Research Article Leopard Panthera pardus fusca Density in ...Leopard Panthera pardus fusca Density in the Seasonally Dry, Subtropical Forest in the Bhabhar of Terai Arc, Nepal KanchanThapa,

Research ArticleLeopard Panthera pardus fusca Density in the Seasonally DrySubtropical Forest in the Bhabhar of Terai Arc Nepal

Kanchan Thapa1 Rinjan Shrestha2 Jhamak Karki3

Gokarna Jung Thapa4 Naresh Subedi5 Narendra Man Babu Pradhan4

Maheshwar Dhakal6 Pradeep Khanal4 and Marcella J Kelly1

1 Department of Fish and Wildlife Conservation Virginia Tech Blacksburg Virginia VA 24061 USA2 Eastern Himalayas Program (WWF-US) CO WWF-Canada 245 Eglinton Avenue E Toronto ON Canada M4P 3J13 Nepal Engineering College-Center for Post Graduate Studies Kathmandu 44600 Nepal4WWF Nepal Kathmandu 44600 Nepal5 National Trust for Nature Conservation Lalitpur 44700 Nepal6Department of National Park and Wildlife Conservation Kathmandu 44600 Nepal

Correspondence should be addressed to KanchanThapa kanchan1vtedu

Received 14 April 2014 Revised 9 June 2014 Accepted 12 June 2014 Published 16 July 2014

Academic Editor Tomasz S Osiejuk

Copyright copy 2014 KanchanThapa et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

We estimated leopard (Panthera pardus fusca) abundance and density in the Bhabhar physiographic region in Parsa WildlifeReserve Nepal The camera trap grid covering sampling area of 289 km2 with 88 locations accumulated 1342 trap nights in 64days in the winter season of 2008-2009 and photographed 19 individual leopards Using models incorporating heterogeneity weestimated 28 (plusmnSE 607) and 2958 (plusmnSE 1044) leopards in Programs CAPTURE and MARK Density estimates via 12 MMDMmethods were 561 (plusmnSE 130) and 593 (plusmnSE 215) leopards per 100 km2 using abundance estimates from CAPTURE and MARKrespectively Spatially explicit capture recapture (SECR) models resulted in lower density estimates 378 (plusmnSE 085) and 348 (plusmnSE083) leopards per 100 km2 in likelihood based program DENSITY and Bayesian based program SPACECAP respectively The 12MMDM methods have been known to provide much higher density estimates than SECR modelling techniques However ourSECRmodels resulted in high leopard density comparable to areas considered better habitat in Nepal indicating a potentially densepopulation compared to other sites We provide the first density estimates for leopards in the Bhabhar and a baseline for long termpopulation monitoring of leopards in Parsa Wildlife Reserve and across the Terai Arc

1 Introduction

The leopard (Panthera pardus fuscaMeyer 1794) is one of themost widely distributed felids across the forested landscapesof the Indian subcontinent [1 2] Being a habitat generalist[3] the leopard has a wider fundamental niche than its largercongener the tiger (Panthera tigris tigris Linnaeus) in termsof the habitat and area it occupies [4] extending from alluvialfloodplains subtropical deciduous moist and dry habitat inlowlands and Siwaliks temperate deciduous forest habitat inmid hills and high mountains to dry alpine forest in theHimalayas [5] Leopards occur sympatrically with tigers inNepal India and Bhutan [6ndash8]

The tiger being an apex predator appeals to the publicand serves as a flagship species [9] Perceived asmore tolerantof anthropogenic influences the leopard on the other handhas received less attention from conservationists despiteits important functional role within ecosystems includingits potential to cause trophic cascades [10] its impact onmesopredators [11 12] and its competitive role within itsguild [13 14] In the human dominated landscape of todayrsquosIndian subcontinent habitat destruction and fragmentationremainmajor threats to leopards [4] and leopard numbers aredeclining due to both direct mortality and decreases in preypopulations [15]

Hindawi Publishing CorporationAdvances in EcologyVolume 2014 Article ID 286949 12 pageshttpdxdoiorg1011552014286949

2 Advances in Ecology

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India

Nepal

China

International boundaryRiverCamera stationSettlementProtected areasBuffer zone

MCP outline

Figure 1 Study areas showing the spatial location of camera traps in the Bhabhar region and the effective sampling area formed by drawinga minimum convex polygon surrounding the outermost camera trap locations The area of MCP (minimum convex polygon) is 289 km2

In contrast to tigers whose population sizes and trendshave been intensively studied in multiple sites across theIndian subcontinent [8 16ndash23] fewer studies have estimatedleopard population sizes across both protected [24ndash26] andnonprotected areas [27] representing an array of habitattypes in India In Nepal even fewer studies are available onleopard demography only representing alluvial floodplainsgrasslands and deciduous forest from Chitwan [28 29] andBardiaNational Parks [30]However there are no estimates ofleopard density from seasonally dry subtropical forest in theBhabhar region Bhabhar is the alluvial apron of sedimentswashed down from the Siwaliks [31] and represents a keyphysiographic region extending to the ldquoTerai zonerdquo across theTerai Arc Landscape (Terai Arc) Hence lack of informationfrom Bhabhar habitat has hindered an overall assessment ofleopard conservation status

We estimate the abundance and density of leopardsfrom the protected area within Bhabhar using a pho-tographic capture-recapture sampling framework [32ndash34]This approach is widely acknowledged as a robust toolfor estimating population abundance of elusive wild catswith individually distinct pelage patterns [15 20] We usetraditional techniques of estimating the density using thead hoc method of adding a buffer area around the polygonformed by connecting outer camera trap locations to accountfor edge effects (animals that do not occur entirely within

trapping grid but have home range overlapping the edge ofthe grid) These buffering methods include using an estimateof home range radius derived from GPS or radio telemetry[35 36] or the common technique of using half of the meanmaximum distance moved (12MMDM) by animals withinthe trap array as a surrogate for home range radius [18 37 38]However these ad hoc approaches have come under scrutinybecause they are heavily influenced by small sample sizecamera spacing and extent of sampling grid relative to theanimalrsquos home range [35 39 40] To address the shortcomingsof the traditional approach in estimating leopard densitywe also use recently developed spatially explicit capturerecapture (SECR) techniques that use spatial informationmore directly in the density estimation process [40ndash43] Wecompare the maximum likelihood [44] and Bayesian [45]SECR approaches in estimating density without the needto estimate an effective trapping area using the traditionalad hoc buffering approach We present the result from bothtraditional and SECR approaches allowing us to compare ourleopard density estimates with other studies in South Asia(Nepal India and Bhutan)

2 Materials and Methods

The study was carried out in Parsa Wildlife Reserve (PWR27∘151015840N 84∘401015840E Figure 1) in the south-central lowland Terai

Advances in Ecology 3

of Nepal Encompassing over 499 km2 PWR is the largestwildlife reserve in the country and is contiguous to ChitwanNational Park to the west PWR is made up mostly of Churiahills (the outermost foothills of Himalayas) and Bhabharregions a rugged and highly porous landscape largely com-prised of coarse alluvial deposits where streams disappearinto permeable sediments [31] PWR has amonsoonal humidclimate with more than 85 of the annual precipitation(2180mm) occurring between July and October The dryseason occurs for 8months betweenNovember and June [46]

The vegetation can best be described as subtropical drydeciduous forest with colonizing Saccharum spontaneum andImperata cylindrica on the dry riverbeds and the floodplainsto a climax Sal (Shorea robusta) forest on Bhabhar andhillsides [47] The reserve supports a diverse mammalianfauna in addition to leopards including carnivores such asthe tiger (Panthera tigris tigris) dhole (Cuon alpinus) stripedhyena (Hyaena hyaena) golden jackal (Canis aureus) Indianfox (Vulpes bengalensis) and ratel (Mellivora capensis) Theprinciple wild prey species of the leopard include large sizeanimals (gt50 kg) gaur (Bos gaurus) sambar (Rusa unicolor)and nilgai (Boselaphus tragocamelus) medium size animals(20ndash50 kg) chital (Axis axis) muntjac (Muntiacus muntjak)and wild pig (Sus scrofa) and small size animals (lt20 kg)common langur (Semnopithecus entellus) and rhesus monkey(Macaca mulatta) The combined ungulate prey density isestimated to be 66 individuals (plusmnSE 11) per km2 [48] Two ofthe settlements comprising approximately 100 households inRambhori and Bhata in the core area of the reserve have beenrecently relocated [49] This is an event that is expected totrigger the recovery of carnivores within the reserve makingour density estimation an important baseline study Illegallivestock grazing along the buffer zone is believed to reduceforage for wild ungulates and livestock has been foundgrazing inside the reserve as far as 5 km from the reserveboundary Photographic evidence from camera trap pictures[50] suggests that illegal poaching of wild prey is a directthreat to the carnivore populations in the reserve

21 Field Methods We conducted a camera trap survey for64 days across a 289 km2 core area of the reserve betweenDecember 2008 and March 2009 We followed the standardstudy design approaches prescribed for large felids at sitesthat had intensive signs of their usage [18 19 51] Wefirst carried out extensive sign surveys for leopards [2 52]across 42 transect routes spread across the core area of thereserve amounting to 702 km searched on footThese transectsurveys enabled us to choose key locations for installingcamera traps and identifying survey blocks that covered alarge area without leaving potential gaps in our survey grid

We selected 88 camera-trap locations spread throughoutthe study area based on the presence of leopard tracksscats scrapes and other signs of use To maximize captureprobability we positioned our camera traps along forestroads trails and dry stream beds the habitat features knownas leopard travel routes [6 53] The spacing between camera-trap locations (Figure 1) was maintained at approximately19 km (plusmnSE 006) [2] At each location we used 2 passive

Figure 2 Identical pelage patterns from the same leopard in PWRas displayed in photographic captures from different camera traps

digital camera traps (Moultrie D50 Moultrie Feeders USA)activated by animal movement and placed on either side ofthe trail to photograph both right and left flanks of leopards[2 32] Each camera trap was active for 24 h and was checkedon alternate days for proper recording of capture events datetime and any possible malfunctions

Wedivided the study area into four ldquotrapping blocksrdquo eachmeasuring an average area of 705 km2 (plusmnSE 754) We placed22 camera stations per block resulting in an average of 322camera stations per 100 km2 Each location was sampled for16 consecutive days resulting in 16 encounter occasions eachconsisting of capture data drawn from one dayrsquos trapping ineach of the blocks [2] After 16 days cameras were moved tothe next block until all 4 survey blocks were completed

22 Individual Identification Two investigators indepen-dently identified the photos for individual identification tobuild consensus on individual leopard identity Individualleopards were identified based on their unique rosette pat-terns on the flanks limbs and forequarters [54] and givenunique identification numbers (Figure 2) as done in otherstudies [24 26 27 55]

23 Population Estimation We followed the traditionalcapture-recapture analytic techniques used for estimatingpopulation sizes of large felids from remote camera data [218 19] We constructed capture histories for each individualleopard fromphotographic captures and assigned them to theappropriate encounter occasions (refer to Otis et al [33] andKaranth and Nichols [2] for detail) We used two separateapproaches to statistically test the assumption of populationclosure

We first used the closure test implemented in programCAPTURE [56]We then used the Stanley and Burnham [57]closure test that assumes only time variation in recaptureprobability using the Pradel model [58] in Program MARKv 51 [59] The Pradel model evaluates geographic closureby estimating the site fidelity (C) immigration (119891) and


recapture probability (119901) with regard to entry and exit intoor out of the sampling area under assumption of the closurefor the leopard population over our 64-day sampling period

We used the closed population models [33] implementedin program CAPTURE for estimation of overall captureprobability (119901) and abundance () using several differ-ent models that can incorporate effects of ecological andsampling-related factors (for details refer to [2 7]) We alsoused theHuggins closed capturewith heterogeneitymodelingplatform in program MARK [33 59 60] to calculate abun-dance estimates These models use a maximum likelihoodframework and we fit 8 models of Otis et al [33] whichallow capture probabilities to vary over time (119901(119905) = 119888(119905)) byindividualrsquos heterogeneity (119901(ℎ) = 119888(ℎ)) due to a behavioralresponse (initial capture being different from recapture prob-abilities 119901(sdot) sdot 119888(sdot)) along with null model (with no variationin capture probabilities 119901(sdot) = 119888(sdot)) and combinations ofthe above factors Model input includes the one time winterseason capture histories with 16 encounter occasions Weranked all the models using sample size-adjusted AkaikersquosInformation Criterion (AIC) [61] and considered all modelswith ΔAICc lt 2 as competing models [62]

24 Density Estimation To convert our abundance estimatesfrom programs CAPTURE and MARK to densities we usedthe traditional 12MMDM [18 37] and full MMDM [63]approaches to calculate the buffer strip surrounding ourcamera traps to determine the effective trap area (ETA)The 12MMDM and full MMDM were calculated from pho-tographed individuals trapped inmore than one location andwe buffered each camera trap and dissolved the overlappingareas to calculate the ETA We then divided our populationestimates from CR (capture-recapture) analysis by total ETAto determine density We used the delta method to calculatethe variance in density estimates [64]

We used two SECR approaches to estimate leoparddensity a maximum likelihoodmodeling framework (SECR-ML) implemented in program DENSITY [41] and a Bayesianmodeling framework (SECR-B) [45 65] implemented in pro-gram SPACECAP [66] These methods allow us to compareour results with recent studies on leopard density estimatesusing SECR-ML models [25 67 68] and using SECR-B [2527 29 67]

In program DENSITY v 50 [41] we first modeled toselect the appropriate detection (observational) process aseither half-normal hazard rate or negative exponentialUsing the selected detection function we then allowed 119892

0

(the capture probability at the hypothetical center of anindividualrsquos home range) and sigma (a function of the scaleof animal movement) to vary using 2-class finite mixture(ℎ2) to represent heterogeneity andor a behavioral response(119887) Thus a hazard detection function model with constant1198920and 2-class finite mixture of sigma would be represented

as HZ 1198920(sdot)sig(ℎ2) We used the estimated log likelihood and

root-pooled spatial variance (RPSV) of varying integrationbuffers [41 69] for determining the appropriate buffer sizeWe ranked all themodels using sample size-adjusted AkaikersquosInformation Criterion (AICc) and considered all models

Table 1 Summary statistics for photographic capture-recapture dataon leopards in PWR

Survey Summary ValueNumber of camera trap stations 88Sampling occasions (1 day each) 16Effort (trap nights) 1342Number of independent photographs 45Leopard activity index (number ofphotographs per 100 trap nights) 335

Total number of individuals caught119872119905+1

19Total number of captures 39Number of individual animals caught once 14Number of individual animals caught morethan once 5

Survey period 21 December 2008ndash3March 2009

with ΔAICc lt 2 as competing models We used the modelaveraging techniques to determine final density estimates[62] We report the unconditional variance estimates for themodel average estimates

For the SECR-B approach we used program SPACECAP[66] implemented in R package v 301 [70] for estimatingleopard density [65] We buffered 15 km around the samplingarea to represent the probable extent of leopard home rangecenters and generated a grid of hypothetical home rangecenters with equally spaced points (119899 = 8150) each 0336 kmapart This resulted in an area of 1389 km2 of leopard habitatover which these activity centers were uniformly distributedafter removing the 674 km2 area of settlements (villagesand agriculture areas Rambhori Bhata Nirmal basti andamlekhganj) We used three standard input data files (animalcapture locations and dates trap deployment dates andlocations and hypothetical activity centers) and we assumedthe half normal detection function We performed 52000iterations of which the initial 2000 were discarded as theburn-in period a thinning rate was set at 20 and we used anaugmentation value of 180 individuals (more than five timesthe expected number of animals) We evaluated results usingthe Geweke diagnostic [71] and 119911-score statistics of |119911-score|more than 16 implying lack of convergence [66] We pro-duced the pixelated density map showing the estimated leop-ard densities per pixel of size 0336 km2 using ArcGIS 101

25 Comparison of Leopard Estimates We compiled infor-mation on leopard densities from protected areas across theirrange in Nepal India and Bhutan and present cross-sitecomparisons of density and habitat type and describe densityestimates based on type of modeling approach used and theircorresponding standard errors

3 Results

31 Sampling Effort and Number of Individual LeopardsCaptured After discarding 66 trap nights of camera mal-functions we amassed 1342 trap nights and obtained 120identifiable photographs comprised of 64 right flanks and 56left flanks of leopard photographs (Table 1) Two investigators


Table 2 Closure test and model selection (after Otis et al [33]) for leopard population size estimation using photographic capture-recapturedata from Parsa Wildlife Reserve in Program CAPTURE DFS is the discriminant function score The119872

119887shows the Behavioral effect while

119872ℎshows the heterogeneity effect and the119872

119900shows the constant null model

Closure Test IndividualsCaught (119872

119905+1)

TotalCaptures (119899) Models DFS Capture

Probability 119901Total Abundance(SE[])

95 ConfidenceInterval (CI)Z p

0988 08383 19 39

119872ℎ 100 00871 28 (607) 22ndash48119872119900 095 01115 22 (245) 20ndash31119872119887ℎ 084 00453 36 (2705) 21ndash173119872119887 062 00453 36 (2705) 21ndash173

Table 3 Model selection summary for geographic closure for the leopard population in Parsa Wildlife Reserve in program MARK Phirepresents the site fidelity 119901 is the recapture probability and119891 is immigration onto the study site Parameter with ldquosdotrdquo indicates a constant valueand with ldquo1rdquo andor ldquo0rdquo indicates parameter is fixed AICc is Akaikersquos information criterion corrected for small sample size and difference inAIC value between top model and 119894th model is represented by ΔAICc Weight of support for each model is AICc weights 120596

Model AICc ΔAICcAICc weights

(120596)Model

likelihoodNumber of

parameters (k) Deviance

[Phi(1)119901(time)119891(0)] 2631642 0 089645 1 16 1000934[Phi(1)119901(time) sdot119891(sdot)] 2684008 52366 006538 00729 17 989144[Phi(sdot)119901(time)119891(0)] 2695798 64156 003626 00404 17 1000934[Phi(sdot)119901(time) sdot119891(sdot)] 275458 122938 000192 00021 18 989144

Table 4 Leopard abundance fromParsaWildlife Reservewas estimated usingHuggins closed captures with heterogeneitymodels in programMARK (Pledger [60] White and Burnham [59])119872

119887[119901(sdot) sdot 119888(sdot)] is the behavioral effect119872

ℎ[119901(ℎ) = 119888(ℎ)] is the heterogeneity effect while

119872119900[119901(sdot) = 119888(sdot)] is the constant null model AICc represents Akaikersquos information criterion corrected for the small sample size Difference

in AIC value between top model and 119894th model (ΔAICc) Weight of support for each model (AICc weights 120596) represents abundanceestimate while SE represents standard errors Model averaged density estimates and standard errors are given in bold lowastrepresents modelselected for inferring parameter estimates

Model AICc ΔAICcAICc Weights

(120596)Model

likelihoodNumber of

parameters (k)Abundance estimate(SE[])

95 Confidenceinterval

119872119887[119901(sdot) sdot 119888(sdot)] 155157 0 037089 1 3 3718 (30564) 2087ndash19504119872ℎ[119901(ℎ) = 119888(ℎ)]

lowast 155226 00695 035823 09659 4 2958 (1044) 2110ndash7230119872119900[119901(sdot) = 119888(sdot)] 155793 06369 026974 07273 2 2186 (250) 1965ndash3157

Model averaged results from top 3 models 3031(2060) 2005ndash14004

independently examined the photos for individual identifica-tion both agreed on the 92 of all the capture events (119899 =42) and we built the capture histories based on consensus(119899 = 39) We excluded the events for which consensus onthe individual identity could not be built due to low qualitypictures We identified 19 individuals visually assessed to be 1year or older (5 males 12 females 2 of unknown sex)

32 Closure Assumption Program CAPTURE closure testresults were consistent with the assumption that the leopardpopulation was closed during the 64-day survey period(Table 2) Additionally the Stanley and Burnham [57] testwas also consistent with the assumption of geographic closureduring the sampling period as the model constraining sitefidelity (C) to 10 and immigration (119891) to 0 and underlyingthe 120582 value to be 1 showed substantially more support thanother models (Table 3)

33 Abundance Estimates The discriminant function anal-ysis in program CAPTURE indicated that the 119872

ℎmodel

incorporating individual heterogeneity was the best fit tothe data (Table 2) The 119872

ℎ(jackknife estimator) is widely

acknowledged as a robust model in estimating abundance oflarge felids from the photographic capture-recapture analyses[2 18] The average capture probability (119901) was 008 withan abundance estimate (SE()) of 28 (plusmnSE 6071) leop-ards (Table 2) In program MARK three models (Table 4)accounted for the 100 of the AIC weights supporting varia-tion in the behavior heterogeneity and constant (null model)capture probabilities Due to imprecise estimates for the119872

119887

model and the fact that leopards individuals are unlikely tohave constant detectability we used the model incorporatingheterogeneity (119872

ℎ) in the abundance estimate (SE()) of

2958 (plusmnSE 1044) leopards and capture probability (119901) of 024(plusmnSE 008)

34 Density Estimates Six individual leopards were capturedmore than once resulting in a MMDM of 52 km and aneffective trapping area of 473 km2 using the buffer strip


Table 5 Model selection results from leopard density estimates using photographic capture-recapture data from Parsa Wildlife Reserve inprogram DENSITY using hazard rate detection function 119892

0is the capture probability at home range center 119904 is the spatial scale parameter of

capture function ℎ2 is the 2-class finite mixture probability for heterogeneity Akaikersquos information criterion adjusted for small sample size byAICc 119908119894 represents Akaike weight while119863

119890 is estimated density (per 100 km2) and SE represents its standard error Model averaged densityestimates and standard errors are given in bold

Site Model AICc ΔAICc 119908119894

119863119890 (SE)

PWR

1198920[sdot]119904[sdot] 49601 0 0428 003 plusmn 0011198920[sdot]119904[ℎ2] 49785 184 0171 004 plusmn 0001198920[119887]119904[sdot] 49874 273 0109 004 plusmn 0011198920[119887]119904[sdot] 49878 277 0107 004 plusmn 0021198920[ℎ2]119904[sdot] 49914 313 009 004 plusmn 0021198920[sdot]119904[119887] 49953 352 0074 003 plusmn 0011198920[ℎ2]119904[ℎ2] 50241 64 0017 004 plusmn 001

Model averaged results from top 7 models 378 plusmn 085

Table 6The posterior summaries fromBayesian spatially explicit capture-recapture (SECR-B) of themodel parameters including the leoparddensity estimates from ParsaWildlife Reserve implemented in SPACECAP [66] along with Geweke diagnostic parameters Sigma is the rangeparameter of the species lam0 is the intercept of expected encounter frequency psi is the ratio of the number of animals present within thestate space 119878 to the maximum allowable number Nsuper is the number of activity centers in 119878 Density (per 100 km2) is Nsuper divided by119878 and |119911 score| greater than 16 implies lack of convergence

Parameter Posteriormean

PosteriorSD

95 LowerHPD level

95 UpperHPD level

Gewekersquos statistics|119911 score|

Sigma 288 040 217 361 15975lam0 002 0005 0012 003 minus15487Psi 039 0098 021 058 07407Nsuper 7732 1860 44 113 minus02563Density 348 083 203 515 minus03725

method (using 12MMDM) The density estimates fromtraditional methods of dividing abundance by the ETA wereof 561 (plusmnSE 130) and 593 (plusmnSE 215) leopards per 100 km2from programs CAPTURE andMARK respectively We alsoused the full MMDM resulting in an effective trapping areaof 73631 km2 and density estimates 385 (plusmnSE 088) and 407(plusmnSE 146) leopards per 100 km2 with CAPTURE andMARKrespectively

Spatially explicit models produced much lower estimatesthan traditional 12MMDM techniques but they were com-parable to the full MMDM methods The estimated loglikelihood and RPSV of 24504 units and 3441m suggestedthe appropriate buffer size of 15000m for SECR-ML inprogram DENSITY Model selection in SECR-ML supportedthe hazard rate detection functionwith detectability (at homerange center 119892

0) and spatial scale (function of movement

119904) both constant (null model) (Table 5) There was somesupport for heterogeneity in the spatial scale as thismodelwaswithin 2ΔAICc of the top modelThemodel averaged densityestimate for PWR based on SECR-ML was 378 (plusmnSE 085)leopards per 100 km2

Using the SECR-B analysis (SPACECAP program) allmodel parameters converged based on Geweke diagnosticstatistics with 119911 scores not more than 16 (Table 6) Weobtained the posterior density estimates of the 348 (plusmnSE 083)leopards per 100 km2 and pixelated density map showing therelative animal densities over the animal home range centers(Figure 3)

35 Comparison with Leopard Densities in Other Areas Thedensity point estimates in PWR via traditional methods(561 and 608) were nearly double the SECR estimates (348and 378) However even the lowest SECR estimate at 348leopards per km2 is highest reported so far in Nepal and iscomparable to 345 leopards per 100 km2 in the adjacent Chit-wan National Park in Nepal At the regional scale howeverour estimates were lower in PWR than the estimates fromIndia (Table 7) Using the traditional 12MMDM approachwhich we suspect overestimates density leopard density inBhutan was found to be low (101 leopards per 100 km2) incomparison to 541 leopards per 100 km2

4 Discussion

Our approach of conducting a sign survey [2] prior to thecamera trap survey aided in fine tuning the survey protocolto maximize detection probability in PWR In addition torelatively higher probability of capturing leopards present inthe sampled area (67 119872

119905+1) and capturing 95 of the

leopards in the first 12 days our approach also demonstratedthat operating cameras for short periods (16 days) per blockand moving them sequentially to new blocks can providereliable estimates of leopard abundance and density withoutviolating the closure assumption during the total samplingperiod of 64 days [5 18 72]

PWR is the only protected area within the forest land-scape of the Terai Arc that offers relatively pristine habitats


Animal density per pixel(pixel size 0336 sq km)

0000000ndash00068000006801ndash00124000012401ndash00192000019201ndash00320000032001ndash0056800

Buffer zoneProtected area

15km habitat maskMinimum convex polygon (MCP)

Camera trap location in BhabharSettlement area

N

0 1 2 4 6 8

(km)

Figure 3 A pixelated densitymap showing relative leopard densities per pixel of size 0336 km2The area ofMCP (minimum convex polygon)is 289 km2

for predators in the Bhabhar regionThis study constitutes thefirst attempt to quantify leopard abundance and density fromprotected areas in the seasonally dry subtropical deciduoushabitat type and Bhabhar physiographic region in the TeraiArc

Because of the prevailing controversy surrounding esti-mating animal densities we used four different approachesto estimate leopard density in the study area Of the fourestimators leopard densities from SECR-ML and SECR-Bmodels were similar [66] but much lower in comparison tobuffer strip method using 12MMDM (Figure 4) Previousstudies also indicated that traditional 12MMDM methodsoverestimate density compared to SECRmethods [22 25 2640 67] While we found estimates using the full MMDMto be similar to the SECR models there is no theoreticalbasis to justify this inference [40 73] since the 12MMDM ismeant to represent a home range radius for buffering cameratraps However it is possible that the extent of the cameratrap grid is simply too small to encompass the true leopardhome range radius for multiple leopards and hence the

12MMDM underestimates distance moved For example astudy that used GPS collars and camera traps simultaneouslyon jaguars showed that actual home range radius was muchlarger than the 12MMDM determined from camera traps[35] Therefore the use of the SECR models may be betterjustified than traditional buffer strip methods in the absenceof telemetryGPS data Understanding the potential pitfallsof traditional techniques which may overestimate density iscrucial formanagers as they require reliable estimates tomakeobjective assessments of the conservation status of a speciesat risk

Program DENSITY allows for selection from multiplepossible observation models using an AIC framework andis computationally efficient and relatively quick [41] unlikeSPACECAP [74] We did not evaluate the effect of ldquosexrdquo as acovariate in density estimates due to inability to determinesex for some of the photographed individual leopards andthe inability of program DENSITY to accept missing valuesIn addition no feature in the current version of SPACECAP(GUI) allows us to incorporate missing data even though


Table 7 Leopard (Panthera pardus fusca) density estimates (per 100 km2) across the array of habitat types from study areas in South Asiabased on traditional meanmaximum distance moved (MMDM) approaches and spatially explicit capture-recapture (SECR) using maximumlikelihood (SECR-ML) and Bayesian (SECR-B) analytical methods 12 MMDM and full MMDM approaches were used in populationestimates from program CAPTURE

Site Habitat type Density estimates Source12 MMDM Full MMDM SECR-ML SECR-B

Chitwan National ParkNepal

Alluvial floodplainsgrassland and deciduousforest

406 (183) 348 (089) 345 (049) [29]

Bardia National ParkNepal


5 () [89]

Jigme Singye WangchuckNational Park Bhutan

Broadleaf to coniferousforest 104 (001) [55]

Akole Tahsil MaharastraIndia$ Irrigated Valley 64 (078) 48 (12) [27]

Chilla Forest Range-RajajiNational Park Indiasect

Moist and dry deciduousforest 1499 (69) [24]

Sariska Tiger ReserveIndia Dry deciduous forest 60 (05) 71 (20) 58 (11) [67]

Satpura Tiger ReserveIndia

Dry and moistdeciduous mixed forest 73 (51)ndash93 (20) 42 (31)ndash62 (16) 404 (137)ndash721 (321) [68]

Mudumalai Tiger ReserveIndia

Subtropical drydeciduous forest 2891 (722) 1341 (267) 1317 (315) 1301 (231) [25]

Manas National ParkIndia

Alluvial floodplain andsub-tropical forest 1130 (29) 340 (082) [26]

Parsa Wildlife ReserveNepal

Subtropical drydeciduous forest 561 (130) 385 (088) 378 (085) 348 (083) This study

based on combination of telemetry studies camera trapping and leopard tracks Estimate is representative of southern most areas of Bardia National Park$A nonprotected areasectPopulation estimates based on programMARK for density estimates

12 MMDM

Full MMDM SECR-ML SECR-B

0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator

Figure 4 Comparative density estimates of leopards in PWR usingtraditional density estimators that buffer trapping grid by 12 or fullmean maximum distance moved (MMDM) among camera trapsand recently developed spatially explicit capture recapture (SECR)models in a maximum likelihood (SECR-ML) or Bayesian (SECR-B) framework

it is computationally possible with Bayesian analysis [7576] We reported both SECR density estimates in order tocompare density estimates across studies that used these

techniques [73 74 77] Also we wanted to evaluate thetradeoff of being able to select among spatial detectionmodels using an information theoretic approach in programDENSITY compared to choosing a spatial detection functionin SPACECAP [74] We expected the posterior estimatesfrom program SPACECAP to be superior as it also reportsinterval estimates of density as direct probabilities withoutproblematic asymptotic assumptions [27 45] The benefitsof SPACECAP include that its nonasymptotic assumptionsappear to fit the low sample size camera trap data better[45] and it can be extended to open models in futurestudiesUltimatelywe found the estimates from the two SECRmethods to be very similar with similar precision

Our results show that PWR harbors high leopard densitysimilar to other protected areas namely Bardia and ChitwanNational Park areas that are considered better habitat Highprey biomass may be the primary reason for unexpectedlyhigh leopard densities in PWR [15] where ungulate preyoccurred at 66 (plusmnSE 11) individuals per km2 [48] Thecurrent densities of medium- and large-sized prey in PWRare probably not sufficient to support a high density tigerpopulation assuming an annual kill rate of 3000 kg per yearper tiger and annual biomass cropping rate of 10 [6] Asa result tiger density in the reserve is low at 087 tigersper 100 km2 [48] and leopards are perhaps able to fully


exploit their preferred prey medium- and small-sized preyperhaps explaining the high density of leopards in PWRAt the regional scale results of this study demonstrate thatSECR leopard densities of 348 per 100 km2 in seasonally drydeciduous forest were lower than SECR estimates in similarsites in India Prey availability and the broad niche width ofleopards in forested landscapes could be the reason for theirvariable densities in the subcontinent

Baseline population estimates for leopards are essentialfor monitoring the effectiveness of conservation initiatives[78] and for guiding conservation decisions In this regardour findings provide useful insights for leopard conservationboth at the local and regional levels At the local level ourresults serve as the benchmark data to assess conservationimpacts of the relocation of ca 100 households from the corearea of PWR At the regional scale leopard demographicassessment across this large conservation complex of theChitwan-Parsa-Valmiki Tiger conservation unit [79 80]which was established for tigers can simultaneously providereliable information on leopard density for the transbound-ary conservation landscape following the model providedby the transboundary Manas conservation landscape acrossIndia and Bhutan [81] Hence long-term monitoring ofleopards with camera trap studies as employed in ourstudy would be useful to understand conservation statusand variation in the population sizes over time and fromlocal to regional scales thereby informing decisionmakers inimplementing sound conservation management recommen-dations

The current Nepalese legislation on wildlife does notrecognize the leopard on the protected animal list [82] yetit is one of the most heavily traded species for its skin [83]The antipoaching strategy designed to protect the tiger andrhinoceros as flagship species in the region [84 85] shouldalso aid in leopard conservation within protected areasHowever the large swathe of unprotected areas in the humandominated landscape of the Terai Arc [86ndash88] representsa considerable challenge in terms of the impact of habitatfragmentation [4] and increasing human wildlife conflict inprotecting this iconic mesopredator

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors are thankful to the Department of NationalParks and Wildlife Conservation for granting permission tocarry out this research They thank the Director General(DNPWC) the Country Representative (WWF Nepal) andthe Member Secretary (NTNC) for their support the coreteam for their technical support Parsa Wildlife Reserve staffBCCNTNC and TALWWF field staff for their assistance inthe survey Thanks are also due to National Fish andWildlifeFoundationSave the Tiger Fund US Fish and WildlifeService WWF UK and WWF International for financial

assistance to the project Kanchan Thapa was supportedby WWF US Kathryn Fuller Fellowship and Virginia Techwhile preparing the paper The authors thank Brian Gerberand Dana Morin for their technical advice in the analysisand Brandon Plunkett for helping construct the leoparddatabase Special thanks are due to Drs John SeidenstickerDale Miquelle and Mark Ryan including two anonymousreviewers for their review and comments in improving theearlier version of this paper

References

[1] IUCN ldquoRed List of Threatened Species Version 2010rdquo Interna-tional Union for Nature Conservation httpwwwiucnredlistorgappsredlistdetails227320

[2] K U Karanth and J D Nichols Monitoring Tigers and TheirPrey A Manual for Researchers Managers and Conservationistsin Tropical Asia Center for Wildlife Studies New York NYUSA 2002

[3] M E Sunquist and F Sunquist Wild Cats of the World TheUniversity Chicago Press Chicago Ill USA 2002

[4] T Dutta S Sharma J E Maldonado T C Wood H S Panwarand J Seidensticker ldquoFine-scale population genetic structure ina wide-ranging carnivore the leopard (Panthera pardus fusca)in central Indiardquo Diversity and Distributions vol 19 no 7 pp760ndash771 2013

[5] K Thapa N M B Pradhan J Barker et al ldquoHigh elevationrecord of a leopard cat in the Kangchenjunga ConservationArea Nepalrdquo Cat News no 58 pp 26ndash27 2013

[6] M E Sunquist The Social Organization of Tigers (Pantheratigris) in Royal Chitawan National Park Nepal SmithsonianContributions to Zoology no 336 Smithsonian InstitutionPress Washington DC USA 1982

[7] K U Karanth and M E Sunquist ldquoPrey selection by tigerleopard and dhole in tropical forestsrdquo Journal of Animal Ecologyvol 64 no 4 pp 439ndash450 1995

[8] S W Wang and D W Macdonald ldquoFeeding habits and nichepartitioning in a predator guild composed of tigers leopardsand dholes in a temperate ecosystem in central Bhutanrdquo Journalof Zoology vol 277 no 4 pp 275ndash283 2009

[9] D Verıssimo T Pongiluppi M C M Santos et al ldquoUsinga Systematic Approach to Select Flagship Species for BirdConservationrdquo Conservation Biology vol 28 no 1 pp 269ndash2772014

[10] NHairston F Smith and L Slobodkin ldquoCommunity structurepopulation control and competitionrdquo American Naturalist vol94 pp 421ndash425 1960

[11] E G Ritchie and C N Johnson ldquoPredator interactions meso-predator release and biodiversity conservationrdquo Ecology Lettersvol 12 no 9 pp 982ndash998 2009

[12] K R Crooks and M E Soule ldquoMesopredator release andavifaunal extinctions in a fragmented systemrdquo Nature vol 400no 6744 pp 563ndash566 1999

[13] P Henschel L T B Hunter U Breitenmoser et al ldquoPan-thera pardusrdquo in Proceedings of the IUCN World ConservationCongress IUCN Red List of Threatened Species 2013

[14] G A Balme R Slotow and L T B Hunter ldquoEdge effects andthe impact of non-protected areas in carnivore conservationleopards in the Phinda-Mkhuze Complex South Africardquo Ani-mal Conservation vol 13 no 3 pp 315ndash323 2010


[15] K U Karanth J D Nichols N S Kumar W A Link and JE Hines ldquoTigers and their prey predicting carnivore densitiesfrom prey abundancerdquo Proceedings of the National Academy ofSciences of theUnited States of America vol 101 no 14 pp 4854ndash4858 2004

[16] A Harihar D L Prasad C Ri B Pandav and S P GoyalldquoLosing ground tigers panthera tigris in the north-westernshivalik landscape of IndiardquoOryx vol 43 no 1 pp 35ndash43 2009

[17] D M Rayan and S W Mohamad ldquoThe importance of selec-tively logged forests for tiger Panthera tigris conservation apopulation density estimate in PeninsularMalaysiardquoORYX vol43 no 1 pp 48ndash51 2009

[18] K U Karanth and J D Nichols ldquoEstimation of tiger densitiesin India using photographic captures and recapturesrdquo Ecologyvol 79 no 8 pp 2852ndash2862 1998

[19] K U Karanth ldquoEstimating tiger Panthera tigris populationsfrom camera-trap data using capture-recapturemodelsrdquoBiolog-ical Conservation vol 71 no 3 pp 333ndash338 1995

[20] K Kawanishi and M E Sunquist ldquoConservation status oftigers in a primary rainforest of Peninsular Malaysiardquo BiologicalConservation vol 120 no 3 pp 329ndash344 2004

[21] P Wegge C P Pokheral and S R Jnawali ldquoEffects of trappingeffort and trap shyness on estimates of tiger abundance fromcamera trap studiesrdquoAnimal Conservation vol 7 no 3 pp 251ndash256 2004

[22] Sunarto M J Kelly S Klenzendorf M R Vaughan MB Hutajulu and K Parakkasi ldquoThreatened predator on theequator multi-point abundance estimates of the tiger Pantheratigris in central SumatrardquoORYX vol 47 no 2 pp 211ndash220 2013

[23] J B Karki B Pandav S R Jnawali et al ldquoEstimating theabundance of Nepalrsquos largest population of tigers Pantheratigrisrdquo Oryx pp 1ndash7 2013

[24] A Harihar B Pandav and S P Goyal ldquoDensity of leopards(Panthera pardus) in the Chilla Range of Rajaji National ParkUttarakhand IndiardquoMammalia vol 73 no 1 pp 68ndash71 2009

[25] R Kalle T Ramesh Q Qureshi and K Sankar ldquoDensity oftiger and leopard in a tropical deciduous forest of MudumalaiTiger Reserve southern India as estimated using photographiccapture-recapture samplingrdquo Acta Theriologica vol 56 no 4pp 335ndash342 2011

[26] J Borah T Sharma D Das et al ldquoAbundance and densityestimates for common leopard Panthera pardus and cloudedleopard Neofelis nebulosa in Manas National Park AssamIndiardquo Oryx vol 48 no 1 pp 149ndash155 2014

[27] V Athreya M Odden J D C Linnell J Krishnaswamy andU Karanth ldquoBig cats in our backyards persistence of largecarnivores in a human dominated landscape in Indiardquo PLoSONE vol 8 no 3 Article ID e57872 2013

[28] J Seidensticker M E Sunquist and C McDougal ldquoLeopardsliving at the edge of the Royal ChitwanNational Park Nepalrdquo inConservation inDeveloping Countries Problems and Prospects JC Daniel and J S Serrao Eds pp 415ndash423 Oxford UniversitvPress Bombay India 1990

[29] T BThapaHabitat suitability evaluation for Leopard (Pantherapardus) using remote sensing and GIS in and around ChitwanNational Park Nepal [PhD thesis] Wildlife Institute of IndiaSaurashtra University Rajkot India 2012

[30] M Odden and P Wegge ldquoKill rates and food consumption ofleopards in Bardia National Park NepalrdquoActaTheriologica vol54 no 1 pp 23ndash30 2009

[31] M Israil M Al-hadithi and D C Singhal ldquoApplicationof a resistivity survey and geographical information system(GIS) analysis for hydrogeological zoning of a piedmont areaHimalayan foothill region IndiardquoHydrogeology Journal vol 14no 5 pp 753ndash759 2006

[32] A F OrsquoConnell J D Nichols and K U S Kaaranta CameraTraps in Animal Ecology Methods and Analyses Springer NewYork NY USA 2011

[33] D L Otis K P Burnham G C White and D R AndersonldquoStatistical inference from capture data of closed populationsrdquoWildlife Monographs vol 2 pp 1ndash13 1978

[34] B K Williams J D Nichols and M J Conroy Analysis andManagement of Animal Populations Academic Press Califor-nia Calif USA 2002

[35] M K Soisalo and S M C Cavalcanti ldquoEstimating the densityof a jaguar population in the Brazilian Pantanal using camera-traps and capture-recapture sampling in combination with GPSradio-telemetryrdquo Biological Conservation vol 129 no 4 pp487ndash496 2006

[36] A Dillon and M J Kelly ldquoOcelot home range overlap anddensity comparing radio telemetry with camera trappingrdquoJournal of Zoology vol 275 no 4 pp 391ndash398 2008

[37] K R Wilson and D R Anderson ldquoEvaluation of two densityestimators of small mammal population sizerdquo Journal of Mam-malogy vol 66 no 1 pp 13ndash21 1985

[38] L R Dice ldquoSome census methods for mammalsrdquoThe Journal ofWildlife Management vol 2 no 3 pp 119ndash130 1938

[39] A Dillon and M J Kelly ldquoOcelot Leopardus pardalis in Belizethe impact of trap spacing and distance moved on densityestimatesrdquo Oryx vol 41 no 4 pp 469ndash477 2007

[40] B D Gerber S M Karpanty and M J Kelly ldquoEvaluatingthe potential biases in carnivore capture-recapture studiesassociated with the use of lure and varying density estimationtechniques using photographic-sampling data of the Malagasycivetrdquo Population Ecology vol 54 no 1 pp 43ndash54 2012

[41] M Efford D Dawson and C Robbins ldquoDENSITY software foranalysing capture-recapture data from passive detector arraysrdquoAnimal Biodiversity and Conservation vol 27 no 1 pp 217ndash2282004

[42] J S Ivan Density Demography and Seasonal Movements ofSnowshoe Hares in Central Colorado Colorado State UniversityFort Collins Colo USA 2011

[43] D L Borchers and M G Efford ldquoSpatially explicit maximumlikelihood methods for capture-recapture studiesrdquo Biometricsvol 64 no 2 pp 377ndash385 2008

[44] M G Efford D K Dawson and D L Borchers ldquoPopulationdensity estimated from locations of individuals on a passivedetector arrayrdquo Ecology vol 90 no 10 pp 2676ndash2682 2009

[45] J A Royle J D Nichols K U Karanth and A MGopalaswamy ldquoA hierarchical model for estimating density incamera-trap studiesrdquo Journal of Applied Ecology vol 46 no 1pp 118ndash127 2009

[46] S Jha and A Karn ldquoClimatic analogues for the administrativedistricts of Nepalrdquo Tribhuvan University Journal vol 23 no 1pp 55ndash64 2001

[47] M Chetri Food habits habitat utilization and conservation ofgaur (Bos gaurus gaurus) in Parsa Wildlife Reserve Nepal [MSthesis] Tribhuvan University Kathmandu Nepal 1999

[48] J B Karki Occupancy and abundance of tigers and their prey inthe Terai Arc Landscape Nepal [PhD thesis] Wildlife Instituteof India Dehradun India 2011


[49] DNPWC Annual Report 2009-2010 Department of NationalPark and Wildlife Conservation Babarmahal KathmanduNepal 2010

[50] K Thapa and S Lohani Tiger Went Up the Hill A Case Studyfrom Parsa Wildlife Reserve WWF Nepal and University ofMissouri 2007

[51] S C Silver L E T Ostro L K Marsh et al ldquoThe use ofcamera traps for estimating jaguar Panthera onca abundanceand density using capturerecapture analysisrdquoOryx vol 38 no2 pp 148ndash154 2004

[52] Y V Jhala Q Qureshi and R Gopal Status of Tigers Co-Predators and Prey in India National Tiger ConservationAuthority Government of India Wildlife Institute of IndiaDehradun India 2008

[53] A Harihar B Pandav and S P Goyal ldquoResponses of leopardPanthera pardus to the recovery of a tiger Panthera tigrispopulationrdquo Journal of Applied Ecology vol 48 no 3 pp 806ndash814 2011

[54] S Miththapala J Seidensticker L Phillips S Fernando JSmallwood and S Miththapala ldquoIdentification of individualleopards (Panthera pardus kotiya) using spot pattern variationrdquoJournal of Zoology vol 218 no 4 pp 527ndash536 1989

[55] S W Wang and D W Macdonald ldquoThe use of camera traps forestimating tiger and leopard populations in the high altitudemountains of Bhutanrdquo Biological Conservation vol 142 no 3pp 606ndash613 2009

[56] E A Rexstad andK P BurnhamUserrsquos guide for interactive pro-gram CAPTURE Animal Conservation Colorado CooperativeWildlife Research Unit Colorado State University Fort CollinsColo USA 1991

[57] T R Stanley andK P Burnham ldquoA closure test for time-specificcapture-recapture datardquo Environmental and Ecological Statisticsvol 6 no 2 pp 197ndash209 1999

[58] R Pradel ldquoUtilization of capture-mark-recapture for the studyof recruitment and population growth raterdquo Biometrics vol 52no 2 pp 703ndash709 1996

[59] G C White and K P Burnham ldquoProgram MARK survivalestimation from populations of marked animalsrdquo Bird Studyvol 46 supplement 1 pp S120ndashS139 1999

[60] S Pledger ldquoUnified maximum likelihood estimates for closedcapture-recapture models using mixturesrdquo Biometrics vol 56no 2 pp 434ndash442 2000

[61] H Akaike ldquoInformation theory and an extension of themaximum likelihood principlerdquo in Proceedings of the 2ndInternational Symposium on Information Theory pp 267ndash281Akademinai Kiado Budapest Hungary 1973

[62] K P Burnham and D R Anderson Model Selection and Mul-timodel Inference A Practical Information-Theoretic ApproachSpringer New York NY USA 2nd edition 2002

[63] R R Parmenter T L Yates D R Anderson et al ldquoSmall-mammal density estimation a field comparison of grid-basedvs web-based density estimatorsrdquo Ecological Monographs vol73 no 1 pp 1ndash26 2003

[64] J D Nichols and K U Karanth ldquoStatistical Concepts esti-mating absolute densities of tigers using capture-recapturesamplingrdquo in Monitoring Tigers and Their Prey A Manual forReseachers Managers and Conservationists in Tropical Asia KU Karanth and J D Nichols Eds pp 121ndash137 Center forWildlife Studies Banglore India 2002

[65] J A Royle KUKaranth AMGopalaswamy andN S KumarldquoBayesian inference in camera trapping studies for a class of

spatial capture-recapture modelsrdquo Ecology vol 90 no 11 pp3233ndash3244 2009

[66] A M Gopalaswamy J A Royle J E Hines et al ldquoProgramSPACECAP software for estimating animal density using spa-tially explicit capture-recapture modelsrdquo Methods in Ecologyand Evolution vol 3 no 6 pp 1067ndash1072 2012

[67] K Mondal K Shankar Q Qureshi S Gupta and P ChourasialdquoEstimation of population and survivorship of leopard Pantherapardus (Carnivora Felidae) through photographic capture-recapture sampling inWestern IndiardquoWorld Journal of Zoologyvol 7 pp 30ndash39 2012

[68] A Edgaonkar Ecology of the Leopard (Panthera Pardus) in BoriWildlife Sanctuary and Satpura National Park India Universityof Florida Gainesville Fla USA 2008

[69] C A Tredick and M R Vaughan ldquoDNA-based populationdemographics of black bears in coastal north Carolina andVirginiardquo Journal of Wildlife Management vol 73 no 7 pp1031ndash1039 2009

[70] R D C Team ldquoThe R foundation for statistical computingVersion 301 Vienna Austriardquo 2013

[71] J Geweke ldquoEvaluating the accuracy of sampling-basedapproaches to the calculation of posterior momentsrdquo inProceedings of the 4th Valencia International Meeting onBayesian Statistics pp 169ndash194 Oxford University PressOxford UK 1992

[72] M W Tobler and G V N Powell ldquoEstimating jaguar densitieswith camera traps problems with current designs and recom-mendations for future studiesrdquo Biological Conservation vol 159pp 109ndash118 2013

[73] M E Obbard E J Howe and C J Kyle ldquoEmpirical comparisonof density estimators for large carnivoresrdquo Journal of AppliedEcology vol 47 no 1 pp 76ndash84 2010

[74] B Gardner J Reppucci M Lucherini and J A Royle ldquoSpatiallyexplicit inference for open populations estimating demo-graphic parameters from camera-trap studiesrdquo Ecology vol 91no 11 pp 3376ndash3383 2010

[75] B Gardner J A Royle M T Wegan R E Rainbolt and P DCurtis ldquoEstimating black bear density using DNA data fromhair snaresrdquo Journal of Wildlife Management vol 74 no 2 pp318ndash325 2010

[76] R E Russell J A Royle R Desimone et al ldquoEstimating abun-dance of mountain lions from unstructured spatial samplingrdquoJournal of Wildlife Management vol 76 no 8 pp 1551ndash15612012

[77] A J Noss B Gardner L Maffei et al ldquoComparison of densityestimationmethods formammal populationswith camera trapsin the Kaa-Iya del Gran Chaco landscaperdquo Animal Conserva-tion vol 15 no 5 pp 527ndash535 2012

[78] T N E Gray and S Prum ldquoLeopard density in post-conflictlandscape Cambodia evidence from spatially explicit capture-recapturerdquo The Journal of Wildlife Management vol 76 no 1pp 163ndash169 2012

[79] E Dinerstein C Loucks AHeydlauff et al Setting Priorities forthe Conservation and Recovery of Wild Tigers 2005ndash2015 2006

[80] E Dinerstein The Return of the Unicornis The Natural His-tory and Conservation of the Greater One-Horned RhinocerosColumbia University Press New York NY USA 2003

[81] J Borah D Wangchuk A Swargowari et al ldquoTigers inthe transboundary manas conservation complex conservationimplications across bordersrdquo PARKS vol 19 p 51 2013


[82] J T Heinen and P B Yonzon ldquoA review of conservation issuesand programs in Nepal from a single species focus towardbiodiversity protectionrdquo Mountain Research and Developmentvol 14 no 1 pp 61ndash76 1994

[83] U R Bhuju R S Aryal and P Aryal Report on the Facts andIssues on Poaching of Mega Species and Illegal Trade in TheirParts in Nepal Transparency International Nepal KathmanduNepal 2009

[84] D Verıssimo I Fraser J Groombridge R Bristol and D CMacMillan ldquoBirds as tourism flagship species a case study oftropical islandsrdquo Animal Conservation vol 12 no 6 pp 549ndash558 2009

[85] K Thapa S Nepal G Thapa S R Bhatta and E Wikra-manayake ldquoPast present and future conservation of the greaterone-horned rhinoceros Rhinoceros unicornis in Nepalrdquo Oryxvol 47 no 3 pp 345ndash351 2013

[86] E Wikramanayake M McKnight E Dinerstein A JoshiB Gurung and J L D Smith ldquoDesigning a conservationlandscape for tigers in human-dominated environmentsrdquo Con-servation Biology vol 18 no 3 pp 839ndash844 2004

[87] E Wikramanayake E Dinerstein J Seidensticker et al ldquoAlandscape-based conservation strategy to double the wild tigerpopulationrdquoConservation Letters vol 4 no 3 pp 219ndash227 2011

[88] S M Barber-Meyer S R Jnawali J B Karki et al ldquoInfluence ofprey depletion and human disturbance on tiger occupancy inNepalrdquo Journal of Zoology vol 289 no 1 pp 10ndash18 2013

[89] M Odden and P Wegge ldquoSpacing and activity patterns ofleopards Panthera pardus in the Royal Bardia National ParkNepalrdquoWildlife Biology vol 11 no 2 pp 145ndash152 2005

Submit your manuscripts athttpwwwhindawicom

Forestry ResearchInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Environmental and Public Health

Journal of



EcosystemsJournal of


MeteorologyAdvances in

EcologyInternational Journal of


Marine BiologyJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Advances in


Environmental Chemistry

Atmospheric SciencesInternational Journal of



Waste ManagementJournal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics


Geological ResearchJournal of

EarthquakesJournal of


BiodiversityInternational Journal of


ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


ClimatologyJournal of


84∘35

9984000998400998400E 998400

84∘40

9984000

998400E 99840084

∘45

9984000

998400E 99840084

∘50

9984000

998400E 84∘55

9984000998400998400E 85

∘09984000998400998400E

84∘35

9984000998400998400E 998400

84∘40

9984000

998400E 99840084

∘45

9984000

998400E 99840084

∘50

9984000

998400E 84∘55

9984000998400998400E 85

∘09984000998400998400E

27∘30

9984000998400998400N

27∘25

9984000998400998400N

27∘20

9984000998400998400N

27∘15

9984000998400998400N

27∘30

9984000998400998400N

27∘25

9984000998400998400N

27∘20

9984000998400998400N

27∘15

9984000998400998400N

N

India

Nepal

China

International boundaryRiverCamera stationSettlementProtected areasBuffer zone

MCP outline

Figure 1 Study areas showing the spatial location of camera traps in the Bhabhar region and the effective sampling area formed by drawinga minimum convex polygon surrounding the outermost camera trap locations The area of MCP (minimum convex polygon) is 289 km2

In contrast to tigers whose population sizes and trendshave been intensively studied in multiple sites across theIndian subcontinent [8 16ndash23] fewer studies have estimatedleopard population sizes across both protected [24ndash26] andnonprotected areas [27] representing an array of habitattypes in India In Nepal even fewer studies are available onleopard demography only representing alluvial floodplainsgrasslands and deciduous forest from Chitwan [28 29] andBardiaNational Parks [30]However there are no estimates ofleopard density from seasonally dry subtropical forest in theBhabhar region Bhabhar is the alluvial apron of sedimentswashed down from the Siwaliks [31] and represents a keyphysiographic region extending to the ldquoTerai zonerdquo across theTerai Arc Landscape (Terai Arc) Hence lack of informationfrom Bhabhar habitat has hindered an overall assessment ofleopard conservation status

We estimate the abundance and density of leopardsfrom the protected area within Bhabhar using a pho-tographic capture-recapture sampling framework [32ndash34]This approach is widely acknowledged as a robust toolfor estimating population abundance of elusive wild catswith individually distinct pelage patterns [15 20] We usetraditional techniques of estimating the density using thead hoc method of adding a buffer area around the polygonformed by connecting outer camera trap locations to accountfor edge effects (animals that do not occur entirely within

trapping grid but have home range overlapping the edge ofthe grid) These buffering methods include using an estimateof home range radius derived from GPS or radio telemetry[35 36] or the common technique of using half of the meanmaximum distance moved (12MMDM) by animals withinthe trap array as a surrogate for home range radius [18 37 38]However these ad hoc approaches have come under scrutinybecause they are heavily influenced by small sample sizecamera spacing and extent of sampling grid relative to theanimalrsquos home range [35 39 40] To address the shortcomingsof the traditional approach in estimating leopard densitywe also use recently developed spatially explicit capturerecapture (SECR) techniques that use spatial informationmore directly in the density estimation process [40ndash43] Wecompare the maximum likelihood [44] and Bayesian [45]SECR approaches in estimating density without the needto estimate an effective trapping area using the traditionalad hoc buffering approach We present the result from bothtraditional and SECR approaches allowing us to compare ourleopard density estimates with other studies in South Asia(Nepal India and Bhutan)

2 Materials and Methods

The study was carried out in Parsa Wildlife Reserve (PWR27∘151015840N 84∘401015840E Figure 1) in the south-central lowland Terai


















0












3 Results








119905+1)




0988 08383 19 39




(120596)Model

likelihoodNumber of









(120596)Model

likelihoodNumber of









ℎmodel




119887











119863119890 (SE)

PWR





PosteriorSD

95 LowerHPD level

95 UpperHPD level









4 Discussion











N

0 1 2 4 6 8

(km)











406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics































0












3 Results








119905+1)




0988 08383 19 39




(120596)Model

likelihoodNumber of









(120596)Model

likelihoodNumber of









ℎmodel




119887











119863119890 (SE)

PWR





PosteriorSD

95 LowerHPD level

95 UpperHPD level









4 Discussion











N

0 1 2 4 6 8

(km)











406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics




















0












3 Results








119905+1)




0988 08383 19 39




(120596)Model

likelihoodNumber of









(120596)Model

likelihoodNumber of









ℎmodel




119887











119863119890 (SE)

PWR





PosteriorSD

95 LowerHPD level

95 UpperHPD level









4 Discussion











N

0 1 2 4 6 8

(km)











406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics




















119905+1)




0988 08383 19 39




(120596)Model

likelihoodNumber of









(120596)Model

likelihoodNumber of









ℎmodel




119887











119863119890 (SE)

PWR





PosteriorSD

95 LowerHPD level

95 UpperHPD level









4 Discussion











N

0 1 2 4 6 8

(km)











406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics




















119863119890 (SE)

PWR





PosteriorSD

95 LowerHPD level

95 UpperHPD level









4 Discussion











N

0 1 2 4 6 8

(km)











406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics




















N

0 1 2 4 6 8

(km)











406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics



















406 (183) 348 (089) 345 (049) [29]



5 () [89]
















12 MMDM


0

1

2

3

4

5

6

7

8

0 1 2 3 4

Num

ber o

f leo

pard

per100

sq k

m

Density estimator











Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics




















Acknowledgments



References


































































































Journal of












Volume 2014

Advances in









Geophysics

































































































Journal of












Volume 2014

Advances in









Geophysics






























































Journal of












Volume 2014

Advances in









Geophysics



























Journal of












Volume 2014

Advances in









Geophysics


















Journal of












Volume 2014

Advances in









Geophysics














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