14
BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research. Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards Author(s): Jan E. Janečka, Bariushaa Munkhtsog, Rodney M. Jackson, Galsandorj Naranbaatar, David P. Mallon, and William J. Murphy Source: Journal of Mammalogy, 92(4):771-783. 2011. Published By: American Society of Mammalogists DOI: http://dx.doi.org/10.1644/10-MAMM-A-036.1 URL: http://www.bioone.org/doi/full/10.1644/10-MAMM-A-036.1 BioOne (www.bioone.org ) is a nonprofit, online aggregation of core research in the biological, ecological, and environmental sciences. BioOne provides a sustainable online platform for over 170 journals and books published by nonprofit societies, associations, museums, institutions, and presses. Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance of BioOne’s Terms of Use, available at www.bioone.org/page/terms_of_use . Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiries or rights and permissions requests should be directed to the individual publisher as copyright holder.

Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

Embed Size (px)

Citation preview

Page 1: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, researchlibraries, and research funders in the common goal of maximizing access to critical research.

Comparison of noninvasive genetic and camera-trapping techniques for surveyingsnow leopardsAuthor(s): Jan E. Janečka, Bariushaa Munkhtsog, Rodney M. Jackson, Galsandorj Naranbaatar, David P.Mallon, and William J. MurphySource: Journal of Mammalogy, 92(4):771-783. 2011.Published By: American Society of MammalogistsDOI: http://dx.doi.org/10.1644/10-MAMM-A-036.1URL: http://www.bioone.org/doi/full/10.1644/10-MAMM-A-036.1

BioOne (www.bioone.org) is a nonprofit, online aggregation of core research in the biological, ecological, andenvironmental sciences. BioOne provides a sustainable online platform for over 170 journals and books publishedby nonprofit societies, associations, museums, institutions, and presses.

Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance ofBioOne’s Terms of Use, available at www.bioone.org/page/terms_of_use.

Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiriesor rights and permissions requests should be directed to the individual publisher as copyright holder.

Page 2: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

Comparison of noninvasive genetic and camera-trapping techniquesfor surveying snow leopards

JAN E. JANECKA,* BARIUSHAA MUNKHTSOG, RODNEY M. JACKSON, GALSANDORJ NARANBAATAR, DAVID P. MALLON, AND

WILLIAM J. MURPHY

Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas

A&M University, College Station, TX 77843-4458, USA (JEJ, WJM)

Institute of Biology, Mongolian Academy of Sciences and Irbis Mongolian Centre, Jukov Avenue 77, Ulaanbaatar 51,

Mongolia (BM, GN)

Snow Leopard Conservancy, 18030 Comstock Avenue, Sonoma, CA 95476, USA (RMJ)

School of Biology, Chemistry and Health Science, Manchester Metropolitan University, Manchester M1 5GD, United

Kingdom (DPM)

* Correspondent: [email protected]

The endangered snow leopard (Panthera uncia) is widely but sparsely distributed throughout the mountainous

regions of central Asia. Detailed information on the status and abundance of the snow leopard is limited because

of the logistical challenges faced when working in the rugged terrain it occupies, along with its secretive nature.

Camera-trapping and noninvasive genetic techniques have been used successfully to survey this felid. We

compared noninvasive genetic and camera-trapping snow leopard surveys in the Gobi Desert of Mongolia. We

collected 180 putative snow leopard scats from 3 sites during an 8-day period along 37.74 km of transects. We

then conducted a 65-day photographic survey at 1 of these sites, approximately 2 months after scat collection. In

the site where both techniques were used noninvasive genetics detected 5 individuals in only 2 days of

fieldwork compared to 7 individuals observed in the 65-day camera-trapping session. Estimates of population

size from noninvasive genetics ranged between 16 and 19 snow leopards in the 314.3-km2 area surveyed,

yielding densities of 4.9–5.9 individuals/100 km2. In comparison, the population estimate from the 65-day

photographic survey was 4 individuals (adults only) within the 264-km2 area, for a density estimate of 1.5 snow

leopards/100 km2. Higher density estimates from the noninvasive genetic survey were due partly to an inability

to determine age and exclude subadults, reduced spatial distribution of sampling points as a consequence of

collecting scats along linear transects, and deposition of scats by multiple snow leopards on common sites.

Resulting differences could inflate abundance estimated from noninvasive genetic surveys and prevent direct

comparison of densities derived from the 2 approaches unless appropriate adjustments are made to the study

design.

Key words: abundance, Gobi Desert, microsatellites, Mongolia, monitoring, Panthera uncia, population survey, scat

E 2011 American Society of Mammalogists

DOI: 10.1644/10-MAMM-A-036.1

The endangered snow leopard (Panthera uncia) occupies

particularly inaccessible mountainous habitat in central Asia,

covering an area of more than 1.2 million km2 across 12

countries (Nowell and Jackson 1996). Snow leopards are elusive

and sparsely distributed, making them extremely difficult to

observe and study in the wild (Nowell and Jackson 1996). As a

result, reliable information is lacking on the number of snow

leopards remaining, locations of peripheral and core popula-

tions, and areas where they are in decline. However, such

knowledge is critical for the conservation of this flagship

species. Considerable effort is underway to determine the status

of the species across its extensive and highly fragmented range

(Ale et al. 2007; Jackson et al. 2009; Janecka et al. 2008; Lovari

et al. 2009; McCarthy et al. 2008; Xu et al. 2008). Survey

methods used must generate reliable quantitative data and be

time efficient, easily repeatable, and affordable.

To date, researchers have relied heavily on field sign to

detect and monitor snow leopards (Ale et al. 2007; Fox et al.

w w w . m a m m a l o g y . o r g

Journal of Mammalogy, 92(4):771–783, 2011

771

Page 3: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

1991; Hussain 2003; Jackson and Hunter 1996; Mallon 1991;

McCarthy and Munkhtsog 1997; Schaller et al. 1987, 1988;

Xu et al. 2008). Spoors, scrapes, scats, and scent-sprayed rocks

constitute evidence used for presence–absence surveys and

abundance indexes (Jackson and Hunter 1996). The high

marking frequency of snow leopards, conspicuousness of the

deposited sign, and use of predictable marking sites located

along ridgelines, saddles, and cliff bases facilitate this

approach (Ahlborn and Jackson 1988). However, many

environmental and biological factors (e.g., time of year,

structure of substrate, available scraping sites, breeding status

of females, age structure, social status, and home-range

overlap—Ahlborn and Jackson 1988; Jackson and Hunter

1996) confound the relationship between snow leopard density

and distribution and frequency of sign, making comparisons

between sites difficult. In addition, sign surveys do not

account for differences in detection probabilities or distinguish

between individual snow leopards, making it largely impos-

sible to derive quantitative population estimates with this

approach.

Photographic and molecular methods incorporating nonin-

vasive sampling offer more-robust techniques for monitoring

carnivore populations (Karanth and Nichols 1998; Kohn et al.

1999). Camera trapping has been used extensively for

estimating abundance and population size of many felids,

including ocelots (Leopardus pardalis—Dillon and Kelly

2008), jaguars (Panthera onca—Soisalo and Cavalcanti

2006), leopards (Panthera pardus—Balme et al. 2009), and

tigers (Panthera tigris—Karanth and Nichols 1998). In

northern India, Jackson et al. (2006) demonstrated the

effectiveness of this approach on snow leopards and concluded

that it was a useful tool for estimating population size where

density exceeds 2 or 3 individuals/100 km2. However, camera

trapping requires substantial investment in field time and

equipment, along with careful attention to the distribution and

placement of cameras. Low captures and detection probabil-

ities as a result of insufficient sampling lead to wide

confidence intervals and poor precision of capture–mark–

recapture estimates of population size (Thompson 2004).

Ruggedness of snow leopard habitat and generally poor access

within study sites increases the amount of time and effort

required to set and maintain camera stations. This limits the

size of areas that can be surveyed within the 45- to 55-day

period recommended for maintaining population closure

(Jackson et al. 2006, 2009).

Noninvasive genetic techniques also enable estimation of

population size and density and are becoming more common

for wildlife monitoring (Schwartz et al. 2007; Waits and

Paetkau 2005). This approach has been used to estimate

population size in diverse species, including tigers (Mondol et

al. 2009), bobcats (Lynx rufus—Ruell et al. 2009), coyotes

(Canis latrans—Kohn et al. 1999), and elephants (Loxodonta

cyclotis—Eggert et al. 2003). The simplest technique is to

sample scats deposited within a study site and attribute the scat

to specific individuals by genotyping variable microsatellite

loci. Two approaches are used for deriving population

estimates from noninvasive genetic data collected in this

manner. The 1st uses multiple sampling occasions and a

capture–mark–recapture model similar to camera-trapping

surveys. The 2nd approach derives population estimates from

the number of times different individuals are observed among

samples collected during a single period. In a tiger study in

Bandipur National Park (India) capture–mark–recapture

estimates of population size from noninvasive genetic

techniques were similar to those from camera trapping

(Mondol et al. 2009).

Efficacy of detecting multiple snow leopards via genetic

analysis of scats recently has been illustrated across diverse

geographic areas (Janecka et al. 2008; Lovari et al. 2009;

McCarthy et al. 2008). McCarthy et al. (2008) found a

correlation between the frequency of snow leopard sign (total

sign per kilometer) and number of individuals genetically

detected via analysis of scats. However, neither estimator was

correlated with camera-trapping results from 2 of 3 study sites

(McCarthy et al. 2008). In addition, McCarthy et al. (2008) did

not derive population estimates from genetic analysis of scats

and so did not directly compare abundance from noninvasive

genetic and camera-trapping techniques.

Both noninvasive genetic and camera techniques are

promising for generating quantitative information on distribu-

tion and abundance of snow leopards. Cost-effective and

reliable methods for estimating snow leopard abundance to

better target conservation initiatives are urgently needed

(Jackson and Fox 1997; McCarthy and Chapron 2003). The

objective of our study was to compare the 2 most common

noninvasive approaches for deriving density estimates in terms

of effort, cost, and ability to detect and recapture individual

snow leopards in the Gobi Desert of Mongolia. Comparison

of results obtained by these approaches, along with the

advantages and disadvantages of each, are important for

making decisions on the investment of resources for

monitoring snow leopards. A priori knowledge of the actual

abundance of snow leopards in the area was not available;

therefore, we made comparisons using information on snow

leopard biology and ecology and previous estimates of

population density.

MATERIALS AND METHODS

Study area.—The distribution of snow leopards in Mongolia

extends into the Gobi Desert (Bannikov 1954). In this region

numerous relatively isolated, low-elevation (1,500–2,500 m)

mountains run west to east, south of the larger Gobi Altai

range. We surveyed Tost Uul (i.e., Tost Mountains;

43u10.89N, 101u34.89E) and Noyon Uul (43u7.89N,

102u0.009E) located approximately 250 km west of Dalan-

zadgad (Fig. 1). These rugged massifs rise out of the desert

and are composed of exposed rock and steep slopes broken by

seasonal drainages. In Dalanzadgad the mean annual precip-

itation for the period between 1961 and 2004 was 1.31 cm

with most falling in July and August (Cheng et al. 2011;

Sternberg et al., in press). Temperatures were typically lowest

772 JOURNAL OF MAMMALOGY Vol. 92, No. 4

Page 4: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

in January (monthly mean 214.5uC, 1961–2004) and highest

in July (monthly mean 21.6uC, 1961–2004—Cheng et al.

2011).

The vegetation is sparse on mountain massifs, with feather

grass (Stipa gobica and S. glareosa), low shrubs (Caragana

spp. and Artemisia frigida), and herbaceous plants (Ajania

spp. and Scorzonera capito) most common. In valleys and

gullies the bush Amygdalus mongolica is often dominant.

Snow leopards prey primarily on ibex (Capra sibirica) and

argali (Ovis ammon) in the Gobi Desert, although the argali is

now rare in many areas. Small mammals also are utilized by

snow leopards and include the Tolai hare (Lepus tolai) and

Pallas’s pika (Ochotona pallasi). Sympatric carnivores that

potentially compete for resources include the gray wolf (Canis

lupus), red fox (Vulpes vulpes), and Eurasian lynx (Lynx

lynx—Bannikov 1954; Bold and Dorjzunduy 1976). Pastoral-

ists primarily graze goats and sheep; they use desert slopes

during summer but live in winter–spring camps closer to the

mountains from October through May. Both the local people

and wildlife are dependent on a small number of wells and

springs because of limited available water.

Scat collection.—Snow leopards were not handled directly

during this project. Three sites were surveyed by collecting

scats that later were analyzed genetically. The 1st site (Noyon)

was in Noyon Uul and the other 2 sites (Tost A and Tost B) in

Tost Uul, ,125 km to the west of Noyon (Figs. 1 and 2).

Knowledgeable local residents guided field teams to areas

actively used by snow leopards. We focused on sites with

known or suspected snow leopard activity to maximize

probability of detection because sampling random locations

would provide low detection rates insufficient for population

size estimates (Otis et al. 1978; Pollock et al. 1990).

Two teams concurrently searched for snow leopard sign

(scrapes, tracks, and scent marks) along different transects

traversing ridgelines, saddles, and rocky outcrops. When sign

was observed, each team followed wildlife trails along well-

defined topographic features and collected snow leopard scats

that were present. Species identification of scats in the field

was based on diameter, segmentation, odor, and proximity to

other snow leopard sign (Jackson and Hunter 1996). Only

scats believed to be of snow leopard origin were collected. A

small sample of each scat (,1 cm2) was stored in 15-ml

centrifuge tubes with silica desiccant. Total numbers of scats,

scrapes, and tracks observed were noted. Length of each

transect was dependent on terrain, amount of scat, and

available time. Transects were continued until approximately

18 samples were collected or 5 km of terrain were covered.

Limits were imposed to avoid uneven representation among

transects and to ensure sufficient time for teams to return to

base camp.

Species, sex, and individual identification of scats.—DNA

was extracted from scats using the Qiagen Stool DNA

extraction kit (Qiagen, Valencia, California) following manu-

facturer recommendations. Species-specific polymerase chain

reaction (PCR) or restriction enzyme assays were not available

for all sympatric carnivores present in our study sites. We

therefore identified species that deposited scats by PCR

amplifying and sequencing a 148-base pair (bp) segment of

the mitochondrial cytochrome-b gene using previously de-

scribed methods (Farrell et al. 2000; Janecka et al. 2008).

Sequences observed (GenBank accessions HQ897983–

HQ897986) were aligned with reference taxa, and a neighbor-

joining phylogeny was reconstructed in PAUP* 4.0b10

(Janecka et al. 2008; Swofford 2003). A scat was identified

when its haplotype was in a monophyletic clade with a

reference species supported by 100% bootstrap values and

exhibited ,3% sequence divergence (Janecka et al. 2008).

Genetically identified snow leopard scats were genotyped at

7 microsatellite loci (PUN082, PUN100, PUN124, PUN132,

PUN225, PUN229, and PUN327—Janecka et al. 2008). Loci

FIG. 1.—Map showing scat transects and camera-trap locations

sampled in the Gobi Desert of Mongolia. Scat transects were buffered

with one-half mean maximum distance moved (MMDM) so that they

appear more noticeable.

FIG. 2.—Map showing the distribution of scat transects and

camera-trap locations within the Tost A study site. Buffers used to

estimate effective study areas for the 2 respective surveys also

are shown.

August 2011 JANECKA ET AL.—SURVEYING SNOW LEOPARD POPULATIONS 773

Page 5: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

were originally characterized in the domestic cat by Menotti-

Raymond et al. (1999) and assayed in snow leopards by Waits

et al. (2007), but the primers used were redesigned from snow

leopard flanking sequences and placed closer to microsatellite

repeats to minimize genotyping errors as a result of PCR

failure, allele dropout, and false alleles (Janecka et al. 2008).

Previous analysis of error rates suggested that 3 independent

PCR replicates were sufficient to minimize genotyping errors

to an acceptable level (Janecka et al. 2008). All 7 loci were

variable in the snow leopard population studied.

Microsatellites were PCR amplified using the methods of

Janecka et al. (2008) and genotyped on an ABI 3730 (Applied

Biosystems, Inc., Foster City, California) in the Molecular

Cytogenetics and Genomics Laboratory (Texas A&M Univer-

sity). Samples were handled in a dedicated pre-PCR area, and

barrier-tips were used to minimize contamination. All PCRs

and genotyping were done in triplicate and analyzed with

extraction blanks. Samples that did not yield �2 consistent

replicates at each locus were discarded from analyses. Alleles

had to be observed �2 times in the 3 PCR replicates to be

included in the consensus genotype. Only samples with

consensus genotypes generated for all 7 loci were included

in the analysis.

Sex identification was performed with felid-specific

primers (Murphy et al. 1999) that amplified a 200-bp intronic

segment of the AMELY gene on the Y chromosome (Janecka

et al. 2008). Amplifications were performed in triplicate

along with 1 male positive control, 1 female positive control,

and 1 negative control. PCR products were run on a 1.5%

agarose gel, stained with ethidium bromide, and visualized

under ultraviolet light. A sample was identified as male if at

least 2 of 3 PCRs gave strong amplification of the AMELY

marker. Samples that yielded PCR product in only 1 PCR

were not assigned to either sex. If no amplification of the

AMELY marker occurred in all 3 replicates, the individuals

were considered females. However, to avoid misidentifying

male samples as female due to poor DNA quality or quantity,

only scats that were genotyped successfully with the

complete 7-microsatellite panel were identified positively as

female.

Analysis of microsatellite data.—Level of PCR failure and

allele dropout were monitored closely, and scat samples with

missing consensus genotypes at �1 loci were excluded to

avoid genotyping errors. We thus excluded 21 snow leopard

scat samples. We estimated PCR failure and allele dropout

rates for 39 scats included in population analyses. We also

estimated the quality index for each locus and sample, and

global quality index as recommended by Miquel et al. (2006).

Quality index can range from 0 to 1; the higher the index,

the lower the error rate. We discarded all samples that

had a quality index , 0.80. Allele number (AN), observed

heterozygosity (HO), expected heterozygosity (HE), and

Hardy–Weinberg equilibrium were calculated in GENALEX

6.0.9 (Peakall and Smouse 2006). The sequential Bonferroni

technique was used to correct for multiple comparisons in

Hardy–Weinberg equilibrium tests (Rice 1989). Probability of

identity (Paetkau and Strobeck 1994) was estimated in

GENALEX for unrelated individuals (PID-unr) and siblings

(PID-sibs—Waits et al. 2001).

Population size estimates from scat survey.—Numbers of

snow leopards detected were compared across transects.

Capture–mark–recapture population estimates were inappro-

priate for our sample set because scats were collected in only 1

survey session. Estimating population size by pooling

noninvasive samples into 1 temporal period previously has

provided robust estimates across diverse taxa and study sites

(Eggert et al. 2003; Kohn et al. 1999; Miller et al. 2005; Ruell

et al. 2009); we therefore used this approach. However, one of

the major weaknesses of pooling scat samples is that the

temporal period remains undefined because the length of time

scats persist in the field is uncertain. In the Gobi the time

period represented can be 2–6 months, based on previous field

experience.

The 1st method used was the maximum-likelihood

estimator of CAPWIRE, which calculates the likelihood of

data fitting hypothesized population sizes (Miller et al. 2005).

CAPWIRE takes into account capture heterogeneity by

assigning individuals into 1 of 2 classes (type A and type B)

and estimating relative capture probabilities. The likelihood

ratio test was used to determine if our data fit the equal capture

probability model or the 2 innate rates model with a rejection

value of 0.05 (based on 1,000 bootstrap replicates). Confi-

dence intervals (CIs) around the population size were

estimated from 1,000 bootstrap replicates.

The 2nd method involved the use of rarefaction estimators

that derive an asymptote (the estimate of population size) from

the curve of the cumulative number of individuals detected

versus number of samples genotyped. These approaches do

not incorporate capture heterogeneity. Three proposed formu-

las, originally described by Kohn et al. (Kohn—1999), Eggert

et al. (Eggert—2003), and D. Chessel (Chessel—Valiere

2002), can be used for calculating the rarefaction curve; all

were implemented in GIMLET 1.3.1 (Valiere 2002). GIMLET

was used to generate 1,000 pseudoreplicates from random

reiterations of sampling order. Population estimates were

derived in R version 1.7.1 (Ihaka and Gentleman 1996) from

the asymptote of the rarefaction equations fitting the data from

the pseudoreplicates generated by GIMLET.

To define area surveyed we buffered transects with mean

maximum distance moved (MMDM) and one-half MMDM

between locations where the same individual was genetically

‘‘recaptured’’ (Karanth and Nichols 2002; Wilson and

Anderson 1985). Polygons were generated from buffers and

areas estimated in ArcGIS 9.3 (ESRI, Inc., Redlands,

California). Density was derived from population estimates

divided by area of buffered transects.

Camera-trapping methods.—Two months after scat collec-

tion a camera-trapping survey described by Jackson et al.

(2009) was conducted in the Tost A site (Figs. 1 and 2). The

approach was based on methods refined during a 2-year

camera-trap study undertaken in the Indian Trans-Himalaya

(Jackson et al. 2006). We deployed 18 camera stations, each

774 JOURNAL OF MAMMALOGY Vol. 92, No. 4

Page 6: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

2–4 km apart (Fig. 2). Two film (analog) cameras triggered by

a TrailMaster 1550 active-infrared sensor (20-s delay, 24-h

continuous operation; Goodson & Associates, Inc., Lenexa,

Kansas) were concealed within natural rock cairns at each

station. Camera traps were positioned along snow leopard

travel routes (e.g., ridgelines, valley edges, and passes); no

baits or lures were used as attractants. We did not relocate

traps during the survey because of limited access and to

maximize the probability of observing all individuals present

within the surveyed area. Camera traps were checked every 2–

10 days to ensure that they were functional and to replace film.

Results of camera trapping are decribed in Jackson et al.

(2009).

Population size estimates from camera-trapping survey.—

We distinguished individual snow leopards from pelage

patterns using the criteria of Jackson et al. (2006). Program

CAPTURE (Karanth and Nichols 1998; White et al. 1982) was

used to estimate population size with the capture–mark–

recapture approach under 1-day, 3-day, 5-day, and 7-day

sampling occasions; details and results of the capture–mark–

recapture analysis are provided in Jackson et al. (2009). Cubs

were excluded from the analysis. Population closure was

tested with the Stanley and Burnham (1999) method. To

define effective area surveyed and estimate snow leopard

densities, polygons were generated in ArcGIS 9.3 by buffering

individual camera traps and the minimum convex polygon of

camera traps using one-half MMDM and MMDM distances

between successive captures of individuals (Karanth and

Nichols 2002).

Cost comparison of surveys.—Effort put forth during

surveys was estimated by multiplying number of trained

personnel and days required to complete work (i.e., person-

days). We calculated total costs of surveys by summing

expenditures for domestic airfares (Ulaanbaatar to Dalanzad-

gad), ground transportation to study sites, food and accom-

modation for team members, expendable field and laboratory

supplies (e.g., film, batteries, and scat collection kits), and

salaries of local staff. Expenditures in the laboratory were

estimated for DNA extraction and species identification

(extraction kits, PCR, and sequencing), individual identifica-

tion (PCR and microsatellite genotyping), and sex identifica-

tion (PCR and agarose electrophoresis). Remote-sensing

cameras, laboratory equipment, indirect costs, and salaries of

staff from the United States were excluded.

RESULTS

Noninvasive genetic survey.—Thirteen transects were sur-

veyed from 8 to 16 March 2007 (Fig. 1). Of the 180 scats

collected we obtained genetic species identification for 81%

(Table 1). Number of scrapes per kilometer ranged from 3.46 in

Noyon to 10.56 in Tost B (Table 2). Tracks were observed in 2

of 3 sites, with 1.01 tracks/km in Noyon and 0.30 tracks/km in

Tost A. The majority of scats collected were red fox (n 5 83),

and the snow leopard was the next most common species

detected (n 5 60). Three wolf or dog scats were observed.

We genotyped the verified snow leopard scats with 7

microsatellites. We used rigorous criteria for allele sizing and

discarded all ambiguous samples (21 of 60) from the analysis.

For the 39 scats used for population analysis our mean PCR

failure rate was 2.44% (range, 0.85–3.42%), and allele

dropout was 0.83% (range, 0.00–1.79%; Appendix I). Mean

quality index for loci was 0.971 (range, 0.957–0.991) and for

scats also 0.971 (range, 0.857–1.00; Appendix I). Global

quality index was 0.966. Our high quality values were a direct

result of excluding all questionable genotypes from the

analysis. Complete genotypes were obtained for 39 samples

(65%), among which a total of 15 unique genotypes were

observed (Table 1).

All 7 loci were polymorphic, with AN ranging from 2 to 5

(X 5 3.6) and HE from 0.18 to 0.70 (X 5 0.51; Appendix II).

No additional snow leopard samples were available from the

study population that would permit an independent estimate of

allelic frequencies. We therefore estimated allelic frequencies

and genetic parameters from unique genotypes observed

during our survey. All loci were in Hardy–Weinberg

TABLE 1.—Species and individual identity (ID) of putative snow leopard scats collected along transects located in Noyon Uul and Tost Uul of

the Gobi Desert. Species ID refers to the numbers of scats genetically attributed to the listed species. n 5 number of scats, X 5 mean number of

scats per transect, NA 5 not applicable.

Scat

Noyon (4 transects) Tost A (4 transects) Tost B (3 transects) Total (11 transects)

n (%) X n X n (%) X n (%) X

All scats 39 (NA) 9.8 84 (NA) 21.0 57 (NA) 19.0 180 (NA) 16.4

Species ID 32 (82) 8.0 70 (83) 17.5 44 (77) 11.0 146 (81) 13.3

Snow leopard 13 (41) 3.3 33 (47) 8.3 14 (32) 3.5 60 (41) 5.5

Red fox 19 (59) 4.8 37 (53) 9.3 27 (61) 6.8 83 (57) 7.5

Wolf 0 (0) 0 0 (0) 0.0 3 (7) 0.8 3 (2) 0.3

Snow leopard scat

Individual ID 10 (77) 2.5 19 (58) 4.8 10 (71) 2.5 39 (65) 3.6

Individuals 6 (NA) 2.0 5 (NA) 3.0 4 (NA) 2.3 15 (NA) 2.5

Males 3 (NA) 1.0 3 (NA) 2.0 3 (NA) 1.3 9 (NA) 1.5

Females 3 (NA) 1.0 2 (NA) 1.0 1 (NA) 1.0 6 (NA) 1.0

August 2011 JANECKA ET AL.—SURVEYING SNOW LEOPARD POPULATIONS 775

Page 7: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

equilibrium. The PID-unr was 0.00016 and PID-sib was 0.01801

with all 7 loci. In conjunction with the Y-linked marker we

identified 9 male and 6 female snow leopards.

Population size estimates.—Fifteen snow leopards were

genetically detected; numbers in each of the 3 sites varied

from 4 (3 males and 1 female) in Tost B to 6 (3 males and 3

females) in Noyon (Table 1). Mean number of captures was

2.7 per snow leopard. Six snow leopards (40%) were observed

only once, and the other 10 a mean of 3.5 times. The most

common male was observed 8 times and the most common

female 5 times. Within the camera-trapped site (Tost A), scat

sampling detected a total of 5 individuals, with 3 males and 1

female observed 3 or more times and 1 female observed only

once. The male that was identified in 8 scats was detected at

this site.

We combined data across all 3 sites to estimate population

size within the entire area sampled. The CAPWIRE program

selected the 2 innate rates model model based on the

difference between the likelihood scores (P 5 0.046).

Population size derived from the maximum-likelihood method

was 19 (95% CI 5 11–26). Mean number of observations per

individual was 2.7, and relative capture probability of type A

versus type B individuals was 4.15. The rarefaction population

size estimates were 16 (95% CI 5 12–19 [Chessel]), 17 (95%

CI 5 12–31 [Eggert]), and 24 (95% CI 5 15–95 [Kohn]).

Previous studies have shown that the Kohn method overes-

timates sizes of small populations (Eggert et al. 2003; Frantz

and Roper 2006; Valiere 2002). In our data set it also produced

unrealistically wide confidence intervals; thus, we excluded it

from subsequent analyses.

We derived density estimates from the CAPWIRE and

Chessel results, which provided an upper and lower range.

Scat transects buffered using one-half MMDM (2.84 km)

yielded survey areas of 155.5 km2 (Noyon), 108.0 km2 (Tost

A), and 59.8 km2 (Tost B), for a total of 323.3 km2. Buffers

provided density estimates of 4.9 (95% CI 5 3.6–5.8) and 5.9

(95% CI 5 3.4–8.0) snow leopards/100 km2. When the 5.69-

km MMDM buffer was used, the total area was 859.2 km2,

yielding densities of 1.8 (95% CI 5 1.3–2.2) and 2.2 (95% CI

5 1.3–3.0) snow leopards/100 km2.

We examined possible effects of violations of population

closure resulting from scats deposited well before our survey

(.2 months prior) by reestimating population size with the

CAPWIRE and Chessel methods after excluding scats

classified as old. During collection, scats were divided into

3 subjective categories: fresh—surface was completely intact

and glossy; recent—majority of surface was intact, very dry,

and slightly cracked; and old—surface was cracked and

visibly deteriorated. Although it was not possible to gauge the

absolute age of scats, this exercise provided insight into the

effects temporal distribution of scat deposition had on

population estimators. We removed 10 scats that were old,

based on our classification scheme. This removal reduced the

number of individuals detected to 13; however, population

estimates were similar to those derived when all scats were

included (CAPWIRE: n 5 19, 95% CI 5 13–29; Chessel: n 5

14, 95% CI 5 10–18).

Camera-trapping effort and capture success.—A contiguous

block of habitat in Tost A (Figs. 1 and 2) was camera trapped

over 65 consecutive days using 18 camera stations (12 May–16

July 2007; 1,114 total trap nights). It took 7 days to deploy all

camera stations. The survey period began after all cameras were

activated. Snow leopards were observed during 47 capture

events (120 total photographs), leading to a capture rate of

10.77 snow leopard photos/100 trap nights (Jackson et al. 2009).

In 13 of 47 capture events a specific individual could not be

identified. Seven snow leopards were detected, including 1

female (SL-1) with 3 cubs (,12 months of age), 2 adult males

(SL-2 and SL-4), and 1 adult (SL-3) of undetermined sex. Three

adults (SL-1, SL-2, and SL-4) and 3 cubs were detected within

the first 4 trap nights. It took an additional 18 nights to detect the

remaining adult snow leopard male, SL-3. The individual

capture rate was 0.63 individuals (including cubs)/100 trap

nights (Jackson et al. 2009).

Capture–mark–recapture history and population esti-

mates.—We captured SL-1 seven times, SL-2 sixteen times,

TABLE 2.—Indexes of snow leopard abundance and effort expended for the 3 sites surveyed using noninvasive genetic techniques in the Gobi

Desert. Sign data were not recorded for 2 of the 5 transects sampled in Noyon. ID 5 identity, NA 5 not applicable.

Abundance index Noyon N (SE) Tost A N (SE) Tost B N (SE) X

Scats (visual ID)/kma 5.13 (2.49) 7.85 (1.00) 7.34 (0.97) 6.77

Scats (genetic ID)/km 1.69 (0.52) 2.74 (0.75) 1.63 (0.69) 2.02

Scrapes/km 3.46 (0.57) 10.45 (3.47) 10.56 (3.03) 8.16

Tracks/km 1.01 (0.32) 0.30 (0.30) 0 0.44

Total signb/km 6.17 (1.15) 13.48 (3.56) 12.20 (3.66) 10.62

Detected individualsc/km 1.08 (0.37) 0.97 (0.21) 0.87 (0.12) 0.97

Detected individuals/100 km2 d 3.86 (NA) 4.63 (NA) 6.68 (NA) 5.06

Effort

Total transect length (km) 8.01 13.39 6.01 9.14

Person-days 4 4 3 3.67

Transect km/100 km2 d 1.00 1.24 1.47 1.24

a Length of transect.b Sum of scrapes, tracks, and snow leopard scats that were genetically verified.c Snow leopards detected by genotyping scat.d Area estimated by buffering transects with one-half mean maximum distance moved (MMDM).

776 JOURNAL OF MAMMALOGY Vol. 92, No. 4

Page 8: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

SL-3 twice, and SL-4 four times. Capture rates declined after

40 days of trapping, particularly for SL-1 and her cubs. Cubs

were not detected after the initial 40 days. Fifty-two percent of

captures were made at 4 trap sites that were located on

actively used travel routes with high abundance of snow

leopard sign. Four camera stations (22%) did not detect any

snow leopards (Jackson et al. 2009).

Data for the capture–mark–recapture analysis are provided

in Jackson et al. (2009) and Appendixes III and IV. Although

CAPTURE indicated a closed population over the entire 65-

day period, the Stanley–Burnham test (Stanley and Burnham

1999) supported closure only for ,57 days. Relative fit tests

could not reject the null Mo model (constant capture

probabilities) over the Mt (variation by time) and Mb

(behavioral response) models. Expected values were too small

to test the Mh (heterogeneity) model and Mh versus Mbh

(behavioral response and heterogeneity).

The simplest model, Mo, was most appropriate for our data

based on these tests. The model selection criteria derived by

CAPTURE also indicated that model Mo provided the best fit

(Appendix III). The Mh model, in which capture probabilities

are heterogeneous as a result of factors including sex, age,

activity patterns, and social status, was 2nd (model selection

criteria 5 0.84–0.95; Appendix III). However, small sample

size does not provide sufficient power to discriminate between

null and more complex models; therefore, we estimated

abundance using the 2 best-fitting models. Population size

estimates were 4 (95% CI 5 4–4) adult snow leopards with

both Mo and Mh (Appendix IV). The narrow confidence

interval indicated that we detected all snow leopards present

during the survey. Capture probabilities were relatively high,

ranging from 0.118 for 1-day trapping occasions to 0.563 for

7-day occasions (Appendix IV), further supporting this

conclusion.

The one-half MMDM was 3.38 km, yielding an effective

area of 264 km2 for individually buffered camera stations and

294 km2 when the minimum convex polygon of stations was

buffered. This resulted in densities of 1.5 (95% CI 5 1.5–1.5)

and 1.4 (95% 5 1.4–1.4) snow leopards/100 km2, respectively

(Table 3). Using the MMDM (6.75 km) as a buffer distance

reduced densities to 0.8 (95% CI 5 0.8–0.8) and 0.7 (95% CI

5 0.7–0. 7) snow leopards/100 km2, respectively.

Comparison of noninvasive genetic and camera-trapping

surveys.—Within Tost A, scats were collected on only 2 field

days compared to a total of 65 days of sampling for camera

trapping (Table 4). Total effort was 149 person-days for

camera trapping of 1 site versus 59 person-days for the genetic

survey of 3 sites (Noyon, Tost A, and Tost B). Genetic

analysis costs at Texas A&M University were $10/sample for

extraction and sequencing (n 5 180 scats), $30/sample for

individual identification (60 snow leopard scats), and $3/

sample for sex identification (60 snow leopard scats).

Excluding equipment purchases, air travel, and United States

salaries, our total survey expenditures were $5,230 for the

genetic survey and $10,800 for camera trapping.

DISCUSSION

Noninvasive genetic survey.—Genetic analysis revealed that

the majority of scats collected were deposited by red fox; only

41% were attributed to snow leopards. This high misidenti-

fication rate was observed despite only sampling scats that

were judged to be from our target species based on their

appearance and associated sign. Red fox scats were found

frequently (76.3% of the total) on active snow leopard scrape

sites. Misidentification of carnivore scat has been reported in

previous field studies incorporating genetics (Davison et al.

2002; Farrell et al. 2000; Perez et al. 2006). The error rate for

snow leopards was 35% in Ladakh (Janecka et al. 2008), 51%

in Mongolia (Janecka et al. 2008), and 41% in China and

TABLE 3.—Camera-trapping density estimates of snow leopards in Tost Uul derived with 2 different buffering methods. Data from Jackson et

al. (2009). Density estimates are individuals per 100 km2. NA 5 not applicable.

Buffer Buffer width (km)

MCP buffereda Individual stations buffered

Area (km2) Density 6 95% CI Area (km2) Density 6 95% CI

One-half MMDMb 3.38 294.4 1.4 6 0 264.0 1.4 6 0

MMDM 6.77 552.5 0.7 6 0 536.9 0.8 6 0

Mean home-range radiusc 7.23 593.6 0.7 6 0 NA NA

a MCP 5 minimum convex polygon.b MMDM 5 mean maximum distance moved by snow leopards between photographic captures.c Radius of average snow leopard home range in Mongolia (from McCarthy et al. 2008).

TABLE 4.—Cost comparison for noninvasive genetic and camera-

trapping surveys of snow leopards in the Gobi Desert of Mongolia.

Camera trapping was conducted in only 1 site (Tost A), whereas the

noninvasive genetic survey included 3 sites (Tost A, Tost B,

and Noyon).

Cost Noninvasive genetic survey Camera-trapping survey

Financial ($US)a 5,230b 10,800

Effort (person-days) 59 150

Fieldc 20 133

Data analysis 39 18

No. team members 3 2 or 3

a Excludes international air travel, equipment costs (i.e., cameras, sensors, thermal

cyclers, etc.), and salaries of United States staff.b DNA analysis costs: $10/sample for species identification, $3/sample for sex

identitfication, and $30/sample for individual identification.c Person-days for camera set up, take down, and monitoring throughout the 65-day

survey; genetic survey includes time required for sampling transects and traveling

between collection sites.

August 2011 JANECKA ET AL.—SURVEYING SNOW LEOPARD POPULATIONS 777

Page 9: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

Kyrgyzstan (McCarthy et al. 2008), suggesting that this is a

prevalent problem with carnivore fecal surveys.

The proportion of misidentified scats varied among

transects, even within the same site (e.g., 38–89% for Tost

B). Confirmed snow leopard scats per kilometer were 2- to 3-

fold lower than those based on visual identification. Because

snow leopard scat is misidentified frequently in the field, and

the proportion might not be consistent across transects, its use

in surveys should be discounted unless corroborated by

genetic analysis. Trained dogs could be incorporated into

snow leopard surveys to improve species-specific scat

detection (Mackay et al. 2008).

Lowest numbers of scrapes were detected in Noyon. In this

area scrapes per kilometer (3.46 scrapes/km) were similar to

those previously observed in Ladakh, India (up to 2.6 scrapes/

km—Fox et al. 1991) and Nepal (2.46 scrapes/km, range 0.28–

4.96 scrapes/km—Ale et al. 2007). Other studies reported only

total sign per kilometer after incorporating visually identified

scats, making it difficult to make additional comparisons

(Hussain 2003; Lovari et al. 2009; McCarthy et al. 2008).

Scrape frequency was 3-fold higher in the Tost sites than in

Noyon, even though numbers of detected individuals per

kilometer were similar (1.1 versus 0.9 individuals/km, respec-

tively). Jackson et al. (2006) noted that sign indexes likely

underestimate abundance based on their camera-trapping data.

Marking behavior in snow leopards is behaviorally, seasonally,

and site-dependent (Ahlborn and Jackson 1988). Such factors

likely influence scraping frequency, limiting the utility of this

type of sign for indexes of abundance, a conclusion consistent

with the discrepancies we observed.

The genetic survey was effective for documenting presence

of snow leopards. This species was detected on 11 of 13

transects sampled across 3 different sites in 8 days of

fieldwork. We obtained enough individual recaptures to make

preliminary estimates of population size. The maximum-

likelihood estimator of CAPWIRE can in some cases

outperform rarefaction methods because it incorporates

heterogeneity in capture probabilities (Miller et al. 2005).

This is largely dependent on how individual capture

probabilities fit the 2-category model, compared to the equal

probability assumed in rarefaction approaches.

We used both models (rarefaction and 2-category) because

we did not have a priori knowledge of capture probabilities.

Our estimate derived using the heterogeneity model was not

significantly different from the rarefaction results. The long

persistence of scat in dry, cold environments makes it difficult

to determine whether pooling samples into 1 temporal period

violated population closure, potentially inflating size esti-

mates. However, excluding samples defined as old did not

change our results substantially.

Camera-trapping survey.—The camera-trapping rate in Tost

Uul was relatively high (10.77 snow leopard photos/100 nights

compared to 0.09, 0.93, and 2.37 snow leopard photos/100

nights in the Tien Shan Mountains of Kyrgyzstan and China—

McCarthy et al. 2008), and 3.5 snow leopard photos/100

nights in the western Sayan Mountains of Russia (Subbotin

and Istomov 2009). Photographic captures were similar to

those observed over 2 successive years in Ladakh, India (9.41

and 15.11 photos/100 nights—Jackson et al. 2006). Capture

probabilities in our survey ranged from 0.12 for 1-day

sampling occasions to 0.56 for 7-day occasions (see Appendix

IV); in contrast, tiger capture probabilities ranged from 0.04

(3-day occasions) to 0.26 (4- to 6-day occasions) for several

surveys (Karanth et al. 2004; Karanth and Nichols 1998). The

mean interval between snow leopard captures was 9.3 nights,

lower than in the Ladakh study (14.5 nights) and substantially

less than the mean for tiger captures (99.4 nights, 19 studies),

but consistent with intervals for areas where tiger densities

approach 10 individuals/100 km2 (Carbone et al. 2001).

Repeated use of well-defined travel routes by snow leopards in

the Gobi appears to enable high capture rates when

appropriate camera-trapping sites are selected.

Unfortunately, high capture rates and detection probabilities

do not necessarily lead to more robust population estimates. It

also is necessary to obtain sufficient samples over an area

large enough to encompass a significant portion of the

population (Otis et al. 1978; Thompson 2004). This is

especially problematic when surveying rare and difficult to

detect carnivores. The small area we were able to survey

within the 65-day period exemplifies the difficulty of meeting

this goal. The narrow confidence intervals and selection of the

simplest capture model suggest that although we may have

detected nearly all individuals within the specific study site

because of the uniform trap layout and high capture rates, our

sample was likely insufficient for estimating population size

with the capture–mark–recapture model (Otis et al. 1978;

White et al. 1982). In addition, the relevance of our estimate

for overall abundance in the region is difficult to assess,

because the area covered was limited.

White et al. (1982) recommended using closed population

models to estimate population size with more than 20

individuals. Even a cursory review of the literature indicates

that camera-trapping surveys of felids rarely attain this

threshold. There is no snow leopard survey that we are aware

in which .10 individuals were observed during an indepen-

dent session (Jackson et al. 2006; McCarthy et al. 2008;

Subbotin and Istomov 2009). Few surveys of other cat species

have had larger sample sizes. For example, only 3 of 15

camera-trapping surveys of tigers had .20 photo-captured

individuals (Harihar et al. 2009). We found only 1 survey of

jaguars that detected .11 individuals (Soisalo and Cavalcanti

2006). Ocelots numbered ,10 in 3 published camera-trap

studies (Haines et al. 2006; Maffei and Noss 2008; Trolle and

Kery 2003). This is a direct result of the limited resources

researchers have when conducting camera-trapping surveys of

species with inherently low densities.

Comparison of noninvasive genetic and camera-trapping

methods.—Each approach provided information on snow

leopard presence–absence, the minimum number of individ-

uals detected, and estimates of abundance. At 1 of the sites

(Tost A) we conducted both surveys for direct comparison of

results. During the noninvasive genetic survey of this site we

778 JOURNAL OF MAMMALOGY Vol. 92, No. 4

Page 10: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

collected 84 scats on 4 transects over the course of only 2 days.

Genetic identification confirmed that 33 originated from snow

leopards. This species was detected on all 4 transects, with a

total of 3 males and 2 females being observed. In Tost A we

found many active scrapes. Two months later the camera-

trapping survey identified 7 individuals (including 3 cubs);

however, it took 11 field days (7 days to deploy cameras and 4

trap nights) to detect 6 and 25 field days to observe all 7.

Recaptures were common with both methods, enabling

population estimates. Population size derived from camera

trapping (4 adults, with additional 3 cubs) was similar to the

number of individuals (5) genetically observed among the

scats collected at this site.

Several important factors must be considered when

comparing camera-trapping and genetic surveys. First, sub-

adults typically are excluded from capture–mark–recapture

estimates. In contrast, age of individuals cannot be inferred

from scat; thus, the data set might include subadults. Camera

trapping also provides date and time of each capture allowing

a definition of sampling periods, but unless the same transects

are sampled repeatedly, no definite temporal bounds exist for

scat surveys. If scats persist in the field for .2 months,

violations to population closure could exist. However, the

number of individuals genetically detected in Tost A was

below that observed by camera trapping, suggesting that

migrants and transients might not significantly affect the

genetic survey. Scat sampling can fail to detect individuals

that have lower marking activity, as was the case with

subadult wolves (Marucco et al. 2009).

Density.—The geographic extent of our study population was

uncertain. This is a problem faced in many wildlife surveys

(Wilson and Anderson 1985), because most cannot cover the

entire geographic area encompassed by a population, often for

logistical reasons alone. Researchers, therefore, typically use

density to compare abundance and population trends, which

requires a careful definition of effective area surveyed (Wilson

and Anderson 1985). One of the most difficult problems to

overcome when monitoring populations is defining this

parameter critical for assessing density and study effort.

A common approach is to buffer the locations sampled

(camera stations or scat transects) with a distance represen-

tative of movement patterns in the area (Wilson and Anderson

1985). Subsequent densities therefore are influenced strongly

by the distribution of the sampled points, the concordance

between the buffered area and home ranges, and the metric

used as the buffer (Balme et al. 2009; Maffei and Noss 2008).

Much debate surrounds how this metric, a surrogate for

movement distances, should be derived (Balme et al. 2009;

Dillon and Kelly 2008; Soisalo and Cavalcanti 2006; Wilson

and Anderson 1985). Previously, studies have shown that

using derivations of the mean of the maximum distance

between successive captures yields reasonable densities

(Jackson et al. 2006; Karanth and Nichols 1998; Silver et al.

2004; Wilson and Anderson 1985).

Some studies also have used mean home-range diameter or

summary statistics of directly observed movements when

telemetry data were available (Balme et al. 2009; Dillon and

Kelly 2008; Soisalo and Cavalcanti 2006). In most snow

leopard field sites telemetry is logistically not feasible

(Jackson and Ahlborn 1989; McCarthy et al. 2005). Applying

telemetry data to different sites or periods could lead to

densities biased by previously observed movement patterns.

We thus chose to use one-half MMDM and MMDM for

estimating our effective areas and densities. In a study of leopards

one-half MMDM provided meaningful density estimates from

camera-trapping data (Balme et al. 2009). However, concurrent

global positioning system telemetry and camera-trapping jaguar

studies found that one-half MMDM underestimated distances

moved, significantly inflating density (Soisalo and Cavalcanti

2006). In another study MMDM buffers resulted in ocelot

densities consistent with radiotracking data (Dillon and Kelly

2008). These discrepancies likely are caused by site-specific

differences in habitat distribution, individual movement, and

distribution of sampling locations.

Our estimates varied from 1.5 individuals/100 km2 from

camera trapping to 5.9 individuals/100 km2 from the

noninvasive genetic survey (using one-half MMDM). Bold

and Dorjzunduy (1976) estimated 4.4 individuals/100 km2 in

Tost Uul, although they did not explain clearly how this

estimate was derived. Snow leopard densities have ranged

from a low of 0.2 individuals/100 km2 in SaryChat

Zapovednik, Kyrgyzstan (McCarthy et al. 2008) to 4.5

individuals/100 km2 (2002) and 8.5 individuals/100 km2

(2003) in Hemis National Park, India (Jackson et al. 2006).

Jackson et al. (2006) noted that narrow spacing of camera

stations in 2003 likely led to an overestimation of density.

Methods used to define effective areas have been inconsistent

among snow leopard surveys. The few studies reporting

density have used either one-half MMDM (Jackson et al.

2009), mean home-range diameter inferred from ungulate

density (McCarthy et al. 2008), or an ad hoc estimation of

study area size (Lovari et al. 2009). This lack of consistency

prevents direct comparisons. A method of defining study areas

should be used consistently across snow leopard surveys

conducted by different research groups.

Despite comparable one-half MMDM distances from scat

and photo captures (2.84 km versus 3.38 km, respectively), the

effective areas covered by the 2 surveys contributed to

substantial differences in density. Densities from the genetic

survey were nearly 4-fold greater than from camera trapping

(,5.4/100 km2 versus 1.4/100 km2). This can be attributed

largely to 2 main factors. First, cubs were excluded in

population estimates from camera trapping; in contrast, all

detected individuals were used in the genetic survey. Second,

the broader spacing of camera stations compared to scats

collected along transects resulted in a greater effective area.

This is clearly seen in Tost A, where the camera survey

covered a 2.5-fold larger area (Fig. 2). In addition, multiple

snow leopards frequently mark and deposit scats in common

sites such as prominent outcrops and saddles (Ahlborn and

Jackson 1988). When transects are selected preferentially to

cover these sites, a large portion of the population can be

August 2011 JANECKA ET AL.—SURVEYING SNOW LEOPARD POPULATIONS 779

Page 11: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

detected within a relatively small area. Finally, the persistence

of scat in cold, dry environments makes it difficult to define

the length of the survey when collected scats are pooled into 1

sampling period, although excluding scats judged to be old did

not significantly change our estimates. The combination of

these factors, particularly the reduced size of the area covered

in each site (Fig. 2), seems to inflate density derived from scat

transects. To minimize such bias in noninvasive genetic

surveys scat transects need to be separated spatially and

oriented to maximize the area surveyed. It can be advanta-

geous to sample a greater number of shorter transects (Ahlborn

and Jackson 1988). These modifications should be combined

with a stratified sampling design that ensures a more uniform

distribution of sampled points than in our study.

Because of the uncertainties involved in deriving density

estimates, Jackson et al. (2009) recommended that surveys

report basic capture indexes (e.g., minimum number of

individuals detected) and trapping effort (e.g., photographs

per 100 trap nights). Similarly, some standardized metrics also

should be used in genetic surveys. We propose reporting

number of genetically verified snow leopard scats per

kilometer and individual snow leopards per kilometer, along

with derived densities. Information on the spatial distribution

of transects and effort per site defined as transect kilometer per

square kilometer also is necessary for meaningful compari-

sons. This would facilitate comparisons between surveys

undertaken in different areas and habitats without relying on

possibly questionable density estimates.

Logistical advantages and disadvantages of methods.—To

attain the target of 20 individuals proposed by White et al.

(1982) a snow leopard survey must cover a large contiguous

area. In regions where densities are ,3 snow leopards/100 km2

field teams would need to survey approximately 700 km2 while

avoiding home-range–size gaps. Given poor access, rough

terrain, and often high altitude of snow leopard habitat, this

presents a logistically daunting task. Assuming 1.5 camera

stations/25 km2, availability of 20 stations (each with 2

cameras), and the ability to set 3 stations daily, it would take

approximately 7 days to set 20 stations covering only 350 km2.

After six 3-day sampling occasions (areas with low capture

probability would likely require more) it would take another

10 days to move the stations and survey an additional 350 km2

over another 18-day period. Seven more days would be required

to take down the stations. A field team would have difficulty

covering ,700 km2 within 60 days. In contrast, a noninvasive

genetic survey could be conducted in a shorter time frame.

Assuming 2 transects within each 25-km2 block, 28 blocks

sampled at a rate of 1 block per day, and 4 additional days for

travel between more distant sites, a genetic survey could cover

the same ,700-km2 area within approximately 32 days.

Noninvasive genetic surveys do not require specialized or

expensive field equipment. The ability to complete transects

rapidly, and the relatively high detection rates, enable more

sites and larger areas to be surveyed. Further, scat surveys

yield DNA samples of populations, thereby greatly expanding

the scope of information that can be generated. The cost also is

substantially lower, in our case ,50% that of camera trapping

even though we sampled 2 additional sites. However, genetic

surveys involve sophisticated laboratory facilities, trained

personnel, and additional time and financial cost to complete

analysis after the field session. Some countries within the

distribution of the snow leopard currently lack the necessary

laboratories for genetic analysis and have restrictions on the

export of fecal material.

Conservation implications.—Quantitative status and distri-

bution surveys of snow leopards are notably difficult compared

to those of other rare carnivores, such as tigers, leopards, or

jaguars that tend to have greater road access in their study areas.

Camera trapping has offered a major advance for research and

conservation of snow leopards. However, this approach is

hampered by the rugged terrain, poor access, field logistics, and

subsequent small sample sizes. Our results suggest that

population estimates in larger areas are more attainable with

noninvasive genetic surveys because of the ability to detect

multiple individuals with less field effort.

Examination of genetic data also can offer invaluable

insight into relatedness, dispersal, population structure, and

landscape connectivity (Schwartz et al. 2007), which is critical

for conservation planning. The information camera trapping

can provide on these topics is limited. Samples collected

during wide-ranging, noninvasive genetic surveys can be used

to address these population-level questions. This approach will

be useful for identifying core snow leopard populations and

landscape-level mapping of corridors that likely play a key

role in the long-term persistence of snow leopards, particularly

in disjunct mountain ranges.

More research is needed to establish optimal strategies for

scat sampling, given different snow leopard densities, habitat

factors, and terrain conditions. Behavior and social structure

influence marking and scat deposition and need to be

understood to model capture probabilites (Marucco et al.

2009). Concurrent genetic surveys and telemetry studies could

explore various sampling strategies under specific environ-

mental and biological conditions (Balme et al. 2009).

However, because of financial and logistical constraints, such

studies will be limited to only a few sites. For results to be

widely applicable research among groups should be coordi-

nated so that the selected sites are representative of snow

leopard populations across most of the range.

Our results suggest that the collection and genetic analysis

of scats offers a more efficient means than camera trapping for

detecting snow leopards in terms of field effort, total effort,

and financial expenditure. The main factor is the reduction in

time required to sample populations. Time spent in the field by

researchers in many areas of central Asia occupied by snow

leopards is severely constrained. Sample size and number of

sampled sites principally limit population surveys. The ability

to collect a greater number of samples over a larger area can

yield better population estimates. Noninvasive genetics,

therefore, has a greater potential for monitoring snow leopards

on a larger scale than camera trapping. The main challenges

that need to be addressed are factors that influence scat

780 JOURNAL OF MAMMALOGY Vol. 92, No. 4

Page 12: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

deposition, violations to population closure, absence of age

information, optimal sampling schemes, and insufficient

capacity for genetic analysis in many countries within the

distribution of the snow leopard.

ACKNOWLEDGMENTS

We appreciate funding generously provided by the Snow Leopard

Conservancy, The National Geographic Society, Leonard X. Bosack

Foundation, Bette M. Kruger Charitable Foundation, Wildlife

Conservation Network, California Institute of Environmental Studies,

Ken and Gabrielle Adelman, Texas A&M University, and an

anonymous individual. We thank the Institute of Biology, Mongolian

Academy of Sciences, and Irbis Mongolia Center for providing permits

and in-country staff. We are grateful for assistance provided in the field

by N. Mijiddorj, Dorji, and Shaarav, and aid from J. Robinson, A.

Royle, J. Hines (Patuxent Wildlife Research Center, United States

Geological Survey), K. Nowell (Cat Action Treasury), and E. Williams

and J. Jencek (San Francisco Zoo). We thank K. Logan and 1

anonymous reviewer for helpful suggestions for our manuscript. San

Francisco Zoo generously provided samples for positive controls. Scat

samples were exported from Mongolia under Mongolia State

Specialized Supervision Inspectorate Agency Permit 271470.

LITERATURE CITED

AHLBORN, G., AND R. JACKSON. 1988. Marking in free-ranging snow

leopards in west Nepal: a preliminary assessment. Pp. 25–49 in

Fifth International Snow Leopard Symposium (H. Freeman, ed.).

International Snow Leopard Trust and Wildlife Institute of India,

Dehradun, India.

ALE, S. B., P. YONZON, AND K. THAPA. 2007. Recovery of snow leopard

Uncia uncia in Sagarmantha (Mount Everest). Oryx 41:89–92.

BALME, G. A., L. T. B. HUNTER, AND R. SLOTOW. 2009. Evaluating

methods for counting cryptic carnivores. Journal of Wildlife

Management 73:433–441.

BANNIKOV, A. G. 1954. Mlekopitayushchie Mongol’skoi Marodnoi

Respubliki (Mammals of the Mongolian People’s Republic).

Academy of Sciences, Moscow, Russia.

BOLD A., AND S. DORJZUNDUY. 1976. Report on snow leopards in the

southern spurs of the Gobi Altai. Proceedings of the Institute of

General and Experimental Biology—Ulaanbaatar 11:27–43.

CARBONE, C., ET AL. 2001. The use of photographic rates to estimate

densities of tiger and other cryptic mammals. Animal Conservation

4:75–79.

CHENG, Y., M. TSUBO, T. Y. ITO, E. NISHIHARA, AND M. SHINODA. 2011.

Impact of rainfall variability and grazing pressure on plant diversity

in Mongolian grasslands. Journal of Arid Environments 75:471–476.

DAVISON, A., J. D. S. BIRKS, R. C. BROOKES, T. C. BRAITHEWAITE, AND J.

E. MESSENGER. 2002. On the origin of faeces: morphological versus

molecular methods for surveying rare carnivores from their scats.

Journal of Zoology (London) 257:141–143.

DILLON, A., AND M. J. KELLY. 2008. Ocelot home range, overlap and

density: comparing radio telemetry with camera-trapping. Journal

of Zoology (London) 275:391–398.

EGGERT, L., J. EGGERT, AND D. WOODRUFF. 2003. Estimating

population sizes for elusive animals: the forest elephants of

Kakum National Park, Ghana. Molecular Ecology 12:1389–1402.

FARRELL, L. E., J. ROMAN, AND M. E. SUNQUIST. 2000. Dietary

separation of sympatric carnivores identified by molecular analysis

of scats. Molecular Ecology 9:1583–1590.

FOX, J. L., S. P. SINHA, R. S. CHUNDAWAT, AND P. K. DAS. 1991. Status

of the snow leopard Panthera uncia in northwest India. Biological

Conservation 55:283–298.

FRANTZ, A. C., AND T. J. ROPER. 2006. Simulations to assess the

performance of different rarefaction methods in estimating

population size using small datasets. Conservation Genetics

7:315–318.

HAINES, A. M., J. E. JANECKA, M. E. TEWES, L. I. GRASSMAN, AND P.

MORTON. 2006. The importance of private lands for ocelot

Leopardus pardalis conservation in the United States. Oryx 40:1–5.

HARIHAR, A., B. PANDAV, AND S. K. GYAL. 2009. Subsampling

photographic capture–recapture data of tigers (Panthera tigris) to

minimize closure violation and improve estimate precision: a case

study. Population Biology 51:471–479.

HUSSAIN, S. 2003. The status of snow leopard in Pakistan and its

conflict with local farmers. Oryx 37:26–33.

IHAKA, R., AND R. GENTLEMAN. 1996. R: a language for data analysis

and graphics. Journal of Computational and Graphical Statistics

5:299–314.

JACKSON, R., AND G. AHLBORN. 1989. Snow leopards (Panthera uncia)

in Nepal: home range and movements. National Geographic

Research 5:161–175.

JACKSON, R., AND J. L. FOX. 1997. Snow leopard conservation:

accomplishments and research priorities. Pp. 128–145 in Proceed-

ings of the 8th International Snow Leopard Symposium (R.

Jackson and A. Ahmad, eds.). International Snow Leopard Trust

and WWF–Pakistan, Seattle, Washington.

JACKSON, R., AND D. O. HUNTER. 1996. Snow leopard survey and

conservation handbook. International Snow Leopard Trust and

United States Geological Survey, Biological Resources Division,

Fort Collins, Colorado.

JACKSON, R. M., B. MUNKHTSOG, D. P. MALLON, G. NARANBAATAR, AND

K. GERELMAA. 2009. Camera-trapping snow leopards in the Tost

Uul region of Mongolia. Cat News 51:18–21.

JACKSON, R., J. D. ROE, R. WANGCHUK, AND D. O. HUNTER. 2006.

Estimating snow leopard population abundance using photography and

capture–recapture techniques. Wildlife Society Bulletin 34:772–781.

JANECKA, J. E., ET AL. 2008. Population monitoring of snow leopards

using noninvasive collection of scat samples: a pilot study. Animal

Conservation 11:401–411.

KARANTH, K. U., R. S. CHUNDAWAT, J. D. NICHOLS, AND N. S. KUMAR.

2004. Estimation of tiger densities in the tropical dry forests of

Panna, Central India, using photographic capture–recapture

sampling. Animal Conservation 7:285–290.

KARANTH, K. U., AND J. D. NICHOLS. 1998. Estimation of tiger

densities in India using photographic captures and recaptures.

Ecology 79:2852–2862.

KARANTH, K. U., AND J. D. NICHOLS. 2002. Monitoring tigers and their

prey: a manual for researchers, managers and conservationists in

tropical Asia. Centre for Wildlife Studies, Bangalore, India.

KOHN, M., E. YORK, D. KAMRADT, G. HAUGHT, R. SAUVAJOT, AND R.

WAYNE. 1999. Estimating population size by genotyping faeces.

Proceedings of the Royal Society of London, B. Biological

Sciences 266:657–663.

LOVARI, S., ETAL. 2009. Restoring a keystone predator may endanger a prey

species in a human-altered ecosystem: the return of the snow leopard to

Sagarmatha National Park. Animal Conservation 12:559–570.

MACKAY, P., D. A. SMITH, R. LONG, AND M. PARKER. 2008. Scat

detection dogs. Pp. 183–222 in Noninvasive survey methods for

carnivores (R. Long, P. Mackay, J. Ray, and W. Zielinski, eds.).

Island Press, Washington, D.C.

August 2011 JANECKA ET AL.—SURVEYING SNOW LEOPARD POPULATIONS 781

Page 13: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

MAFFEI, L., AND N. J. NOSS. 2008. How small is too small? Camera

trap survey areas and density estimates for ocelots in the Bolivian

Chaco. Biotropica 40:71–75.

MALLON, D. P. 1991. Status and conservation of large mammals in

Ladakh. Biological Conservation 56:110–119.

MARUCCO, F., D. H. PLETSCHER, L. BOITANI, M. K. SCHWARTZ, K. L.

PILGRIM, AND J. D. LEBRETON. 2009. Wolf survival and population

trend using non-invasive capture–recapture techniques in the

Western Alps. Journal of Applied Ecology 46:1003–1010.

MCCARTHY, K., T. FULLER, M. MING, T. M. MCCARTHY, L. WAITS, AND

K. JUMABAEV. 2008. Assessing estimators of snow leopard

abundance. Journal of Wildlife Management 72:1826–1833.

MCCARTHY, T. M., AND G. CHAPRON (EDS.). 2003. Snow leopard

survival strategy. International Snow Leopard Trust and the Snow

Leopard Network, Seattle, Washington.

MCCARTHY, T. M., T. K. FULLER, AND B. MUNKHTSOG. 2005.

Movements and activities of snow leopards in southwestern

Mongolia. Biological Conservation 124:527–537.

MCCARTHY, T. M., AND B. MUNKHTSOG. 1997. Preliminary assessment

of snow leopard sign surveys in Mongolia. Pp. 57–65 in Eighth

International Snow Leopard Symposium (R. Jackson and A.

Ahmad, eds.). International Snow Leopard Trust and WWF–

Pakistan, Seattle, Washington.

MENOTTI-RAYMOND, M., ET AL. 1999. A genetic linkage map of

microsatellites in the domestic cat (Felis catus). Genomics 57:9–23.

MILLER, C. R., P. JOYCE, AND L. P. WAITS. 2005. A new method for

estimating the size of small populations from genetic mark–

recapture data. Molecular Ecology 14:1991–2005.

MIQUEL, C., E. BELLEMAIN, C. POILLOT, J. BESSIERE, A. DURAND, AND P.

TABERLET. 2006. Quality indexes to asses the reliability of

genotypes in studies using noninvasive sampling and multiple-

tube approach. Molecular Ecology Notes 6:985–988.

MONDOL, S., K. U. KARANTH, N. B. KUMAR, A. M. GOPALASWAMY, A.

ANDHERIA, AND U. RAMAKRISHNA. 2009. Evaluation of non-invasive

genetic sampling methods for estimating tiger population size.

Biological Conservation 142:2350–2360.

MURPHY, W. J., S. SUN, Z. Q. CHEN, J. PECON-SLATTERY, AND S. J.

O’BRIEN. 1999. Extensive conservation of sex chromosome

organization between cat and human revealed by parallel radiation

hybrid mapping. Genome Research 9:1223–1230.

NOWELL, K., AND P. JACKSON. 1996. Wild cats: status survey and

action plan. IUCN—World Conservation Union, Gland, Switzer-

land.

OTIS, D. L., K. P. BURNHAM, G. C. WHITE, AND D. R. ANDERSON. 1978.

Statistical inference from capture data on closed animal popula-

tions. Wildlife Monographs 62:1–135.

PAETKAU, D., AND C. STROBECK. 1994. Microsatellite analysis of

genetic-variation in black bear populations. Molecular Ecology

3:489–495.

PEAKALL, R., AND P. E. SMOUSE. 2006. GENEALEX 6: genetic

analysis in Excel. Population genetic software for teaching and

research. Molecular Ecology Notes 6:288–295.

PEREZ, I., E. GEFFEN, AND O. MOKADY. 2006. Critically endangered

Arabian leopards Panthera pardus nimr in Israel: estimating

population parameters using molecular scatology. Oryx 40:295–301.

POLLOCK, K. H., J. D. NICHOLS, C. BROWNIE, AND J. E. HINES. 1990.

Statistical inference for capture–recapture experiments. Wildlife

Monographs 107:1–97.

RICE, W. R. 1989. Analyzing tables of statistical tests. Evolution

43:223–225.

RUELL, E. W., S. P. RILEY, M. R. DOUGLAS, J. P. POLLINGER, AND K. R.

CROOKS. 2009. Estimating bobcat population sizes and densities in

a fragmented urban landscape using noninvasive capture–recapture

sampling. Journal of Mammalogy 90:129–135.

SCHALLER, G. B., H. LI, T. REN, AND M. QIU. 1987. The snow leopard

in Xinjiang, China. Oryx 22:197–204.

SCHALLER, G. B., J. R. REN, AND M. J. QIU. 1988. Status of the snow

leopard Panthera uncia in Qinghai and Gansu provinces, China.

Biological Conservation 45:179–194.

SCHWARTZ, M. K., G. LUIKART, AND R. S. WAPLES. 2007. Genetic

monitoring as a promising tool for conservation and management.

Trends in Ecology & Evolution 22:25–33.

SILVER, S. C., ET AL. 2004. The use of camera traps for estimating

jaguar (Panthera onca) abundance and density using capture/

recapture analysis. Oryx 38:148–154.

SOISALO, M. K., AND S. M. C. CAVALCANTI. 2006. Estimating the

density of a jaguar population in the Brazilian Pantanal using

camera-traps and capture–recapture sampling in combination with

GPS radio-telemetry. Biological Conservation 129:487–496.

STANLEY, T. R., AND K. P. BURNHAM. 1999. A closure test for time-

specific capture–recapture data. Environmental and Ecological

Statistics 6:197–209.

STERNBERG, T., D. THOMAS, AND N. MIDDLETON. In press. Drought

dynamics on the Mongolian steppe, 1970–2006. International

Journal of Climatology.

SUBBOTIN, A. E., AND S. V. ISTOMOV. 2009. The population and status

of snow leopards Uncia uncia (Felidae, Carnivora) in the western

Sayan mountain range. General Biology 6:846–849.

SWOFFORD, D. L. 2003. PAUP*: phylogenetic analysis using

parsimony (*and other methods), version 4b 10. Sinauer Associ-

ates, Inc., Publishers, Sunderland, Massachusetts.

THOMPSON, W. L. (ED.). 2004. Sampling rare or elusive species:

concepts, designs and techniques for estimating population

parameters. Island Press, Washington, D.C.

TROLLE, M., AND M. KERY. 2003. Estimation of ocelot density in the

Pantanal using capture–recapture analysis of camera-trapping data.

Journal of Mammalogy 84:607–614.

VALIERE, N. 2002. GIMLET: a computer program for analysing genetic

individual identification data. Molecular Ecology Notes 2:377–379.

WAITS, L. P., V. A. BUCKLEY-BEASON, W. E. JOHNSON, D. ONORATO,

AND T. MCCARTHY. 2007. A select panel of polymorphic

microsatellite loci for individual identification of snow leopards

(Panthera uncia). Molecular Ecology Notes 7:311–314.

WAITS, L. P., G. LUIKART, AND P. TABERLET. 2001. Estimating the

probability of identity among genotypes in natural populations:

cautions and guidelines. Molecular Ecology 10:249–256.

WAITS, L. P., AND D. PAETKAU. 2005. Noninvasive genetic sampling

tools for wildlife biologists: a review of applications and

recommendations for accurate data collection. Journal of Wildlife

Management 69:1419–1433.

WHITE, G. C., D. R. ANDERSON, K. P. BURNHAM, AND D. L. OTIS. 1982.

Capture–recapture and removal methods for sampling closed popula-

tions. Los Alamos National Laboratory, Los Alamos, New Mexico.

WILSON, K. R., AND D. R. ANDERSON. 1985. Evaluation of two density

estimators of small population size. Journal of Mammalogy 66:13–21.

XU, A., ET AL. 2008. Status and conservation of snow leopard Panthera

uncia in theGouliRegion,KunlunMountains,China.Oryx42:460–463.

Submitted 2 February 2010. Accepted 21 January 2011.

Associate Editor was Jonathan A. Jenks.

782 JOURNAL OF MAMMALOGY Vol. 92, No. 4

Page 14: Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards

APPENDIX I

Microsatellite genotyping error rates observed among 39 snow

leopard scats individually identified in our study and used to estimate

population size. The quality index was estimated using the methods

of Miquel et al. (2006). PCR 5 polymerase chain reaction.

Locus PCR failure (%) Allele dropout (%) Quality index

PUN082 3.42 1.11 0.957

PUN100 0.85 0.00 0.991

PUN124 3.42 0.96 0.957

PUN132 1.71 0.00 0.983

PUN225 2.56 1.79 0.966

PUN229 2.56 1.10 0.966

PUN327 2.56 0.00 0.974

X 2.44 0.83 0.971

APPENDIX II

Microsatellite diversity observed among snow leopards sampled

with scats in Noyon Uul and Tost Uul of the Gobi Desert. AN 5 total

number of alleles, AE 5 effective number of alleles, HO 5 observed

heterozygosity, HE 5 expected heterozygosity, HWE (P) 5 Hardy–

Weinberg equilibrium P-value, PID-unr 5 probability 2 unrelated

individuals will have an identical genotype.

Locus AN AE HO HE HWE (P) PID-unr

PUN082 2 2.0 0.67 0.50 0.19 0.37

PUN100 5 2.8 0.60 0.65 0.56 0.17

PUN124 4 3.4 0.93 0.70 0.44 0.15

PUN132 3 1.2 0.20 0.18 0.98 0.67

PUN225 3 2.1 0.53 0.53 0.83 0.33

PUN229 5 2.9 0.60 0.66 0.43 0.18

PUN327 3 1.6 0.47 0.37 0.71 0.44

X 3.6 2.3 0.57 0.51 0.59 0.33

APPENDIX III

Selection criterion values estimated by program CAPTURE for

different capture probability models. Mo 5 constant capture

probabilities; Mh 5 heterogeneity; Mb 5 behavioral response; Mbh

5 behavioral response and heterogeneity; Mt 5 variation by time;

Mth 5 variation by time and heterogeneity; Mtb 5 variation by time

and behavioral response; Mtbh 5 variation by time, behavioral

response, and heterogeneity. Data from Jackson et al. (2009).

Sampling

Occasion

Capture model

Mo Mh Mb Mbh Mt Mth Mtb Mtbh

1-day 1.00 0.95 0.37 0.60 0.00 0.31 0.44 0.64

3-day 1.00 0.95 0.41 0.65 0.00 0.34 0.46 0.69

5-day 1.00 0.84 0.26 0.58 0.00 0.42 0.31 0.65

7-day 1.00 0.80 0.23 0.55 0.00 0.40 0.26 0.63

APPENDIX IV

Capture probability and population size estimates of snow leopards camera trapped in Tost Uul using the 2 best-fitting capture models. Mo 5

constant capture probabilities, Mh 5 heterogeneity. Data from Jackson et al. (2009).

Sampling occasion

Mo Mh

Capture probability

Estimated abundance

6 SE (95% CI) Capture probability

Estimated abundance

6 SE (95% CI)

1-day 0.118 4 6 0.063 (4–4) 0.118 4 6 0.375 (4–4)

3-day 0.303 4 6 0.066 (4–4) 0.303 4 6 0.266 (4–4)

5-day 0.432 4 6 0.090 (4–4) 0.432 4 6 0.215 (4–4)

7-day 0.563 4 6 0.080 (4–4) 0.563 4 6 0.165 (4–4)

August 2011 JANECKA ET AL.—SURVEYING SNOW LEOPARD POPULATIONS 783