCA and Phu (17\% compared to 18.4\%; Wegge et al., 2012). This
difference could be explained by the greater availability of small
livestock in the Phu Valley (Wegge et al., 2012; Thapa, 2005) compared
to the KCA, providing snow leopards with a smaller prey target. The
higher proportion of prey to predators around Manang (159:1; Oli, 1994)
compared to KCA (59:1) could also explain this difference in Naur
consumption. As well as the KCA having less ``easy'' small livestock
prey available compared to Dolpa, Manang and the Phu Valley (Devkota et
al., 2013; Wegge et al., 2012; Oli, 1994), snow leopards in the area
also share habitat with two other apex predators, the common leopard
along the forested- edge habitat in the south (Thapa et al., 2013) and
grey wolf (Subba et al., 2017) in the north which will add significantly
to the competitive pressures on Naur populations. Further long-term
studies examining snow leopard, common leopard and wolf interactions are
therefore needed.

The current situation indicates that available wild prey biomass is
insufficient to support the estimated snow leopard population in the
KCA. Therefore, livestock play an important role for the survival of
snow leopard here in Nepal and elsewhere (Khanal et al., 2020; Mishra et
al., 2016; Johansson et al., 2015; Sharma et al., 2015; Wegge et al.,
2012; Shehzad et al., 2012; Oli et al., 1993). In most of the snow
leopard's range, current community conservation initiatives focus on
compensation and incentive, rather than preventative measures. Recently
introduced Community- Managed Livestock Insurance Schemes in the KCA are
offering huge promise for the long-term survival of snow leopards in the
area by reducing the incentive for retribution killings (Gurung et al.,
2011). However, financial compensation for livestock losses alone will
not solve the problem (Thapa, 2006; Jackson, 2015). Along with this
scheme improvement, there needs to be a focus on preventive measures
such as improved livestock guarding as well as engaging and educating
local people to be citizen scientists on the importance of snow leopard
conservation, involving them in long-term monitoring programs and
promotion of ecotourism.

\textbf{ACKNOWLEDGEMENTS} Permission was granted to conduct the study by
the Department of National Parks and Wildlife Conservation (Ref.:
689/069/070). We are grateful to WWF International for awarding us the
PRINCE BERNHARD scholarship (Grant no. 9Z0533.01) and WWF Nepal for the
research grant to undertake this study. We also want to thank the Nepal
Engineering College (nec-cps) especially the Director Prof. Dr. Khem Raj
Sharma, Associate Prof. Dr. Narayan Kumar Shrestha, Associate Prof.
Jhamak Bahadur Karki and Asst. Prof. Mr. Robert Dongol including Prof.
Mukesh Kumar Chalishe (TU) for their guidance and critical review during
drafting the paper.

Our sincere thanks go to members of the Snow Leopard Conservation
Committees of KCA who collected the scat samples and also assisted us in
gathering the Naur and domestic stock data in the field. We also
acknowledge the Kangchenjunga Conservation Area Management Council and
Kangchenjunga Conservation Area Project for mobilizing the Citizen
Scientists who helped us gather reliable data from field. The team at
the Center for Molecular Dynamics Nepal (CMDN) generously processed and
analyzed our fecal samples in the lab. Finally, we are grateful to Mr.
Gokarna Jung Thapa (GIS expert, WWF Nepal) for assisting us in preparing
the map.

\textbf{Conflict Of Interest}

None.

\textbf{AUTHOR CONTRIBUTION}

Kamal Thapa: Study design, data collection, analysis and drafting and
finalize.

Natalie Schmitt: Review and editing

Narendra Man Babu Pradhan: Study design, review.

Hem Raj Acharya: Study design and writing.

Santosh Rayamajhi: Review and edit.

\textbf{DATA ACCESSIBILITY}

The scat sample locations, DNA Sequences and Microsatellite Genotypes
are available at the Dryad Digital Repository
https://doi.org/10.5061/dryad.vq83bk3rn



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\textbf{Figure 1.} Study area location within prime snow leopard habitat
in the Kangchenjunga Conservation Area, northern Nepal where we
conducted snow leopard scat collection and Naur and livestock surveys
during Spring (April-May) 2012. Blue dots denote putative snow leopard
scat collection sites in the sampling grids (4*4km\textsuperscript{2}).

\textbf{Figure 2.} Location points of individual snow leopard identified
by fecal DNA analysis within the four major blocks of potential snow
leopard habitat (Ramjer, Khambachen, Yagma and Gola) of the KCA during
the Spring Season (April- May) 2012.

\textbf{Table 1.} Summary of Model Parameters for Snow Leopard Fecal DNA
analysis from the KCA, 2012. (Bayesian p-value based on individual
encounters: 0.75); Sigma- Encounter probability, Lamda- Detection
probability at trap location that considered home range, Psi- Data
augmentation parameter (the ratio of the number of animals actually
presents within S to the maximum allowable number), Nsuper- Population
size, and Density- Animals per 100 km\textsuperscript{2}).\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Item & Mean & SD & CI 95\% (Lower Level) & CI 95\% (Upper
Level)\tabularnewline
\midrule
\endhead
Sigma & 1.73 & 3.23 & 0.21 & 1.19\tabularnewline
lam0 & 0.05 & 0.09 & 0.03 & 0.06\tabularnewline
Psi & 0.08 & 0.02 & 0.04 & 0.11\tabularnewline
Nsuper & 23.57 & 3.04 & 19 & 29\tabularnewline
Density & 3.9 & 0.8 & 3.1 & 4.8\tabularnewline
\bottomrule
\end{longtable}

\par\null

\textbf{Table 2.} Demographic Population Structure of Naur in the KCA,
2012. Table describes sex and age of Naur within each sampling block.
The Group category gives the total number of Naur individuals within the
sampling block, FE = the number of Adult Females (more than 2 years), YO
= the number of Young (below one year age of both male and female), YE =
the number of Yearlings (in between one and two year ages of both male
and female), YM = the number of Young Males (above two and below four
years), MM = the number of Middle Males (above five and below seven
years), BM = the number of Big Males (above seven years) and UI = the
number of those Unidentified (all ages both male and female) Thapa, 2007
adopted from Wegge, 1979.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
Block & Group & FE & YO & YE & YM & MM & MM & UI & Total\tabularnewline
\midrule
\endhead
Ramjer & 4 & 42 & 20 & 7 & 10 & 5 & 5 & 15 & 104\tabularnewline
Khambachen & 17 & 267 & 99 & 37 & 37 & 78 & 89 & 27 & 634\tabularnewline
Yagma & 16 & 177 & 82 & 64 & 50 & 43 & 41 & 53 & 510\tabularnewline
Gola & 8 & 54 & 34 & 21 & 15 & 13 & 8 & 11 & 156\tabularnewline
Total & 45 & 540 & 235 & 129 & 112 & 139 & 143 & 106 &
1404\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3.} Available Standing Biomass of Yak and Naur in the KCA,
2012. We assume an average weight of 150 kg for a yak and an average
weight of 34 kg for a Naur (adopted from Wegge et al., 2012).\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Block & No. of Yak & Biomass (kg) & No. of Naur & Biomass
(kg)\tabularnewline
\midrule
\endhead
Gola & 459 & 68,850 & 156 & 5,304\tabularnewline
Yagma & 601 & 90,150 & 510 & 17,340\tabularnewline
Khambachen & 804 & 120,600 & 634 & 21,556\tabularnewline
Ramjer & 186 & 27,900 & 104 & 3,536\tabularnewline
Total & 2,050 & 307,500 & 1,404 & 47,736\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4.} Count and Frequency of Prey Items in the Snow Leopard
Diet From Microscopic Analysis of Fecal Material in the KCA (n=73),
2012.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Prey Species & Count & \% of Frequency\tabularnewline
\midrule
\endhead
Naur & 70 & 34.3\tabularnewline
Musk deer & 11 & 5.4\tabularnewline
Pika & 19 & 9.3\tabularnewline
Yak & 40 & 20\tabularnewline
Cow & 7 & 3\tabularnewline
Goat & 35 & 17\tabularnewline
Sheep & 10 & 5\tabularnewline
Unidentified & 12 & 6\tabularnewline
Total & 204 & 100\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 5.} Estimation of Prey Species Consumed in a Year by a
Snow Leopard in the KCA, 2012. A= assumed weight of the prey species; B=
Y (i.e. Y=1.98+0.035*A); C= Frequency of occurrence in scats; D= Biomass
consumed (B*C); E= Biomass eaten by a snow leopard assuming a rate of
3.7 kg/day; F= No. of prey species consumed by a snow leopard/ year
based on E divided by A.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Prey Species & Estimated mass (A) & Biomass/ scat (B) correction factor
& Frequency of occurrence (C) & Biomass Consumed (D) & Biomass eaten (E)
& No. of prey consumed by a snow leopard (F)\tabularnewline
\midrule
\endhead
Blue sheep & 34 & 3.17 & 70 & 221.9 & 710.08 & 21\tabularnewline
Musk deer & 16.5 & 2.56 & 3 & 7.67 & 24.55 & 2\tabularnewline
Pika & 0.3 & 1.99 & 15 & 29.86 & 95.54 & 318\tabularnewline
Yak & 150 & 7.23 & 10 & 72.3 & 231.36 & 2\tabularnewline
Cow & 140 & 6.88 & 3 & 20.64 & 66.05 & 0.5\tabularnewline
Goat & 25 & 2.86 & 11 & 31.41 & 100.50 & 4\tabularnewline
Sheep & 30 & 3.03 & 1 & 3.03 & 9.70 & 0.3\tabularnewline
Others & 2 & 2.05 & 16 & 32.8 & 104.96 & 52\tabularnewline
Biomass (KG) consumed by a snow leopard/year & Biomass (KG) consumed by
a snow leopard/year & Biomass (KG) consumed by a snow leopard/year &
Biomass (KG) consumed by a snow leopard/year & 420 & 1343
&\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Table 6. Utilization, Availability and Preference of Prey Species in
Snow Leopard Diet within the KCA (2012).\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Species\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Utilization\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Utilization\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Availability\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
90\% Bonferroni confidence interval\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
90\% Bonferroni confidence interval\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Preference\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Counts\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Proportion\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Proportion\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Lower\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Upper\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Blue sheep\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
70\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.38\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.44\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
+\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Musk deer\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Pika\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NA\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yak\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.18\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.52\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.30\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cow\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.03\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.03\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Goat\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Sheep\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.03\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.02\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig/Fig}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fih/Fih}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

