Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong Phayayen – Khao Yai Forest Complex, Thailand

Accepted Manuscript Title: Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong Phayayen - Khao Yai Forest Complex, Thailand Authors: Dusit Ngoprasert, George A. Gale PII: DOI: Reference:

S1616-5047(18)30086-7 https://doi.org/10.1016/j.mambio.2019.02.003 MAMBIO 41088

To appear in: Received date: Accepted date:

28 March 2018 21 February 2019

Please cite this article as: Ngoprasert D, Gale GA, Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong Phayayen - Khao Yai Forest Complex, Thailand, Mammalian Biology (2019), https://doi.org/10.1016/j.mambio.2019.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

17 February 2019 Please address correspondence to: George A. Gale

Running head: tiger and dhole in Dong Phayayen-Khao Yai

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Conservation Ecology Program, King Mongkut’s University of Technology Thonburi, 49 Thakham, Bangkhuntien, Bangkok, 10150, Thailand

Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong

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Dusit Ngoprasert and George A. Gale*

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Phayayen - Khao Yai Forest Complex, Thailand

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Affiliation

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King Mongkut’s University of Technology Thonburi, Conservation Ecology Program 49 Thakham, Bangkhuntien, Bangkok 10150, THAILAND

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* Correspondent: [email protected]

Abstract

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Large carnivores have been declining due to a combination of factors including habitat loss and fragmentation, prey loss, and direct persecution. Tiger Panthera tigris and dhole Cuon alpinus are endangered and emblematic of problems facing large carnivores globally. We estimated tiger density, dhole occupancy and prey availability within the Dong Phayayen - Khao Yai Forest Complex, a World Heritage Area in Thailand that has potential as a ‘recovery site’ for both

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species. Camera traps were set near bait stations designed for bear monitoring. A Bayesian spatial capture-recapture approach was used to estimate tiger density and occupancy of dhole and their prey. Camera traps were deployed in two areas, Khao Yai (78 locations, December 2009-

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May 2011) and Dong Phayayen (45 locations, December 2012-August 2014). Tiger was not detected in Khao Yai. We detected 9 tigers (2 male, 4 females, and 3 unknown sex) in Dong Phayayen. Tiger density was 2.1 (95% CI 0.5–5.3) individuals per 100 km2 based on an

individual heterogeneity model. Dhole occupancy was higher in Khao Yai (64%) than Dong

Phayayen (55%). Prey occupancy was 9–53% higher in Dong Phayayen. Wild pig Sus scrofa had

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the highest occupancy rates, followed by gaur Bos gaurus, sambar Rusa unicolor and muntjac

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Muntiacus muntjac, respectively. Although Dong Phayayen’s tiger density was lower compared

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to populations estimated in some better-known protected areas, our data suggest it has potential

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as a regional tiger (and perhaps dhole) recovery site. However, Dong Phayayen, like many sites in the region, faces significant threats from wildlife hunting and rosewood (Dalbergia spp.)

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poaching that need to be addressed urgently if this small population is going to survive even the

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KEYWORDS

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near term.

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Camera trapping, Cuon alpinus, density estimation, Panthera tigris, spatial capture-recapture

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Introduction

Top predators often serve as keystone species and are critical in structuring ecosystems

(Beschta and Ripple, 2009; Ritchie and Johnson, 2009; Terborgh et al., 2001). Additionally, large predators are highly suitable umbrella species for conservation planning (Sergio et al.,

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2008) because of their sensitivity to forest degradation and their large home ranges typically overlap with other species of conservation concern (e.g., Morrison et al., 2007). However, large carnivores have been in decline over the last century due to a combination of habitat loss and

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fragmentation, loss of their prey base, and direct persecution (Karanth and Chellam, 2009; Lynam, 2010; Oswell, 2010). In the Asian tropics, large carnivores have spatial requirements

that often exceed the spatial extent of protected areas (Curran et al., 2004; Laidlaw, 2000). At edges of reserves where wildlife habitats lie adjacent to human-settled lands, large carnivores

face elevated mortality risks due to poaching and retribution killings for attacks on livestock and

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humans (Harcourt et al., 2001; Ogada et al., 2003; Woodroffe and Ginsberg, 1998).

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Tiger (Panthera tigris) and dhole (Cuon alpinus) are endangered species, declining in

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most parts of their geographic ranges; tigers for example are now concentrated within less than

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7% of remaining habitat (Walston et al., 2010b). An estimated 4000 tigers survive in the wild (Goodrich et al., 2015) and the best assessment suggests the situation for dhole is even more dire

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(<2500 mature individuals) (Kamler et al., 2015). Although Southeast Asia is estimated to

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contain approximately 50% of the global tiger population (Goodrich et al., 2015), very little is

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known about their distribution and population status outside of a few well-studied sites e.g. Huai Kha Khaeng (Duangchantrasiri et al., 2016). Dholes are also widespread, but are largely

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unstudied and lack reliable population estimates (Kamler et al., 2015) with only two published

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assessments rangewide (Majumder et al., 2011; Selvan et al., 2014). Knowledge of a population’s basic ecology and current population size is critical to

assessing and managing extinction risk. Although there have been several density estimates for tigers within Southeast Asia (Duangchantrasiri et al., 2016; Johnson et al., 2006; Kawanishi and Sunquist, 2004; Linkie et al., 2008; Lynam, 2010; Steinmetz et al., 2013; Sunarto et al., 2013;

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Wibisono et al., 2009), recent data suggest that populations of tigers and other large carnivores have collapsed regionally (Rostro-García et al., 2016; Walston et al., 2010a). In general, Southeast Asia lacks recent information on the status of large carnivores but has seen the greatest

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range contractions (Wolf and Ripple, 2017). Thus, while bycatch data are less than ideal for making management decisions, nearly all recent data regarding the status of endangered carnivores has potential conservation value in Southeast Asia.

In this region, Thailand developed its protected area system earlier compared to other

countries except for Singapore (ICEM, 2003). However, Thailand’s natural areas are still facing

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fragmentation and loss of habitat which has led to a situation where areas outside formal

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protection (83% of land area) may hold little if any potential for supporting large carnivores.

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Further, approximately 25% of the protected areas in Thailand are less than 200 km2 in size

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(Tantipisanuh et al., 2016). Thus, only in the largest (>4,000 km2) protected area complexes does any significant core intact and undisturbed habitat remain, and hard-edges are actively

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maintained at the boundaries of all reserves due to the lack of buffer zones, which poses risks for

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large carnivores and other wildlife (Kinnaird et al., 2003; Ngoprasert et al., 2007). Currently,

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only three main landscapes support tiger and dhole together in Thailand; the Western Forest Complex (WEFCOM) a global priority site for tiger conservation and recovery within mainland

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Southeast Asia with a combined area of 18,000 km2 (Duangchantrasiri et al., 2016; Harihar et al. 2018), Dong Phayayen – Khao Yai forest complex (DPKY), which is approximately one-third of

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the WEFCOM area, and Kaeng Krachan forest complex (KKFC), which is one-fourth of WEFCOM. Previous reports indicate tigers are at extremely low densities in KKFC (Lynam, 2003; Steinmetz et al., 2013), while reports from DPKY indicate that there may be a small, but

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viable tiger population, although little is known about dhole (Department of National Park Wildlife and Plant Conservation, 2014). Here we estimated density and occupancy for tiger and dhole from bycatch data as part of

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baseline assessments for one of the few tiger and dhole populations remaining in Southeast Asia. Although DPKY has some of the most extensive protected forest in Thailand, this complex is

subjected to heavy poaching and disturbance in some parts. In particular, past logging (Lynam et al., 2006) as well as recent illegal logging of rosewood (Dalbergia cochinchinensis) were

observed during our study. Here we focused on 1) estimating density of tiger, 2) estimating

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occupancy of dhole, and 3) occupancy of their prey by using camera trap surveys that were

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initially designed for bear monitoring (see below). We were particularly interested in knowing

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the size and occupancy of the current populations to assess the level of urgency regarding their

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likelihood of persistence. Finally, we also determined human disturbance in the Dong Phayayen

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Materials and methods

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as a baseline indicator of the threats to the long-term survival of large carnivores.

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Study area

The study was conducted in the Dong Phayayen-Khao Yai Forest Complex, DPKY-FC

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(14° 00’ to 14° 33’ S and 101° 05’ to 103° 14’ E, which is comprised of five protected areas organized into two sections, the Khao Yai section which consists of Khao Yai National Park

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2,168 km2 and the Dong Phayayen section which encompasses Thap Lan National Park 2,236 km2, Pang Sida National Park 844 km2, Ta Phraya National Park 594 km2 and Dong Yai Wildlife Sanctuary 313 km2. The DPKY-FC has been a World Heritage site since July 2005. It has a land area 6,155 km2 with altitudes ranging from 100 m to 1,351 m (Khao Rom summit, Khao Yai

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National Park). Precipitation in DPKY-FC varies. In general, higher elevations receive greater rainfall than lower elevations. The average annual precipitation was 2,200 mm from 1994-2007 (Brockelman et al., 2011). The dominant vegetation was primary evergreen forest for the entire

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complex. Mixed deciduous forest occupies a small proportion of the complex (5%). However, past human use areas, such as villages and agricultural areas were represented in the park today as grassland or secondary forest (18%) (Lynam et al., 2006; Srikosamatara and Hansel, 2000).

We sampled five areas within DPKY-FC, Khlong E-Tow (KET), Khlong Samor-Pun (KSP) and

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Khao Kampang (KKP) in Khao Yai, and DP1 and DP2 in Dong Phayayen.

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Khao Yai National Park - At the KET and KSP study sites, we set camera stations approximately

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1.5 to 2 km apart (Ngoprasert et al., 2012). We used this distance so that even bears (Asiatic

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black bear Ursus thibetanus and Malayan Sun bear Helarctos malayanus) with small home ranges (e.g., the annual home range of the sun bear is as small as 6 km2 in Borneo; Wong et al.,

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2004) had access to multiple stations. Nineteen baited camera trap stations were set in KET

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during December 2009–February 2010 (except during the first survey period where we only used

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15 stations). A polygon encompassing the outermost camera locations covered 33 km2. We set 18 camera trap stations in KSP during March–May 2010; the trapping polygon covered 40 km2.

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We changed the bait, batteries, and memory card of each camera every three weeks, and used these three-week periods as sampling occasions for capture–recapture analysis. We set 41

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camera trap stations at the KKP study site. We used four-kilometre distances between camera stations for KKP during December 2010–May 2011; the trapping polygon covered 135 km2. Based on data from Huai Kha Khaeng, female tiger home ranges average 70 km2 (Simcharoen et al., 2014), so 4-km spacing (>4 camera trap stations per home range) should have been sufficient

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to ensure all tigers had some probability of capture (Karanth et al., 2011). There were four sampling occasions at KKP, each with a duration of four weeks. Dong Phayayen - At DP1, we set up 23 camera trap stations from December 2012 to May 2013.

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There were four sampling occasions at each study site, each with a duration of 3 weeks. We used four-kilometre distances between camera stations following the simulation results from the Khao Yai data, and the trapping polygon covered 249 km2. At the DP2 study area, we set up 22 camera trap stations from November 2013 to August 2014. There were four sampling occasions at each study site, each with duration of six weeks. We used four-kilometre distances between camera

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stations and the trapping polygon covered 256 km2. Trapping duration varied depending on the

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logistical constraints of each study area and safety issues (particularly from heavily armed

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poachers).

Camera trapping

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Our camera trap surveys were specifically designed for bear, as such, tiger and dhole

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detections were bycatch (see below for our discussion of the limitations of such a design). As

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tigers and dholes were attracted to the bear baits, tiger density could be assessed by using the unique pelage patterns of individuals (Karanth & Nichols, 1998). Male and female tigers were

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distinguished by the presence or absence of testicles. We used passive infrared-triggered digital video scouting camera-traps, “HCO Scoutguard SG565FV” (HCO Outdoor Products, Georgia,

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USA), to photograph animals visiting bait stations. Three camera traps were mounted on trees approximately 3–4 m apart and facing each other in a triangular arrangement (Ngoprasert et al., 2012). At each station, six kilograms of beef was suspended above the ground at the center point between the three cameras; the infrared beams were set below the bait, one meter above the

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ground. Bait stations were setup as ‘non-reward’ so bears (as well as tiger and dhole) could not reach it. Several previous studies have used bait to increase the capture probability of large carnivores and still achieve unbiased estimates of density (Braczkowski et al., 2016; du Preez et

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al., 2014; Gerber et al., 2012). Camera-traps were set to take still pictures without a delay between triggering. Camera traps were active 24 hours per day. We used the last day a camera

was working to calculate the number of trap-nights. A total of 3–4 sampling occasions was used to construct the capture matrix. We classified photographs as independent detections only if the time between photographs was greater than or equal to 30 minutes (O'Brien et al., 2003).

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Although there are limitations of our bycatch data to estimate tiger density and dhole

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occupancy because study designs are typically species-specific (e.g. home-range, movement

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patterns, etc.), tiger and Asiatic black bear appear to have similar home-range sizes based on two

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previous studies conducted in Thailand (Simcharoen, 2014; Chongsomchai, 2013). Furthermore, spacing among stations was similar between our study and tiger surveys in Huai Kha Khaeng

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(Duangchantrasiri et al., 2016). Our maximum trap spacing was 4 km, slightly larger than Huai

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Kha Khaeng, but we assumed that tiger in Dong Phayayen ranged more widely than in Huai Kha

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Khaeng, because of lower prey availability. Greater trap spacing of our design would not cause a bias in density estimation in this case because all tiger in the area would have had at least some

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chance of being detected (see Results). Using our data to estimate dhole occupancy was also reasonable based on a daily

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movement of 2.5 km for dhole in Thailand (Grassman et al., 2005; Jenks et al., 2012b). Our trap spacing, approximately equal to an average day’s movement of dhole, was sufficient to minimize the probability of detecting the same group of animals at different traps during the same trapping occasion (Selvan et al., 2014).

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Density estimation from photographic capture-recapture For traditional population abundance estimates, populations should be demographically and geographically closed (Otis et al., 1978). However, spatial capture-recapture methods do not

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assume geographic closure, but they do assume demographic closure. We tested the demographic closure assumption using the “closure.test” function in the secr package (Efford, 2018) following Otis et al. (1978). We used spatial capture–recapture modeling (SCR) to estimate the density of tigers using a Bayesian model (Royle et al., 2009). SCR fits a spatial model of detection and a

spatial model for the distribution of activity centers. The detection probability in SCR is modeled

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as a function of distance from capture locations to each animal’s activity center. The detection

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function has two parameters: g0 the probability of capture at the activity center of an animal and

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sigma (σ) the spatial scale of movement, which is derived from distances among recapture

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locations of individual animals (Efford et al., 2009). Because home ranges of tigers can vary considerably in size among sites (Simcharoen et al., 2014) and no tiger home range data were

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available for Dong Phayayen, we used a uniform flat prior (non-informative prior) for sigma.

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Because of the small number of detections, we did not incorporate sex as a covariate in the

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model. We fit a constant model (SCR0) by using data augmentation, where M = 200 with a uniform distribution of individuals over the state-space (Royle et al., 2009). We also modeled the

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possibility that capture probability varied by individual (SCRh). We compared between two models based on the deviance information criterion, DIC (Royle et al., 2014; Spiegelhalter et al.,

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2002). We created a buffer (state-space) which was initially larger than the boundaries of the Dong Phayayen forest complex thereby including unsuitable, non-forest habitat. The unsuitable habitat was then clipped out using a polygon shape file of the forest complex boundary (statespace = 3,500 km2). We used 300,000 Markov chain Monte Carlo (MCMC) iterations of three

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chains with a thinning rate of 30 and adaptation 1500, but no burn-in (M. Meredith, Biodiversity Conservation Society Sarawak, pers. comm. 2016). We implemented the SECR analysis with program JAGS (Plummer, 2003) through package “jagsUI” version 1.4.4 (Kellner, 2016) in R

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software version 3.3.2 (R Core Team, 2018). Model convergence was assessed using the R-hat value, where a value close to 1 indicated convergence (Gelman and Hill, 2007), and also by examining the trace plots of the chains. Site occupancy

Camera-trapping was used to determine the presence or absence of dholes and their prey

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(gaur, sambar, wild pig, and muntjac) in the study areas. Species detection probability (p) was

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defined as the probability of detecting the species during a sampling occasion if it was present in

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the area (Pollock et al., 2002). Detection histories were constructed for dholes and their prey at

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each camera trap station using 1-day occasions, 182 day-occasions in total. However, based on dhole vagility and the trap spacing there was still some risk of detecting the same group in more

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than one trap during the same sampling occasion. Therefore, spatial correlation of dhole

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detections was evaluated using the variogram function in the gstat package (Gräler et al., 2016;

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Pebesma, 2004). Two trap locations were removed from the analysis because the trap spacing between these stations was less than 2 km. We estimated the probability of a species detection

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and probability of occupancy by using a Bayesian framework for single-season occupancy models (Royle and Dorazio, 2008). Models were fitted using modified BUGS code for JAGS

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(Kery and Royle, 2016). We fitted and compared the constant model psi(.), p(.) and a site covariate model psi(Sites), p(.). Uninformative priors were used for all parameters in both models. We also used informative priors where the models from uninformative priors fail ed to convergence for dhole. The informative prior (mode 0.5, concentration 2.2) for psi was

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obtained from naïve occupancy estimates from previous reports of dhole in Khao Yai (Jenks et al., 2011; Lynam et al., 2006). The posterior parameter estimates were based on a MCMC analysis. Model convergence was assessed using the R-hat value, and also by examining the

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trace plots of the chains (Gelman et al., 2014).

Human disturbance

Siamese rosewood, a target of extensive illegal trade in Cambodia, Lao PDR, Thailand and Vietnam for the luxury furniture market in China (Humphreys, 2016; Siriwat and Nijman

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2018a; Siriwat and Nijman 2018b), was present in the dry evergreen forests of Dong Phayayen,

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but not in our study areas of Khao Yai (Ngoprasert et al., 2011). Due to the conspicuousness of

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our camera trap set-up, we thought it unlikely that photo records would provide a representative

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sample of human activity; instead we counted the number of cut rosewood stumps along transects as a proxy index of human disturbance. Here rosewood poachers had set camps, and

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some hunted wildlife at least opportunistically (see Discussion below). Each transect was 300 m

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long x 10 m wide covering a total of 10.8 ha. We surveyed the transects from May 2015 to July

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2015 (24 transects in DP1 and 12 transects in DP2). Transects covered the same areas as our

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camera trapping stations.

Results

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Large carnivores We used a total of 123 camera stations in 5 study areas from December 2009 to

August 2014. In Khao Yai we set up 78 camera stations and 45 camera stations in Dong Phayayen. We captured 27 independent photos of tiger, and 40 photos of dhole during a total

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34,329 trap-days (Table 1). Tiger photographs were obtained from 13 camera stations corresponding to a naïve occupancy of 11% within the Dong Phayayen-Khao Yai forest complex. We did not detect tiger sign/photos in Khao Yai; however, we detected 9 tigers (2

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male, 4 females, and 3 unknown sex) in Dong Phayayen. Tigers detected included 2 females and 2 individuals of unknown sex in DP1. One male, 2 females and 2 of unknown sex were detected in DP2. None of the tigers were detected in both DP1 and DP2. The maximum

distance of tiger movements was calculated from two movements between camera traps in the DP1 study site derived from two female tigers which moved 14,518 and 14,147 m.

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Detection models were estimated from pooled data of individual capture histories from both

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study areas (DP1 and DP2). The closure test results suggested that our data did not violate

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population closure assumptions (Z = 0.93, p = 0.81). The individual heterogeneity model best

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fit our data according to DIC model selection SCRh 234.2 vs. SCR0 266.5. Density was 2.1 ±SD 1.4 (95% CI 0.5–5.3) individuals per 100 km 2 based on the SCRh model, and sigma

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21.1±SD 18.3 km. The range of capture probabilities was 0.01 – 0.03 for the nine individual

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tigers. Abundance (N) ranged from 16 tigers to 186 tigers for the 95% credible interval,

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however the upper limit was clearly an unrealistic number of tigers. Based on the constant model (SCR0), density was estimated at 1.4 ±SD 1.0 (95% CI 0.4–4.5) individuals per 100

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km2, with a movement parameter (sigma) of 15.7±SD 8.9 km and probability of capture at an

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activity center (g0) equal to 0.01±SD 0.01. Dholes were trapped in 15% of all trap stations. The detection rate of dhole was poor

for both models. The model with ‘park’ (Khao Yai versus Dong Phayayen) as a covariate had the lowest DIC compared to pooled data from all sites (Table 2) and estimated the

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probability of occupancy at 64% in Khao Yai and 55% in Dong Phayayen. Detection probability using pooled data or stratified by area yielded detection rates of 0.004 – 0.012.

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Prey occupancies We estimated two models, one assuming a constant probability of occupancy and

detection probability psi(.), p(.) and a second assuming the probability of occupancy differed between the two sections of DPKY, but with a constant detection probability psi(Park), p(.). All models converged with R-hat close to 1. Wild pig had the highest level of occupancy,

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followed by gaur, muntjac, and sambar respectively (Table 2). Red muntjac had lower

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occupancy in Khao Yai but ~60% occupancy in Dong Phayayen. Gaur was distributed widely

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(40 – 50%) across the whole forest complex, but also had a relatively low detection

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probability (Table 2). Sambar had a lower occupancy compared to other ungulates. On

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Human disturbance

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statistical certainty (Table 2).

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average, prey occupancy was 9 – 53% higher in Dong Phayayen than Khao Yai with high

No cut rosewood stems were found inside Khao Yai, but we recorded 125 cut stems in

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Dong Phayayen from both transects and opportunistic sampling. We conducted 24 transects in DP1 and only 12 transects in DP2, due to the presence of armed poachers. Only 4 cut stems

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were recorded along transects with an estimate of 3.7 cut stems per 10 ha. Most of the cut stems were found outside transects during opportunistic observations, however we could not calculate the survey effort.

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Discussion As was the case for previous studies (Lynam et al., 2006; Jenks et al., 2011), we detected no tigers in Khao Yai National Park, but we found a small population of tigers in

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the Dong Phayayen section of the forest complex. Our estimates suggested more tigers were present but undetected, with the implication that Dong Phayayen could be another potential

stronghold of tigers in Southeast Asia (Harihar et al., 2018). Tigers are surviving in the Dong Phayayen forest complex probably because of its higher prey abundance than Khao Yai.

However, the result was likely reversed for dhole, which had higher occupancy in Khao Yai

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despite the lower prey abundance. The lack of competition from tigers in Khao Yai may

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explain the greater relative abundance of dhole (Steinmetz et al., 2013).

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Tiger densities derived from our constant model (SCR0) and individual heterogeneity

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model (SCRh) were similar; the 95% credible intervals of the two models overlapped almo st entirely. However, the individual heterogeneity model suggested that tiger movement

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patterns may have been affected by human disturbance in Dong Phayayen. We could not

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model human disturbance directly in our model because of the limited human disturbanc e

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data. However, all movement information of tiger came from only DP1, an area with a relatively lower level of human disturbance based on our direct field observations (fewer

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direct encounters with poachers). The DP2 study area was highly disturbed at the time of our surveys. No recaptures were obtained from the five tigers we detected in this area. The

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density estimates were imprecise, as indicated by the very wide 95% CI and low capture probability. The low probability of capture suggested that it was possible that more tigers were alive in Dong Phayayen. Accordingly, the result from our model suggested at least seven more tigers were present but not detected; in partial support of this, 6 tigers were

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observed in Pang Sida National Park from February to June 2013 (Department of National Park Wildlife and Plant Conservation, 2014), which is contiguous with the southern part of Dong Phayayen, all but one of these were different individuals from those observed during

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our study. Our density estimation was based on bycatch data from a bear monitoring program.

Thus, although this is the first published field estimate from DPKY, the study design was not ideal for low-density animals like tiger. However, our results will likely benefit future tiger

research in DPKY and other low-density sites by providing tangible parameters for setting up

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better targeted study designs specifically for tiger. The movement parameter estimate of

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tigers in Dong Phayayen was at least 3 times larger than Huai Kha Khaeng Wildlife

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Sanctuary (Duangchantrasiri et al., 2016) and central Sumatra (Sunarto et al., 2013). Our

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prey data cannot be compared directly with Huai Kha Khaeng, but the movement parameter suggests that tigers in Dong Phayayen may be living in a habitat with a much lower prey

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density than Huai Kha Khaeng. Comparisons among tiger density estimates in this region

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indicated that the tiger population in Dong Phayayen was similar to others in Southeast Asia

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(Table 3). It also highlights the potential of Dong Phayayen as a future tiger source site (Wikramanayake et al., 1998). For example, if the existing population in Dong Phayayen

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could be recovered, it might potentially serve as a source for repopulating Khao Yai in the

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future, which as noted below, likely lost all of its tigers several years ago (Lynam, 2010). We did not detect tigers from our camera trap survey in Khao Yai. In this area, the

last known tiger was photographed in 2001 (only 1 individual, Lynam et al., 2006), and the last signs were detected by survey teams in 2005 (Jenks et al., 2011). It is highly likely that tigers have been extirpated from Khao Yai as indicated by the lack of detections from four

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independent surveys, including camera trap surveys in 2003–2007 (217 locations of 6,260 trap-nights, Jenks et al., 2011), 309 sign transects of 154.5 km walked in 2008 (Ngoprasert et al., 2011), sign transect surveys conducted in 2015 (15 transects of 300 m x 10 m) for large

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mammals in the same area as the single tiger footprint of 2005 (D. Ngoprasert, unpublished data), and this study. We speculate that the low prey abundance caused by poaching for local consumption and trade of wild ungulate meat were partly the cause of the tiger extirpation (Table 2). Another possible factor could include direct poaching. However, we assumed

Dong Phayayen and Khao Yai received similar hunting pressure but the larger landscape of

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Dong Phayayen may be able to support more prey than Khao Yai, which is approximately

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2,000 km2 smaller than Dong Phayayen.

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In Khao Yai, dhole occupancy was 16% higher than Dong Phayayen, and although the

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credible interval was rather wide, there was still 88% certainty that it was indeed higher. Because diets of tiger and dhole greatly overlap (Selvan et al., 2013; Wang and Macdonald,

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2009), the absence of tigers in Khao Yai for 12-16 years might be benefiting dholes. In Khao

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Yai, without tigers, dholes may be able to persist on the lower prey abundance. In contrast,

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we suggest that food competition between tiger and dhole was higher in Dong Phayayen because of the extensive spatial overlap between them. However, dholes tend to feed on

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smaller prey than tiger (Karanth and Sunquist, 1995), particularly muntjac and sambar (Grassman et al., 2005; Kamler et al., 2012; Wang and Macdonald, 2009). It is possible dhole

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and tiger co-exist in Dong Phayayen because there are for now enough large prey (gaur and sambar) for tigers and sufficient medium-sized and smaller prey (wild pig and muntjac) for dholes (Steinmetz et al., 2013). Our results indicated there was a higher prey occupancy (9– 53%) in Dong Phayayen compared to Khao Yai with a high level of certainty. Future

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management should urgently consider steps to monitor ungulate populations for both of these large carnivores as prey occupancies indicate that their populations were relatively low (Table 2), and further prey reductions would presumably greatly increase the risk of the local

SC RI PT

extinction of tigers and dhole. Our occupancy estimates for dhole in Dong Phayayen and Khao Yai were reasonably precise, but there were limitations that require caution. First, the presence of both tigers and dholes in Dong Phayayen renders the use of single-species occupancy models, which do not account for the co-occurrence of a dominant competitor (tiger), as potentially imprecise for

U

estimating the occupancy of dhole (a subordinate competitor) (Steinmetz et al. , 2013).

N

However, the number of detections was too small (dhole was detected at only three camera

A

trap stations in Dong Phayayen) compared to the number of required parameter estimates for

M

us to utilize co-occurrence models. Further, our trap spacing for dhole was potentially insufficient for preventing the detection of the same group of animals at different traps

D

during the same trapping occasion. We found four camera trap stations out of a total 19

TE

stations with detections where dholes were observed twice within a day. It was not possible

marks.

EP

to ascertain whether these were the same group of animals because of the lack of individual

CC

In addition to the problem of maintaining an adequate prey base, rosewood poaching is also a serious issue for the conservation of these large carnivores and their prey. We also

A

suspect that the high value of rosewood is likely promoting increased illegal activity in the region’s forests including the poaching of prey species (Beale et al., 2018). Based on Department of National Parks, Wildlife and Plan Conservation (DNP) records, approximately one hundred offenders were arrested during the study period in Dong Phayayen, but none

17

were arrested in Khao Yai for rosewood poaching (Appendix1). Nevertheless, we were unable to get quantifiable information regarding hunting pressure related to rosewood poaching in our study areas. Here the threat is unlikely due to the loss of habitat from

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logging, but rather human disturbance and/or hunting of ungulates for food while rosewood poachers camp in the forest. For example, we found wild pig meat inside a bag of a logger adjacent to a rosewood logging track in May of 2013. Poaching of large carnivore prey was

detected in both Dong Phayayen and Khao Yai during our survey from 2009–2015 and at least

eight wild pig, six sambar and three guar were reported killed. However, we found no reports of

U

tiger/tiger parts seizures around our study areas (DNP 2018, unpublished criminal cases report).

N

Although we were unable to determine the exact number of people involved in these illegal

A

cutting operations, we did photo document armed loggers and poachers and we also had direct

M

encounters with them. Thus, as suggested above, monitoring of large carnivores and their prey as well as anti-poaching are urgently needed. Fortunately, government agencies are currently

TE

involvement.

D

implementing anti-poaching operations in the study area with multiple organizational

EP

Dong Phayayen-Khao Yai forest complex is probably protecting Thailand’s secondlargest tiger population along with a significant, but currently undetermined, dhole

CC

population in both Khao Yai and Dong Phayayen. It is encouraging that tigers and dholes continue to persist despite the relatively high level of illegal human activity (as noted here)

A

in and around this complex. However, we lack long-term population data, and it is likely that these populations are declining. Furthermore, the facts that Thailand likely has only three landscapes remaining with viable or potentially viable tiger populations, and the total potential habitat for dholes is perhaps only ~10,500 km 2 (Jenks et al., 2012a) are additional

18

causes of concern. If tiger and dhole are faring so poorly in a country, which at least in theory, has a well-developed system of protected areas, with significant resources to devote to conservation, further suggests these two species are close to regional extinction.

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Our research indicates that the two focal areas of this study fit roughly within the recently defined paradigm as “recovery sites” for tigers (Harihar et al. 2018). However, our results also suggest that without long-term, comprehensive protection they may eventually

lose populations of key prey and consequently lose their large carnivores. Maintenance and recovery of predators such as tigers and dholes will therefore require an extensive effort

U

aiming to maintain and enhance prey diversity and abundance (Chapron et al., 2008; Harihar

A

N

et al., 2018).

M

Acknowledgements

D

All research was carried out with the necessary approvals and permits from the Thai Department

TE

of National Parks, Wildlife and Plant Conservation and King Mongkut’s University of Technology Thonburi. Funding for this work was supported by the King Mongkut’s University

EP

of Technology Thonburi (WOR1-2557–2558), National Science and Technology Development Agency (NSTDA P-11-00390), International Association for Bear Research and Management-

CC

IBA Research & Conservation grant, and The Asahi Glass Foundation (Research Grant 2013).

A

Special thanks to Murray Efford for initial study design, Mike Meredith and Ngumbang Juat for their help with data analysis, and Naruemon Tantipisanuh for her assistance with making the GIS maps. Thanks also to Antony J. Lynam for his valuable comments on an earlier draft of the manuscript. Finally, thanks to Mauro Lucherini and three anonymous reviewers for their comments guiding the revision of the manuscript.

19

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Table 1. Number of camera trapping sites within our five study areas where large carnivores and their prey were detected in the two sections of the Dong Phayayen - Khao Yai Forest Complex (Khao Yai - Dong Phayayen). Khlong E-Tow (KET), Khlong Samor-Pun (KSP) and

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Khao Kampang (KKP) in Khao Yai National Park, and DP1 and DP2 in Dong Phayayen. Number of independent photographs of each species indicated in parenthesis.

Khao Yai

Dong Phayayen

KET

KSP

KKP

DP1

DP2

Total

Tiger

0 (0)

0 (0)

0 (0)

6 (13)

7 (14)

13 (27)

Dhole

5 (12)

4 (7)

6 (16)

3 (4)

19 (40)

Gaur

5 (19)

8 (17)

3 (3)

10 (28)

8 (36)

34 (103)

Sambar

2 (3)

3 (7)

0 (0)

3 (4)

5 (31)

13 (45)

Wild pig

6 (9)

8 (14)

11 (19)

21 (169)

19 (313)

65 (524)

Red muntjac

6 (16)

3 (4)

2 (3)

10 (26)

14 (86)

35 (135)

Total trap sites

19

18

41

23

22

123 34329

N 1 (1)

A

M

D

TE

EP

U

Site

3007

3246

6191

8639

13246

Avg. days*

61±10

67±2

70±17

132±37

168±13

CC

Trap-nights

A

* Average number of survey days for each camera-trap station ±SD.

30

N U SC RI P

Table 2. A comparison between Khao Yai and Dong Phayayen sections of the Dong Phayayen - Khao Yai Forest Complex in the occupancy (95% credible intervals in parenthesis) of dhole and their prey based on camera trap photographs.

Model

Dhole

psi(.), p(.)

0.24 (0.14 – 0.40)

psi(Park), p(.)

0.64 (0.51 – 0.73)

ED

psi(.), p(.)

PT

psi(Park), p(.)

Wild pig

A

Red muntjac

Dong Phayayen

0.55 (0.50 – 0.67)

Difference (%)

16

Certainty (%)

88

0.44 (0.32 – 0.59) 0.36 (0.21 – 0.56)

psi(.), p(.)

0.15 (0.08 – 0.24)

psi(Park), p(.)

0.10 (0.02 – 0.18)

CC E

Sambar

M

Species

Gaur

Khao Yai

A

psi

psi(.), p(.)

0.56 (0.47 – 0.66)

psi(Park), p(.)

0.34 (0.23 – 0.46)

psi(.), p(.)

0.36 (0.26 – 0.47)

psi(Park), p(.)

0.18 (0.08 – 0.29)

0.50 (0.34 – 0.68)

0.19 (0.08 – 0.32)

0.87 (0.77 – 0.96)

0.57 (0.41 – 0.72)

14

9

53

39

88

91

100

99

p

DIC

0.012 (0.006 – 0.019)

147.19

0.004 (0.003 – 0.007)

144.84

0.014 (0.010 – 0.018)

248.66

0.014 (0.010 – 0.018)

248.27

0.019 (0.012 – 0.027)

116.29

0.020 (0.013 – 0.028)

110.13

0.044 (0.039 – 0.049)

529.07

0.045 (0.040 – 0.049)

485.00

0. 022 (0.018 – 0.027)

238.77

0.023 (0.019 – 0.028)

213.65

31

Table 3 Density estimates and 95% confidence intervals (CI) for tiger sites in Southeast Asia.

Location

Density/100 km2

95% CI

Method

Reference

Taman Negara National Park, Malaysia

1.89

1.89–6.62

AMDM

Kawanishi & Sunquist 2004

Gunung Basor Forest Reserve,

2.49–4.99

1.55

1.30–2.93

1.09

0.72–2.18

1/2MMDM

0.66

0.44–1.31

MMDM

2.59

NA

MMDM

Linkie et al 2008

Lynam et al 2009

Rayan & Wan Mohamad 2009

1.80 <1

NA

Huai Kha Khaeng Wildlife Sanctuary,

ED

Thailand

M

A

Riau Province, Indonesia

1.80–6.40

N

Batang Gadis National Park, Indonesia

U

Malaysia

MMDM

PT

Hukaung Tiger Reserve, Myanmar

2.95

SC RI

Kerinci Seblat National Park, Indonesia

PT

Dong Phayayen-Khao Yai, Thailand

2.01

MMDM

Wibisono et al 2009

SCR-

Sunarto et al 2013

Maximum likelihood

1.51–2.53*

SCR-

Duangchantrasiri et al

Bayesian

2016 This study

1.40a

0.40–4.50*

SCR-

2.10b

0.50–5.30*

Bayesian

* 95% credible interval; a Constant model; b individual heterogeneity model; 1/2MMDM = half of mean

CC E

maximum distance moved; MMDM = mean maximum distance moved; AMDM = absolute maximum

A

distance moved; SCR = spatial capture-recapture.

32

Appendix 1. Summary of criminal cases from the Dong Phayayen – Khao Yai forest complex. No. of offenders

No. of cases involving

Dong Phayayen

Khao Yai

Dong Phayayen

Khao Yai

2009

13

0

6

0

2010

7

0

13

0

2011

30

0

32

0

2012

74

2

63

2013

136

5

170

2014

310

59

610

2015

253

65

218

SC RI

Year

PT

rosewood poaching

4 5

N

U

32 30

A

CC E

PT

ED

M

http://portal.dnp.go.th/Content?contentId=2134

A

Source: Department of National Parks, Wildlife and Plant Conservation, Thailand.

33