Carnivores Research in 2007-2020 Western Forest Complex

CARNIVORES RESEARCH IN 2007-2020 WESTERN FOREST COMPLEX

ค ำน ำ

การอนุรักษ์และจัดการสัตว์ป่าและถิ่นอาศัยนั้น จ าเป็นต้องด าเนินการภายใต้ข้อมูลพื้นฐาน ทางวิชาการ เพื่อให้การวางแผนการอนุรักษ์และการจัดการสัตว์ป่ามีแนวทางการด าเนินงานที่มีประสิทธิภาพ เหมาะสมกับสัตว์ป่าแต่ละชนิดและในแต่ละพื้นที่ การศึกษาวิจัยด้านสัตว์ป่าเพื่อให้ทราบข้อมูลทางด้านประชากร ชีววิทยา นิเวศวิทยา ลักษณะถิ่นอาศัยของสัตว์ป่า จึงเป็นข้อมูลที่ส าคัญส าหรับก าหนดและวางแผนการอนุรักษ์ สัตว์ป่า เพื่อให้การบริหารจัดการทรัพยากรธรรมชาติของประเทศมีประสิทธิภาพยิ่งขึ้น กลุ่มงานวิจัยสัตว์ป่า ภายใต้การด าเนินงานของสถานีวิจัยสัตว์ป่าเขานางร า ซึ่งเป็นหน่วยงานใน ส านักอนุรักษ์สัตว์ป่า กรมอุทยานแห่งชาติ สัตว์ป่า และพันธุ์พืช ได้รับมอบหมายภารกิจส าคัญในการสนับสนุน ข้อมูลวิชาการเพื่อช่วยงานจัดการด้านสัตว์ป่าและถิ่นอาศัยในพื้นที่ป่าอนุรักษ์ โดยสถานีวิจัยสัตว์ป่าเขานางร า ได้ด าเนินโครงการศึกษาวิจัยต่าง ๆ เพื่อศึกษาความหลากหลายทางชีวภาพ ชีววิทยาและนิเวศวิทยาของสัตว์ป่า ในพื้นที่ป่าอนุรักษ์ รวมถึงการส ารวจและติดตามสถานภาพสัตว์ป่าที่ส าคัญในพื้นที่มรดกโลกทางธรรมชาติ เขตรักษาพันธุ์สัตว์ป่าทุ่งใหญ่ - ห้วยขาแข้ง ซึ่งเป็นส่วนหนึ่งของผืนป่าตะวันตก เป็นพื้นที่ที่มีความหลากหลาย ทางชีวภาพและความชุกชุมของสัตว์ป่าสูง ทั้งกลุ่มสัตว์เลี้ยงลูกด้วยนม นก สัตว์เลื้อยคลาน และสัตว์สะเทินน้ า สะเทินบก อีกทั้งยังเป็นแหล่งอาศัยที่ส าคัญของสัตว์ป่าที่ใกล้สูญพันธุ์ และมีความส าคัญต่อระบบนิเวศในกลุ่ม ของสัตว์ผู้ล่าขนาดใหญ่ ทั้งเสือโคร่ง เสือดาว รวมถึงสัตว์กีบขนาดใหญ่ที่เป็นเหยื่อของสัตว์ผู้ล่าเหล่านั้น ผลงานวิจัยที่ได้ด าเนินการมาอย่างต่อเนื่องเป็นระยะเวลามากกว่า 10 ปี เป็นที่ยอมรับทั้งในประเทศและระดับ นานาชาติ และมีส่วนส าคัญในการสนับสนุนการบริหารจัดการพื้นที่มรดกโลกทางธรรมชาติเขตรักษาพันธุ์สัตว์ป่า ทุ่งใหญ่ - ห้วยขาแข้ง กรมอุทยานแห่งชาติ สัตว์ป่า และพันธุ์พืช หวังเป็นอย่างยิ่งว่า หนังสือ “Carnivore Research in Western Forest Complex 2007 - 2020” ฉบับนี้ ซึ่งเป็นการรวบรวมผลงานวิจัยด้านสัตว์ป่าในพื้นที่เขตรักษาพันธุ์สัตว์ป่า ห้วยขาแข้ง - ทุ่งใหญ่นเรศวร ที่ได้รับการตีพิมพ์และเผยแพร่เพื่อเป็นข้อมูลในการอนุรักษ์และการจัดการสัตว์ป่า ทั้งในระดับประเทศและระดับนานาชาติ จะเป็นแนวทางการด าเนินงานวิจัยด้านสัตว์ป่าให้กับพื้นที่ป่าอนุรักษ์อื่น ๆ รวมถึงเป็นการรวบรวมข้อมูลให้สะดวกต่อการค้นหาและน ามาใช้ เพื่อน าไปสู่การวางแผนการจัดการเพื่ออนุรักษ์ และคุ้มครองสัตว์ป่าและถิ่นอาศัยในพื้นที่มรดกโลกทางธรรมชาติเขตรักษาพันธุ์สัตว์ป่าทุ่งใหญ่ - ห้วยขาแข้ง อย่างมีประสิทธิภาพต่อไป

นายธัญญา เนติธรรมกุล อธิบดีกรมอุทยานแห่งชาติ สัตว์ป่า และพันธุ์พืช

INTRODUCTION

Conservation and management of wildlife and their habitats require basic academic research on planning and guidelines for each species and area. Wildlife research on population, biology, ecology and habitat are important to wildlife conservation and natural resource management planning. Wildlife Research Division which administrate Khao Nang Rum Wildlife Research Station, under Department of National Parks, Wildlife and Plant Conservation, is established to support academic research for management of wildlife and their habitats in protected areas. Khao Nang Rum Wildlife Research Station focuses research on biodiversity, biology and ecology of wildlife, including key wildlife species surveying and monitoring in Thung Yai- Huai Kha Khaeng Wildlife Sanctuaries, one of the World Heritage sites. It is an area with high biodiversity and abundance of wildlife including mammals, birds, amphibians and reptiles, also the endangered species habitats. There are mainly large predators in the ecosystem such as tigers and leopards along with their large preys. The contribution of Khao Nang Rum Wildlife Research Station, which has continually been done for more than 10 years and accepted by international research community, also supports the management of Thung Yai-Huai Kha Khaeng Wildlife Sanctuary World Heritage. Department of National Parks, Wildlife and Plant Conservation hopefully that the publish of “Carnivores Research in Western Forest Complex 2007 – 2020 ”, a collection of wildlife research in Thung Yai-Huai Kha Khaeng Wildlife Sanctuaries, will be distribute and becomes a guideline research in other protected areas. Additionally, this easy-to-access collection of research can be used in effective management planning for wildlife conservation and protection in Thung Yai-Huai Kha Khaeng Wildlife Sanctuary World Heritage.

Mr. Thanya Netithammakun Director General Department of National Parks, Wildlife and Plant Conservation

CONTENTSCONTENTS

Page

 Monitoring of the Leopard Population at Khao Nang Rum In Huai Kha Khaeng 1 Wildlife Sanctuary Saksit Simcharoen and Somphot Duangchantrasiri

 How many tigers Panthera tigris are there in Huai Kha Khaeng Wildlife Sanctuary, 14 Thailand? An estimate using photographic capture-recapture sampling Saksit Simcharoen, Anak Pattanavibool, K. Ullas Karanth, James D. Nichols and N. Samba Kumar

 Home range size and daytime habitat selection of leopards in Huai Kha Khaeng 21 Wildlife Sanctuary, Thailand Saksit Simcharoen, Adam C.D. Barlow, Achara Simcharoen, and James L.D. Smith

 Female tiger Panthera tigris home range size and prey abundance: important 30 metrics for management Achara Simcharoen, Tommaso Savini, George A. Gale, Saksit Simcharoen, Somphot Duangchantrasiri, Somporn Parkpien and James L.D. Smith

 Ecological factors that influence sambar (Rusa unicolor) distribution and abundance 38 in western Thailand: implications for tiger conservation Achara Simcharoen, Tommaso Savini, George A. Gale, Erin Roche, Vijak Chimchome and James L. D. Smith

 Non-Panthera cat records from big cat monitoring in Huai Kha Khaeng Wildlife 45 anctuary Saksit Simcharoen, Mayuree Umponjan, Somphot Duangchantrasiri and Anak Pattanavibool

 Dynamics of a low-density tiger population in Southeast Asia in the context 50 of improved law enforcement Somphot Duangchantrasiri, Mayuree Umponjan, Saksit Simcharoen, Anak Pattanaviboo, Soontorn Chaiwattana, Sompoch Maneerat, N. Samba Kumar, Devcharan Jathanna, Arjun Srivathsa, and K. Ullas Karanth

 A context-sensitive correlated random walk: A new simulation model for movement 60 Sean C. Ahearn, Somayeh Dodge, Achara Simcharoen, Glenn Xavier And James L.D. Smith

 Ecological Covariates at Kill Sites Influence Tiger (Panthera tigris) Hunting Success 82 in Huai Kha Khaeng Wildlife Sanctuary, Thailand Somporn Pakpien, Achara Simcharoen, Somphot Duangchantrasiri, Vijak Chimchome, Nantachai Pongpattannurak and James L. D. Smith

CONTENTSCONTENT S

 Tiger and leopard diets in western Thailand: Evidence for overlap and potential 89 consequences Achara Simcharoen, Saksit Simcharoen, Somphot Duangchantrasiri, Joseph Bump and James L.D. Smith

 Impact of prey occupancy and other ecological and anthropogenic factors on 95 tiger distribution in Thailand's western forest complex Somphot Duangchatrasiri, Pornkamol Jornburom, Sitthichai Jinamoy, Anak Pattanvibool, James E. Hines, Todd W. Arnold John Fieberg and James L. D. Smith

 Weights of gaur (Bos gaurus) and banteng (Bos javanicus) killedby tigers in Thailand 105 Supawat Khaewphakdee, Achara Simcharoen, Somphot Duangchantrasiri Vijak Chimchome, Saksit Simcharoen and James L. D. Smith

 Spatial and temporal analysis of leopards (Panthera pardus), their prey and tigers 113 (Panthera tigris) in Huai Kha Khaeng Wildlife Sanctuary, Thailand Apinya Saisamorn, Prateep Duengkae, Anak Pattanavibool, Somphot Duangchantrasiri, Achara Simcharoen and James L.D. Smith

 Diet composition of the golden jackal (Canis aureus L.) during the dry season 123 in west-central Thailand Saksit Simcharoen, Achara Simcharoen, Sasitorn Hasin, Francesca Cuthbert and J. L. David Smith

 Diet of the Large Indian Civet (Viverra zibetha L., 1758) in west-central Thailand 135 Saksit Simcharoen, Achara Simcharoen, Sasitorn Hasin, Francesca Cuthbert and J. L. David Smith

Thai J. For. 27 : 68-80 (2008) ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 27 : 68-80 (2551)

Original article

Monitoring of the Leopard Population at Khao Nang Rum In Huai Kha Khaeng Wildlife Sanctuary

Saksit Simcharoen1 Somphot Duangchantrasiri1

1 Research Division, Department of National Park, Wildlife and Plant Conservation, Pahonyotin Road, Chatuchak, Bangkok 10900 Received: November 20, 2007 Accepted: June 18, 2008

ABSTRACT

Leopard population was monitered using capture-recapture technique. Camera traps were set up for 3 years (1996-1999) over a 115.88 km2 area (A) around Khao Nang Rum Wildlife Research Station, Huai Kha Khaeng Wildlife Sanctuary. Eighteen leopards were photographed including 4 adult females, 3 sub-adult females, 3 adult males, 3 sub-adult males and 5 males that could not be identified by age class. Four black leopards were individually identified: one leopard was collared and 3 leopards were identified by their size, sex, photograph time and locations. Closure test indicated that the leopard population was closed (p>0.05). The estimation of population size of leopard using model Mh are 10, 10 and 11 leopards for three sessions respectively. Log- normal-based 95% confidence interval ranged from 10 to 29 leopards for the first session, from 9 to 17 leopards for the second session and from 11 to 26 leopards for the third session. Estimated leopard densities were decreased from 7.88 ± 5.82 to 5.21 ± 3.12 and 4.86 ± 2.29 leopards/ 100 km2 respectively. The average leopard density in this study was 5.98 leopards/ 100 km2. This highly leopard density indicated that the Western Forest Complex is an important area for leopard conservation. Moreover, data on the abundance and density of leopard will help us to understand their present status in the study area and evaluate habitat quality and success of the management.

Keywords: Leopard, Panthera pardus, Population, Huai Kha Khaeng Wildlife Sanctuary

INTRODUCTION

The leopard is an important predator loss, depletion of prey and direct hunting. It has in the tropical rainforest ecosystem. It plays an been listed on Appendix I of the Convention of important role in predator-prey interactions. It International Trade of Endangered Species of regulates prey populations and maintains Wild Fauna and Flora (CITES) since 1983. natural selection by influencing prey behavior Monitoring of leopard population is important and directly affects the vigor of prey populations to the planning and management to maintain and indirectly, the health of the whole ecosystem. them. However, leopard is an endangered large felid Leopard is secretive and usually noc- which shows a declining trend due to habitat turnal animals. Thus, methods for estimating 2

ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 2 : 68-80 (2551) 69 abundance of wild cats such as tiger and manage and conserve the leopard in the wild. leopard in the past used counting tracks of Data on the abundance and density of leopard individual animal identified by measurement of will help us to understand their present status their tracks (Riordan, 1998) and track shape in the study area and evaluate habitat quality (Panwar, 1979). However, this method has and success of the management. These factors serious problems associated with field surveys are the principal objectives of this study. and some assumptions are erroneous (Karanth 1987, 1988, 1995). Capture-recapture is a common approach MATERIALS AND METHODS to investigating the abundance of marked animals; capture histories of animals can be Study Area used for determining the number of undetected The study was carried out in the forests animals present. There are statistical models to around Khao Nang Rum Wildlife Research be employed for estimating the both of open Station (N. Lat. 15º25´-15º31´ and E. Long. and closed populations. In a closed population, 99º15´-99º20´) within Huai Kha Khaeng Wildlife it is assumed that there are no births/deaths/ Sanctuary (Figure 1), which forms part of an 2 immigration/emigration during the time of the 18,727 km protected area network known as survey. the Western Forest Complex. The area has rugged, Camera trapping is probably the best hilly topography, mean annual temperature in possible available technique to build up capture 20 years from 1986 to 2005 was 24.64ºc and histories that can be analyzed in readily available annual rainfall of 1447 mm. The area supports capture-recapture software such as CAPTURE four vegetation type dry evergreen forest, hill (Otis et al., 1978; White et al., 1982) and evergreen forest, mixed deciduous forest and MARK (White and Burnham, 1999). This approach dry dipterocarp forest depending on rainfall already has been used for leopards in many patterns and edaphic factors (Srikosamatara, area (Jenny, 1996; Stander et al., 1997; 1993; Tunhikorn et al., 2004). Khorozyan, 2003; Kostyria et al., 2003; Spalton Camera Trapping et al., 2006) and also has been applied to other This study was begun to document cryptic felids such as tiger (Panthera tigris) the presence of leopards and other mammals. (Karanth, 1995; Karanth and Nichols, 1998; Consequently camera trapping was done on Kawanishi and Sunquist, 2004), jaguar an ad hoc basis, without strictly following optimal (Panthera onca) (Silveira et al., 2003; Wallace survey protocols (Nichols and Karanth, 2002). et al., 2003; Maffei et al., 2004; Silver, 2004), However, The following field protocols were snow leopard (Uncia uncia) (Spearing, 2002) helpful for analyzing the data within a formal and ocelot (Trolle and Kerv, 2003). Moreover, capture-recapture framework. they are linked with movement, distribution, Camera trapping was carried out for activity patterns, habitat use and reproductive 3 years (1996-1999) over a 115.88 km2 area information that are important for wildlife (A) around Khao Nang Rum Wildlife Research conservation (Spalton et al., 2006; Silveira et Station (Figure 2), Huai Kha Khaeng Wildlife al., 2003). Sanctuary. The area (A) was determined by a The majority of past research has taken minimum convex polygon around all camera place in sub-Saharan Africa, India, Sri Lanka locations. Camera traps were deployed for 3-4 and Nepal. In Thailand, there has been little months a year. research concerning the abundance, behavior Trailmaster® (Goodson Associates, and some aspects of leopard ecology. However, Inc., Kansas, USA) camera traps were set on the detailed knowledge of leopard abundance the trails and roads where leopard tracks or has not been fully studied and is necessary to other secondary signs were frequently found. 3

70 Thai J. For. 27 : 68-80 (2008)

Figure 1. Map of Huai Kha Khaeng Wildlife Sanctuary and the study area. 4

ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 2 : 68-80 (2551) 71 Camera trap sites, camera trap areas, and buffer areas. buffer areas, and trap camera sites, trap Camera Figure 2. 5

72 Thai J. For. 27 : 68-80 (2008)

Camera traps were mounted on trees 3-4 m size using the computer program CAPTURE from the path, with the infrared beam set 45 (Otis et al., 1978; White et al., 1982; Rexstad cm above the ground (Karanth, 1995; Karanth and Burnham, 1991). The program estimates and Nichols, 1998; Kostyria, 2003). Camera abundance of closed population under seven traps were set out in pairs to capture the opposing, models (Mh, Mb, Mt, Mbh, Mth, Mtb, Mtbh that asymmetrical spot patterns of the passing vary by h-heterogeneity, b-behavior, t-time) leopards (Karanth, 1995). The distance between using M0, in which probabilities are constant is unit pairs was 1.0-3.0 km, with GPS locations null the hypothesis (White et al., 1982). In taken for all sites. Cameras were checked CAPTURE, the closure test for the number of every 2-3 days to change batteries and film individual is constant during the overall study and to document the presence of animal tracks. period and is computed for the subset of the Leopard Identification data defined by the capture frequencies. However, Leopards are patterned animals that the validity test can not be devised because of we can individually identify from their natural problems in behavioral responses and time markings. Rosettes and spot patterns of individual trends (White et al., 1982). However, we assumed leopard are asymmetrical on the two flanks. Both that the population closed because the short sides of each animal had to be photographed period. The study would end before significant simultaneously. The photographs are needed immigration could occur. In this study, we used for clear identification. However the obtained, model Mh in which capture histories vary by photographs viewed only on one side, can be individual heterogeneity because the estimator ˆ linked to the left or right profile from camera for model Mh (the jackknife, Nh ) is the most ˆ ˆˆˆ trapping and radio collared leopard to identify robust of the five estimators ( Nbhbt,,, NNN0 ) individual leopards. Unclear animal photographs (White et al., 1982). In addition, this model was on one side were not used for analysis. Black widely used for estimate abundance for large leopards could not be identified clearly by their cats (Karanth and Nichols, 1998; Kostyria et spot pattern. However, they were identified by al., 2003) that are territorial animals in which their size, sex and photograph locations which home range size and trapping depend on social relate to photograph time. position and spatial location of the animal Animal photographs were organized (Karanth, 1995). separately for each individual leopard and the Estimating Density date, time, photograph location, age class and Animal density can be estimated using sex were recorded. Leopards were given all abundance estimates from CAPTURE. Density ID from their collar number or new ID was is defined as: created for non-collared animal. Estimating Abundance Dˆˆ= N/() AWˆ (1) The capture history was used to describe capture frequencies. Data were recorded in Dˆ is the estimated animal density, Nˆ an X matrix consisting of i animals in rows and is the estimated abundance and AWˆ () is the t trapping occasions in columns, assuming a estimated effective area in which photographed value of either "0" if the animal was not photo- animals live; W is the boundary strip of width graphed or a "1" if it was photographed. We that is added around the perimeter of the area used five trapping occasions for all sites with in which the camera traps were set. each occasion containing 3 day. The capture Variance in animal density is calculated histories of individual leopards were used in from variance in estimated abundance and the framework of capture-recapture theory to effective area: estimate capture probabilities and population 6

ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 27 : 68-80 (2551) 73

⎡⎤115.88 km2. The research activition for each varˆ AWˆ 2 ⎢⎥()() varˆ ()N var(ˆ DDˆˆ ) =+ session were carried out for 3-4 months during ⎢⎥⎡⎤2 ˆ 2 (2) the dry season. A total of 2,094 trap nights ⎢⎥AWˆ N ⎣⎦⎣⎦() included 650 trap nights between December Variance in estimated abundance was 1996 and March 1997, 620 trap nights between computed by program CAPTURE. The effective December 1997 and February 1998 and 824 area includes the area where camera trap sites trap nights between December 98 and March 99. This traps sampled each location, on an were plotted and connected on the edge to form average for about 15 days. Leopards were the perimeter and the buffer area. The buffer detected 106 times during 2,094 trap-nights; 43, width (Wˆ ) was calculated using the mean ˆ 39 and 24 times for the three sessions respectively maximum distance moved by leopards ( d ) (Table 1). between camera trap sites divided by two Abundance (Karanth and Nichols, 1998). Eighteen leopards were captured including 4 adult females, 3 sub-adult females, ˆ ˆ (3) W = d / 2 3 adult males, 3 sub-adult males and 5 males Variance in buffer width was calculated that could not be identified by age class. From using variance in mean maximum distance a total of 27 black leopard photographs, 15 moved by leopards. Let d denote the maximum photographs were separated for 4 individual i leopards; one leopard had a collar and 3 leopards distance moved between camera trap sites for were identified by their size, sex, photographed animal i and m denote the number of animals time and locations. L055 and L195 were that cameras trapped at least twice: captured every session. Five leopards were ˆ varˆˆ()Wdˆ =× 0.25 var (4) captured for two sessions and eleven leopards ( ) were capture during only one session. 2 m ˆ Capture frequency between sexes = dd11− ∑ i ) was difference: males were photographed 1.81 varˆ dˆ = ( (5) ( ) mm()−1 times more than females. The mean capture rate of males was 0.77 detections/ 100 trap Variance in estimated effective area was nights for adults and 0.18 detections/100 trap calculated following Karanth and Nichols (1998): nights for sub-adults. The mean female capture 2 rate was 0.43 and 0.10 detections/100 trap- ˆ 2 ˆˆ varˆˆ()AW()=+ 4π ( c W ) var () W (6) nights for adults and sub-adults respectively. The average number of photograph locations This estimation approximates each of the for individual leopards were 0.56, 0.28, 0.13 and sampled areas as a circle in which c is a constant 2 0.10 locations/100 trap nights for adult males, ˆ ˆ that is calculated from AW()=+π c W adult females, sub-adult males and sub-adult ˆ () ; AW() is the estimated effective area that females respectively. Both mean capture rate includes the camera trap area and the buffer and number of photograph locations ranked from area and Wˆ is the buffer width. most to least value as determined by number of detections and photographed locations per 100 RESULTS AND DISCUSSION trap-nights are: adult male, adult female, sub-adult male and sub-adult female. The highest capture Camera Trap Effort rate male was L055 which was photographed Camera traps were set up around 1.88 detections/100 trap-nights; L390, the Khao Nang Ram WRS ranging from 39 to 56 highest capture rate female, was photographed locations per session and covered an area of 0.61 detections/100 trap-nights. 7

74 Thai J. For. 27 : 68-80 (2008) Table 1. Summary statistics for camera trap data on leopards in HKK Table 8

ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 2 : 68-80 (2551) 75

Closure tests indicated that leopard area and the boundary strip were 126.93, population was closed (Table 2). The estima- 191.76 and 226.44 km2 (Figure 2). In these areas, tion of population size of leopard using model the estimated population sizes were 10, 11 and 11

Mh are 10, 10 and 11 leopards for the three leopards respectively. We used these estimates sessions respectively. Log-normal-based 95% in conjunction with Equation 1 to estimate leopard confidence interval ranged from 10 to 29 leop- density. We found that the estimated leopard ards for the first session, from 9 to 17 leopards densities decreased from 7.88 ± 5.82 to for the second session and from 11 to 26 leop- 5.21 ± 3.12 and 4.86 ± 2.29 leopards/ 100 km2 ards for the third session. Estimated capture respectively (Table 4). The average leopard probability over all sampling occasions varied density in this study was 5.98 leopards/ 100 km2. between 0.80 and 0.91. Estimated values of Discussions average capture probability were varied ranging Results of the study indicated that, 126 from 0.33 to 0.44 (Table 2). photographs of leopards during 2,094 trap-nights Density (6.02 photographs/100 trap-nights) which was For the three sessions, the camera trap higher than the ones which were obtained from areas were 42.05, 57.86 and 96.93 km2 respec- the study on leopard in Kaeng Khachan National tively. The number of animals which the camera Park (KKNP), Thailand (3.25 photographs/100 trapped at least twice was six for all three trap-nights, Ngoprasert, 2004) and in Southwest sessions. Average maximum distances moved Primorski Krai (4.69 photographs/100 trap- by photographed leopards ranged from 3.19 to nights, Kostyria et al., 2003). In addition, the 4.65 km. The estimated boundary strip widths estimated capture probabilities of this study were 1.59(SD=0.30), 2.33(SD=0.40) and (0.44, 0.42 and 0.33) are higher than the results 1.81(SD=0.15) km respectively (Table 3). The were obtained from the study in KKNP (0.27, estimated effective area from the first to the using model Mh) and Southwest Primorski Krai third sessions, which included the camera trap (0.20, using model Mh).

Table 2. Estimated abundance and capture probabilities of leopards in HKK under model Mh of program CAPTURE

The average maximum distances low temperature and snow cover in winter, between capture sites, was varied from 3.19 which are extremal conditions for leopard to 4.65 km, and with the significance level which (Kostyria et al., 2003). These conditions was lower than the one obtained form the South- probably impacted the distances which were west Primorski Krai (9.70 km). Southwest higher than the ones obtained from the other Primorski Krai is in the temperate zone with study areas including KKNP and this study. 9

76 Thai J. For. 27 : 68-80 (2008)

Table 3. Calculated effective area using half the mean maximum distance moved by leopards caught on more than one occasion

Table 4. Estimated leopard density and detectable change using program CAPTURE and calculated effective area A(W)

Moreover, the obtained male leopard Estimated density using a framework photographs was 1.81 times which was more of capture-recapture averaged 5.98 leopards/ than female leopard photographs. In the same 100 km2. The densities were 7.88 ± 5.82 to way, Ngoprasert (2004) reported that capture 5.21 ± 3.12 and 4.86 ± 2.29 leopards/ 100 km2, frequency between sexes was differenced. which were close to densities using radio track- Male leopards had recapture more than female ing in the same period (6.3 leopards/100 km2). leopards (T-test, p=0.01). Santiapillai et al. The average density using radio tracking method (1982) reported that male leopards were visually were 10.1, 6.9, 3.7 and 4.5 leopard/100 km2 observed more frequently (72%) than female from 1996 to 1999 respectively. In the same leopards in Ruguna National Park parallels the times and area, both methods gave nearly observations of Muckenhirn and Eisenberg densities and with a decreasing trend. Further- (1973) in Wilpattu National Park and Bailey more the average leopard densities were greater (1993) in Kruger National Park. Bailey (1993) than in KKNP (4.78 leopards/100 km2) reported that males were captured with less (Ngoprasert, 2004) and much greater than in trapping effort than females and were observed Southwest Primorski Krai (1.2 ± 0.2 leopards/ more often (63%) than females along tourist 100 km2) (Kostryria et al., 2003). In Huai Kha roads although, females were observed more Khaeng Wildlife Sanctuary, Rabinowitz (1989) often (68%) and more than males along fire- estimated leopard density was lower (4 leopards/ break roads. He suggested that the females 100 km2) than the obtainul result from this study. avoided contact with human more than the Based on the comparison with the another males. The data support the contention that the method, the average leopard density in this study females were shyer than males (Santiapillai et was lower than in Tsavo National Park, Kenya al., 1982). (Hamilton, 1976); Cape Province, South Africa 10

ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 2 : 68-80 (2551) 77

(Norton and Henley; 1987) and Tai National covered an area of 115.88 km2 around Khao Park, Ivory Coast (Jenny, 1996) but higher than Nang Rum Research Station, Huai Kha in Sri Lanka (Clark, 1901); Wilpattu National Khaeng Wildlife Sactuary. Eighteen leopards Park, Sri Lanka (Eisenberg and Lockhart, were photographed included 4 adult females, 3 1972); Serengeti National Park, Tanzania sub-adult females, 3 adult males, 3 sub-adult (Schaller, 1972; Cavallo, 1993); Kalahari males and 5 males that could not be identified Desert, Southern Africa (Bothma and Le Riche, by age class. From a total of 27 black leopard 1984); Stellenbosch, Cape Province (Norton and photographs, 15 photographs were separated Lawson, 1985); Kruger National Park, South for 4 individual leopards; one leopard had a Africa (Bailey, 1993); and North-eastern Namibia collar and 3 leopards were identified by their (Stander et al., 1997). size, sex, photographed time and locations. The trend of leopard density decreased Closure tests indicated that leopard throughout the study period which during the population was closed. The estimates of same period the tiger density increased from population size of leopard using model M are 1.71 0.99 to 2.27 1.12 and 2.94 2.26 tigers/ h ± ± ± 10, 10 and 11 leopards for the three sessions 100 km2 respectively. Seidensticker (1976) recorded respectively. Log-normal-based 95% confidence that a tiger may have appropriated a kill from interval ranged from 10 to 29 leopards for the a leopard, and he believed that social dominance first session, from 9 to 17 leopards for the second is a major factor in tiger-leopard interaction. He found that leopard used areas not which were session and from 11 to 26 leopards for the third frequented by tiger in order to minimize their chance session. Estimated leopard densities decreased of encounter. Moreover, we found a female from 7.88 ± 5.82 to 5.21 ± 3.12 and 4.86 ± 2.29 2 black leopard was killed in the study area. The leopards/ 100 km respectively. The average 2 animal died as a result of one powerful bite to the leopard density was 6.0 leopards/ 100 km . chest over the heart which is only one clue and we found female tiger tracks around it. We believed that this leopard was killed by a tiger. ACKNOWLEDGEMENTS The average estimated density in this study can roughly estimate the leopard population We would like to express our great size in Huai Kha Khaeng Wildlife Sanctuary, appreciation to the National Park, Wildlife and which covered an area of 2780.14 km2, as 166 Plant Conservation Department for giving me leopards. If the habitat quality in the Western the opportunity to work and study at Khao Nang Forest Complex, which covered an area of Rum Wildlife Research Station. We gratefully 18,727 km2, were the as same as in this study acknowledge the late Mr. Mark Graham and area, estimated leopard population size was WWF (Thailand) for supporting the camera 1,120 leopards. The data indicated that The trap. We are particularly appreciated Prof. Western Forest Complex was an important Dr.Ullas Karanth, Director Wildlife Conser- area for leopard conservation. Moreover, data vation Society (India), for explanation and on the abundance and density of leopard will suggestion about the estimation of leopard helped us to understand their present status in population. We especially thank Mr. Onsa the study area and evaluate habitat quality and Norrasan, Mr. Precha Prommakun and Mr. success of the management. Pakawat Ponak for their help in the field study. We would like to give special thank to Ms. Ardith Eudey who helped editing our English CONCLUSION AND manuscript. Our appreciation also is due to Ms RECOMMENDATION Namkhang Saelee for completing our manuscript. We would like to thank everyone who Monitoring of leopard population was helped and supported this study. 11

78 Thai J. For. 27 : 68-80 (2008)

REFERENCES

Bailey, T. N. 1993. The African Leopard Karanth, K. U. 1987. Tiger in India: a critical Ecology and Behavior of a Solitary review of field censuses, pp. 118-131. Felid. Columbia University Press, In R. L. Tilson and U. S. Seal, eds. New York. Tigers of the World: The Biology, Bothma, J. D. P. and E. A. N. Le Riche. 1984. Biopolitics, Management and Aspects of the ecology and the Conservation of an endangered behavior of the leopard Panthera species. Noyes Publications, Park pardus in the Kalahari Desert. Ridge, NJ, USA. Supplement to Koedoe. :259-279. ______1988. Analysis of predator-prey Carbone, C., S. Christie, K. Conforti, T. balance in Bandipur tiger reserve with Coulson, N. Franklin, J. R. Ginsberg, reference to census reports. Journal M. Griffiths, J. Holden, K. Kawanishi, of the Bombay Natural History M. Kinnaird, R. Laidlaw, A. Lynam, Society 85: 1-8. D. W. MacDonald, D. Martyr, C. ______1995. Estimating tiger (Panthera McDougal, L. Nath, T. O. O’Brian, J. tigris) populations from camera-trap Seidensticker, J. L. D. Smith, M. data using capture-recapture models. Sunquist, R. Tilson, and W.N.Wan Biological Conservation 71: 333- Shahruddin. 2001. The use of 338. photographic rates to estimate ______and J. D. Nichols. 1998. Estimation densities of tigers and other cryptic of tiger densities in India using mammals. Animal Conservation 4: photographic captures and recaptures. 75-80. Ecology 79: 2852-2862. Cavallo, J. 1993. A study of leopard behaviour Kawanishi, K. and M. Sunquist. 2004. and ecology in the Seronera Valley, Conservation status of tigers in a Serengeti National park. Serengeti Wildlife Research Centre Scientific primary rainforest of peninsular Report 1990-1992. Malaysia. Biological Conservation Clark, A. 1901. Sport in the Low-country in 120: 333-348. Ceylon. Tisara Prakasakyo, Dehiwela, Khorozyan, I. 2003. Camera Photo-trapping Sri Lanka. of the Endangered Leopards Eisenberg, J.F. and M. Lockhart. 1972. An (Panthera pardus) in Armenia: a ecological reconnaissance of Wipattu Key Element of Species Status National Park, Ceylon. Smithsonian Assessment. Contributions to Zoology 101: 1- Kostyria, A. V., A. S. Skorodelov, D. G. 118. Miquelle, V.V. Aramilev, D. Hamilton, P. H. 1976. The movements of McCullough. 2003. Report on a Leopards in Tsavo National Park, census of Far-Eastern Leopards Kenya as Determined by Radio Using Camera Traps in South-west Tracking. M. Sc. Thesis, University Primorskii Province, Winter 2002- of Nairobi. 2003. Wildlife Conservation Society. Jenny, D. 1996. Spatial organization of leopards Maffei, L., E. Cuellar and A. Noss. 2004. One (Panthera pardus) in Tai National thousand jaguars (Panthera onca) in Park, Ivory Coast: is rain forest habitat Bolivia’s Chaco? camera trapping in a tropical haven? Journal of Zoology Kaa-Iya National Park. Journal of 240: 427-440. Zoology 262: 295-304. 12

ÇÒÃÊÒÃÇ¹ÈÒÊµÃ 2 : 68-80 (2551) 79

Muckenhirn, N. A. and J. F. Eisenberg. 1973. Rexstad, E. and K. P. Burnham. 1991. User’ Home ranges and predation of Ceylon s Guide for Interactive Program leopard, pp. 142-175. In R.L. Eaton, CAPTURE: AbundanceEstimation ed. The World’s cats 1(1). World of Closed Animal Populations. Wildlife Safari, Winston, Oregon. Colorado State University, Corolado, Ngoprasert, D. 2004. Effects of Roads, USA. Selected Environmental Variables Riordan, P. 1998. Unsupervised recognition of and Human Disturbance on Asiatic individual tigers and snow leopards Leopard (Panthera pardus) in from their footprints. Animal Conservation 12 : 252-262. Kaeng Krachan National Park. M. Santiapillai, C., M. R. Chambers and N. Sc. Thesis, King Mongkut’s University Ishwaran. 1982. The leopard, of Technology Thonburi. Panthera pardus fusca (Meyer, Nichols, J. D. and K. U. Karanth, 2002. 1794), in the Ruhuna National Park, Statistical concepts: Estimating Sri Lanka, and observations relevant absolute densities of Tigers using to its conservation. Biological Capture-recapture sampling, pp. 121- Conservation 23: 5-14. 137. In K. U. Karanth and J. D. Schaller, G. B. 1972. The Serengeti Lion. Nichols, eds. Monitoring Tigers and University of Chicago Press, London. their Prey: A manual for Researchers, 480 pp. Managers and Conservationists in Seidensticker, J. 1976. On the ecological Tropical Asia. Bangalore: Centre for separation between tigers and Wildlife Studies. leopards. Biotropica 8 (4): 225-234. Norton, P. M. and A. B. Lawson. 1985. Radio Silveira, L., A. T. A. Jacomo, and J. A. F. Diniz- tracking of leopards and caracals in Filho. 2003. Camera trap, line transect the Stellenbosh area, Cape Province. census and track surveys: a South African Journal of Wildlife comparative evaluation. Biological Research 15: 17-24. Conservation 114: 351-355. ______and S. R. Henley. 1987. Home range Silver, S. C. 2004. The use of camera traps for and movements of male leopard in the estimating Jaguar (Panthera onca) Cerdarberg wilderness area, Cape abundance and density using capture- recapture analysis 38: 148-154. Province. South African Journal of . Oryx Spalton, J. A., H. M. Al Hikmani, D. Willis and Wildlife Research 17 (2): 41-48. A. S. B. Said. 2006. Critically Otis, D. L., K. P. Burnham, G. C. White and endangered Arabian leopards D. R. Anderson. 1978. Statistical Panthera pardus nimr persist in the inference from capture data on Jabal Samhan Nature Reserve, Oman. closed animal populations. Wildlife Oryx 40 (3): 287-294. Monographs 62: 1-35. Spearing, A. 2002. A note on the prospects Panwar, H. S. 1979. A note on tiger census for snow leopard census using technique based on pugmark tracings. photographic capture. Proceeding Tigerpaper 6: 16-18. of the snow leopard survival summit, Rabinowitz, A. 1989. The density and behavior Snow leopard survival summit, ISLT. of large cats in a dry tropical forest Srikosamatara, S. 1993. Density and biomass mosaic in Huai Kha Khaeng Wildlife of large herbivores and other mammals Sanctuary, Thailand. Natural History in a dry tropical forest, western Bulletin of the Siam Society 37 (2): Thaiand. Journal of Tropical 235-251. Ecology 9: 33-43. 13

80 Thai J. For. 27 : 68-80 (2008)

Stander, P. E., P. J. Haden, Kaqece and Ghau. Wallace, R. B., H. Gomez, G. Ayala and F. 1997. The ecology of asociality in Espinoza. 2003. Camera trapping for Nabibian leopards. Journal of jaguar (Panthera onca) in the Tuichi Zoology, London 242: 343-364. valley, Bolivia. Journal of Neotropical Trolle, M., and M. Kerv. 2003. Estimation of Mammals 10: 133-139. ocelot density in the pantanal using White, G. C., D. R. Anderson, K. P. Burnham capture-recapture analysis of camera and D. L. Otis. 1982. Capture- trap data. Journal of Mammalogy 84: Recapture and Removal Methods 607-614. for Sampling Closed Populations. Tunhikorn, S., J.L.D. Smith, T. Prayurasiddhi, Los Alamos National Laboratory M. Graham, P. Jackson and P. Cutter Publication LA-8787-NERP, NM, (Eds.). 2004. Saving Thailand’s USA. tigers: An action plan. Bangkok: ______and K. P. Burnham. 1999. Program Ministry of Natural Resources and MARK: Survival rate estimation from Environment. Department of National both live and dead encounters. Bird Park, Wildlife, and Plant Conservation. Study 46: 120-139. 14

Oryx Vol 41 No 4 October 2007

How many tigers Panthera tigris are there in Huai Kha Khaeng Wildlife Sanctuary, Thailand? An estimate using photographic capture-recapture sampling

Saksit Simcharoen, Anak Pattanavibool, K. Ullas Karanth, James D. Nichols and N. Samba Kumar

Abstract We used capture-recapture analyses to esti- Western Forest Complex c. 720 tigers. Although based mate the density of a tiger Panthera tigris population in on ﬁeld protocols that constrained us to use sub-optimal the tropical forests of Huai Kha Khaeng Wildlife Sanctuary, analyses, this estimated tiger density is comparable to Thailand, from photographic capture histories of 15 distinct tiger densities in Indian reserves that support moderate individuals. The closure test results (z 5 0.39, P 5 0.65) prey abundances. However, tiger densities in well- provided some evidence in support of the demographic protected Indian reserves with high prey abundances closure assumption. Fit of eight plausible closed models to are three times higher. If given adequate protection we the data indicated more support for model Mh,which believe that the Western Forest Complex of Thailand incorporates individual heterogeneity in capture probabil- could potentially harbour .2,000 wild tigers, highlight- ities. This model generated an average capture probability ing its importance for global tiger conservation. The p^ 5 0.42 and an abundance estimate of NbðSEb½NbÞ 5 19 monitoring approaches we recommend here would be (9.65) tigers. The sampled area of AbðWÞðSEb½AbðWÞÞ 5 useful for managing this tiger population. 477.2 (58.24) km2 yielded a density estimate of DbðSEb½DbÞ 5 3.98 (0.51) tigers per 100 km2. Huai Kha Khaeng Wildlife Keywords Camera traps, capture-recapture models, Sanctuary could therefore hold 113 tigers and the entire Panthera tigris, population estimation, Thailand, tiger.

Introduction to obtain reliable estimates of tiger densities at a large number of sites across the 1.2 million km2 geographic The tiger Panthera tigris is categorized as Endangered on range of the species (Seidensticker et al., 1999). the IUCN Red List (IUCN, 2006) and during the past Thailand is a key tiger range state, with 25% of its land 3 decades substantial efforts have been invested in tiger area under forest cover, 16% of it being managed un- conservation by governments and non-governmental der wildlife and national park protection legislation agencies. However, these efforts are constrained by (Pattanavibool & Dearden, 2002). In addition, increasing a lack of reliable data on the distribution as well as societal wealth and an ofﬁcial commitment to science- densities of wild tiger populations. Furthermore, dis- based tiger conservation (Tunhikorn et al., 2004) make semination of putative ‘tiger numbers’ (Jackson, 1993), Thailand a critical region for tiger conservation. Conse- often based on guesswork or demonstrably faulty meth- quently, attempts have been made to map accurately the ods (Karanth, 1987, 1988; Karanth et al.,2003)masksareal distribution of tiger populations in Thailand from ﬁeld scarcity of reliable data. Therefore, there is an urgent need surveys (Rabinowitz, 1993, 1999; Smith et al., 1999; Tunhikorn et al., 2004; WEFCOM, 2004). However, to

Saksit Simcharoen Wildlife Research Division, Department of National Park, use such maps for managing wild tiger populations there Plant, and Wildlife Conservation, Paholyotin Road, Chatuchak, Bangkok is an additional need to estimate densities and sizes of 10900, Thailand. individual tiger populations at speciﬁc sites. This critical

Anak Pattanavibool Wildlife Conservation Society -Thailand Program, P.O.Box need has been enunciated in Thailand’s national action 170, Laksi, Bangkok 10210, Thailand. plan for tigers (Tunhikorn et al., 2004). The national plan 2 K. Ullas Karanth (Corresponding author) and N. Samba Kumar Wildlife also identiﬁes the 18,000 km Western Forest Complex, Conservation Society-India Program, Centre for Wildlife Studies, 26-2, Aga which contains 17 protected areas, including Huai Kha Abbas Ali Road (Apt: 430), Bangalore, Karnataka-560 042, India. E-mail Khaeng Wildlife Sanctuary, as the most important tiger [email protected] conservation area in the country. James D. Nichols US Geological Survey, Patuxent Wildlife Research Center, Although reliable estimation of tiger abundance is Laurel, Maryland 20708, USA. difﬁcult because of their elusive behaviour and naturally Received 1 February 2006. Revision requested 25 September 2006. low densities, recent development of automated camera Accepted 20 December 2006. traps and their application within a formal framework

447 ª 2007 FFI, Oryx, 41(4), 447–453 doi:10.1017/S0030605307414107 Printed in the United Kingdom 15

448 S. Simcharoen et al.

of capture-recapture population sampling (see Karanth Kha Khaeng Wildlife Sanctuary (Fig. 1). The area is et al., 2004b, for a review) have enabled investigators to rugged and hilly over altitudes of 200-1,600 m, has an obtain rigorous density estimates in India (Karanth & annual temperature range of 10-35°C and annual pre- Nichols, 1998; Karanth et al., 2004a,c), Nepal (Wegge cipitation of c. 1,500 mm. It supports four vegetation et al., 2004), Malaysia (Kawanishi & Sunquist, 2004) and types: dry deciduous dipterocarp forests, mixed decidu- Indonesia (O’Brien et al., 2003). Unlike previous tiger ous forest, dry evergreen forest, and hill evergreen forest, monitoring approaches based on footprint total counts depending on rainfall patterns and edaphic factors (Panwar, 1980), radio-telemetry (Sunquist, 1981; Smith, (Srikosamatara, 1993; Tunhikorn et al., 2004; WEFCOM, 1993) or raw photographic trapping rates (Carbone 2004). From earlier food habit studies in the area (Petdee, et al., 2001), capture-recapture methods can effectively 2000), principal prey species of tigers are wild pig Sus deal with the typical inability of surveys to detect all scrofa,sambarCervus unicolor,commonmuntjacMuntiacus individual tigers present in an area (i.e. detection prob- muntjac,bantengBos javanicus and gaur Bos frontalis.Other ability P ,1; Williams et al., 2002). Photographic capture- potential tiger prey include wild buffalo Bubalus bubalis recapture sample surveys of tigers conducted in habitats and Malayan tapir Tapirus indicus. ranging from evergreen, semi-deciduous and deciduous forests to alluvial grasslands (O’Brien et al., 2003; Karanth et al., 2004a; Kawanishi & Sunquist, 2004; Wegge et al., Methods 2004) show that reliable estimates can be generated at Field methods relatively low densities of 2-3 tigers per 100 km2, although their variances tend to be large because of the The original goal was to document the presence of tigers small number of traps typically deployed in such studies. and other mammals in the area using camera-trap A recent study (Karanth et al., 2006) that integrated techniques. Therefore, trapping was done on an ad hoc photo-capture data across space and time employing basis without employing recommended survey proto- the Robust Design (Pollock et al., 1990; Lebreton et al., cols (Karanth et al., 2002; Nichols & Karanth, 2002). 1992; Kendall et al., 1997; Williams et al., 2002) demon- Twelve Trailmaster (Goodson & Associates, Lenexa, strated the power of capture-recapture analyses to detect USA) and 10 CamTrakker (CamTrakker, Georgia, USA) changes in the temporal dynamics of a tiger population. units were deployed to cover a 211 km2 area using 103 However, prior to this study, there has not been an trap locations (Fig. 1). estimate of tiger abundance in Thailand based on Trapping was carried out from 9 February 2004 to capture-recapture analyses. Here we present the results 1 February 2005 using 14 clusters of trap locations. These of a post hoc capture-recapture analysis of camera trap clusters are analogous to trapping blocks (Nichols & survey data collected in Huai Kha Khaeng Wildlife Karanth, 2002), with each block consisting of c. seven Sanctuary during 2004-2005. The objectives of our anal- trap locations. The sampling effort varied among blocks: ysis were to: (1) Assess the potential for employing eight locations were trapped for .20 days, 49 locations camera trap surveys in the semi-deciduous forests that for 16-19 days, 12 locations for c. 15 days and the form a large proportion of tiger habitat in Thailand remaining 34 locations were trapped for ,15 days. On (Tunhikorn et al., 2004). (2) Analyse the tiger photo- average there were c. 15 trap-days at each location, and capture data in a formal capture-recapture sampling this trapping effort was uniform across the study area. framework (Otis et al., 1978; White et al., 1982; Williams The moving of traps among blocks did not follow a strict et al., 2002) to generate estimates of capture probability, pre-designed sequence and was driven by logistics as population size, effectively sampled area and tiger well as opportunities for setting traps at tiger kill sites. density based on survey protocols developed in India However, in combination, data from all these blocks (Karanth et al., 2002; Nichols & Karanth, 2002). (3) covered the area evenly (Fig. 1). Assess whether tiger densities in Huai Kha Khaeng are Of particular concern for the analysis was the long comparable to densities recorded in ecologically similar survey duration of 12 months, resulting in the possibility semi-deciduous forest sites in India (Karanth et al., of the sampled tiger population being demographically 2004c). (4) Examine the general implications of our open (Otis et al., 1978). Given the high turnover of results for understanding tiger ecology and monitoring individuals in tiger populations (Karanth et al., 2006), wild tiger populations in Thailand. such lack of closure could bias estimates of population size. However, the following aspects of the survey encouraged us to attempt a post-hoc statistical analysis Study area of these data under a formal capture-recapture sampling This study was carried out in the forests around Khao framework: (1) There were two opposing cameras at Nang Rum research station within the 2,780 km2 Huai each trap location, at a distance of c. 3-5 m from the

ª 2007 FFI, Oryx, 41(4), 447–453 16

Tiger density in tropical forests of Thailand 449

Fig. 1 The Khao Nang Rum camera-trap survey area in Huai Kha Khaeng Wildlife Sanctuary. Inset shows the Sanctuary’s location (HKK) in Thailand. anticipated path of moving tigers at c. 45 cm height, that capture data from well spaced locations were which obtained good photographs of both flanks, en- included in every sampling occasion. We constructed abling unambiguous identification of individuals. (2) The five sampling occasions based on the calendar dates on camera trap locations were selected based on signs of which each location was trapped (Otis et al., 1978; Karanth past tiger activity to maximize capture probabilities, & Nichols, 1998). Because of low capture rates, tiger resulting in a relatively large number (n 5 17) of in- photo-capture data from three successive calendar dates dividual tigers being photo-captured. (3) The maximum at each trapping location were combined before being spacing between any two trap locations was ,2.3 km, assigned to a specific sampling occasion. We thus ensured thus ensuring that there were no holes in the sampled that equal trapping effort was expended and the entire area and that every tiger in the sampled population had area was sampled during each of the sampling occasions. a non-zero probability of being photo-captured during The individual tiger capture histories in the standard each sampling occasion. X-matrix format (Otis et al., 1978; White et al., 1982) were analysed using models developed for closed populations (Otis et al., 1978; White et al., 1982) implemented in Analysis the software CAPTURE (Rexstad & Burnham, 1991). We Given the potential for lack of demographic closure tested the population closure assumption against our (Karanth et al., 2002; Williams et al., 2002) in the data we data. The closure test (Otis et al., 1978; White et al., 1982) would have preferred to use open model analyses implemented in CAPTURE is based on the number of (Karanth et al., 2006). However, the lack of simultaneous sample periods separating the times of first and last natural temporal coverage of the entire survey area capture for each animal caught at least twice. If animals (because we had to construct our sampling occasions are entering and/or leaving the sampled population as described above) precluded this option. Therefore, we during the survey period, the time between first and last constructed closed model capture histories following captures should be shorter on average than if all animals survey design 4 of Nichols & Karanth (2002), ensuring were present during the entire survey period.

ª 2007 FFI, Oryx, 41(4), 447–453 17

450 S. Simcharoen et al.

The capture-recapture models implemented in CAP- Table 1 Capture histories of tigers photo-trapped in Huai Kha TURE consider potential effects of behavioural response Khaeng Wildlife Sanctuary, Thailand, during 2004-2005.

of tigers to camera trapping (e.g. trap-avoidance: model Sampling occasion Mb), time-speciﬁc variation (e.g. weather changes over Identiﬁcation no. 12345Age/sex* the 3-day sampling occasions: model Mt), and heterogeneity among individual animals (e.g. caused by factors HKT-101 11111F such as territorial status or trap access: model Mh), as well HKT-102 10111F

as more complex models such as Mbh,Mth,Mtb and Mtbh HKT-103 10101F that incorporate occurrence of the effects of heterogeneity, HKT-104 11111F HKT-105 11111F trap response and time in different combinations. HKT-106 11111M We ﬁtted the null model M0 and each of the above seven HKT-107 00011M models to our data using CAPTURE (Rexstad & Burnham, HKT-108 01011F 1991) and examined results of goodness-of-ﬁt and between- HKT-109 10000F model tests, and the overall discriminant function, to HKT-11000100M HKT-11100001F guide the selection of an appropriate model for the data. HKT-11201000U The selected model was then used for estimating cap- HKT-11300100F b ture probabilities bp and abundance N. We estimated the HKT-11511000C effectively sampled area using an approach evaluated by HKT-11601000C Wilson & Anderson (1985), and computed tiger densities HKT-11400001M HKT-11710100F by dividing the population size by the sampled area. This computational approach is fully described elsewhere *F, female .12 months; M, male .12 months; U, unknown sex .12 (Karanth & Nichols, 1998; Nichols & Karanth, 2002). months; C, cubs ,12 months (not included in the capture-recapture analysis).

Results between photo-captures was 0.90-16.05 km, with a mean Photographic captures of tigers value of 7.11 km. Using the approach described more In a total sampling effort of 1,509 trap-days we obtained fully elsewhere (Karanth & Nichols, 1998; Nichols & 124 tiger photographs (59 right flanks, 57 left flanks, four Karanth, 2002), we estimated the effectively sampled area b b b 2 frontal, four rear) of 15 individual tigers judged to as AðWÞðSE½AðWÞÞ 5 477.2 (58.24) km . be .12 months of age (10 females, four males, one of unknown sex). Individual tigers could be clearly identified Tests for population closure and model selection from stripe patterns (Karanth et al., 2002) and were given unique identification numbers (HKT-101-HKT-117). The statistical test for population closure implemented We obtained both left and right profile photos for 12 in CAPTURE (Rexstad & Burnham, 1991) was consistent individuals, and three more animals were identified with the assumption that our tiger population was from only left profiles. Capture data for two cubs were closed during the survey period (z 5 0.39, P 5 0.65). excluded from the analysis. Because of the long (12 months) survey period, we The capture histories generated from the field survey would have liked to consider open models as well but (Table 1) show that the number of individuals caught the ad hoc field sampling design precluded this possi-

was small (Mt+1 515), as expected in a low to medium bility. We assumed that our data supported the closure density tiger population (Karanth et al., 2004c). Four assumption, albeit not strongly. animals were caught in all ﬁve sampling occasions, one The test for presence of individual heterogeneity in

was caught in four occasions, two animals were caught capture probabilities showed that the null model M0 was thrice, two others twice and six individual tigers were rejected in favour of the model incorporating heterogeneity 2 caught only once. We expected this low recapture rate Mh (v 5 10.07, df 5 1, P ,0.002). The goodness-of-ﬁt

for several individuals to induce substantial uncertainty test results for models Mh and Mb (incorporating trap- in our estimates. response behaviour) provided no evidence of lack of ﬁt (v2 5 3.85, df 5 4, P 5 0.43 and v2 5 2.57, df 5 4, P 5 0.64, respectively). The tests also did not reject the Estimates of effectively sampled area 2 null model M0 in favour of alternative models Mb (v 5

The polygon formed by the outer-most camera traps 0.77, df 5 1, P 5 0.38) or Mt (time-speciﬁc variation in (Fig. 1) was 211 km2. For the 10 individual tigers that capture probabilities; v2 5 2.86, df 5 4, P 5 0.58).

were caught more than once, the maximum distance Model Mbh, which accommodates heterogeneity as well as

ª 2007 FFI, Oryx, 41(4), 447–453 18

Tiger density in tropical forests of Thailand 451 trap response was not favoured over the more par- Based on comparisons of this ad hoc study with 2 simonious Mh model (v 5 0.67, df 5 2, P 5 0.72). earlier surveys in India that employed more rigorous The overall discriminant function model selection ﬁeld protocols (Karanth et al., 2002; Nichols & Karanth, algorithm in CAPTURE (Rexstad & Burnham, 1991) 2002), we recommend the following modiﬁcations to scored the competing models as: M0 5 0.88, Mh 5 1.00, future camera trap studies of tigers in the area: (1) The Mb 5 0.38, Mbh 5 0.57, Mt 5 0.0, Mth 5 0.41, Mtb 5 number of camera traps employed in this study was

0.37, Mtbh 5 0.64. The higher scoring model Mh is more small (10-15). To improve robustness of the statistical likely to have generated the observed capture history inferences of tiger abundance we recommend deploy- data in comparison to lower scoring models. This choice ment of at least 40-50 traps, so that the sampled area, the of model Mh in statistical tests reported above is consis- potential number of tiger-exposed traps, and recapture tent with past results (Karanth et al., 2004c) as well as rates can all be increased. (2) The camera trap survey aspects of tiger biology. Resident breeding tigers maintain duration should be shorter, preferably ,6 weeks, to home ranges that overlap between the sexes. Addi- avoid potential violation of population closure assump- tionally, some individuals in the population are non- tions. Furthermore, a pre-designed field survey protocol breeding ’floaters’, which may not have stable home (Nichols & Karanth, 2002), which can generate data ranges (Sunquist, 1981; Smith, 1993; Karanth & Sunquist, amenable to straightforward construction of capture 2000). These space use patterns, as well as location of our histories, should be employed. A larger number of traps camera traps in relation to home ranges of individuals, would make it easier to implement such a survey design. were likely to induce differences in capture probabilities (3) It would be useful to sample this tiger population among individual tigers. photographically on an annual basis to estimate its size and density, as well as other parameters such as longer Estimates of capture probability, tiger population term rates of survival, recruitment, and permanent and size and density temporary emigration. Robust Design and other recent refinements in capture-recapture analyses (Pollock et al., The tiger capture histories (Table 1) were used to 1990; Lebreton et al., 1992; Kendall et al., 1997; Williams generate parameter estimates under model M using h et al., 2002) facilitate such analyses (Karanth et al., 2006). the jackknife estimator (Burnham & Overton, 1978; Otis Reliable monitoring of the responses of tiger population et al., 1978) implemented in CAPTURE, which per- dynamics to threats and conservation interventions formed well in earlier photographic capture studies of can be an effective component of long-term adaptive tigers (Karanth & Nichols, 1998; Karanth et al., 2004c). management. The estimated average capture probability per sampling The observed mean density of c. 4 tigers per 100 km2 occasion (bp) was 0.42. The total population size estimate in this study was comparable to the density of 3.3-7.3 (Nb) was 19 tigers with a standard error ðSEb½NbÞ of 3.9 tigers per 100 km2 measured in ecologically similar tigers. Based on the sampled area AbðWÞðSEb½AbðWÞÞ 5 disturbed semi-deciduous forests such as Tadoba, 477.2 (58.24) km2, the estimated density of tigers in the Bhadra, Melghat, Pench and Panna reserves in India area was DbðSEb½DbÞ 5 3.98 (0.51) tigers per 100 km2. (Karanth et al., 2004c). However, better protected Indian These estimates exclude cubs ,12 months in age, which reserves that are ecologically comparable to Huai Kha generally comprise 20-25% of wild tiger populations Khaeng, such as Kanha, Bandipur and Nagarahole, (Smith, 1993; Kenny et al., 1995). support tiger densities that are thrice as high (c.12 tigers per 100 km2). The Huai Kha Khaeng landscape Discussion lacks an abundant, social cervid such as the chital Axis We have demonstrated in this study that non-invasive axis that accounts for 13-95% of prey numbers recorded photographic sampling is a potentially useful method in Indian reserves. However, Eld’s deer Cervus eldi, for estimating densities of tigers in the tropical forests of which was extirpated from Huai Kha Khaeng in histor- the Western Forest Complex in Thailand and therefore ical times, is such a species. probably for other similar areas in South-east Asia. Our study area of 477 km2 around Khao Nang Rum Ecological factors, such as climate, topography and research station forms 17% of the area of Huai Kha present tiger density levels permit the application of Khaeng Wildlife Sanctuary and 2.7% of the Western this method. The overall probability of capturing a tiger Forest Complex. Prima facie, this area appears to sup- present in the sampled area during the entire survey port low densities of ungulate prey (Srikosamatara, b (Mt+1/N 5 0.79) was ,1. Therefore, it is critical to 1993), and consequently a relatively low density of c.4 use the capture-recapture sampling-based approach tigers per 100 km2. If the entire landscape surrounding (Williams et al., 2002) to deal with the fact that not all Khao Nang Rum research station supports comparable tigers present in the study area are likely to be detected. tiger densities, Huai Kha Khaeng Sanctuary could hold

ª 2007 FFI, Oryx, 41(4), 447–453 19

452 S. Simcharoen et al.

113 tigers, and the entire Western Forest Complex c. 720 W.N. (2001) The use of photographic rates to estimate tigers. densities of tigers and other cryptic mammals. Animal Conservation 4 Given the similarity of vegetation types, climate and , , 75–79. IUCN (2006) 2006 IUCN Red List of Threatened Species. IUCN, prey composition between semi-deciduous forests of Gland, Switzerland [http://www.redlist.org, accessed 21 India and Thailand, their ecological productivities August 2007]. should be comparable. Furthermore, given the similarity Jackson, P. (1993) The status of the tiger in 1993. Cat News, 19, in composition of their ungulate prey assemblages, 5–11. potential maximum prey densities and hence tiger Karanth, K.U. (1987) Tigers in India: a critical review of field censuses. In Tigers of the World: The Biology, Biopolitics, densities should also be similar. Based on tiger density Management and Conservation of an Endangered Species (eds R.L. data from well protected Indian reserves (Karanth et al., Tilson & U.S. Seal), pp. 118–132. Noyes Publications, Park 2004c), we speculate that Huai Kha Khaeng Sanctuary Ridge, USA. could potentially hold 338 tigers, and the entire Western Karanth, K.U. (1988) Analysis of predator-prey balance in Forest Complex .2,000 tigers, highlighting the impor- Bandipur Tiger Reserve with reference to census reports. Journal of the Bombay Natural History Society, 85, 1–8. tance of this area for global tiger conservation. Major Karanth, K.U., Chundawat, R.S., Nichols, J.D. & Kumar, new conservation initiatives followed on from this N.S. (2004a) Estimation of tiger densities in the tropical study, in particular improved law enforcement under dry forests of Panna, Central India, using photographic the joint initiatives of the Thailand government and the capture-recapture sampling. Animal Conservation, 7 Wildlife Conservation Society, and we have also imple- , 285–290. Karanth, K.U., Kumar, N.S. & Nichols, J.D. (2002) Field surveys: mented an improved camera-trap monitoring system estimating absolute densities of tigers using capture- that employs standard closed model photographic recapture sampling. In Monitoring Tigers and their Prey: A capture-recapture sampling of ,60 days duration Manual for Researchers, Managers and Conservationists in (Karanth et al., 2002) using 136 trap sites to sample Tropical Asia (eds K.U. Karanth & J.D. Nichols), pp. 139–152. effectively an area of 1,260 km2. Centre for Wildlife Studies, Bangalore, India. Karanth, K.U. & Nichols, J.D. (1998) Estimating tiger densities in India from camera trap data using photographic captures and recaptures. Ecology, 79, 2852–2862. Acknowledgements Karanth, K.U., Nichols, J.D. & Kumar, N.S. (2004b) We are grateful to the Department of National Park, Photographic sampling of elusive mammals in tropical forests. In Sampling Rare or Elusive Species (ed. W.L. Wildlife, and Plant Conservation, Government of Thompson), pp. 229–247. Island Press, Washington, DC, USA. Thailand, for supporting this work. We gratefully ac- Karanth, K.U., Nichols, J.D., Kumar, N.S., Link, W.A. & Hines, knowledge encouragement received from the following J.E. (2004c) Tigers and their prey: predicting carnivore officials of the department: Chatchawan Pisdamkhom, densities from prey abundance. Proceedings of National Soontoon Chaiwattana, Kalyanee Boonkerd, Boosabong Academy of Sciences (USA), 101, 4854–4858. Karanth, K.U., Nichols, J.D., Seidensticker, J., Dinerstein, E., Kanchanasakha, and Suchitra Changtragoon. We thank Smith, J.L.D., McDougal, C., Johnsingh, A.J.T., Chundawat, the Wildlife Conservation Society, New York, for sup- R.S. & Thapar, V. (2003) Science deficiency in conservation porting the involvement of AP, KUK and NSK in this practice: the monitoring of tiger populations in India. Animal study and for partial funding, and the US Geological Conservation, 6, 141–146. Survey’s Patuxent Wildlife Research Center for support- Karanth, K.U., Nichols, J.D., Kumar, N.S. & Hines, J.E. (2006) Assessing tiger population dynamics using photographic ing the involvement of JDN. We acknowledge WWF– capture-recapture sampling. Ecology, 87, 2925–2937. Thailand for providing 10 camera traps to start the Karanth, K.U. & Sunquist, M.E. (2000) Behavioural correlates of study. We are grateful to our enthusiastic team of predation by tiger (Panthera tigris), leopard (Panthera pardus) research assistants, Boonyang Srichan, Sompoad Daung- and dhole (Cuon alpinus) in Nagarahole, India. Journal of sirichantra and Somporn Pakpein for their dedicated Zoology, London, 250, 255–265. Kawanishi, K. & Sunquist, M.E. (2004) Conservation status of fieldwork. We are particularly grateful to James E. Hines tigers in a primary rainforest of Peninsular Malaysia. for assistance with data analysis. Biological Conservation, 120, 329–344. Kendall, W.L., Nichols, J.D. & Hines, J.E. (1997) Estimating References temporary emigration and breeding proportions from capture-recapture data with Pollock’s Robust Design. Ecology, Burnham, K.P. & Overton, W.S. (1978) Estimation of the size 78, 563–578. of a closed population when capture probabilities vary Kenny, J.S., Smith, J.L.D., Starfield, A.M. & McDougal, C.W. among animals. Biometrika, 65, 625–633. (1995) The long-term effects of tiger poaching on population Carbone, C., Christie, S., Conforti, K., Coulson, T., Franklin, N., viability. Conservation Biology, 9, 1127–1133. Ginsberg, J.R., Griffiths, M., Holden, J., Kawanishi, K., Lebreton, J.D., Burnham, K.P., Clobert, J. & Anderson, D.R. Kinnaird, M., Laidlaw, R., Lynam, A., MacDonald, D.W., (1992) Modelling survival and testing biological hypotheses Martyr,D.,McDougal,C.,Nath,L.,O’Brien,T.,Seidensticker,J., using marked animals: a unified approach with case studies. Smith, D., Sunquist, M., Tilson, R. & Wan Shaharuddin, Ecological Monographs, 62, 67–118.

ª 2007 FFI, Oryx, 41(4), 447–453 20

Tiger density in tropical forests of Thailand 453

Nichols, J.D. & Karanth, K.U. (2002) Statistical concepts: Srikosamatara, S. (1993) Density and biomass of large estimating absolute densities of tigers using capture- herbivores and other mammals in a dry tropical forest, recapture sampling. In Monitoring Tigers and their Prey: western Thailand. Journal of Tropical Ecology, 9, 33–43. A Manual for Researchers, Managers and Conservationists Sunquist, M.E. (1981) Social organisation of tigers Panthera tigris in Tropical Asia (eds K.U. Karanth & J.D. Nichols), pp. in Royal Chitwan National Park, Nepal. Smithsonian 121–137. Centre for Wildlife Studies, Bangalore, India. Contributions to Zoology, 336, 1–98. O’Brien, T.G., Kinnaird, M.F. & Wibisono, H.T. (2003) Crouching Tunhikorn, S., Smith, J.L.D., Prayurasiddhi, T., Graham, M., tigers, hidden prey: Sumatran tigers and prey populations Jackson, P. & Cutter, P. (eds) (2004) Saving Thailand’s in a tropical forest landscape. Animal Conservation, 6, Tigers: An Action Plan. Ministry of Natural Resources and 131–139. Environment, Department of National Parks, Wildlife and Otis, D.L., Burnham, K.P., White, G.C. & Anderson, D.R. Plant Conservation, Bangkok, Thailand. (1978) Statistical inference from capture data on closed WEFCOM (2004) GIS Database and its Applications for animal populations. Wildlife Monographs, 62, 1–135. Ecosystem Management. The Western Forest Complex Panwar, H.S. (1980) A note on tiger census technique based Ecosystem Management Project, Department of National on pugmark tracings. Cheetal, 22, 40–46. Park, Wildlife, and Plant Conservation, Bangkok, Pattanavibool, A. & Dearden, A. (2002) Fragmentation and Thailand. wildlife in montane evergreen forest, northern Thailand. Wegge, P., Pokheral, C.P. & Jnawali, S.R. (2004) Effects of Biological Conservation, 107, 155–164. trapping effort and trap shyness on estimates of tiger Petdee, A. (2000) Feeding habits of the tiger (Panthera tigris) abundance from camera trap studies. Animal Conservation, in Huai Kha Khaeng Wildlife Sanctuary by fecal analysis.MSc 7, 251–256. thesis, Faculty of Forestry, Kasetsart University, Bangkok, White, G.C., Anderson, D.R., Burnham, K.P. & Otis, D.L. (1982) Thailand. Capture-Recapture Removal Methods for Sampling Closed Pollock, K.H., Nichols, J.D., Brownie, C. & Hines, J.E. (1990) Populations. Los Alamos National Laboratory publication no. Statistical inference for capture-recapture experiments. LA-8787-NERP. Los Alamos, USA. Wildlife Monographs, 107, 1–97. Williams, B.K., Nichols, J.D. & Conroy, M.J. (2002) Analysis and Rabinowitz, A. (1993) Estimating the Indochinese tiger Panthera Management of Animal Populations. Academic Press, San tigris corbetti population in Thailand. Biological Conservation, Diego, USA. 65, 213–217. Wilson, K.R. & Anderson, D.R. (1985) Evaluation of two density Rabinowitz, A. (1999) The status of the Indochinese tiger: estimators of small mammal population size. Journal of separating fact from ﬁction. In Riding the Tiger: Tiger Mammalogy, 66, 13–21. Conservation in Human Dominated Landscapes (eds J. Seidensticker, S. Christie & P. Jackson), pp. 148–165. Cambridge University Press, Cambridge, UK. Rexstad, E. & Burnham, K.P. (1991) User’s Guide for Biographical sketches Interactive Program CAPTURE. Colorado Cooperative Wildlife Research Unit, Colorado State University, Fort Saksit Simchareon’s area of interest is the study of tigers and Collins, USA. leopards in the dry forests of Thailand using radio telemetry Seidensticker, J., Christie, S. & Jackson, P. (1999) Preface. and camera trapping. He has carried out intensive ecolog- In Riding the Tiger: Tiger Conservation in Human Dominated ical studies on these species over the past 3 years. Anak Landscapes (eds J. Seidensticker, S. Christie & P. Jackson), pp. Pattanavibool is interested in examining large mammal xv–xix. Cambridge University Press, Cambridge, UK. ecology and conservation issues in Thailand within a land- Smith, J.L.D. (1993) The role of dispersal in structuring the scape ecological framework. K. Ullas Karanth developed Chitwan tiger population. Behaviour, 124, 165–195. camera trap surveys in India with a focus on integrating Smith, J.L.D., Tunhikorn, S., Tanhan, S., Simcharoen, S. & them with modern animal sampling methods. James D. Kanchanasaka, B. (1999) Metapopulation structure of tigers in Nichols works on development of rigorous sampling and Thailand. In Riding the Tiger: Tiger Conservation in Human analytical methodologies for assessing wildlife populations. Dominated Landscapes (eds J. Seidensticker, S. Christie & N. Samba Kumar specializes in developing ﬁeld protocols P. Jackson), pp. 166–175. Cambridge University Press, for surveying large mammals in Asian forests. Cambridge, UK.

ª 2007 FFI, Oryx, 41(4), 447–453 21

Author's personal copy

BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250

available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/biocon

Home range size and daytime habitat selection of leopards in Huai Kha Khaeng Wildlife Sanctuary, Thailand

Saksit Simcharoena, Adam C.D. Barlowb,*, Achara Simcharoenc, James L.D. Smithb aResearch Division, Department of National Park, Wildlife and Plant Conservation, Pahonyotin Road, Chatuchak, Bangkok 10900, Thailand bFisheries, Wildlife and Conservation Biology Department, University of Minnesota, 1980 Folwell Avenue, St. Paul, MN 55108, United States cConservation Areas Regional Ofﬁce 12, Kositai Road, Muang, Nakhornsawan 60000, Thailand

ARTICLE INFO ABSTRACT

Article history: Leopards (Panthera pardus) are endangered in South East Asia yet little is known about Received 23 November 2007 which resources need to be secured for their long-term conservation or what numbers of Received in revised form this species this region can support. This study uses radio telemetry to investigate seasonal 14 June 2008 variation in habitat selection and home range size of Leopards in Huai Kha Khaeng Wildlife Accepted 22 June 2008 Sanctuary, Thailand. Over a five year period, 3690 locations were recorded from nine indi- Available online 10 August 2008 viduals. The mean ± standard error of fixed kernel home range size for six adult females was 26 ± 8.2 km2, for two adult males was 45.7 ± 14.8 and for two sub-adult females was Keywords: 29 km2 ± 5.5. Adult female wet and dry season home range sizes did not differ significantly. Compositional analysis One adult male showed an increase in home range size from dry to wet seasons. Estimated Fixed kernel density was 7 adult females/100 km2, which suggests 195 adult female leopards living in Leopard Huai Kha Khaeng alone, thus highlighting the larger Western Forest Complex’s potential Panthera pardus contribution to leopard conservation. Compositional analysis of second and third order Telemetry habitat selection suggested mixed deciduous and dry evergreen forest types, flat slope and areas close to stream channels are important landscape features for leopards. These results can help formulate a much needed conservation strategy for leopards in the region. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction such as the leopard have a chance of long-term survival (Trisurat, 2006). Without research-based conservation plan- The leopard (Panthera pardus) is a widespread but endangered ning in the region, remaining leopard populations face an large felid. Habitat destruction, poaching and prey depletion uncertain future (Rabinowitz, 1989; Weber and Rabinowitz, have created a discontinuous patchwork of leopard popula- 1996). tions throughout Asia, Africa, the Middle East and south east- An essential component of conservation planning is iden- ern Europe (Bailey, 1993; Uphyrkina et al., 2001). Persecution tifying important resources that relate to population persis- of leopards has led to listing of the species on Appendix 1 of tence (Alldredge and Ratti, 1992; Marker and Dickman, the Convention of International Trade of Endangered Species 2005). The ‘‘importance’’ of a resource can be measured by of Wild Fauna and Flora. its perceived contribution to an animal’s survival or reproduc- In Thailand, leopards are particularly threatened with hab- tion (Garshelis, 2000). However, for short-term conservation itat loss and prey depletion (Rabinowitz, 1989; Grassman, planning it is necessary to identify key resources during the 1999), and there are few contiguous areas left where large cats considerable time needed to investigate habitat-speciﬁc

* Corresponding author: Tel.: +1 612 626 1213. E-mail addresses: [email protected] (S. Simcharoen), [email protected] (A.C.D. Barlow), [email protected] (A. Simcharoen). 0006-3207/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2008.06.015 22

Author's personal copy

BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250 2243

demographic parameters for long-lived animals with low lined in this paper. Our investigation builds substantially on reproduction rates such as the leopard (Nielson et al., 2006). this earlier work by taking advantage of the largest sample Importance can potentially be inferred by ‘habitat selection’’ size of its kind in Asia, a longer study time and the develop- which refers to a behavioral response resulting in dispropor- ment of more advanced analysis techniques. This study does tionate use of habitat types that may affect the animal’s fit- not identify causal factors that drive spatial dynamics or den- ness (Block and Brennan, 1999). Habitat selection may vary sity. Instead it focuses on providing baseline data on popula- due to seasonal variation in resource availability, so studies tion status and identifies important landscape features for should take this into account to identify resources important leopards that can be used for conservation planning on a na- at only certain times of year (Schooley, 1994). tional or regional scale. Although habitat selection for leopards is covered by some studies (Bailey, 1993; Marker and Dickman, 2005), information 2. Materials and methods is lacking for Asia. A camera trap study in Kaeng Krachen National Park in Thailand found that leopard habitat use in- 2.1. Study area creased with distance from human habitation (Ngoprasert et al., 2007). However, no study in Thailand has conclusively The study site consisted of a valley and surrounding hills in identified, either seasonally or otherwise, leopard selection Huai Kha Khaeng Wildlife Sanctuary around Khao Nang of other geographical features likely to influence their distri- Rum Wildlife Research Station and Sub Pha Pa Guard Post bution, such as forest type, topography or water courses. (Fig. 1). Average annual rainfall for the area, as recorded at In addition, management will benefit from improved Khao Nang Rum, was 1447 mm. The rainfall pattern delin- understanding of leopard home range, which can be consid- eates wet (May–October) and dry (November–April) seasons ered a more-or-less restricted area where an animal moves (Walker and Rabinowitz, 1992). Elevation varies from 121 m during its normal activities (Harris et al., 1990). Information at the valley bottom up to the highest surrounding hill at on leopard home range size will allow managers to assess 1350 m. HKK is a well protected sanctuary with human activ- population status and model responses to different threat ities largely limited to research, tourism and an annual mush- scenarios. Information regarding leopards’ spatial require- room harvest. HKK also experiences annual fires originating ments and habitat needs can then be used with existing satel- in adjacent agricultural land. These fires are generally of lite coverages to guide management of the species on a low intensity and affect a small proportion of the sanctuary. landscape-level, as has been done for tigers (Panthera tigris) Forest types include mixed deciduous, dry deciduous dip- (Sanderson et al., 2006). terocarp, dry evergreen and hill evergreen (Rabinowitz, 1989). Considering the leopard’s elusive nature and naturally low In addition to leopard, HKK’s large mammal assemblage in- densities, radio telemetry is probably the best available cludes tiger (Panthera tigris), Asiatic black bear (Ursus thibet- means to investigate both habitat selection and home range anus), Malayan sun bear (Helarctos malayanus), banteng (Bos size (Bailey, 1993). This technique has been used extensively javanicus), gaur (Bos gaurus), sambar (Cervus unicolor), Malaysian in the leopard’s African range (Norton and Lawson, 1985; tapir (Tapirus indicus) and elephant (Elephas maximas)(Rabino- Bailey, 1993; Jenny, 1996; Mizutani and Jewell, 1998; Marker witz and Walker, 1991; Srikosamatara, 1993). The 2780 km2 and Dickman, 2005). In Asia it has been used in Nepal HKK Wildlife Sanctuary is part of the larger 18,727 km2 Wes- (Seidensticker, 1976; Sunquist, 1983; Odden and Wegge, tern Forest Complex (WEFCOM), and is made up of 17 protected 2005), India (Karanth and Sunquist, 1995, 2000) and Thailand areas. This whole area is one of the largest intact contiguous (Rabinowitz, 1989; Grassman, 1999). Rabinowitz (1989) col- forests left in South East Asia (Trisurat, 2006). lected telemetry data on two leopards in Huai Kha Khaeng Wildlife Sanctuary, and Grassman (1999) collected data on 2.2. Leopard capture three leopards in Kaeng Krachen National Park. Both studies suggested there may be slight increases in male leopard home Wooden box traps (2 m long, 0.8 m wide, 1 m high) were bai- range size during the wet season, possibly in response to ted with live or dead chickens and placed along jeep tracks wider dispersion of prey (Rabinowitz, 1989; Grassman, 1999). and animal paths. Placement was based on presence of sec- We conducted a radio telemetry study in Huai Kha Khaeng ondary sign such as tracks and scrapes, which indicated reg- Wildlife Sanctuary (HKK), Thailand to investigate seasonal ular leopard travel routes (Rabinowitz, 1989). All traps were variation in (1) home range size and (2) selection of vegeta- checked on a daily basis. Captured leopards were immobilized tion, slope and stream habitat types. We chose to focus on using Ketamine (5 mg/kg) and Xylazine (2 mg/kg), delivered second and third order habitat selection, which reflects an by a jab stick. The Xylazine was later antagonized with animal’s choice of a home range within the landscape and Yohimbine (0.05 mg/kg). Leopards were fitted with a VHF radio habitat patches within a home range, respectively (Johnson, collar (Telonic Inc., Mesa, Arizona and Advanced Telemetry 1980). We also included analysis of combined season habitat Systems, Minnesota, USA) and released at the site of capture. selection to determine if this pooled data source would have Leopards were observed until they regained full conscious- failed to identify temporally important resources, as has been ness and walked away. noted for other studies (Schooley, 1994). To give an indication of population status for the area, we estimated density of 2.3. Home range estimation adult female leopards. Rabinowitz’s (1989) previous study of leopards in HKK Each day, an attempt was made to locate all radio-collared established the trapping and monitoring methodology out- leopards using standard telemetry procedures (Mech, 1983). 23 Author's personal copy

2244 BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250

Fig. 1 – Map of Western Forest Complex and Huai Kha Khaeng Wildlife Sanctuary, Thailand.

To minimize possible error resulting from a moving animal, tive purposes with other studies the minimum convex three teams, positioned at different locations, took bearings polygons (MCP) were also calculated. of the same leopard simultaneously. Initial bearings were The computer program BIOTAS v1.03 (Ecological Software generally taken from ridge tops or other vantage points before Solutions, Florida) was also used to determine 95% MCP and descending to the valley ﬂoor to acquire additional bearings if 95% FK home ranges (Worton, 1989), with the FK smoothing necessary. All points were recorded using a geographical posi- factor determined by least squares cross validation (LSCV). tioning system. Seaman and Powell (1996) found that FK estimators gave little To reduce the likelihood of autocorrelation, which is the bias when smoothed by LSCV, and made good distribution lack of independence between pairs of observations at given estimates compared to the adaptive kernel approach. Leopard distances in time or space (Legendre, 1993), only one radio home ranges were determined for each study year (combined location per day was used for each leopard. If more than seasons) and for wet and dry seasons within each year. one location was taken for an individual leopard in a day then Kernohan et al. (2001) noted that kernel estimators performed the location taken closest to 12 p.m. was used. well, even with as little as 50 data points, so this was used as To assess home range size we used the ﬁxed kernel (FK) the minimum threshold for calculating a home range within a method (Worton, 1989), which is not effected by grid size or time period. A mean home range size was estimated for each placement (Silverman, 1986), and can estimate density isop- leopard, and a grand mean for all leopards in the same demo- leths of any shape (Seaman and Powell, 1996). For compara- graphic group. A Wilcoxon signed rank t-test (a = 0.05), was 24

Author's personal copy

BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250 2245

used to test for differences between wet and dry home range Leban, 1999). Compositional analysis overcomes the problems sizes. that can result from (1) assessing habitat selection without The density of adult females was calculated by construct- taking into account non-independence of proportions (also ing a 100% MCP around all adult female locations for the year known as the unit sum constraint), and (2) inappropriate level with the most collared animals of that demographic group. of sampling and sample size (Aebischer et al., 1993). Telemetry error was estimated by the distance between The minimum number of individuals needed for composi- estimated (determined by radio telemetry and program tional analysis is six (Aebischer et al., 1993), thus for this LOASTM 4.0 b, Ecological Software Solutions) and actual (re- study all leopards with adequate number of locations (n =9) corded using a GPS) locations of a spare radio collar that were pooled together, irrespective of demographic group. was moved around the forest. Mean telemetry error, calcu- If selection was significantly non random (p < 0.05), the lated by distance between estimated and actual collar posi- habitat types were ranked from most to least selected, using tions (n = 28) was 150 ± 76 m (range = 300–30 m). This level a matrix of mean and standard deviation of log ratio differ- of error was acceptable considering that it was lower than ences for all habitat types. that used by Dickson and Beier (2002), whose approach we Classification and analysis of GIS coverages was done followed. using GIS software ArcView 3.3 (ESRI, Redlands, California). The number of unsuccessful location attempts was not re- The study area was defined by a minimum convex polygon corded, so missing data were calculated as the total number around the combined locations of all leopards, which totaled of days when a collar was active but not located. 172.3 km2. Habitat classes analyzed were vegetation, slope, and 2.4. Habitat selection water courses (Fig. 2). Vegetation class included three broad types; dry dipterocarp, dry evergreen and mixed deciduous In this study ‘‘habitat type’’ (e.g. dipterocarp forest) represents forest. There was also a small amount of hill evergreen, a category of variation in characteristics within a ‘‘habitat but since this comprised <1% of the study area it was class’’, which signifies a physical, biological or geographical merged with dry evergreen; the habitat type with most sim- coverage (e.g. vegetation). For statistical analysis of habitat ilar characteristics. selection using location data, we used the compositional The slope class was created using a 20 m elevation con- analysis approach (Aitchison, 1986; Aebischer et al., 1993) tours to define slope angle for each 30 m grid square. Slope using Program Resource Selection for Windows (RSW 1.00Ó class was divided into three types measured in degrees from

Fig. 2 – Habitat class, types and composition in the Huai Kha Khaeng study area. 25

Author's personal copy 2246 BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250 level; flat slope type was 0–12 degrees, moderate was 13–24 3. Results degrees, and steep was 25–49 degrees. The water course class was constructed using a 150 m buf- 3.1. Animal capture and telemetry effort fer around all streams and rivers in the study area. Area within this buffer was termed stream type, whilst outside was Ten leopards were captured (seven females and three males). termed dry type. Although somewhat arbitrary, we consid- All animals recovered from immobilization and were released ered the 150 m buffer width to take into account resources with a VHF radio collar. The radio-collared leopards were fol- (such as prey and travel routes) that may be distributed close lowed over five years (1994–1999) with each animal tracked for to the water channels (Grassman, 1999). a mean of 25 ± 16 months. One male leopard had too few loca- To circumvent the problems associated with using tions (12) to be included in further analysis. In total, 3690 loca- point data for habitat selection analysis (Rettie and tions were recorded from the remaining nine leopards. The McLoughlin, 1999), a 150 m buffer (the mean estimated mean number of daily locations for each of these leopards telemetry error) was created around each leopard location. was 337 ± 234 (Table 1). This was done to take into account both telemetry error The mean percentage of available tracking days when and the potential importance of habitat close to the ani- locations were acquired (not including M855 whose home mal’s position (Rettie and McLoughlin, 1999; Dickson and range was not estimated) was 44.5% and ranged from 31 to Beier, 2002). 73.7% (Table 1). This included days when no attempt was Following Dickson and Beier (2002), we looked at second made to acquire a location and days when, despite efforts, order selection using compositional analysis to compare pro- no location was obtained. portions of available habitat types in the study area to selected habitat within all area encompassed by buffered 3.2. Home range size leopard locations for combined, wet (May–October) and dry (November–April) season locations. Adult female leopards (n = 6) had a mean annual 95% FK For third order selection we compared the proportion of home range of 26 ± 8.2 km2, a wet season home range (n = 5) available habitat in multiyear 95% fixed kernel home ranges of 25.8 ± 7.8 km2 and a dry season home range (n = 6) of (for combined, wet and dry locations) to the selected habitat 29.2 km2 (±12.5). Mean 95% FK combined seasons’ home range encompassed by the buffered locations within the home size for the two sub-adult females still living in their natal range (Dickson and Beier, 2002). areas was 29 ± 5.5 km2. A Wilcoxon paired rank t-test For both orders of selection, results can be unsound if (a = 0.05) revealed no significant difference (p = 0.06) between one or more animals are not recorded in one of the habitat wet and dry season adult female 95% FK home ranges. Of note types within a habitat class, in which case the zero value was an old adult female F025 who expanded her home range can be replaced by a number less than 0.1 times the next from 30.9 km2 in year one to 76.7 km2 in year two followed by lowest observed value (Aebischer et al.,1993). However, in a reduction to 14.4 km2 in year three, moving between back this study all leopards were recorded in all classes of all hab- and forth between the adjacent territories of F046 and F195. itat types. Both sub-adult females resided within their mother’s

Table 1 – Tracking effort and Fixed Kernal home range sizes of study leopards LPD Age Mo. Loc. Aq. (%) Combined seasons Wet season Dry season n 95% FK (km2) Range (km2) n 95% FK (km2) Range (km2) n 95% FK (km2) Range (km2)

F025 A 31 412 50.8 3 40.7 (±32.3) 14.4–76.7 2 38.8 (±34.5) 14.4–63.2 2 46.4 (±19.5) 32.6–60.2 F035 A 23 275 41.7 3 20.7 (±4.9) 15.5–21.5 2 18 (±5.9) 13.8–22.1 1 22.7 22.7–22.7 F046 A 55 848 50.8 5 28.9 (±13.3) 8.7–45.49 4 24.7 (±8) 17.5–36 4 34.9 (±7.6) 28.4–45.3 F195 A 40.5 603 40.5 5 26 (±9.1) 18.1–38.3 2 23 (±4.4) 19.9–26.3 3 38.2 (±20.9) 20.4–61.2 F345 A 30 302 31 3 17.8 (±7) 11.9–25.5 1 24.5 – 3 16.7 (±5.4) 11.9–22.6 F390 A 13 160 41.8 1 22 – 0 – – 1 15.9 –

Mean 32 433 43 6 26.0 (±8.2) – 5 25.8 (±7.8) – 6 29.2 (±12.5) – F035 SA 4.5 154 73.7 1 25 – – – 0 - – F467 SA 28.5 353 40.53 3 32.9 (±2.6) 30.2–35.5 2 30.6 (±2.5) 28.8–32.4 2 27.4 (±4.1) 24.4–30.3

Mean 16.5 254 57 2 29 (±5.5) – 1.0 30.6 – 1 27.4 1 M055 A 35 422 31.2 3 56.1 (±8.5) 46.5–62.6 2 72.3 (±4.16) 69.4–75.3 2 55.2 (±21) 40.4–70 M550 A 13 168 43.2 1 35.2 – 0 – – 1 41.3 – M855 A 2 12 20 0 – – 0 – – 0 – –

Mean 16.5 201 32 2 45.7 (±14.8) – 1 72.3 – 2 48.3 (±9.8) –

Note: LPD is leopard identity, F = female, M = male, A = adult, SA = sub-adult, Mo. = number of months tracked, Aq. % = the percentage of available tracking days when locations were aquired, n is the number of periods with >50 recorded locations, and for the mean is the number of leopards. Leopard F035 has been listed twice; once as a sub-adult and once as an adult after she had moved out of her natal area and established another home range elsewhere. 26 Author's personal copy

BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250 2247

territories and had home ranges within the general size range of the paired t-test results testing selection between habitats of adult females. Sub-adult F035 then dispersed and estab- were significant (Table 3). lished a home range to the North East of her natal area, and was classed as an adult for the later period. The two adult 3.3.2. Slope males had annual home ranges of 56.1 ± 8.5 km2 and At the second order level slope types were selected signi- 35.2 km2. One adult male mean wet season home range ficantly differently from random for combined (k = 0.14, 72.3 ± 4.16 km2 was larger than its mean dry season home p < 0.001), wet (k = 0.04, p < 0.001) and dry (k = 0.08, p < 0.0001) range of 55.2 ± 21 km2 (Table 1). periods. In all cases, low slope was ranked first followed by The mean 95% MCP for adult females for combined, wet medium and steep. For each period the difference between and dry seasons were 22.8 ± 8.6 km2, 21.4 ± 12.1 km2 and rankings of each slope type was significant (p < 0.05) (Table 3). 20.2 ± 7.9 km2, respectively. For the same time periods, sub- Third order selection analysis inferred significant differ- adult females had 95% MCPs of 24.2 ± 6.9 km2, 17.4 km2, and ences for combined (k = 0.32, p < 0.05), and dry (k = 0.14, 15.3 km2. The two males had combined, wet and dry 95% p < 0.001) seasons. As slope increased selection decreased, MCPs of 36.7 ± 10.3 km2, 47.5 km2 and 32.2 ± 7.1 km2 (Table 2). with flat slope always being selected significantly more Estimated density from year three, when five adult fe- (p < 0.05) than moderate and steep slope (Table 3). males were recorded, was 7 adult female leopards/100 km2. 3.3.3. Water courses 3.3. Habitat selection At the second order level, leopards selected significantly more stream type (the area encompassed by the 150 m buffer sur- 3.3.1. Vegetation rounding all streams and rivers) for combined (k = 0.76, Evaluating second order selection (study area vs. 95% FK), p < 0.0001), wet (k = 0.21, p < 0.001) and dry (k = 0.07, leopards showed significant habitat selection for combined p < 0.0001) seasons. At the third order level there was no sig- (k = 0.4, p < 0.05), and wet seasons (k = 0.29, p < 0.05) . In both nificant difference between proportions of selected and avail- cases dry dipterocarp was ranked last and selected signifi- able stream habitat types (Table 3). cantly less (p < 0.05) than mixed deciduous and dry evergreen. The combined season result ranked dry evergreen before 4. Discussion mixed deciduous, whereas for the wet season mixed deciduous was ranked first. However, in both cases, the difference 4.1. Home range size between mixed deciduous and dry evergreen was not significant, i.e. the ranking of these habitat types can be considered We found no discernable difference in adult female 95% FK interchangeable (Aebischer et al., 1993)(Table 3). home range sizes for wet and dry seasons, despite differing For third order (95% FK vs. buffered locations within 95% environmental conditions which potentially affect resource FK), only wet season locations produced a significant overall distribution and availability. Similar findings were noted for result (k = 0.04, p < 0.05); dry dipterocarp was selected the leopards in Namibia (Marker and Dickman, 2005). Two previ- most, followed by dry evergreen and mixed deciduous. None ous telemetry studies in Thailand suggested male leopards

Table 2 – Minimum convex polygon home range sizes of study leopards LPD Age Combined seasons Wet season Dry season n 95% MCP (km2) Range (km2) n 95% MCP (km2) Range (km2) n 95% MCP (km2) Range (km2)

F025 A 3 42.5 (±28.9) 22–63 2 42.5 (±28.9) 22–63 2 31.9 (±20.0) 17.7–46 F035 A 3 14.0 (±4.5) 10.8–17.2 2 14.0 (±4.5) 10.8–17.2 1 10.6 – F046 A 5 14.0 (±9.7) 15.4–21.5 4 14.0 (±9.7) 15.4–21.5 4 19.5 (±4.7) 13.3–24.5 F195 A 5 16.1 (±0.6) 15.7–16.6 2 16.1 (±0.6) 15.7–16.6 3 22.3 (±10.1) 13.4–33.3 F345 A 3 20.3 – 1 20.3 – 3 12.4 (±8.8) 6.7–22.47 F390 A 1 – – 0– – 1 24.4 –

Mean A 6 21.4 (±12.1) – 5 21.4 (±12.1) – 6 20.2 (±7.9) –

F035 SA 1 – – – – 0– – F467 SA 3 17.4 (±2.7) 15.5–19.3 2 17.4 (±2.7) 15.5–19.3 2 15.3 (±2.9) 13.2–17.3 Mean SA 2 17.4 – 1.0 17.4 – 1 15.3 –

M055 A 3 47.5 (±15.0) 36.8–58 2 47.5 (±15.0) 36.8–58 2 37.2 (±2.0) 35.8–38.6 M550 A 1 – – 0– – 1 27.2 – M855 A 0 – – 0– – 0– –

Mean A 2 47.5 – 1 47.5 – 2 32.2 (±7.1) –

Note: LPD is leopard identity, F = female, M = male, A = adult, SA = sub-adult, Leopard F035 has been listed twice; once as a sub-adult and once as an adult after she had moved out of her natal area and established another home range elsewhere. 27 Author's personal copy

2248 BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250

Table 3 – Compositional analysis results of leopard habitat selection Habitat class Selection order Season k p Habitat type ranking

Vegetation 2nd Combined 0.4 <0.05 Dry EG > mix. decid dry dipt 2nd Wet 0.29 <0.05 Mix. decid > dry EG dry dipt 2nd Dry 0.53 0.06 – 3rd Combined 0.8 0.30 3rd Wet 0.04 <0.05 Dry dipt > dry EG > mix. decid 3rd Dry 0.74 0.69 –

Slope 2nd Combined 0.14 <0.001 Flat medium steep 2nd Wet 0.17 <0.001 Flat medium steep 2nd Dry 0.08 <0.0001 Flat medium steep 3rd Combined 0.32 <0.05 Flat moderate steep 3rd Wet 0.56 0.07 3rd Dry 0.14 <0.001 Flat moderate > steep

Stream 2nd Combined 0.76 <0.0001 Stream dry 2nd Wet 0.21 <0.001 Stream dry 2nd Dry 0.07 <0.0001 Stream dry 3rd Combined 0.69 0.07 – 3rd Wet 0.94 0.49 – 3rd Dry 0.82 0.12 –

k is the Wilk’s lamda statistic computed from the matrix of log ratio differences and p = probability of their being no overall habitat selection. Habitats are listed in order of rank, from most to least selected, with > > indicating a signiﬁcant difference in selection between two habitat types. For vegetation class dry eve. = dry evergreen, mix. dec. = mixed deciduous and dry dip. = dry dipterocarp. For slope class ﬂat = 0–12 degrees, moderate = 13–24 degree and steep = 25–49 degrees. For stream class stream = all area within 150 m from water courses and dry = all area not within 150 m from water courses.

may increase home range sizes in the wet season 4.2. Habitat selection (Rabinowitz, 1989; Grassman, 1999). This is supported by data in this study from one male leopard which had larger home Discerning leopard resource requirements by identifying ranges recorded in the wet season (72.3 ± 4.16 km2) compared what habitat types individuals are selecting, presumes that to the dry season (55.2 ± 21 km2). Grassman (1999) suggested animals choose those which will increase survival and repro- that this effect may be in response to relatively diffuse distri- duction potentials. However, when attempting to characterize bution of prey in the wet season, but in HKK adult female home selection there may be problems associated with two main ranges recorded in our study, which should also reflect prey underlying assumptions: that recorded observations can be density (Bailey, 1993), were not affected in the same way. used to infer habitat selection, and that evidence of selection The mean MCP home range for adult females reported in is related to fitness and population growth (Porter and this study (22.8 km2 ± 8.6) is the largest so far recorded in Asia. Church, 1987; Alldredge and Ratti, 1992; Garshelis, 2000). Nev- Compared to the other studies in Thailand, Rabinowitz (1989) ertheless, investigating how a species selects different fea- recorded one MCP home range of 11.4 km2 (42 locations) in tures of its environment is an essential step towards HKK, and Grassman (2000) one of 8.8 km2 (92 locations) in assigning importance to those features, with the acknowl- Kaeng Krachan National Park. Rabinowitz’s (1989) female edged caveat that subsequent measurements of survival leopard home range size was within the range recorded in and reproduction parameters will be needed (Garshelis, 2000). our study, but substantially below the mean. Likewise we re- This study focused on daytime habitat selection and leop- corded larger adult male MCP home ranges (44 km2 ± 14.6 and ards may select habitat differently at the third order level dur- 29.4 km2) compared to 27 km2 (Rabinowitz, 1989) and 17.3– ing the night; an area for further research. A larger sample 18 km2 (Grassman, 1999). However, the difference between size would be required to determine variation in selection studies was probably also influenced by study time and sam- for different demographic groups; in this study male, female, ple size. adult and sub-adult leopards were pooled together. The individual home ranges we recorded showed high var- Also, the inferences of the selection results would have iability and are probably underestimates, considering that on been strengthened by understanding prey distribution with average we did not acquire locations on 55.5% of available respect to habitat types, but no comparable prey abundance tracking days for each leopard. The use of satellite collars in data set was collected during the study period. future studies should make comparisons between data sets Acknowledging the caveats, the results of this study sug- easier, cut necessary study time and avoid some of the prob- gest that mixed deciduous and dry evergreen are potentially lems associated with missing data and telemetry error. important habitats for leopards, which selected home ranges The density estimate of 7 adult female leopards/100 km2 within the landscape with proportionally more of those vege- recorded is still probably a conservative estimate of carrying tation types than available for the combined and wet season capacity, considering there may have been undetected indi- periods. However, the third order selection was more ambig- viduals residing in the same area. uous; within wet season home ranges (the only significant 28 Author's personal copy

BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250 2249

result), dry dipterocarp was ranked fist, followed by dry ever- Plant Conservation Department. Additional funding for the green and mixed deciduous, but the difference between each project came through generous donations from World Wild- rankings was not significant. life Fund (Thailand). We are very grateful to the hard work The results for the slope and stream habitat classes were of many people that assisted with data collection in the more conclusive; low gradient and stream areas seem to be field. These included assistant researchers Prathom important landscape features for leopards when choosing a Boontawee, Preecha Prommakun, and Somporn Pakpien home range. This result is consistent with the findings of and Khao Nang Rum staff Onsa Norrasan, Saiphech Ngoprasert et al. (2007) who found that leopard use of habitat Toomnoi and Thaworn Thadwijit. Dr. Utis Kutintara was greater closer to streams. Within home ranges leopards (Kasetsart University), Chatchawan Pisdamkham (former also selected low gradient areas but did not show any prefer- chief of HKK) and Dr. Alan Rabinowitz, (Wildlife Conserva- ence to stream habitat. However, stream and slope class are tion Society) all provided valuable guidance to this study. probably correlated to some degree so these results should This preparation of this paper was aided by Namkhang not be considered as totally independent. Saelee and improved by comments from Dave Garshelis, In general, analysis of the pooled data correctly identified Peter Cutter, Francie Cuthbert and Christina Greenwood. selected habitat types identified by the seasonal data. However, for second order wet season selection, mixed deciduous and dry evergreen were correctly identified as important but were ranked differently. For third order selection of vege- REFERENCES tation, the pooled data failed to identify the wet season selection but, as noted before, this result was not conclusive. Aebischer, N.J., Robertson, P.A., Kenward, R.E., 1993. 4.3. Conservation implications Compositional analysis of habitat use from animal radio- tracking data. Ecology 74, 1313–1325. Aitchison, J., 1986. The statistical analysis of compositional data. Thailand’s protected area system mostly comprises small, Chapman and Hall, London, England. fragmented, rugged, higher altitude forest areas. With the Alldredge, J.R., Ratti, J.T., 1992. Further comparison of some exception of parts of the Western Forest Complex, including statistical techniques for analysis of resource selection. the study area, large, extensive, contiguous lowland forest Journal of Wildlife Management 56, 1–9. areas are now mostly gone due to conversion into agricultural Bailey, T.N., 1993. The African Leopard. Ecology and behavior of a land (Trisurat, 2006). The continued fragmentation of remain- solitary field. Colombia University Press, New York. Block, W.M., Brennan, L.A., 1999. The habitat concept in ing forests is a serious threat to Thailand’s large mammal fauna ornithology: theory and applications. Current Ornithology 11, (Pattanavibool and Dearden, 2002). Other leopard-range coun- 35–91. tries in Southeast Asia that still have large areas of lowland for- Dickson, B.G., Beier, L.R., 2002. Home-range and habitat selection est should consider initiating conservation strategies that by adult cougars in Southern California. Journal of Wildlife ensure good representation of this rich and diverse habitat. Management 66, 1235–1245. Even though much of the potential leopard habitat in Thai- Garshelis, D.L., 2000. Delusions in habitat evaluation: measuring use, selection, and importance. In: Boitani, L., Fuller, T.K. land is legally protected, wildlife managers must still decide (Eds.), Research Techniques in Animal Ecology; Controversies how to prioritize use of limited resources. Focusing conserva- and Consequences. Columbia University Press, New York, pp. tion efforts on umbrella species such as tiger, leopard and ele- 111–164. phant may be the most effective and immediately available Grassman, L.I., 1999. Ecology and behavior of the Indochinese strategy to improve future prospects for much of the remain- leopard in Kaeng Krachan National Park, Thailand. Natural ing terrestrial ecosystems in the country. Based on the rough History Bulletin of the Siam Society 47, 77–93. density estimates in this study, the 2780 km2 of HKK alone Harris, S.W., Cresswell, J., Forde, P.G., Trewhella, W.J., Woollard, T., Wray, S., 1990. Home range analysis using could hold at least 195 adult female leopards, and the 2 radio-tracking data; a review of problems and techniques 18,727 km of WEFCOM many times that number. This high- particularly as applied to mammals. Mammal Review 20, lights the area’s importance as a priority conservation area 97–123. for leopards as well as for tigers (Simcharoen et al., 2007). Jenny, D., 1996. Spatial organization of leopards (Panthera pardus) The leopard home range and habitat selection data pre- in Tai National Park, Ivory Coast: is rain forest habitat a sented in this study can be used to identify other areas within tropical haven? Journal of Zoology 240, 427–440. the existing protected areas network important for leopards. Johnson, D.H., 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, This information, in combination with previously accumu- 65–71. lated knowledge on leopards from the region (Rabinowitz, Karanth, K.U., Sunquist, M.E., 1995. Prey selection by tiger, leopard 1989; Grassman, 1999; Ngoprasert et al., 2007), should contrib- and dhole in tropical forests. Journal of Animal Ecology 64, ute to the formulation of a national landscape-level conserva- 439–450. tion plan for the species. Karanth, K.U., Sunquist, M.E., 2000. Behavioral correlates of predation by tiger (Panthera tigris), leopard (Panthera pardus) and dhole (Cuon alpinus) in Nagarahole, India. Journal of Acknowledgments Zoology 250, 255–265. Kernohan, B.J., Gitzen, R.A., Millspaugh, J.J., 2001. Analysis of animal space use and movements. In: Marzluff, J.M. (Ed.), This research was only possible with the kind permission Radio Tracking and Animal Populations. Academic Press, San and logistical support of the National Parks, Wildlife and Diego. 29

Author's personal copy

2250 BIOLOGICAL CONSERVATION 141 (2008) 2242– 2250

Leban, F., 1999. Resource Selection for Windows 1.00. Sanderson, E., Forrest, J., Loucks, C., Ginsberg, J., Dinerstein, E., Legendre, P., 1993. Spatial autocorrelation: trouble or new Seidensticker, J., Leimgruber, P., Songer, M., Heydlauff, A., paradigm? Ecology 74, 1659–1673. O’Brien, T., Bryja, G., Klenzendorf, S., Wikramanayake, E., 2006. Marker, L.L., Dickman, A.J., 2005. Factors affecting leopard Setting Priorities for the Conservation and Recovery of Wild (Panthera pardus) spatial ecology, with particular reference to Tigers: 2005–2015. The Technical Assessment. WCS, WWF, Namibian farmlands. South African Journal of Wildlife Smithsonian, NFWF–STF, Washington DC. Research 35, 105–115. Schooley, R.L., 1994. Annual Variation in Habitat Selection: Mech, L.D., 1983. Handbook of animal radio-tracking. University Patterns Concealed by Pooled Data. Journal of Wildlife of Minnesota Press, Minneapolis. Management 58, 367–374. Mizutani, F., Jewell, P.A., 1998. Home-range and movements of Seaman, D.E., Powell, R.F., 1996. An evaluation of the accuracy of leopards on a livestock ranch in Kenya. Journal of Zoology 244, kernel estimators for home range analysis. Ecology 77, 2075– 269–286. 2085. Ngoprasert, D., Lynam, A.J., Gale, G.A., 2007. Human disturbance Seidensticker, J., 1976. On the ecological separation between affects habitat use and behaviour of Asiatic leopard Panthera tigers and leopards. Biotropica 8, 225–234. pardus in Kaeng Krachan National Park Thailand. Oryx 41, 343– Silverman, B.W., 1986. Density estimation for statistics and data 351. analysis. Chapman and Hall, London, United Kingdom. Nielson, S.E., Stenhouse, G.B., Boyce, M.S., 2006. A habitat-based Simcharoen, S., Pattanavibool, A., Karanth, K.U., Nichols, J.D., framework for grizzly bear conservation in Alberta. Biological Kumar, N.S., 2007. How many tigers Panthera tigris are there in Conservation 130, 217–229. Huai Kha Khaeng Wildlife Sanctuary, Thailand? An estimate Norton, P.M., Lawson, A.B., 1985. Radio tracking of leopards and using photographic capture-recapture sampling. Oryx 41, 447– caracals in the Stellenbosch area, Cape Province. South 453. African Journal of Wildlife Research 15, 17–24. Srikosamatara, S., 1993. Density and biomass of large herbivores Odden, M., Wegge, P., 2005. Spacing and activity patterns of and other mammals in a dry tropical forest, western Thailand. leopards Panthera pardus in the Royal Bardia National Park, Journal of Tropical Ecology 9, 33–43. Nepal. Wildlife Biology 11, 145–152. Sunquist, M., 1983. Dispersal of three radio tagged leopards. Pattanavibool, A., Dearden, P., 2002. Fragmentation and wildlife in Journal of Mammalogy 64, 337–341. montane evergreen forests, northern Thailand. Biological Trisurat, Y., 2006. Applying gap analysis and a comparison index Conservation 107, 155–164. to evaluate protected areas in Thailand. Environmental Porter, W.F., Church, K.E., 1987. Effects of environmental pattern Management 39, 235–245. on habitat preference analysis. Journal of Wildlife Uphyrkina, O., Johnson, W.E., Quigley, H., Miquelle, D., Marker, L., Management 51, 681–685. Bush, M., O’Brian, S.J., 2001. Phylogenetics, genome diversity Rabinowitz, A., 1989. The density and behavior of large cats in a and origin of modern leopard, Panthera pardus. Molecular dry tropical forest mosaic in Huai Kha Khaeng Wildlife Ecology 10, 2617–2633. Sanctuary, Thailand. Natural History Bulletin of the Siam Walker, S., Rabinowitz, A., 1992. The small-mammal community Society 37 (2), 235–251. of a dry-tropical forest in central Thailand. Journal of Tropical Rabinowitz, A., Walker, S.R., 1991. The carnivore community in a Ecology 8, 57–71. dry tropical forest mosaic in Huai Kha Khaeng Wildlife Weber, W., Rabinowitz, A., 1996. A global perspective on large Sanctuary, Thailand. Journal of Tropical Ecology 7, 37–47. carnivore conservation. Conservation Biology 10, Rettie, J.W., McLoughlin, P.D., 1999. Overcoming radio telemetry 1046–1054. bias in habitat-selection studies. Canadian Journal of Zoology Worton, B.J., 1989. Kernel methods for estimating the utilization 77, 1175–1184. distribution in home range studies. Ecology 70, 164–168. 30

Female tiger Panthera tigris home range size and prey abundance: important metrics for management

A CHARA S IMCHAROEN,TOMMASO S AVINI,GEORGE A. GALE,SAKSIT S IMCHAROEN S OMPHOT D UANGCHANTRASIRI,SOMPORN P AKPIEN and J AMES L.D. SMITH

Abstract Tigers Panthera tigris are highly threatened South Asia to the temperate forests of Far East Russia and continue to decline across their entire range. Actions (Sunquist et al., 1999). However, the species is highly to restore and conserve populations need to be based on threatened across its entire range by poaching, habitat loss science but, in South-east Asia, information on ecology and prey depletion (Kenny et al., 1995; Wikramanayake and behaviour of tigers is lacking. This study reports the et al., 1998; Karanth et al., 2004). Its distribution is greatly relationship between the home range size of female tigers restricted; populations that are viable, at least in the short and prey abundance, using data from radio-collared tigers in term, occur only in Bangladesh, Bhutan, India, Indonesia, Huai Kha Khaeng Wildlife Sanctuary, Thailand, and Malaysia, Nepal, Russia, and Thailand (Seidensticker, 2010; published data from other studies. A total of 11 tigers, four Walston et al., 2010). The species is extinct in Bali, Java, males and seven females, were fitted with global position- southern China and central Asia, and populations in other ing system collars, to estimate home ranges using 95 and range countries are now severely reduced and at imminent 100% minimum convex polygons (MCP). Prey abundance risk of extinction because of their small size (Carroll & was estimated by faecal accumulation rates. The mean home Miquelle, 2006). range size of male tigers was 267 and 294 km2 based on 95 and In response to these significant declines wildlife 100% MCPs, respectively; the mean female home range size managers are attempting a number of strategies, alone or was 70 and 84 km2, respectively. Territories of male and in combination, to increase the number of tigers. Efforts female tigers had little overlap, which indicated both sexes include restoring tiger habitat, re-establishing habitat were territorial. Mean densities of the prey species sambar connectivity, restoring prey populations, reducing poaching Rusa unicolor, barking deer Muntiacus muntjac and large of tigers and their prey, and reintroducing animals to the bovids were 7.5, 3.5 and 3.0 km−2, respectively. When female wild (Seidensticker, 2010). All of these management options home range size and prey abundance were compared at six require an understanding of the relationship of tigers to locations in Thailand, and at other sites in India, Nepal, their prey, and of the influence of ecological variables on Bangladesh and Russia, a significant negative correlation both tigers and their prey. Although ecological variables can was found between prey abundance and home range size. affect tigers directly, many are likely to have a pronounced Monitoring this relationship can provide managers with influence on the density of the tiger’s prey. Current models metrics for setting conservation goals. suggest that, in the absence of heavy poaching, the density of tigers is primarily determined by prey abundance Keywords Huai Kha Khaeng Wildlife Sanctuary, Panthera (Karanth et al., 2004; Wegge et al., 2009; Harihar et al., tigris, protected area management, satellite telemetry 2009). Documentation of a positive relationship between tiger populations and their prey does not, however, explain the behavioural mechanisms underlying population growth Introduction or resilience. As reported for many solitary felids, breeding female tigers in Nepal and Russia occupy defended home he tiger Panthera tigris occurs in a wide range ranges (Seidensticker, 1976; Sunquist, 1981; Smith et al., 1987; of climates and habitats, from the tropical forests of T Goodrich et al., 2010) and individual females have the sole responsibility for raising their young, using prey they acquire within their home range. Although prey abundance ACHARA SIMCHAROEN* (Corresponding author), TOMMASO SAVINI and GEORGE A. GALE School of Bioresources and Technology, King varies widely between these two countries, females in both Mongkut’s University of Technology, Thonburi, Thailand. E-mail simtom@ localities are usually able to establish breeding territories windowslive.com large enough to raise young. Because previous studies have SAKSIT SIMCHAROEN,SOMPHOT DUANGCHANTRASIRI and SOMPORN PAKPIEN demonstrated that female home range size is a function Department of National Parks, Wildlife and Plant Conservation, Thailand of prey density (Smith et al., 1987; Miquelle et al., 2010) we JAMES L.D. SMITH Department of Fisheries, Wildlife and Conservation Biology, ’ University of Minnesota, USA hypothesize that this relationship holds across the tiger s range and that mean female home range size may be used *Also at: Department of National Parks, Wildlife and Plant Conservation, Thailand to calculate local carrying capacity for tigers. Received 31 May 2012. Revision requested 13 August 2012. The number of female territories in a population Accepted 10 October 2012. determines that population’s recruitment potential

2 A. Simcharoen et al.

and ultimately its viability and resilience. Thus, a measurement of the number of breeding female tigers that can exist in a given area (i.e. carrying capacity) is a useful metric for managers. This measurement would be particularly useful if tiger carrying capacity could be approximated by estimating the abundance of prey in a unit of habitat. With this information, managers would be able to predict the response of breeding female home range sizes to changes in prey abundance, as well as to identify if tiger poaching, prey poaching or habitat quality were limiting tiger population size. We used data from six resident breeding female tigers ﬁtted with global positioning system (GPS) satellite collars in Huai Kha Khaeng Wildlife Sanctuary in western Thailand during 2005–2011 to explore the relationship between female home range size and prey density. We compared our results to those from studies in four other protected areas in which tiger home range size and prey abundance have been previously estimated. The objectives of this study are to (1) estimate home range size and home range overlap of tigers in western Thailand, (2) correlate female tiger home range size and prey density across the tiger’s range, and (3) demonstrate the signiﬁcance of female home range size and prey abundance as two important metrics for reserve managers.

Study area

FIG. 1 Huai Kha Khaeng Wildlife Sanctuary, with the prey The study was conducted in the Huai Kha Khaeng Wildlife survey transects in six female tiger home ranges. The Sanctuary (Fig. 1), which, in conjunction with the wildlife dark-shaded area on the inset indicates the location of the sanctuaries Thung Yai East and West, is designated as a main map in Thailand, on the border with Myanmar. UNESCO World Heritage Site. This protected area complex supports the largest tiger population in Thailand (DNP, tiger prey species occur in high numbers in the area: banteng 2010). In Huai Kha Khaeng Wildlife Sanctuary the estimated Bos javanicus, gaur Bos gaurus, sambar Rusa unicolor and tiger density is 3.98 per 100 km2 (Simcharoen et al., 2007). wild boar Sus scrofa (Srikosamatara, 1993). The sanctuary is 2,780 km2, altitudes are 200–1,600 m, annual temperature range is 8–38° C and mean annual Methods rainfall is 1,375 mm (Khao Nang Rum Wildlife Research Station, unpubl. data). Normally the lowest temperatures Capture and radio collaring occur in January and the highest in April. The dry season (November–April) has a mean rainfall of 298 mm and the During June 2005–August 2011 we captured tigers in parts wet season (May–October) a mean of 1,088 mm. The area of Huai Kha Khaeng Wildlife Sanctuary that represent has four main vegetation types, the occurrence of which the range of habitat types and topographic features in depends on rainfall patterns and edaphic conditions: the Sanctuary. Healthy live domestic cows were placed mixed deciduous forest (48%), dry evergreen forest (25%), along trails where repeated recent tiger scent marks hill evergreen forest (13%) and dry deciduous dipterocarp occurred. When a cow was killed three or four leg-hold forest (7%; WEFCOM, 2004). snares were set near the cow to capture the tiger when it In addition to the tiger, other carnivores in the sanctuary returned to feed the next night. The snares were designed include the leopard Panthera pardus, clouded leopard by P. Pratumratanatarn, an experienced technician with Neofelis nebulosa, marbled cat Pardofelis marmorata, the Department of National Parks, Wildlife and Plant leopard cat Prionailurus bengalensis, Asiatic jackal Canis Conservation. Captured tigers were anaesthetized with a aureus, wild dog Cuon alpinus, Asiatic black bear Ursus mixture of tiletamine HCL and zolazepam HCL (Zoletil; thibetanus and sun bear Helarctos malayanus. Four major Virbac Laboratories, Carros, France) at a dose of 4 mg kg−1

http://journals.cambridge.org Downloaded: 25 Mar 2014 IP address: 115.67.101.28 32

Tiger home range and prey abundance 3

(Kreeger & Arnemo, 2007). At the time of capture we prey density is an important factor determining the size recorded sex, age and reproductive status. The age of each of tiger home ranges. However, we include 100% MCP tiger was estimated from tooth eruption, tooth wear and estimates here also to facilitate comparison with other staining. Reproductive status of females was classified as studies. nulliparous if nipples were pink; dark nipples indicated To determine if a female home range was used a female had produced young. Animal handling and exclusively by a single female to obtain food for herself immobilization were undertaken in accordance with the and her young, we defined neighbouring females as those University of Minnesota IACUC protocol 0906A67489. that had adjacent (with insufficient space or evidence for Breeding tigers were fitted with one of three radio collar there to be another female living in between) and potentially models: Advanced Telemetry Systems (ATS) GPS collar overlapping home ranges during the same time period. model G2000 (Isante, Minnesota, USA), Telonics Argos Overlaps were based on the frequency a female used the Terrestrial Transmitter (Mesa, Arizona, USA), or Vectronic same geographical locations as her female neighbours Aerospace GmbH GPS Plus (Berlin, Germany). The ATS (at least once per month). As we did not have data for all collars were programmed to acquire locations every 2 hours. female home ranges surrounding our study females we Telonics and Vectronic collars were programmed to could only estimate the minimum percentage overlap (100% fi acquire locations every hour. All radio collars were MCPs), de ned here as HRij 5 Aij/Ai, where HRij is the released with drop-off mechanisms. ATS and Telonics proportion of individual i’s home range that is overlapped models had to be recovered to download data but the by individual j’s home range, Ai is the area of individual Vectronic model transmitted data via Iridium satellites and i’s home range and Aij is the area of overlap between the the data were available from the Vectronic website. Output two individuals’ home ranges (Kernohan et al., 2001). The of all collars included date, time, latitude, longitude, altitude home range sizes and degree of overlap of radio collared and fix status. We used location data from both two- and tigers were calculated using the ArcView v. 3.3 (ESRI, three-dimensional fixes. Redlands, USA) extension package Home Range (Rodgers & Carr, 1998).

Home range size and overlap Prey abundance

We used 95 and 100% minimum convex polygons (MCP) We estimated prey abundance during the dry season to estimate home range size of tigers (Jennrich & Turner, (December–April) for 2009–2011. In addition, prey abun- 1969). Because the 100% MCP can be influenced by outliers, dance in the entire Sanctuary has been monitored since the 95% MCP is often preferred, to avoid inflation of 2006 as part of sanctuary management; these data sug- the estimate (Harris et al., 1990; White & Garrott, 1990). gested that prey density was approximately constant during The MCP is also the most widely used home range estimator 2006–2011 (Wildlife Conservation Society, unpubl. data). (Harris et al., 1990), which facilitates comparisons with We used faecal accumulation rate techniques (Bailey & other studies. To determine the number of locations needed Putman, 1981) to estimate prey abundance in the Sanctuary. to obtain stable home range areas we conducted a bootstrap Four c. 100 km2 areas that encompassed the six female tiger simulation in which home range size was estimated from home ranges were selected to represent the range of habitat randomly selected locations. We began with 50 locations types and topographic features in the Sanctuary. Two sites and increased the sample size by 50 until all radio locations were in the central valley of the Sanctuary along the lower were included in the calculations. We considered the and upper portions of the Huai Kha Khaeng River. The home range established when the number of days or the lower site was characterized by mixed deciduous forest. number of fixes reached an asymptote (Harris et al., 1990). The upper site was a combination of mixed deciduous The kernel method, in contrast to the MCP, is best as an and dry evergreen forest and the habitat was more rugged estimator of the probability of use within a home range than at the lower site. A third site, near the Khao Nang Rum and thus can address questions related to third-order Wildlife Research Station, was a drier area away from the selection (sensu Johnson, 1980). But the kernel method can Huai Kha Khaeng River. It was composed of a combination be problematic for estimating home range size, especially of dry dipterocarp and mixed deciduous habitat. The fourth as the number of locations increases with the use of GPS site was in the northern part of the Sanctuary, and also collars (Downs & Horner, 2008). Kernel estimates are away from the Huai Kha Khaeng River. It was characterized particularly inflated when there is a large number of by mixed deciduous and dry evergreen habitat in locations along a ‘hard’ boundary, which is often the case rugged terrain. for animals, such as tigers, that defend home ranges (Downs A total of 90 square line transects, 200 × 200 m, were & Horner, 2008). Therefore, we believe that the 95% MCP randomly located in each of the four sites (Fig. 1) to survey is a suitable estimator for testing our hypothesis that gaur and banteng, as dung of these species is relatively

4 A. Simcharoen et al.

rare. In addition, 40 circular plots of 20 m2 were placed 20 m between female home range size and prey abundance at the apart on each of these transects, to sample the more local level. Across the tiger’s range we reviewed information common ungulates (sambar, barking deer Muntiacus on tiger home range size and prey abundance from five muntjac and wild boar). All ungulate pellet groups were studies in Nepal, India, Bangladesh and Russia that had counted and removed from all transects and circular plots. used radio telemetry to estimate home range size. In all Following removal, new faecal accumulation was measured studies home range size was estimated by either the 95 or for a period of 30 days (over this time period no entire 100% MCP. Using data from earlier studies we estimated pellet groups were lost to decomposition or other natural prey biomass for each site as the product of prey density processes during the dry season). A pellet group consisted and mean body weight (Dhungel & O’Gara, 1991; Karanth of .15 pellets. Pellet groups that overlapped were dis- & Sunquist, 1992). We then used simple linear regression to tinguished by size and colour, which became lighter as predict home range size in relation to prey abundance at the pellets aged. local level and Spearman’s rank correlation to determine if We searched for gaur and banteng dung piles up to 2 m the same relationship holds at a range-wide level (our results either side of the transects. Observed piles were counted from Huai Kha Khaeng Wildlife Sanctuary combined with and marked, and the perpendicular distance from the published data). Probabilities , 0.05 were considered transect was measured. We could not distinguish gaur and significant. banteng dung and therefore combined the data, as ‘large bovid’. To determine absolute abundance of prey species we used published defecation rates of large bovids (9.5 dung Results piles per day) and barking deer (7.5 pellet groups per day; Srikosamatara, 1993; Sukmasuang, 2001). For sambar we Home range size and overlap recorded the defecation rate of a group of six animals of A total of 11 tigers, seven resident breeding females and four mixed sex and age (one male, two females and three young) resident breeding males, were fitted with GPS collars. One at Khao Pratapchang Wildlife Breeding Centre. To control female and one male were collared twice, at separate times, for possible seasonal differences in defecation rate, observa- and home range estimates were made for each period for tions at the Centre were made in the same season as the field each individual as size and shape of home ranges changed study. During a 5-day period deer grazed undisturbed; in a over time. The mean number of days used for home range second 5-day period deer were stimulated so that they were analysis was 152 ± SD 72 days (range 77–358 days). The mean more active than normal. Deer were fed a diet of natural time to reach an asymptote in home range size was browse that was cut near the breeding centre, to provide 77 ± SD 35 days and the mean number of fixes to reach an a diet similar in species and cellulose composition to their asymptote was 418 ± SD 160 locations. The mean number of natural diet. The mean number of pellet groups per locations used to estimate home range was 1,502 ± SD 1,215. individual per day was 9.5 ± SD 1.41 (n 5 6). We calculated Mean home range (95% MCP) of the resident females deer and barking deer density as D 5 (P/A)/(Df Ds), where (n 5 8) was 70.2 ± SD 33.2 km2 and that of the resident P 5 the number of pellet groups found on the second visit, males was 267.6 ± SD 92.4 km2 (n 5 5; Table 1). Mean home A 5 the total sample area (km2), Df 5 the defecation rate range estimated with the 100% MCP was 84.2 ± SD 40.8 km2 and Ds 5 the number of days between the two visits. (n 5 8) and 294.1 ± SD 100.3 km2 (n 5 5) for the resident Distance v. 5.0 (Thomas et al., 2006) was used to estimate the males and females, respectively (Table 1). The ratio of male density of dung, which was then used to estimate large bovid to female tiger home range size was 3.8 : 1. abundance. Data were right truncated at 1.2 m and grouped Home range overlap was examined from three pairs of into 0.2 m intervals to improve model fit. Uniform and adjacent females and one pair of adjacent males. Mean half-normal models for the detection function were fitted overlap was 4.5 ± SD 3% for the females and 18% for the against the data, using cosine adjustments (Buckland et al., males. We used five female home ranges within three male 2001). The best model selection and adjustment terms were home ranges to examine overlap between male home ranges based on Akaike’s Information Criterion (AIC; Akaike, and sympatric female home ranges. Male home ranges 1974). We calculated large bovid density BD 5 Dd/(DfDs), overlapped nearly the entire sympatric home range of the where Dd 5 bovid dung density, Df 5 defecation rate of females (90 ± SD 15%, n 5 5). bovids, and Ds 5 number of days between the two visits.

Prey abundance Home range size and prey abundance Mean densities of sambar and barking deer were We estimated prey biomass in six resident female tiger 7.5 ± SD 6 and 3.5 ± SD 2.6 km−2 over the four sites. Our home ranges in the Sanctuary to determine the correlation sample of wild boar dung (n 5 77) was insuﬃcient for

http://journals.cambridge.org Downloaded: 25 Mar 2014 IP address: 115.67.101.28 34

Tiger home range and prey abundance 5

TABLE 1 Estimates, using 95 and 100% minimum convex polygons (MCP), of the home range size of 11 individual collared tigers (one male and one female were collared twice) in Huai Kha Khaeng Wildlife Sanctuary, Thailand (Fig. 1).

Home range size (km2) Tiger Data collection period No. of ﬁxes 95% MCP 100% MCP Male 1 May–Aug. 2005 542 246.1 291.2 Male 2 Feb.–Oct. 2008 333 174.8 197.0 Male 3 Apr.–June 2009 1,235 281.2 289.3 Male 4 Apr.–July 2009 1,899 218.5 234.0 Male 5 June–Nov. 2011 2,659 417.5 459.0 Female 1 Feb.–July 2005 470 57.0 69.84 Female 2 Jan.–Dec. 2005 529 52.9 78.2 Female 3 Feb. 2007–Feb. 2008 738 122.3 155.9 Female 4 Feb.–June 2010 1,537 117.1 133.5 Female 5 Mar.–July 2010 2,126 61.6 75.3 Female 6 Dec. 2009–July 2010 4,585 31.0 36.6 Female 7 Aug.–Nov. 2010 608 75.6 78.3 Female 8 May.–Nov. 2011 2,274 44.1 46.0 precise density estimation and therefore we used the density of wild boar in the Sanctuary (2.4 ± SD 0.05 km−2) estimated by Sukmasuang (2001). To estimate large bovid density by distance sampling we ﬁrst tested models of dung observability based on distance from the transects. During the surveys 105 bovid dung piles were detected. The best model for observability based on the lowest AIC score was a uniform cosine distribution. The bovid dung density was estimated to be 860 ± SD 126 km−2 and, from this, bovid density was estimated to be 3.0 ± SD 0.4 km−2.

Home range size and prey abundance FIG. 2 Correlation between the home range size of six female The simple linear regression indicated there was a sig- tigers and prey biomass in Huai Kha Khaeng Wildlife Sanctuary fi (Fig. 1). The linear regression indicates that the home range ni cant negative correlation between size of female fi 2 size of female tigers is signi cantly correlated (P , 0.05) with home range and prey biomass (r 5 0.70,P, 0.05,n5 6). prey density. This relationship appeared to be log-linear as a log transformation of the home range size gave a better (Litvaitis et al., 1986) and Canadian lynx Lynx canadensis fi 2 t (r 5 0.85,P, 0.05,n5 6; Fig. 2). At a regional scale (Ward & Krebs, 1985). Schaller (1972), Orsdol et al. (1985) (Table 2) we found the same relationship (Spearman and Hayward et al. (2007) also demonstrated this relation- rs 5 0.88,P, 0.005,n5 6). ship for large African felids. To test our hypothesis that a female’s home range Discussion should be large enough to support the prey requirements for a female and her cubs from birth until they disperse at Our results support the hypothesis that variation in the c. 18 months (Smith et al., 1987), we needed a home range home range size of female tigers is partially explained by estimator that defined the area in which a female acquires variation in prey density. The relationship appears to be food. We also needed to test the degree to which females log-linear, probably reflecting the biological upper and have exclusive home ranges. Choice of home range lower limits of home range sizes within the study area. This estimators depends on the question being addressed relationship indicates that home range size of female tigers is (Kie et al., 2010) and therefore we calculated home range relatively constant when prey biomass is . 5,000 kg km−2, as both the 95 and 100% MCPs (Smith et al., 1987; indicating minimum home ranges of 10–20 km2 (Fig. 2). Chundawat et al., 1999; Karanth & Sunquist, 2000; In carnivores a direct relationship between higher prey Goodrich et al., 2010; Barlow et al., 2011). For female tigers abundance and smaller home range sizes has been home ranges are not only related to prey abundance reported in many species, including bobcat Felis rufus but are exclusively used by an individual female.

6 A. Simcharoen et al.

Tigers in Russia and Nepal exhibit territorial behaviour as a strategy to ensure access to adequate prey to raise their young to dispersal age (Smith et al., 1987, Goodrich et al., 2010). This behaviour is also common in other solitary female carnivores (Sandell, 1989). We did not document aggressive behaviour among females as an indicator of territorial behaviour but we did ﬁnd that territorial overlap among females is small. For example, three pairs of adjacent females had an overlap of only 4%, which is similar Reza et al. (2002) Prey study Bhattarai & Kindlman (2012) This study to estimates from Nepal, where the overlap of females was 3.5–7% (Smith et al., 1987), and Russia, where female tigers had a mean overlap of 9% (Goodrich et al., 2010). Factors other than prey, such as poaching of tigers, may reduce competition among females and result in home ranges larger than needed to supply food resources. These conditions may confound the negative relationship between female tiger home range size and prey abundance. When density of tigers was low in Nepal during periods Barlow et al. (2011) Reference Karanth & Sunquist (2000)Smith et al. (1987) Karanth & Sunquist (1995) Goodrich et al. (2010)This study Stephens et al. (2006) Chundawat et al. (1999) Chundawat & Sharma (2008) when habitat was recovering from human disturbance, tiger home ranges were larger than needed and this allowed females to make room for their dispersing daughters (Smith et al., 1987). ) Home range study 2 − In contrast it is widely reported that home range size for male solitary carnivores is not inﬂuenced by food require- s range, including this study. 436 Biomass density (kg km 2,511 ments, as males attempt to hold territories that encompass as many females as possible (Sandell, 1989). The home range size, and thus the number of females that a male may mate with, is constrained by the energy and time requirements expended visiting females to track their reproductive status, and patrolling boundaries to discourage intruding males that may kill cubs (Sandell, 1989). Mean male home range size in Huai Kha Khaeng Wildlife Sanctuary was 3.8 times larger than the mean female home range. This ratio is ) Prey abundance

2 similar to the ratio of home range size between male and

12 Distance sampling 4,297 female tigers in Russia and Nepal (Smith et al., 1987; Goodrich et al., 2010). Although our sample size for the correlation between home range and prey is small, it is currently the best MCP home range size (km information available. Tracking the relationship between female home range size and prey abundance is a useful tool for managers because it provides them with a metric for 2 7 54 21 Distance sampling 4,257 setting management goals. Hayward et al. (2007) suggested that the next step in reﬁning the relationship between female home range size and prey abundance is to analyse the Male Female Male Female Method No. of animals 1 1 243 27 Distance sampling 4,057 1 1 32 17 Distance sampling 5,482 6 21 1,160 394ecological Track count factors that predict prey abundance. Detailed information on female home range size, prey abundance and the ecological factors that determine prey abundance will allow managers to develop appropriate management actions. For example, when female home range sizes are larger than needed to supply adequate prey for breeding females to raise their young, managers should investigate the possibility that tigers are being poached. Alternatively, 2 Summary data for tiger home range size and prey abundance at six study sites across the tiger ’ when prey abundance is below the potential carrying ABLE Panna, India T Study site Nagarahole, India Chitwan, Nepal Sundarbans, Bangladesh Sikhote-Alin, Russia Huai Kha Khaeng, Thailand 4 7capacity 267 predicted 70by Pellet count environmental variables, managers

http://journals.cambridge.org Downloaded: 25 Mar 2014 IP address: 115.67.101.28 36

Tiger home range and prey abundance 7

should examine whether prey are being poached. In this mangrove ecosystem for the species’ conservation. Oryx, contrast, if the mean size of home ranges is at the minimum 45, 125–128. size given the prey density, then the only management BHATTARAI, B.P. & KINDLMANN,P.(2012) Habitat heterogeneity as the key determinant of the abundance and habitat preference option to increase the viability of the tiger population is to of prey species of tiger in the Chitwan National Park, Nepal. enlarge the area available. If in the next few years female Acta Theriologica, 57, 89–97. home range size increases in Huai Kha Khaeng Wildlife BUCKLAND, S.T., ANDERSON, D.R., BURNHAM, K.P., LAAKE, J.L., Sanctuary, managers should investigate if it is because of BORCHERS, D.L. & THOMAS,L.(2001) Introduction to Distance declining prey abundance or an increase in tiger poaching. Sampling: Estimating Abundance of Biological Populations. Oxford University Press, Oxford, UK. Faced with continued threats to tigers, conservationists CARROLL,C.&MIQUELLE, D.G. (2006) Spatial viability analysis of have focused on strategies that emphasize conserving source Amur tiger Panthera tigris altaica in the Russian Far East: the role of populations (Walston et al., 2010) or entire landscapes protected areas and landscape matrix in population persistence. (Sanderson, 2010). These efforts provide important guide- Journal of Applied Ecology, 43, 1056–1068. lines, and strategic conservation planning is essential. CHAPRON, G., MIQUELLE, D.G., LAMBERT, A., GOODRICH, J.M., LEGENDRE, S. & CLOBERT,J.(2008) The impact on tigers of Poaching, reduced carrying capacity and the loss of the poaching versus prey depletion. Journal of Applied Ecology, fi land area available to tigers have been identi ed as the key 45, 1667–1674. threats to the species (Kenny et al., 1995; Chapron et al., CHUNDAWAT, R.S., GOGATE, N. & JOHNSINGH, A.J.T. (1999) 2008; Seidensticker, 2010). Managers can use the relation- Tigers in Panna: preliminary results from an Indian tropical ship between female home range size and prey abundance, dry forest. In Riding the Tiger: Tiger Conservation in Human-Dominated Landscapes (eds J. Seidensticker, S. Christie along with long-term camera-trap monitoring of tiger – fi & P. Jackson), pp. 123 129. Cambridge University Press, populations, to examine the threats speci c to tigers in each Cambridge, UK. protected area. CHUNDAWAT, R.S. & SHARMA,K.(2008) Tiger prey in a tropical dry forest: an assessment of abundance and of biomass estimation derived from distance sampling. Journal of the Bombay Natural Acknowledgements History Society, 105, 64–72. DHUNGEL, S.K. & O’GARA, B.W. (1991) Ecology of the hog deer in We acknowledge the Department of National Parks, Royal Chitwan National Park, Nepal. Wildlife Monographs, 119, 3–40. Wildlife and Plant Conservation, and especially DNP (2010) Thailand Tiger Action Plan 2010–2022. Department of Chatchawan Pitdumkam and Theerapat Prayurasiddhi, for National Parks, Wildlife and Plant Conservation, Bangkok, supporting this work. We also appreciate the help provided Thailand. by the former chiefs of Huai Kha Khaeng Wildlife DOWNS, J.A. & HORNER, M.W. (2008)Effects of point pattern Sanctuary, Sunthorn Chaiwatana and Apicha Yusombune. shape on home-range estimates. Journal of Wildlife Management, – Funding was provided by PTTEP (Thailand), the U.S. Fish 72, 1813 1818. GOODRICH, J.M., MIQUELLE, D.G., SMIRNOV, E.N., KERLEY, L.L., and Wildlife Service, The Royal Golden Jubilee PhD QUIGLEY, H.B. & HORNOCKER, M.G. (2010) Spatial structure Programme (Thailand) and the Biodiversity Research and of Amur (Siberian) tigers (Panthera tigris altaica) on Sikhote-alin Training Programme (Thailand). We are also grateful to Biosphere Zapovednik, Russia. Journal of Mammalogy, 91, 737–748. research assistants Onsa Norasarn, Thavorn Thadvichit, HARIHAR, A., PANDAV,B.&GOPAL, S.P. (2009) Responses of tiger Kerkpol Wongchu, Boonyang Srichan, Yingbune (Panthera tigris) and their prey to removal of anthropogenic influences in Rajaji National Park, India. European Journal Chongsomchai, Kerati Phetong, Nattakarn Pengmark, of Wildlife Research, 55, 97–105. Namkang Saelee and Parinyakorn Worawan for their help HARRIS, S., CRESSWELL, W.J., FORDE, P.G., TREWHELLA, W.J., with the capture of tigers and field data collection. We are WOODLARD, T. & W RAY,S.(1990) Home-range analysis using particularly grateful to Francesca J. Cuthbert, Erin Roche, radio-tracking data—a review of problems and techniques Anak Pattanavibool, Vijak Chimchom, John Fieberg particularly as applied to the study of mammals. Mammal Review, 20, 97–123. and Adam Barlow for their comments the article and for HAYWARD, M.W., O’BRIEN,J.&KERLEY, G.I.H. (2007) Carrying suggestions regarding data analysis. capacity of large African predators: predictions and tests. Biological Conservation, 139, 219–229. JENNRICH, R.I. & TURNER, F.B. (1969) Measurement of non-circular References home range. Journal of Theoretical Biology, 22, 227–237. JOHNSON, D.H. 1980. The comparison of usage and availability AKAIKE, H. (1974) A new look at the statistical model identification. measurements for evaluating resource preference. Ecology, 61, 65–71. IEEE Transactions on Automatic Control, 19, 716–723. KARANTH, K.U., NICHOLS, J.D., KUMAR, N.S., LINK, W.A. & HINES, J. BAILEY, R.E. & PUTMAN, R.J. (1981) Estimation of fallow deer E. (2004) Tigers and their prey: predicting carnivore densities from (Dama dama) populations from faecal accumulation. Journal prey abundance. Proceedings of the National Academy of Sciences of Applied Ecology, 18, 697–702. of the United States of America, 101, 4854–4858. BARLOW, A.C.D., SMITH, J.L.D., AHMAD, I.U., HOSSAIN, A.N.M., KARANTH, K.U. & SUNQUIST, M.E. (1992) Population structure, RAHMAN,M.&HOWLADER, A. (2011) Female tiger Panthera tigris density and biomass of large herbivores in the tropical forests home range size in the Bangladesh Sundarbans: the value of of Nagarahole, India. Journal of Tropical Ecology, 8, 21–35.

8 A. Simcharoen et al.

KARANTH, K.U. & SUNQUIST, M.E. (1995) Prey selection by tiger, SMITH, J.L.D., MCDOUGAL, C.W. & SUNQUIST, M.E. (1987) Female leopard and dhole in tropical forests. Journal of Animal Ecology, land tenure system in tigers. In Tigers of the World (eds R.L. Tilson 64, 439–450. & U.S. Seal), pp. 97–109. Noyes Publications, Saddle River, USA. KARANTH, K.U. & SUNQUIST, M.E. (2000) Behavioural correlates SRIKOSAMATARA, S. (1993) Density and biomass of large herbivores of predation by tiger (Panthera tigris), leopard (Panthera pardus) and other mammals in a dry tropical forest, western Thailand. and dhole (Cuon alpinus) in Nagarahole, India. Journal of Zoology, Journal of Tropical Ecology, 9, 33–43. 250, 255–265. STEPHENS, P.A., ZAUMYSLOVA, O.YU., MIQUELLE., D.G., KENNY, J.S., SMITH, J.L.D., STARFIELD, A.M. & MCDOUGAL, C. MYSLENKOV, A.I. & HAYWARD, G.D. (2006) Estimating population (1995) The long-term effects of tiger poaching on population density from indirect sign: track counts and the Formozov– viability. Conservation Biology, 9, 1127–1133. Malyshev–Pereleshin formula. Animal Conservation, 9, 339–348. KERNOHAN, B.J., GITZEN, R.A. & MILLSPAUGH, J.J. (2001) Analysis SUKMASUANG,R.(2001) Ecology of barking deer (Muntiacus spp.) of animal space use and movements. In Radio Tracking and Animal in Huai Kha Khaeng Wildlife Sanctuary. PhD thesis. Kasetsart Populations (eds J.J. Millspaugh & J.M. Marzluff), pp 125–166. University, Bangkok, Thailand. Academic Press, San Diego, USA. SUNQUIST, M.E. (1981) The social organization of tigers (Panthera KIE, J.G., MATTHIOPOULOS, J., FIEBERG, J., POWELL, R.A., tigris) in Royal Chitwan National Park, Nepal. Smithsonian CAGNACCI, F., M ITCHELL, M.S. et al. (2010) The home-range Contributions to Zoology, 336, 1–98. concept: are traditional estimators still relevant with modern SUNQUIST, M.E., KARANTH, K.U. & SUNQUIST,F.(1999) Ecology, telemetry technology? Philosophical Transactions of the Royal behaviour and resilience of the tiger and its conservation needs. Society B, 365, 2221–2231. In Riding the Tiger (eds J.C.S. Seidensticker & P. Jackson), pp. 5–18. KREEGER, T.J. & ARNEMO, J.M. (2007) Handbook of Wildlife Chemical Cambridge University Press, Cambridge, UK. Immobilization. Sunquest Print, China. THOMAS, L., LAAKE, J.L., STRINDBERG, S., MARQUES, F.F.C., LITVAITIS, J.A., SHERBURNE, J.A. & BISSONETTE, J.A. (1986) BUCKLAND, S.T., BORCHERS, D.L. & MARQUES, T.A. (2006) Bobcat habitat use and home range size in relation to prey density. Distance 5.0. Release 2. Research Unit for Wildlife Population The Journal of Wildlife Management, 50, 110–117. Assessment, University of St. Andrews, UK. MIQUELLE, D.G., GOODRICH, J.M., SMIRNOV, E.N., STEPHENS, P.A., WALSTON, J., ROBINSON, J.G., BENNETT, E.L., BREITENMOSER, U., ZAUMYSLOVA, O.Y., CHAPRON, G. et al. (2010) The Amur Tiger: FONSECA, G.A.B., GOODRICH, J. et al. (2010) Bringing the tiger A Case Study of Living on the Edge. Oxford University Press, back from the brink—the six percent solution. PLoS Biology, Oxford, UK. 8,e1000485. ORSDOL, K.G., HANBY, J.P. & BYGOTT, J.D. (1985) Ecological correlates WARD, R.M.P. & KREBS, C.J. (1985) Behavioural response of lynx to of lion social organization (Panthera leo). Journal of Zoology, 206, declining snowshoe hare abundance. Canadian Journal of Zoology, 97–112. 63, 2817–2824. REZA, A.H.M.A., FEEROZ, M.M. & ISLAM, M.A. (2002) Prey species WEFCOM (WESTERN FOREST COMPLEX)(2004) GIS Database and density of Bengal tiger in the Sundarbans. Journal of the Asiatic its Applications for Ecosystem Management. The Western Forest Society of Bangladesh, Science, 28, 35–42. Complex Ecosystem Management Project, Department of National RODGERS, A.R. & CARR, A.P. (1998) HRE: the Home Range Park, Wildlife, and Plant Conservation, Bangkok, Thailand. Extension for Arc View. User’s Manual. Ontario Ministry of Natural WEGGE, P., O DDEN, M., POKHAREL, Pd.C. & STORAAS, T. (2009) Resources, Peterborough, Canada. Predator–prey relationships and responses of ungulates and their SANDELL,M.(1989) The mating tactics and spacing behaviour of predators to the establishment of protected areas: a case study solitary carnivores. In Carnivore Behaviour, Ecology and Evolution of tigers, leopards and their prey in Bardia National Park, Nepal. (ed. J.L. Gittleman), pp. 164–182. Cornell University Press, Biological Conservation, 142, 189–202. New York, USA. WHITE, G.C. & GARROTT, R.A. (1990). Analysis of Wildlife Radio SANDERSON, E.W. (2010) Setting priorities for tiger conservation: Tracking Data. Academic Press, San Diego, USA. 2005–2015. In Tigers of the World: The Science, Politics, and WIKRAMANAYAKE, E.D., DINERSTEIN, E., ROBINSON, J.G., Conservation of Panthera tigris (eds R. Tilson & P.J. Nyhus), KARANTH, U., RABINOWITZ, A., OLSON, D. et al. (1998) An pp. 143–161. Academic Press, San Diego, USA. ecology-based method for defining priorities for large mammal SCHALLER, G.B. (1972) The Serengeti Lion: A Study of Predator–Prey conservation: the tiger as case study. Conservation Biology, Relations. University of Chicago Press, Chicago, USA. 12, 865–878. SEAMAN, D.E. & POWELL, R.A. (1996) An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology, 77, 2075–2085. Biographical sketches SEIDENSTICKER,J.(1976) On the ecological separation between tigers and leopards. Biotropica, 8, 225–234. A CHARA S IMCHAROEN specializes in tiger ecology and tiger SEIDENSTICKER,J.(2010) Saving wild tigers: a case study in biodiversity movement behaviour. T OMMASO S AVINI’ S research includes the loss and challenges to be met for recovery beyond 2010. Integrative behavioural ecology of primates and pheasants. G EORGE A. GALE Zoology, 5, 285–299. conducts research on the ecology and population dynamics of birds SIMCHAROEN, S., PATTANAVIBOOL, A., KARANTH, K.U., and mammals. S AKSIT S IMCHAROEN studies the ecology and habitat NICHOLS, J.D. & KUMAR, N.S. (2007) How many tigers Panthera use of tigers and leopards. S OMPHOT D UANGCHANTRASIRI is in tigris are there in Huai Kha Khaeng Wildlife Sanctuary, Thailand? charge of tiger monitoring in the Western Forest Complex of Thailand. An estimate using photographic capture–recapture sampling. Oryx, S OMPORN P AKPIEN undertakes research on tigers and their prey. 41, 447–453. J AMES L. D. SMITH investigates landscape-scale tiger ecology.

http://journals.cambridge.org Downloaded: 25 Mar 2014 IP address: 115.67.101.28 38

Conservation & Ecology RAFFLES BULLETIN OF ZOOLOGY 62: 100–106 Date of publication: 11 March 2014 http://zoobank.org/urn:lsid:zoobank.org:pub:3528409A-62CE-4F2D-8713-06D47FF98585

Ecological factors that inﬂ uence sambar (Rusa unicolor) distribution and abundance in western Thailand: implications for tiger conservation

Achara Simcharoen1, Tommaso Savini1, George A. Gale1, Erin Roche2, Vijak Chimchome3 & James L. D. Smith4

Abstract. Prey density is declining throughout the tiger’s (Panthera tigris) range and knowledge of the ecological factors that affect prey distribution and abundance remains surprisingly limited for this globally endangered species. In this study, we examined the ecological variables infl uencing the abundance of sambar (Rusa unicolor), the dominant prey species for the tiger across its global southern range. We also identifi ed the scale at which these variables impact sambar distribution in Huai Kha Khaeng Wildlife Sanctuary, a high tiger density site in Southeast Asia. The fecal pellet group accumulation method was used to estimate an index of sambar abundance. Pellet groups were counted along 360 line transects randomly placed among four approximately 100 km2 sites that encompassed six female tiger home ranges. The relationship between sambar pellet-group counts and 10 environmental variables was investigated using generalised linear mixed models. The sambar abundance index was negatively associated with distance to the largest river in the study area, elevation, and the amount of dry deciduous dipterocarp forest cover. Distribution and abundance of sambar were positively associated with relatively fl at areas of river valleys, presumably due to the quality of vegetation available for foraging and greater visibility for detecting predators compared to other portions of the study area. This study is the fi rst to identify the importance of wide alluvial valleys to tiger prey and suggests this habitat is critical for securing one of the largest tiger source populations in Southeast Asia.

Key words. Panthera tigris corbetti, tiger prey, habitat selection, Huai Kha Khaeng Wildlife Sanctuary

INTRODUCTION 1995; Hayward et al., 2012), studies (e.g., Ngampongsai, 1987; Padmalai et al., 2003; Matsubayashi et al., 2007 Approximately 60% of the published studies on tiger Bhattarai & Kindlmann, 2012) have rarely quantiﬁ ed the (Panthera tigris) diet report that the sambar (Rusa unicolor) habitat preferences of the tiger’s primary prey, the sambar. is the dominant prey species in terms of biomass in South Understanding the habitat requirements of this species is and Southeast Asia (e.g., Seidensticker & McDougal, clearly needed to predict the distribution and carrying capacity 1993; Karanth & Sunquist, 1995; Biswas & Sankar, 2002). of tigers, and where appropriate, to manage the habitat to Additionally, Hayward et al. (2012) reported that sambar improve conditions for preferred prey. The sambar is also an are one of the two most preferred prey species throughout important prey species because its widespread distribution the entire range of tigers, and hypothesized that sambar in Asia largely overlaps that of the tiger in South Asia, importance in the tiger diet is a consequence of the nearly South China and Southeast Asia (Corbett & Hill, 1992). 1:1 predator to prey weight ratio between these two species, Across its range, the sambar is highly adaptive; it occurs a common relationship reported for large cats. Ackerman in a wide diversity of habitats, ranging from ocean shores (1986) provided an ecological rationale for this ratio in the to subalpine regions, and consumes a varied diet including mountain lion (Puma concolor) whereby a female needs coarse grasses, woody browse, broad-leaved foliage, fruit, and to kill prey her size, or larger, to meet her own energetic partially submerged water plants (Geist, 1998). In Thailand, requirements as well as those of her maturing offspring sambar is the largest of the cervid species and historically which can weigh as much or more than she does prior to it had the widest distribution in the region (Lekagul & their independence. Despite previous research on tiger diet McNeely, 1977; Francis, 2008). Given the signiﬁ cance of and the proposed optimum prey size (Karanth & Sunquist, sambar in the diet of tigers and widespread documentation of prey depletion across the tiger’s range, including Thailand (Ramakrishnan et al., 1999; Johnson et al., 2006; Datta et

1Conservation Ecology Program, King Mongkut’s University of Technology, Thonburi, al., 2008), a better understanding of the ecological factors Thailand; Email: [email protected] (*corresponding author) that inﬂ uence sambar abundance and spatial distribution is 2Department of Biological Sciences, University of Tulsa, USA needed in critical tiger habitats. The objectives of this study 3Department of Forest Biology, Kasetsart University, Thailand were to: 1) assess an index of abundance and distribution of 4Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, sambar in the Huai Kha Khaeng Wildlife Sanctuary (HKK) USA a key site for tigers in western Thailand; and 2) identify © National University of Singapore ecological factors associated with sambar distribution. This ISSN 2345-7600 (electronic) | ISSN 0217-2445 (print) information will provide conservation managers with a set

100 39

RAFFLES BULLETIN OF ZOOLOGY 2014 of metrics to select and evaluate management actions for within four sites where home ranges of tiger have been sambar and tigers. studied (Fig. 1). This deer survey was primarily designed to investigate food availability in different tiger home ranges MATERIAL AND METHODS to represent the array of ecological diversity within the sanctuary. These sites cover about 14% of the total sanctuary Study area. This study was conducted in HKK, which area and represent the three major forest types present (we is located in western Thailand (15°00'–15°50'N, 99°00'– excluded hill evergreen forest which covers approximately 99°19'E) and represents the core of a forest complex 13% of the sanctuary). Although not optimal, we are confi dent consisting of 17 contiguous protected areas (collectively that data obtained in this survey can be cautiously used to known as the Western Forest Complex or WEFCOM). This explain the sambar distribution in the entire sanctuary. Each region supports the largest tiger population in Thailand (DNP, of the four sites is approximately 100 km2 encompassing six 2010). The sanctuary is 2,780 km2 and elevation varies from female tiger home ranges. These sites were also chosen to 200–1,600 m. It has a tropical monsoonal climate with an represent the range of ecological conditions in HKK that annual temperature ranging from 8°C in January to 38°C might refl ect differences in both deer density and tiger home in April. Total rainfall averages 1,386 mm (2000–2011), range size. Two sites (KBD and KYD) were in the central but most (78%) occurs in the wet season (May–October). valley of the sanctuary along the lower and upper portions From late November until April, dry season forest fi res are of the Huai Kha Khaeng River. The other sites (KNR and common. This seasonal variation in temperature and rainfall KPP) were drier areas away from Huai Kha Khaeng River. results in a general dry deciduous forest mosaic. Depending For this project, deer sampling did not include the full on rainfall patterns, edaphic factors, and fi re frequency, range of the ecosystems found in the WEFCOM, but was four primary vegetation types occur: mixed deciduous limited to a subsample due to the intensive sampling needed forest (48%); dry evergreen forest (25%); hill evergreen to estimate sambar numbers and the limited budget and forest (13%); and dry deciduous dipterocarp forest (7%) personnel. Specifi cally, we did not sample the roughly 22% (WEFCOM, 2004). A central feature of HKK is the 100 of HKK above 900 m because sambar are rarely observed km long Huai Kha Khaeng River, which drains the central in this steep terrain (Trisurat et al., 2010). At each site we valley from north to south (Fig. 1). Many temporary and two smaller permanent streams originate in the rugged hills and empty into the main river. The lower part of this central valley is wide and less steep and is characterised by richer alluvial soils than the upper part of the valley. The HKK tiger population is currently estimated to be between 59–77 breeding individuals (DNP, 2010). Mean female home range size is 70 km2 and mean male home range size is 267 km2 (Simcharoen et al., 2014). The fi ve major prey species of the tiger in HKK are banteng (Bos javanicus), sambar, gaur (B. gaurus), wild boar (Sus scrofa), and red muntjac (Muntiacus muntjak), but banteng, sambar, and gaur account for 87% of the biomass consumed by tigers (Petdee, 2000). In large parts of WEFCOM, however, sambar and other large prey species of tigers are absent or in decline (Steinmetz et al., 2010).

Sambar distribution and relative abundance. An index of sambar abundance was estimated by using the fecal pellet group accumulation method. This technique has been widely applied in ungulate habitat studies to obtain both absolute estimates as well as indices of deer abundance (Rogers et al., 1958; Mitchell et al., 1977; Bailey & Putman, 1981). The method is typically applied in habitats in which animals are difﬁ cult to count directly or where the assumptions of distance sampling are likely violated (e.g., in dense habitat animals secretively move from sight before being observed; Smart et al., 2004; Wegge & Storaas, 2009). Because > 70% of the habitat in HKK (e.g., dry evergreen forest, mixed deciduous forest) has dense ground cover, we assumed that sambar would be difﬁ cult to count directly. Field work was conducted during the dry season (November–April) between 2009 and 2011.

Fig.1 Location of Huai Khaeng Wildlife Sanctuary in western Within the Wildlife Sanctuary we focused our investigation Thailand and the 360 sampling units contained within the four on deer distribution and relative abundance in areas located areas represent six female tiger home ranges.

101 40

Simcharoen et al.: Ecological factors for sambar prey of tiger randomly located 90 transects each forming a 200 × 200 m variables were then added to the model. We also investigated square, totalling 800 m in diameter, for a total of 360 square the interaction between the distance from Huai Kha Khaeng transects across the four sites. In each square transect, we River and the elevation as a variable because our previous place 40 circular plots, each measuring 20 m2, approximately experience suggested that both tigers and sambar avoided 20 m apart (for a total of 3,600 plots per site and 14,400 higher elevations near the main river. We accepted the model plots in total). We cleared plots of all pellet groups and then with the lowest AIC value as the best representation of the returned to the plots after 30 days to estimate the rate of relationship between pellet group counts and ecological pellet group accumulation. Pellet groups were counted and factors (Burnham & Anderson, 2010). The analysis was distinguished from each other following Simcharoen et al. performed using the MASS package (Venables & Ripley, (2014). We considered the total number of pellet groups 2002) in program R (R Core Team, 2012). divided by the total number of transects as an index of sambar abundance and number of sambar pellet groups within each RESULTS transect as an index of habitat use. Sambar distribution and index of abundance. We searched Ecological variables. Based on previous work by Trisurat et sambar pellet groups on a total of 360 square transects in al. (2010) and our knowledge of the species, we identifi ed four sites representing six female tiger home ranges that 10 ecological variables which we hypothesized to potentially ranged from 200–900 msl in altitude and included three main infl uence sambar distribution and abundance in HKK. Nine habitat types recorded in HKK: 75.3% MD, 8.9% DD, and were derived using a GIS database prepared by the Western 15.7% DE. Habitat proportions for each site were 72% MD, Forest Complex Ecosystem Management Project, Department 5% DD, and 23% DE for KPP; 73% MD, 9% DD, and 18% of National Parks, Wildlife and Plant Conservation DE for KYD; 70% MD, 21% DD, and 9% DE for KNR; (WEFCOM, 2004). Variables included terrain ruggedness, and 88% MD, 2% DD, and 10% DE for KBD. The median slope, elevation (m), distance to all streams (m), distance to slope for the square transects ranged from 0–23%. Sambar Huai Kha Khaeng River (m), distance to a salt lick (m), and pellet groups were found in 63% of all square transects. In average low slope patch size (areas with slopes shallower total, we recorded 1,041 pellets groups (102 in KPP, 444 in than 10%). Because Rotenberry et al. (2006) emphasised KYD, 26 in KNR, and 469 in KBD). Per habitat type we the need to consider multiple scales in habitat use studies recorded 48 pellet groups in DD forest, 246 in DE forest, we measured slope and the average low slope patch size at and 747 in MD forest. The mean number (±SD) of pellet two scales (150 and 600 m radius from the center of each groups recorded in all square transects was 2.9±4.6. The transect). These scales are our best estimate of the range of mean number of pellets in a pellet-group was 32±14. The areas in which ecological variables would attract a sambar relative abundance of sambar in HKK was 3,615±3,180 pellet to the vicinity of our transects. Vegetation type was recorded groups km-2. A Mann-Whitney U test was used to examine during our fi eld surveys and classifi ed for each of the 1,440 the effect of distance to the main river on sambar pellet circular plots. For each circular plot we recorded vegetation abundance. There was a signifi cant difference in the median type as either mixed deciduous forest (MD), dry deciduous number of sambar pellet groups in the square transects of dipterocarp forest (DD), dry evergreen forest (DE) or bamboo KBD and KYD, sites located close to the Huai Kha Khaeng forest (BB). The habitat type for each square transect was River (Fig. 1; median=3 pellet groups square transect-1; assigned as the proportion of the habitat types recorded in abundance=6,340±245 pellets group km-2), and the median its 40 circular plots. Bamboo was later lumped with mixed in the other two sites KNR and KPP, located further from deciduous forests both in our square transects and in the the Huai Kha Khaeng River (Fig. 1), ( median=0 pellets overall forest type availability map in the WEFCOM GIS. group square transect-1; abundance=889±746 pellets group Terrain ruggedness was measured as a vector ruggedness km-2); (Mann-Whitney U test, Z=−9.31, p=0.05, n=180). measure (VRM) using an ArcView script. Ruggedness values range from 0 (no terrain variation) to 1 (complete terrain Ecological variables. Results from the correlation tests variation). The value of natural terrain ranges from 0–0.4 suggested that sambar pellet abundance had a strong negative (Sappington et al., 2007). We defi ned “slope” as the median association with the distance to the Huai Kha Khaeng slope and the “average low slope patch size” as the total low River (r=−0.34, p<0.0001). Sambar pellets were negatively slope area within a 150 m and 600 m radius. associated with the occurrence of dry deciduous dipterocarp forest (r=−0.16, p=0.001) and positively associated with Data analysis. We investigated the relationship between the average low slope patch size within a 600 m radius sambar pellet-group counts (response variable) and the above (r=0.16, p=0.001) and 150 m radius of square transect centers 10 environmental variables using generalised linear mixed (r=0.14, p=0.004). Terrain ruggedness was discarded from models with negative binomial error terms. We did not the variables tested because it was highly correlated with standardise these variables when performing the regression slope (r=0.76) and slope was more correlated with pellet analyses. To reduce possible effects of multicollinearity, we group abundance. The remaining nine ecological factors discarded one independent variable of each tested pair if the were deemed to be uniquely associated with pellet group between variable association had an r-value>0.5 (Torres et al., counts (Table 1). Following model selection, only three of the 2011).The choice of which correlated variable to remove was ecological variables (dist. hkk, DD, and elev) were included in based on the relative strength of its Pearson correlation with the best-fi t model (Table 2). Our top model suggests that the the frequency of pellet counts. The remaining independent interaction between distance to the main river and elevation

102 41

RAFFLES BULLETIN OF ZOOLOGY 2014

Table 1. Variables used to identify factors potentially affecting sambar (Rusa unicolor) distribution and their levels of signiﬁ cance (Pearson correlations).

Variable p-value r-value Distance to main stream (dist.hkk) <0.001 −0.340 Average low slope patch size of 600 radius (patch.600) 0.001 0.166 Habitat type : dry dipterocarp (DD) 0.001 −0.162 Average low slope patch size of150 radius (patch.150) 0.004 0.140 Elevation (elev). 0.080 −0.092 Habitat type : Mixed deciduous (MD) 0.100 0.086 Distance to salt lick (salt lick) 0.500 −0.035 Slope 600 radius (slp.600) 0.729 −0.018 Slope 150 radius (slp.150) 0.733 −0.017 Habitat type : Dry evergreen (DE) 0.770 0.015 Distance to any stream (dist.st) 0.995 0.0003

Table 2. Candidate models of sambar (Rusa unicolor) occurrence in Huai Kha Khaeng Wildlife Sanctuary, Thailand. Here we report

Akaike’s Information Criterion values (AIC), the difference in AIC rank relative to the top model (∆AIC), the relative model weights (wi), the number of parameters in the model (k), and the model deviance (Dev.). Full variable names and abbreviations are listed in Table 1.

Candidate Model AIC ∆AIC wi k Dev. Dist.hkk*elev+DD 1446.2 0 0.56 6 359.2 Dist.hkk+DD 1447.9 1.7 0.24 4 360.1 Dist.hkk+DD+patch.600 1448.7 2.5 0.16 5 360.4 Dist.hkk+patch.600 1452.5 6.3 0.02 4 361.3

Table 3. Regression coefﬁ cient estimates and standard errors (SE) for in this habitat type however, we do not have evidence to the top-supported models for sambar abundance (Table 2, model 1). support this interpretation.

Variable Estimate SE Sambar abundance. Sambar is an important tiger prey intercept 2.417e+00 3.307e-01 species and is preferred in the diet of tigers throughout dist.hkk −5.860e-04 1.296e-04 Southeast and South Asia (Hayward et al., 2012). Historically, elev −1.714e-03 6.793e-04 sambar occurred throughout the tiger’s range, with the − DD 2.445e-02 1.040e-02 exception of northern China and the Far East of Russia dist.hkk*elev 6.222e-07 2.838e-07 where it is replaced by a close relative, the red deer, Cervus elaphus, (Miquelle et al., 2010, Hayward et al., 2012). affect sambar abundance (regression parameter estimate for However, sambar is one of several large mammal species the interaction of dist.hkk*elev.=6.22×10-7, SE=2.84×10-7, that has recently experienced major declines primarily due p<0.01). Sambar abundance was greatest at low elevations to habitat degradation and poaching (Wikramanayake et al., near the Huai Kha Khaeng River (Fig. 2; Table 3). Sambar 1998, Linkie et al., 2003, O’Brien et al., 2003) and is now abundance was negatively associated with increases in DD consider globally threatened (Timmins et al., 2008). Pellet habitat (regression parameter estimate for DD=−2.44 x10-2, groups were used as an index of sambar abundance. Although SE=2.84×10-2, p<0.01) it is important not to assume that indices are automatically linked to the actual abundance of the species, we felt that DISCUSSION fecal accumulation provides a reasonable estimate of relative abundance because it appeared that decomposition rates were This study examined sambar distribution and index of similar across habitat types. abundance at landscape and micro scales in relation to habitat variables and it focused on habitats typical of the Ecological variables. We examined nine ecological variables core area of the tiger population in Thailand’s Western hypothesized to predict sambar pellet abundance. Pellet group Forest Complex. Our results indicated that the distribution abundance appeared to be correlated with distance to Huai and index of abundance of sambar in HKK was related to Kha Khaeng River. The river valley is topographically ﬂ atter distance from the main river (Huai Kha Khaeng River) and than the rest of the sanctuary and it has a relatively high elevation; abundance was greater in areas closest to the main percentage of grass species (Kruuk et al., 1994). Shrestha river at lower elevations where the predominant habitat (2004) reported that ungulates in lowland Nepal prefer type is mixed deciduous forest. In addition, dry deciduous similar low-lying areas, particularly ﬂ ood plains with grass dipterocarp was negatively correlated with the index of and riverine forests. Trisurat et al. (2010) noted that sambar abundance. This could be related to a lower defecation rate avoid steep terrain and prefer open habitat; Bagchi et al.

103 42

Simcharoen et al.: Ecological factors for sambar prey of tiger

(2003) also suggested that sambar preferred grassland and water itself that makes the main river attractive to sambar; dense shrubs closer to water in India. Additionally, McKay instead, a complex of ecological parameters derived from & Eisenberg (1974) noted that sambar are sedentary and do other geologic and geographic features of the valley likely not shift their ranges seasonally. Our study aimed to model produce a desirable mixed deciduous forest habitat. Gallery sambar distribution in relation to ecological variables and to forests, the typical habitat along the Huai Kha Khaeng river, identify the importance of wide alluvial valleys to tiger prey. described as both seasonally ﬂ ooded areas and non-ﬂ ooded areas of mixed deciduous forest (Chimchome et al., 1998), Distance to the main river had the highest correlation with might have a higher food availability due to the constant pellet group abundance, but it is important to note that, in water supply (Budke et al., 2008) compared to the same contrast, there was no correlation of deer pellet abundance habitat type found away from this permanent water source. to distance of smaller permanent streams. It is clearly not Moreover, this habitat had denser understudy vegetation

Fig. 2 The distribution of elevations at which transects were placed (a). The distribution of distances from the Huai Kha Khaeng River (HKK) at which transects were placed (b). The combinations of elevation and distance to HKK River at which pellet groups were found (each dot represents a single transect) (c). The number of pellet groups found in relation to distance from the HKK River at three elevations (where dots represents transects) (d). Dots represent transects and lines represent the predicted number of pellet groups using our top-supported model (Table 2, model 1) solved at mean covariate values and one of three elevations to illustrate the interaction of elevation with distance to the HKK River.

104 43

RAFFLES BULLETIN OF ZOOLOGY 2014 which might offer better concealment from predators. On a ACKNOWLEDGEMENTS geological time scale, gradual erosion has likely deposited richer soils near the main river resulting in the growth of We thank the U.S. Fish and Wildlife Service (Rhinoceros more nutritional ungulate forage in this zone. Similarly, higher and Tiger Conservation Fund), PTT Exploration and soil moisture is more likely at lower elevations near the main Production Public Company Limited, Royal Golden Jubilee river. These two factors are represented in the interaction Grant, and Biodiversity Research Training Thailand, Grant between the main river and elevation. Soil quality and number BRT-T-254010 for funding this research. We also moisture might also be expected to affect food availability thank the Department of National Parks, Wildlife and Plant following Shrestha’s (2004) observation of sambar preference Conservation for study permission. Todd Arnold provided for the fl ood plains of Nepal and similarly in India sambar’s statistical advice and Mayuree Umpormjan provided GIS preference for lower elevations (Bagchi et al., 2003). data. Saksit Simcharoen, Sompot Duangchantrasiri, Somporn Pakpien, and Peter Cutter helped develop the prey survey Although overall presence of water was not highly correlated design in HKK. Kirati Phetthong, Thani Daoruang, Dolroman with sambar numbers, lack of water combined with poorer Chatson, and KNR staff organised the prey fi eld surveys. soils found in dry deciduous dipterocarp forest may account Finally, we thank Anak Pattanavibool and Francie Cuthbert for the lower density of sambar in this vegetation type. The for comments on earlier drafts of this manuscript. negative response to dry deciduous dipterocarp may refl ect a seasonal shift of sambar away from this habitat. For LITERATURE CITED example, Srikosamatara (1993) reported that sambar density in HKK in the dry season was lower than the wet season. Ackerman BB, Lindzey FG & Hemker TP (1986) Predictive However, even in the wet season ground cover is limited energetics model for cougars. In: Miller SD & Everett D (eds.) and grass is much sparser in dry deciduous dipterocarp forest Cats of the World: Biology, Conservation, and Management. National Wildlife Federation, Washington DC. Pp. 333–352. (Smitinand, 1977). The lower relative abundance of sambar Bagchi S, Goyal SP & Sankar K (2003) Prey abundance and in dry deciduous dipterocarp forest versus mixed deciduous prey selection by tigers (Panthera tigris) in a semi-arid, dry forest is further indicated by a preliminary analysis of long- deciduous forest in western India. Journal of Zoology, London, term camera trapping data where detection in DD was two 260: 285–290. times lower than in MD (S. Duangchantrasiri, unpublished Bailey RE & Putman RJ (1981) Estimation of fallow deer (Dama data). Finally, a study in Nagarahole India demonstrated that dama) populations from faecal accumulation. Journal of Applied the density of sambar in mixed deciduous forest habitat was Ecology, 18: 697–702. Bhattarai BP & Kindlmann P (2012) Habitat heterogeneity as the seven times that of dry deciduous dipterocarp forest (Karanth key determinant of the abundance and habitat preference of & Sunquist, 1992). Therefore, a number of factors appear prey species of tiger in the Chitwan National Park, Nepal. to make this forest type less attractive to sambar than the Acta Theriol, 57: 89–97. alluvial valleys. Biswas S & Sankar K (2002) Prey abundance and food habit of tigers (Panthera tigris tigris) in Pench National Park, Madhya Conservation implications. Our results have subtle but Pradesh, India. Journal of Zoology, London, 256: 411–420. important implications for sambar and tiger conservation. Budke JC, Jarenkow JA & Oliveira-Filho AT (2008) Tree community features of two stands of riverine forest under different fl ooding With widespread prey depletion occurring globally in regimes in Southern Brazil. Flora, 203: 162–174. response to habitat degradation and poaching (Sanderson et Burnham KP & Anderson DR (2010). Model Selection and Multi- al., 2002; Linkie et al., 2003; O’Brien et al., 2003), increased model Inference: A Practical Information-Theoretic Approach. patrolling and other forms of management (e.g., restoration Springer-Verlag, New York. 488 pp. of degraded land, management on trans-boundary lines Corbett GB & Hill JE (1992) The Mammals of the Indomalayan between tiger ranges) to reduce human impacts are needed. Region. A Systematic Review. Oxford University Press, USA. Given fi nancial constraints, it is important to target sambar 488 pp. Chimchome V, Vidhidharm A, Simchareon S, Bumrungsri S & management efforts in areas where there is likely to be a Poonswad P (1998) Comparative study of the breeding biology reasonable return for conservation efforts. For example, and ecology of two endangered hornbill species in Huai Kha investment to restore sambar numbers in rugged terrain in Khaeng Wildlife Sanctuary,Thailand. In: Poonswad P (ed) Kuiburi National Park, Thailand, met with little success The Asian Hornbills: Ecology and Conservation. Biodiversity (Steinmetz et al., 2009). Our study suggests that even with Research and Training Program, National Center for Genetic signifi cant effort, such rugged habitat is not likely to support a Engineering and Biotechnology. Pp. 111–136. dense population of sambar. We recommend that prime areas Datta A, Anand MO & Naniwadekar R (2008) Empty forests: large carnivore and prey abundance in Namdapha National Park, to target should be the wide interior valleys of the WEFCOM, north-east India. Biological Conservation, 141: 1429–1435. and elsewhere in Asia where the habitat is more favorable. DNP (2010) Thailand Tiger Action Plan 2010–2022. Department In WEFCOM, some of these valleys, though they occur in of National Parks, Wildlife and Plant Conservation, Bangkok, wildlife sanctuaries, are currently used by small villages Thailand. 55 pp. that inhabited the area before it was designated as a wildlife Francis CM (2008). A Field Guide to the Mammals of Thailand sanctuary. Our results suggest that these valleys should be a and South-East Asia. New Holland Publishers, UK. 392 pp. high priority for management because the habitat is suitable Geist V (1998) The Deer of the World, Their Evolution, Behavior, and Ecology. Stackpole Books, Mechanicsburg, MA. 432 pp. and lie within the area of the largest source population of Hayward MW, Jędrzejewski W & Jêdrzejewska B (2012) Prey tigers in Southeast Asia (Walston et al., 2010). preferences of the tiger Panthera tigris. Journal of Zoology, 296: 221–231.

105 44

Simcharoen et al.: Ecological factors for sambar prey of tiger

Johnson A, Vongkhamheng C, Hedemark M & Saithongdam T Sappington JM, Longshore KM & Thompson DB (2007) (2006) Effects of human-carnivore conﬂ ict on tiger (Panthera Quantifying landscape ruggedness for animal habitat analysis: tigris) and prey populations in Lao PDR. Animal Conservation, a case study using bighorn sheep in the Mojave Desert. Journal 9: 421–430. of Wildlife Management, 71: 1419–1426. Karanth KU & ME Sunquist (1992) Population structure, density Seidensticker J & McDougal C (1993) Tiger predatory behavior, and biomass of large herbivores in the tropical forests of ecology and conservation. Zoological Society of London Nagarahole, India. Journal of Tropical Ecology, 8: 21–35. Symposium, 65: 105–125. Karanth KU & ME Sunquist (1995) Prey selection by tiger, leopard Shrestha MK (2004) Relative Ungulate Abundance in a Fragmented and dhole in tropical forests. Journal of Animal Ecology, 64: Landscape: Implications for Tiger Conservation. Unpublished 439–450. PhD Thesis, University of Minnesota, USA. 99 pp. Kruuk H, Kanchanasaka B, O’Sullivan S & Wanghongsa S (1994) Simcharoen A, Savini T, Gale GA, Simcharoen S, Duangchantrasiri Niche separation in three sympatric otters Lutra perspicillata, S, Pakpien S & Smith JLD (2014) Female tiger home range L. lutra and Aonyx cinerea in Huai Kha Khaeng, Thailand. size and prey abundance: important management metrics. Biological Conservation, 69: 115–120. Oryx, in press. Lekagul B & McNeely JA (1977) Mammals of Thailand. Kulusapa, Smart JCR, Ward AI & White PCL (2004) Monitoring woodland Bangkok. 760 pp. deer populations in the UK: an imprecise science. Mammal Linkie M, Martyr DJ, Holden J, Yanuar A, Hartana AT, Sugardjito J Review, 34: 99–114. & Leader-Williams N (2003) Habitat destruction and poaching Smitinand T (1977). Vegetation and ground cover of Thailand. threaten the Sumatran tiger in Kerinci Seblat National Park, Department of Forest Biology, Kasetsart University, Technical Sumatra. Oryx, 37: 41–48. Paper, 1: 160–171. Matsubayashi H, Lagan P, Abd. Sukor JR & Kitayama K (2007) Srikosamatara S (1993) Density and biomass of large herbivores Seasonal and daily use of natural licks by sambar deer (Cervus and other mammals in a dry tropical forest Western, Thailand. unicolor) in a Bornean tropical rain forest. Tropics, 17: 81–85. Journal of Tropical Ecology, 9: 33–43. McKay GM & Eisenberg JF (1974) Movement patterns and habitat Steinmetz R, Seuaturien N, Chutipong W, Chamnankit C & Poonil utilization of ungulates in Ceylon. In: Geist V & Walther F (eds.) B (2009) The Ecology and Conservation of Tigers and their The Behavior of Ungulates and its Relation to Management. Prey in Kuiburi National Park, Thailand. WWF Thailand and IUCN Publication, Morges, Switzerland. Pp.708–721. Department of National Parks, Wildlife and Plant Conservation, Miquelle DG, Goodrich JM, Smirnov EN, Stephens PA, Zaumyslova Bangkok, Thailand. 58 pp. OY, Chapron G, Kerley L, Murzin AA, Hornocker MG & Steinmetz R, Chutipong W, Seuaturien N, Chirngsarrd E & Quiley HB (2010) The Amur tiger: a case study of living Khaengkhetkarn M (2010) Population recovery patterns on the edge. In: Loveridge AJ & Macdonald WD (eds.) The of Southeast Asian ungulates after poaching. Biological Biology and Conservation of Wild Felid. Oxford University, Conservation, 143: 42–51. Oxford, UK. Pp. 325–340. Timmins RJ, Steinmetz R, Sagar Baral H, Samba Kumar N, Mitchell B, Rowe JJ, Ratcliffe PR & Hinge M (1977) The Ecology Duckworth JW, Anwarul Islam Md, Giman B, Hedges S, Lynam of Red Deer: a Research Review Relevant to Their Management AJ, Fellowes J, Chan BPL & Evans T (2008) Rusa unicolor. in Scotland. Institute of Terrestrial Ecology, Cambridge, UK. IUCN Red List of Threatened Species. Version 2013.1. www. 74 pp. iucnredlist.org (Accessed 06 September 2013) Ngampongsai C (1987) Habitat use by the sambar (Cervus unicolor) Torres RT, Santos J, Linnell JDC, Virgós E & Fonseca C (2011) in Thailand: a case study for Khao-Yai National Park. In: Factors affecting roe deer occurrence in a Mediterranean Wemme CM (ed.) Proceedings of the Biology and Management landscape, Northeastern Portugal. Mammalian Biology, 76: of Cervidae, Smithsonian Institution Press, USA. Pp. 289–298. 491–497. O’Brien TG, Kinnaird MF & Wibisono HT (2003) Crouching Trisurat Y, Pattanavibool A, Gale GA & Reed DH (2010) Improving tigers, hidden prey: Sumatran tiger and prey populations in a the viability of large-mammal populations by using habitat and tropical forest landscape. Animal Conservation, 6: 131–139. landscape models to focus conservation planning. Wildlife Padmalal UKGK, Takatsuki S & Jayasekara P (2003) Food habits Research, 37: 401–412. of sambar Cervus unicolor at the Horton Plains National Park, Venables WN & Ripley BD (2002) Modern Applied Statistics with Sri Lanka. Ecological Research, 18: 775–782. S. Fourth Edition. Springer, New York, 495pp. Petdee A (2000) Feeding Habits of the Tiger Panthera tigris Walston J, Robinson JG, Bennett EL, Breitnmoser U, Fonseca (Linnaeus) in Huai Kha Khaeng Wildlife Sanctuary by Fecal GAB, Goodrich J, Gumal M, Hunter L, Johnson A, Karanth Analysis. Unpublished MSc Thesis, Kasetsart University, KU, Williams NL, MacKinnon K, Miquelle D, Pattanavibool Bangkok, 92 pp. [In Thai] A, Poole C, Rabinowitz A, Smith JLD, Stokes EJ, Stuart SN, R Core Team (2012) R: A Language and Environment for Statistical Vongkhamheng C & Wibisono H (2010) Bringing the tiger back Computing. R Foundation for Statistical Computing, Vienna, from the brink- the six percent solution. PLoS Biology, 8: 1–4. Austria. http://www.R-project.org (Accessed 06 September WEFCOM (2004) GIS Database and its Applications for Ecosystem 2013). Management. The Western Forest Complex Ecosystem Ramakrishnan U, Coss RG & Pelkey NW (1999) Tiger decline Management Project, Department of National Park, Wildlife, caused by the reduction of large ungulate prey: evidence and Plant Conservation, Bangkok, Thailand. 228 pp. from a study of leopard diets in southern India. Biological Wegge P & Storaas T (2009) Sampling tiger ungulate prey by Conservation, 89: 113–120. the distance method: lessons learned in Bardia National Park, Rogers G, Julander O & Robinette WL (1958) Pellet-group counts Nepal. Animal Conservation, 12: 78–84. for deer census and range-use index. Journal of Wildlife Wikramanayake ED, Dinerstein E, Robinson JG, Karanth U, Management, 22: 193–199. Rabinowitz A, Olson D, Mathew T, Hedao P, Conner M, Rotenberry JT, Preston KL & Knick ST (2006) GIS-based niche Hemley G & Bolze D (1998) An ecology-based method for modeling for mapping species habitat. Ecology, 87: 1458–1464. deﬁ ning priorities for large mammal conservation: the tiger as Sanderson EW, Jaiteh M, Levy MC, Redford KH, Wannebo AV case study. Conservation Biology, 12: 865–878. &Woolmer G (2002) The human footprint and the last of the wild. Bioscience, 52: 891–904.

106 45 original contribution

SAKSIT SIMCHAROEN1, MAYUREE UMPONJAN2*, SOMPHOT DUANGCHANTRASIRI1 AND law, Thailand’s Office of Natural Resources ANAK PATTANAVIBOOL2 and Environmental Policy and Planning has reported, based on expert opinions, the sta- Non-Panthera cat records from tus of threatened species in Thailand and listed jungle cat and flat-headed cat as ‘critically big cat monitoring in Huai Kha endangered’, marbled cat as ‘endangered’, and clouded leopard, fishing cat and Asiatic Khaeng Wildlife Sanctuary golden cat as ‘vulnerable’ species; leopard cat is the only species considered nationally A camera-trapping deployment for tiger Panthera tigris monitoring in Huai Kha Khaeng of least concern (Nabhitabhata & Chan-ard Wildlife Sanctuary HKK, in the Western Forest Complex WEFCOM of Thailand, was 2005). carried out intensively between 2005 and 2009. The deployment’s annual setup in- Non-Panthera cats in the wild in Thailand cluded an average of 162 camera-trap locations with more than 2,000 trap-nights and have received less attention than the two covered almost 1,000 km2. Many other wildlife species were photographed including large cats, tiger and leopard. Leopard cat was small and medium (non-Panthera) cats. This analysis explores the potential use of studied in HKK in the late 1980s (Rabinowitz the system to monitor cat species other than tiger and leopard Panthera pardus. In 1990). From the late 1990s to mid 2000s came five years, leopard and tiger, major targets of the deployment, were camera-trapped a string of publications: leopard cat in Kaeng in 653 and 483 notionally independent events respectively. Among non-Panthera cats, Krachan National Park, Southern Thailand leopard cat Prionailurus bengalensis was the most common, with 155 events. Inde- (Grassman 1998), clouded leopard in Khao Yai pendent events of three other non-Panthera cats were rare: ten of Asiatic golden cat National Park, Northeastern Thailand (Austin Catopuma temminckii, six of mainland clouded leopard Neofelis nebulosa, and only & Tewes 1999), and leopard cat and marbled two of marbled cat Pardofelis marmorata. Leopard cat in HKK used mixed deciduous cat in Phu Khieo Wildlife Sanctuary, Northern forest heavily and showed an obvious crepuscular and nocturnal activity pattern. central Thailand (Grassman & Tewes 2000, The camera-trapping deployment for tigers in HKK could be used to monitor leopard 2002, Grassman et al. 2005). Since 2005, re- cats, but different deployment designs would be necessary for other non-Panthera sources and man power have been heavily in- cats at this site. vested in conservation of Panthera species especially tiger (Simcharoen et al. 2007, Lynam South-east Asia is home to nine small and cats are under-represented in field studies 2010, Stokes 2010), in Thailand’s Western 31 medium cat species (i.e. excluding genus Pan- (Grassman et al. 2005). Four of the seven spe- Forest Complex WEFCOM. thera). Of these, seven occur in Thailand (all cies are categorised as globally threatened WEFCOM is categorised as a Tiger Conserva- those of mainland Southeast Asia): jungle cat by The IUCN Red List of Threatened Species. tion Landscape Class I (one that has habitat to Felis chaus, leopard cat, fishing cat Prionai- In Thai law, marbled cat is listed as ‘endan- support at least 100 tigers, evidence of breed- lurus viverrinus, flat-headed cat P. planiceps, gered’ and the rest as ‘protected’ under the ing, minimal-moderate levels of threat, and Asiatic golden cat, marbled cat and clouded Wildlife Preservation and Protection Act B. E. conservation measures in place), and Global leopard (Wilson & Mittermeier 2009). In Thai- 2535 (A. D. 1992) (Wildlife Conservation Divi- priority (highest probability of persistence of land as in much of the world, non-Panthera sion 1992, Boonboothara 1996). Besides the tiger populations over the long term; Diner- stein et al. 2006). Within WEFCOM, HKK is a core area where tiger and leopard ecology has been thoroughly studied, and populations estimated (Simcharoen et al. 2007, 2008). Camera-trapping started in a systematic man- ner in 2005, following the setup described in Karanth & Nichols (2002). Although designed for tigers, the deployment also photographed non-Panthera cats and many other species. This study uses by-catch from the long-term camera-trapping deployment in HKK to (1) examine the records of non-Panthera cats, and present what can be learned about status and natural history, and (2) discuss whether the programme generates sufficient non-Panthera cat records to allow these species’ conservation status to be monitored using such deployments.

Study Area Fig. 1. Location of Huai Kha Khaeng Wildlife Sanctuary, the major habitat types, and the Huai Kha Khaeng Wildlife Sanctuary (15°00’- locations of camera-traps. 15°50’N/99°00’- 99°19’E) is one of the best-

Non-Panthera cats in South-east Asia 46 Simcharoen et al.

2005 2006 2007

2008 2009

Fig. 2. Locations of camera-traps in Huai Kha Khaeng Wildlife Sanctuary showing where Fig. 3. Camera-trap points where Asiatic leopard cat was detected (red dots) and not detected (black dots) each year during 2005– golden cat, clouded leopard and marbled 2009. The background shows forest types. cat were detected in HKK WS 2005-2009.

known protected areas in Thailand (WEFCOM mountain range plays an important role in more rain in the west and less in the east, 2004; Fig. 1). It covers 2,780 km2 and is part blocking the southwest monsoon flowing in a variation causing significant differences in of a much bigger (18,000 km2) protected area from Myanmar. The southern part of HKK is vegetation type. network called the Western Forest Complex generally lower with many small hills of 700- HKK consists of mixed deciduous forest over WEFCOM. HKK was declared a wildlife sanc- 800 m high (Forest Research Centre 1997). almost half of the sanctuary. The other fo- tuary in 1972. Currently there are 19 ranger The climate is a mix of tropical and sub-trop- rest types include dry evergreen (25%), hill stations, located mostly along the eastern ical, has three seasons: the hot dry season evergreen (14%), dry dipterocarp (7%) and boundary, to protect HKK from poaching and of March-April with average temperature of bamboo forest (4%) (WEFCOM 2004). The land encroachment (WEFCOM 2004). 24°-38°C, the rainy season of May-October open dominant forest types of mixed decidu- HKK is part of the Dawna Range, north of the with 23°-34° C, and the cool dry season of ous and dry dipterocarp occur at elevations Tenasserim Range, separating northwestern December-February with 18°-21°C (Forest of 450-900 m. The forest is sometimes mixed Thailand from Myanmar. HKK topography is Research Centre 1997). The average annual with bamboos (major bamboo species: Bam- more mountainous to the north and west of rainfall is about 1,500 mm with the minimum busa arundincea, B. burmanica, Dendrocala- the area, with ridges exceeding 1,000 m. This in January and maximum in October. There is mus strictus, Gigantochloa albociliata). The

CATnews Special Issue 8 Spring 2014 47 non-Panthera cats in Huai Kha Khaeng Wildlife Sanctuary dominant tree species in the crown layer in- Table 1. The number of notionally independent events for cat species during camera- clude Afzelia xylocarpa, Tetrameles nudifora, trapping in Huai Kha Khaeng Wildlife Sanctuary during 2005-2009. When both cameras Lagerstroemia tomentosa, L. duperreanna, in a pair photographed an animal, this is recorded as only one record. Shorea obtusa, S. siamensis, Dipterocarpus Number of notionally independent events obtusifolis and D. tuberculatus (Forest Re- Species 2005 2006 2007 2008 2009 Total search Centre 1997). Tiger 107 68 91 111 106 483 Methods Leopard 133 138 139 115 128 653 For this study, data from camera-trapping Clouded leopard 22 1016 collected between 2005 and 2009 were ana- Asiatic golden cat 03 12410 lysed. The deployments occurred mainly in Leopard cat 9 24 56 12 54 155 the two open dominant forest types, given Marbled cat 10 0102 that the main target species was the tiger. Total camera-trap-nights 2,241 2,020 2,467 2,804 2,731 Tigers prefer open forests where, with their grass base, large ungulates such as gaur staff, with support from two wildlife biolo- of small and medium cat (Table 1). No do- Bos gaurus and banteng B. javanicus mostly gists, with more than five years of experience mestic cats Felis catus were captured during reside (Prayurasiddhi 1997). The camera- of camera trapping, in case of doubts. All these surveys. Tables 1 and 2 also contain trapping areas covered about 1,000 km2 of photographs of cats were scanned, put into results for tiger and leopard, for comparison this near-optimal tiger habitat. Almost 80% a database and identification of all photo- with the smaller species; detailed analysis of of camera-trap locations were in mixed de- graphs listed as non-Panthera were assessed Panthera data will be published elsewhere. ciduous and dry dipterocarp forests, 17% in independently by J. W. Duckworth. Records degraded evergreen, and the rest in other were calculated in terms of: 1) number of in- Number of notionally independent events vegetation types. dependent events, and 2) number of camera- Each year the camera-traps were deployed Several camera-trap models, including trap stations detecting the species. To assess for more than 2,000 trap-nights with a total CamTrakker, Bushnell and Scoutguard, were conservation status, the photographs at one of 12,263 trap-nights over the five years. set up following the standard method used camera-trap station are not independent if The numbers of independent events for non- for monitoring tigers, detailed in Karanth & they show the same animal. This problem is Panthera cats are much lower than Panthera Nichols (2002). Camera-traps were located reduced by presenting the number of camera- cats (Table 1). Leopard cat was the most fre- 33 mainly along forest roads and animal trails, trap stations recording the species, although quently detected small cat. Clouded leopard and at salt licks. At each location camera- even this will not exclude non-independent and golden cat events ranged from very few traps were set in a pair, each unit 3-5 m from records if multiple camera-trap stations are to none per year; marbled cat was detected the path and about 45 cm above ground. No within a typical individual’s home range. No- only twice (Supporting Online Material SOM bait was used. Camera-traps were set to tionally independent events are defined as Table T1). function throughout the 24-hour cycle. one or more photographs of one or more ani- The spacing between camera-trap locations mals of the same species at a given camera- Number of camera-trap stations detecting was about 3-4 km, based on female tiger trap location, separated by no more than 30 the species home-range (Karanth & Nichols 2002). With minutes. Between 150 and 190 camera-trap stations about 180 camera-trap locations each year, Camera-trap locations were overlaid with a were set each year, covering almost 1,000 trapping was divided into eight blocks of habitat map interpreted from LANDSAT 5 TM km2. Leopards and tigers were the most 20-25 trapping locations. The camera-traps 2002 (WEFCOM 2004) to determine the veg- widely detected cat species (Table 2). Among were left in each block for 15-20 days before etation cover at each location. non-Panthera cats, leopard cat had the wid- being relocated to another block. Two blocks est detection, but even so each year less than were sampled simultaneously. Trapping nor- Results one-sixth of camera-trap stations detected mally started in January and finished by mid Tiger-focussed camera-trapping in HKK be- leopard cats. The other three cats were found May. For an optimal setting of cameras, lo- tween 2005 and 2009 captured four species at very few stations. cations within a block were moved slightly between years. Thus, spacing between ca- Table 2. The number of camera-trap stations recording each species in Huai Kha mera-trap locations used in different years Khaeng Wildlife Sanctuary during 2005-2009. was frequently well below 3-4 km, and the Number of stations where the species were recorded total number of camera-trap locations at Species 2005 2006 2007 2008 2009 Total which some species were found over the five years exceeded the 180 total camera-trap lo- Tiger 58 46 52 67 64 287 cations per year. Leopard 77 61 76 61 63 338 The total of camera-trap-nights is the sum Clouded leopard 121015 of the number of nights each pair of cam- Asiatic golden cat 0312410 eras was open functioning at all camera- Leopard cat 9 14 29 7 33 92 trap locations. Species identification from Marbled cat 100102 photographs was carried out by the project Total camera-trap locations 155 136 156 180 186

Non-Panthera cats in South-east Asia 48 Simcharoen et al.

basis for the 1989 report warrants a review. Jungle cat apparently occurs predominantly in deciduous forest in South-east Asia ( Duck- worth et al. 2005), so parts of HKK might be expected to support it. However, no records were obtained from this intensive camera- trapping survey, mostly in deciduous forest, despite reasonable trapping rates described in other studies (e.g. Gray et al. 2014), suggesting that jungle cat is rare or even absent from HKK. The other small cat of Thailand, the flat-headed cat, does not occur this far north (Wilting et al. 2010).

Fig. 4. Clouded leopard on 3 June 2006, 23:49 h. Habitat: Mixed deciduous forest. Small cat community In HKK, leopard cat is common but golden Leopard cat habitat use and activity pattern Discussion cat, clouded leopard and marbled cat were Leopard cat was the only small cat with suf- Leopard cat all recorded only rarely. Focused camera- ficient camera-trap records (92 locations in five Leopard cat is the only small cat species so far trapping in HKK’s evergreen forests might years) for an analysis of habitat use (SOM T2). studied intensively in multiple parts of Thai- find these three species more often, but they Caution is required in interpretation because land (Rabinowitz 1990, Grassman et al. 2005). are evidently rare in HKK’s deciduous forest. patterns may be biased by the selection of Similarly, it is the only species with enough Observations in other areas suggest that camera-trap locations, and refer only to the camera-trap detections in HKK for a confident leopard cat population increases when larger late dry season. Almost 70% of camera-trap discussion of abundance and habitat use at predators, such as golden cat and clouded locations with leopard cat detection were in the site, albeit only for the late dry season. leopard, are eliminated (Wilson & Mittermei- mixed deciduous forest (SOM T2; Fig. 2), while It was photographed in many habitat-types, er 2009). Release of leopard cat population the other two open canopy forest types, dry coinciding with its generally wide habitat use with reduction of interspecific competition 34 dipterocarp (10%) and degraded dry evergreen (Wilson & Mittermeier 2009). In HKK the high from golden cat and marbled cat is plausible, forests (15%) were used to a lesser extent. encounter rates in mixed deciduous forest because the three species presumably share Leopard cat was also the only small cat spe- may simply reflect disproportionate survey similar small prey such as rodents, reptiles, cies with enough data to allow for the analysis effort. However, the low encounter rate in birds, amphibians and insects. However, it is of activity patterns. At least in the late dry sea- dry dipterocarp forest relative to survey ef- less likely for clouded leopard, which preys son, it is nocturnal, with the main activity start- fort corroborates earlier findings in HKK that on larger animals such as porcupines (Hystri- ing after 18:00 h and peaking during 19:00 h it uses mixed deciduous and dry evergreen cidae), pigs Sus spp., young sambar Rusa uni- - 22:00 h and fluctuating from 22:00 h to 06:00 forests more than dry dipterocarp forest with color, muntjacs Muntiacus spp., chevrotains h. It is almost inactive by day (SOM Figure F1). its lower dry-season grass base, and thus Tragulus spp. and palm civets (Paradoxurinae) lower cover and prey (Rabinowitz 1990). Wet- (Wilson & Mittermeier 2009). In this study, Morphology of Asiatic golden cat season surveys, when dry dipterocarp forest clouded leopard seems to use evergreen for- Of the 10 records of golden cat, seven were has rich understorey growth, might reveal a est more frequently than leopard cat, which is of golden animals and three of grey ones. very different habitat use. found more in deciduous forest. These results found leopard cat to be crepuscular and nocturnal, with very few pho- Conclusions and management implica- tographs by day. Radio-collared leopard cats tions in Phu Khieo Wildlife Sanctuary, northeast- Intensive camera-trap deployment for tigers ern Thailand, in more evergreen habitats, in Huai Kha Kheang Wildlife Sanctuary from showed somewhat more daytime activity, 2005 to 2009 captured six cat species: tiger, while still being mainly crepuscular and noc- leopard, clouded leopard, golden cat, marbled turnal (Grassman et al. 2005). cat and leopard cat. Tiger and leopard were recorded often. Of the non-Panthera cats, Other non-Panthera cats leopard cat was found commonly whereas Fishing cat was reported in the Master Plan golden cat, clouded leopard and marbled cat of HKK in 1989 (Thailand Faculty of Forestry were rarely found. 1989). It was not detected in the 2005-2009 Thus, camera-trapping for tigers in Huai Kha camera-trap deployment, which covered Khaeng Wildlife Sanctuary provides useful large areas including near streams, and data to study abundance patterns, activity seems very unlikely to occur there presently. rhythms, and habitat use of leopard cat, but Fig. 5. Leopard cat on 23 April 2006, Because individuals of this species are often data are too sparse for a similar analysis of 14:59 h. Habitat: Mixed deciduous forest. misidentified, (Duckworth et al. 2009), the clouded leopard, golden cat and marbled cat.

CATnews Special Issue 8 Spring 2014 49 non-Panthera cats in Huai Kha Khaeng Wildlife Sanctuary

Moreover, annual numbers of leopard cat independent events fluctuated considerably during this five-year study, making it difficult to use this method to assess population trends during short periods of time. To monitor clouded leopard, golden cat and marbled cat, other camera-trapping study designs would need to be experimented with, such as placing more camera-trap stations in evergreen forests, or around fruiting trees with high rodent concentration.

Acknowledgements Fig. 6. Asiatic golden cat on 16 March 2006 08:04 h. Habitat: Hill evergreen forest. Funding for this study was from US Fish and Wild- life Service: Rhino and Tiger Conservation Fund, galensis) in a subtropical evergreen forest Simcharoen S., Pattanavibool A., Karanth K. U., Panthera, and Liz Claiborne and Art Ortenberg in southern Thailand. Zoological Society La Nichols J. D. & Kumar N. S. 2007. How many Foundation. We are grateful to the Thai govern- Torbiera, Scientific Report 4, 9-21. tigers Panthera tigris are there in Huai Kha ment’s Department of National Parks, Wildlife and Grassman, L. I. & Tewes M. E. 2000. Marbled cat Khaeng Wildlife Sanctuary, Thailand? An es- Plant Conservation and the Wildlife Conservation pair in northeastern Thailand. Cat News 33, 24. timate using photographic capture-recapture Society (WCS) for the logistics and technical sup- Grassman, L. I., & Tewes M. E. 2002. Marbled cat sampling. Oryx 41, 447-453. port. Special thanks go to the staff of Kao Nang in northeastern Thailand. Cat News 36, 19-20. Stokes E. J. 2010. Improving effectiveness of pro- Ram Wildlife Research Station, of WCS Thailand Grassman Jr L. I., Tewes M. E., Silvy N. J. & Kreeti- tection efforts in tiger source sites: developing and of WCS India. yutanont K. 2005. Spatial organization and diet a framework for law enforcement monitoring of leopard cat (Prionailurus bengalensis) in using MIST. Integrative Zoology 5, 363-377. References northern-central Thailand. Journal Zoological Thailand Faculty of Forestry 1989. The master plan Austin S. C. & Tewes M. E. 1999. Ecology of the Society of London, 266, 45-54. for Huai Kha Khaeng Wildlife Sanctuary. Fac- clouded leopard in Khao Yai National Park, Gray T. N. E., Phan C., Pin C. & Prum S. 2014. The ulty of Forestry, Kasetsart University, Bangkok. 35 Thailand. Cat News 31, 17-18. status of jungle cat and sympatric small cats in WEFCOM 2004. GIS Database and its applications Boonboothara K. 1996. Wildlife Preservation and Cambodia’s Eastern Plains. Cat News Special for ecosystem management. The Western For- Protection Act B.E.2535. The Information Of- Issue 8, 31-35. est Complex Ecosystem Management Project fice, the Royal Forest Department, Bangkok. IUCN. 2011. The IUCN Red List of Threatened Spe- (WEFCOM), Department of National Parks, Dinerstein E., Loucks C., Heydlauff A., Wikra- cies. Version 2011.2 http://www.iucnredlist.org. Wildlife and Plant Conservation, Bangkok. manayake E., Bryja G., Forrest J., Ginsberg Karanth K. U. & Nichols J. D. 2002. Monitoring ti- Wildlife Conservation Division 1992. The Wildlife J., Klenzendorf S., Leimgruber P., O’Brien T., gers and their prey. Centre of Wildlife Studies, Preservation and Protection Act 1992. The Roy- Sanderson E., Seidensticker J. & Songer M. Bangalore, India. al Forest Department, Bangkok. In Thai. 2006. Setting priorities for the conservation Lynam A. J. 2010. Securing a future for wild In- Wilson D. E. & Mittermeier R. A. (Eds). 2009. and recovery of wild tigers: 2005-2015. A dochinese tigers: transforming tiger vacuums Handbook of the mammals of the world. Vol. 1. User’s Guide. WWF, WCS, Smithsonian, and into tiger source sites. Integrative Zoology 5, Carnivores. Lynx Ediciones, Barcelona. NFWF-STF, Washington, D.C. and New York. 324-334. Wilting A., Cord A., Hearn A. J., Mohamed A., Duckworth J. W., Poole C. M., Tizard R. J., Walston Nabhitabhata J. & Chan-ard, T. 2005. Thailand Red Traeholdt C., Cheyne S. M., Sunarto S., Jayasi- J. L. & Timmins R. J. 2005. The Jungle Cat Felis Data: Mammals, Reptiles, and Amphibians. lan M.-A., Ross J., Shapiro A. C., Sebastian A., chaus in Indochina: a threatened population of Office of Natural Resources and Environmental Dech S., Breitenmoser C., Sanderson J., Duck- a widespread and adaptable species. Biodiver- Policy and Planning, Bangkok. worth J. W. & Hofer H. 2010. Modelling the sity and Conservation 14, 1263-1280. Prayurasiddhi T. 1997. The ecological separation species distribution of flat-headed cats (Prion- Duckworth J. W., Shepherd C. R., Semaidi G., of gaur (Bos gaurus) and banteng (Bos javani- ailurus planiceps), an Endangered South-East Schauenberg P., Sanderson J., Roberton S. I., cus) in Huai Kha Khaeng Wildlife Sanctuary, Asian small felid. PLoS One 5, 1-18. O’Brien T. G., Maddox T., Linkie M., Holden J., Thailand. PhD thesis, University of Minnesota, & Brickle N. W. 2009. Does the fishing cat in- U.S.A. Supporting Online Material SOM Tables T1, T2 and habit Sumatra? Cat News 51, 4-9. Rabinowitz A. R. 1990. Notes on the behavior and Figure F1 are available at www.catsg.org/catnews Forest Research Centre. 1997. Application of re- movements of leopard cats Felis bengalensis mote sensing and GIS for monitoring forest in a dry tropical forest mosaic in Thailand. Bio- 1 Department of National Parks, Wildlife and land use change in Huai Kha Khaeng Wildlife tropica 22, 397-403. Plant Conservation, 61 Paholyotin Road, Cha- Sanctuary. Final Report to the Royal Forest Simcharoen S., Barlow A., Simcharoen A. & Smith tuchak, Bangkok, 10900, Thailand Department by Faculty of Forestry, Kasetsart J. D. 2008. Home range size and daytime habi- 2 Wildlife Conservation Society Thailand Pro- University, Bangkok. tat selection of leopards in Huai Kha Khaeng gram, 55/295 Muangthong Thani, Project 5, Grassman L. I. 1998. Movements and prey se- Wildlife Sanctuary, Thailand. Biological Con- Chaengwattana Road, Pakkred, Nonthaburi, lection of the leopard cat (Prionailurus ben- servation 141, 2242-2250. 11120, Thailand *

Non-Panthera cats in South-east Asia 50 Contributed Paper Dynamics of a low-density tiger population in Southeast Asia in the context of improved law enforcement

Somphot Duangchantrasiri,∗ Mayuree Umponjan,† Saksit Simcharoen,∗ Anak Pattanavibool,†‡ Soontorn Chaiwattana,∗ Sompoch Maneerat,∗ N. Samba Kumar,§∗∗ Devcharan Jathanna,§ Arjun Srivathsa,§ ¶ and K. Ullas Karanth§†† ∗Department of National Parks, Wildlife and Plant Conservation, Paholyotin Road, Chatuchak, Bangkok 10110, Thailand †Wildlife Conservation Society, Thailand Program, 55/295 Muangthong Thani 5, Chaengwattana Road, Pakkred, Nonthaburi 10210, Thailand ‡Faculty of Forestry, Department of Conservation, Kasetsart University, Bangkok 10900, Thailand §Centre for Wildlife Studies, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560 070, India ∗∗Wildlife Conservation Society, India Program, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560 070, India ††Wildlife Conservation Society, Global Conservation Program, 2300 Southern Boulevard, Bronx, NY 10460, U.S.A.

Abstract: Recovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture-recapture methods. From 2006 to 2012, we sampled across 624–1026 km2 with 137–200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture-recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood-based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km2, abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture–recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82–90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low-density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade.

Keywords: abundance estimation, camera traps, carnivores, overhunting, patrolling, population dynamics, spatial capture-recapture models

La Din´amica de una Poblacion´ de Tigres con Baja Densidad en el Sureste de Asia dentro del Contexto de una Aplicacion´ Mejorada de la Ley Resumen: Recuperar las poblaciones pequenas˜ de las especies amenazadas es una importante estrategia global de conservacion.´ Sin embargo, monitorear la recuperacion´ esperada generalmente depende de ´ındices

¶Address correspondence to A. Srivathsa, email [email protected] Paper submitted May 4, 2015; revised manuscript accepted October 10, 2015. 1 Conservation Biology, Volume 00, No. 0, 1–10 C 2015 Society for Conservation Biology DOI: 10.1111/cobi.12655 51 2 Tiger Population Dynamics in Thailand inciertos de abundancia en lugar de estimados demograficos´ rigurosos. Para contrarrestar la gran amenaza causada por la cacer´ıa furtiva de tigres (Panthera tigris), el Gobierno de Tailandia establecio´ un sistema intensivo de patrullaje en 2005 para proteger y recuperar la poblacion´ fuente mas´ grande en el Santuario Huai Kha Khaeng. Simultaneamente,´ evaluamos las dinamicas´ de esta poblacion´ de tigres durante los siguientes ocho anos˜ con rigurosos métodos fotograficos´ de captura-recaptura. De 2006 a 2012 muestreamos a lo largo de 624–1026 km2 con 137–200 trampas camara.´ Las camaras´ desplegadas durante 21,359 d´ıas de trampa produjeron registros fotograficos´ de 90 individuos distinguibles. Usamos métodos espaciales de captura- recaptura y modelo bayesiano cerrado para estimar anualmente la abundancia de los tigres. Los estimados de abundancia estuvieron integrados por analisis´ de modelo abierto basados en la probabilidad para estimar la tasa anual y las tasas generales de supervivencia, reclutamiento y cambios en la abundancia. Los estimados de los parametros´ demograficos´ fluctuaron ampliamente: la densidad anual vario´ entre 1.25 y 2.01 tigres/ 100 km2, la abundancia entre 35 a 58 tigres, la supervivencia entre 79–6 y 95.5% y el reclutamiento anual de 0 a 25 tigres. El numero´ de individuos distinguibles que fue fotografiado demuestra el valor de los métodos de captura-recaptura para la evaluacion´ de las dinamicas´ poblacionales de especies raras y elusivas que son identificables gracias a marcas naturales. Posiblemente por causa de la presion´ ejercida por la caza furtiva, la densidad general de los tigres en Huai Kha Khaeng fue 82–90% mas´ baja que en sitios ecologicamente´ comparables de India. Sin embargo, el patrullaje intensivo después de 2006 parecio´ reducir la caza furtiva y estuvo correlacionado con el mejoramiento marginal de la supervivencia y reclutamiento de los tigres. Nuestros resultados sugieren que la recuperacion´ de las poblaciones de tigres con baja densidad puede ser mas´ lenta de lo esperado por las estrategias globales actuales enfocadas en la duplicacion´ del numero´ de tigres en una década.

Palabras Clave: carn´ıvoros, din´amicas poblacionales, estimacion´ de la abundancia, exceso de caza, modelos espaciales de captura-recaptura, patrullaje, trampas c´amara

Introduction 3000–4000 individuals in <200 years. Direct killing for commerce or because of human–carnivore conflict, Protected areas have facilitated the persistence and recov- overhunting of prey, and habitat loss have driven this ery of many large mammal species (Margules & Pressey decline (Walston et al. 2010; Wikramanayake et al. 2000; Brooks et al. 2009). In the context of escalating 2011). Currently, about 70% of surviving wild tigers are threats (Schipper et al. 2008) and consequent population concentrated within <7% of remaining habitat. These are declines (Ceballos & Ehrlich 2002), models of “source source sites that support breeding populations, without population recovery” have gained support (Walston et al. which tigers cannot survive across wider landscapes 2010). This approach relies on the measurement of pop- (Walston et al. 2010). Although Southeast Asia contains ulation recoveries of target species as its central metric of approximately 50% of remaining tiger habitat, no study effectiveness (Walpole et al. 2001; Williams et al. 2002; of population dynamics exists for any tiger population in Soule et al. 2005). However, rigorous empirical studies this region. Responding to threats, after 2006, the gov- of ongoing species recoveries under this model (Walsh ernment of Thailand adopted a tiger conservation model & White 1999) are scarce. based on source population recovery. In collaboration Large carnivores have long been flagships of species with the Wildlife Conservation Society (WCS), it estab- recovery efforts, often serving as umbrellas for overall lished systematic, intensified foot patrols in Huai Kha biodiversity (Caro & O’Doherty 1999). Given their nat- Khaeng (HKK) Wildlife Sanctuary to reduce hunting pres- urally low population densities, wide-ranging behaviors, sures on tigers and prey species (WCS-Thailand 2007). and elusiveness, rigorously monitoring their populations Simultaneously, a scheme for rigorous tiger population is often difficult (Palomares et al. 2010). In addition to monitoring developed in India (Karanth & Nichols 2002; abundance, estimation of demographic vital rates (sur- Karanth et al. 2006, 2011a) was initiated. These matched vival, recruitment) that drive carnivore population re- conservation interventions offer a rare opportunity to ex- sponses is also critical (Hebblewhite et al. 2003; Lambert amine whether a correlative relationship exists between et al. 2006; Balme et al. 2009). Because of methodological increased law enforcement efforts and tiger abundance. challenges, many recovery programs rely on surrogate in- Camera-trap surveys and capture-recapture (CR) meth- dices of carnivore abundance, threat levels, and efficacy ods have expanded the scope of noninvasive study of of conservation efforts instead of using rigorous demo- carnivores with identifiable natural markings (Williams graphic assessments. et al. 2002; Karanth et al. 2006). Because of high rates The tiger (Panthera tigris) represents a good case of mortality, reproduction, and turnover (Karanth et al. study of endangerment of a flagship species. Its global 2006; Goodrich et al. 2008), tiger populations need to be range has contracted by approximately 93%, and assessed annually. Such annual surveys, combined with its global population has declined to approximately open-model analyses across years, can yield estimates of

Conservation Biology Volume 00, No. 0, 2015 52 Duangchantrasiri et al. 3 density and abundance and vital rates that drive changes in these factors (Karanth et al. 2006; van de Kerk et al. 2013). We applied methodological advances in spatial capture–recapture (SCR) (Borchers & Efford 2008; Royle et al. 2009) in the first-ever study of tiger population dynamics in Southeast Asia. In our study of tigers in HKK Wildlife Sanctuary, Thailand from 2005 to 2012, we sought to estimate tiger photo-capture probabilities, tiger density and abundance, and rates of population change from 2005 to 2012; estimate survival and recruitment rates, which drive population dynamics; examine spatial and temporal patterns in tiger-population parameters; and explore the possible effect of the recovery strategy employed in HKK on these population parameters. We hypothesized that illegal hunting of tigers and prey species depressed tiger densities in HKK prior to 2006 (Karanth et al. 2004). We anticipated that tiger numbers in HKK would improve in response to increased protection efforts and sought to measure the rate and magnitude of this response.

Methods

Study Area HKK (2780 km2) is embedded in the 18,000 km2 West- ern Forest Complex (WEFCOM) in Thailand (15°00’N– 15°50’N and 99°00’E–99°19’E) (Fig. 1). The area supports dry-deciduous Dipterocarp forest, mixed deciduous forest, dry-evergreen forest, hill evergreen forest, and bamboo-mixed secondary forest, depending on local rainfall, soil, and past human disturbances. To counter threats from illegal hunting and logging, HKK was established as a wildlife sanctuary in 1972, and several villages were Figure 1. Location of Huai Kha Khaeng (HKK) resettled from within HKK from 1972 to 1991. However, Wildlife Sanctuary in the Western Forest Complex there are about 30 villages along HKK’s eastern bound- (WEFCOM), Thailand. ary that are a significant source of illegal hunting (WCS- Thailand 2007; Department of National Parks 2010). The sanctuary supports one of the largest tiger popula- ing was likely the cause for depressed densities, suggest- tions in Southeast Asia (Simcharoen et al. 2007). It is also ing that increased law enforcement efforts were required. an important conservation area for several threatened We conducted our study after Simcharoen et al.’s (2007). ungulates that are tiger prey, including gaur (Bos gaurus), banteng (Bos javanicus), and Eld’s deer (Cervus Field Survey eldii) (Bhumpakphan 1997; Prayurasiddhi 1997). Other principal prey species are wild pig (Sus scrofa), sam- Simcharoen et al. (2007) surveyed 477 km2 in HKK from bar (Rusa unicolor), red muntjac (Muntiacus muntjak), 2004 to 2005. In 2005, we surveyed an adjacent area. and, potentially, wild buffalo (Bubalus bubalis) and These 2 surveys did not follow CR sampling protocols Malayan tapir (Tapirus indicus) (Petdee 2000). Livestock because survey durations far exceeded the recommended are limited to the edges outside HKK (Jotikapukkana et al. 45–60 d, violating the population closure assumption 2010). (Karanth & Nichols 2002). Therefore, we included 24 Simcharoen et al. (2007) estimated the potential carry- tigers detected just before our survey only as a starting ing capacity of tigers at approximately 700 in HKK and data point of our systematic surveys, which spanned the approximately 2000 in the WEFCOM landscape, which subsequent 7 years from 2006 to 2012. highlight the region’s global importance for tiger conser- We positioned paired camera traps along anticipated vation. Their results, however, raised concerns that tiger tiger travel routes to simultaneously photograph both densities were low at <4 tigers/100 km2 and that poach- flanks. Trap locations were selected to maximize photo

Conservation Biology Volume 00, No. 0, 2015 53 4 Tiger Population Dynamics in Thailand

Table 1. Details of camera-trap sampling from a survey of wild tigers in Huai Kha Khaeng Wildlife Sanctuary (HKK), Thailand, from 2005 to 2012. No. of days Sampling No. of trap Trap-array Effort No. of tigers Cumulative no. of 2 Year of sampling midpoint t (years) locations area (km ) (trap days) detected (Mt+1) tigers detected 2005 481 6 Oct 2004 1.41 155 524 910 24 24 2006 185 4 Mar 2006 1.04 137 624 2156 27 36 2007 135 20 Mar 2007 1.01 156 991 2597 26 41 2008 145 23 Mar 2008 0.92 180 982 2999 34 50 2009 149 20 Feb 2009 1.08 194 1094 2919 33 61 2010 143 22 Mar 2010 0.97 181 934 2935 29 70 2011 128 13 Mar 2011 1.02 183 905 2974 32 83 2012 144 19 Mar 2012 – 200 1026 3869 35 90

captures of tigers and were spaced at around 1.5– SCR analyses, we used daily sampling occasions; a trap 3 km apart (Karanth & Nichols 2002, 2010). The number deployment matrix indicated locations that were active of trap locations varied by year (Table 1). Because of (or not) on specific days. For the CAPTURE analyses, human-power and equipment limitations, we used stan- sampling occasions were constructed by combining data dard block trapping (Karanth & Nichols 2002). We used across blocks (i.e., captures from the first day from all shorter secondary sampling periods, nested within the blocks were assigned to the first sampling occasion). 8 annual primary periods (Karanth et al. 2011a, 2011b). Tiger abundance (Nˆ ) (population size) for each year However, logistical constraints of surveying the rugged, was obtained by multiplying Dˆ by the study area size roadless terrain resulted in our secondary samples ex- (2780 km2). We used the cell-specific (pixel-level) tiger ceeding the 45–60 d recommended duration (Table 1) densities obtained annually from SCR analyses (Royle (Karanth & Nichols 2002). et al. 2009) to examine variations in tiger density within the HKK study area. The rate of population change between successive years was estimated from the ratios Analyses Nˆ t+1/Nˆ t for the corresponding interval t (Karanth et al. We used the pattern-matching software EXTRACTCOM- 2006; Karanth et al. 2011b). We also performed conven- PARE (Hiby et al. 2009) to compare tiger photographs and tional closed-model CR analyses implemented in program subsequently verified the matched individuals visually. CAPTURE to generate a second set of density and abun- These unique tiger identities generated standard individ- dance estimates. ual capture-history matrices with daily samples (Karanth For estimating recapture and survival probabilities et al. 2011a). SCR analyses require additional information for tigers between successive annual surveys, we used on locations of traps and captures. As per standard tiger- Cormack–Jolly–Seber (CJS) models (Cormack 1964; Jolly survey protocols (Karanth et al. 2004, 2006), because 1965; Seber 1965). The apparent survival rate (φ) ac- of low capture rates for cubs, all demographic data and counts for losses from both mortality and permanent analyses pertained to tigers >1 year of age. emigration (Williams et al. 2002). Recapture probability Unlike conventional CR methods that rely on an ad hoc (p) here is the probability that an individual is captured in buffer area around the trap array to deal with geographic a primary sampling occasion, following its initial capture. closure, SCR models directly integrate animal movement The 4 biologically plausible scenarios for the tiger popu- data into modeling of the detection process. They also lation, implemented in program MARK (Cooch & White better model individual heterogeneity in capture proba- 2009), included models in which survival and recapture bility arising from the spatial configuration of tiger home probabilities varied over time [φ(t),p(t)]; survival and re- ranges in relation to the trap array (Royle et al. 2009). capture probabilities were constant over time [φ(.),p(.)]; We relied on a Bayesian MCMC-based SCR approach only recapture probability varied over time [φ(.),p(t)], (Royle et al. 2013) implemented in program SPACECAP and only survival varied over time [φ(t),p(.)]. We assessed (Gopalaswamy et al. 2012) to estimate tiger densities (Dˆ ). the fit of these 4 CR models to our capture-history data We set the state–space S at 4600 km2 based on a home- with Akaike’s information criterion corrected for small range diameter 3 times larger than observed in field stud- sample sizes (AICc ) (Burnham & Anderson 2002). ies (Royle et al. 2013). We used 52,000 MCMC iterations We first converted the annual survival rate estimates φˆ with an initial burn-in of 2000. The augmentation value ( t) derived from the open model analysis to estimates (maximum number of animals that could exist within of survival over the interval between successive primary φˆt the state space) was set at 150, approximately 5 times sampling occasions ( t ). We then used annual estimates the number of tigers we captured each year. We used the of tiger abundance (Nˆ ) and survival to compute annual Geweke (1992) statistic reported by program SPACECAP recruitment arising from in situ reproduction and immi- to check for convergence of the MCMC chains. For the gration. Recruitment (Bˆ ) was computed for each interval

Conservation Biology Volume 00, No. 0, 2015 54 Duangchantrasiri et al. 5 from 2006 to 2012 as the difference between estimated directly as higher tiger survival rates. The increase in abundance Nˆ t+1 in year t + 1 and the expected number of survival rate could manifest indirectly, such that in the φˆt survivors (i.e., Nt t ) from the previous year t (Pollock longer term tiger survival rate would improve as a result et al. 1990). of increased prey densities. Similarly, we expected that Considered together, our above estimates of density, the effect of increased patrolling on recruitment (entry of abundance, population change, survival, and recruitment new individuals >1 year old into the population, either in provided us with direct, comprehensive measures of the situ or through immigration) could be direct (increased dynamics of the wild tiger population in HKK at annual cub survival because of decreased hunting of adults) or intervals and averaged across the entire study period. indirect with a time lag (due to increased cub survival and immigration resulting from higher prey densities). We could not explore potential time lags in the response Assessment of Protection Measures of tiger survival or recruitment in relation to patrol efforts The management interventions concurrent with our because patrol data were available only for 2005 onwards. camera-trapping studies were driven by administrative We tried to test plausible relationships between tiger and social contingencies. Therefore, our examination of survival and recruitment rates, and between annual pa- the relationship between monitoring and management trol effort and rates of detection of poaching incidents interventions is purely observational, typical of conser- (as a measure of patrolling efficacy) using the temporal vation science. Although it was not possible to deter- symmetry modeling approach (Pradel 1996). We con- mine causal relationships due to the lack of a suitable sidered population models in which survival probability experimental design (replication, random assignments (φˆ), recapture probability (ˆp), and per capita recruitment of treatments, and controls), we nonetheless looked for rate ˆf either varied over time or were constant (but not correlative support for the hypothesis that increased law the model in which all 3 varied over time, due to con- enforcement efforts results in increased tiger abundance. founding of parameters). We also modeled survival and Based on theoretical models of tiger population viabil- recruitment as functions of total annual patrol effort and ity (Karanth & Stith 1999) and field studies (Karanth et al. detection rates of poaching incidents. 2004), we hypothesized that poaching of ungulates for local trade and of tigers for global commerce are drivers Results of tiger decline in HKK. The increased patrols in HKK Estimates of Tiger Capture Probabilities, from 2006 to 2012 aimed to reduce poaching, which in Densities, and Abundance turn would stabilize the tiger population and eventually lead to the recovery to its potential carrying capacity. For each year (excluding 2005), depending on logisti- Anecdotal information from antipoaching efforts and cal feasibility, we sampled approximately the same area law enforcement experience in HKK indicated that small (Fig. 2; Table 1). Our surveys yielded photo captures of groups of hunters temporarily camped in the forest, typ- 90 individual tigers (Table 1) (33 males, 50 females, 7 ically operating on foot. They poison carcasses of wild tigers of unknown sex). prey to lure and subsequently kill tigers (Department The movement parameter in the SCR model showed of National Parks 2013). After 2006, the authorities ex- tiger movement parameter ranged from 3.424 to panded the patrol range, intensified patrol networks, and 6.249 km and the expected basal encounter rate of an collected data on spatial and temporal deployment of pa- individual in a sampling occasion ranged from 0.050 to trols. Information on law enforcement, including data on 0.016 (Table 2). We were unable to perform separate cases of poaching of prey animals, was gathered and main- analyses by sex because of sample size constraints. Tiger tained using the MIST patrolling system software (Stokes densities Dˆ ranged from 1.27 (SD 0.19) to 2.09 (SD 0.39) 2010). The annual number of patrol days increased from tigers/100 km2. Population size Nˆ generally ranged from 1031 to 3316 and distances patrolled increased from 41 (SE 1.3) to 58 (SE 2.1) animals, except in 2011 when 5,979 km to 12,907 km (Fig. 3). there was a steep decline to 35 (SE 1.0) animals (Table The patrol teams recorded direct observations of 2). Estimated spatial densities of tigers in HKK from 2005 poaching and evidence of past occurrences, such as pres- to 2012 are presented in Fig. 2. The year-to-year change ence of snares or traps, camps, and animal carcasses. ratio for successive years t and t + 1 ranged from 0.70 to Poachers were arrested either during patrols or later 1.58. The geometric mean of the finite rate of population based on prior intelligence, following which the wildlife change over the entire study period was 0.99 (SE 0.23), managers prosecuted them. Thus, substantial effort was indicating a virtually stable tiger population. invested to deter, prevent, and catch poachers. We speculated that increases in patrol effort and in Survival Rates and Recruitment encounter rates of poaching signs of tigers and their prey would reflect intensified patrolling in the corresponding The best-fit CJS model (Supporting Information) held year and that the effects of these patrols would manifest capture probability constant across years, but allowed

Conservation Biology Volume 00, No. 0, 2015 55 6 Tiger Population Dynamics in Thailand

Figure 2. Estimated spatial pattern of tiger densities per 0.336 km2 pixel (generated with program SPACECAP) and camera-trap locations in Huai Kha Khaeng Wildlife Sanctuary from 2005 to 2012.

Table 2. Annual estimates of tiger densities (Dˆ ), abundance (Nˆ ), and model parametersa with posterior standard deviations (SD)b based on photographic capture data from Huai Kha Khaeng Wildlife Sanctuary (HKK), Thailand, 2005–2012. Year σˆ (SD) λˆ0 (SD) ψˆ (SD) Dˆ (SD) Dˆ 95% CI Nˆ (SE) 2005 4.080 (0.38) 0.050 (0.007) 0.483 (0.10) 1.82 (0.35) 1.19–2.53 50.60 (1.85) 2006 3.424 (0.37) 0.020 (0.004) 0.545 (0.11) 2.09 (0.39) 1.33–2.81 58.10 (2.06) 2007 4.512 (0.49) 0.013 (0.003) 0.385 (0.08) 1.46 (0.25) 0.97–1.95 40.59 (1.32) 2008 5.288 (0.40) 0.016 (0.002) 0.386 (0.06) 1.54 (0.21) 1.12–1.95 42.81 (1.11) 2009 4.275 (0.36) 0.016 (0.003) 0.427 (0.07) 1.69 (0.24) 1.23–2.16 46.98 (1.27) 2010 3.635 (0.29) 0.026 (0.004) 0.414 (0.07) 1.60 (0.26) 1.14–2.08 44.48 (1.37) 2011 6.249 (0.57) 0.012 (0.002) 0.323 (0.06) 1.27 (0.19) 0.88–1.60 35.31 (1.00) 2012 3.495 (0.21) 0.023 (0.003) 0.392 (0.06) 2.01 (0.26) 1.51–2.53 55.88 (1.37) a Parameters: movement, σˆ; basal encounter rate, λˆ0; proportion of MCMC-data augmented individuals that are real, (ψˆ ). bValues derived from spatial capture–recapture analyses in program SPACECAP. See “Methods” section for descriptions of spatial capture– recapture model parameters and details on abundance estimation. survival rates to vary [φ(t),p(.)]. Overall capture proba- Protection Measures bility, annual survival and interval survival rates for tigers Official reports showed that patrol teams detected 88 car- were estimated under this model (Table 3). Annual sur- casses of prey species. Three incidents of tiger poaching vival rates ranged from 79.6% to 95.5% (Table 3), re- were detected and successfully investigated, although it is sulting in an overall survival rate of 82% (derived from likely that not all such cases were detected. Overall, inten- the constant survival model [φ(.),p(.)]) (Supporting Infor- sification of foot patrols appeared to reduce incidences mation). Annual recruitment of new individuals into the of poaching (Fig. 3). The negative correspondence be- population (0–24 tigers) varied, but it increased gradually tween increased law enforcement and extent of poaching during the study, except for a drop during the 2010–2011 was supported by different types of evidence, including interval (Table 3).

Conservation Biology Volume 00, No. 0, 2015 56 Duangchantrasiri et al. 7

Table 3. Estimates of tiger abundance (N ), recapture probability ( pˆ ), annual survival (φˆ ), interval survival (φˆ t), and recruitment (Bˆ ) from open-model Cormack–Jolly–Seber analysesa based on photographic capture data from for Huai Kha Khaeng Wildlife Sanctuary (HKK), Thailand, from 2005 to 2012.b

Year Nˆ (SE) ˆp (SE) φˆ (SE) φˆt (SE) Bˆ (SE) 2005 50.60 (1.85) – 0.80 (0.08) 0.73 (0.08) – 2006 58.10 (2.06) 0.87 (0.03) 0.80 (0.08) 0.79 (0.08) 0 (5.03) 2007 40.59 (1.32) 0.87 (0.03) 0.96 (0.06) 0.95 (0.06) 5.17 (2.86) 2008 42.81 (1.11) 0.87 (0.03) 0.87 (0.05) 0.88 (0.05) 9.32 (2.67) 2009 46.98 (1.27) 0.87 (0.03) 0.59 (0.08) 0.57 (0.08) 16.81 (4.22) 2010 44.48 (1.37) 0.87 (0.03) 0.86 (0.05) 0.86 (0.05) 0 (2.70) 2011 35.31 (1.00) 0.87 (0.03) 0.90 (0.09) 0.90 (0.09) 24.05 (3.40) 2012 55.88 (1.37) 0.87 (0.03) – – – aAnalyses performed using the selected model [φ(t), p(.)] in program MARK. bSee “Methods” section for details on how abundance, interval survival, and recruitment were computed.

Figure 3. Foot-patrol efforts and encounter rates of poaching incidents from 2006 to 2012 in Huai Kha Khaeng Wildlife Sanctuary. number of poaching incidents (Pearson’s correlation r = patrolling efficacy) positively affected recruitment, the −0.39; P < 0.01) and detection of old poaching camps effect was very weak, as indicated by the small estimated (r = –0.55; P < 0.01), traps and snares (r =−0.87; P < β-coefficient (βˆ (SE) = 0.05 (0.02)) and the fact that the 0.01), and poached animal carcasses (r = –0.35; P < 0.2). 95% CIs straddled zero (−0.007 to 0.108). Models with Increased patrolling was positively correlated to numbers survival rate as a function of total annual patrol effort or of arrested poachers (r = 0.29; P > 0.2), although the encounter rate of poaching signs received little support. relationship was not significant. Because no clear increases in population density were Discussion evident from 2005 to 2012, we could not directly explore effects of increased patrolling on tiger abundance Utility of Noninvasive Capture–Recapture Sampling or density. The temporal symmetry analysis indicated Our results showed that the number of individual tigers that a single model did not receive clear support from captured M + (and camera-trapping rates) did not < t 1 the data because 5 models had AICC 2 (Supporting vary monotonically with tiger densities or abundance Information). Among these, the models with the low- (Table 1). We were also able to map spatial density vari- est AICc value included survival probability as a func- ations within the HKK study area for each year (Fig. 2). tion of time and per capita recruitment as a function of Because a fully developed SCR modeling approach for poaching sign encounter rate (Supporting Information). demographically open populations does not currently Although poaching sign encounter rates (a surrogate for exist, we integrated our closed-model SCR estimates of

Conservation Biology Volume 00, No. 0, 2015 57 8 Tiger Population Dynamics in Thailand abundance with likelihood-based, nonspatial, open- number of breeding females, in adult or cub survival, model CR analyses. However, open-model extensions and in subadult dispersal (Sunquist 1981; Smith 1993; of the SCR currently under development may permit Goodrich et al. 2008). addressing questions of demography more thoroughly With over 50 tigers, HKK probably supports the largest in terms of age, sex-specific social organization, and land source population of wild tigers outside the Indian sub- tenure. continent, highlighting its strategic importance for range- wide tiger recovery. Tiger survival rates were generally Study Limitations stable and reasonably high across all annual intervals in our study, except 2009–2010 when survival dipped to The potential violation of demographic closure assump- 59%. The overall survival rate of 82% at HKK compares fa- tion in our secondary samples (Table 1) may have resulted vorably with the rate of 77% observed in the high-density in some overestimation of tiger abundance due to under- protected population in Nagarahole (Karanth et al. 2006). estimation of basal encounter rate and potential turnover Annual recruitment rates of tigers (from in situ repro- of individuals. Given that the sampling durations were duction and immigration) showed a gradual increase and similar over time (excluding 2005), we expect compa- attained relatively higher levels toward the end of our rable magnitudes of bias in basal encounter rate and in study period. We speculate that the dip in survival in estimates of abundance across all years. Therefore, esti- 2009–2010 likely caused the drop in recruitment in sub- mated rates of recruitment and population change may sequent intervals, depressing the population size as ob- be relatively unbiased. Our tiger density estimates were served in 2011. Subsequent rebound of survival rates in lower than those reported by Simcharoen et al. (2007), 2010–2011 and 2011–2012 resulted in a large recruitment likely because the 477 km2 area they sampled supports of 24 tigers into the population in 2011–2012, raising the the highest tiger density within HKK due to its history of population size to 56 tigers by 2012. protection and their reliance on conventional CR analysis Although per capita recruitment into the sampled pop- with the half MMDM buffer probably resulted in density ulation appeared to respond favorably to increased detec- estimates higher than SCR analyses (Sharma et al. 2010; tion of poaching signs, the influence was not significant. Karanth et al. 2011a). We contend that tiger densities Responses in survival rates of adult tigers to increased pa- in HKK were not substantially higher in the area we trolling may still occur indirectly through increased prey sampled before our study. Variation in the number of densities, which is likely to manifest after a time lag of trap locations over time was fully accounted for in the several years. Although recruitment into the population SCR analyses, which included the spatial and temporal of tigers >1 year old may increase slightly through greater deployment of camera traps. The CJS analysis was robust cub survival, we speculate that far greater increases in to increase in sampled area after 2005 because capture survival are likely to occur after a time lag of several histories were conditioned on first captures and there years (through cub survival and immigration) in response was no strong reduction in the trap array area over time. to increase and stabilization of prey densities following Estimation of recruitment and rate of population change increased patrol effort. Overall, our results do not pro- were both sensitive to increases in the sampled area; vide unambiguous evidence of an increase in the tiger therefore, we excluded data from the first year of sam- population size in HKK between 2006 and 2012. pling (2005) when estimating these quantities. Finally, our indices of poaching intensity were subject to the same problems that weaken all indices of abundance. Fu- Conservation and Management Implications ture monitoring of poaching would benefit from survey Excessive hunting is a recognized driver of species de- protocols that enable these poaching intensity indices to clines globally. Establishment of effective protected areas be corrected for detectability. has been pivotal to reversing species declines and pre- venting extinctions of many taxa (Hoffmann et al. 2010). Tiger Population Dynamics in Huai Kha Khaeng In this context, the endangerment of the iconic tiger Tiger densities in HKK currently appear to be well below has received significant conservation focus for nearly potential levels (Supporting Information), similar to den- 5 decades. Walston et al. (2010) argue that preventive sities in Indian reserves such as Pench (Madhya Pradesh), law enforcement focused on tiger population sources Tadoba, and Bhadra (3.3–4.9 tigers/100 km2 from 2000 to (typically located in protected areas) is the most effec- 2004), which had relatively short histories of protection tive species recovery strategy, whereas Wikramanayake (Karanth et al. 2004). Tiger densities showed a substan- et al. (2011) advocate efforts across wider landscapes. Re- tial fluctuation of 62% (1.3–2.1 tigers/100 km2) over the gardless of these nuances, rigorous assessment and long- 7 years of our study (Table 2). The fluctuation across 9 term monitoring of tiger population response is critical years was even higher at 152% (8.6–21.7 tigers/100 km2) as demonstrated by our results. in Nagarahole, India (Karanth et al. 2011b), despite much Even expensive and intensive conservation interven- higher densities. Such fluctuations are inherent to tiger tions are rarely accompanied by such assessments (Walsh populations and arise from demographic stochasticity in & White 1999; Sutherland et al. 2004; Balme et al. 2009).

Conservation Biology Volume 00, No. 0, 2015 58 Duangchantrasiri et al. 9

Historical evidence from tiger reserves in India and Nepal of capture probabilities, abundance, trap array, buffer suggests that 10–15 years of intensive protection of width, sampled area, and tiger densities derived source sites is required before prey populations attain from closed-model conventional capture–recapture anal- ecologically optimal densities. Thereafter, range sizes of yses (Appendix S2), model-selection statistics from breeding female tigers shrink in response (Sunquist 1981; Pradel’s (1996) temporal symmetry analyses in HKK Smith 1993; Karanth et al. 1999), leading to denser pack- (Appendix S3), and spatial intensity of patrols in HKK ing of breeders and increased tiger abundance. from 2006 to 2012 (Appendix S4) are available as part of We found that antipoaching efforts in HKK intensified the on-line article. The authors are solely responsible for substantially after 2006. These efforts were correlated the content and functionality of these materials. Queries with declines in evidence of poaching and possibly led (other than absence of the material) should be directed to improvements in tiger survival and recruitment rates, to the corresponding author. although we were not able to demonstrate a clear relationship. A major criminal gang engaged in tiger and Literature Cited prey poaching was apprehended in 2011 (Department of Balme G, Slotow R, Hunter L. 2009. Impact of conservation interven- National Parks 2013). In one incident, camera-trap survey tions on the dynamics and persistence of a persecuted leopard (Pan- teams detected poaching of 3 tigers. Vigorous follow up thera pardus) population. Biological Conservation 142:2681–2690. by managers led to apprehension of the poachers. It is Bhumpakphan N. 1997. Ecological characteristics and habitat utilization plausible that the drop in tiger survival during 2008– of gaur (Bos gaurus H. Smith, 1827) in different climatic sites. Thesis, Kasetsart University, Bangkok, Thailand. 2009 was a consequence of increased levels of organized Borchers DL, Efford M. 2008. Spatially explicit maximum likelihood poaching. Increases in tiger densities were not evident methods for capture–recapture studies. Biometrics 64:377–385. after 7 years, possibly due to these continued poaching Brooks TM, Wright SJ, Sheil D. 2009. Evaluating the success of conser- pressures or because of limitations in our methods. vation actions in safeguarding tropical forest biodiversity. Conserva- Targeted management and increased protection have tion Biology 23:1448–1457. Burnham KP, Anderson DR. 2002. Model selection and multimodel enabled rapid recovery of populations in some felid inference: a practical information-theoretic approach. Springer- species after negative impacts were controlled (Lindzey Verlag, New York. et al. 1992; Balme et al. 2009). In contrast, our results indi- Caro TM, O’Doherty G. 1999. On the use of surrogate species in con- cate that population recoveries of tigers in the face of preservation biology. Conservation Biology 13:805–814. vailing levels of poaching pressures in Southeast Asia are Ceballos G, Ehrlich PR. 2002. Mammal population losses and the extinction crisis. Science 296:904–907. likely to be much slower and uncertain, as seen in HKK. Cooch EG, White GC. 2009. Using MARK- a gentle introduction. Avail- Therefore, the goal of doubling wild tiger populations in able from http://www.phidot.org/software/ (accessed April 2013). 10 years set by the Global Tiger Initiative (2013) appears Cormack RM. 1964. Estimates of survival from the sighting of marked to be unsupported on the basis of current evidence. animals. Biometrika 51:429–438. Department of National Parks. 2010. Wildlife conservation in Thailand. Department of National Parks Report, Wildlife and Plant Conserva- Acknowledgments tion, Bangkok. Department of National Parks. 2013. Wildlife conservation in Thailand. We thank the Wildlife Conservation Society, New York, Department of National Parks Report, Wildlife and Plant Conserva- for supporting our research. We are grateful to the tion, Bangkok. following donors for funding support: The U.S. Fish Geweke J. 1992. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Pages 169–193 in Bernardo and Wildlife Service, U.S. Department of State, Liz Clai- JM, editor. Bayesian statistics 4. Oxford University Press, Oxford, borne and Art Ortenberg Foundation, Panthera, Save UK. the Tiger Fund, Disney Worldwide Conservation Fund, Global Tiger Initiative. 2013. Global Tiger Recovery Program Implemen- National Geographic Society, and Petroleum Authority tation Plan 2013–14. The World Bank, Washington, DC. of Thailand: Production & Exploration (PTT-EP). We Goodrich J, Kerley L, Smirnov E, Miquelle D, McDonald L, Quigley H, Hornocker M, McDonald T. 2008. Survival rates and causes of also acknowledge support from the Department of Na- mortality of Amur tigers on and near the Sikhote-Alin Biosphere tional Parks, Wildlife and Plant Conservation (Thailand), Zapovednik. Journal of Zoology 276:323–329. The Royal Thai Police and Kasetsart University-Thailand. Gopalaswamy AM, Royle JA, Hines JE, Singh P, Jathanna D, Kumar We are grateful to J.D. Nichols, J.A. Royle, and A.M. NS, Karanth KU. 2012. Program SPACECAP: software for estimating Gopalaswamy for useful discussions on methods and to animal density using spatially explicit capture–recapture models. Methods in Ecology and Evolution 3:1067–1072. V.R. Goswami for analytical assistance. Hebblewhite M, Percy M, Serrouya R. 2003. Black bear (Ursus ameri- canus) survival and demography in the Bow Valley of Banff National Park, Alberta. Biological Conservation 112:415–425. Supporting Information Hiby L, Lovell P, Patil N, Kumar NS, Gopalaswamy AM, Karanth KU. 2009. A tiger cannot change its stripes: using a three-dimensional model to match images of living tigers and tiger skins. Biology Letters Model-selection statistics for the open-model Cormack– 5:383–386. Jolly–Seber analyses used to estimate survival and de- Hoffmann M, et al. 2010. The impact of conservation on the status of tection probability (Appendix S1), annual estimates the world’s vertebrates. Science 330:1503–1509.

Conservation Biology Volume 00, No. 0, 2015 59 10 Tiger Population Dynamics in Thailand

Jolly GM. 1965. Explicit estimates from capture-recapture data with Prayurasiddhi T. 1997. The ecological separation of gaur (Bos gaurus) both death and immigration-stochastic model. Biometrika 52:225– and banteng (Bos javanicus) in Huai Kha Khaeng Wildlife Sanc- 247. tuary, Thailand. Wildlife Conservation, University of Minnesota, St. Jotikapukkana S, Berg A,˚ Pattanavibool A. 2010. Wildlife and human Paul, Minnesota. use of buffer-zone areas in a wildlife sanctuary. Wildlife Research Royle JA, Chandler RB, Sollmann R, Gardner B. 2013. Spatial capture- 37:466–474. recapture. Academic Press, New York. Karanth KU, Nichols JD. 2002. Monitoring tigers and their prey: a Royle JA, Karanth KU, Gopalaswamy AM, Kumar NS. 2009. Bayesian manual for wildlife researchers, managers and conservationists in inference in camera trapping studies for a class of spatial capture- tropical Asia. Centre for Wildlife Studies, Bangalore. recapture models. Ecology 90:3233–3244. Karanth KU, Nichols JD. 2010. Non-invasive survey methods for assess- Schipper J, Chanson JS, Chiozza F, Cox NA, Hoffmann M, Katariya ing tiger populations. Pages 241–261 in Tilson RL, Nyhus PJ, editors. V, Lamoreux J, Rodrigues AS, Stuart SN, Temple HJ. 2008. The Tigers of the world: science, politics and conservation of Panthera status of the world’s land and marine mammals: diversity, threat, tigris. Elsevier, New York. and knowledge. Science 322:225–230. Karanth KU, Nichols JD, Kumar NS. 2011a. Estimating tiger abundance Seber GAF. 1965. A note on multiple-recapture census. Biometrika from camera trap data: field surveys and analytical issues. Pages 97– 52:249–259. 117 in O’Connell AF, Nichols JD, Karanth KU, editors. Camera traps Sharma RK, Jhala Y, Qureshi Q, Vattakaven J, Gopal R, Nayak K. 2010. in animal ecology: methods and analyses. Springer, Tokyo. Evaluating capture-recapture population and density estimation of Karanth KU, Nichols JD, Kumar NS, Jathanna D. 2011b. Estimation of tigers in a population with known parameters. Animal Conservation demographic parameters in a tiger population from long-term cam- 13:94–103. era trap data. Pages 145–161 in O’Connell AF, Nichols JD, Karanth Simcharoen S, Pattanavibool A, Karanth KU, Nichols JD, Kumar NS. KU, editors. Camera traps in animal ecology: methods and analyses. 2007. How many tigers Panthera tigris are there in Huai Kha Springer, Tokyo. Khaeng Wildlife Sanctuary, Thailand? An estimate using photo- Karanth KU, Nichols JD, Kumar NS, Hines JE. 2006. Assessing tiger pop- graphic capture-recapture sampling. Oryx 41:447–453. ulation dynamics using photographic capture-recapture sampling. Smith JLD. 1993. The role of dispersal in structuring the Chitwan tiger Ecology 87:2925–2937. population. Behaviour 124:165–195. Karanth KU, Nichols JD, Kumar NS, Link WA, Hines JE. 2004. Tigers Soule ME, Estes JA, Miller B, Honnold DL. 2005. Strongly interacting and their prey: predicting carnivore densities from prey abundance. species: conservation policy, management, and ethics. BioScience Proceedings of the National Academy of Sciences of the United 55:168–176. States of America 101:4854–4858. Stokes EJ. 2010. Improving effectiveness of protection efforts in tiger Karanth KU, Stith BM. 1999. Prey depletion as a critical determi- source sites: developing a framework for law enforcement monitor- nant of tiger population viability. Pages 100–113 in Seidensticker ing using MIST. Integrative Zoology 5:363–377. J, Christie S, Jackson P, editors. Riding the tiger: tiger conserva- Sunquist ME. 1981. The social organization of tigers (Panthera tigris) tion in human dominated landscapes. Cambridge University Press, in Royal Chitwan National Park, Nepal. Smithsonian Contributions Cambridge, United Kingdom. to Zoology 336:1–98. Karanth KU, Sunquist ME, Chinnappa KM. 1999. Long term monitoring Sutherland WJ, Pullin AS, Dolman PM, Knight TM. 2004. The need of tigers: lessons from Nagarahole. Pages 114–122 in Seidensticker for evidence-based conservation. Trends in Ecology & Evolution J, Christie S, Jackson P, editors. Riding the tiger: tiger conserva- 19:305–308. tion in human dominated landscapes. Cambridge University Press, van de Kerk M, de Kroon H, Conde DA, Jongejans E. 2013. Carnivora Cambridge, United Kingdom. population dynamics are as slow and as fast as those of other mam- Lambert CMS, Wielgus RB, Robinson HS, Katnik DD, Cruickshank HS, mals: implications for their conservation. PLOS ONE 8: (e70354) Clarke R, Almack J. 2006. Cougar population dynamics and viability DOI: 10.1371/journal.pone.0070354. in the Pacific Northwest. Journal of Wildlife Management 70:246– Walpole MJ, Morgan-Davies M, Milledge S, Bett P, Leader-Williams N. 254. 2001. Population dynamics and future conservation of a free-ranging Lindzey FG, Van Sickle WD, Laing SP, Mecham CS. 1992. Cougar popu- black rhinoceros (Diceros bicornis) population in Kenya. Biological lation response to manipulation in southern Utah. Wildlife Society Conservation 99:237–243. Bulletin 20(2):224–227. Walsh PD, White LJ. 1999. What it will take to monitor forest elephant Margules CR, Pressey RL. 2000. Systematic conservation planning. Na- populations. Conservation Biology 13:1194–1202. ture 405:243–253. Walston J, Robinson JG, Bennett EL, Breitenmoser U, da Fonseca GA, Palomares F, Rodrigues A, Revilla E, Lopez-Bao J, Calzada J. 2010. As- Goodrich J, Gumal M, Hunter L, Johnson A, Karanth KU. 2010. sessment of the conservation efforts to prevent extinction of the Bringing the tiger back from the brink—the six percent solution. Iberian lynx. Conservation Biology 25:4–8. PLOS Biology 8: (e1000485) DOI: 10.1371/journal.pbio.1000485. Petdee A. 2000. Feeding habits of the tiger (Panthera tigris) in Huai WCS-Thailand. 2007. Building a monitoring system for tiger conserva- Kha Khaeng Wildlife Sanctuary by fecal analysis. Faculty of Forestry, tion in the Western Forest Complex, Thailand. A final report to US Kasetsart University, Bangkok. Fish and Wildlife Service. WCS Thailand, Bangkok. Pollock KH, Nichols JD, Brownie C, Hines JE. 1990. Statistical infer- Wikramanayake E, Dinerstein E, Seidensticker J, Lumpkin S, Pandav ence for capture-recapture experiments. Wildlife Monographs 107: B, Shrestha M, Mishra H, Ballou J, Johnsingh A, Chestin I. 2011. 3–97. A landscape-based conservation strategy to double the wild tiger Pradel R. 1996. Utilization of capture-mark-recapture for the study population. Conservation Letters 4:219–227. of recruitment and population growth rate. Biometrics 52:703– Williams BK, Nichols JD, Conroy MJ. 2002. Analysis and management 709. of animal populations. Academic Press, California, USA.

Conservation Biology Volume 00, No. 0, 2015

View publication stats 60

A context-sensitive correlated random walk: A new simulation model for movement

Sean C. Ahearna,∗ Somayeh Dodgeb,d∗, Achara Simcharoenc, Glenn Xavierd, James L.D. Smithb

International Journal of Geographical Information Science (in press, August 2016)

aCity University of New York – Hunter College bUniversity of Minnesota, Twin Cities cDepartment of National Parks, Plant Conservation, Thailand dUniversity of Colorado, Colorado Springs

Abstract Computational Movement Analysis focuses on the characterization of the trajectory of individuals across space and time. Various analytic techniques, including but not limited to random walks, brownian motion models, and step selection functions have been used for modeling movement. These fall under the rubric of signal models which are divided into deterministic and stochastic models. The difficulty of applying these models to the movement of dynamic objects (e.g. animals, humans, vehicles) is that the spatiotemporal signal produced by their trajectories a complex composite that is influenced by the geography through which they move (i.e. the network or the physiography of the terrain), their behavioral state (i.e. hungry, going to work, shopping, tourism, etc.), and their interactions with other individuals. This signal reflects multiple scales of behavior from the local choices to the global objectives that drive movement.preprint In this research we propose a stochastic simulation model that incorporates contextual factors (i.e. environmental conditions) that affect local choices along its movement trajectory. We show how actual GPS observations can be used to parameterize movement and validate movement models, and argue that incorporating context is essential in modeling movement.

keywords: Movement model; stochastic models, agent-based simulation; environmental context; behavior; movement pattern, scale.

∗Corresponding authors. Email: [email protected], [email protected]

1 61

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

1 Introduction

Movement is essential to almost all organisms. The advent of inexpensive and ubiqui- tous positioning technologies such as Global Positioning Systems (GPS) has resulted in unprecedented datasets that can be used for quantitative assessment of the movement of individuals (i.e. animals, humans) and their collective dynamics (Galton, 2005). As a consequence, the study of movement has gained significant momentum in Geographic Information Science (GIScience) and its applications in movement ecology (Demˇsaret al., 2015; Holyoak et al., 2008), mobility and transportation (Tribby et al., 2016; Song et al., 2015), behavioral studies (Gonzálezet al., 2008; Sang et al., 2011; Torrens et al., 2012), and public health (Glasgow et al., 2014; Lu and Fang, 2014), to name but a few. Numerous deterministic and stochastic methods have been developed to model movement, generate synthetic trajectories, and analyze patterns of movement (Schick et al., 2008; Laube, 2014; Dodge et al., 2016). These methods include various extensions of time geography approaches (Winter and Yin, 2010; Hornsby and Egenhofer, 2002; Miller, 1991), Lévyflights (Jiang et al., 2009; Rhee et al., 2011), random walks (Ahearn et al., 2001; Batty et al., 2003; Gautestad and Mysterud, 2005; Codling et al., 2008; Technitis et al., 2014), brownian bridges (Horne et al., 2007; Kranstauber et al., 2012; Buchin et al., 2012), Step Selection Functions (Squires et al., 2013; Thurfjell et al., 2014), and hidden Markov models (Franke et al., 2004). These models mainly employ information about movement parameters (e.g. speed, distance, turn angle) and time to simulate possible trajectories in space and time (i.e random walks and Lévyflights), as well as to quantify a probable space accessible or used by dynamic objects (i.e. time geography and brownian bridge models). Most studies concentrate on the pattern of space use and visit probability (Downs and Horner, 2009; Kie et al., 2010; Benhamou and Riotte-Lambert, 2012; Song and Miller, 2014) while others have examined the complex interaction between movement behavior and variation in environmental conditions (Ovaskainen, 2004; Morales et al., 2005; Moorcroft et al., 2006; Forester et al., 2007; Ovaskainen et al., 2008; Ahearn et al., 2010). While significant progress has been made, a key question with respect to existing models is how well do they capture: the local choices that agents make as they move through their environment, their interaction with other individuals (Yuan and Nara, 2015), and their own geographic strategies for resource usage (Ahearn and Smith, 2005; Dodge etpreprint al., 2014; Dodge, 2016). The spatiotemporal signal produced by movement trajectories is in fact a complex composite that reflects multiple scales of behavior from the local choices to the global objectives that drive movement. Move- ment encapsulates several components: the internal states and behavioral states of the moving entity, and the space and context (i.e. environmental factors) through which it moves in time (Dodge, 2016). Existing approaches often confound movement patterns associated with external factors with those patterns associated with global scale behaviors. Accurate simulation models for movement should incorporate and deconstruct the environmental and behavioral drivers of movement for more reliable

2 62

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

and realistic representations. Simulation of movement is essential for studying and predicting patterns and behavioral responses of moving agents to varying conditions and their interactions with other individuals. This study introduces a context-sensitive simulation model for movement based on the concept of the correlated random walk (Codling et al., 2008). The proposed model is diﬀerent from existing approaches because it integrates environmental context into modeling movement as an integral part of each local choice along the movement trajectory. The strength of our approach is that the model is parameterized and validated using real movement data. The parametrization of the model is achieved through the development of probability distributions that relate actual movement to its context. We assess our proposed model on movement of endangered tigers (Panthera tigris) in West-central Thailand. The case study investigates the inﬂuence of geography and physiography (i.e. the shape of home range and landscape characteristics) on tiger’s movement characteristics.

2 Related Work on Modeling Movement

Random walks have broadly been used to model change or movement at local and global scales (David and Perry, 2013). A two dimensional or spatial random walk describes the probability of moving in a direction from the current position in space. Codling et al. (2008) provide a comprehensive review of diﬀerent random walk models applied in biology and ecology applications. The simplest version of random walk is called the uncorrelated random walk. In this model, the next direction of movement is independent of the direction of the previous movement step. In contrast, the correlated random walk (CRW) involves a persistence in the direction of successive movement steps by introducing a correlation to the preceding directions (Turchin, 1998). Previous studies suggest that the movement of organisms is less random and CRWs are better suited to represent movement (David and Perry, 2013). In addition to the local persistence in direction, random walks can involve an external bias to maintain a global direction of movement from an origin towards a destination (Ahearn et al., 2001; Technitis et al., 2014). Random walks form the building block of many agent- based simulation models for movement (Ahearn et al., 2001; Batty et al., 2003; Tang and Bennett,preprint 2010; Torrens et al., 2012; Technitis et al., 2014). Ahearn et al. (2001) built an agent-based model to simulate the collective dynamics of male and female tigers (with or without their cubs), and their interactions with prey. Their simulation is based on a correlated random walk by introducing external biases (i.e. location of other tigers and location of prey) that are dynamic and a function of the animal’s state. Gautestad and Mysterud (2005) proposed a multi-scale random walk (MSR) which does not make the assumption of a low-order Markovian process (i.e. the current state is a function of the immediate previous state) that many models do. Their work uses the MSR to model the multiple spatiotemporal scales over which animals operate (Gautestad and Mysterud, 2010). In another study, Technitis et al.

3 63

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

(2014) introduced a point to point correlated random walk model (i.e. for given origin and destination locations) based on a set of assumptions on speed capacities of moving individuals and a time budget, akin to time geography approaches. Random walk models are often used to generate a trajectory (i.e. sequence of locations over time). In contrast, time geography and brownian bridge movement models mainly quantify a continuos space accessible by a moving individual. As such these models can represent space utilization distribution or visitation probability in space and/or in time. Time geography models are based on the concept of Hägerstrand’s space-time prism (Hägerstrand,1970). In time geography, accessibility is defined as a space that a moving entity can possibly reach given a time budget and a maximum speed (Miller, 1991). A variety of time geography approaches have been proposed to model movement in a planar space (Winter and Yin, 2010; Song and Miller, 2014), or in a network space (Song et al., 2015). The brownian bridge model quantifies the probability of being in a location between a start (A) and an endpoint (B) taking into account the distance between the points and the overall time that it takes to traverse from A to B (Horne et al., 2007; Kranstauber et al., 2012). Most existing simulation models are parametrized using a set of assumptions and rules based on the dynamic capacities of the moving agents (e.g. speed capacities, time budget, turn angle patterns). It is now possible to calibrate models based on the distribution functions and correlations of movement parameters (e.g. speed, turn angle) computed from real observations, thus obviating the need for such assumptions. An- other limitation of existing movement models is that many of them ignore the internal states, behaviors, and context (i.e. external factors) that results in a set of movement patterns (Dodge, 2016; Dodge et al., 2016). They mainly consider dynamic capacities of moving individuals and external biases such as destination or interactions between moving individuals (Miller, 2015). A number of researchers have begun to incorporate context into their understanding and modeling of movement (Ovaskainen, 2004; Morales et al., 2005; Moorcroft et al., 2006; Forester et al., 2007; Ovaskainen et al., 2008). Ovaskainen (2004) and Ovaskainen et al. (2008) used a segmented landscape with different diffusion coefficients for each landscape type with separate specification for the boundary condition. Their research provides a strong case for understanding movement in heterogeneous landscapes. Moorcroft et al. (2006) proposed a mechanistic home range model for carnivores that incorporates conspecific avoidance, rough terrain avoidance and habitat selection to derive home range patterns. The models resulting probabilitypreprint density functions were tested against the home range derived from observations of coyote GPS locations and demonstrated a strong “goodness of fit”. Schick et al. (2008) provide an excellent summary of these models and other existing approaches for modeling animal movement. The models they reviewed focus on three areas: simulating realistic movement, understanding organism-environment interaction and its effect on movement, and inferential models for predicting movement where data may be incomplete. In conclusion they find that while models have attempted to understand organism-environment interaction, “none of these models has an ability to test for how landscape features actually influence the movement

4 64

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016 process” (Schick et al., 2008). They propose a conceptual model that explicitly incorporates state, location, and habitat suitability as factors governing the choice of an individual as it moves from place to place. A more recent development in understanding the relationship between environment and movement is the Step Selection Function (SSF) (Thurfjell et al., 2014). The mechanism for this model is the Resource Selection Function (RSF) which usually uses a Logistic Regression to relate the probability of selecting the next resource unit to a set of environmental correlates deﬁned by their frequency of use and availability (Thurfjell et al., 2014). While this is a powerful new method to understand these relationships, it has been used in a limited way to simulate movement. For instance, it has been applied to create a probability surface of use for each resource unit to calculate a least-cost path to generate point to point movement (Squires et al., 2013). This in essence is a deterministic model of movement in contrast to the stochastic model we propose in this paper.

3 Methodology

This section presents a new context-sensitive correlated random walk (CsCRW) method and compares it with a standard correlated random walk (CRW) to better understand the contribution of geography and contextual factors to an individual’s trajectory. The models are implemented in a Monte Carlo agent-based simulation environment in which trajectories are simulated within an area of interest (e.g. deﬁned by the shape of a home range (Powell and Mitchell, 2012)). The simulated trajectories are used to generate a visitation probability surface for evaluation and comparison of the models, as described below.

3.1 Drivers of Movement Movement occurs in response to the internal state of a moving agent (e.g. the physi- ological state of being hungry) which results in a change in its behavioral states (e.g. hunting, patrolling) (Ahearn and Smith, 2005). The behavioral states of a moving agent are realized as patterns of movement that occur at different spatial and temporal scales (Dodge, 2016). Environmental factors (e.g. slope, vegetation density, prey density and the existencepreprint of trails) often affect local scale choices in movement (e.g. direction and rate of movement). These choices are made based on local conditions under the broader constraints of more global scale behaviors driven by internal states of the moving agent (Figure 1). In this paper, context refers to environmental factors that may influence the movement of an agent at local scales. Understanding the contribution of individual’s behavior at different scales to the observable patterns of movement is critical for their proper interpretation. In this paper we focus on the local scale choices affected by environmental context; how to calibrate them and how to validate their importance.

5 65

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

internal state behavioral state (e.g. biological state) (e.g. movement goal)

environmental correlates (e.g. slope, vegetation)

local choices

patterns of movement

Figure 1: Drivers of movement

3.2 Correlated random walk (CRW) The algorithm implemented here (Figure 2) is a modiﬁed version of the standard CRW (Turchin, 1998) to simulate a trajectory T (equation 1). The algorithm ﬁrst selects a random starting point (Step 1) and picks a persistence value for direction (p) from a random uniform distribution (Step 2). If it is less than the input value of persistence in direction, as calculated from the actual GPS observations, then the agent persist in the previous direction with a small probability of deviation (e.g. standard deviation 10 degrees, Step 3.a). If it is more, then the agent can move with a much larger turn angle (e.g. standard deviation 45 degrees, Step 3.b) to a new direction (α, equation 2), to reduce back tracking. Next, a distance d is selected from the probability density function derived from GPS observations (Step 4, equation 3). The agent is then moved for distance d in direction α forming a vector (Step 5, equation 4). This process is repeated to generate a trajectory in the area of interest using the same numberpreprint of points as the actual GPS trajectory (Figure 2). The formal representation of the process is as follows:

6 66

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

T = {(x0, y0, t0), (x1, y1, t1), .., (xi−1, yi−1, ti−1), (xi, yi, ti), ..., (xn, yn, tn)} (1) ( ◦ 2 10 if p ≥ persistence αi = αi−1 + N(0, σ ), where σ = (2) 45◦ otherwise 2 2 di = t ∗ χ (µ), where χ (µ) is the distribution of speed obtained from GPS(3)

xi,t = d ∗ cos αi + xi−1,t−1 (4)

yi,t = d ∗ sin αi + yi−1,t−1

1. pick a random start 2. pick a direction location in persistence probability the area of value (p) start interest

x0,y0 yes p ≥ persistence

3.a. pick a random direction (a) from a normal distribution

Highest direction probability is in the direction of movement

probability Lowest direction probability 0 10-10 next turn angle primary movement direction

3.b. pick a random direction (a)

probability in each direction probability 0 45-45 primary movement direction next turn angle

4. pick a distance (d) from 5. move d meters in

a c c 2 distribution the selected xj,yj direction (a) a d xi,yi µ

preprintyes no end no_pts > MAX

Figure 2: Correlated Random Walk (CRW)

3.3 Context-sensitive correlated random walk (CsCRW) The CsCRW is a modiﬁcation of the CRW algorithm to account for contextual factors (i.e. slope) in generating a context-sensitive trajectory (T c, equation 5). The

7 67

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

movement of the agent across space is simulated on the per-pixel basis rather than by a vector as in the CRW model, to incorporate local choices made by the agent based on context as it moves through space. Contextual factors are modeled by using a probability density function P (c) to determine the next probable move (Step 5.1, Figure 3, equation 6). This function is approximated by a chi-square distribution χ2(µ), derived from the relationship between the agent’s locations (i.e. GPS observations) and the environmental context values (i.e slope use). The CsCRW algorithm follows the same process described for the CRW until Step 4 in Figure 2. Instead of moving along a vector in direction α for distance d, the CsCRW moves one pixel at a time based on context (Step 5.1, Figure 3). At each step, the pixel which is in direction α and its two adjacent neighbors become possible choices for the agent’s next move (Step 5.2). Among the three choices, the pixel whose context value is closest to the random context variable (c) selected from P (c) is chosen (Step 5.3, equation 7), formally:

c T = {(x0, y0, c0, t0), .., (xi−1, yi−1, ci−1, ti−1), (xi, yi, ci, ti), ..., (xn, yn, cntn)} (5) P (c) = χ2(µ), where χ2(µ) is distribution of context values used by an agent(6)

ci = Cj| min(|Cj − c|), where j ∈ J = {1, 2, 3} (7)

(xi,t, yi,t) = (X(ci), Y (ci)), where X, Y are the coordinates of choice ci (8)

The agent is moved to the selected pixel (Step 6, equation 8 ) and the process continues until the selected distance d is reached. When that happens it returns to Step 2 of CRW (Figure 2). This process is repeated to generate a trajectory in the area of interest using the same number of points as the actual GPS trajectory. This approach could in fact include any number of contextual factors (m) that can be combined through a joint probability function (P(c) in equation 10), formally:

ci = {c1, c2, ..., cm}, where m is the number of contextual factors (9) m Y P (c) = P (ck) (10) k=1 3.4 Assessmentpreprint of models To assess the CRW and CsCRW models a surface for probability of visitation is created for each model in the area of interest (e.g. home range). Each surface is created using a Monte Carlo simulation of 1,000 trajectories and intersecting the trajectories with the raster of the area of interest and accumulating the number of visits for each cell. To evaluate how well the surface for probability of visitation captures the actual visitation probability of the moving entity, its GPS trajectory is compared with a random trajectory of equivalent size. Our premise for evaluation is that the best model will result in higher cell counts along the GPS trajectory, than for the random trajectory.

8 68

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

5.1. pick a c c 2 random context variable (c) step 4., CRW

5.3. select a pixel with 5.2. take three pixels according to the a context value selected direction from step 3. closest to c (from 5.1.)

6. move to the selected pixel

(xj,yj)

(xi,yi)

is distance d no reached? current pixel (xi,yi) yes

step 2., CRW

Figure 3: Context-sensitive Correlated Random Walk (CsCRW)

4 Case Study and Results

4.1 Dataset To evaluate our proposed methods we use movement data for two female tigers tracked in Huai Kha Kaeng Wildlife Sanctuary in Thailand. Both tigers were ﬁtted with a Vectronic Aerospace GmbH GPS Plus collar. Tiger T 7203 was tracked between December 2009 and July 2010 with a sampling rate of one hour, and tiger T 10727 was tracked between December 2012 and January 2015 with a sampling rate of four hours. Following removal of outliers and erroneous GPS observations, T 7203 consisted a total of 4874 points and T 10727 consisted of 3946 GPS points. Figure 4 shows trajectories and computed homepreprint ranges of the two tigers, and the elevation maps of the areas. The home range of the tigers are calculated using both a 100% Minimum Convex Polygon (MCP) (Mohr, 1947) (shown in magenta in Figure 4) and a Characteristic Hull Polygon (CHP) (Duckham et al., 2008) (shown in dark blue). The characteristic hull polygon is generated from the Delaunay triangulation (Okabe et al., 1992) of the set of GPS observations. The CHP is extracted from the Delaunay triangulation through the removal of triangles determined by the chi-shape algorithm, resulting in a bounding hull with non-convex edges (Duckham et al., 2008). This method was selected for the analysis because it is one of the most parsimonious methods for determining a home range, as can be seen in the comparison with the commonly used

9 69

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

MCP approach (Downs and Horner, 2009) (Figure 4). The generated home ranges using the Delaunay triangulation method resulted in a 34.26 km2 boundary for tiger T 7203 (93.7% of MCP), and a 99.92 km2 boundary for T 10727 (89.1% of MCP). As seen in Figure 4, the home ranges of the tigers show a diversity of terrain. Both tiger tracks are annotated with elevation and slope information from the digital elevation model (DEM) of the study areas obtained from ASTER GDEM dataset1 (30 meter resolution) using a bilinear interpolation (Dodge et al., 2013).

Vietnam ! Myanmar ! !! !! ! ! ! ! ! !! ! ! !! !!!!! ! ! ! !!!!!!!! !!!!!!! !! !!! ! !! ! ! ! !! ! ! ! !! !!!!! ! !! !! ! ! !! ! ! ! ! ! !! ! ! !! ! ! ! !! !!! !!!!!!!!!!!!!!!!! ! ! !! !!!!!!!!!! ! ! !! ! ! ! ! ! ! Thailand ! ! ! !! ! ! !!! !! ! ! ! ! ! !! ! ! ! ! !!! ! ! ! ! ! ! ! !!!!! !!!! !!! ! ! !!!! !!!!!!!!!!!! ! ! ! !! ! ! ! !!! !! !! !!!!!!!!!!! ! !!!!!! ! !!!!!! ! !! ! !!!!!!!!!!!!!! ! !! !!!!! !!!!!!!!!!!! ! !!! ! ! ! ! !!!!!!!!! ! Cambodia ! !! ! ! !!!!!!!!!!! !!!! ! !!!!!!!!!!!!! !! !! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !!!!!!!! ! ! !! !!!! ! ! ! !!!!!!! ! !!!! ! ! !! !!!!!!!!!!!!!!! ! !!!!!! ! !! !! ! !! ! ! ! ! ! !!!!!!! ! ! ! ! ! !!!!! ! ! ! ! ! ! !! !! ! ! !! !!!!!!!!!!! ! !!! ! ! !! ! ! !! !!!! !! !!!! !! !! ! ! !!! ! !!! !!!!!!!! ! ! ! !!!!! ! ! ! ! !! !!!! ! ! !!! ! !! ! ! !! ! !!!!!!!!!! ! ! ! !!!!!!!!!!!! ! ! !!! ! ! ! ! !!! !! ! !! ! ! ! ! !!!!!!!!!!!!!!!! ! ! ! ! ! !!!!!!! ! !!!! ! !!!!!!!! ! !!! !!!!!!!!!! !! !!!! !!!!! ! ! !!!!! !!!! !!! !!! ! ! !!!!!!!! ! ! !!! ! ! ! !!!!! !!!! ! ! !!!!!!!!!! !! ! !! ! !!!! !! ! !!! ! !!!!!!! !! ! !!! !!! ! !! ! !!!!!!!!! !!!!!!! ! !! !!!! ! ! !!! !!! ! !!!!!! ! ! ! ! !! !!! ! ! !!! ! ! !!!!!!!! ! ! !! !! ! ! ! ! ! !! !! ! !!!! !! ! ! ! !!! ! ! !! !!!!!!!!!! ! !! ! ! ! ! !!!!!!!!!! ! !!!!!!!!!!!! !! ! ! ! ! !! !!! ! !!!! !!!!!!! ! ! ! !! !!!!! ! !!! ! ! ! !! ! ! !!!!!!!!!!!!!! ! !! ! ! ! !!! ! ! !!! !!!!!! !! !!!! ! ! !!!! ! !!! !! !!!! ! ! ! ! !! ! ! ! !!!! ! !! !!! ! ! ! !! !!!!!!!!!!! ! !!!!!!!! ! ! ! !! !!! ! ! ! ! ! !! ! ! !!!!! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! !! ! !!!! !!! ! !! !! ! !! ! !!! ! !! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! !!! !! ! !!!! ! ! !! ! ! ! ! ! ! !!!!!! ! ! ! !!!! !!!!!!!! ! ! !!!!!! ! ! ! ! ! ! ! !!!!!!!!!!!!!!!!!!! !!! !!!!!!!! ! !!!!! !! ! !!!!!!!!!!!!!!!!!!!!!!!! !! !!!! !! ! !!!!! !!!!!!!!! !! !!!!!!!!!!!!!!! !! !!!!!! !! !!!! ! ! !!!!!!!!!!!!!! !! !!!!!!!!!! ! ! !! !! ! !!!!!!!!!! !! ! ! !!!!!!!!! !!! ! !! !! ! ! !!!!! !! ! !!!! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! !!! ! ! ! !!!!!!!!! !! ! ! !! ! !! !!! ! !!!!!!!! !! ! ! !! !!!!!! ! ! ! !!!! ! ! !! !!!! ! ! !!!!!!!!!!!! ! ! !!!!!!!!!! ! ! !!!! ! !!!!! ! !! ! ! !!!!!!!!!!!!! ! !!! ! !!! ! !! !!! !!!!!! ! !!!! !!! !! !!! !! ! ! ! !!!! ! !! ! ! ! !! ! ! ! !!!!!!!!!! ! ! !!! ! !! ! ! !! !! ! ! ! ! ! ! ! ! ! !! !! ! !!! !! !! ! ! ! ! ! ! ! !!!!!!! ! !!!!! ! !!!! ! ! ! ! !! ! ! ! ! !! !! !! !! !!! !!!!!!!!!!!! ! ! !! ! !!!!!!!! ! ! ! ! ! !! ! ! !! !!! !!!!! !!! ! !! !!! ! ! ! ! ! !!!!! !!!! !! !!!! ! !! !! ! ! ! ! ! ! !!! ! ! ! !! ! ! !!!! !!!! ! ! !!! ! !! ! ! ! ! ! ! ! ! ! ! ! ! !!!!!! ! ! ! ! ! ! ! ! !!! ! ! ! !!! ! !!!! !! ! ! ! !!!! ! ! ! !!!!!!!!!!!! ! !!! ! ! !! ! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! !!!! ! ! ! ! ! ! ! !!!! ! !! !!!!! ! ! ! !! ! !!! ! ! !! !! !! !!!!! !! ! ! ! !!!!!!!!!!!!!!!!! !!!!!! ! !! !! ! !! !!! ! !! ! ! ! ! ! !!!! !! !! !!! ! ! !! ! !!!! ! ! ! ! ! !! ! ! !!!! ! !!!!!!!!!! ! ! ! ! ! ! !!! !!!!!!!!!!! !!!! ! ! ! ! !!!!!!!!!!!!! ! ! ! !! ! !!!!!!!!! !!! !!!!!!!!!!!! ! ! ! ! !!! ! ! !! !!!!!!!!! !! ! ! ! !!!! ! ! !! !!!! ! ! !!!!!!! !! !! !!!!!!!!!!!!!!!!! ! !! !! ! ! ! !!!!! !! ! !!!! !! ! ! !!!!! ! ! ! !!!! !!! ! ! ! ! ! ! ! !! ! !!!!! ! ! ! ! ! !!!!!!!! ! ! ! !! !!!!!!!! ! ! Observed GPS Point !!! !!!!!!!!!! ! ! !!! ! ! ! ! ! ! ! !!!! !!! ! ! ! !! !!!!! ! !!! ! !!! ! !!! ! ! ! Observed Trajectory !!! !!!! ! !! !!!! ! !!! ! ! !!!! !!!!!!!! ! ! !!!!! !! !!! !!!!!!!!!!! ! ! !! !!! ! ! ! !!!!! !! !! !!! ! !!!!!!!!! ! ! ! ! ! !! ! !!!! !! ! ! !!!!!!!!!!!!!!!!!!!!!!!! ! ! ! !!!! ! ! Characteristic Hull Polygon ! !!!!!!!!!!!!!! ! !!!! ! ! !! ! !!!!! !!!! ! ! !! !!!!!!!!!!!!!! !!!!!!!!! ! !!!! ! ! ! ! !!! !!!!!!! ! !! ! ! ! ! !!!! ! !!!!!!!!!!!!!!!!!!! ! !!! ! ! ! ! !!!!!! !! ! MCP 100% !! ! !!!! !!!! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !!! !! ! ! ! !! ! Elevation (m) ! !!! !!!! ! !!! !!!!!!!!! ! ! !!! ! !!!!!!! !!! ! !!!!!! High : 1946 ! ! !!!!!!!!! ! ! ! ! !!! !!!!!! ! !!! ! Low : 93 ! ! ! !! 0 0.5 1 2 Km ! ! !!

(a) T7203 trajectory and home range

!! ! !! !! ! ! Vietnam ! Myanmar !! ! ! ! ! ! ! !

! !!! ! ! ! ! Thailand ! ! ! Cambodia ! ! !! ! ! ! ! ! ! ! !!!!! ! ! !!!! ! !!!! ! ! ! ! ! ! ! !!!!!!!!! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !!!!!! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !!! ! !! ! ! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! !! ! !!!!!!!!! ! ! ! ! !!! ! !!!!!!!! ! ! !!!! !! ! !!! ! ! ! !!!!!! !!! !! !!!!! !! ! ! ! !! ! ! !!!!! !!!! ! ! ! ! !!!!!! ! !!!!!!!! ! !!!!!! !! !! ! ! !!! !! ! !!!!!!!!! !!!!! !!!!!! !!!! ! !! ! ! ! ! !!!! !! !! !! ! !! ! ! !!!!!!!!!!!!!! !!!!! ! !! ! !!!!!! ! !! ! !! !!! !!!!!!!! !!! !!!!! ! !!! !!! !! ! !! ! !!! !!! !!!!! ! !!!! !! ! ! ! ! !!!! ! ! !!! !!! ! ! !!!!!! !!!! ! !! !! ! !!! ! !!!!!!!!!!!! ! !!!!!!!!!!!!!!!!! !! ! !!! ! ! !! ! !! ! !!!! !!!!! ! ! ! ! ! ! ! ! ! ! !! !! !! !!!!! !! ! ! ! !!!!!!! ! !!!! ! ! ! ! ! ! !! ! ! ! ! ! !!! !!!! ! ! ! ! ! !!!!!!! ! ! ! ! !! ! ! ! ! ! ! !!! !!! !! ! ! !!!! !! !! ! ! ! ! !!!!! ! ! ! ! ! !!!!!!! !!!!!!! ! ! ! ! !! ! ! !!!! !!!!!!!!! !!!! ! !!!!!!! ! ! ! !! !! ! ! !!!! ! ! !!!!!! ! !!!!!! ! ! ! !! ! ! !!!!!! ! ! ! ! !! ! ! ! !!!! ! !!!! !! !! ! !!!!! !! ! ! ! !!!! !! !! !!!! ! ! ! !! ! !!! ! !!!!!!!! !!!!!!! ! ! ! ! ! !!!!! ! !!!!!!!!! !!! ! ! !!!!!!!!!!!!! ! !!!!!!! ! !!! !! !!!!!! !!!!! !! ! !!!!!!! ! ! !!! !!! ! !! !!!!! !!! ! !!!!!!! !! ! ! !!! ! ! !!!! ! !! ! !!! ! !! !! !!! ! ! ! ! !!!! ! !!!!!!!! !!!! ! ! ! !! !! !!!! ! !! ! ! !!! !!!! ! ! !!!!! ! !!! !!!!!!!! !! !!! !!! ! !!!!!! !!!!!!!!!!!!!! ! !!!! !! ! ! ! ! ! ! ! !!!!!!!!!! !! ! ! !!! ! ! ! !!! ! ! ! ! !! !!!!!!!! !!! ! ! ! ! !! ! ! ! ! ! ! !!!! ! ! !! ! ! ! !!! ! ! ! ! !! !!!! !! !!!!! !!! ! !! !!! ! !!! ! ! ! !!!!! !! ! !! ! ! ! !!! ! !! ! !! ! ! !! ! !!! !!! ! ! ! !!!! ! !! ! ! ! ! ! !! !!! !!!! ! !!!! ! ! !! !!!!!!!! ! ! !!!! !!!!! !! ! ! ! ! ! ! ! ! ! ! ! !!!!!!!!! ! !!!!!!!!!!!!!! !! ! ! ! ! ! ! !! ! !! ! !!!! ! ! ! ! ! ! ! !! ! !!!!! !! ! ! ! ! !!!!!! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! !!!!!!!!!!!!!! !!!! ! ! !!! !! ! ! ! !!!! ! ! !!! ! ! ! ! ! !!!!!! !!!!! !!!!!!!!! ! ! ! ! ! !!! !! !! ! !! !!!!! ! !!! !!!!!! !! ! ! ! ! ! ! !!! ! !!! !!!!!!!!! !!!! !! !! !!! ! ! ! ! !!!!!!!!!!!! ! !! ! !!!! ! ! ! !! ! ! !!! ! ! ! ! !! ! !! ! !!! ! !! ! ! ! ! ! ! !! ! ! !!!!!!! !!!!!!!! ! ! ! !! ! ! ! !! ! !! !!!!! !!!! ! ! ! !! ! ! !! ! !! ! ! ! ! !!!!!!!!!!! ! ! ! ! !!! ! !!! ! ! !! ! ! !! !!!!! ! !! ! ! !! !!!!!!!!! !! ! !!! ! !!!!!!!!!! ! ! ! ! !!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!! ! !! ! ! ! ! !! ! ! ! ! ! !! !! ! ! ! !!!!!! ! !! !!! ! ! ! !!! ! !!! !!!! ! ! ! ! !! ! ! !!! !!!! ! ! !! ! ! !!!!!!!!!!!!!!!!! !!! !!! ! !!! ! ! !! !!!!!!!! !! ! !!!!! ! ! !!!!!! !! ! !!! !!!! !! !!!!! !! ! ! ! !!!!!! ! ! ! !!!!!!!!!! ! ! !!!!!!!!!!!!!!!! !!!!!!!! !!!!!!!!!! !! ! ! !! ! ! !!!!!! ! !!!! !!!!!!!!!!!!! ! ! ! ! ! !!!! !! !! ! !!! ! !!!!!!!!!!!!!!! !!! !! ! ! ! ! !! ! !!! ! ! ! !!!! ! ! ! ! ! ! ! ! ! ! ! !!!!! !!!! ! ! !! ! ! ! ! ! ! ! !!!!!!!!!!!!!! ! ! ! !! ! !! ! ! !! ! ! !!!!!! !! !! !!! ! !!!! !! ! ! !! ! ! !!!!!!!!!! ! ! !! !!!! !!! ! ! !!! ! ! ! ! ! !! ! ! !! ! !! ! !!! !!! ! ! ! ! ! !!! ! !! ! ! ! !! ! ! ! ! ! ! !! !! ! !!! ! ! ! ! !! ! !!!! ! !! !!!! ! ! ! ! !! !!!!!!!!! ! ! ! ! !!!! !!!! !! ! ! ! !!!!! ! ! ! !! ! ! !! Observed GPS Point ! !! !!!!! ! !!! ! ! ! !!! !!!! ! !!! ! ! ! ! ! ! ! !! !! ! !! ! !! ! !!! ! ! ! !!!! ! ! !! ! ! ! ! Observed Trajectory !! ! !! ! ! ! !!!! ! !!! ! ! ! ! !! ! ! !!!!!!!! !!! !! ! ! ! !!!! ! ! ! ! !! !!!!! ! !!!!!!!! ! !!! ! ! !!! ! ! ! ! !! !! ! ! ! ! Characteristic Hull Polygon !! !!!!!!!! !!!!! ! ! ! ! ! !!! !! ! !!! ! ! ! ! ! !!!! !!! !! ! ! ! ! !!!! ! ! ! !!!!!!!! !! ! ! ! ! !!!! ! ! preprint! ! !! !!! ! !! ! ! !!!! !!! MCP 100% ! ! !!!!!!!! ! ! ! ! !!! ! ! !! ! !! ! ! ! ! ! ! ! ! ! ! Elevation (m) ! ! ! ! !! ! ! ! ! ! !!!! !! ! High : 1946 ! ! ! !!! !! ! ! ! !!! ! !!!! !!! Low : 93 0 1 2 4 Km !!

(b) T10727 trajectory and home range

Figure 4: The trajectories and home ranges of two female tigers, and the elevation maps of the study areas.

1Data source: http://asterweb.jpl.nasa.gov/gdem.asp

10 70

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

4.2 Model Parameterization Three model parameters are calibrated from analysis of the tigers GPS data. These parameters are: directional persistence, speed of movement, and slope preference. Directional persistence and speed of movement are used to parametrize both the CRW and CsCRW models, while the slope persistence is used for the CsCRW model, as an example of a context variable. Directional persistence is calculated as the ratio of the total number of turn angles between −20◦ and +20◦ to the total number of GPS vectors and resulted in a value of 0.3. To parametrize speed of movement, a probability density function is created using 1-hourly GPS observations and estimated by a χ2 distribution with a mean of 0.24 km/hr (Figure 5). To parameterize slope preference probability density functions are created from the slope annotated GPS observations of the two tigers, and were estimated with χ2(µ = 5.8◦) for T 7203 and χ2(µ = 6.1◦) for T 10727, as shown in Figure 6. It is important to remark that the data of the two tigers resulted in very similar mean estimates for the χ2 distributions of their slope use. It is necessary to note that since speed and directional persistence are spatial- temporal measures, they are computed only using the 1-hour data set for parame- terizing both models. However, since slope preference is a spatial phenomenon, the temporal resolution of the data has no eﬀect on estimation of slope use. Therefore the diﬀering temporal resolutions of the two tigers do not impact the model parameterization or the results of the validations.

T7203 T10727 0.15 0.15 mean = 0.24 km/h chi−square chi−square mu = 5.8 deg mu = 6.1 deg 0.10 0.10 density density density 0.05 0.05 0 1 2 3 4 5 6

0.0 0.5 1.0 1.5 2.0

speed values (km/h) preprint0.00 0.00 0 5 10 15 20 25 30 0 10 20 30 40 Figure 5: Kernel density plot (a) slope values (degree) (b) slope values (degree) of speed values obtained from GPS observations, estimated Figure 6: Kernel density plots of slope values obtained with a χ2 distribution of µ = from GPS observations of T 7203 and T 10727, ﬁtted with 0.24 km/hr χ2 distributions of µ = 5.8◦ and µ = 6.1◦, respectively.

11 71

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

4.3 Results 4.3.1 Correlated random walk (CRW) Speed distribution and directional persistence obtained from model parameterization are used as input to calculate the distance and direction for each successive move in the correlated random walk model (as described in Section 3.2). Figures 7(a) and 8(a) show probability of visitation surfaces, generated through a Monte Carlo simulation using a CRW model (as described in Section 3.4), for tigers T 7203 and T 10727. The resolution of the generated probability of visitation surfaces is the same as the DEM (30 meters). The highest visitation counts (shown in red) are away from the borders of the home ranges and the areas near the borders show lower visitation counts in blue. As seen in the Figures 7(a) and 8(a), this model captures the impact of geography (i.e. location within the home range) on visitation probability.

4.3.2 Context-sensitive correlated random walk (CsCRW) The sensitivity of tiger movement to slope (i.e. slope preference) for the CsCRW model is parametrized for the two tigers using χ2 distributions of µ = 5.8◦ and µ = 6.1◦ as shown in Figure 6. These functions are used to incorporate slope selection for each successive move in the CsCRW model (as described in Section 3.3). Figures 7(b) and 8(b) show probability of visitation surfaces, generated through a Monte Carlo simulation using a CsCRW model (as described in Section 3.4), for tigers T 7203 and T 10727. The resolution of the generated probability of visitation surfaces is the same as the DEM (30 meters). As seen in the ﬁgures, this model captures the impact of terrain on the visitation probability of the agent. The areas in blue (i.e. low visitation) correspond to the high slope areas or hilltops, which are diﬃcult to reach for tigers. These rougher terrains, as highlighted on slope maps on Figures 7(c) and 8(c), result in the creation of corridors of high visitation for the CsCRW model (i.e. higher visitation counts shown in red) along river bottoms, valleys, and ridge lines.

4.4 Assessment Two tiger trajectories from GPS observations are used for validation. For each tiger, a random trajectory of the same number of points as the tiger trajectory is generated (i.e. 4874 pointspreprint for T 7203, and 3946 points for T 10727). The visitation count for each pixel along the tiger trajectory and the random trajectory is extracted from the probability of visitation surfaces of both models (i.e. CRW and CsCRW) as described in Section 3.4. The results are presented as boxplots in Figure 9 and summarized using means, medians and their differences for the tiger and random trajectories (Table 1). Both CRW and CsCRW models result in significant higher means and medians for both tiger GPS trajectories as compared to the random trajectories, using a T- squared test (p < .0001). The CsCRW model shows much higher mean and median differences for visitation counts (i.e. diff in Table 1) than the CRW model for both tigers. For T 7203, the CsCRW mean difference is 245.4 (visits per cell), while the

12 72

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

Figure 7: Visitation counts for the home range of tiger T 7203 obtained from (a) CRW and (b) CsCRW models; and (c) the slope map for tiger T 7203. The area of the home range is 34.26 km2.

Table 1: Comparative assessment of CRW and CsCRW models for tigers T 7203 and T 10727. CRW CsCRW tiger random diﬀ tiger random diﬀ mean 1002.0 928.5 73.5 1144.0 898.6 245.4 T7203 median 1056.0 1028.0 28.0 908.0 684.5 223.5 mean 385.7 349.9 35.8 367.7 307.9 59.8 T10727 median 396.0 378.0 18.0 355.0 307.0 48.0

CRW mean difference is 73.5. For T 10727, the CsCRW mean difference is 59.8, while the CRW mean difference is 35.8. The CRW model shows a low variance (Figure 9) because it captures the relative uniformity in space use by the tiger towards the middle of its home range. More visits occur toward the center of the home range and away from the boundary due to the need to cross the middle of the home range when moving from one part of the home range to another. However, as seen on Figures 7(a) and 8(a) the modelpreprint does not reflect the lower space use and movement corridors used by the tigers in the rougher terrain of the home ranges (illustrated in Figure 4). In contrast, the CsCRW model has a much higher variance with a pronounced skew above the median (Figure 9). This can be attributed to the fact that the CsCRW model incorporates context (i.e. slope) into the choice of movement and better captures the corridors which tigers use more frequently. For instance, high visitation corridors, shown by the CsCRW model, in the eastern slopes of T 7203 (Figures 7) and in the northwestern part of the home range of T 10727 (Figure 8), coincide with the actual tiger movement track as seen in Figure 4. The more distinct corridors of modeled surfaces can also be observed in the more rugged portions of both home ranges. This

13 73

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

Figure 8: Visitation counts for the home range of tiger T 10727 obtained from (a) CRW and (b) CsCRW models; and (c) the slope map for tiger T 10727. The area of the home range is 99.92 km2. is not observed in the CRW model for either tiger’s home range. It is important to note that due to the difference in the size of the two home ranges, and therefore the difference in the scales of two figures, the influence of micro-topography is seen more pronouncedly in Figure 7 than Figure 8.

5 Discussion

The underlying principle of this paper is that modeling trajectories using geometric metrics (e.g. path sinuosity, step length, turn angle, etc.) ignores the fact that a trajectory is a complex and composite signal of various behaviors that occur across multiple spatial and temporal scales. The goal of this paper is to demonstrate that local choices (i.e. decisions and behavior at local scales) play a major role in where a moving agent (e.g. tiger) is likely to go. Two stochastic models are developed and examined, which estimate the space use and movement by an individual. These models simulate agents to move through space using two algorithms, a correlated random walk (CRW) and a context-sensitive correlated random walk (CsCRW). The CRW results in apreprint space use that is more pronounced away from the boundary (in this case the tiger’s home range). It captures the global patterns of space use for an area by an individual. In contrast, the CsCRW model incorporates the contextual factors (in this case slope) which inﬂuence the strategies for local movements by an individual. The premise of our analysis is that local scale eﬀects must be understood and modeled before regional and global scale behaviors can be deduced from a trajectory. For instance, a tiger that is patrolling its home range boundary (i.e. global scale behavior), still makes choices at local scales (i.e. following contour lines or moving along natural trails) to traverse its home range. There may be external biases driving

14 74

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

CRW t7203 CsCRW t7203 visitation counts visitation counts 0 1000 2000 3000 0 1000 2000 3000

tiger random tiger random

CRW t10727 CsCRW t10727 visitation counts visitation counts 0 200 400 600 800 0 200 400 600 800

tiger random tiger random

Figure 9: Boxplots of visitation counts of CRW and CsCRW models for random trajectories and tigers T 7203 and T 10727 tiger’s global trajectory, but how the tiger reaches its goal-oriented destination is influenced by local choices. Therefore we make a distinction between local choice models (i.e. CsCRW) and biased CRW. The external biases constrain the possible paths to the goal but the local choices are largely independent of theses biases. Other approaches such as Step Selection Function (SSF) can also be used to understand the relationshipspreprint between local scale environmental correlates and movement choice based on available resources, however, their use has been confined to deterministic models for movement (Squires et al., 2013) in contrast to the stochastic approach we have taken in this research. A number of contextual factors play a role in local movement patterns and space use of an individual. The strength of the CsCRW model is that it can incorporate multiple contextual factors. In the case of the tiger this could include slope, vegetation density, prey density or proximity to the boundary. Parametrization of these factors can be made using actual field observations and GPS locations. In fact, the strength of our approach is that the models are calibrated and validated through

15 75

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016 actual observations. In the case of the CsCRW model implemented in this research the parametrization is done using the tiger’s occurrence on different slope classes. A similar process could be used to parametrize other contextual information that contributes to tiger movement choices. In this study, we validated our model using two different trajectories of two tigers. These trajectories were different in terms of their geography (i.e. geometry of their home ranges), physiography (i.e. terrain type and topographic features), as well as the temporal resolution of their GPS observations. The comparative analysis of the two home ranges suggests that the proposed simulation model successfully incorporates the influence of geography and topography (i.e. context) on space use and local choices made by the tigers. Movement is driven by an individual’s state and the associated behaviors that occur at different spatial and temporal scales (Ahearn et al., 2001; Gautestad and Mysterud, 2005). The resulting trajectory of movement is therefore a complex composite (i.e. signal) that is influenced by geography through which individuals move (i.e. the network or the physiography of the terrain), their behavioral state (i.e. hungry, going to work, shopping, tourism, etc.) and their interaction with other individuals. The study of movement aims at understanding these components and the scales over which they occur (Dodge, 2016; Dodge et al., 2016). In this research we use a spatiotemporal stochastic simulation procedure that incorporates space and environmental context as two important components of the signal associated with a trajectory. We see context as a driver for the local choices that individuals may make, that are nested within more global scale objectives driven by the animals behavioral state. For instance a tiger patrols its home range at certain intervals, however the path it takes may be influenced by the characteristics of the terrain and vegetation. Thus the pattern of movement is a reflection of both of these factors at two very different scales. In essence by modeling these contextual parameters we are separating them out from the patterns of movement which are attributed to the agents behaviors state.

6 Conclusion and Future Work

The main contribution of this study is to integrate environmental context in a spatiotemporal simulation model for movement, and to use real tracking observations to parameterize andpreprint validate the simulation. Three parameters (i.e. directional persistence, slope use, speed) were calibrated in this research from GPS tiger tracking data of two tigers. Two diﬀerent models (CRW and CsCRW) were developed and evaluated for estimating probability of visitation (or space use) by a tiger within its home range. The CsCRW model shows the most promise for its ability to incorporate multiple contextual variables that inﬂuence movement choices at local scales in space and time. The spatiotemporal simulation proposed in this research incorporates the three important components of movement: space, time, and context as in the framework suggested by Dodge (2016). This research did not model the state and objectives of

16 76

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016 individuals and the resulting higher level behaviors (i.e. patrolling, hunting, tourism, shopping) which drive large scale movement patterns. Future extension of this method will incorporate these larger scale behaviors.

Acknowledgments

S. Dodge and G. Xavier work was supported under the 2015 UCCS Committee on Research and Creative Works (CRCW) award and the UCCS College of Letters, Arts, and Sciences Student-Faculty Research Awards. The authors wish to thank the reviewers for their insightful and constructive comments.

References

Ahearn, S. C., David, J. L., Joshi, A. R. and Ding, J. (2001), ‘TIGMOD: an individual-based spatially explicit model for simulating tiger/human interaction in multiple use forests’, Ecological Modelling 140, 81–97.

Ahearn, S. C. and Smith, J. (2005), Modeling the interaction between humans and animals in multiple use forests: a case study of Panthera tigris, in D. Maguire, M. Goodchild and M. Batty, eds, ‘GIS, Spatial Analysis, Modeling’, ESRI, Red- lands, California, chapter 18, pp. 387–403.

Ahearn, S. C., Smith, J., Simchareon, A., Simchareon, S. and Garcia, J. (2010), Modeling the relationship between patterns of movement of Panthera tigris and its behavioral states, in B. Gottfried, P. Laube, A. Klippel, N. Van de Weghe and R. Billen, eds, ‘the 1st Workshop on Movement Pattern Analysis, MPA’10’, Zurich Switzerland, pp. 143–146.

Batty, M., DeSyllas, J. and Huxbury, E. (2003), ‘The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades’, International Journal of Geographical Information Science 17(7), 673 – 697. URL: http://discovery.ucl.ac.uk/242/

Benhamou, S. and Riotte-Lambert, L. (2012), ‘Beyond the utilization distribution: Identifying homepreprint range areas that are intensively exploited or repeatedly visited’, Ecological Modelling 227, 112–116. URL: http://dx.doi.org/10.1016/j.ecolmodel.2011.12.015

Buchin, K., Sijben, S., Arseneau, T. and Willems, E. P. (2012), ‘Detecting movement patterns using Brownian bridges’, Proceedings of the 20th International Conference on Advances in Geographic Information Systems pp. 119–128.

Codling, E. A., Plank, M. J. and Benhamou, S. (2008), ‘Random walk models in biology’, Journal of The Royal Society Interface 5(25), 813–834.

17 77

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

David, O. and Perry, G. L. W. (2013), Spatial simulation: exploring pattern and process, Wiley-Blackwell. URL: http://onlinelibrary.wiley.com/book/10.1002/9781118527085

Demˇsar,U., Buchin, K., Cagnacci, F., Saﬁ, K., Speckmann, B., Van de Weghe, N., Weiskopf, D. and Weibel, R. (2015), ‘Analysis and visualisation of movement: an interdisciplinary review’, Movement Ecology 3(1).

Dodge, S. (2016), From observation to prediction: The trajectory of movement research in GIScience, in H. Onsrud and W. Kuhn, eds, ‘Advancing Geographic Information Science: The Past and Next Twenty Years’, GSDI Association Press, chapter 9, pp. 123–136.

Dodge, S., Bohrer, G., Bildstein, K., Davidson, S. C., Weinzierl, R., Bechard, M. J., Barber, D., Kays, R., Brandes, D., Han, J. et al. (2014), ‘Environmental drivers of variability in the movement ecology of turkey vultures (cathartes aura) in north and south america’, Philosophical Transactions of the Royal Society of London B: Biological Sciences 369(1643), 20130195.

Dodge, S., Bohrer, G., Weinzierl, R., Davidson, S. C., Kays, R., Douglas, D., Cruz, S., Han, J., Brandes, D. and Wikelski, M. (2013), ‘The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data’, Movement Ecology 1(1), 3.

Dodge, S., Weibel, R., Ahearn, S. C., Buchin, M. and Miller, J. A. (2016), ‘Analysis of movement data’, International Journal of Geographical Information Science 30(5), 1–10. URL: http://dx.doi.org/10.1080/13658816.2015.1132424

Downs, J. A. and Horner, M. W. (2009), ‘A characteristic-hull based method for home range estimation’, Transactions in GIS 13(5-6), 527–537.

Duckham, M., Kulik, L., Worboys, M. and Galton, A. (2008), ‘Eﬃcient generation of simple polygons for characterizing the shape of a set of points in the plane’, Pattern Recognition 41(10), 3224–3236. Forester, J. D., Ives,preprint A. R., Turner, M. G., Anderson, D. P., Fortin, D., Beyer, H. L., Smith, D. W. and Boyce, M. S. (2007), ‘State-space models link elk movement patterns to landscape characteristics in Yellowstone National Park’, Ecological Monographs 77(2), 285–299.

Franke, A., Caelli, T. and Hudson, R. J. (2004), ‘Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models’, Ecological Modelling 173(2-3), 259–270. URL: http://www.sciencedirect.com/science/article/pii/S0304380003003983

18 78

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

Galton, A. (2005), Dynamic collectives and their collective dynamics, in ‘Spatial Information Theory, Lecture Notes in Computer Science’, Vol. 3693, Springer, pp. 300–315. URL: http://www.springerlink.com/index/514wxvd6fe70y9w4.pdf Gautestad, A. O. and Mysterud, I. (2005), ‘Intrinsic scaling complexity in animal dispersion and abundance’, The American naturalist 165(1), 44–55. Gautestad, A. O. and Mysterud, I. (2010), ‘The home range fractal: from random walk to memory-dependent space use’, Ecological Complexity 7(4), 458–470. Glasgow, M. L., Rudra, C. B., Yoo, E.-H., Demirbas, M., Merriman, J., Nayak, P., Crabtree-Ide, C., Szpiro, A. A., Rudra, A., Wactawski-Wende, J. and Others (2014), ‘Using smartphones to collect time–activity data for long-term personal- level air pollution exposure assessment’, Journal of Exposure Science and Envi- ronmental Epidemiology . González,M. C., Hidalgo, C. A. and Barabási,A.-L. (2008), ‘Understanding individual human mobility patterns’, Nature 453(7196), 779–82. URL: http://www.ncbi.nlm.nih.gov/pubmed/18528393 Hägerstrand,T. (1970), ‘What about people in Regional Science?’, Papers of the Regional Science Association 24(1), 6–21. Holyoak, M., Casagrandi, R., Nathan, R., Revilla, E. and Spiegel, O. (2008), ‘Trends and missing parts in the study of movement ecology’, Proceedings of the National Academy of Sciences of the United States of America 105(49), 19060–5. Horne, J. S., Garton, E. O., Krone, S. M. and Lewis, J. S. (2007), ‘Analyzing animal movements using Brownian bridges’, Ecology 88(9), 2354–2363. Hornsby, K. and Egenhofer, M. (2002), ‘Modeling moving objects over multiple gran- ularities’, Annals of Mathematics and Artificial Intelligence 36(1), 177–194. Jiang, B., Yin, J. and Zhao, S. (2009), ‘Characterizing the human mobility pattern in a large street network’, Physical Review E(80), 021136. Kie, J. G., Matthiopoulos,preprint J., Fieberg, J., Powell, R. A., Cagnacci, F., Mitchell, M. S., Gaillard, J.-M. and Moorcroft, P. R. (2010), ‘The home-range concept: are traditional estimators still relevant with modern telemetry technology?’, Philosophical Transactions of the Royal Society of London B: Biological Sciences 365(1550), 2221–2231. Kranstauber, B., Kays, R., Lapoint, S. D., Wikelski, M. and Safi, K. (2012), ‘A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement’, Journal of Animal Ecology 81(4), 738–746. Laube, P. (2014), Computational movement analysis, Springer.

19 79

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

Lu, Y. and Fang, T. (2014), ‘Examining personal air pollution exposure, intake, and health danger zone using time geography and 3D geovisualization’, ISPRS International Journal of Geo-Information 4(1), 32–46. URL: http://www.mdpi.com/2220-9964/4/1/32/

Miller, H. J. (1991), ‘Modeling accessibility using space-time prism concepts within geographical information systems’, International journal of geographical information systems 5(3), 287–301. URL: http://www.tandfonline.com/doi/abs/10.1080/02693799108927856

Miller, J. A. (2015), ‘Towards a better understanding of dynamic interaction metrics for wildlife: a null model approach’, Transactions in GIS 19(3), 342–361.

Mohr, C. O. (1947), ‘Table of equivalent populations of north american small mammals’, The American Midland Naturalist 37(1), 223–249.

Moorcroft, P. R., Lewis, M. A. and Crabtree, R. L. (2006), ‘Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellow- stone’, Proceedings of the Royal Society – Biological Sciences 273(1594), 1651– 1659.

Morales, J. M., Fortin, D., Frair, J. L. and Merrill, E. H. (2005), ‘Adaptive models for large herbivore movements in heterogeneous landscapes’, Landscape Ecology 20(3), 301–316. URL: http://link.springer.com/10.1007/s10980-005-0061-9

Okabe, A., Boots, B. and Sugihara, K. (1992), Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, John Wiley & Sons, Inc., New York, NY, USA.

Ovaskainen, O. (2004), ‘Habitat-speciﬁc movement parameters estimated using mark- recapture data and a diﬀusion model’, Ecology 85(1), 242–257.

Ovaskainen, O., Rekola, H., Meyke, E. and Arjas, E. (2008), ‘Bayesian methods for analyzing movements in heterogeneous landscapes from mark–recapture data’, Ecology 89(2),preprint 542–554. Powell, R. A. and Mitchell, M. S. (2012), ‘What is a home range?’, Journal of Mam- malogy 93(4), 948–958.

Rhee, I., Shin, M., Hong, S. and Lee, K. (2011), ‘On the levy-walk nature of human mobility’, IEEE/ACM Transactions on Networking (TON) 19(3), 630–643. URL: http://dl.acm.org/citation.cfm?id=2042974

Sang, S., O’Kelly, M. and Kwan, M.-P. (2011), ‘Examining commuting patterns: results from a journey-to-work model disaggregated by gender and occupation’, Urban Studies 48(5), 891–909.

20 80

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

Schick, R. S., Loarie, S. R., Colchero, F., Best, B. D., Boustany, A., Conde, D. A., Halpin, P. N., Joppa, L. N., McClellan, C. M. and Clark, J. S. (2008), ‘Under- standing movement data and movement processes: current and emerging directions.’, Ecology letters 11(12), 1338–50. URL: http://www.ncbi.nlm.nih.gov/pubmed/19046362 Song, Y. and Miller, H. J. (2014), ‘Simulating visit probability distributions within planar space-time prisms’, International Journal of Geographical Information Science 28(1), 104–125. URL: http://www.tandfonline.com/doi/abs/10.1080/13658816.2013.830308 Song, Y., Miller, H. J., Zhou, X. and Proffitt, D. (2015), ‘Modeling visit probabilities within network-time prisms using Markov techniques’, Geographical Analysis 48(1), 18?42. URL: http://doi.wiley.com/10.1111/gean.12076 Squires, J. R., DeCesare, N. J., Olson, L. E., Kolbe, J. A., Hebblewhite, M. and Parks, S. A. (2013), ‘Combining resource selection and movement behavior to predict corridors for Canada lynx at their southern range periphery’, Biological Conservation 157, 187–195. URL: http://dx.doi.org/10.1016/j.biocon.2012.07.018 Tang, W. and Bennett, D. (2010), ‘Agent-based modeling of animal movement: A review’, Geography Compass 4(7), 682–700. Technitis, G., Safi, K. and Weibel, R. (2014), ‘From A to B, randomly: An endpoint- to-endpoint random trajectory generator for animal movement’, International Journal Geographical Information Science 29(6), 912–934. Thurfjell, H., Ciuti, S. and Boyce, M. S. (2014), ‘Applications of step-selection functions in ecology and conservation’, Movement Ecology 2(1), 4. URL: http://www.movementecologyjournal.com/content/2/1/4 Torrens, P. M., Nara, A., Li, X., Zhu, H., Griffin, W. a. and Brown, S. B. (2012), ‘An extensible simulation environment and movement metrics for testing walking behavior in agent-based models’, Computers, Environment and Urban Systems 36(1), 1–17.preprint URL: http://dx.doi.org/10.1016/j.compenvurbsys.2011.07.005 Tribby, C. P., Miller, H. J., Brown, B. B., Werner, C. M. and Smith, K. R. (2016), ‘As- sessing built environment walkability using activity-space summary measures’, Journal of transport and land use 9(1), 187–207. Turchin, P. (1998), Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants, Sinauer Associates, Sunderland, MA, USA. URL: http://www.getcited.org/pub/100319196

21 81

A context-sensitive correlated random walk Ahearn et al., IJGIS 2016

Winter, S. and Yin, Z.-C. (2010), ‘Directed movements in probabilistic time geography’, International Journal of Geographical Information Science 24(9), 1349– 1365. URL: http://www.tandfonline.com/doi/abs/10.1080/13658811003619150

Yuan, M. and Nara, A. (2015), Space-Time Integration in Geography and GIScience: Research Frontiers in the US and China, Springer Netherlands, Dordrecht, chapter Space-Time Analytics of Tracks for the Understanding of Patterns of Life, pp. 373–398.

preprint

22 82

Research Article

Tropical Conservation Science Volume 10: 1–7 Ecological Covariates at Kill Sites ! The Author(s) 2017 Reprints and permissions: Influence Tiger (Panthera tigris) sagepub.com/journalsPermissions.nav DOI: 10.1177/1940082917719000 Hunting Success in Huai Kha Khaeng journals.sagepub.com/home/trc Wildlife Sanctuary, Thailand

Somporn Pakpien1,2, Achara Simcharoen1, Somphot Duangchantrasiri1, Vijak Chimchome2, Nantachai Pongpattannurak2, and James L. D. Smith3

Abstract Despite significant knowledge of tiger ecology, information on hunting behavior is limited because tigers hunt in habitats where they are difficult to observe. From May 2013 to June 2015, we visited kill sites of eight female radio-collared tigers (Panthera tigris) to identify prey species of this species in Huai Kha Khaeng Wildlife Sanctuary, Thailand. At 150 kill sites, 11 mammalian species were identified from skeletal remains or hair samples. Sambar (Rusa unicolor), banteng (Bos javanicus), and gaur (Bos gaurus) composed 95.1% of tiger prey biomass. A subset of 87 kill sites was paired with 87 randomly selected sites within the home ranges of five of the eight radio-collared tigers to determine the influence of prey abundance and other ecological variables on hunting success. At each site, geomorphic and ecological covariates were sampled in 900 m2 square plots. A generalized linear model was used to investigate differences between kill sites and random sites. Mean relative prey abundance at kill sites was significantly lower than relative prey abundance at random sites (77.8 and 139.3 tracks/ha, respectively) indicating tigers did not kill in areas of higher relative prey abundance. Model selection was used to examine 12 landscape features that potentially influence kill site location. In the best model, low shrub cover and high crown cover were highly significant; tree density was included in this model but was not significant. This is the first study to demonstrate that kill location requires a combination of landscape features to first detect and then successfully stalk prey.

Keywords cluster locations, Huai Kha Khaeng Wildlife Sanctuary, hunting success, tiger kill site characteristics, tiger prey

Animals should seek habitat with adequate food, cover, second order habitat selection which focuses on where nest/den sites, or other resources critical for survival female tigers settle and the relationship of prey abun- (Manly, McDonald, Thomas, McDonald, & Erickson, dance to their territory size. These authors found an 2002). For female felids, sufficient food to raise young inverse relationship between female territory size and is often their primary resource need, and natural selection is expected to drive foraging decisions to optimize food 1 intake and minimize energy expenditure (Krebs & Department of National Parks, Wildlife and Plant Conservation, Thailand 2Faculty of Forestry, Kasetsart University, Thailand Davies, 1993). Food demands of female tigers increase 3Department of Fisheries, Wildlife, and Conservation Biology, University of rapidly as cubs mature and mothers continue to be the Minnesota, USA primary provider until their young are approximately Received 30 April 2017; Revised 8 June 2017; Accepted 9 June 2017 1.5 years old; at this time, male offspring are often larger than their mothers (Smith, McDougal, & Corresponding Author: Achara Simcharoen, Department of National Parks, Wildlife and Plant Miquelle, 1989). Johnson (1980) proposed a hierarchical Conservation, Protected Area Regional Office 12, 19/47 Kositai Road, model as a framework by which animals efficiently meet Nakhonsawan, Thailand. their resource needs. Simcharoen et al. (2014) studied Email: [email protected]

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 83

2 Tropical Conservation Science the abundance of large prey. Within a female’s territory, (Figure 1) which is characterized by mixed deciduous, dry third and fourth order resource selection includes selec- dipterocarp, and dry evergreen forest. The average tion of denning, resting, and hunting sites. Here, we focus annual rainfall (1375 mm) is divided into a wet season on kill site selection. (May–October), with a mean of 1088 mm of rain and a Many studies have shown that high prey abundance dry season (November to April) with a mean of 298 mm. is the primary factor that predicts hunting success The temperature reaches 40 C in April at the end of the (Litvaitis, Sherburne, & Bissonette, 1986; Murray, dry season. The tiger is the largest carnivore in this eco- Boutin, O’Donoghue, & Nams, 1995; Palomares, system and its density ranges from 1.25 to 2.01 tigers/km2 Delibes, Revilla, Calzada, & Fedriani, 2001; Spong, (Duangchantrasiri et al., 2016). Phetdee (2000) identified 2002). Alternatively, Hebblewhite, Merrill, and 16 prey species consumed by tigers but the primary prey McDonald (2005) propose a landscape hypothesis that were animals 100 kg, represented by large ungulates predators prefer habitat where it is easier to kill their pri- including sambar (Rusa unicolor), banteng (Bos javani- mary prey. Following Hollings (1959), Hebblewhite et al. cus), gaur (B. gaurus), and water buffalo (Bubalus buba- simplify predation into the instantaneous probability of lis), which characterize 89.8% of tiger diet in this region. encounter followed by the conditional probability of successfully killing prey. This landscape hypothesis suggests Data Collection and Analysis that landscape features such as slope, ruggedness, and various aspects of horizontal cover first favor prey detec- Kill site data. Potential kill sites were identified using cluster tion and once prey is detected, catchability will be favored analysis of hourly location data from eight female satellite (Hebblewhite et al., 2005). Our study examines resource radio-collared tigers (Vectronic Aerospace GmbH, selection by hunting female tigers to evaluate the import- Germany; radio collaring was in accordance with the ance of prey abundance and landscape attributes that University of Minnesota IACUC protocol 0906A67489). If affect hunting success (Stephens & Krebs, 1986). we obtained >2 locations with consecutive movement dis- Once a prey animal is detected, felids typically tances < 100 m within 48 h, sites were identified as locations approach it using available vegetative cover (Elliott, where a tiger had potentially killed a prey animal (Figure 2; Cowan, & Hollings, 1977). Concealment allows them to Miller et al., 2013). We investigated these sites on foot and if hunt by ambushing prey by stalking and then chasing a kill was located, species, sex, and age class of prey were prey for a short distance (Kruuk, 1986; Caro & identified from skeletal material, hair, and hoofs (Lekagul & Fitzgibbon, 1992; Sunquist & Sunquist, 1989). Tigers McNeely, 1977; Phetdee, 2000). We used Phetdee’s (2000) (Panthera tigris) employ this strategy, stalking or pelage and skeletal size criteria to classify juvenile versus ambushing prey from cover (Schaller, 1967; Sunquist & adult. Gaur, banteng, and sambar were classified as adult Sunquist, 1989). Cover, however, conceals predators so when > 9 months old, and wild boar were classified as that hunting success is improved and also reduces prey adult when 6 months old. Kills were found at 150 sites but detection by predators (Balme, Hunter, & Slotow, 2007). only 87 of these, used by 5 female satellite radio-collared Thus, the landscape hypothesis must balance first detec- tigers, were investigated to study kill site characteristics. At tion of prey and then stalking success. Both of these fac- kill sites, we identified the actual kill site, which could be tors are components of Holling’s (1959) theoretical identified from the drag marks or the presence of the rumen, framework; predators should select habitats to maximize which is usually removed before the animal is dragged. Kill both aspects of hunting success (Hayward & Kerley, sites were compared with 87 randomly sampled sites 2005). The objectives of our study were as follows: (a) (excluding kill sites) from the home ranges of the five identify the main prey species consumed by tigers and female collared tigers. (b) determine relative importance of prey abundance and the ecological variables that influence hunting suc- Prey abundance data. To test the hypothesis that tigers kill cess of tigers in Huai Kha Khaeng Wildlife Sanctuary, prey in areas of high prey abundance, we assessed relative Thailand. prey abundance at both kill and random sites. At each site, we searched for tracks and dung of sambar, banteng, gaur, water buffalo, wild boar (Sus scrofa), and muntjac Method (Muntiacus muntjac) within four 10 -m radius subplots Study Area which were oriented in cardinal directions 30 m from the site center. An independent sample t-test was used to com- The study was conducted between May 2013 to June 2015 pare the relative prey abundance at kill and random sites. in Huai Kha Khaeng Wildlife Sanctuary, Thailand (15 310 N, 99 160 E) which is located in the eastern portion Ecological covariate data. To examine factors that influence of the Western Forest Complex (WEFCOM). The study kill site location, we chose 12 ecological covariates that was concentrated in the northern part of the Sanctuary we hypothesized might influence tiger hunting success. 84

Pakpien et al. 3

Figure 1. Map of Huai Kha Khaeng Wildlife Sanctuary showing the locations of 150 sites where tiger-killed prey were found and identified and 87 kill and 87 random sites where ecological correlate data were measured to investigate tiger kill site characteristics.

A30 30 m plot was placed at each kill and random site to (R Core Team, 2015). The importance of ecological vari- quantify eight of these ecological features. Vegetation struc- ables in the top performing models was assessed based on ture at these plots was characterized as number of shrubs, their respective z-values, the associated probability of each percent of crown cover, number of barrier features (e.g., variable’s beta coefficient, and the 94.5% confidence inter- fallen logs, lianas), bamboo clump density, basal area of val of the beta estimates. trees, tree density, visibility, and slope (Table 1). Visibility at both kill and random sites were measured as percentage visible of a 50 100-cm board placed at the center of each Results plot (Nudds, 1977). These variables were combined with Prey Species and Prey Abundance four geographic variables generated using ArcMap 9.3 (Esri, Redlands, CA) that were also hypothesized to influ- Eleven prey species were identified at 150 kill sites ence catchability; these were distances to permanent stream, (Table 2). Sambar, banteng, and gaur composed 95.1% distance to seasonal stream, distance to salt lick, and eleva- of biomass of these kills. Analysis of hair texture, teeth, tion. Collinearity between these 12 variables was assessed and skeletal remains indicated that tigers killed adult prey prior to analysis using Spearman’s rank correlation test. 67.6% of the time or three times more frequently than A set of generalized linear models with binomial error dis- juvenile prey (22.7%); we could not classify age of 9.7% tributions and a logit link function was used to evaluate the of kills. Mean relative abundance of prey sign at 87 kill ecological variables that best differentiate kills sites from sites (77.9 sign/ha, n ¼ 77, SD ¼ 72.3) was significantly random sites. The most likely model was selected using lower than at 87 randomly selected nonkill sites (139.2 Akaike’s information criteria (Burnham & Anderson, sign/ha, n ¼ 77, SD ¼ 112.3 sign; t ¼4.04, df ¼ 129.79, 2002). Statistical analyses were performed in R software p ¼ .00009). 85

4 Tropical Conservation Science

Ecological Covariates To identify the ecological variables that best predict kill site characteristics, we first examined colinearity and eliminated distance to salt licks, which was highly correlated with elevation (r ¼ .73) as well as distance to permanent streams (r ¼ .87). The model with lowest Akaike information criteria included three variables: low shrub density, high crown cover, and low tree density (Table 3). The deviation of z-values from zero and their associated p values indicated the strength of each variable in the top two models. In the top model, the strongest deviation of z from zero was shrub cover (z ¼2.813, p > jzj¼.0049).

Table 2. The Number of Tiger’s Kills and Biomass of Species in 150 Kills in Huai Kha Khaeng Wildlife Sanctuary, Thailand.

Species No. kills Weight Biomass (%)

Sambar 76 212 55.52 Banteng 27 287 26.70 Gaur 13 287 12.86 Wild boar 18 37 2.29 Elephant 3 200 2.07 Porcupine 5 8 0.14 Muntjac 3 20 0.21 Hog badger 2 10 0.07 Serow 1 30 0.10 Figure 2. (a) We found this sambar kill by visiting sites where a Pangolin 1 3 0.01 tiger was located three or more times in consecutive 1 hr GPS fixes. (b) We occasionally placed camera traps at kill sites to obtain Langur 1 9 0.03 additional information on a tiger’s condition and, for females, their Note. Mean weight of animals killed by tiger are from Karanth and Sunquist reproductive status (photo: Thailand Tiger Project). (1995).

Table 1. Description of the 12 Ecological Covariates That Were Used for Comparison of Kill and Random Sites.

Potential impact on Name of variable Unit Description kill site selection

Tree density N/ha Total number of trees diameter > 4.5 cm in 30 30 plot Detection of prey Basal area of trees BA/ha Total basal area of trees diameter > 4.5 cm in 30 30 plot Detection of prey Shrub cover N/ha Total number of shrubs height 30–100 cm in 30 30 plot Detection of prey Bamboo N/ha Total number of bamboo clumps in 30 30 plot Impacts prey escape Barrier feature N/ha Total of fallen trees and climbers in 30 30 plot Impacts prey escape Crown cover % Mean percentage of crown cover in 30 30 plot Shade impacts prey vigilance using convex spherical densitometer Visibility % Mean percentage of visibility in each cardinal direction (30 m) Detection of prey Slope % Mean of slope measured by clinometer in cardinal direction Impacts hunting success Elevation m. Generated using ArcMap 9.3 Impacts hunting success Distance to permanent m Generated using ArcMap 9.3 Impacts prey abundance stream Distance to seasonal stream m Generated using ArcMap 9.3 Impacts prey abundance Distance to salt lick m Generated using ArcMap 9.3 Impacts prey abundance

Note. We identify potential impact of each variable on kill site selection. 86

Pakpien et al. 5

Table 3. Summary Statistics for the Top Four Models of Kill Site We initially thought that high prey abundance would Characteristics With an Accumulative Weight of 0.90 for Tigers in be a good predictor of kill sites because several studies on Huai Kha Khaeng Wildlife Sanctuary, Thailand. kill site habitat selection by large carnivores support the Independent Delta_ AICc Cum. hypothesis that kill site location within an animal’s home variables K AICc AICc Wt Wt LL range is largely influenced by prey abundance (Davidson et al., 2012). Furthermore, a strategy to hunt in areas of cvþtrþsh 4 224.64 0.00 0.52 0.52 108.20 high prey abundance, especially by adult females that cvþtrþshþbr 5 226.21 1.57 0.24 0.76 107.92 need to meet the energetic demands of feeding their off- cvþsh 3 228.04 3.41 0.09 0.85 110.95 spring, should optimize energy gained at the lowest risk cvþshþbr 4 229.21 4.58 0.05 0.90 110.49 cost (Heurich et al., 2016). Our study, however, did not support the prey abundance-hypothesis that killing suc- Note. AIC: Akaike information criteria; cv: Crown cover; tr: Tree density; sh: Shrub cover; br: Barrier feature. Resource use was estimated from tiger kill cess, and thus energy, are maximized by hunting in areas sites and resource availability was estimated from random locations that of high prey abundance. On the contrary, we found that were chosen from tiger locations within their home range. kill sites had a significantly lower prey abundance than random sites located along a tiger’s route of travel. We do not know the extent to which prey may have avoided kill The next most important variable was crown cover sites, but found no literature indicating prey shift their (z ¼ 2.721, p > jzj¼.0065). There was only weak support range. for the third variable, tree density (Table 3). The second Thus, our findings led us to evaluate an alternative set best model included a fourth ecological correlate, bar- of landscape hypotheses that certain habitat attributes rier cover, but its z-value was not significant (>0.05). are more important to killing success than prey abun- The third- and fourth-ranked models were subsets dance. Several previous studies also support landscape of Models 1 and 2 and garnered weights of 9% and hypotheses that carnivores select habitats where prey 5%. These top four models had an accumulative weight are more susceptible to predation (Balme et al., 2007; of 90%). Belotti et al., 2013; Davidson et al., 2012; Hopcraft, Sinclair, & Packer, 2005). Of the 12 ecological correlates we examined to explain Discussion tiger hunting location, low cover was the most important Identification of Prey at Kill Sites variable in all of the top-ranked models. This was surprising because tigers, lions (Panthera leo), and other felids We identified only 11 mammalian prey species at kill sites favor stalking to within a short distance followed by a as compared with 16 species reported in an analysis of relatively short chase. Thus, adequate cover is essential. scats (Phetdee, 2000) from the same area. Larger prey in Lions and tigers accelerate faster than many of their prey, our study (sambar, gaur, and banteng) composed 95.1% but their top speed peaks much below that of their prey of the biomass of kills we identified, which was higher so they must initiate an attack at a close distance (Elliott than the 88% biomass of these prey reported by Phetdee et al., 1977). Furthermore, their large, muscular body (2000). It is not surprising that prey identified from makes it energetically costly to maintain high speed kill sites are biased toward larger prey species because over a long chase. we identified kill sites by noting a sequence of clumped However, large cats must first detect their prey, thus 1-hr interval locations. Small kills could be processed they need an optimal combination of habitat structure to before we noticed a clump. Also we visited kill sites first locate, and then successfully stalk and ambush their a mean of 8 days after a kill and the scattering of prey (Lamprecht, 1978; Murphy & Ruth, 2010). To small kills made them more difficult to find. However, understand the role of different ecological covariates in identifying smaller prey is less important to under- large carnivore hunting success, Hebblewhite et al. (2005) standing kill site selection because, as shown by scat suggest using Holling’s (1959) theoretical framework to surveys, these animals compose 8.5% of the tiger’s diet decompose hunting success into two components: first, Phetdee (2000). the instantaneous probability of encounter followed by Three of our 87 kills were elephants (Elephas max- the conditional probability of a successful stalk leading imus) < 1 year in age, which is an observation not to a kill. In this context, less cover at a site with generally reported in the previous scat study (Phetdee, 2000). We high cover would be favored to increase the initial prob- speculate that young elephants were not found in the past ability of detection. The median cover at kill sites was because, prior to the mid-1990s, elephant poaching was 10,410 shrubs/ha. Given that kills are made at sites with widespread. With improved management beginning in considerably less cover than random sites (med- the 1990s, elephant numbers and recruitment have ian ¼ 14,190 shrubs/ha), areas with lower cover have ade- increased (Kanchanasaka, 2010). quate cover for tigers to successfully hunt. 87

6 Tropical Conservation Science

Table 4. The Beta Coefficients for the Best Model Which Included Low Shrub Cover, High Crown Cover and Low Tree Density.

Estimate coefficient Std. error z value Pr(>jzj) 95% CI

6.323 e-01 7.209 e-01 0.877 0.38046 Shrub cover 5.572 e-05 1.981 e-05 2.813 0.00490** 9.642 e-05–0.000 Crown cover 2.243 e-02 8.245 e-03 2.721 0.00651** 6.763 e-03–0.039 Tree density 3.988 e-04 5.285 e-04 0.755 0.45044 1.459 e-03–0.0006

Note. The z-values for shrub cover and crown cover were highly significant (p < 0.001).

The second most important ecological correlate was Author Contributions crown cover. It is unclear what advantage crown cover offers hunting tigers. Mysterud (1996) hypothesized that Field research and analysis: SP, AS, SD; Research design: SP, AS, VC, NP, Advice on statistical analysis: NP; Manuscript prepar- crown cover provides shade which is sought by deer for ation: SP, AS, JS. sites to rest and ruminate. He reported that roe deer (Capreolus capreolus) prefers to bed down below dense canopy to seek shade: similarly, in Thailand, sambar Acknowledgments and muntjac also bed at sites with high over story cover The authors would like to thank Chorpaka Vichittrakulchai, (Brodie & Brockelman, 2009). If indeed, crown cover cre- Ekaphol Plaidaeng, Kittisak Thongvichit, Supawat ates preferred resting sites, it may have a consequence of Khaewphakdee, Onsa Norrasarn, and Zack Beach for assistance reducing vigilance in resting and ruminating ungulates in the field. Saksit Simcharoen provided advice throughout the study. The authors also thank Oranuch Mahaphant for supporting (Blanchard & Fritz, 2007). this project. The authors appreciate comments from Francie Low tree density was also an ecological correlate in the Cuthbert on an earlier version of the manuscript. top two supported models, but in neither was the probability of its beta coefficient significant (Table 4). In the second- ranked model, ground-level barrier (e.g., fallen trees, lianas, Declaration of conflicting interests dense tangled vegetation) was a forth ecological correlate. The author(s) declared no potential conflicts of interest with respect We hypothesize that barriers such as fallen trees increase to the research, authorship, and/or publication of this article. cover for stalking tigers and they may also limit escape options for prey, but the probability associated its beta Funding coefficient was also not significant (Table 4). Models 3 The author(s) disclosed receipt of the following financial support and 4 were subsets of Models 1 and 2 and again low for the research, authorship, and/or publication of this article: shrub cover and high crown cover were significant. Funding was provided by USFWS, PTTEP, and the Department of National Parks, Wildlife and Plant Conservation, Thailand. Implications for conservation. Globally, significant remaining tiger habitat is fragmented into small units, and as a result, References most tiger populations are critically small (Kenny, Allendori, Balme, G., Hunter, L., & Slotow, R. (2007). Feeding habitat selec- McDougal, & Smith, 2014; Walston et al., 2010). Managers tion by hunting leopards Panthera pardus in a woodland have only three options for increasing the viability of tiger savanna: Prey catchability versus abundance. Animal populations. They can increase the land base that supports Behavior, 74, 589–598. tigers, increase connectivity between populations or increase Belotti, E., Cˇ erveny´, J., Sˇustr, P., Kreisinger, J., Gaibani, G.Bufka, prey abundance, especially where human activities have L. (2013). Foraging sites of Eurasian lynx Lynx lynx: Relative resulted in prey depletion (Karanth & Stith, 1999). Fire man- importance of microhabitat and prey occurrence. Wildlife agement has been widely used to reduce shrub cover in Huai Biology, 19, 188–201. Kha Khaeng, especially of less nutritious species and to pro- Blanchard, P., & Fritz, H. (2007). Induced or routine vigilance vide young, more nutritious high-protein grasses (Sunquist while foraging. Oikos, 116, 1603–1608. & Sunquist, 1989) for ungulates and ultimately tiger prey. Brodie, J. F., & Brockelman, W. Y. (2009). Bed site selection of red muntjac (Muntiacus muntjak) and sambar (Rusa unicolor)in Although we found tigers kill more often in areas of low a tropical seasonal forest. Ecological Research, 24, 1251–1256. shrub cover, if cover is reduced too much, adequate cover for Burnham, K. P., & Anderson, D. R. (2002). Model selection and stalking prey will be limited. We suggest that altering fire multimodel inference: A practical information-theoretical management to create smaller burns will potentially create a approach. New York, NY: Springer-Verlag. mosaic between burned and nonburned areas which may Caro, T. M., & Fitzgibbon, C. D. (1992). Large carnivores and their optimize cover for prey detection, but also produce adequate prey: The quick and the dead. In: M. J. Crawley (ed.) Natural cover for tigers. enemies: The population biology of predators parasites and 88

Pakpien et al. 7

diseases (pp. 117–142). Oxford, England: Blackwell Scientific Litvaitis, J. A., Sherburne, J. A., & Bissonette, J. A. (1986). Bobcat Publications. habitat use and home range size in relation to prey density. The Davidson, Z., Valeix, M., Loveridge, A. J., Hunt, J. E., Johnson, P. Journal of Wildlife Management, 50, 110–117. J., Madzikanda, H., & Macdonald, D. W. (2012). Environmental Manly, B. F., McDonald, L., Thomas, D., McDonald, T. L., & determinants of habitat and kill site selection in large carnivore: Erickson, W. P. (2002). Resource selection by animals: Scale matters. Journal of Mammalogy, 93, 677–685. Statistical design and analysis for field biology. Netherlands: Duangchantrasiri, S., Umponjan, M., Simcharoen, S., Springer. Pattanavibool, A., Chaiwattana, S., Maneerat, S., ...Karanth, Miller, C. S., Hebblewhite, M., Petrunenko, Y. K., Serodkin, I. V., K. U. (2016). Dynamics of a low-density tiger population in DeCesare, N. J., Goodrich, J. M., & Miquelle, D. G. (2013). Southeast Asia in the context of improved law enforcement. Estimating Amur tiger (Panthera tigris altaica) kill rates and Conservation Biology, 30, 639–648. potential consumption rates using global positioning system col- Elliott, J. P., Cowan, I. M., & Hollings, C. S. (1977). Prey capture lars. Journal of Mammalogy, 94, 845–855. by the African lion. Canadian Journal of Zoology, 55, Murphy, K., & Ruth, T. (2010). Diet and prey selection of a perfect 1811–1828. predator. In: M. Hornocker, & S. Negri (Eds.). Cougar: ecology Hayward, M. W., & Kerley, G. I. H. (2005). Prey preferences of the and conservation (pp. 118–137). Chicago, IL: University of lion (Panthera leo). Journal of Zoology, 267, 309–322. Chicago Press. Hebblewhite, M., Merrill, E. H., & McDonald, T. L. (2005). Spatial Murray, D. L., Boutin, S., O’Donoghue, M., & Nams, V. O. (1995). decomposition of predation risk using resource selection func- Hunting behavior of sympatric felid and canid in relation to tions: An example in wolf-elk predator-prey system. Oikos, 111, vegetative cover. Animal Behaviour, 50, 1203–1210. 101–111. Mysterud, A. (1996). Bed-site selection by adult roe deer Capreolus Heurich, M., Zeis, K., Ku¨chenhoff, H., Mu¨ller, J., Belotti, E., capreolus in southern Norway during summer. Wildlife Biology, Bufka, L., & Woelfing, B. (2016). Selective predation of a 2, 101–106. stalking predator on ungulate prey. PLoS One, 11, e0158449. Nudds, T. D. (1977). Quantifying the vegetative structure of wild- Holling, C. S. (1959). The Components of predation as revealed by life cover. Wildlife Society Bulletin, 5, 113–117. a study of small-mammal predation of the European sawfly. The Palomares, F., Delibes, M., Revilla, E., Calzada, J., & Fedriani, J. Canadian Entomologist, 91, 293–320. M. (2001). Spatial ecology of Iberian lynx and abundance of Hopcraft, J. G. C., Sinclair, A. R. E., & Packer, C. (2005). Planning European rabbits in southwestern Spain. Wildlife Monographs, for success: Serengeti lions seek prey accessibility rather than 148, 1–36. abundance. Journal of Animal Ecology, 74, 559–566. Phetdee, A. (2000). Feeding habits of the tiger (Panthera tigris Johnson, D. H. (1980). The comparison of usage and availability Linnaeus) in Huai Kha Khaeng Wildlife Sanctuary by fecal measurements for evaluating resource preference. Ecology, 61, analysis (MSc Thesis). Kasetsart University, Bangkok, 65–71. Thailand. Kanchanasaka, B. (2010). Status and distribution of large mammals R Core Team. (2015). R: A language and environment for statis- in Thailand. Bangkok, Thailand: Department of National Parks, tical computing. Retrieved from http://www.R-project.org/ Wildlife and Plant Conservation. Schaller, G. B. (1967). The deer and the tiger: A study of wildlife in Karanth, K. U., & Stith, B. M. (1999). Prey density as a critical India. Chicago, IL: University of Chicago Press. determinant of tiger population viability. In: J. Seidensticker, Simcharoen, A., Savini, T., Gale, G. A., Simcharoen, S., S. Christie, & P. Jackson (Eds.). Riding the tiger: Tiger conser- Duangchantrasiri, S., Pakpien, S., & Smith, J. L. D. (2014). vation in human-dominated landscapes (pp. 110–113). Female tiger Panthera tigris home range size and prey abun- Cambridge, England: Cambridge University Press. dance: Important metrics for management. Oryx, 48, 370–377. Kenny, J., Allendori, F. W., McDougal, C., & Smith, J. L. D. Smith, J. L. D., McDougal, C., & Miquelle, D. (1989). Scent mark- (2014). How much gene flow is needed to avoid inbreeding ing in free-ranging tigers, Panthera tigris. Animal Behaviour, depression in wild tiger populations? Proceedins of the Royal 37, 1–10. Society Publishing B, 281, 20133337. Spong, G. (2002). Space use in lions, Panthera leo, in the Selous Krebs, J. R., & Davies, N. B. (1993). An introduction to behav- Game Reserve: Social and ecological factors. Behavioral ioural ecology. Oxford, England: Blackwell Scientific Ecology and Sociobiology, 52, 303–307. Publications. Sunquist, M. E., & Sunquist, F. C. (1989). Ecological constraints on Kruuk, H. (1986). Interactions between Felidae and their prey spe- predation by large felids. In: J. L. Gittleman (ed.) Carnivore cies: A review. In: S. D. Miller, & D. D. Everett (Eds.). Cats of behavior, ecology, and evolution (pp. 283–301). New York, the world: Biology, conservation and management NY: Springer. (pp. 333–352). Washington, DC: National Wildlife Federation. Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. New Lamprecht, J. (1978). The relationship between food competition Jersey: Princeton University Press. and foraging group size in some larger carnivores. Ethology, 46, Walston, J., Robinson, J. G., Bennett, E. L., Breitenmose, U., 337–343. Fonseca, G. A. B. F., Goodrich, J., ...Wibisono, H. (2010). Lekagul, B., & McNeely, J. A. (1977). Mammals of Thailand. Bringing the tiger back from the brink-the six percent solution. Bangkok, Thailand: Kurusapha. Plos Biology, 8, 1–4. 89

Food Webs xxx (2018) e00085

Contents lists available at ScienceDirect

Food Webs

journal homepage: www.journals.elsevier.com/food-webs

Tiger and leopard diets in western Thailand: Evidence for overlap and potential consequences☆

Achara Simcharoen a, Saksit Simcharoen a, Somphot Duangchantrasiri a, Joseph Bump b, James L.D. Smith b,⁎ a Protected Area Administration, Ofﬁce 12, Department of National Parks, Wildlife, and Plant Conservation, Nakhon Sawan, Thailand b Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, 2003 Upper Buford Cr., St. Paul, MN 55108, USA. article info abstract

Article history: Interference competition by tigers, Panthera tigris, is widely reported to reduce leopard, Panthera pardus, density Received 25 April 2018 or cause its shift to more a marginal habitat. In Southeast Asia, lack of a medium sized prey, spotted deer (Axis Accepted 29 April 2018 axis), and increased consumption of sambar (Rusa unicolor) by leopards amplifies dietary overlap between Available online xxxx these two large felids. In our study area in western Thailand, leopard density was 2.4 times that of tigers. Using scat analysis we estimated prey biomass in the diet of each species to examine resource competition between these species. Tigers had a 0.89 spatial overlap with leopards and leopards a 0.92 overlap with tigers. Larger prey in this system (N100 kg) composed 89.8 Ackerman's coefficient, ACF, and 79.3% Chakrabati's coefficient, CCF of the biomass in tiger diet and 47.0% (ACF) and 45.3% (CCF) of the biomass in leopard diet. Dietary overlap of prey N100 kg versus smaller prey (≤37 kg) between these felids was 74.4% (ACF) and 81.2% (CCF). Dense cover at our site may reduce interference competition from tigers. In turn, high leopard density and leopard consumption of young sambar likely increases resource competition with tigers and thus potentially reduces tiger density. Given current small tiger population sizes, it is important to recognize that leopards may compete with and potentially suppress tiger numbers at some locations throughout their range and this topic needs further study. © 2018 Published by Elsevier Inc.

1. Introduction et al., 2010), India (Harihar et al., 2011) and Thailand (Steinmetz et al., 2013) indicate tiger avoidance and are viewed as evidence of interfer- Interference and resource competition are expected to govern the ence competition by tigers toward leopards. However, in India's West- interactions and persistence of sympatric large carnivores. Interference ern Ghats, tigers did not spatially or temporally displace leopards competition occurs when a dominant species aggressively reduces the (Karanth and Sunquist, 1995, 2000; Ramesh et al., 2012). abundance of the less dominant species (Case and Gilpin, 1974)andit Direct observations of interference competition are rare, but have has been widely reported among many mammalian carnivore guilds been reported in many carnivore guilds (Caro and Stoner, 2003; (Caro and Stoner, 2003; Levi and Wilmers, 2012). Resource competition Palomares and Caro, 1999) including tigers and leopards. For example, follows when two species exploit the same, limiting resource, e.g. high tigers killed 5 leopards in 21 months within an area of 7 km2 in Chitwan overlap in diet, and it is also common among carnivore guilds National Park, Nepal (McDougal, 1988). In Thailand, 3 leopards killed by (Hayward et al., 2007). Both competitive mechanisms are expected to tigers were discovered over a period of several years in Huai Kha influence the interaction of large co-occurring felids, specifically tigers Khaeng Wildlife Sanctuary (HKK) (S. Simcharoen unpuplished data). To- and leopards (Harihar et al., 2011; Karanth et al., 2017; Odden et al., gether, the decline in density of leopards or their displacement to the 2010; Seidensticker, 1976). periphery of reserves, and the additional observations of tigers killing Tigers and leopards, the two largest obligate carnivores in temperate leopards, establishes that interference competition by tigers can impact and tropical Asia, evolved to exploit the radiation of large ungulates in leopards (Harihar et al., 2011). the late Pleistocene (Sunquist et al., 1999). Leopards co-occur every- Diet specialization and overlap between competing predators are fre- where tigers reside except in the Sundarbans and Sumatra. Shifts in quently examined to assess potential resource competition. Several au- leopard home ranges from interior to edge habitats in Nepal (Odden thors report high dietary overlap between leopards and tigers (Andheria et al., 2007; Harihar et al., 2011; Karanth and Sunquist, 1995, 2000; Ramesh et al., 2012; Wegge et al., 2009). These studies in South ☆ AS, SS, SD collected field data; AS, SS, JS analyzed data; AS, SS, JB, JS contributed to Asia found spotted deer, Axis axis,andsambar,Rusa unicolor,torepresent writing the manuscript. ⁎ Corresponding author. the highest biomass overlap in the diet of tigers and leopards. In contrast, E-mail address: [email protected] (J.L.D. Smith). in Southeast Asia, Axis spp. are absent, resulting in a prey size gap between

2 A. Simcharoen et al. / Food Webs xxx (2018) e00085 wild boar, Sus scrofa, and the several times larger sambar (Karanth and later analysis (Simcharoen, 2008, Phetdee, 2000). To identify prey, a ref- Sunquist, 1995). Lack of a medium-sized deer species, and consumption erence collection was created from hair samples acquired from 30 spe- of sambar by both tigers and leopards, potentially increase resource com- cies; hair was obtained from kills, captive animals and museum petition between these two species. This potential is unexamined but im- specimens. To examine variation within species, and from different lo- portant to regional management and conservation of other critically small cations on an individual, we sampled several animals of each species tiger populations that co-occur with leopards. and obtained hair from each animal at five sites: brow, back, shoulder, To assess whether or not a prey size gap increases potential resource thigh and stomach. Five morphological aspects of hair: color, cross sec- competition between tigers and leopards, we estimated the extent to tion, medulla, scale pattern and diameter (at mid-point) were used to which dietary overlap is found in the biomass of prey species eaten by identify prey species (Moore et al., 1974; Reynolds and Aebischer, tigers and leopards. Using this estimate, we discuss the potential for re- 1991). Cross sections were prepared by imbedding hair in paraffin, source competition. In our system, dietary overlap is key to understand- cross sectioning and mounting the sections on glass slides (Reynolds ing why the density of leopards is 2.4 times greater than the density of and Aebischer, 1991). To prepare hair for medullar examination it was tigers (Simcharoen et al., 2007). placed in xyline until pigments were removed. Depending on species, hair was kept in xyline from one day for lightly pigmented hair to nearly 2. Materials and methods a month for heavily pigmented samples (e.g. wild boar and Asiatic black bear, Ursus thibetanus), and then mounted on slides. Scale pattern was 2.1. Study area categorized based on a set of reference drawings by (Brunner and Coman, 1974). The cuticle form (shape of scale structure) of each hair This study was conducted in an approximately 200 km2 area near was characterized at the base, middle and end of each sample. Hair Khao Nang Ram Research Station (KNR) in HKK, Uthaithani Province, specimens were not collected for two prey species (porcupine, Hystrix Thailand. As in much of Thailand, rainfall is highly seasonal with a dry brachyuran, and pangolin, Manis javanica) because quills and scales season (November–April; mean precipitation is 280 mm) and a wet are easily identified in scats. season (May–October; mean precipitation is 1225 mm). These condi- Scrape length and width and track size found at scat sites were used tions result in a monsoonal forest that is categorized into four general to distinguish species producing scats (Cutter, 2014). Maximum leopard cover types: dry dipterocarp forest (13%, 200–400 MSL); mixed decidu- pad widths were measured from front tracks of males; minimum tiger ous forest (46%, 400–950 MSL), dry evergreen forest (18%, 400–1000 pad widths were measured from rear tracks of females. MSL) and hill evergreen forest (15%, 1000–1500 MSL) (Trisurat, 2004). Cleaned and dry hair samples were first compared to the reference collection by examining the cuticle pattern, medulla structure and 2.2. Spatial overlap of tiger and leopard scats color. If hairs could not be identified by these three characteristics, then hair diameter and cross section morphology were used to further From 1996 to 1998 scats of tigers and leopards were georeferenced distinguish hair sample species. Because juveniles and adults of the and collected during daily ground surveys as we searched for sign same species had distinctive hair morphology, we were able to deter- along an extensive network of trails, two-track roads, streams and mine relative proportions of young (b1 year) and old prey species in ridges within the study area (Walsh and White, 1999). To determine if tiger and leopard diet for the four largest species (gaur, Bos gaurus; ban- either felid was avoiding competition from the other species, by spatial teng, Bos javanicus; sambar and wild boar. For scats composed of hair segregation, we constructed utilization distributions of the locations of from two or three species, we estimated each species as present in tiger and leopard scats using the Kernel method (Worton, 1989) and es- half or one third of a scat, respectively (Link and Karanth, 1994). timated spatial overlap by assessing the utilization distribution overlap index (UDOI) (Fieberg and Kochanny, 2005). 2.5. Quantification of prey biomass

2.3. Tiger and leopard density in study area Estimating biomass based on scats of prey species that vary greatly in size overemphasizes smaller prey items because they contain a larger We used camera trap data of individually recognized animals and a amount of indigestible surface area (e.g. hair) to unit weight ratio than closed capture recapture model during the same time period do large species. We converted frequency of scats to relative biomass (1996–1999) (Simcharoen et al., 2007, 2008). These data were analyzed of each species in tiger and leopard diets using a correction factor first and averaged over 3 annual seasons using “Capture” software developed for wolves (Floyd et al., 1974) and subsequently recalibrated (Simcharoen et al., 2008). for mountain lions, Felis concolor (Ackerman et al., 1984) and used widely for other large felids. Following this previously established 2.4. Scat collection and prey identification method and a review of diet studies of tigers and leopards (Hayward et al., 2012; Karanth and Sunquist, 1995), we calculated biomass per Scats were only collected if they were associated with scrapes and scat of both species using the regression, tracks so that species producing the scat could be confirmed. Feces were deposited at the rear of scrapes and we never found scats of Y ¼ 1:980 þ 0:035X: ð1Þ other species or a second tiger or leopard scat associated with a scrape. However, when an animal was establishing a territory there were often This linear model does not take into account satiation when carni- zones of intense scrapes with and without scats. Using measurements vores consume prey considerably larger than their own body weight. from scrapes and tracks made at camera trap sites and zoos we classified Therefore, we also analyzed our data using a non-linear correction fac- scats as tiger or leopard (Cutter, 2014)(Table 1). Scats were washed and tor (Chakrabarti et al., 2016) that accounts for tiger (2) and leopard hair and animal parts were dried, labeled, sorted by color and stored for (3) satiation when consuming larger prey species,

Y ¼ 0:033−0:025 exp−4:28X ð2Þ Table 1 Measurements of tiger and leopard scrapes and tracks at scats. Y ¼ 2:171−1:671 exp−0:056X ð3Þ N Track pad width Scrape length Scrape width

Tiger 61 Not b7.0 cm Not b35 cm Not b19 cm In our discussion we explain the reasons for and consequence of Leopard 95 Not N6.4 cm Not N25 cm Not N15 cm reporting the results calculated by both correction factors. Estimates of 91

A. Simcharoen et al. / Food Webs xxx (2018) e00085 3 average weight of each species consumed by tigers and leopards were monitor lizards) were found in leopard scats (Table 2). The biomass of based on Karanth and Sunquist (1995). Estimates of mean weight of a species ≥100 kg (banteng, sambar, gaur, water buffalo, Bubalus bubalis prey species consumed by leopards was considerably less than the and Asiatic black bear) in the tiger's diet was 89.9% using Ackerman's mean weight consumed by tigers (Table 2). coefficient (ACF) and 79.3% using Chakrabati's coefficient (CCF); for leopards the proportion of biomass of large mammals was 46.9% 2.6. Seasonal diet and dietary overlap (ACF) and 45.3% (CCF) (Table 3). The highest annual single species that appeared in both tiger and leopard scats (i.e. overlap) was sambar; To determine diet overlap, we reviewed different methods and, fol- for ACF it was 28.7% for tigers and 36.0% for leopards; for CCF it was lowing the recommendation of Smith and Zaret (1982),wechose 29.2% for tiger and 35.9% for leopard. Annual dietary overlap between ti- Pianka's (1974) index of dietary overlap as the most appropriate mea- gers and leopards for large (≥100 kg) versus medium and small species sure because it has been widely used in similar carnivore diet studies. (≤100 kg) was 74.9% (ACF) and 81.0% (Table 3) (CCF). Overlap ranges from 0 to 1.0 and generally overlap N0.6 is considered high; importantly, for all measures of diet overlap there is a bias to un- 4. Discussion derestimate diet overlap as the number of diet categories increases (Smith and Zaret, 1982). Because our study measured dietary overlap At our study site the density of leopards is more than twice the den- to assess potential resource competition between tigers and leopards, sity of tigers. One possible explanation for this difference is that low di- we calculated diet overlap for larger prey in this system (≥100 kg) ver- etary overlap allows leopards to occur at high density by reducing sus medium and small prey (≤37 kg); however, we also report overlap resource competition. This hypothesis, however, is not supported by of all species. Typically, no prey are available between 37 and 100 kg our results; we found high diet overlap in large prey consumed by tigers in this system (Table 2). and leopards. The highest overlap in diet occurred for sambar, which in We categorized scats collected during November–April as dry sea- part may be explained by the absence of a medium-sized deer (e.g. spot- son samples and those obtained during May–October as wet season ted deer) that is the dominant biomass in the leopard's diet in South samples. Because the same number of scats were not obtained in each Asia (Andheria et al., 2007; Hayward et al., 2012). At only one site in season, the relative abundance of prey was estimated in each season South Asia, Pakke Tiger Reserve, Eastern Himalayan lowlands, India, and averaged to obtain annual abundance. For each of the four largest was there a similar high dietary overlap (0.85) and also an absence of prey species, the ratio of young to adults was categorized based on a medium-sized deer (spotted deer) (Selvan et al., 2013). At Pakke clear differences in hair characteristics of young and older animals. Tiger Reserve, sambar composed 63% of tiger diet and 42% of leopard diet. 3. Results Dietary overlap may have increased because hunting and habitat change has resulted in extirpation or caused extinction of 3 species of We collected 252 tiger scats and analyzed 246 containing mammal medium-sized deer in Thailand (Humphrey and Bain, 1990; McShea remains. We also obtained 371 leopard scats and analyzed 356 contain- et al., 1999). Eld's deer, Rucervus eldii, once widespread in SE Asia occurs ing mammal, snake and lizard remains. All scats were located within an only in restricted areas in Cambodia (Bhumpakphan et al., 2004); hog approximately 200 km2 study area. Visually we did not detect spatial deer, Axis porcinus, is extirpated from Thailand although there are cur- differences between the distributions of the two data sets (Fig. 1). Our rently attempts to reintroduce both these species (Prasanai et al., estimate of the probability of tiger scat spatially overlapping leopards 2012). A third medium-sized species, Schomburgk's deer, Rucervus scats was 0.89 and leopard scats spatially overlapping tiger scats was schomburgki, is extinct (Humphrey and Bain, 1990). Absence of these 0.92. species reduces the breadth of prey resources available to large felids and may explain the high dietary overlap between tigers and leopards. 3.1. Diet In addition to dietary overlap, we found high spatial overlap of scats (Fig. 1). We did not identify the individuals depositing scats so it is pos- Sixteen species of mammals were identified in tiger scats and 14 sible that individual tigers and leopards overlap less than the overlap of species of mammals plus a general category of reptiles (e.g. snakes; tiger and leopard scats indicates (Rabinowitz, 1989; Simcharoen et al.,

Table 2 Relative biomass of mammal prey species in the diet of tigers and leopards in HKK using Ackerman et al.'s (1984) and Chakrabarti et al.'s (2016) correction factors for biomass per scat.

Species Tiger Leopard

Weight Ackerman Chakrabarti Weight Ackerman Chakrabarti

Banteng, Bos javanicus 287 44.5% 35.5% 85 9.9% 8.4% Gaur, Bos gaurus 287 14.0% 11.2% 85 0.7% 0.6% Water buffalo, Bubalus bubalis 287 1.2% 0.9% 85 0.0% 0.00% Sambar, Rusa unicolor 212 28.7% 29.2% 62 36.0% 35.9% Asiatic black bear, Ursus thibetanus 100 1.5% 2.5% 20 0.3% 0.4% Wild boar, Sus scrofa 37 3.7% 8.3% 37 15.5% 17.7% Chinese serow, Capricornis milneedwardsi 30 0.0% 0.0% 30 1.1% 1.4% Barking deer, Muntiacus muntjak 20 3.5% 7.6% 20 11.4% 13.5% Golden jackal, Canis aureus 10 0.3% 0.5% 10 0.2% 0.1% Hog badger, Arctonyx collaris 10 0.2% 0.4% 10 0.0% 0.00% Phayre's langur, Trachypithecus phayrei 9 0.1% 0.1% 9 3.2% 3.1% Macaque, Macaca sp 8 0.1% 0.2% 8 6.4% 6.0% Malayan porcupine, Hystrix brachyuran 8 1.4% 2.4% 8 10.7% 10.1% White-handed gibbon, Hylobates lar 5.5 0.2% 0.3% 5.5 0.7% 0.6% Common palm civet, Paradoxurus hermaphroditus 3.5 0.1% 0.2% 3 0.0% 0.0% Small Indian civet, Viverricula indica 3 0.1% 0.2% 3 0.6% 0.4% Sunda pangolin, Manis javanica 3 0.4% 0.5% 3 0.0% 0.0% Rodents/hares 0.5 1.0% 0.5% Asian slow loris, Nycticebus bengalensis 1 2.0% 1.1% Snakes/monitor lizards 1.5 0.5% 0.3% 92

4 A. Simcharoen et al. / Food Webs xxx (2018) e00085

Fig. 1. Study area in the northeast portion of Huai Kha Khaeng Wildlife Sanctuary, west central Thailand. Points indicate the location of scats.

2008, 2014). In the Western Ghats and elsewhere in South Asia other in- other sites not displacing leopards? One explanation is that prey is not vestigators also reported high dietary and spatial overlap (Harihar et al., limiting tigers at sites with high dietary and spatial overlap so tigers tol- 2011; Karanth and Sunquist, 1995, 2000; Odden et al., 2010; Ramesh erate leopards. This argument is counter to Karanth et al. (2004),who et al., 2012; Seidensticker, 1976). report tiger density is positively related to prey density. We interpret The contrasting data on the degree of interference competition this relationship to mean that prey abundance limits tiger abundance poses an interesting question. Why at some localities is there strong ev- and that intraspeciﬁc competition exists. Further evidence of intraspe- idence of tigers spatially displacing and even killing leopards and at ciﬁc competition in tigers is the lack of home range overlap (Smith 93

A. Simcharoen et al. / Food Webs xxx (2018) e00085 5

Table 3 understanding the role of leopards as competitors with tigers is a Percentage biomass of species N100 kg and b100 kg for tiger and leopard using Ackerman topic that merits further investigation. et al.'s (1984) and Chakrabarti et al.'s (2016) correction factors for converting scats to prey biomass. Pianka's (1974) dietary overlap (0–1) was calculated using data from both correction factors. Acknowledgments Ackerman Chakrabarti Tiger Leopard Tiger Leopard Funding was provided by World Wildlife Fund-Thailand and a Prey N100 kg 89.8% 47.0% 79.3% 45.3% USFWS (F15AP00762) grant to the University of Minnesota. Smith's Prey b100 kg 10.2% 53.0% 20.7% 54.7% contribution to this study was supported by the USDA National Institute Overlap N100 kg vs b100 kg 0.74. 0.81 of Food and Agriculture (Project No. MIN-41-002). We express deep gratitude to: Dr. Schwan Tunhikorn for permission to conduct this research; Drs. Naris Bhumpakpun, Virayuth Lauhajinda and Obhat et al., 1989; Goodrich et al., 2010). In combination with the lack of home Khobkhet for their data analysis recommendations; Onsa Norasarn range overlap, Simcharoen et al. (2014) demonstrate that female home and Precha Phrommakul for assistance with field work; Phakawat range size is inversely dependent on prey abundance. Phonak and Wiworachit Musikwong for assistance in reference slide Together evidence of intraspecific competition among tigers and the preparation; and Kosol Tunkjiphitaks for his advice on microtechnique widespread report of interference competition by tigers toward leopards of hair cross-sections. Finally, we thank Drs. Francesca Cuthbert and is indicative of resource competition. Additionally, from an evolutionary Melvin Sunquist for comments on an earlier draft of this manuscript. perspective, interference competition implies resource competition (Odden et al., 2010). Odden and colleagues suggest that “different eco- References logical settings” indicate “there is ‘no single key to coexistence’”. The spatial and dietary overlap that we report in combination with Ackerman, B.B., Lindzey, F.G., Hemker, T., 1984. Cougar food habits in Southern Utah. J. Wildl. 48, 147–155. the high density of leopards found in our study area raises several im- Andheria, A.P., Karanth, K.U., Kumar, N.S., 2007. Diet and prey profiles of three sympatric portant questions. What is the impact of approximately 5 female leop- large carnivores in Bandipur Tiger Reserve, India. J. Zool. 273, 168–175. ards overlapping the territories of an estimated 2 female tigers when a Bhumpakphan, N., Sukmasuang, R., Chaiyarat, R., 2004. Thailand: the crossroad where substantial portion of the diet of both species is sambar? Karanth and two Eld's deer subspecies existed. In: Pukazhenthi, B. (Ed.), Workshop on Eld's Deer Conservation and Restoration. Khao Kheow Open Zoo, Chonburi Thailand, Sunquist (1995) report that the mean size of sambar eaten by tigers is pp. 10–14. 212 kg and the mean size eaten by leopards is 70 kg. It is argued that Brunner, H., Coman, B.J., 1974. The Identification of Mammalian Hair. Inkata Press, by partitioning the size of sambar tiger and leopard reduce dietary com- Melbourne. Caro, T.M., Stoner, C.J., 2003. The potential for interspecific competition among African petition. This would only occur if there was compensatory mortality of carnivores. Biol. Conserv. 110, 67–75. young sambar not eaten by leopards. We have no evidence to make Case, T.J., Gilpin, M.E., 1974. Interference competition and niche theory. Proc. Natl. Acad. this assumption. Only tiger and leopards are predating sambar and dur- Sci. 71, 3073–3077. Chakrabarti, S., Jhala, Y.V., Dutta, S., Quershi, Q., Kadivar, R.F., Rana, V.J., 2016. Adding con- ing regular Smart Patrolling there was no evidence of poaching or other straints to predation through allometric relation of scats to consumption. J. Anim. forms of mortality of sambar and other large ungulates in this core area Ecol. https://doi.org/10.1111/1365-265612508. of HKK (Duangchantrasiri et al., 2016). We agree with previous sugges- Cutter, P., 2014. Landscape-scale Conservation Planning and Wildlife Monitoring in Southeast Asia. PhD Thesis. University of Minnesota Available:. http://hdl.handle. tions that interference competition is likely linked to and is a behavior to net/11299/167771. reduce resource competition (Odden et al., 2010; Steinmetz et al., Duangchantrasiri, S., Umponjan, M., Simcharoen, S., Pattanavibool, A., Chaiwattana, S., 2013). Maneerat, S., ... Karanth, K.U., 2016. Dynamics of a low-density tiger population in Southeast Asia in the context of improved law enforcement. Conserv. Biol. 30, Given high prey overlap, the remaining question is, why is interfer- 639–648. ence competition by tigers not more effective at reducing leopard den- Fieberg, J., Kochanny, C.O., 2005. Quantifying home-range overlap: the importance of the sity in HKK? We speculate that two factors reduce interference utilization distribution. J. Wildl. Manag. 69, 1346–1359. competition at our study site. First, female tiger home range size is Floyd, T.J., Mech, L.D., Jordan, P.A., 1974. Relating wolf scat content to prey consumption. J. Wildl. Manag. 42, 528–532. larger than elsewhere in HKK (Simcharoen et al., 2014) and at sites Goodrich, J.M., Miquelle, D.G., Smirnov, E.N., Kerley, L.L., Quigley, H.B., Hornocker, M.G., where interference competition occurs in India and Nepal (Harihar 2010. Spatial structure of Amur (Siberian) tigers (Panthera tigris altaica) on – et al., 2011; Lovari et al., 2014; Odden et al., 2010; Ramesh et al., Sikhote-Alin Biosphere Zapovednik, Russia. J. Mammal. 91, 737 748. Harihar, A., Pandav, B., Goyal, S., 2011. Responses of leopard Panthera pardus to the recov- 2012). Lower density of tigers reduces encounter. Furthermore, higher ery of a tiger Panthera tigris population. Appl. Ecol. 48, 806–814. vegetation cover in Huai Kha Khaeng compared to more open habitat Hayward, M.W., O'Brien, J., Kerley, G.I.H., 2007. Carrying capacity of large African preda- in Bardia National Park, Nepal, and Raaji National Park, India (Harihar tors: predictions and tests. Biol. Conserv. 139, 219–229. Hayward, M., Jędrzejewski, W., Jêdrzejewska, B., 2012. Prey preferences of the tiger et al., 2011; Odden et al., 2010) likely further reduces the encounter Panthera tigris. J. Zool. 286, 221–231. rate between tigers and leopards. Although some level of interference Humphrey, S.R., Bain, J.R., 1990. Endangered Animals of Thailand. Sandhill Crane Press, competition does occur in HKK (Saksit Simcharoen, unpublished data), Gainesville, FL. Karanth, K.U., Sunquist, M.E., 1995. Prey selection by tiger, leopard and dhole in tropical a density of 2.4 times as many leopards as tigers suggests that interfer- forests. J. Anim. Ecol. 64, 439–450. ence competition has had only a modest impact on leopards in our Karanth, K.U., Sunquist, M.E., 2000. Behavioral correlates of predation by tiger (Panthera study area. The combination of lower tiger density and greater cover tigris), leopard (Panthera pardus), and dhole (Cuon alpinus) in Nagarahole, India. J. Zool. Lond. 250, 255–265. may lower encounter rate between tigers and leopards. And in turn re- Karanth, K.U., Nichols, J.D., Kumar, N.S., Link, W.A., Hines, J.E., 2004. Tigers and their prey: duced interference competition increases resource competition. This in- predicting carnivore densities from prey abundance. Proc. Natl. Acad. Sci. USA 101: ference merits further investigation and it is important to recognize that 4854–4858. https://doi.org/10.1073/pnas.0306210101. the dynamic between interference and resource completion is expected Karanth, K.U., Srivathsa, A., Vasudev, D., Puri, M., Parameshwaran, R., Kumar, N.S., 2017. Spatio-temoral interactions facilitate large carnivore sympatry across a resource gra- to vary (Odden et al., 2010). dient. Proc. R. Soc. B 284:20161860. https://doi.org/10.1098/rspb.2016.1860. Kenney, J.S., Allendorf, F., Smith, J.L.D., 2014. How much gene flow is needed to avoid in- – 5. Conservation implications breeding depression in wild tiger populations? Proc. R. Soc. B 281, 1 8. Levi, T., Wilmers, C.C., 2012. Wolves-coyotes-foxes: a cascade among carnivores. Ecology 93:921–929. https://doi.org/10.1890/11-0165-1. For critically small tiger populations, leopards could have an impact Link, W.A., Karanth, K.U., 1994. Correcting for overdispersion in tests of prey selectivity. on tiger population viability (Steinmetz et al., 2013). Because tiger pop- Ecology 75, 2546–2549. Lovari, S., Pokharel, C.P., Jnawali, S.R., Fusani, L., Ferretti, F., 2014. Coexistence of the tiger ulation sizes are low enough across the tiger's range that concern about and the common leopard in a prey-rich area: the role of prey partitioning prey-rich viability exists in a time frame of b100 years (Kenney et al., 2014), area: the role of prey partitioning. J. Zool. 295, 122–131. 94

6 A. Simcharoen et al. / Food Webs xxx (2018) e00085

McDougal, C., 1988. Leopard and tiger interactions at Royal Chitwan National park, Nepal. Simcharoen, S., Pattanavibool, A., Karanth, K.U., Nichols, J.D., Kumar, N.S., 2007. How many J. Bombay Nat. Hist. Soc. 3, 609–610. tigers Panthera tigris are there in Huai Kha Khaeng Wildlife Sanctuary Thailand? An McShea, W.J., Leimgruber, P., Aing, M., Monfort, S.L., Wemmer, C., 1999. Range collapse of estimate using photographic capture–recapture sampling. Oryx 41, 447–453. a tropical cervid (Cervus eldi) and the extent of remaining habitat in central Simcharoen, S., Barlow, A.C.D., Simcharoen, A., Smith, J.L.D.S., 2008. Home range size and Myanmar. Anim. Conserv. Forum 2, 173–183. daytime habitat selection of leopards in Huai Kha Khaeng Wildlife Sanctuary, Moore, T.D., Spence, L.E., Dugnolle, C.G., 1974. Identification of dorsal guard hairs of some Thailand. Biol. Conserv. 14, 447–453. mammals of Wyoming. Wyo. Game Fish Dept. Bul. 14, 1–177. Simcharoen, A., Simcharoen, S., Duangchantrasiri, S., Pakpien, S., Gale, G., Savini, T., Smith, Odden, M., Wegge, P., Fredricksen, T., 2010. Do tigers displace leopards? If so, why? Ecol. J.L.D., 2014. Female tiger home range size and prey abundance: important manage- Res. 25, 875–881. ment metrics. Oryx 8, 370–377. Palomares, F., Caro, T., 1999. Interspecific killing among mammalian carnivores. Am. Nat. Smith, E.P., Zaret, T.M., 1982. Bias in estimating niche overlap. Ecology 63, 1248–1253. 153, 492–508. Smith, J.L.D., McDougal, C., Miquelle, D., 1989. Scent marking in free-ranging tigers, Phetdee, A., 2000. Feeding Habits of the Tiger (Panthera tigris) in Huai Kha Khaeng Wild- Panthera tigris. Anim. Behav. 37, 11–27. life Sanctuary by Fecal Analysis. M.Sc. Thesis. Kaesetsart University. Steinmetz, R., Seuaturien, N., Chutipong, W., 2013. Tigers, leopards and dholes in a half- Pianka, E.R., 1974. Niche overlap and diffuse competition. Proc. Natl. Acad. Sci. 71, empty forest: assessing species interactions in a guild of threatened carnivores. 2141–2145. Biol. Conserv. 163, 68–78. Prasanai, K., Sukmasuang, R., Bhumpakphan, N., Wajjwalku, W., Nittaya, K., 2012. Popula- Sunquist, M., Karanth, K.U., Sunquist, F., 1999. Ecology, behaviour and resilience of the tion characteristics and viability of the introduced hog deer (Axis porcinus) in Phu tiger and its conservation needs. In: Seidensticker, J., Christie, S., Jackson, P. (Eds.), Khieo Wildlife Sanctuary, Thailand. Songklanakarin. J. Sci. Technol. 34, 263–271. Riding the Tiger: Tiger Conservation in Human-dominated Landscapes. Cambridge Rabinowitz, A.R., 1989. The density and behavior of large cats in a dry tropical forest mo- University Press, Cambridge, pp. 5–18. saic in Huai Kha Khaeng Wildlife Sanctuary, Thailand. Nat. Hist. Bull. Siam Soc. 37, Trisurat, Y., 2004. GIS Database and Its Applications for Ecosystem Management. Western 235–251. Forest Complex, Ecosystem Management Project (WEFCOM), National Park, Wildlife Ramesh, T., Kalle, R., Sankar, K., Qureshi, Q., 2012. Spatio-temporal partitioning among and Plant Conservation Department. large carnivores in relation to major prey species in Western Ghats. J. Zool. https:// Walsh, P.D., White, L.J.T., 1999. What it will take to monitor forest elephant populations. doi.org/10.1111/j.1469-7998.2012.00908.x. Conserv. Biol. 13, 1194–1202. Reynolds, J.C., Aebischer, N.J., 1991. Comparison and quantification of carnivore diet by Wegge, P., Odden, M., Pokharel, C.P., Storaas, T., 2009. Predator-prey relationships and re- faecal analysis: a critique recommendations, based on a study of the fox Vulpes vulpes. sponses of ungulates and their predators to the establishment of protected areas: a Mammal Rev. 21, 97–122. case study of tigers, leopards and their prey in Bardia National Park, Nepal. Biol. Seidensticker, J., 1976. On the ecological separation between tigers and leopards. Biotro. 8, Conserv. 142, 189–202. 225–234. Worton, B.J., 1989. Kernel methods of estimating the utilization distribution in home- Selvan, K.M., Veeraswami, G.G., Lyngdoh, S., Habib, B., Hussain, S.A., 2013. Prey selection range studies. Ecology 70, 277–298. and food habits of three sympatric large carnivores in a tropical lowland forest of the Eastern Himalayan biodiversity hotspot. Mamm. Biol. 78, 296–303. Simcharoen, S., 2008. Ecology of the Leopard (Panthera pardus) in Hual Kha Khaeng Wild- life Sanctuary. Ph.D. Thesis. Kasetsart University. 95 Received: 25 June 2018 | Revised: 20 October 2018 | Accepted: 19 November 2018

DOI: 10.1002/ece3.4845

ORIGINAL RESEARCH

Impact of prey occupancy and other ecological and anthropogenic factors on tiger distribution in Thailand's western forest complex

1Wildlife Research Division, Department of National Parks, Plant, and Wildlife Abstract Conservation, Bangkok, Thailand 1. Despite conservation efforts, large mammals such as tigers (Panthera tigris) and 2 University of Minnesota, Saint Paul, their main prey, gaur (Bos gaurus), banteng (Bos javanicus), and sambar (Rusa uni‐ Minnesota color), are highly threatened and declining across their entire range. The only large 3Wildlife Conservation Society Thailand Program, Nonthaburi, Thailand viable source population of tigers in mainland Southeast Asia occurs in Thailand’s 4Patuxent Wildlife Research Center, U.S. Western Forest Complex (WEFCOM), an approximately 19,000 km2 landscape of Geological Survey, Laurel, Maryland 17 contiguous protected areas. Correspondence 2. We used an occupancy modeling framework, which accounts for imperfect detec- Pornkamol Jornburom, Wildlife Conservation Society Thailand Program, tion, to identify the factors that affect tiger distribution at the approximate scale Nonthaburi, Thailand. of a female tiger’s home range, 64 km2, and site use at a scale of 1‐km2. At the Email: [email protected] larger scale, we estimated the proportion of sites at WEFCOM that were occupied Funding information by tigers; at the finer scale, we identified the key variables that influence site‐use Wildlife Conservation Society and developed a predictive distribution map. At both scales, we examined key anthropogenic and ecological factors that help explain tiger distribution and habitat use, including probabilities of gaur, banteng, and sambar occurrence from a companion study. 3. Occupancy estimated at the 64‐km2 scale was primarily influenced by the combined presence of all three large prey species, and 37% or 5,858 km2 of the landscape was predicted to be occupied by tigers. In contrast, site use estimated at the scale of 1 km2 was most strongly influenced by the presence of sambar. 4. By modeling occupancy while accounting for imperfect probability of detection, we established reliable benchmark data on the distribution of tigers in WEFCOM. This study also identified factors that limit tiger distributions; which managers can then target to expand tiger distribution and guide recovery elsewhere in Southeast Asia.

KEYWORDS large landscape survey, multiple scale occupancy, Panthera tigris, prey, tiger, Western Forest Complex

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Ecology and Evolution. 2019;9:2449–2458. www.ecolevol.org | 2449 96 2450 | DUANGCHATRASIRI eT Al.

1 | INTRODUCTION gap remains about the tiger's distribution. Previous tiger–habitat relationship studies throughout WEFCOM were limited and used Tigers (Panthera tigris) are highly threatened and declining across inconsistent spatial and temporal scales (Kanchanasaka et al., 2010; their entire range. In Thailand, the subspecies, P.t. corbetti, has de- Rabinowitz, 1993; Smith, Ahern, & McDougal, 1998; WEFCOM, clined from approximately 500 individuals distributed in 13 forest 2004). They also neglected to account for imperfect detection, which complexes (landscapes) in 1994–1995 (Smith, Tunhikorn, Tanhan, can obscure the underlying ecological processes that determine dis- & Simcharoen, and Kanchanasaka, 1999) to an estimated 190– tribution and habitat relationships, especially when surveys are con- 250 in 10 forest complexes in 2004 (Kanchanasaka, Tunhikorn, ducted over a large landscape (Pollock et al., 2002). Therefore, it is Vinitpornsawan, Prayoon, & Faengbubpha, 2010). By 2015, a single difficult to reliably compare results across these studies. To establish large viable population remained in the Western Forest Complex reliable benchmark data on tiger distribution patterns, we conducted (WEFCOM), an approximately 19,000 km2 landscape of 17 con- spatially replicated occupancy surveys throughout WEFCOM, simi- tiguous protected areas. Three protected areas, Thung Yai East lar to those being applied across other tiger landscapes (Harihar & (TYE), Thung Yai West (TYW), and Huai Kha Khaeng (HKK), which Pandav, 2012; Hines et al., 2010; Karanth et al., 2011). together form a World Heritage Site (6,400 km2), represent the Occupancy models applied to multiple spatial scales, with both core of WEFCOM. This core area supports the highest density of landscape and fine‐scale predictors, have been demonstrated to tigers on the landscape (Duangchantrasiri et al., 2016; Simcharoen, be effective in addressing wildlife conservation needs (Scott et al., Pattanavibool, Karanth, Nichols, & Kumar, 2007) and contains one 2002) and have been used specifically to help inform conservation of only four remaining source populations of tigers worldwide that strategies of multiple species, including tigers (Wikramanayake et al., have, under current management, a high probability of being via- 2011). At a regional level, landscape‐wide assessment of tiger dis- ble for the next 100 years (Kenney, Allendorf, McDougal, & Smith, tribution facilitated the identification of source populations, meta‐ 2014). Elsewhere in Southeast Asia tiger populations are on the population structure, and functional corridors that allow individuals verge of extirpation. to move through habitat impacted by human disturbances (Karanth WEFCOM is part of an even larger conservation landscape in et al., 2011; Ranganathan, Chan, Karanth, & Smith, 2008; Smith et the Tenasserim Range, which forms Thailand's western border with al., 1998). At a finer scale, space‐use patterns in source areas provide Myanmar (WEFCOM, 2004). Because of its size and geographic ex- insights into local factors driving habitat use, which can help inform tent, WEFCOM provides critical habitat for tigers in the region. The local management options (Sunarto et al., 2015; Vinitpornsawan, landscape also supports numerous other threatened wildlife species 2013). In summary, analyses that include multiple spatial scales including the tiger's main prey: gaur (Bos gaurus), banteng (Bos ja‐ can improve understanding of tiger–habitat relationships (Johnson, vanicus), wild water buffalo (Bubalus arnee), and sambar (Rusa uni‐ 1980; McDonald, Erickson, Boyce, & Alldredge, 2012). color) (Simcharoen, Simcharoen, Duangchantrasiria, Bump, & Smith, In this study, we seek to: (a) identify how anthropogenic pres- 2018). Data from camera‐trapping and radio telemetry suggest that sures, landscape features, and prey occupancy determine tiger distri- HKK alone supports an estimated 35 to 58 tigers (Simcharoen et bution and habitat use, (b) estimate the proportion of area occupied 2 al., 2007; Duangchantrasiri et al., 2016). Female home ranges there (true occupancy) by tigers at a landscape scale using a 64 km grid, 2 vary from 35 to 105 km2 (Simcharoen, Savini, Gale, Simcharoen, et (c) model habitat relationships of tigers at a 1‐km scale to determine al., 2014). Elsewhere in WEFCOM, tiger distribution and abundance drivers of habitat use, and (d) develop a predictive distribution map and the quality of habitat are less well known. However, ranger pa- for tigers based on spatially explicit site use models. We conclude trols suggest tigers are likely absent from large parts of WEFCOM, by providing guidance on possible alternative management options. and their major prey is similarly reduced (Jornburom, 2016). This lack of knowledge restricts planning efforts to monitor and manage other 2 | MATERIALS AND METHODS protected areas in this landscape. To prioritize and strengthen the future protection of tigers throughout WEFCOM, it is important to 2.1 | Study area determine where tigers occur. Recent studies using occupancy modeling have greatly increased From November 2010 to December 2012, Thailand's Department of understanding of how habitat connectivity, human disturbance, and National Parks, Plants, and Wildlife Conservation (DNP), the Wildlife prey availability affect tiger occurrence. Important factors impacting Conservation Society (WCS, Thailand), and the World Wildlife Fund tigers include forest type and extent of vegetation cover (Sunarto, (WWF, Thailand) conducted an occupancy survey that included el- Kelly, Parakkasi, & Hutajulu, 2015), connectivity of habitat (Joshi, ephants (Elephus maximus), tigers and the main prey of tigers: ban- Vaidyanathan, Mondol, Edgaonkar, & Ramakrishnan, 2013; Yumnam teng (Bos javanicus), gaur (Bos gaurus), and sambar (Rusa unicolor) in et al., 2014), and anthropogenic impacts such as livestock and human the Western Forest Complex (WEFCOM) (Figure 1). Collectively, settlements (Harihar & Pandav, 2012; Karanth, Gopalaswamy, these three large ungulates comprise 88%–95% of the tiger's prey in Kumar, Nichol, & MacKenzie, 2011; Sunarto et al., 2015). In most WEFCOM (Pakpien et al., 2017; Simcharoen et al., 2018). This land- 2 of WEFCOM and elsewhere in Thailand, a significant knowledge scape covers 19,600 km and consists of 17 contiguous protected 97 DUANGCHATRASIRI eT Al. | 2451

FIGURE 1 Study area and designed sample units of tiger in Western Forest Complex (WEFCOM), Thailand (2010–2012). The map shows the spatial distribution of surveyed grid cells (those with >10% forest cover). Inset: location of the study area in Thailand is outlined by a red box. NP: National Park; WS: Wildlife Sanctuary.

areas making it the largest intact protected area in Thailand and all 2.2 | Tiger occupancy survey of southern Asia (Figure 1). The study area ranges in elevation from 200 to 2,180 m but Our data were collected as part of a multispecies large mammal sur- the dominant elevation varies from 600–1,000 m, and slopes were vey that included elephants, tigers and gaur, banteng, and sambar, generally moderate (15%–30%). The major vegetation types include the main prey of tigers. We used a 64 km2 grid, the approximate size mixed deciduous + bamboo (MD 48.3%), dry evergreen (DE 27.5%), of a female tiger home range, to determine tiger occupancy. Within tropical hill evergreen forest (HE 9.6%), savannah grassland (GR each grid cell, linear survey routes, composed of 1‐km spatial repli- 7.3%), agriculture (AG 4.0%), and dry dipterocarp forest (DD 2.2%) cates, were delineated with the number of replicates proportional to (WEFCOM, 2004). Additionally, activities from 139 human commu- the amount of forest habitat within the grid cell. A maximum route nities inside and <3 km from WEFCOM borders greatly affect the length of 15 km per 64‐km2 grid cell was used if the entire cell was complex (WEFCOM, 2004). Evidence indicates these human set- forested and contained no villages or agricultural land. Grid cells with tlements reduce abundance or led to shifts in distribution of large <10% forest cover were not surveyed under the assumption that mammals (Duengkae, Maneerat, Pattanavibool, & Marod, 2004). they were unlikely to be occupied by tigers. To determine habitat Despite establishment of 149 ranger stations in WEFCOM, poach- use in addition to occupancy, each 1‐km survey unit was divided into ing, illegal logging, and harvesting of non‐timber forest products 100‐m spatial replicates. Thus, to estimate occupancy there were a continue to negatively impact the distribution and abundance of maximum of 15 1‐km replicates and to estimate habitat use within tigers and other large mammals (Duangchantrasiri et al., 2016). 1‐km2 grid cells there were ten 100‐m replicates. Detections (e.g., 98 2452 | DUANGCHATRASIRI eT Al. direct sightings, scats, pugmarks/tracks, carcasses, scent marks, and evergreen forest (DE), mixed deciduous forest (MD), and dry dip- vocalizations) were recorded in each 100‐m subsegment on typical terocarp forest (DD) (WEFCOM, 2004). For geophysical variables, tiger travel routes that included wildlife trails, mineral licks, forest we used a digital elevation model to obtain elevation, slope, and ter- roads, and river banks. Additionally, in each 100‐m subsegment, rain ruggedness. In addition, distance to rivers and streams (stream) substrate condition, habitat type, human activities, and evidence of and low slope areas (<10% slope) within 1‐km or 3‐km buffers along domestic animals were recorded. Because of the high logistic cost of streams (flat1km or flat3km) were also included as covariates that re- traveling to random transects, we adopted the widely used modifi- flect habitat quality near streams (Linkie, Chapron, Martyr, Holden, cation that reduced travel time by surveying linked replicates along & Leader‐Williams, 2006). These spatial covariates were calculated a linear route (Aing, Halls, Oken, Dobrow, & Fieberg, 2011; Hines et using ArcMap 10.3 (ESRI) and ERDAS IMAGINE 2013 software. al., 2010). Surveys were conducted in the dry season (January–May, Field‐based covariates were collected by surveyors as they October–December) to ensure that scat persistence was consistent. walked transects searching for signs of humans and domestic an- Surveying during the dry season also helps reduce heterogeneity in imals including tracks, tree cutting, gun shells, and campfires. detection probability induced by rainfall variation (Royle & Nichols, Substrate condition (SUB), which was hypothesized to impact detec- 2003). tion, was recorded as soft soil, hard soil, or leaf litter. The presence This sampling design allowed us to analyze occupancy at two of domestic animals (domestic animal) was a binary variable coded as spatial scales or second‐order habitat selection and site use or third‐ “1” for presence or “0” for absence. order habitat selection (Johnson, 1980). For occupancy, we used 64‐km2 grid cells as “sites” and 1‐km segments as spatial replicates. 2.4 | Data analyses For site use, 1‐km2 grid cells were used as sites and 100‐m segments were used as spatial replicates. We applied a first‐order Markovian model (Hines et al., 2010; Karanth et al., 2011) to account for spatial dependence between adjacent replicates. This model compensates for the lack of spatial 2.3 | Selection of ecological and independence of replicate surveys by including segment‐level oc- anthropogenic variables cupancy (parameterized by θ0, θ0 and θπ) and detection probability We extracted Geographic Information System (GIS)‐based ecologi- (p) conditional on neighboring segment‐level occupancy. The spatial cal covariates from GIS public domain data and DNP's WEFCOM da- dependence in segment‐level occupancy (θ) is captured by θ0 if the tabase. To predict occupancy and site use of tigers, we considered species is present locally but was not present in the previous spatial five groups of factors: (a) availability of prey, (b) human disturbance, replicate, θ1 if the species is present locally and was present in the (c) forest covers (d) distance to streams, and (e) terrain (for sources previous spatial replicate, and θπ for the first replicate where there is and further details see Supporting Information Tables S1–S13). no prior information to inform segment‐level occupancy. The covariates chosen for modeling were selected based on a pri- We developed models depicting habitat relationships at two spa- ori knowledge of prey and habitat preferences of tigers (Smith et al., tial scales to better understand tiger ecology and to address conser- 1998; Wegge, Odden, Pokharel, & Storaas, 2009; Harihar & Pandav, vation needs (Johnson, 1980). We used different subscripts on ̂ to

2012). Previous studies in Thailand found that banteng, gaur, and differentiate estimates at these two scales; ̂ 64 refers to occupancy 2 sambar were principal components of tiger diets (Simcharoen et al., probability at the home‐range (64‐km ) scale, whereas ̂ 1 refers to 2018). We considered three prey predictors (a) bovidae: gaur + ban- probability of site use at the 1‐km2 scale. We modeled probabilities teng, (b) sambar, and (c) all prey species combined (Jornburom, 2016). of occupancy (ψ)and detection (p) of tigers at both scales as linear Prey covariates were obtained from a companion analysis that es- functions of the above‐mentioned covariates using a logit link func- timated probabilities of habitat use (ψ) at the site scale of (1‐km2), tion (MacKenzie et al., 2002). while estimating detection (p) at the scale of 0.1 km. All prey was the probability of use by at least 1 of the 3 large prey species. Further, 2.4.1 | Model development we hypothesized that tigers would be less likely to occur in parts of WEFCOM that experience high human disturbance. We also ex- To analyze occupancy and site use, we used a sequence of four pected human activity would have a greater impact on occupancy steps described in more detail below. First, we examined all poten- than correlates related to habitat heterogeneity. To assess the im- tial covariates and eliminated highly correlated variables. Next, we pact of human activities, we included a measure of relative abun- grouped ecological correlates into five categories and selected one dance of domestic animals (domestic), distance from villages (village), covariate in each category for subsequent analysis. These variables and distance from roads (road). Forest areas that have been con- were additive with no interaction terms. These five variables served verted to shifting cultivation (agri) inside WEFCOM were considered as a global occupancy model (Burnham & Anderson, 2002; Duren, as a measure of habitat degradation. Buler, Jones, & Williams, 2011). Third, we held the global model For habitat‐related covariates, we used Thematic Mapper™ constant to select the most important detection covariate for each data to calculate the proportion of forest (forest), and four separate scale. Finally, we used the best detection model from step 3 to ex- continuous variables: percentage of hill evergreen forest (HE), dry amine our candidate set of models to identify the most important 99 DUANGCHATRASIRI eT Al. | 2453 predictors for occupancy and site use (Karanth et al., 2011; Royle & while holding all other covariates constant. We calculated variances Nichols, 2003). of predicted values using the delta method as implemented in the To select the appropriate covariates to develop occupancy mod- CAR package in R (Fox, 2016). els, we first explored the correlations among ecological variables to avoid collinearity. We considered correlation coefficients <0.7 as ac- 2.4.2 | Estimation of overall tiger occupancy ceptable to include two ecological variables (Dormann et al., 2013).

For correlated variables ≥0.7, we selected the covariate considered To estimate overall tiger habitat occupancy ( ̂ 64) within WEFCOM most representative based on its ecological relevance and availabil- (total proportion of the landscape occupied by tigers taking into acity across a wider area (Fieberg & Johnson, 2015; Giudice, Fieberg, count imperfect detection), we used the top ranked model (Burnham & Lenarz, 2012). & Anderson, 2002) and averaged predictions from all 309 grid cells. We grouped variables into five categories: availability of prey, Because our sampling was a near‐complete survey of the WEFCOM human disturbance, forest cover, proximity to stream, and terrain landscape with 309 grids of 64 km2, we were able to estimate 309 ∑ ai ̂ i Ψ= ̄ i=1 (Table 1). To select the factors included in our “global model,” we overall tiger occupancy in the WEFCOM as 15,672 where ai is modeled covariates in each category in a univariate fashion and the forested area in cell i (i.e., total area of potential tiger habitat chose the factor with the lowest AIC value in each category to incor- is 15,672 km2) (Karanth et al., 2011; Srivathsa, Karanth, Jathanna, porate into our global model. Kumar, & Karanth, 2014). We used a parametric bootstrap (Efron & We then used the global model for occupancy to evaluate vari- Tibshirani, 1994) to compute covariance and the standard error of ables that influenced detection. Detection probability (p) was mod- overall tiger occupancy ( ̂ 64). eled as a function of substrate type, presence of domestic animals, To map tiger distribution, we employed our best‐supported model and area of low slope (<10% slope) within 1 km or 3 km from streams. for tiger site use (1 km2) to help managers identify the key factors In this step, we held occupancy constant using the “global model” impacting spatial distribution of tigers (Lakshminarayanan, Karanth, (MacKenzie et al., 2006). Goswami, Vaidyanathan, & Karanth, 2015). Mapping at the scale of The final step was to use the best model for detection to eval- occupancy (64 km2) or use (1 km2) produced very similar maps, but uate a set of models to predict occupancy at the 64 km2 scale and the finer‐scale map provides better visualization of habitat use that site use at 1 km2 scale (Karanth et al., 2011). For these models, we managers need in making decisions. We used a probability of use >0.6 kept θ0, θ1, and θπ constant (Karanth et al., 2011; Thapa & Kelly, as a convenient metric to indicate high‐quality tiger habitat. 2017). Estimates of coefficients and standard errors (β, SE(β)) were used to determine effect sizes and direction of influence of covariates on the probabilities of occupancy, site use, and detection. Prior 3 | RESULTS to modelling, all continuous covariates were centered and scaled,

(xi −x̄i)∕SD(xi), to facilitate estimation of parameters using numerical We surveyed a total of 3,517 1‐km segments distributed in 309 optimization techniques (Donovan & Hines, 2007) and to facilitate (64 km2) grid cells across WEFCOM to determine occupancy. comparisons among competing variables. Further, each segment was subsampled to produce 35,170 100‐m Estimates of regression parameters and the associated variance– subsegments to evaluate habitat use at a fine scale. We detected covariance matrix derived from program PRESENCE version 12.7 tiger sign in 82 of 309 grid cells, which yielded naïve occupancy of (Hines, 2006) were used to explore the effect of individual covariates 0.27.

TABLE 1 Model selection results and estimated coefficients (β(SE)) for best‐supported models of tiger occupancy estimates at 64‐ 2 2 km scale (ψ64) and 1‐km scale (ψ1)

Estimated β (SE)e

Modela Domestic b c d e Tiger ψ64 ωi K Dev. β0 (SE) Prey Forest Elevation animal Stream

(All prey + forest + elev + 0.58 11 1,318.84 −2.08 (0.49) 1.20 (0.37) 1.19 (0.51) 0.72 (0.30) −0.85 (0.17) −1.59 (0.32) domestic + stream)

Estimated β (SE)e Modela b c d e Tiger ψ1 ωi K Dev. β0 (SE) Sambar Stream Domestic animal

(Sambar + stream + 0.99 9 3,701.16 −2.24 (0.23) 1.61 (0.05) −0.54 (0.15) −3.33 (0.10) domestic) a 2 0 1 π 0 1 The model specification for the parameters at 64‐ km scale (ψ64) θ , θ , θ , and pt was: θ (.), θ (.), pt (flat3km)[<10% slope within 3‐km buffers along π 2 0 1 π 0 1 π b streams], θ (.) and at 1‐ km scale (ψ1) θ , θ , θ , and pt was: θ (.), θ (.), pt (flat1km)[<10% slope within 1‐km buffers along streams], θ (.). The AICc model weight. cNumber of parameters. dTwice the negative log likelihood. eEffect sizes (beta estimates) are based on standardized data. See Appendix 1 for a complete list of occupancy models. 100 2454 | DUANGCHATRASIRI eT Al.

Exploratory analysis revealed substantial correlation between For site use (1 km2), the most important predictor was sambar distance from villages and all variables estimating prey site use: vil- presence. In addition to sambar, the best‐supported model included lage and gaur (Pearson's r = 0.81), village and sambar (r = 0.79), village a negative effect of distance from streams and negative effect of and Bovidae (r = 0.77), and village and all prey (r = 0.81). Therefore, domestic livestock. This model garnered 99% of the model weights we dropped distance from villages from further analysis, but empha- (Table 1). The probability of tiger site use increased from 20% to size that the high positive correlation with prey demonstrates that 80% as the probability of sambar occupancy increased from approx- distance from villages was an important driver of prey occupancy imately 40% to 75% when holding other predictors at their mean (Supporting Information Tables S1–S13). values (Figure 3). Model selection results and coefficient estimates are presented in Supporting Information Tables S1–S13. We generated a predicted distribution map using model‐based 3.1 | Influence of covariates on tiger occupancy and probabilities of tiger site use ≥0.60 (ranging from 0 to 1) from the site use best‐supported model for site use. Areas of predicted tiger site use The global model at the 64‐ km2 scale included the variables: all prey, (≥0.60) were restricted to the east‐central and northeastern regions proportion of forest, elevation, distance from streams, and relative of WEFCOM (Huai Kha Khaeng, east and west Thung Yai, Umpang, abundance of domestic animals. At the 64‐ km2 scale of site use, the and Mae Wong) (Figure 4). Site use throughout much of the remain- same ecological variables were chosen, except “all prey” was replaced der of the landscape was less contiguous, consisting largely of scat- by “sambar.” At both scales, we found that the model incorporating tered ‘“islands”’ of predicted (site use) ≥0.60. low slope areas along streams was the best model for detection. At the home‐range scale (64‐km2), all prey together (i.e., gaur, ban- 3.2 | Estimate of tiger occupancy teng, and sambar) was the most important predictor of occupancy based on the size of standardized regression coefficients (β) (Table 1). We estimated tiger occupancy ̂ 64 to be 0.37 (SE 0.06) for the 309 Other important factors for predicting occupancy were proportion of surveyed cells or 5,858 km2 (SE 1,758 km2) of the 15,600 km2 of po- forest, elevation, relative abundance of domestic animals, and distance tential habitat. This estimate was 21% larger than the naïve estimate from streams. The AIC‐best model with 58% of the model weight re- of 0.27. vealed that prey availability, proportion of forest, and elevation were positively correlated with tiger occupancy, and relative abundance of domestic animals was negatively correlated with tiger occupancy 4 | DISCUSSION (Table 1). The probability of tiger occupancy increased from 20% to 80% as the probability of “all prey” site use increased from approx- Our study is the first modeling effort to incorporate occupancy imately 30% to 80% when holding other variables at their mean of prey species as a covariate in tiger occupancy models. Despite (Figure 2). A complete set of 32 models and model‐specific regression widespread use of occupancy modeling, very little research has coefficients are presented in Supporting Information Tables S1–S13. been published on how prey occupancy influences the distribution

FIGURE 2 Relationship between the highly influential covariates (based on regression coefficient (β) and 95% CI from best‐supported model) and the probability of tiger occupancy in WEFCOM, Thailand (2010–2012). Effect sizes (beta estimates) are based on standardized data while holding the other covariates at their mean values. Tick marks on the X‐axis show density of data values in 64 km2 grid cell. See Supporting Information Tables 1 and 2 for description of covariates 101 DUANGCHATRASIRI eT Al. | 2455

FIGURE 3 Relationship between the highly influential covariates (based on regression coefficient (β) and 95% CI from best‐supported model) and the probability of tiger site use in FIGURE 4 Spatially explicit predictions map of tiger site use WEFCOM, Thailand, 2010–2012. Effect sizes (beta estimates) are constructed from the best‐supported occupancy model developed based on standardized data. Tick marks on the X‐axis show density at 1‐km2 scale based on the analysis of occupancy surveys (2010– 2 of data values in 1 km grid cell. See Appendix 1 for description of 2012) in WEFCOM, Thailand covariates of large carnivores (Andresen, Everatt, & Somers, 2014; Everatt, Our study analyzed occupancy at two spatial scales and yielded Andresen, & Somers, 2015). Incorporation of prey variables into an important difference. At the 64‐km2 scale, the model with all models has often been done using indices such as overall prey bio- large prey was the highest ranked model. Total large prey biomass mass or density (Robinson, Bustos, & Roemer, 2014), general pres- was also highly inversely correlated to the size of a female tiger's ence or absence of prey (Alexander, Gopalaswamy, Shi, Hughes, & home range (Simcharoen, Savini, Gale, Simcharoen, et al., 2014). Riordan, 2016; Vinitpornsawan, 2013), relative abundance indices However, within a tiger's home range, tigers preferred localized (Chanchani, Noon, Bailey, & Warrier, 2016), or photo‐trap rates areas dominated by sambar. This result is consistent with the fact (Sunarto et al., 2015). Our study considered only the probability that across the tiger's range, sambar is both a preferred prey and the of large prey occupancy because large prey comprises 89% of tiger dominant prey biomass in the diet of tigers (Hayward, Jędrzejewski, prey biomass (Simcharoen et al., 2018). We also examined other & Jedrzejewska, 2012). These differences show the importance of natural and anthropogenic features to determine the relative influ- analyzing data at multiple scales. ence of these correlates in shaping tiger distribution in WEFCOM. While tiger occupancy is largely influenced by prey availability, Identification of environmental and anthropogenic factors affect- we also found that tiger occupancy decreased with greater distance ing the distribution of tigers not only increases our understand- from streams (Figure 3). We suspect these areas are crucial for tiger ing of tiger occupancy, but also helps target those correlates that site use because low slope forests near streams are also preferred can be managed to increase tiger distribution (Fieberg & Johnson, habitat of sambar and banteng (Jornburom, 2016; Simcharoen, Savini, 2015). Gale, Roche, et al., 2014). Tigers also occupied areas of higher altitude 102 2456 | DUANGCHATRASIRI eT Al. in the core of WEFCOM; this finding might be attributed to avoiding co‐management has been effective in reducing poaching and habitat human activities near villages, especially in the west and the south of degradation in forests in close proximity to villages (Carter, Shrestha, WEFCOM. Thus low elevation areas per se may not limit tigers, but, Karki, Pradhan, & Liu, 2012). instead, livestock grazing at low elevation may degrade the habitat of Our research shows that the low occupancy of tigers (37%) was the tiger's primary prey. Also, poaching sign is more common at lower largely a consequence of the absence of large prey. Managers need elevations (Jornburom, 2016; Vinitpornsawan, 2013). to identify the factors that limit the distribution of large mammals Our study demonstrated that large parts (63%) of the WEFCOM and test new options to increase large ungulate distribution so that landscape were devoid of tigers and that tiger habitat use was con- WEFCOM remains source population able to support tiger resto- centrated in core protected areas in the north (Figure 4), whereas ration efforts elsewhere in Thailand and Southeast Asia. there were only a few scattered patches that were identified as potential habitat in southern areas where tigers were historically ACKNOWLEDGMENTS widely distributed (Smith et al., 1999). These current patterns of tiger distribution show a positive response to the presence of large We sincerely thank Government of Thailand; DNP, WCS, and ungulates and a negative response to domestic cattle grazing. WWF for support in conducting this survey. The Rhino and Tiger Previous research has shown the relationship of tiger density to prey Conservation Fund of the U.S. Fish and Wildlife Service and the density (Karanth, Nichols, Kumar, Link, & Hines, 2004; Simcharoen, Liz Claiborne and Art Ortenberg Foundation (LCAOF) provided fi- Savini, Gale, Simcharoen, et al., 2014). Given that distribution and nancial support. The second author is the recipient of the WCS abundance of large ungulates are critical to the distribution and graduate scholarship for study at the University of Minnesota and abundance of tigers, additional research is needed to identify the Conservation Biology summer grant while this manuscript was pre- key ecological correlates that drive ungulate distribution and abun- pared, USA. J. Fieberg, T.W. Arnold, J.L.D. Smith's contribution to dance in western Thailand. the research was supported by the USDA National Institute of Food and Agriculture. We thank K. Ulas Karanth and WCS India team for training and designed occupancy field methodology. Any use of 5 | CONCLUSIONS trade, product of firm names is for descriptive purposes and does not imply endorsement by the U.S. Government. Our results suggest that low tiger occurrence in WEFCOM is primarily due to low abundance of large prey and Jornburom (2016) CONFLICT OF INTEREST attributed their low abundance to human activities near villages. Several other occupancy studies note that scarcity of natural prey None declared. near villages is a consequence of degradation of habitat by livestock grazing (Harihar & Pandav, 2012; Karanth et al., 2011). However, AUTHORS’ CONTRIBUTIONS relocating villagers, who have long historical residence, may not be an accepted management strategy. Furthermore, globally there SD, SJ, PJ, AP conceived the ideas and designed field methodology; can be strong opposition to forced resettlement (Clements, Suon, SD, PJ, SJ collected the data; PJ, JH, TA, JF, JS analyzed the data; PJ, Wilkie, & Milner‐Gulland, 2014, 2007). The well‐established Smart JF, TA, JS contributed critically to the drafts and all authors gave final Patrolling system has had an overall strong protection impact in approval for publication. WEFCOM, but has not increased prey abundance or eliminated domestic livestock grazing or subsistence poaching near villages DATA ACCESSIBILITY (Duangchantrasiri et al., 2016). Thus increasing tiger population size may depend on reducing certain activities in the vicinity of villages. Presence/Absence of tiger in WEFCOM Thailand: Dryad https://doi. Jornburom (2016) modeled the potential significant increase in org/10.5061/dryad.7h8f15s tiger prey distribution if such a scenario applied to just nine strategi- cally located villages in Thung Yai East and West. Her model exam- ORCID ined the impact on tigers if sufficient incentives and co‐management reduced livestock grazing and subsistence hunting near villages. Pornkamol Jornburom https://orcid.org/0000‐0003‐1831‐2902 Currently, a grant from United Nations Development Program's Todd W. Arnold https://orcid.org/0000‐0002‐7920‐772X Global Environmental Facility has funded two Thai Non‐governmental Organizations to establish pilot programs that are targeting nine Karen villages embedded in Thung Yai East (seven villages) and REFERENCES Thung Yai West (two villages) with the goal of establishing co‐man- Aing, C., Halls, S., Oken, K., Dobrow, R., & Fieberg, J. (2011). A Bayesian agement (United Nations Development Program, 2015). Specific hierarchical occupancy model for track surveys conducted in a se- objectives of co‐management in these and future studies could ries of linear, spatially correlated sites. Journal of Applied Ecology, 48, be beneficial to tiger conservation. In a similar situation in Nepal, 1058–1517. https://doi.org/10.1111/j.1365‐2664.2011.02037.x 103 DUANGCHATRASIRI eT Al. | 2457

Alexander, J. S., Gopalaswamy, A. M., Shi, K., Hughes, J., & Riordan, P. Hines, J. E. (2006). PRESENCE ver 11 – software to estimate patch occu- (2016). Patterns of Snow Leopard site use in an increasingly human‐ pancy and related parameters. USGS‐PWRC. Retrieved from http:// dominated landscape. PLoS ONE, 11(5), e0155309. https://doi. www.mbr‐pwrc.usgs.gov/software/presence.html (accessed April org/10.1371/journal.pone.0155309 2013). Andresen, L., Everatt, K. T., & Somers, M. J. (2014). Use of site occupancy Hines, J. E., Nichols, J. D., Royle, J. A., MacKenzie, D. I., Gopalaswamy, A. models for targeted monitoring of the cheetah. Journal of Zoology, M., Kumar, N., & Karanth, K. U. (2010). Tigers on trails: Occupancy 292(3), 212–220. https://doi.org/10.1111/jzo.12098 modeling for cluster sampling. Ecological Applications, 20(5), 1456– Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel 1466. https://doi.org/10.1890/09‐0321.1 inference: A practical information‐theoretic approach. Berlin, Germany: Johnson, D. H. (1980). The comparison of usage and availability mea- Springer Science & Business Media. surements for evaluating resource preference. Ecology, 61(1), 65–71. Carter, N. H., Shrestha, B. K., Karki, J. B., Pradhan, N. M. B., & Liu, J. https://doi.org/10.2307/1937156 (2012). Coexistence between wildlife and humans at fine spatial Jornburom, P. (2016). The distribution of elephants, tigers and tiger prey in scales. Proceedings of the National Academy of Sciences of the United Thailand’s Western Forest Complex. PhD thesis, U. of Minnesota. States of America, 109(38), 15360–15365. https://doi.org/10.1073/ Joshi, A., Vaidyanathan, S., Mondol, S., Edgaonkar, A., & Ramakrishnan, pnas.1210490109 U. (2013). Connectivity of tiger (Panthera tigris) populations in the Chanchani, P., Noon, B. R., Bailey, L. L., & Warrier, R. A. (2016). Conserving human‐influenced forest mosaic of central India. PLoS ONE, 8(11), tigers in working landscapes. Conservation Biology, 30(6), 649–660. e77980. https://doi.org/10.1371/journal.pone.0077980 https://doi.org/10.1111/cobi.12633 Kanchanasaka, B., Tunhikorn, S., Vinitpornsawan, S., Prayoon, U., & Clements, T., Suon, S., Wilkie, D. S., & Milner‐Gulland, E. J. (2014). Faengbubpha, K. (2010). Status of large mammals in Thailand (In Impacts of protected areas on local livelihoods in Cambodia. Thai). Bangkok, Thailand: Wildlife Research Division, National Parks, World Development, 64, S125–S134. https://doi.org/10.1016/j. Wildlife and Plant Conservation Department. worlddev.2014.03.008 Karanth, K. U., Gopalaswamy, A. M., Kumar, N. S., Nichol, J. D., & Donovan, T. M., & Hines, J. (2007). Exercises in occupancy modeling and MacKenzie, D. I. (2011). Monitoring carnivore populations at the estimation. http://www.uvm.edu/rsenr/vtcfwru/spreadsheets/ landscape scale: Occupancy modelling of tigers from sign sur- occupancy/ veys. Journal of Applied Ecology, 48, 1048–1056. https://doi. Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … org/10.1111/j.1365‐2664.2011.02002.x Lautenbach, S. (2013). Collinearity: A review of methods to deal with Karanth, K. U., Nichols, J. D., Kumar, N. S., Link, W. A., & Hines, J. E. it and a simulation study evaluating their performance. Ecography, 36, (2004). Tigers and their prey: Predicting carnivore densities from 27–46. https://doi.org/10.1111/j.1600‐0587.2012.07348.x prey abundance. Proceedings of the National Academy of Sciences Duangchantrasiri, S., Umponjan, M., Simcharoen, S., Pattanavibool, A., of the United States of America, 101(14), 4854–4858. https://doi. Chaiwattana, S., Maneerat, S., … Karanth, K. U. (2016). Dynamics of org/10.1073/pnas.0306210101 a low‐density tiger population in Southeast Asia in the context of Kenney, J., Allendorf, F. W., McDougal, C., & Smith, J. L. (2014). How improved law enforcement. Conservation Biology, 30(6), 639–648. much gene flow is needed to avoid inbreeding depression in wild tiger https://doi.org/10.1111/cobi.12655 populations? Proceedings of the Royal Society B: Biological Sciences, Duengkae, P., Maneerat, S., Pattanavibool, A., & Marod, D. (2004). 281(1789), 20133337. https://doi.org/10.1098/rspb.2013.3337 Response of bird assemblages to the abandoned settlement areas in Lakshminarayanan, N., Karanth, K. K., Goswami, V. R., Vaidyanathan, S., Thung Yai Naresuan. Kasetsart Journal (Natural Science), 41, 371–376. & Karanth, K. U. (2015). Determinants of dry season habitat use by Duren, K. R., Buler, J. J., Jones, W., & Williams, C. K. (2011). An improved Asian elephants in the Western Ghats of India. Journal of Zoology, multi‐scale approach to modeling habitat occupancy of northern 298(3), 169–177. https://doi.org/10.1111/jzo.12298 bobwhite. The Journal of Wildlife Management, 75(8), 1700–1709. Linkie, M., Chapron, G., Martyr, D. J., Holden, J., & Leader‐Williams, https://doi.org/10.1002/jwmg.248 N. (2006). Assessing the viability of tiger subpopulations in a frag- Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. Boca mented landscape. Journal of Applied Ecology, 43(3), 576–586. https:// Raton, FL: CRC Press. doi.org/10.1111/j.1365‐2664.2006.01153.x Everatt, K. T., Andresen, L., & Somers, M. J. (2015). The influence of prey, MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., pastoralism and poaching on the hierarchical use of habitat by an & Langtimm, C. A. (2002). Estimating site occupancy rates when detec- apex predator. African Journal of Wildlife Research, 45(2), 187–196. tion probabilities are less than one. Ecology, 83(8), 2248–2255. https:// https://doi.org/10.3957/056.045.0187 doi.org/10.1890/0012‐9658(2002)083[2248:ESORWD]2.0.CO;2 Fieberg, J., & Johnson, D. H. (2015). MMI: Multimodel inference or mod- MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Hines, J. E., els with management implications? Journal of Wildlife Management, & Bailey, L. L. (2006). Occupancy estimation and modeling: Inferring 79(5), 708–718. https://doi.org/10.1002/jwmg.894 patterns and dynamics of species occurrence. New York, NY: Academic Fox, J. (2016). Applied regression analysis and generalized linear models (3rd Press. ed.). Sage deltaMethod function in the car: Companion to Applied McDonald, L. L., Erickson, W. P., Boyce, M. S., & Alldredge, J. R. (2012). Regression. R package version 3.0‐0. Modeling vertebrate use of terrestrial resources. In N. J. Silvy (Ed.), Giudice, J., Fieberg, J., & Lenarz, M. (2012). Spending degrees of freedom The wildlife techniques manual: research (7th ed.) (pp. 410–428). in a poor economy: A case study of building a sightability model for Baltimore, MD: University Press. Moose in northeastern Minnesota. Journal of Wildlife Management, Pakpien, S., Simcharoen, A., Duangchantrasiri, S., Chimchome, V., 76, 75–87. https://doi.org/10.1002/jwmg.213 Pongpattannurak, N., & Smith, J. L. D. (2017). Ecological covariates at Harihar, A., & Pandav, B. (2012). Influence of connectivity, wild prey kill sites influence tiger (Panthera tigris) hunting success in Huai Kha and disturbance on occupancy of tigers in the human‐dominated Khaeng wildlife sanctuary. Thailand Tropical Conservation Science, 10, Western Terai Arc Landscape. PLoS ONE, 7(7), e40105. https://doi. 1–7. https://doi.org/10.1177/1940082917719000. org/10.1371/journal.pone.0040105 Pollock, K. H., Nichols, J. D., Simons, T. R., Farnsworth, G. L., Bailey, Hayward, M. W., Jędrzejewski, W., & Jedrzejewska, B. (2012). Prey pref- L. L., & Sauer, J. R. (2002). Large scale wildlife monitoring studies: erences of the tiger Panthera tigris. Journal of Zoology, 286(3), 221– Statistical methods for design and analysis. Environmetrics, 13(2), 231. https://doi.org/10.1111/j.1469‐7998.2011.00871.x 105–119. https://doi.org/10.1002/env.514 104 2458 | DUANGCHATRASIRI eT Al.

Rabinowitz, A. (1993). Estimating the Indochinese tiger. Panthera ti‐ Sunarto, S., Kelly, M. J., Parakkasi, K., & Hutajulu, M. B. (2015). Cat gris carbetti population in Thailand. Biological Conservation, 65(3), coexistence in central Sumatra: Ecological characteristics, spatial 213–217. and temporal overlap, and implications for management. Journal of Ranganathan, J., Chan, K. M., Karanth, K. U., & Smith, J. L. D. (2008). Zoology, 296(2), 104–115. https://doi.org/10.1111/jzo.12218 Where can tigers persist in the future? A landscape‐scale, den- Thapa, K., & Kelly, M. J. (2017). Prey and tigers on the forgotten trail: High sity‐based population model for the Indian subcontinent. prey occupancy and tiger habitat use reveal the importance of the Biological Conservation, 141(1), 67–77. https://doi.org/10.1016/j. understudied Churia habitat of Nepal. Biodiversity and Conservation, biocon.2007.09.003 26(3), 593–616. https://doi.org/10.1007/s10531‐016‐1260‐1 Robinson, Q. H., Bustos, D., & Roemer, G. W. (2014). The applica- United Nations Development Program (2015). Strengthening capacity tion of occupancy modeling to evaluate intraguild predation in a and incentives for wildlife conservation in the Western Forest Complex. model carnivore system. Ecology, 95(11), 3112–3123. https://doi. United Nations Development Program Project Document. org/10.1890/13‐1546.1 Vinitpornsawan, S. (2013). Population and spatial ecology of Tigers and Royle, J. A., & Nichols, J. D. (2003). Estimating abundance from re- Leopards relative to prey availability and human activity in Thung peated presence‐absence data or point counts. Ecology, 84(3), Yai Naresuan (East) Wildlife Sanctuary, Thailand. PhD thesis, U. 777–790. https://doi.org/10.1890/0012‐9658(2003)084[077 Massachusetts. 7:EAFRPA]2.0.CO;2 WEFCOM (2004). GIS Database and its applications for ecosystem manage‐ Scott, J. M., Heglund, P. J., Morrison, M. L., Haufler, J. B., Raphael, M. G., ment. Bangkok, Thailand: Department of National Park, Wildlife, and Wall, W. A., & Samson, F. B. (2002). Predicting species occurrences: Plant Conservation. issues of scale and accuracy. Washington, DC: Island Press. Wegge, P., Odden, M., Pokharel, C. P., & Storaas, T. (2009). Predator–prey Simcharoen, A., Savini, T., Gale, G. A., Roche, E., Chimchome, V., & Smith, relationships and responses of ungulates and their predators to the J. L. (2014). Ecological factors that influence sambar (Rusa unicolor) establishment of protected areas: A case study of tigers, leopards distribution and abundance in western Thailand: Implications for and their prey in Bardia National Park, Nepal. Biological Conservation, tiger conservation. Raffles Bulletin of Zoology, 62, 100–106. 142(1), 189–202. https://doi.org/10.1016/j.biocon.2008.10.020 Simcharoen, A., Savini, T., Gale, G. A., Simcharoen, S., Duangchantrasiri, Wikramanayake, E., Dinerstein, E., Seidensticker, J., Lumpkin, S., Pandav, S., Pakpien, S., & Smith, J. L. (2014). Female tiger Panthera tigris home B., Shrestha, M., Than, U. (2011). A landscape‐based conservation range size and prey abundance: Important metrics for management. strategy to double the wild tiger population. Conservation Letters, Oryx, 48(3), 370. 4(3), 219–227. https://doi.org/10.1111/j.1755‐263X.2010.00162.x Simcharoen, A., Simcharoen, S., Duangchantrasiria, S., Bump, J., & Smith, Yumnam, B., Jhala, Y. V., Qureshi, Q., Maldonado, J. E., Gopal, R., Saini, J. L. D. (2018). Tiger and leopard diets in western Thailand: Evidence S., … Fleischer, R. C. (2014). Prioritizing tiger conservation through for overlap and potential consequences. Food Webs, 15, e00085. landscape genetics and habitat linkages. PLoS ONE, 9(11), e111207. https://doi.org/10.1016/j.fooweb.2018.e00085 https://doi.org/10.1371/journal.pone.0111207 Simcharoen, S., Pattanavibool, A., Karanth, K. U., Nichols, J. D., & Kumar, N. S. (2007). How many tigers Panthera tigris are there in Huai Kha Khaeng Wildlife Sanctuary, Thailand? An estimate using photographic capture‐recapture sampling. Oryx, 41(04), 447–453. https:// SUPPORTING INFORMATION doi.org/10.1017/S0030605307414107 Smith, J. L. D., Ahern, S. C., & McDougal, C. (1998). Landscape analysis of tiger Additional supporting information may be found online in the distribution and habitat quality in Nepal. Conservation Biology, 12(6), Supporting Information section at the end of the article. 1338–1346. https://doi.org/10.1046/j.1523‐1739.1998.97068.x Smith, J. L. D., Tunhikorn, S., Tanhan, S., Simcharoen, S., & Kanchanasaka, B. (1999). Metapopulation structure of tigers in Thailand. In J. How to cite this article: Duangchatrasiri S, Jornburom P, Seidensticker, S. Christie, & P. Jackson (Eds.), Riding the tiger: Tiger conservation in human‐dominated landscapes (pp. 166–175). Jinamoy S, et al. Impact of prey occupancy and other Cambridge, UK: University Press. ecological and anthropogenic factors on tiger distribution in Srivathsa, A., Karanth, K. K., Jathanna, D., Kumar, N. S., & Karanth, K. Thailand's western forest complex. Ecol Evol. 2019;9:2449– U. (2014). On a dhole trail: Examining ecological and anthropo- 2458. https://doi.org/10.1002/ece3.4845 genic correlates of dhole habitat occupancy in the Western Ghats of India. PLoS ONE, 9(6), e98803. https://doi.org/10.1371/journal. pone.0098803 105

Received: 10 July 2019 | Revised: 6 March 2020 | Accepted: 17 March 2020 DOI: 10.1002/ece3.6268

ORIGINAL RESEARCH

eights of gaur ( os a r s) and banteng ( os avanic s) illed by tigers in Thailand

1 e r en o ore iolog c l o ore r e r niver i ng o Abstract h il nd he ri r re o iger cro ch o o h- i h een de le ed red c- 2 e r en o ion l r ildli e nd ing he ili o lre d li i ed h i o or iger . o e er nder nd he l n on erv ion hon w n h il nd e r en o i herie ildli e nd e en o which wo o he l rge re ecie g r Bos gaurus) nd n eng Bos ja‐ on erv ion iolog niver i o vanicus) con ri e o he iger die we e i ed he ver ge i e o he e ecie Minnesota, Minneapolis, MN, USA illed iger . hi in or ion i needed o ore cc r el c lc l e io o Correspondence he e ecie in he iger die nd o devi e r egie o incre e iger c rr ing ch r i ch roen e r en o ion l r ildli e nd l n on erv ion c ci where h i i r g en ed nd li i ed in we -cen r l h il nd. e ed hon w n h il nd. e or ll cl ed loc ion o 24 elli e r dio-coll red iger o iden i heir ill il: i o window live.co sites and obtained mandibles from 82 gaur and 79 banteng. Kills were aged by teeth Funding information eruption sequence, sectioning the M1 molar and counting cementum annuli. Of all h il nd); h i o nd ion; Rabbit in the Moon Foundation; USFWS gaur killed, 45.2% were adults; of all banteng killed, 55.7% were adults. The average hinocero iger nd; ion l weight of banteng killed was 423.9 kg, which was similar to the 397.9 kg average n i e o ood nd gric l re weigh or g r. he e n weigh o o h re ecie i .5–4.5 i e gre er h n he redic ed 1:1 re erred re o red or r io. n he ence o edi - i ed re illing he e l rger ni l e e eci ll cri ic l or e le iger rovi- ioning ne rl inde enden o ng when le o ring re lre d l rger h n he o her. hi i he ir d o re en d on he ver ge weigh o g r nd n eng illed in o h- i nd he e re l gge h he e re e re ecie o rge in iger re recover e or .

KEYWORDS large ungulate, prey biomass, tiger, WEFCOM

1 | INTRODUCTION ore i ol ed iger o l ion . on erv ion e or re oc ed on rever ing he e rend enl rging he l nd e h or cro he iger r nge h i lo degr d ion nd r g en- individ l iger o l ion hro gh co ni ore nd er ion hv e re l ed in red ced h i ni h or ller one rogr he ddi ion o ro ec ed re nd incre ing

hi i n o en cce r icle nder he er o he re ive o on ri ion icen e which er i e di ri ion nd re rod c ion in n edi rovided he origin l wor i ro erl ci ed. 2020 he hor . Ecology and Evolution li hed ohn ile on d.

5152 | www.ecolevol.org Ecology and Evolution. 2020;10:5152–5159. 106 HAe HA Dee eT Al. 5153 connec ivi e ween or ong o l ion . owever o iger prey classes (Andheria, Karanth, & Kumar, 2007) differs from South o l ion re i ol ed ro e ch o her ng n h n h n i . no e hree edi - i ed deer Axis porcinis, Panolia eldi, Karanth, & Smith, 2008), and thus, conservation of this endangered and Rucervus shcomburki) hv e een e ir ed in o h- i elid i l rgel n inde enden o co e o n ge en e or o 2019) re l ing in l rge re i e g e ween wild o r con erve e ch e r e o l ion. en he l nd i no v il le Sus scrofa) and other <38 kg prey and the next larger prey, the sam- o incre e he e en o h i or i rove connec ivi . n hi i - r Rusa unicolor) who e v er ge weigh i e i ed o e 212 g ion he r c ic l l ern ive i o i rove h i li . hi c- r n h n i 1995). n o h- i wo o he hree ion con i o ei her re oring degr ded h i o h i or l rge iger re re g r Bos gaurus) nd n eng Bos javanicus) higher io o re or hro gh red c ion in o ching o o h ig re 1). ie in or ion eci ic o o h- i i needed o re nd iger . he go l o ll he e c ivi ie i o incre e he c r- i rove nder nding o how o incre e iger c rr ing c ci in r ing c ci o he h i o h he i e nd h he vi ili o hi r egion where h i i r g en ed nd li i ed. o l ion i g en ed in i ll li i ed l nd e. hi d e i ed e n weigh o g r nd n eng illed r ing oin or i roving iger c rr ing c ci o r - iger . he e v er ge will hel el cid e he role o he e wo phrase Chakrabarti et al. (2016), we need to know “what and how o he l rge re ecie in he region l die o iger . ing he ch iger e . hi nowledge co ined wi h re nd h i e ro ch r n h nd n i 1995) we e i ed he e en rovide n ger wi h he in or ion o e i e ro or ion o e nd ge cl o g r nd n eng iger ill i e c rren nd o en i l re iger c rr ing c ci o eci ic rend o ined li hed weigh o e ch ge nd e cl . he ro- erve or l nd c e. die o wh nd how ch iger e or ion o he e i e cl e l o rovide in igh in o iger red - n l ed iger die collec ing c o de er ine re richne nd ion r egie . r one e orelli nd e hen 2011) gener li ed o iden i he iger ri r re . ie die ic ll e i e h he re o red or io r io h ronger i c on he io con ri ion o e ch re ecie in iger die n- l rger c rnivore h n i doe on ller c rnivore . w rd e l. l ing c o de er ine he io ingle c o e ch ecie (2006), Hayward, O’Brien, and Kerley (2007) refined this generality re re en . o redic he o i re i e or ever l l rge c rnivore ; or McNab (1980) investigated the nonlinear relationship between iger he r io w re or ed o e 1:1 w rd edr e ew i r ce re nd io o l in er o energe ic need o ecie . hi e r ce re o rel ion hi de er ine the biomass of prey that a single scat represents. Floyd, Mech, and Jordan (1978) used feeding trials to estimate the biomass of a prey ecie ingle wol c re re en ed. ew e r l er c er n Lindzey, and Hemker (1984) modified this approach to develop a re- gre ion e ion or o n in lion Puma concolor). heir odel h een ed widel o e i e he io in he die o n large felid species including most tiger diet studies prior to 2016. More recently, Chakrabarti et al. (2016) revised Ackerman et al.’s (1984) analysis to take into account satiation, prey digestibility, nd c rc e o her c rnivore h cv enge ill o ri ril l rge re . hi revi ion gge h io in c re che n o e where he io er c doe no incre e wi h increasing prey size. The model from Chakrabarti et al. (2016) is based on he v er ge r io o re weigh o c rnivore weigh . r d con ider he i c o v er ge re weigh o g r nd n eng wo o he l rge iger re on he die o iger i n o h he odel (Ackerman et al., 1984; Chakrabarti et al., 2016). o h odel rel on e i ing he v er ge weigh o e ch e- cie con ed iger . r n h nd n i 1995) ioneered rigoro e i e o he v er ge weigh o he iger in re . he de er ined he ercen ge o ge nd e cl e illed iger nd ed li hed weigh o e ch ge cl o c lc l e he e n weigh o e ch o he iger in re ecie . he e weigh hv e e en l een ed in o iger die die . n o h- i new nd cc r e e i e o v er ge re weigh e eci ll o FIGURE 1 wo o he l rge re o iger in i h h eng l rge re e e eci ll i or n or e i ing re io (a) Male tiger feeding on gaur (Bos gaurus). ) e le iger nd her in iger die ec e he co le en o l rge- nd edi - i ed c eeding on n eng B. javanicus) 107 5154 HAe HA Dee eT Al.

edr ew 2012). he e gener li ion rovide in igh in o evo- h o he i wo ld e ) le iger ill ller ercen ge o l rger l ion r d n ic o he e ecie he ddi ion l ch llenge or d l le g r h n ller d l n eng ) e le iger which con erv ion iologi nd n ger i o nder nd he c or v er ge wo hird he weigh o d l le iger ill ller er- h re l in devi ion ro he e gener li ie h re i or - cen ge o d l le o o h ecie co red wi h le iger n o con erv ion. or e le in o h- i iger de- nd c) e le iger ill ewer le o o h ecie co red wi h end ore on l rger re h n in o h i d e o he e ir ion le . i i or n o no e h o r re e rch doe no i l re o hree edi - i ed ecie o deer i ch roen i ch roen re erence w rd e l. 2012); we i l re or i e e nd Duangchantrasiri, Bump, & Smith, 2018). ge cl o g r nd n eng illed iger i n o r d re . ing den l nn li nd horn ch r c eri ic o cl i ge nd e o g r nd n eng co ined wi h li hed weigh or he e classes (Ahrestani, 2018; Hoogerwerf, 1970), enabled us to calcu- 2 | MATERIALS AND METHODS l e he v er ge weigh o g r nd n eng illed iger . he e weigh re e o e i ing he io o he e ecie in he 2.1 | Study area die o iger . ec e d l le g r re 1. i e l rger h n d l le n eng nd le o o h ecie re .5 i e l rger h n he d w cond c ed in i h h eng ildli e nc r w rd e l. 2012) o i 1:1 red or o re r io we h - (HKK) (2,780 km2) nd h he highe den i o iger in h il nd o he i e h he e ecie nd e eci ll he l rge i e cl e re 1.25–2.01 100 2) (Duangchantrasiri et al., 2016). HKK is also the ro ching he i i e li i o iger re . or or hi core sanctuary in a complex of 17 protected areas that form the

FIGURE 2 oc ion o i h h eng ildli e nc r ) nd h o ng ildli e e e rch ion ) in he e ern ore Complex (WEFCOM), Thailand 108 HAe HA Dee eT Al. 5155

18,727 km2 Western Forest Complex (WEFCOM) (Figure 2). This ro en nd ec ioned wi h icro o e o cre e 15–20 longi - co le or he l rge iger o l ion in h il nd 2010) din l cro - ec ion l lice h were o n ed on gl lide. lice and perhaps the 4th largest globally (Kenney, Allendorf, McDougal, were ined wi h ie lood nd l eled; ined ec ion were i h 2014). co ined wi h d cen re erve h ng i e en l e ined 10 gni ic ion nd ce en nn li re n ildli e nc r nd h ng i re n e were co n ed ig re ). e concl ded h he loc l ingle r in ildli e nc r i con idered o rce or re-e li hing iger e on re l ed in ingle nn li ern in we ern h il nd nd throughout the region. Much of the rest of WEFCOM is subject to con ir ed hi co ring nn li d o horn ern hre ni re de le ion ro o ching nd here ore i i e i ed h i- rin 2011). ingle ce en nn li in ro ic l ng l e w gers occur in only 37% of WEFCOM (Duangchatrasiri et al., 2019). first reported by Spinage (1976). We also followed Ahrestani and i co o ed o o r in vege ion e : i ed decid o rin 2011) in gro ing ge in o he ollowing cl e : c lve forest (48.3%), dry evergreen forest (24.7%), hill evergreen forest (0–1 year), juveniles (>1 to 3 years), young adult (>3 to 6 years), and (13.4%), and dry deciduous dipterocarp forest 6.9% (Trisurat, 2004). mature adults (>6‐years). Although elevation ranges from 200 to 2,180 m, it generally varies o de er ine e n re i e illed iger we l i lied he from 600 to 1,000 m. HKK has South‐East Asia's highest diversity of re enc o e ch ge cl or or d l he e nd ge cl l rge ng l e which incl de g r n eng r nd w er - he e i ed weigh o h cl . ge cl weigh were derived lo Bubalus bubalis); oge her he e ecie co o e 90. o iger from Ahrestani (2018) and Hoogerwerf (1970). Estimated mean sizes diet. An additional 12 species constitute the other 9.7% of the diet of prey species killed by tigers are used by Ackerman et al. (1984) and (Simcharoen et al., 2018). Pakpien et al. (2017) documented 11 of the Chakrabarti et al. (2016) to calculate the biomass represented by a tiger's 16 known prey species at kill sites. Our study site coincided ingle c o e ch re ecie . e l o co red he e n weigh geogr hic ll wi h he e wo re e rch loc ion . o g r nd n eng illed iger . o ev l e he h o he i h g r nd n eng re ro ching he li i o re i e illed i- ger we cond c ed chi- red e co ring he n er o 2.2 | Data collection and analysis ill le nd e le iger o d l le g r nd n eng.

At kill sites (15 00–15 40 N, 99 00–99 25 E), we collected the lower ndi le o e ch g r or n eng or ging. e de er ined 3 | RESULTS e o d l ni l ed on he con ig r ion o horn hre ni 2018). The ages of calves and juveniles were determined by teeth From June 2005 to May 2017, we visited kill sites of 24 radiocol- er ion e ence ce c en ing 2009). or d l re lared tigers (9 males and 15 females) and recorded a total of 82 gaur we e r c ed he ir ol r ro he ndi le w hed he oo h and 79 banteng kills based on carcass or skeletal remains. Of all gaur in w er dec lci ied i in we cid ol ion 5 ) nd i- illed 15.9 were d l le nd 29. were d l e le ; d l nally rinsed it again in water to stop decalcification (Klevezal, 1996; male banteng comprised 29.1% of kills and 26.6% of kills were adult Spinage, 1976). Each molar was then dehydrated in isopropyl alcohol, e le . le 1). n con r c lve co o ed 9 o g r ill

FIGURE 3 le 1) nd 2) re ro n eng; he le i e igh e r old (BHKK28); the right is 13 years old 5 ). ce en den ine l c do ce en nn li 109 515 HAe HA Dee eT Al. versus 26.6% of banteng kills. As a consequence, despite the fact he l nd e needed o or l rger ore vi le o l ion g r le were ro i el 1. i e hev ier h n le n eng (Ranganathan et al., 2008). In this situation, the primary option for nd g r e le were 1.1 i e he weigh o e le n eng he n ger who ee o incre e o l ion i e i o i rove h i- average weights of both gaur killed (397.9 kg) was less than the aver- c rr ing c ci . o cco li h hi i i cri ic l o now which ge weigh o n eng illed 42 .9 g) le 1). ecie iger e nd he i or nce o e ch ecie in he die ed on he v er ge e nd ge cl weigh nd he n - o iger . hi in or ion co e ri ril ro revio l - er o ill in e ch cl he v er ge weigh o d l g r ill w lished diet studies. For example, Simcharoen et al. (2018) reported 737.8 kg and they composed 83.7% of the biomass of gaur killed h g r nd n eng wo o he l rge re h iger con e by tigers. Similarly, the mean adult banteng killed weighted 652.2 kg compose 46%–59% of the tiger's diet. However, in that study, the and adults composed 85.6% of biomass of this species killed by ti- authors used Karanth and Sunquist’s (1995) average gaur weight of gers. Adults composed 48.8% of gaur and 79.4% of banteng killed by 287 kg for both gaur and banteng. Our study estimated the aver- le iger ; where d l g r nd n eng co o ed 41.1 nd ge weigh o he e ecie in h il nd ollowing i il r ro ch 37.8% of female kills, respectively (Table 2). o r n h nd n i 1995). re e en o e n weigh one o he redic ion h we gge ed wo ld e or or o he e ecie illed iger w needed ec e n i nd he h o he i h g r nd n eng re ro ching he i e li i r n h e i e w he onl revio rigoro e i e - of tiger prey were significant. Prediction 1: Male tigers killed fewer li hed. r her ore he io o l rge ovid in he iger die d l g r n 21) co red wi h d l n eng n = 27), but the i higher in o h- i co red wi h o h i ec e di erence w no igni ic n χ2 = 0.75 (1), p = .386). Prediction 2: edi - i ed re co on in he iger die in o h i e.g. e le l o illed ewer d l le ovid n 11) h n d l e le Axis axis) i l c ing. e o nd he v er ge i e o g r illed iger ovid n 21) he di erence w no igni ic n χ2 2.1 1) in o h- i i ne rl 1. 5 i e gre er h n he v er ge i e p = .063). Prediction 3: Females did not kill significantly fewer adult in o h i . hi di erence e ri ed o he c h d l g r nd n eng n ) h n le iger did n = 48) (χ2 .1 1) male gaur represent only 14.6% of gaur kills in South Asia versus p .090). iven h o r hree redic ion were no or ed 25.5% of kills in South‐East Asia. In contrast in South Asia, 58% of igni ic n re l o r h o he i h g r nd n eng re ne r he ill were c lve ver 9 in o h- i . e do no ec l e er i e li i o iger re i n o con ir ed. on he e di erence he co ld re lec v rie o ecologic l or eh vior l di erence e.g. degree o cover herd i e ). n i or n e ion i how doe o r revi ed e i e o e n 4 | DISCUSSION re i e o he e wo iger re i c iger die e i e nd o- tential tiger habitat carrying capacity. Using Ackerman et al. (1984) n i or n con ri ion o hi d i he re-ev l ion o line r e ion o r re-e i e o he e n weigh o g r nd n- the role of bovids in the diet of tigers. Most tiger populations are eng illed iger incre e he e i ed io er c ro ll nd ne r cri ic l i e hre hold where vi ili i hre ened 12 to approximately 16 kg per scat, thus substantially increasing the enne e l. 2014). r her ore he ori o he e ll o - io e i e o he e wo ecie in he iger die . owever l ion c rren l l c i le h i o ide o re erve o e nd Chakrabarti et al. (2016) regression of scat biomass weights does

TABLE 1 Mean weights of gaur (Bos gaurus) nd n eng B. javanicus) illed iger ed on n er o ill i n di eren e nd ge classes and published weights of those classes from Ahrestani (2018) and Hoogerwerf (1970)

Adult male Adult female Young adult u enile Calf

6 years 6 years 3 6 years 1 to 3 years 0 1 year

Species wt wt wt wt wt Mean weight

r 1 25.6 900 24 19.5 650 6 7.3 388 7 8.5 200 2 9.0 50 397.9 n eng 2 2.9 700 21 22.8 600 6 7.6 356 8 10.1 200 21 26.6 50 42 .9

TABLE 2 ercen ge nd n er o e e nd ge cl e o g r B. gaurus and B. Javanicus) illed le nd e le iger

Juv. 1 to Tiger se species Male years Female years Yg. Ad 3 to years 3 years Calves 1 year

Male r 20.9 9) 27.9% (12) 11.6% (5) 9. 4) 0.2 1 ) e le 10. 4) 30.8% (12)’ 2.6% (1) 7.7% (3) 48.7% (19) Male n eng 47.1% (16) 2.4 11) 8.8% (3) 2.9 1) 8.8% (3) e le 15.6% (7) 22.2 10) 6.7% (3) 15.6% (7) 40.0% (18) 110 HAe HA Dee eT Al. 515 no incre e he er c io ec e heir e ion re che h r r i in he re o incor or e il i e nd cv enging n o e when he re weigh i ro i el e l o he in die die . weigh o he red or. h r r i nd colle g e e er - sive argument that tigers may reach satiation (“gut fill”) before com- le el con ing n en ire g r or n eng c rc . he e end 5 | CONSERATION IMPLICATIONS their argument with the suggestion: “since handling costs and risk of in rie ll incre e wi h re i e w rd erle 2005) we r re e en o v er ge re weigh o g r nd n eng o l e h ro ic l elid wo ld end o g in ore red ing illed iger i c e i e o he ro or ion o io in on re ro ghl e l o or le h n heir own i e nd no ro he die o iger . iger die die hv e r di ion ll relied on he l rger re . v er ge weigh o re ecie e i ed r n h nd n i w rd nd erle 2005) e cogen rg en wh 1995) ingle loc ion in ndi . her iger die die hv e hen do iger in h il nd ill or ro or ion o heir re h ed di eren v er ge re weigh rovided li le or ing weigh 4–4.5 i e ore h n heir own ver ge od weigh e hodolog or he e e i e . iven wide re d re de le ion h devi ing ro w rd e l. 2012) redic ion o re erred r n h i h 1999) co led wi h he red ced l nd e or - re i e he n wer e he ill wh i v il le. l ho gh ing n iger o l ion nd li i ed o or ni ie o incre e e le iger illed ewer d l g r nd n eng n ) h n h i connec ivi c re l e en o ll ec o he ood le iger n = 48), they did kill 11 adult male bovids (31% of adult e or iger i needed. igoro odel or de er ining he io- male bovids killed by tiger). Ackerman et al.’s (1984) prey biomass o ecie in iger die need o e or ed cc r e odel doe no ddre g ill ccording o h r r i ield d on he v er ge weigh o ecie illed nd he ro or- et al.’s (2016) model, a large portion of the 81 adult gaur and ban- ion o c rc e con ed. oge her hi in or ion will llow eng ill re no ili ed iger . he e hor gge h n ger o give he e en ion o riori i ing re ecie or c venger l role in con ion o l rger iger ill . e re or ion h hi o ic h received in or h eric ro e gree. iger o en le ve c rc e in he he o he d o ee and Australia (Di Marco et al., 2017). Our study suggests that gaur h de drin w er nd re r ic l rl when ill i de ore nd n eng re i or n rge ecie or con erv ion e or o en environ en . c venger e he e o or ni ie o eed h re ore he iger re e in o h- i . r her ore on iger c rc e . n con r i e le wi h l rge c ill in Duangchatrasiri et al. (2019) and Jornburom (2016) have shown that den e cover ne r w er he nd her o ring re li el o re in he re or ion o he e ovid c n hv e or i c on he di - ne r nd here i li le o or ni or o her ecie o c v- tribution of tigers in WEFCOM. enge. However, neither Chakrabarti et al.’s or Ackerman et al.’s re e rch or revio die die h ve de el e i ed he ACKNOWLEDGMENTS role o c venging or i i c on he io o l rge re in e re gr e l o he e r en o ion l r ildli e nd he iger die . er r ho ogr h h ve doc en ed n l n on erv ion overn en o h il nd or or ing hi o her ecie ill i e ; or e le we recen l recorded hree wor . e h n he h il nd iger ro ec o orn ien i n w er oni or aranus salvator) 1.4 long) nd ever l re r on ongchoo ir i h hong o dol er oon wild o r Sus scrofa) c venging n d l n eng ill e ween hor ichi r lch i hol l id eng i i hongvichi iger eeding o n li hed d ). n orr rn i hog rd nd n orn h n h n Another unexplored factor in Chakrabarti and colleagues’ model or i nce wi h ield wor . e re l o gr e l o ol i he e n weigh o iger ; he ed n v er ge iger weigh e - n ch i hi or hi dvice on he icro- echni e o oo h lon- timated by Prater and Barruel (1971). In their model, our re‐esti- gi din l ec ion nd lin le ndrovn leve l n i e o e o g r nd n eng weigh no i c die e i e Development Biology, Russian Academy of Sciences, Moscow, for ec e g ill nd c venging li i iger ili ion o l rge ovid . develo ing he echni e or re ding ce en ge nn li. nding owever o r new weigh e i e e i or n when - or r dio-coll ring iger w rovided he h il nd) h i ing in o cco n he co ined con ion o e le nd l rge Rukpa Foundation, Rabbit in the Moon Foundation, and the USFWS c eeding on ill. e le iger o en wi h in er ir h in erv l o hinocero iger nd; i h con ri ion o hi d w - 22 on h erle owling o ho i - le 200 ; i h or ed he ion l n i e o ood nd gric l re & McDougal, 1991), are rarely without cubs. Furthermore, tigresses (Project No. MIN‐41‐002). con in e o e he ri r rovider when o ng le re l rger h n heir o her nd d gh er re ne rl her i e. i h 2– o - CONFLICT OF INTEREST spring, a 136 kg female is providing for the equivalent of 3–4 adults here i n o con lic o in ere . rior o her li er eco ing inde enden ; heir co ined il weight is 408–544 kg. If Chakrabarti's model were adjusted to re- AUTHOR CONTRIBUTION lec he e n weigh o o her nd her o ng i wo ld cl ri he Supawat Khaewphakdee: c r ion e l); or l n l- role o l rge ovid in he die o iger . e l n o coll or e wi h ysis (equal); Methodology (equal); Writing‐original draft 111 515 HAe HA Dee eT Al.

e l); ri ing-review edi ing e l). Achara Simcharoen: low-den i iger o l ion in o he i in he con e o i - roved l w en orce en . Conservation Biology 30, 639–648. https:// once li ion e l); c r ion e l); or l n l - doi.org/10.1111/cobi.12655 i e l); nding c i i ion e l); nve ig ion e l); ngch r iri . orn ro . in o . nvi ool . ine . Methodology (equal); Project administration (equal); Resources . rnold . . i h . . . 2019). c o re occ nc e l); ervi ion e l); ri ing-origin l dr e l); nd o her ecologic l nd n hro ogenic c or on iger di ri ion in h il nd we ern ore co le . Ecology and Evolution 9 2449– ri ing-review edi ing e l). Somphot Duangchantrasiri: 2458. https://doi.org/10.1002/ece3.4845 once li ion oring); c r ion e l); or l Dyce, K. M., Sack, W. O., & Wensing, C. J. G. (2009). Textbook of veteri‐ analysis (supporting); Methodology (supporting); Project ad- nary anatomy (4th ed.). St. Louis, Missouri: P. Rudolph. ini r ion e l). iak Chimchome: Methodology (sup- Floyd, T. J., Mech, L. D., & Jordan, P. A. (1978). Relating wolf scat con- or ing); ervi ion or ing); ri ing-origin l dr en o re con ed. Journal Wildlife Management 42(3), 528–532. https://doi.org/10.2307/3800814 or ing); ri ing-review edi ing or ing). Saksit Hayward, M. W., Henschel, P., O’Brien, J., Hofmeyr, M., Balme, Simcharoen: once li ion e l); c r ion e l); G., & Kerley, G. I. H. (2006). Prey preferences of the leopard Formal analysis (supporting); Methodology (supporting); Project Panthera pardus). Journal of Zoology 270, 298–313. https://doi. d ini r ion or ing). James L. D. Smith: once li ion org/10.1111/j.1469‐7998.2006.00139.x Hayward, M. W., Jedrzejewski, W., & Jedrzewska, B. (2012). Prey pref- or ing); or l n l i e l); nding c i i ion e l); erence o he iger Panthera tigris. Journal of Zoology 286 221–2 1. Methodology (supporting); Supervision (equal); Writing‐original https://doi.org/10.1111/j.1469‐7998.2011.00871.x dr e l); ri ing-review edi ing e l). Hayward, M. W., & Kerley, G. I. H. (2005). Prey preferences of the lion Panthera leo). Journal Zoological Society of London 267 09– 22. https://doi.org/10.1017/S0952 83690 5007508 DATA AAILABILITY STATEMENT Hayward, M. W., O’Brien, J., & Kerley, G. I. H. (2007). Carrying capacity of ge o g r nd n eng re o iger: v il le ro r d l rge ric n red or : redic ion nd e . Biological Conservation Digital Repository https://doi.org/10.5061/dryad.mpg4f 4qwl. 139, 219–229. https://doi.org/10.1016/j.biocon.2007.06.018 Hoogerwerf, A. (1970). Udjung Kulon: The land of the last javan rhinoceros. eiden e herl nd : . . rill. ORCID . 2019). he ed i o hre ened ecie . er ion 2019– . Supawat Khaewphakdee h : orcid. e rieved ro h : www.i cnr edli .org org/0000‐0002‐5114‐4747 Jornburom, P. (2016). The distribution of elephants, tigers and tiger prey in Achara Simcharoen https://orcid.org/0000‐0003‐2797‐8327 Thailand’s western forest complex. St. Paul, Minnesota: University of Minnesota. PhD dissertation. Karanth, K. U., & Stith, B. M. (1999). Prey depletion as a critical determi- REFERENCES n ion o iger o l ion vi ili . n . eiden ic er . hri ie . Ackerman, B. B., Lindzey, F. G., & Hemker, T. P. (1984). Cougar food hab- c on d .) Riding the tiger: Conservation in human dominated land‐ i in o hern h. Journal of Wildlife Management 48(1), 147–155. scapes . 100–11 ). ridge . .: ridge niver i re . https://doi.org/10.2307/3808462 Karanth, K. U., & Sunquist, M. E. (1995). Prey selection by tiger, leopard Ahrestani, F. S. (2018). Bos frontalis nd Bos gaurus r iod c l : ovid e). nd dhole in ro ic l ore . Journal of Animal Ecology 64 4) 4 9– Mammalian Species 50 959) 4–50. h : doi.org 10.109 ec 450. https://doi.org/10.2307/5647 ie e 004 Kenney, J., Allendorf, F. W., McDougal, C., & Smith, J. L. D. (2014). hre ni . . rin . . . 2011). ge nd e de er in ion o ow ch gene low i needed o void in reeding de re ion in g r Bos Gaurus ovid e). Mammalia 75 151–155. h : doi. wild iger o l ion Proceedings of the Royal Society B: Biological org/10.1515/MAMM.2010.078 Sciences 281, 20133337. https://doi.org/10.1098/rspb.2013.3337 Andheria, A. P., Karanth, K. U., & Kumar, N. S. (2007). Diet and prey Kerley, G. L. H., Cowling, R. L. M., Boshoff, A. F., & Sims‐Catley, R. (2003). ro ile o hree ric l rge c rnivore in ndi r iger ion or he con erv ion o l rge nd edi - i ed - e erve ndi . Journal of Zoology 273(2), 169–175. https://doi. l in he e lori ic egion ho o o h ric . Biological org/10.1111/j.1469‐7998.2007.00310.x Conservation 112, 169–190. https://doi.org/10.1016/s0006 r one . e orelli . e hen . . 2011). he igger he ‐3207(02)00426 ‐3 co e he h rder he ll: od i e nd re nd nce in l - Klevezal, G. A. (1996). Recording structures of mammals: Determination of ence red or– re r io . Biology Letters 7 12– 15. h : doi. age and econstruction of life history. o erd e herl nd : . . org/10.1098/rsbl.2010.0996 l e . h r r i . h l . . . re hi . div r . . n McNab, B. K. (1980). Food habits, energetics, and the population biol- V. J. (2016). Adding constraints to predation through allometric rela- og o l . American Naturalist 116(1), 106–124. https://doi. ion o c o con ion. Journal of Animal Ecology 85, 660–670. org/10.1086/283614 https://doi.org/10.1111/1365‐2656.12508 ien . i ch roen . ngch n r iri . hi cho e . Di Marco, M., Chapman, S., Althor, G., Kearney, S., Besancon, C., Pongpattannurak, N., & Smith, J. L. D. (2017). Ecological Covariates Butt, N., … Watson, J. E. M. (2017). Changing trends and per- ill i e n l ence iger Panthera tigris) n ing cce in i i ing i e in hree dec de o con erv ion cience. Global h h eng ildli e nc r h il nd. Tropical Conservation Ecology and Conservation 10, 32–42. https://doi.org/10.1016/j. Science 10, 1–7. https://doi.org/10.1177/19400 82917 719000 gecco.2017.01.008 Prater, S. H., & Barruel, P. (1971). The book of indian animals rd ed.). . 2010). Thailand tiger action plan 2010 2022. ng o h il nd: o ndi : r l i or ocie . e r en o ion l r ildli e nd l n con erv ion. Ranganathan, J., Chan, K. M. A., Karanth, K. U., & Smith, J. L. D. Duangchantrasiri, S., Umponjan, M., Simcharoen, S., Pattanavibool, A., (2008). Where can tigers persist in the future? A landscape‐scale, Chaiwattana, S., Maneerat, S., … Karanth, K. U. (2016). Dynamics of den i - ed o l ion odel or he ndi n con inen . 112 HAe HA Dee eT Al. 515

Biological Conservation 141(1), 67–77. https://doi.org/10.1016/j. Management Project (WWFCOM), National Park, Wildlife and Plant biocon.2007.09.003 on erv ion e r en. i ch roen . i ch roen . ngch n r iri . . i h J. L. D. (2018). Tiger and leopard diets in western Thailand: Evidence or overl nd o en i l con e ence . Food Webs 15, e00085. How to cite this article: h ew h dee i ch roen https://doi.org/10.1016/j.fooweb.2018.e00085 ngch n r iri hi cho e i ch roen i h . Smith, J. L. D., & McDougal, C. (1991). The contribution of variance in life- i e re rod c ion o e ec ive o l ion i e in iger . Conservation eigh o g r Bos gaurus) nd n eng Bos javanicus) illed Biology 5, 484–490. https://doi.org/10.1111/j.1523‐1739.1991. iger in h il nd. Ecol Evol. 2020;10:5152–5159. h :doi. 00 55. org/10.1002/ece3.6268 Spinage, C. A. (1976). Incremental cementum lines in the teeth of tropical ricn l. Journal of Zoology Society of London 178, 117–131. https://doi.org/10.1111/j.1469‐7998.1976.tb022 67.x ri r . 2004). GIS database and its applications for ecosystem manage‐ ment. ng o h il nd: he e ern ore o le co e 113

FOLIA OECOLOGICA – vol. 46, no. 2 (2019), doi: 10.2478/foecol-2019-0010

Spatial and temporal analysis of leopards (Panthera pardus), their prey and tigers (Panthera tigris) in Huai Kha Khaeng Wildlife Sanctuary, Thailand

Apinya Saisamorn1,2, Prateep Duengkae1*, Anak Pattanavibool2, Somphot Duangchantrasiri3, Achara Simcharoen4, James L.D. Smith5

1Special Research Unit for Wildlife Genomics, Department of Forest Biology, Faculty of Forestry, Kasetsart University, Bangkok 10900, Thailand 2Wildlife Conservation Society, Thailand Program, 55/295 Muangthong Thani 5, Chaengwattana Road, Pakkred, Nonthaburi 10210, Thailand 3Department of National Parks, Wildlife and Plant Conservation, Paholyotin Road, Chatuchak, Bangkok 10110, Thailand 4Department of National Parks, Wildlife and Plant Conservation, Protected Area Regional Office 12, 19/47 Kositai Road, Nakhonsawan, Thailand 5Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, 2003 Upper Buford Cr., St. Paul, MN 55108, USA

Abstract Saisamorn, A., Duengkae, P., Pattanavibool, A., Duangchantrasiri, S., Simcharoen, A., Smith, J.L.D., 2019. Spatial and temporal analysis of leopards (Panthera pardus), their prey and tigers (Panthera tigris) in Huai Kha Khaeng Wildlife Sanctuary, Thailand. Folia Oecologica, 46: 73–82.

Despite their extensive distribution globally, recent reports indicate leopards are declining, especially in Southeast Asia. To support conservation efforts we analyzed the behavioral interactions between leopards (Panthera pardus), their prey, and tigers to determine if leopards fine-tune their activity to maximize contact with four prey species (sambar; wild boar; barking deer; banteng) and avoid tigers and if prey alter their temporal activity in response to variation in their relative abundance ratio with leopards. A lower density of sambar in the northern part of our study area and a lower density of wild boar and a higher density of tigers in the southern part allowed us to examine fine-grained differences in the behavior of leopards and their prey. We used camera trap data to investigate spatial and temporal overlap. Differences in tiger relative abundance did not appear to impact the temporal activity of leopards. Leopards had similar cathemeral activity at all sites with highest activity at dawn and dusk. This behavior appears to be a compromise to provide access to diurnal wild boar and barking deer and nocturnal sambar and banteng. Sambar showed higher temporal avoidance of leopards in the north where its RAI was lowest; in contrast, wild boar had the highest temporal avoidance in the south where its density was lowest. This is the first study in Southeast Asia to quantify spatial and temporal interactions between the leopard, its primary ungulate prey, and the tiger. It provides new insights for conserving this declining subspecies.

Keywords leopard, prey, tiger, spatial activity, temporal activity

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 114

Introduction major prey despite the likelihood that activity patterns differ among species. Furthermore, in carnivore guilds, inter- Leopards (Panthera pardus) have the widest species dis- ference competition by larger carnivores impacts smaller tribution among large felids; they are documented from carnivores, usually the next smaller species (Donadio and southern Africa through the Middle East to South, South- Buskirk, 2006; Palomares and Caro, 1999). Interference east and Northeast Asia (Miththapala et al., 1996; Up- competition by tigers towards leopards is widely reported hyrkina et al., 2001). Currently, eight subspecies are (Karanth and Sunquist, 1995; Mondal et al., 2011; Ra- recognized (Kitchener et al., 2017). However, leopards makrishnan et al., 1999; Ramesh et al., 2012; Schaller, have experienced recent declines in distribution, and in- 1967) and it results in lower leopard density in core areas creasing reports describe population extirpation in many used by tigers as leopards shift to peripheral habitat where localities (Stein et al., 2016). Causes of widespread de- larger prey preferred by tigers are less abundant. However, cline, as also reported for tigers (Panthera tigris), are due the degree of interference competition varies; at two sites to habitat loss, degradation and fragmentation, as well as in South Asia, Pakke Tiger Reserve, Arunachal Pradesh poaching and prey depletion (Stein et al., 2016). Here we (Selvan et al., 2013) and the Western Ghats (Karanth et focus on a population of Indochinese leopard (Panthera al., 2017; Karanth and Sunquist, 1995; Karanth and pardus delacouri), which is found in Southeast Asia and Sunquist, 2000) high spatial overlap was reported in these southern China. Rostro-García et al. (2016) report that felids. Karanth et al. (2017) suggested temporal parti- this subspecies now occurs in only 6.2% of its historical tioning facilitates co-occurrence by reducing the probabil- range in Southeast Asia, and that only seven viable popu- ity of leopards encountering tigers thus allowing leopards lations remain: three in Myanmar, one in Cambodia, one to spatially coexist with tigers. in Malaysia and two in Thailand. High dietary overlap between leopards and tigers Given the newly recognized concern regarding sta- has also been reported and is referred to as resource tus of the leopard in Southeast Asia, knowledge of its be- competition (Karanth et al., 2017). Resource compe- havioral interactions with its major prey, and with tigers, tition between leopards and tigers may be more intense is critical for efforts to conserve this species. An earlier in Southeast Asia than in South Asia, in part because the study on the ecology of leopards in Southeast Asia (Sim- most common prey species of leopards in South Asia, charoen et al., 2018) was conducted in Huai Kha Khaeng the medium sized spotted deer (Axis axis) (Andheria et Wildlife Sanctuary (HKK), Thailand, which supports the al., 2007; Karanth and Sunquist, 1995; Mondal et al., largest populations of leopards and tigers in the region 2011; Schaller, 1967) is absent in Southeast Asia. Fur- (Kenney et al., 2014; Rostro-García et al., 2016). Re- thermore, in Southeast Asia, three other medium-size deer, cent studies of leopard and tiger diets report a high degree comparable in size to the spotted deer, have been extir- of dietary overlap at several sites in South Asia (Lovari, pated (Lekagul and Mcneely, 1977) thus creating a gap 2015; Selvan et al., 2013) and also at our study site in in prey size for leopards between wild boar and sambar. HKK (Simcharoen et al., 2018). Simcharoen and col- This size gap may explain the leopard’s reliance on young leagues found that the species of highest biomass in the sambar as its major prey (Simcharoen et al., 2018). The leopard’s diet was sambar (Rusa unicolor) (36%); other sambar is also the second most abundant prey species of major species were wild boar (Sus scrofa) (16.5%), bark- tigers and the high dietary overlap indicates resource com- ing deer (Muntiacus muntjak) (12.5%) and banteng (Bos petition (Simcharoen et al., 2018). In this study, we ex- javanicus) (9.1%). These four species composed 78% of amine the activity overlap of leopards and their prey and the leopard’s diet and are the focus of this study. Optimum hypothesize that 1) leopards should adjust their activity foraging theory predicts that predators should alter their to maximize contact with all of their major prey, 2) leop- spatial and temporal activity to increase encounter rates ards should locally fine-tune their activity to favor overlap with prey and where prey abundance varies spatially, they with the most abundant of these prey species, and prey should show a local preference for the species with high- in turn should show greatest avoidance of leopards when est density (Charnov, 1976) or the highest probability of their density is lowest, and 3) leopards should adjust their detection (Holling, 1959). In turn, prey often respond to temporal activity in response to higher tiger density where predators by temporal and spatial avoidance, which has the probability of encounter with tigers is higher. Studying been characterized as the ecology of fear (Brown et al., the temporal and spatial relationship of leopards and their 1999). The fear response of prey species, however, must major prey, and the potential impact of tigers on these dy- be balanced by the need to forage (Creel et al., 2007). namics, is critical to conservation of leopards. The consequence of interactions between predator and prey result in predators adjusting their temporal and spatial activity to correlate with the activity of their Materials and methods prey (Karanth and Sunquist, 2000; Linkie and Ridout, 2011). And in response, prey may alter their activity to Study site avoid either temporal and/or spatial overlap with predators (Kronfeld-Schor and Dayan, 2003). Given the leopard’s The study was conducted in Huai Kha Khaeng, a 2,780 diverse ungulate diet, it needs to adjust its spatial and tem- km2 protected area, which is one of three protected areas poral activities to maximize opportunities to capture all designated as a World Heritage Site. It forms the core of

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 115

a ~19,000 km2 landscape, the Western Forest Complex site. These subsamples had different relative abundances (WEFCOM) of Thailand, which is the largest remaining of sambar, wild boar and tigers and thus provided a com- contiguous protected area in mainland Southeast Asia. parison of ecological factors influencing activity patterns. HKK is primarily composed of deciduous forest (83%), Temporal activity was measured as the number of photo- dry dipterocarp forest (13%), dry evergreen forest (3%), graphs of each species obtained during each hour of the and unknown (1%) forest type (Trisurat, 2004). The el- day. We then characterize temporal activity as the percent- evation in HKK ranges from 160 to 1,687 m above mean age of time an animal was diurnal (i.e. active ~0600–1859) sea level. Annual average precipitation is 1,164 mm and (Van Schaik and Griffiths, 1996). Diurnal activity was the average temperature ranges from 7 °C to 31 °C. classified as strongly diurnal (≥85%), mostly diurnal (61– 84%), cathemeral (40–60%), mostly nocturnal (39–16%) Data collection and strongly nocturnal (≤15%). We then compared these general differences in diurnal activity between the leopard Camera trap data were collected during January–May in and each of its four major ungulate prey species. 2013 and 2015 as part of a long-term project to moni- For a more fine grained estimate of temporal over- tor large carnivores and their major prey: sambar, gaur, lap we measured the extent of overlap in activity between banteng, wild boar and barking deer. We set 203 camera leopards and prey using the kernel density package in R to trap stations covering an area of approximately 1,000 determine the coefficient of overlapping (Δ), whichranged km2. These stations were established along animal trails, from 0 (no overlap) to 1 (complete overlap) (Ridout and ridges and streams, and near saltlicks, to maximize im- Linkie, 2009). Overlap was defined as the area under the age capturing opportunities of these species. The inter-trap curve expressed by taking the minimum of two kernel distance was 3–4 km. A pair of cameras was installed at density estimates at each time point. As recommended each station on trees about 50 cm above the ground (Fig. by Ridout and Linkie (2009), we used their Δ4 method 1). At each trap site, cameras were deployed for 15 days for large sample size to estimate the probability density following a protocol developed by Duangchantrasiri et function of overlap. al. (2016). Photographs from trap stations provided spatial We also calculated both temporal and spatial Spear- and temporal data used for estimating spatial and temporal man rank correlation coefficients (Azlan and Sharma, overlap among leopards, their prey and tigers. Images of 2006; Ramesh et al., 2012) performed in R software (R the same species at a camera station were considered to Core Team, 2017) to determine if leopards and their ma- be independent if they were: 1) non-consecutive images jor prey have either spatial or temporal differences in over- (e.g., taken after another species had been photographed), lap among sites with different prey densities. 2) consecutive images with a time interval between shots > 30 min, or 3) consecutive images in which each animal could be individually identified (e.g., different sex or age Results class) (O’Brien et al., 2003; Ramesh et al., 2012; Yasu- da, 2004). These data were used to analyze the activity Using 203 trap stations (6,225 trap nights) we obtained in- of leopards and their prey. We also calculated the relative dependent images of 324 leopards, 981 sambar, 682 wild abundance index (RAI) of leopards, tigers and the leop- boar, 1,169 barking deer, 137 banteng and 276 tiger. The ard’s 4 major prey species as the total number of photo- RAI of leopards ranged from 5.2 over all sites to a high of graphs per 100 trap nights (Antony et al., 2013; Carbone 6.2 in the south and 5.6 in the north (Table 1). For sambar et al., 2001). The RAI ratio of prey and tiger to leopard the RAI was 18.5 at KBD (south) or 2.4 times higher than RAI was used to determine if spatial or temporal over- at KNR (north) 7.8. This difference in relative abundance lap of prey was influenced by differences in the relative of sambar resulted in a sambar to leopard RAI ratio ~2× abundance ratio of prey or tigers to leopards among sites. higher at KBD compared to KNR (Table 1). In contrast the RAI, a presence only statistic, is often criticized because RAI of wild boar was 15.3 at KNR or 1.7 times higher at bias in detection among species varies (Sollmann et al., KNR than at KBD and the leopard wild boar ratio was ~2× 2013); however, given the same general habitat character- higher in KNR compared to KBD. Barking deer had simi- istics across our study area, the between species bias of lar RAI for all 3 sites (15.4 at KNR to 18.2 at KBD and RAI should not vary among different parts of our study 18.8 over all sites). The ratio of barking deer to leopard area and thus the ratio of leopard RAI to the RAI of each RAI was highest among the prey species we examined, prey species, and to tigers, provides a reasonable index but very similar at KBD and KNR, and the ratio for ban- to compare relative abundance differences across sites teng to leopard was similar across all sites (Table 1). within HKK. Behavioral responses between leopards and their prey Data analysis Sambar We analyzed data across all camera trap sites and at two Spearman spatial correlation between leopards and sam- subsamples, Khao Nang Rum (KNR) in the northern and bar was significantly correlated overall (p < 0.05) and Khao Ban Dai (KBD) in the southern part of our study highly correlated at KBD (p = 0.07) (Table 2), and was

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 116

Fig. 1. Camera trap stations in Huai Kha Khaeng Wildlife Sanctuary (HKK) during January–May in 2013 and 2015. Fig. 1. Camera trap stations in Huai Kha Khaeng Wildlife Sanctuary (HKK) during January–May in 2013 and 2015.

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 117 112 - lowest at KNR where sambar relative abundance was low-

RAI est (Table 1). Sambar also had the highest temporal separation at KNR where it was strongly nocturnal (89%) versus

Tiger the leopard’s cathemeral (54% diurnal) behavior. Further evidence of stronger avoidance by sambar at low density Images

sites was low temporal kernel overlap between leopards

and sambar at KNR (Δ4 = 0.62) (Fig. 2). At KBD where sambar density was double that at KNR, kernel overlap Leopard Banteng/ RAI ratioRAI was higher (Δ4 = 0.83) and both sambar and leopards were

cathemeral. nights. nights. RAI Wild boar

Banteng Wild boar had variable Spearman spatial overlap. It was significant across the entire study area, but not significant Images

at the KNR (northern sub-site) or KBD (southern sub-site) tos/100 trap

(Table 2); Spearman temporal correlation was also variable across sites. It was least significant at KBD (p = 0.80) deer/ Barking Leopard RAI ratioRAI and the kernel spatial density overlap was also lowest at

KBD (Δ4 = 0.61) (Fig. 2) where its RAI and the leopard

RAI and wild boar RAI ratio was lowest. Also at KBD the wild boar was strongly diurnal (90%), which resulted in

the strongest avoidance of cathemeral leopards (53%). In 270 270 18.2 2.9 24 1.6 0.3 112 7.5 268 268 15.4 2.7 44 2.5 0.4 40 2.3 Barking deer contrast, at KNR, the cathemeral leopard and the mostly Images diurnal wild boar had the highest kernel temporal overlap

estimate (Δ4 = 0.78) (Table 3, Fig. 2).

Leopard Barking deer RAI ratioRAI Wild boar/ Wild

2.1 1,169 18.8 3.6 137 2.2 0.4 276 4.4 There was no significant Spearman spatial overlap among

RAI 8.8 1.4 8.8 the three sites and temporal overlap was only significant at

the combined sites, but not at either the northern or southern sites. Among the three sites, diurnal activity ranged Wild boarWild from 65–77%, which resulted in the closest temporal over- Images

lap among the three prey species to the cathemeral leopard

130 (53–55% diurnal). The temporal kernel overlap was higher overall at KNR and lower at KBD. Both barking deer and 3.0 3.0 682 11 .0 Sambar/ Leopard RAI ratioRAI leopards showed bimodal peak activity levels after dawn

. and before dusk at all sites. However, among the 4 prey RAI

species, the spatial correlation of leopard and barking deer

was lowest and in fact, it was negative at both KNR and KBD. Sambar Images Banteng

981 15.8 Spearman spatial correlation was not significant at the RAI northern and southern sites, but nearly significant overall

(p = 0.06) among the combined sites; there was no Spear-

Leopard man temporal overlap at any of the sites (Table 3). Banteng

Images temporal activity ranged from nocturnal at the combined sites, nocturnal at KNR but, although the temporal kernel

overlap between leopards and banteng was lowest at KBD 225 324 5.2 Trap night (Δ4 = 0.62), with only 24 photos (14 diurnal and 10 nocturnal at KBD) we draw no conclusions about the dynamics between leopard and banteng at this site. Table 1. Number of camera of 1. Number Table in (KNR) trapsand Rum Khao trap and nightsNang area, in 2 the area subsetsof study the study pho the is of number index (RAI) abundance Relative south. in (KBD) Dai the andthe Ban north Khao to leopards the prey ratios ratioare of RAI Trap In summary, the leopard’s cathemeral temporal activ- stations ity pattern appears to be a compromise that provides ac- 50 1,485 92 6.2 275 18.5 3 .0 47 1,741 98 cess 5.6 to 136 the more 1.4 7.8 267 15.3 2.7 nocturnal sambar and the more diurnal

203 6, wild boar and barking deer. Both sambar and wild boar All Site showed the strongest temporal avoidance of leopards at the site where each of these species occurred at the low- KBD (South)KBD KNR (North)KNR Table 1. Number of camera traps and trap nights in the study area and 2 subsets of the study area, Khao Nang Rum (KNR) in the north and Khao Ban Dai (KBD) in the south. Relative abun Table dance index (RAI) is the number of photos/100 trap nights. RAI ratios are ratio prey to leopards.

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 118

est density. Elsewhere they have a broader activity period. of sambar, wild boar, banteng, and tiger varied across our Barking deer had the highest temporal overlap with leop- study area, we examined two subsets of data. One was at ards, but the lowest spatial overlap. The pattern for ban- KNR where wild boar relative density was high compared teng was less clear because of low sample size at KBD. to the relative density of sambar and the other was at KBD where the relative density of sambar was high compared The impact of tigers on leopards to the relative density of wild boar. We also examine if there were differences in leopard activity between KBD Tiger density did not appear to impact either the density and KNR in response to tigers having a RAI nearly 3 times or temporal activity of leopards. Leopard RAI was similar higher at KBD compared to KNR. ranging from 5.2 for all sites, 5.6 at KNR and 6.2 at KBD Overall, leopards exhibited the highest activity at despite the estimate that tiger RAI was 3× greater at KBD or just after dawn and before or at dusk. This cathemeral compared to KNR. Leopard temporal activity also was not activity pattern was very similar to that found in North- impacted by the difference in tiger density. Its pattern was east China (Yang et al., 2018). It may be a strategy that cathemeral at all three sites (diurnal activity ranged from maximizes encounters with the more diurnal wild boar and 53–55%) (Table 3). We have no temporal activity data for barking deer as well as the nocturnal sambar. The extent tigers from 2013 and 2015. to which leopards or banteng adjust their behavior in response to each other is unknown because our sample size Discussion of banteng at KBD was small. Occasionally leopards have been observed feeding on banteng killed by tigers, but diet This is the first study in mainland Southeast Asia that data from HKK indicates that 70% of banteng remains in quantifies spatial and temporal interactions between the the diet of leopards are from young (Simcharoen et al., leopard, the region’s second largest felid, and its primary 2018). Additionally, a tiger is not likely to abandon or al- ungulate prey. It also examined the temporal activity pat- low a leopard to obtain much meat from a small banteng tern of leopards to determine if it was impacted by tiger kill. Therefore, it is unlikely that these scats were produced density. Although the leopard has a diverse diet of approxi- from foraging on tiger kills. In summary, each of these four mately 20 species, 74.2% of its diet consists of a combina- species are important components of the leopard’s diet and tion of six species medium size adult and young of large demonstrate a strategy of maximizing energy intake. size ungulates (Simcharoen et al., 2018). These prey are The spatial and temporal overlap of the leopard with especially critical food resources for female leopards, who its two primary prey species, sambar and wild boar, varied continue to provide food for their young prior to disper- across sites suggesting that the leopard may shift its ac- sal (Simcharoen et al., 2007, 2008). Because the density tivity to focus on its most abundant prey species. Brown Table 2. Spearman spatial and temporal rank correlation of leopard activity in relation to four major prey in HKK Table 2. Spearman spatial and temporal rank correlation of leopard activity in relation to four major prey in HKK Table 2. Spearman spatial and temporal rank correlation of leopard activity in relation to four major prey in HKK Spatial Temporal Variables Spatial Temporal Variables Spearman correlation p-value Spearman correlation p-value Spearman correlation p-value Spearman correlation p-value All All Leopard and sambar *0.26 <0.05 0.19 0.37 Leopard and sambar *0.26 <0.05 0.19 0.37 Leopard and wild boar *0.24 <0.05 0.29 0.16 Leopard and wild boar *0.24 <0.05 0.29 0.16 Leopard and barking deer 0.04 0.57 *0.48 <0.05 Leopard and barking deer 0.04 0.57 *0.48 <0.05 Leopard and banteng 0.13 0.06 0.15 0.50 Leopard and banteng 0.13 0.06 0.15 0.50 KNR (North) LeopardKNR and (North) sambar 0.19 0.21 –0.15 0.49 Leopard and sambar 0.19 0.21 –0.15 0.49 Leopard and wild boar 0.05 0.74 0.38 0.07 Leopard and wild boar 0.05 0.74 0.38 0.07 Leopard and barking deer –0.12 0.42 0.22 0.30 Leopard and barking deer –0.12 0.42 0.22 0.30 Leopard and banteng 0.19 0.19 –0.02 0.91 Leopard and banteng 0.19 0.19 –0.02 0.91 KBD (South) LeopardKBD and (South) sambar 0.26 0.07 0.13 0.53 Leopard and sambar 0.26 0.07 0.13 0.53 Leopard and wild boar –0.02 0.90 0.06 0.80 Leopard and wild boar –0.02 0.90 0.06 0.80 Leopard and barking deer –0.11 0.44 0.05 0.82 Leopard and barking deer –0.11 0.44 0.05 0.82 Leopard and banteng –0.01 0.94 –0.03 0.90 Leopard and banteng –0.01 0.94 –0.03 0.90

*p-value < 0.05 *p-value < 0.05

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC

Table 3. The daily activity of species was classified based on the percentage of diurnal activity (06:00–17:59): strongly Table 3. The daily activity of species was classified based on the percentage of diurnal activity (06:00–17:59): strongly diurnal (≥ 85%), mostly diurnal (84–61%), cathemeral (60–40%), mostly nocturnal (39–16%) and strongly nocturnal (≤15%) diurnal (≥ 85%), mostly diurnal (84–61%), cathemeral (60–40%), mostly nocturnal (39–16%) and strongly nocturnal (≤15%)

Site Time Leopard Sambar Wild boar Barking deer Banteng Site Time Leopard Sambar Wild boar Barking deer Banteng All 06:00–17:59 176 (54%) 270 (28%) 547 (80%) 803 (69%) 50 (36%) All 06:00–17:59 176 (54%) 270 (28%) 547 (80%) 803 (69%) 50 (36%) 18:00–05:59 148 (46%) 711 (72%) 135 (20%) 366 (31%) 87 (64%) 18:00–05:59 148 (46%) 711Mostly (72%) 135 (20%) 366 (31%) 87Mostly (64%) Cathemeral Mostly Mostly diurnal Mostly diurnal Mostly Cathemeral nocturnal Mostly diurnal Mostly diurnal nocturnal nocturnal nocturnal KNR (North) 06:00–17:59 54 (55%) 15 (11%) 199 (75%) 175 (65%) 9 (20%) KNR (North) 18:0006:00––05:5917:59 4454 (45%)(55%) 12115 (11%)(89%) 19968 (25%) (75%) 17593 (36%) (65%) 359 (20%)(80%) 18:00–05:59 44 (45%) 121Strongly (89%) 68 (25%) 93 (36%) 35Mostly (80%) Cathemeral Strongly Mostly diurnal Mostly diurnal Mostly Cathemeral nocturnal Mostly diurnal Mostly diurnal nocturnal nocturnal nocturnal KBD (South) 06:00–17:59 49 (53%) 142 (51%) 107 (90%) 208 (77%) 14 (58%) KBD (South) 18:0006:00––05:5917:59 4349 (47%)(53%) 134142 (49%)(51%) 10713 (10%) (90%) 20862 (23%) (77%) 1014 (42%)(58%) 18:00–05:59 43 (47%) 134 (49%) 13 (10%) 62 (23%) 10 (42%) Cathemeral Cathemeral Strongly diurnal Mostly diurnal Cathemeral Cathemeral Cathemeral Strongly diurnal Mostly diurnal Cathemeral 119

Table 2. Spearman spatial and temporal rank correlation of leopard activity in relation to four major prey in HKK

Spatial Temporal Variables Spearman correlation p-value Spearman correlation p-value All Leopard and sambar *0.26 <0.05 0.19 0.37 Leopard and wild boar *0.24 <0.05 0.29 0.16 Leopard and barking deer 0.04 0.57 *0.48 <0.05 Leopard and banteng 0.13 0.06 0.15 0.50

KNR (North) Leopard and sambar 0.19 0.21 –0.15 0.49 Leopard and wild boar 0.05 0.74 0.38 0.07 Leopard and barking deer –0.12 0.42 0.22 0.30 Leopard and banteng 0.19 0.19 –0.02 0.91

KBD (South) Leopard and sambar 0.26 0.07 0.13 0.53 Leopard and wild boar –0.02 0.90 0.06 0.80 Leopard and barking deer –0.11 0.44 0.05 0.82 Leopard and banteng –0.01 0.94 –0.03 0.90

*p-value < 0.05

Fig. 2. Estimate of daily activity pattern of leopards and four major prey for all camera trap sites (row 1), northern subset of camera sites at KNR (row 2) and southern subset of camera trap sites at KBD (row 3). Table 3. The daily activity of species was classified based on the percentage of diurnal activity (06:00–17:59): strongly

Tablediurnal 3. The (≥ daily 85%), activity mostly of diurnal species (84 was–61%), classifiedcathemeral based (60 on– 40%),the percentage mostly nocturnal of diurnal (39 –activity16%) and(06:00–17:59): strongly nocturnal strongly (≤15%) diurnal (≥ 85%), mostly diurnal (84–61%), cathemeral (60–40%), mostly nocturnal (39–16%) and strongly nocturnal (≤15%)

Site Time Leopard Sambar Wild boar Barking deer Banteng All 06:00–17:59 176 (54%) 270 (28%) 547 (80%) 803 (69%) 50 (36%) 18:00–05:59 148 (46%) 711 (72%) 135 (20%) 366 (31%) 87 (64%) Mostly Mostly Cathemeral Mostly diurnal Mostly diurnal nocturnal nocturnal KNR (North) 06:00–17:59 54 (55%) 15 (11%) 199 (75%) 175 (65%) 9 (20%) 18:00–05:59 44 (45%) 121 (89%) 68 (25%) 93 (36%) 35 (80%) Strongly Mostly Cathemeral Mostly diurnal Mostly diurnal nocturnal nocturnal KBD (South) 06:00–17:59 49 (53%) 142 (51%) 107 (90%) 208 (77%) 14 (58%) 18:00–05:59 43 (47%) 134 (49%) 13 (10%) 62 (23%) 10 (42%) Cathemeral Cathemeral Strongly diurnal Mostly diurnal Cathemeral

et al. (1999) and Garrott et al. (2007) found similar prey poral avoidance where their density was lowest. In KNR the switching in wolves. Garrott et al. (2007), also noted that leopard sambar RAI was half the ratio at KBD and the tem- prey in turn modify their temporal behavior in response to poral overlap was Δ4 = 0.62 compared to an overlap of 0.83 changing predation risk. Both sambar and wild boar avoid- at KBD. We found a similar but geographically reverse pat- ance behavior was stronger when these species occurred at tern for wild boar. At KNR the RAI ratio of leopard to wild lower density because the probability of an individual sam- boar was ~ half the ratio at KBD and the Δ4 = 0.61 versus a bar or wild boar encountering a leopard is lower where there Δ4 = 0.78. Thus, our data support the second hypothesis that are more prey individuals per leopard. Thus, based strictly on predators locally fine-tune their activity to favor overlap with the probability of encounter (Holling, 1959), we expected the most abundant of these prey species and prey avoid over- and found that sambar and wild boar had the strongest tem- lap with predators where their density is lowest.

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 120

Our third hypothesis that leopards should adjust their National Science and Technology Development Agency activity to have less overlap with tigers where tiger RAI (NSTDA), Thailand and J.L.D. Smith’s contribution to was highest was rejected. The tiger RAI at KBD was 3× this research was supported by the USDA National Insti- the RAI at KNR and female tiger home range size at KBD tute of Food and Agriculture. was reported to be ~31 km2 versus a home range size at KNR of ~62 km2 (Simcharoen et al., 2014). Together these data suggest the density of tigers at KBD was at least References double the density at KNR, but leopards did not adjust their cathemeral activity pattern in response to tiger home range Andheria, A.P., Karanth, K.U., Kumar, N.S., 2007. Diet size or RAI. Lack of evidence of effective interference and prey profiles of three sympatric large carnivores competition by tigers toward leopards at HKK is interest- in Bandipur Tiger Reserve, India. Journal of Zool- ing, because it is widely reported to occur in South Asia ogy, 273 (2): 169–175. https://doi.org/10.1111/j.1469- (Odden et al., 2010; Harihar et al., 2011; Steinmetz et 7998.2007.00310.x al., 2013). However, Karanth et al. (2017) also reported Azlan, J.M., Sharma, D.S.K., 2006. The diversity and activ- lack of interference competition in Southwest India. Hol- ity patterns of wild fields in a secondary forest. Oryx, 40 ling (1959) modeled predation success as consisting of (1): 36–41. https://doi.org/10.1017/S0030605306000147 encounter rate and capture success. Simcharoen et al. Brown, J.S., Laundre, J.W., Gurung, M., 1999. The ecol- (2018) suggest that low encounter rate as a consequence of ogy of fear: optimal foraging, game theory, and trophic dense cover might also explain the lack of evidence of in- interactions. Journal of Mammalogy, 80 (2): 385–399. terference competition. Maputla et al. (2015) also found https://doi.org/10.2307/1383287 that leopard prey selection was more important than the Carbone, C., Christie, S., Conforti, K., Coulson, T., Franklin, N., Ginsberg, J.R., Griffiths, M., Holden, influence of lion movement patterns in South Africa. De- J., Kawanishi, K., Kinnaird, M.F., Laidlaw, R., Lynam, spite the large number of documented cases of interference A., Macdonald, D., Martyr, D., McDougal, C., Nath, competition, modern statistical modeling emphasizes that L., O’Brien, T.G., Seidensticker, J., Smith, D.J.L., different ecological correlates should result in different Sunquist, M.E., Tilson, R., Shahruddin, W.N.W., predictions so it should not be surprising that the degree of 2001. The use of photographic rates to estimate densities interference competition varies across the leopard’s range. of tigers and other cryptic mammals. Animal Conserva- Given that both leopards and tigers are threatened tion, 4: 75–79. across Southeast Asia and that their largest populations Charnov, E.L., 1976. Optimum foraging and the marginal occur in WEFCOM, research at HKK contributes to the value theorem. Theoretical Population Biology, 9: 129– understanding of interspecies dynamics between these two 136. large felids. There is no clear evidence of tigers reducing Creel, S., Christianson, D., Liley, S., Winnie, J.A., 2007. the density of leopards or altering activities in HKK, but in Predation risk affects reproductive physiology and de- response to widespread interference competition by tigers mography of elk. Science, 315 (5814): 960. toward leopards elsewhere, and the increasing decline of Donadio, E., Buskirk, S.W., 2006. Diet, morphology, and leopards in Southeast Asia (Rostro-García et al., 2016), interspecific killing in carnivora. The American Natural- managers need a conservation strategy that encompasses ist, 167 (4): 524–536. doi: 10.1086/501033 threats to both species. Unfortunately, rigorous field stud- Duangchantrasiri, S., Umponjan, M., Simcharoen, S., ies on the behavioral dynamics of carnivores and their prey Pattanavibool, A., Chaiwattana, S., Maneerat, S., are exceedingly difficult (Garrott et al., 2007) and the Kumar, N.S., Jathanna, D., Srivathsa, A., Karanth, difficulty is compounded where two large predators with K.U., 2016. Dynamics of a low-density tiger population overlapping diets occur. HKK has a history of long-term in Southeast Asia in the context of improved law enforce- research on tigers and leopards and is an ideal site for more ment. Conservation Biology, 30 (3): 639–648. https://doi. intensive behavioral studies which are needed to drive sci- org/10.1111/cobi.12655 Garrott, R.A., Bruggeman, J.E., Becker, M.S., Kalin- entific management of these co-occurring species. owski, S.T., White, P.J., 2007. Evaluating prey switching in wolf–ungulate systems. Ecological Applications, 17 (6): 1588–1597. doi: 10.1890/06-1439.1 Acknowledgements Harihar, A., Pandav, B., Goyal, S.P., 2011. Responses of leopard Panthera pardus to the recovery of a tiger We thank the Wildlife Conservation Society for support- Panthera tigris population. Journal of Applied Ecol- ing our research and we are grateful to the Liz Claiborne ogy, 48 (3): 806–814. https://doi.org/ 10.1111/j.1365- and Art Ortenberg Foundation and Department of National 2664.2011.01981.x Parks, Wildlife and Plant Conservation for funding sup- Holling, C.S., 1959. Some characteristics of simple types of port. We acknowledge the staff of Khao Nang Ram Re- predation and parasitism. The Canadian Entomologist, 91 search Station for their many contributions in the field. (7): 385–398. https://doi.org/10.4039/Ent91385-7 Prateep Duengkae is the recipient of “Wildlife habitat res- Karanth, K. U., Srivathsa, A., Vasudev, D., Puri, M., toration for prey species of tiger in Dong Phayayen-Khao Parameshwaran, R., Kumar, N.S., 2017. Spatio-tempo- Yai Forest Complex” (P-18-51249) Research Grant, the ral interactions facilitate large carnivore sympatry across

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 121

a resource gradient. Proceedings of the the Royal Society. O’Brien, T.G., Kinnaird, M.F., Wibisono, H.T., 2003. Biological Sciences, 284 (1848): 20161860. https://doi. Crouching tigers, hidden prey: Sumatran tiger and prey org/10.1098/rspb.2016.1860 populations in a tropical forest landscape. Animal Con- Karanth, K.U., Sunquist, M.E., 1995. Prey selection by ti- servation, 6 (2): 131–139. https://doi.org/10.1017/ ger, leopard and dhole in tropical forests. The Journal of s1367943003003172 Animal Ecology, 64 (4): 439–450. doi: 10.2307/5647 Odden, M., Wegge, P., Fredriksen, T., 2010. Do tigers dis- Karanth, K.U., Sunquist, M.E., 2000. Behavioural corre- place leopards? If so, why? Ecological Research, 25 (4): lates of predation by tiger (Panthera tigris), leopard (Pan- 875–881. doi: 10.1007/s11284-010-0723-1 thera pardus) and dhole (Cuon alpinus) in Nagarahole, Palomares, F., Caro, T.M., 1999. Interspecific killing among India. The Zoological Society of London, 250: 255–265. mammalian carnivores. The American Naturalist, 153 https://doi.org/10.1111/j.1469-7998.2000.tb01076.x (5): 492–508. doi: 10.1086/303189 Kenney, J., Allendorf, F.W., McDougal, C., Smith, J.L., R Core Team. 2017. R: a language and environment for sta- 2014. How much gene flow is needed to avoid inbreed- tistical computing. Vienna, Austria: Foundation for Statis- ing depression in wild tiger populations? Proceedings of tical Computing. https://www.r-project.org/ the Royal Society B. Biological Sciences, 281 (1789): Ramakrishnan, U., Coss, R.G., Pelkey, N.W., 1999. Tiger 20133337. https://doi.org/10.1098/rspb.2013.3337 decline caused by the reduction of large ungulate prey: Kitchener, A.C., Breitenmoser-Würsten, Ch., Eizirik, evidence from a study of leopard diets in southern India. E., Gentry, A., Werdelin, L., Wilting, A., Yamagu- Biological Conservation, 89 (2): 113–120. https://doi. chi, N., Abramov, A.V., Christiansen, P., Driscoll, C., org/10.1016/s0006-3207(98)00159-1 Duckworth, J.W., Johnson, W., Luo. S.-J., Meijaard, E., O’Donoghue, P., Sanderson, J., Seymour K., Bru- Ramesh, T., Kalle, R., Sankar, K., Qureshi, Q., Bennett, ford, M., Groves, C., Hoffmann, M., Nowell, K., Tim- N., 2012. Spatio-temporal partitioning among large mons, Z., Tobe, S., 2017. A revised taxonomy of the Feli- carnivores in relation to major prey species in Western dae. The final report of the Cat Classification Task Force Ghats. Journal of Zoology, 287 (4): 269–275. https://doi. of the IUCN/SSC Cat Specialist Group. Cat News Special org.10.1111/j.1469-7998.2012.00908.x Issue, 11: 80. Ridout, M.S.., Linkie, M., 2009. Estimating overlap of daily Kronfeld-Schor, N., Dayan, T., 2003. Partitioning of time activity patterns from camera trap data. Journal of Agri- as an ecological resource. Annual Review of Ecology, cultural, Biological, and Environmental Statistics, 14 (3): Evolution, and Systematics, 34 (1): 153–181. https://doi. 322–337. https://doi.org/10.1198/jabes.2009.08038 org/10.1146/annurev.ecolsys.34.011802.132435 Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Lekagul, B., McNeely, J.A., 1977. Mammals of Thailand. Ritchie, E.G., Hebblewhite, M., Berger, J., Elmhagen, Bangkok: Kurusapha Ladpao Press. 758 p. B., Letnic, M., Nelson, M.P., Schmitz, O.J., Smith, Linkie, M., Ridout, M.S., 2011. Assessing tiger-prey in- D.W., Wallach, A.D., Wirsing, A.J., 2014. Status and teractions in Sumatran rainforests. Journal of Zool- ecological effects of the world’s largest carnivores. Sci- ogy, 284 (3): 224–229. https://doi.org/10.1111/j.1469- ence, 343 (6167): 1241484. doi: 10.1126/science.1241484 7998.2011.00801.x Rostro-García, S., Kamler, J.F., Ash, E., Clements, G.R., Lovari, S., Pokheral, C.P., Jnawali, S.R., Fusani, L., Fer- Gibson, L., Lynam, A.J., McEwing, R., Naing, H., Pa- retti, F., 2015. Coexistence of the tiger and the common glia, S., 2016. Endangered leopards: range collapse of leopard in a prey-rich are: the role of prey partitioning. the Indochinese leopard (Panthera pardus delacouri) in Journal of Zoology, 295: 122–131. Southeast Asia. Biological Conservation, 201: 293–300. Lynam, J.A., Jenks, E.K., Tantipisanuh, N., Chutipong, https://doi.org/10.1016/j.biocon.2016.07.001 W., Ngoprasert, D., Gale, A.G., Steinmetz, R., Suk- Schaller, G.B., 1967. The deer and the tiger. Chicago: The masuang, R., Bhumpakphan, N., Lon, I., Grassman, J., University of Chicago Press. 370 p. Kitamura, S., Reed, H.D., Baker, C.M., McShea, W., Selvan, K.M., Veeraswami, G.G., Lyngdoh, S., Habib, B., Songsasen, N., Leimgruber, P. 2013. Terrestrial activity Hussain, S.A., 2013. Prey selection and food habits of patterns of wild cats from camera-trapping. The Raffles three sympatric large carnivores in a tropical lowland for- Bulletin of Zoology, 61 (1): 407–415. est of the Eastern Himalayan Biodiversity Hotspot. Mam- Maputla, N.W., Maruping, N.T., Chimimba, C.T., Fer- reira, S.M., 2015. Spatio-temporal separation between malian Biology – Zeitschrift für Säugetierkunde, 78 (4): lions and leopards in the Kruger National Park and the 296–303. https://doi.org/10.1016/j.mambio.2012.11.009 Timbavati Private Nature Reserve, South Africa. Global Simcharoen, A., Savini, T., Gale, G.A., Simcharoen, S., Ecology and Conservation, 3: 693–706. https://doi.org. Duangchantrasiri, S., Pakpien, S., Smith, J.L.D., 10.1016/j.gecco.2015.03.001 2014. Female tiger Panthera tigris home range size Miththapala, S., Seidensticker, J., O’Brien, S.J., 1996. and prey abundance: important metrics for manage- Phylogeographic subspecies recognition in leopards (Pan- ment. Oryx, 48 (3): 370–377. https://doi.org/10.1017/ thera pardus): molecular genetic variation. Conservation s0030605312001408 Biology, 10: 1115–1132. Simcharoen, A., Simcharoen, S., Duangchantrasiri, S., Mondal, K., Gupta, S., Qureshi, Q., Sankar, K., 2011. Bump, J., Smith, J.L.D., 2018. Tiger and leopard diets Prey selection and food habits of leopard (Panthera par- in western Thailand: Evidence for overlap and poten- dus fusca) in Sariska Tiger Reserve, Rajasthan, India. tial consequences. Food Webs, 15: e00085. https://doi. Mammalia, 75 (2): 201–205. https://doi.org/10.1515/ org/10.1016/j.fooweb.2018.e00085 mamm.2011.011 Simcharoen, S., 2007. The relationship between environ-

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 122

mental factors and leopards in Huai Kha Khaeng Wildlife org/10.1016/j.biocon.2012.12.016 Sanctuary Uthai Thani. Journal of Wildlife in Thailand, Trisurat, Y., 2004. GIS database and its applications for 14: 65–79. ecosystem management. The Western Forest Complex Simcharoen, S., Barlow, A.C.D., Simcharoen, A., Smith, Ecosystem Management Project, Department of National J. L.D., 2008. Home range size and daytime habitat selec- Park, Wildlife, and Plant Conservation, Bangkok, Thai- tion of leopards in Huai Kha Khaeng Wildlife Sanctuary, land. 228 p. Thailand. Biological Conservation, 141 (9): 2242–2250. Uphyrkina, O., Johnson, W.E., Quigley, H., Miquelle, D., https://doi.org/10.1016/j.biocon.2008.06.015 Marker, L.L., Bushs, M., O’Brien, S. J., 2001. Phylo- Sollmann, R., Mohamed, A., Samejima, H., Wilting, A., genetics, genome diversity and origin of moder leopard, 2013. Risky business or simple solution – relative abun- Panthera pardus. Molecular Ecology, 10: 2617–2633. dance indices from camera-trapping. Biological Con- Van Schaik, C. P., Griffiths, M., 1996. Activity periods of servation, 159: 405–412. https://doi.org/10.1016/j.bio- Indonesian rain forest mammals. Biotropica, 28 (1): 105– con.2012.12.025 112. doi: 10.2307/2388775 Stein, A.B., Athreya, V., Gerngross, P., Balme, G., Yang, H., Zhao, X., Han, B., Wang, T., Mou, P., Ge, J., Henschel, P., Karanth, U., Miquelle, D., Ros- Feng, L., 2018. Spatiotemporal patterns of Amur leop- tro, S., Kamler, J.F., Laguardia, A., 2016. Pan- ards in northeast China: influence of tigers, prey, and thera pardus. The IUCN Red List of Threatened Spe- humans. Mammalian Biology, 92: 120–128. https://doi. cies 2016: e.T15954A50659089. [cit. 2019-05-3]. org/10.1016/j.mambio.2018.03.009 http://dx.doi.org/10.2305/IUCN.UK.2016-1.RLTS. Yasuda, M., 2004. Monitoring diversity and abundance of T15954A50659089.en mammals with camera traps: a case study on Mount Tsu- Steinmetz, R., Seuaturien, N., Chutipong, W., 2013. kuba, central Japan. Mammal Study, 29 (1): 37–46. Tigers, leopards, and dholes in a half-empty forest: assessing species interactions in a guild of threatened car- Received July 13, 2019 nivores. Biological Conservation, 163: 68–78. https:/doi. Accepted October 2, 2019

Unauthentifiziert | Heruntergeladen 22.12.19 12:57 UTC 123 Malayan Nature Journal 2020, 72(3), 275-286

Diet composition of the golden jackal (Canis aureus L.) during the dry season in west-central Thailand

SAKSIT SIMCHAROEN1, ACHARA SIMCHAROEN1, SASITORN HASIN2, FRANCESCA CUTHBERT3 and J. L. DAVID SMITH3

Abstract: No previous field investigations have been conducted on the diet of golden jackals (Canis aureus L.) in South-East Asia. The object of this study was to understand the role of canids in food webs in the Huai Kha Khaeng Wildlife Sanctuary in west-central Thailand where the diet of this species is not influenced by anthropogenic food sources. We studied diet composition by examining the contents of 84 scats collected during the dry seasons in 2004-2006. Prey and food items were identified using a reference collection maintained at Khao Nang Rum Research Station and recorded as percent frequency of occurrence (%), or the number of scats with remains of a particular prey or food species compared with the total number of scats. Results confirmed golden jackals are opportunistic generalist feeders, as reported elsewhere in their range. Unlike other studies, given the larger percent of meat consumed by jackals in our study area, their primary prey species are insects. We found they consumed significant insect prey (94% of scats), specifically ants (Dorylus sp.) and termites (Macrotermes sp.), but scats had low composition of vertebrate prey except for meat scavenged from large ungulate carcasses at tiger kill sites. Jackals did not feed on human food sources and given their low consumption of meat.

Keywords: canid, diet, Huai Kha Khaeng, scats, Macrotermes, Dorylus, Nycticebus

INTRODUCTION

The golden jackal (Canis aureus) has the most northern range of the world’s three jackal species and is widely distributed globally; it commonly occurs in North and East Africa, Southeastern Europe, and South and South-East Asia (Giannatos et al., 2010; Hoffman et al., 2018). Jackals reach their eastern distribution in Thailand, Laos, Cambodia and Vietnam (Lekakul and McNeely, 1977; Giannatos et al., 2010; Negi, 2014). This species occupies a range of habitats including deserts and savannahs (Giannatos et al., 2010), forests (Giannatos et al., 2010; Jenks et al., 2012) and locations disturbed by humans (Debnath and Choudhury, 2103; Akrim et al., 2019). The IUCN Red Data Book lists the status of the golden jackal as Least Concern and increasing globally (Hoffman et al., 2018), particularly where it associates closely with humans (Jaeger et al., 2007; Giannatos et al., 2010; Borkowski et al., 2011). The diet of golden jackals has been well-studied in many portions of its range and is reported to include both animals and plants (Jaeger et al., 2007, Radović and Kovačić, 2010; Borkowski et al., 2011; Alam et al., 2015; Prerna et al., 2015; Khan et al., 2017). This carnivore is an efficient predator, feeding on small and large animals (i.e. vertebrates and invertebrates) and is often reported as a scavenger at carcasses killed by larger predators

1 Research Division, Department of National Park, Wildlife and Plant Conservation, Pahonyotin Road, Chatuchak, Bangkok 10900, Thailand. 2Innovation of Environmental Management, College of Innovative Management, Valaya Alongkorn Rajabhat University Under the Royal Patronage, C 13180, Thailand. 3Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, MN. USA. Corresponding author: Sasitorn Hasin / E-mail address: [email protected]

275 124 such as leopards (Panthera pardus) and tigers (Panthera tigris) (Negi, 2014). Anthropogenic food resources (e.g. domestic, animals, refuse, crops) are also commonly consumed where available (Radović and Kovačić, 2010; Borkowski et al., 2011). Fleming et al. (2017) describe the species as a mesopredator that forages individually or in small groups. Knowledge of carnivore diets is essential to assess competition with other species, to understand impact on prey populations and to evaluate conservation status (Mukherjee et al., 2004, Hoffmann et al., 2018). The diet composition of the golden jackal has been investigated across Europe (Giannatos et al., 2010; Ćirović et al., 2014; Lanszki et al., 2018), Africa and the Middle East (Borkowski et al., 2011; Temu et al., 2018) and Asia (Sankar, 1988; Alam et al., 2015; Khan et al., 2017; Akrim et al., 2019) and variation in diet composition has been related to different habitats worldwide (Hoffman et al., 2018). Although knowledge of golden jackal diet composition is extensive for most of its range, no study has investigated this species’ diet in South-East Asia, This information is needed to understand the role of canids in food webs locally, in west-central Thailand, as well as in a global context. This study is unusual in that jackals occupied habitat within a large protected area where they had no access to anthropogenic sources of food and identification of prey was accomplished using an extensive local reference collection. The aim of this study was to investigate the diet composition of the golden jackal in their scat contents during the dry season in west-central Thailand

METHODS Study site This study was conducted along the roads between the headquarters of Huai Kha Khaeng Wildlife Sanctuary (HKK) to the Khao Nang Rum Wildlife Research Station (KNRS), located in Uthai Thani Province, in west-central Thailand (Figure 1). The Sanctuary is 2,574 km2 and elevations range from 200-1600 m (Khao Nang Rum Wildlife Research Station, unpubl. data). The primary forest consists of four forest types including mixed deciduous, dry deciduous dipterocarp, dry evergreen and hill evergreen. The HKK is a natural forest and is a well-protected sanctuary with human activities largely limited to research (Simcharoen et al., 2014). In our study, jackals were recorded in a mosaic of undisturbed forest near streams composed of all forest types except hill evergreen (Conforti,1996; Simcharoen, 1998). Average annual rainfall is 1,375 mm, and temperatures range from 8-38°C. Rainfall patterns delineate wet (May–October) and dry (November–April) seasons (KNRS meteorological station 2016 data).

Scat Collection Golden jackal diet was determined by analysis of scats that were collected by research teams at KNRS. All samples were found along 16 km of forest trails located in dry dipterocarp forest and mixed deciduous forest. We collected 84 samples during the dry season, November to April, 2004-2006. Scats were identified based on shape and size (Figure 2). Jackal scats are cylindrical and rounded at both ends with diameter (N=9) between 17.8± 0.9 SD mm to 26.46 ± 1.36 SD mm in width. Additionally we matched scats with jackal tracks to confirm identity (Figure 2). At collection, each scat was stored in an air tight plastic bag and taken to the laboratory at KNRS for analysis of food items. A GPS location for each scat was recorded along with date of collection (Figure1).

276 125

Figure 1 Map of Khao Nang Ram (KNR) Wildlife Research Station, Huai Kha Khaeng Wildlife Sanctuary (HKK) showing locations of scat sampling sites along the main road (black circles). Identification of Food items To identity items in jackal scats, we used methodology followed for other diet studies conducted at the KNR Research Station (Petdee, 2000). Undigested food items from the scats were sorted by washing each scat in a small container with detergent, sifting the material using a fine meshed sieve (0.5 x 0.5 mm), and then drying it. All undigested parts (e.g. teeth; hair; bones; plant seeds; arthropod exoskeletons; other body parts including wings, legs, eyes, ovipositor, head, thorax, abdomen) were separated (Figures 3 and 4). These were dried at 35-40 °C and stored in a paper bag. Non-food items like stones, sand and leaf litter in scats were not included in scat analysis.

277 126

Figure 2 (a-b) Scat of the Jackal in the study area, and commonly found with their footprint. Jackal’s scat are normally multiple tubes dropped.

An identified taxon of vertebrate remains confirmed directly by an expert or by comparing it to a voucher specimens at KNRS and Natural History Museum (NSM). In this study, mammal hairs were identified to genus and species-level using the 60 species hair collection at KNRS obtained from live mammals in captive breeding centers or from carcasses of animals killed by tigers (Panthera tigris) in the KNRS study area. Mammal hair from species not recorded in HKK (Lekakul and McNeely, 1977) was obtained from specimens in the NSM, Bangkok, for additional reference. For each sample, the reference consisted of 25 hairs from each of five different body locations (e.g. forehead, shoulder, and haunch, dorsal and ventral regions of the torso). These hair samples were used to prepare a reference collection of permanent slides including cross-sections, scale and medulla impressions. Hair morphology was classified as type of hair, colour, cuticle, scale, cortex and medulla following Brunner and Coman (1974). For amphibians and reptiles, we compared material found in scats to previously prepared samples of skeletal material collected at the KNRS and identified to family based on skull, hip, leg, foot and vertebrae; tortoise shells were also used to confirm identity. Arthropods were identified using systematic keys (Aoki, 2015); insects were identified using voucher specimens in the KNRS and insect collection at NSM and confirmed by an entomologists. Plant seeds were identified by comparing it to a specimen in the Forest Herbarium (BKF) at Department of National Parks, Wildlife and Plant Conservation.

Data analysis Quantifying the number of individuals of each food item in a scat was not possible. Therefore, we estimated diet composition by reporting percent frequency of occurrence (FO %), which is defined as the number of scats with remains of a particular prey or food species compared with the total number of scats (Borkowski et al., 2011). As a result, only presence and absence data were used to report diet composition.

278 127

Figure 3 Food remain in Jackal’s scat (JS) were found in this study: JS number 51; (a1) plant leave, (a2) animal hair, (a3) sand, (a4) ant and their closed up; (b) Dorylus sp. and (d) Polyrhachis sp. and Pachycondyla sp., (a5) grass leaf and seed and (a6 and c) termite (Macrotermes spp.). JS number 38; (e1) animal hair, (e2) bone and (e3) plant leave. JS number 63; (f1, g) termite (Macrotermes spp.), (f2 and f4) insect, (f3, h) ant (Dorylus sp.), (f5) animal hair and bone and (f6) plant leave.

279 128

Figure 4 Food remain in Jackal’s scat (JS) were found in this study: JS number 33; (a1) termite worker and (a2, b) soldier (Macrotermes spp.), (a3) ant, (a4) grass leaf and seed, (a5, c) cicada and beetles, (a6) plant leave and sand, and (a7, d) giant tropical scorpion; JS number 18; (e1) seed of Vitex limonifolia, (e2) plant leave and sand, (e3, f) giant tropical scorpion, (e4, g1) small mammal teeth, (e4, g2) crab’s cheliped and (e5, h) head of termites (Macrotermes spp.).

280 129

RESULTS We identified Arthropoda remains in 79 scats (94% of scats) (Table 1). Three classes of Arthropoda were identified in the following frequencies of occurrence: Insecta (94% of scats), Arachnida (2.4%) and Chilopoda (1.2 %). Jackals regularly consumed a significant number of insects, particularly termites (94% of scats), ants (61.9%) and beetles (23.8%). Dominant insect species in the food remain were termites in the genus Macrotermes spp. (79.8%) and the ant species Dorylus orientalis (51%). Vertebrates were represented by 4 classes; 31% of scats contained evidence of Amphibia, Reptilia, Aves and Mammalia. Order Artiodactyla was the dominant representative of this taxon and remains were found in 26.2% of scats. The less common taxa across the phyla were Amphibia (1.2%), Aves (1.2 %), Primates (1.2 %), Reptilia (4.8%) and Rodentia (4.8%). Plant material was identified in 21 % of jackal scats and represented the more typical flowering plants (21.4%) as well as grasses (36.9%). The most common plant in jackal scats was Vitex limonifolia (14.2%), a fruit-producing tree widespread in the study area. Table 1. Frequency of occurrence (FO%) of animal and plant remains in golden jackal scats. Grass and Canis aureus hair were excluded from data analysis (indicated by *).

Phylum/Kingdom /Class/Superclass, Order /Family/ Species n (N=84) FO (%) Arthropoda 79 94.0 Malacostraca (Crab) 1 1.2 Arachnida Araneae 2 2.4 Chilopoda 1 1.2 Insecta 79 94.0 Blattodea 2 2.4 Coleoptera (Scarabaeidae, etc.; Beetles) 20 23.8 Hemiptera (Cicadidae, etc.) 5 6.0 Hymenoptera (Formicidae; Ants) 52 61.9 Aenictus sp. 1 1.2 Camponotus sp. 1 1.2 Carebara affinis 5 6.0 Carebara diversus 3 3.6 Carebara silene 1 1.2 Crematogaster rogenhoferi 1 1.2 Crematogaster sp. 1 1.2 Diacamma sp.1 1 1.2 Diacamma sp.2 6 7.1 Dorylus orientalis 43 51.2 Odontoponera denticulata 6 7.1 Pachycondyla astuta 1 1.2 Pachycondyla sp. 2 2.4 Pheidole sp. 3 3.6 Polyrhachis sp. 4 4.8 Tetramorium sp.1 1 1.2 Isoptera (Termitidae; Termites) 79 94.0 Macrotermes spp. 67 79.8 Termitidae spp. 12 14.3 Mantodea (Mantidae) 1 1.2 Orthoptera (Acrididae, Gryllidae) 15 17.9 Phasmatodae (Phasmatidae) 2 2.4

281 130

Chordata 26 31.0 Amphibia 1 1.2 Reptilia 4 4.8 Aves 1 1.2 Mammalia 20 23.8 Primates (Lorisidae); Nycticebus sp. 1 1.2 Artiodactyla 22 26.2 Bovidae: Bos javanicus 7 8.2 Cervidae: Rusa unicolor 10 11.2 Muntiacus muntjak 7 12.1 Rodentia (Spalacidae) 4 4.8 Rhizomys pruinosus 1 1.2 Other-Rodentia 3 3.6 Carnivora; Canis aureus* 27 32.3

Plantae Magnoliopsida 18 21.4 Lamiales (Verbenaceae) Vitex limonifolia 12 14.2 Vitex canescens 1 1.2 Rosales (Moraceae) Ficus sp. 1 1.2 Malpighiales (Euphorbiaceae) Bridelia retusa 1 1.2 Bridelia stipularis 1 1.2 Solanales (Solanaceae) Capsicum annuum 1 1.2 Gentianales (Rubiaceae) Morinda coreia 1 1.2 Malvales (Malvaceae ) Microcos tomentosa 2 2.4 Grewia sp. 1 1.2 Poales Grass * 31 36.9

DISCUSSION This study confirmed the golden jackal as an omnivore and scavenger, which other investigators have clearly established. Our study, however, makes several important contributions to broader knowledge of jackal diet by providing (1) information on prey consumed by jackals in South-East Asia, a location in the golden jackal’s range where diet has not previously been reported, (2) insight on jackal diet in a natural habitat not influenced by anthropogenic food sources, and (3) details on vertebrate and invertebrate prey not previously described for Canis aureus.

282 131

Comparison of golden jackal diet in Thailand and locations in South Asia Although generalist feeders, jackals in our study consumed large quantities of insects (94% of scats); < 50% of the jackal scats consisted of vertebrates (31%) and fruits (21%). And, based on scat composition, large quantities of food items appeared often to be ingested opportunistically during one foraging event. This observation confirms jackals take advantage of diverse resources that are seasonally abundant in HKK. General jackal diet in our study area was in contrast to diets in South Asia (India and Pakistan) where scat contents were primarily comprised of animal carcasses, rodents and reptiles (Sankar, 1988; Alam et al., 2015; Khan et al., 2017) as well as domestic animals (Akrim et al., 2019). Several authors reported diets were supplemented with insects and fruits (Sankar, 1988; Akrim et al., 2019) but insects were not identified as an important diet group. However, our studies were conducted in the dry season and we do not know how diet may vary in the wet season or how wet season diet in HKK might compare with food consumed in other regions. But, Lanszki and Heltai (2002) found no seasonal differences in golden jackal diet in Hungary. Several sites in South Asia were also in proximity to anthropogenic food sources which impacted diet at those locations. We found no evidence that jackals in our study consumed any foods related to human activities. We did note, however, that high insect consumption in HKK was similar to that reported in Serengeti National Park (Wyman, 1967; Lamprecht, 1978; Fleming et al., 2017; Temu et al., 2018). Moreover, the presence of Jackal hair in the scat (>31 %) are most likely a result of self-grooming. Jackals are also reported as consumers of fruits globally and our study also documented fruits (seeds) in scats during the dry season. The most common species was Vitex limonifolia (14.2% of scats), a common forest tree species that produces abundant fruit in mixed deciduous and dry dipterocarp forests in HKK. This plant is reported to have a “sweet” taste and it has medicinal value for humans (Rani and Sharma, 2013). This plant was often found along jackal foraging routes in the study area. We suggest that the golden jackal may function as a seed disperser (Herrera 1989) in natural forests but additional study is needed to confirm this hypothesis. Finally, undigested grasses and other plant material was more likely to be a consequence of the jackals eating the rumen and its contents, in the ungulate carcasses (Figure 5).

Noteworthy items in the diet of golden jackals in west-central Thailand. Despite the overall diversity of jackal diet in our study area, of special interest is that this species consumed significant arthropod prey; > 60 % of the jackal diet consisted of insects, especially termites and ants. And, of those identified, the most abundant were Macrotermes spp. (79.8%) and Dorylus orientalis (51%) which characterized jackal diet during the dry season. We suggest that jackals may feed on these insects by licking them from the ground when the workers come to the forest floor at night to conduct swarm raids for food (Darlington, 1997; Berghoff et al., 2003; Abou-Houly, ‎2010; Simcharoen, pers. observation). Macrotermes is a genus of fungus rearing termites composed of multiple species that are common in South-East Asia. They typically build mounds occupied by thousands of individuals. Dorylus orientalis, is a species of driver ant that may live in colonies of over a million individuals. Swarming groups of both genera clearly can provide significant protein for foraging jackals during certain seasons. Given the larger percent of meat consumed by jackals in our study area, their primary prey species are insects, an important high-protein food resource as also reported for black-backed jackals (Canis mesomelus) in Africa (Grafton, 1965; Goldenberg et al., 2010) as well as golden jackals in Tanzania (Temu et al., 2018). Interestingly, >22% of the scats contained identifiable remains of mammals. This result revealed that the jackals had consumed ungulate flesh, and this could have been undetected when the jackal had not swallowed hair in its meal. Moreover, in case of plant, we noted that plant material identified in the scats was not digested, therefore had no nutritional input to the diet.

283 132

Another noteworthy discovery was the remains of a loris, likely the Asian slow loris (Nycticebus bengalensis) (Francis, 2008), in one of the jackal scats. This record is of interest because all loris species are highly arboreal although they are known to make rare visits to the forest floor. Little is known about their predators as records are very limited, but reports specify snakes and predatory birds. Our discovery appears to be the first report of a loris as prey for a jackal.

Jackals as scavengers at tiger and leopard kill sites Among the vertebrates, the dominant mammal species found in jackal scats were from order Artiodactyla, including barking deer or muntjak (Muntiacus muntjak), sambar (Rusa unicolor) and banteng (Bos javanicus); all are commonly recorded at kill sites of tigers and leopards (Jenks et al., 2015). We confirmed that jackals do feed on carcasses of these species by placing camera traps at locations where large cats had made a kill (Figure 5). Scavenging behavior is reported widely in other portions of the jackal’s range, most often in relation to feeding on human food sources, including dead livestock (Borkowski et al., 2011). Foraging on anthropogenic food sources in areas of high human density appears to have replaced scavenging at natural kill sites as occurs in protected areas distant from human habitation.

Figure 5 A golden jackal attacked a carcass of a banteng (Bos javanicus) at a tiger kill site as recorded by a remote camera.

Acknowledgements : We are grateful to Mr. Punya Suksomkit who assisted with field work.

284 133

REFERENCES

Abou-Houly, H.E. ‎2010. Investigation of flow through and around the Macrotermes michaelseni termite mound skin. PhD dissertation, Loughborough University. 273 pp. (Unpublished). Akrim, F., Mahmood, T., Nadeem, M.S., Dhendup, T., Fatima, H. and Andleeb, S. 2019. Diet composition and niche overlap of two sympatric carnivores: Asiatic jackal, Canis aureus, and Kashmir hill fox Vulpes vulpes griffithii, inhabiting Pir Lasura National Park, Northeastern Himalayan region, Pakistan. Wildlife Biology 1: 1–9. Alam, M.S., Khan, J.A., Njoroge, C.H., Kumar, S. and Meena, R.L. 2015. Food preferences of the Golden Jackal, Canis aureus, in the Gir National Park and Sanctuary, Gujarat, India. Journal of Threatened Taxa 7: 6927–6933 Aoki, J. 2015. Pictorial Keys to Soil Animals of Japan, 2nd edition. Tokai University Press, Hadano, Japan, 1969 p. Bekele, T., Bekele, A. and Balakrishnan, M. 2008. Feeding ecology of the African Civet, Civettictis civetta, in the Menagesha-Suba State Forest, Ethiopia. Small Carnivore Conservation 39: 19–24. Berghoff, S.M., Maschwitz, U. and Linsenmair K.E. 2003. Hypogaeic and epigaeic ant diversity on Borneo: evaluation of baited sieve buckets as a study method. Tropical Zoology 16 : 153–163. Borkowski, J., Zalewski, A. and Manor, R. 2011. Diet composition of golden jackals in Israel. Annales Zoologici Fennici 48 : 108–118. Brunner, H. and Coman, B.J. 1974. The identification of mammalian hair. Inkata Press, Melbourne, 176 pp. Ćirović, D., Penezić, A., Milenković, M. and Paunović, M. 2014. Winter diet composition of the golden jackal (Canis aureus L., 1758) in Serbia. Mammalian Biology 79 : 132–137. Conforti, K. 1996. Status and Distribution of Small Carnivores in Huai Kha Khaeng/Thung Yai Wildlife Sanctuary, West-Central Thailand. MS Thesis, University of Minnesota. (Unpublished). Darlington, J. P. E. C. 1997. Comparison of nest structure and caste parameters of sympatric species of Odontotermes (Termitidae, Macrotermitinae) in Kenya. Insectes Sociaux 44(4) : 393–408. Debnath, D. and Choudhury, P. 2103. Population estimation of golden jackal (Canis aureus) using different methods in various habitats of Cachar district, southern Assam. Indian Forester 139 (10) : 888–894. Fleming, P.J.S., Nolan, H., Jackson, S.M., Ballard, G-A., Begsen, A., Brown, W.Y., Meek, P.D., Midsud, G., Pal, S.K. and Sparkes, J. 2017. Roles for the Canidae in food webs reviewed: Where to they fit?. Food Webs (12) : 14-34 Francis, C.M. 2008. A Guide to the Mammals of Southeast Asia. Princeton University Press, Princeton, New Jersey, and Oxford, United Kingdom. 392 p. Giannatos, G., Karypidou, A., Legakis, A. and Polymeni, R. 2010. Golden jackal (Canis aureus L.) diet in Southern Greece. Mammalian Biology 75(3) : 227–232. Goldenberg, M., Goldenberg, F., Funk, S.M., Henschel, J. and Millesi, E. 2010. Diet composition of black-backed jackals, Canis mesomelas, in the Namib Desert. Folia Zoologica. 59(2) : 93–101. Grafton, R.N. 1965. Food of the Black-backed Jackal: A Preliminary Report. Zoologica Africana 1 : 41-53 Herrera, C.M. 1989. Frugivory and seed dispersal by carnivorous mammals and associated fruit characteristics in undisturbed Mediterranean habitats. Oikos 55 : 250–262. Hoffman, M., Arnold, J., Duckworth, J.W., Jhala, Y., Kamler, J.F. and Krofel, 2018. Canis aureus. The IUCN Red List of Threatened Species 2018: e.T118264161A46194820. Retrieved from URL: http://dx.doi.org/10.2305/IUCN.UK.2018–2.RLTS.T118264161A46194820. en. Assessed on 28 April 2019. Jaeger, M.M., Emdadul, H., Parvin, S. and Bruggers, R.L. 2007. Daytime cover, diet and space-use of golden jackals (Canis aureus) in agro-ecosystems of Bangladesh. USDA National Wild life Research Center - Staff Publications. 701 p. Jenks, K.E. Songsasen, N. and Leimgruber, P. 2012. Camera trap records of dholes in Khao Ang Rue Nai Wildlife Sanctuary, Thailand. Canid News Retrieved from URL: https://www.canids.org/canidnews/15/Camera_trap_records_of_dholes_in_Thailand.pdf. Assessed on 28 April 2019.

285 134

Jenks, K.E., Aikens, E.O., Songsasen, N., Calabrese, J., Fleming, C., Bhumpakphan, N., Wanghongsa, S., Kanchanasaka, B., Songer, M. and Leimgruber, P. 2015. Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape. Raffles Bulletin of Zoology 63 : 546–554. Khan, K.A., Khan, J.A. and Mohan, N. 2017. Winter food habits of the Golden Jackal, Canis aureus, (Mammalia: Carnivora: Canidae) in Patna Bird Sanctuary, Uttar Pradesh, India. Journal of Threatened Taxa 9(9) : 10656–10661. Klare, U., Kamler, J.F. and Macdonald, D. W. 2011. A comparison and critique of different scat-analysis methods for determining carnivore diet. Mammal Review. Retrieved from URL: https://doi.org/10.1111/j.1365-2907.2011.00183.x| Assessed on 2 April 2019. Lamprecht, J. 1978. On the diet, foraging and interspecific food competition of jackals in the Serengeti. Zeitschrift fur Säugertierkunde 43 : 210–223. Lanszki, J. and Heltai, M. 2002. Feeding habits of golden jackal and red fox in south-western Hungary during winter and spring. Mammalian Biology 67(3) : 129–136. Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: First telemetry evidence of a natal dispersal. Mammalian Biology 88 : 81–84. Lekakul, B. and McNeely, J. 1977. Mammals of Thailand. Kurusapa, Ladprao Press, Thailand. 1974 pp. Mukherjee, S., Goyal, S.P., Johnsingh, A.J.T. and Leite Pitman, M.R.P. 2004. The importance of rodents in the diet of jungle cat (Felis chaus), caracal (Caracal caracal) and golden jackal (Canis aureus) in Sariska Tiger Reserve, Rajasthan, India. Zoological Society of London 262: 405–411. Negi, T. 2014. Review on current worldwide status, distribution, ecology and dietary habits of golden jackal, Canis aureus. Octa Journal of Environmental Research 2(4) : 338–359 Petdee, A. 2000. Feeding habits of the tiger (Panthera tigris Linnaeus) in Huai Kha Khaeng Wildlife Sanctuary by fecal analysis. MS Thesis (in Thai), Kasetsart University, Bangkok, Thailand. 92 pp. (Unpublished). Prerna, S., A. Edgaonkar and Dubey, Y. 2015. Diet composition of Golden Jackals Canis aureus (Mammalia: Carnivora: Canidae) in Van Vihar National Park, India, a small enclosed area. Journal of Threatened Taxa 7(8) : 7422–7427. Radović, A. and Kovačić J.A. 2010. Diet composition of the golden jackal (Canis aureus L.) on the Peninsula, Dalmatia, Croatia. Periodicum Biologorum 112(2) : 219–224. Rani, A. and Sharma, A. 2013. The genus Vitex: A review. Pharmacogn. 7(14) : 188–198. Sankar, K. 1988. Some observations on food habits of Jackal (Canis aureus) in Keoladeo National Park, Bhagalpur, as shown by scat analysis. Journal of the Bombay Natural History Society 85(1): 185–186. Simcharoen, S. 1998. Home range and habitat use of a male Asiatic Jackal, Canis aureus, at Khao Nang Rum Wildlife Research Center, Thailand. Natural History Bulletin of the Siam Society 46 : 3–15. Simcharoen, A., Savini, T., Gale, G. A., Simcharoen, S., Duangchantrasiri, S., Pakpien, S. and Smith, J. L. 2014. Female tiger Panthera tigris home range size and prey abundance: Important metrics for management. ORYX 48(3) : 370–377. Temu, S.E., Nahonyo, C.L. and Moehlman, P.D. 2018. Diet composition of the golden jackal (Canis aureus) in the Ngorongoro crater, Tanzania. Tanzania Journal of Science 44(1) : 52-61 Wyman, J. 1967. The jackals of the Serengeti. Animals 10 : 79–83.

286 135 Malayan Nature Journal 2020, 72(3), 287-293

Diet of the Large Indian Civ et (V iv erra z ib et h a L., 1 7 5 8 ) in west-central Thailand

SAKSIT SIMCHAROEN1, ACHARA SIMCHAROEN1, SASITORN HASIN2, FRANCESCA CUTHBERT3 and J. L. DAVID SMITH3

Abstract: Diet studies of the large indian civet (V iv erra z ibetha ) are limited in Asia. The diet composition of this species is important to study because in many parts of its range the V . z ibetha is still common; yet in other areas, it is declining rapidly due to hunting for meat. This study analyz ed 124 scats collected at civet latrines in Huai Kha Khaeng Wildlife Sanctuary, Thailand, to determine diet composition in a natural landscape where the species is still common. Using a reference collection, preys were identified from 4 phyla and 13 classes representing a remarkable diversity of food items. Vertebrates and invertebrates composed a major portion of this species’ diet. This knowledge establishes a strong foundation for future studies on the important role of this species as a bio-indicator of change in forest ecosystems in Asia and its influence on prey population dynamics as well as an agent of seed dispersal.

Keywords: large indian civet, small carnivores, tropical rain forest, diet composition, scat analysis

INTRODUCTION

Large indian civet (V iv erra z ibetha ) is omnivorous, feeding on a wide diversity of vertebrates including fish, amphibians, reptiles and small mammals, as well as invertebrates and plants (especially fruits) (Simcharoen et al., 1990, 1999; Timmins et al., 2016). The larger role of this civet species in influencing population dynamics of its prey (i.e. potential trophic cascades) as well as the function civets may play in dispersal of seeds in the ecosystems(Corlett, 1998; Corlett, 2002; Nakashima et al., 2010). It and other ground foraging civets defecate in a network of dispersed civetries (Baladrishnan and Mullu, 2014). These latrines can be > 1m in diameter and be active for > 5 years (Bekele et al., 2008). Sometimes latrine use is discontinued for several years and then initiated again (Abiyu et al., 2015). Studies in Africa also report that many tree seeds have a higher germination rate after passing through the gut of a civet (Bekele et al., 2008), and the micro environment of latrines may serve as an ideal seed bed for germinated seeds (Abiyu et al., 2015).

1Research Division, Department of National Park, Wildlife and Plant Conservation, Pahonyotin Road, Chatuchak, Bangkok 10900, Thailand. 2Innovation of Environmental Management, College of Innovative Management, Valaya Alongkorn Rajabhat University Under the Royal Patronage, C 13180, Thailand. 3Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, MN. USA. Corresponding author: Sasitorn Hasin / E-mail address: [email protected]

287 136

Our study was undertaken in Huai Kha Khaeng Wildlife Sanctuary (HKK) to determine the diet of the V. zibetha in a natural ecosystem and to better understand its natural diet in Thailand. The aim of this study was to investigate the diet composition of the V. zibetha in their scat contents in west-central Thailand.

MATERIALS AND METHODS Study site This study was conducted within 16 km of Khao Nang Ram Wildlife Research Station (KNRS), located in HKK, Uthai Thani Province, in western Thailand (Figure 1) from 1995 to 1997. The Sanctuary is 2,574 km2 in size, and elevations range from 200–1600 masl. HKK is part of the Western Forest Complex (WEFCOM), a group of 17 protected areas (18,727 km2), which together form the largest contiguous forest complex in southern Asia. HKK consists of four major forest types including mixed deciduous, dry deciduous dipterocarp, dry evergreen and hill evergreen (Simcharoen et al. 1999).

Figure 1. Map of Khao Nang Ram (KNR) Wildlife Research Station, Huai Kha Khaeng Wildlife Sanctuary (HKK), and locations of scat sampling sites at civetries. The research site was located in a mosaic of undisturbed forest composed of mixed deciduous, dry dipterocarp, dry evergreen and hill evergreen where human activities are limited to research. The temperatures range from 8-38 °C and annual rainfall is 1,375 mm; the rainfall pattern delineates wet (May–October) and dry (November–April) seasons (the KNR-WRS meteorological station 2016).

288 137

Collection of scat samples The diet of the V. zibetha was determined by analysis of scats. These were collected at civetries, or regularly used latrines (Figure 2A-2B). Civetries are typically located in natural hollows in the bare ground and are < 1 m2 in diameter. Up to six individuals have been observed to use the same latrine (pers. obs., S. Simcharoen). A GPS unit was used to record location of civetries and the date of collection of each sample was recorded; scats were placed in paper bags to dry and not mold prior to washing for analysis of food items.

Figure 2. (A) Viverra zibetha use of latrine sites, and (B) it scats.

Identification of food items Each scat was washed with detergent, and after drying, the scat contents were processed through a fine meshed sieve (0.5 × 0.5 mm). All undigested parts (e.g. teeth, hair, bones, plant seeds, arthropod exoskeletons and body parts (e.g. wings, legs, eyes, ovipositor, head, thorax, abdomen) and shells were separated. All were dried at 35–40 degrees Celsius and each was again stored in a paper bag. Stones, sand and leaf litter in scats were recorded, but not included in data analysis.

289 138

All cleaned, undigested animal and plant remains were identified by comparing these items with voucher specimens of vertebrate, invertebrate and plant material in a previously established animal and plant collection (HKK-collection). Mammalian hair was identified to genus- and species-level by comparing it to a hair reference collection from carcasses of animals killed by tigers in HKK and from live animals at captive breeding centers. This reference collection consisted of 20 hairs from each of five different body locations (e.g. forehead, shoulder, haunch, dorsal and ventral parts of the torso). These hair samples were used to prepare a reference collection of permanent slides including cross-sections and medulla impressions. Hairs from scats were cross sectioned and mounted on permanent slides for morphological identification (Petdee, 2000). Measurement and indices used included hair color, size, scale and medullar patterns (Petdee, 2000; Brunner and Coman, 1974). For amphibians and reptiles, we compared skeletal material in scat to a previously prepared collection of skeletal material of species collected at Khao Nang Ram Research Station; for tortoises, the carapace and plastron were also used. Invertebrates were identified by using systematic keys of Aoki (2015) and material was also compared with voucher specimens in an extensive invertebrate collection at HKK.

Data analysis We estimated diet composition by using frequency of occurrence (FO). Each food class was calculated as the number of scats in which a prey class occurred divided by the total number of scats collected (Corbett, 1989; Jothish, 2011).

RESULTS Scats of the V. zibetha were collected every 1–2 weeks from 3 latrines and one scat was in a trap. We analyzed a total of 121 scats and identified undigested food remains from 4 main groups (Table 1; Appendix Table 1).

Table 1. Actual number of scats in which that food items was represented (n) and frequency of occurrence (FO%) of animal and plant remains in the V. zibetha scats.

Super class/class/subclass No. scats F(%): N=121 Mollusca 14 12 Gastropoda (forest land snails) 14 12 Arthropoda (6 Classes) 118 98 Arachnida (spiders, scorpions) 51 42 Crustacea 29 24 Insecta 115 95 Chilopoda (centipedes) 26 21 Diplopoda (millipedes) 22 18 Chordata (5 Classes) 112 92 Osteichthyes; (Class Actinopterygii: Fish) 11 9 Amphibia (frogs) 38 31 Reptilia (lizards, snakes, softshelled turtles) 76 63 Aves 21 17 Mammalia 99 82 Plantae 68 56

290 139

Plants were subdivided into a general category, grasses, which were found in 56% of all scats, and other flowering plants, which consisted of 15 groups comprising 12 genera. (Appendix 1). The most common species were Cassis fistula (21%) and Vitex sp. (10%; Table 1). Cassia fistula is a common forest tree species that produces abundant berries eaten by civets. We also identified fruits of four domesticated plants, banana, watermelon, chili and papaya, in civet scats. These fruits were commonly grown near ranger stations in HKK. Invertebrate remains included 5 major classes with the following frequency of occurrence: Insecta (95%), Arachnida (42%), Crustacea (24%), Chilopoda (21%) and Diplopoda (18%). We also noted that the V. zibetha regularly captured and consumed a significant number of poisonous or toxic invertebrates (e.g. scorpions; centipedes). ertebrate remains were also categorized into 5 prey classes: Actinopterygii, Amphibia, Reptilia, Aves and Mammalia. Mammals and reptiles were the dominant Chordata found in 82% and 52% of scats, respectively. The least common classes across all phyla were the land snail (Cyclophorid) and fishes (Osteichthyes) with 11% and 9% recorded, respectively (Appendix 1).

DISCUSSION Our results indicate that invertebrates and vertebrates were the major portions of the V. zibetha’s diet. Its diet is similar to that reported for the African civet, Civettictis civetta, (Amiard et al., 2015; Bekele et al., 2008) and from previous the V. zibetha studies (Simcharoen, 1990; Rabinowitz and Walker, 1991; Rabinowitz, 1991). Although, we did not observe foraging activity, radio-collared the V. zibetha were primarily active at night (Simcharoen, 1999) and reports by others (Timmins et al., 2016) describe this species as a ground forager as they are very rarely observed in trees. Further evidence that this civet is a nocturnal forager is that both its major vertebrate prey (snakes and rodents) and invertebrate prey (beetles, crickets and grasshoppers) are active in the leaf litter primarily at night (Hernández et al., 2002). Nocturnal foraging may also reduce detection by its main predators, medium and large-sized felids (Simcharoen, 2008). We also found hair of large mammals in the V. zibetha scats indicating that this species scavenges on carcasses of banteng, Bos javanicus; sambar, Rusa unicolor; muntjac, Muntiacus muntjak; and wild boar, Sus scrofa. Additional documentation of this behavior was observed in a video recording from a camera directed at a banteng carcass in HKK. In Khao Phaeng Ma non-hunting area, a ranger also photographed the V. zibetha capturing and eating fish along the edge of a pond. Global climate patterns may be altering soil moisture and other edaphic factors in the forests of South-East Asia. These changes cause physical stress to trees, reducing their survival, especially if the rate of seed dispersal does not correspond to the rate of climate change. However, for trees with a restricted seed shadow, civets and other mammals and birds may offer a mechanism to allow trees to shift their distribution rapidly enough to track changing environmental conditions (Herrera, 1989; Corlett, 1998; Jothish, 2011). Marino et al. (2009) proposed that use of latrines can disperse seeds considerably further than the normal seed shadow of many tree species. Civets and many other small carnivores and birds contribute to the spatial pattern of forest landscape structure and this role is of current interest as a mechanism by which many tree species in the tropics may be able to compensate for shifting ecological conditions resulting from climate change. For conservationists and protected area managers, protection of many less charismatic species like the V. zibetha is important to biodiversity conservation because its diverse diet places it in a highly linked, but poorly understood position in the ecosystem in which it resides. In conclusion, our results indicate that the V. zibetha has a diverse diet and, like many other civets, plays a multifunctional role as an omnivore, seed disperser and consumer

291 140 of significant diversity of arthropods and mammals. Its importance in each of these positions is poorly understood and very challenging to study (Corbett, 1989). Although the large indian civet is still distributed widely across its range, it is also sought after as an exotic source of meat and this threat to its conservation is likely to increase in the near future. The role of the V. zibetha as a bio-indicator of forest ecosystems is likely a productive topic to pursue with future research. This study result is important for several reasons: 1). Although listed as “least concern” by IUCN (Timmins et al., 2016), the V. zibetha is declining rapidly in some global locations due to habitat loss and hunting for meat. For example, in South China, this civet is absent presumed due to hunting pressure (Timmins et al., 2016). Furthermore, meat from this species, and its close relative the small indian civet (Viverricula indica) is regularly sold in “game restaurants” near protected areas in West Central Thailand (Simcharoen, 1999), despite protected status in this country. 2) This study result is also valuable because it was conducted in an undisturbed, natural landscape and used a reference collection and hair analysis methodology to identify prey species in scats collected at civet latrines. Thus, it is the largest and most thorough study of the V. zibetha diet to date. As conservation efforts seek to understand the impact of humans in changing natural landscapes, improved knowledge of the V. zibetha diet and its potential role as a bio-indicator of forest habitat dynamics (Baladrishnan and Mullu, 2014) is critical where populations of this species still remain robust.

Acknowledgements :We are grateful to Mr. Punya Suksomkit who assisted with field work. REFERENCES

Abiyu, A., Teketay, D., Glatzel, G. and Gratzer, G. 2015. Tree dispersal by African civets in the Afromntane highlands: too long a latrine to be effective for tree population dynamics. African Journal of Ecology 53 : 588-591. Amiard, P. J., Kruger, C. V.and Mullers, R. H. E. 2015. The diet of African Civet Civettictis civetta in two vegetation types of the Savannah biome in South Africa. Small Carniv. Conserv. 53 : 4–12. Aoki, J. 2015. Pictorial Keys to Soil Animals of Japan, 2nd edition. Tokai University Press, Hadano. Baladrishnan, M. and Mullu, D. 2014. Ecology of African Civet (Civettictis civetta) in Arba Minch Forest, Arba Minch, Ethiopia. Science, Technology and Arts Research Journal 3 : 99-102. Bekele, T., Bekele, A. and Balakrishnan, M. 2008. Feeding ecology of the African Civet Civettictis civetta in the Menagesha-Suba State Forest, Ethiopia. Small Carniv. Conserv. 39 : 19–24. Brunner, H. and Coman, B. J. 1974. The identification of mammalian hair. Inkata Press, Melbourne. Choudhury, A., Duckworth, J. W., Timmins, R., Chutipong, W., Willcox, D. H. A., Rahman, H., Ghimirey, Y. and Mudappa, D., 2015. Viverricula indica. The IUCN Red List of Threatened Species 2015: e.T41710A45220632. Retrieved from http://www. IUCN.org. Assessed on 8 April 2019 Corlett, R. T. 1989. Frugivory and seed dispersal by vertebrates in the Oriental (Indomalayan) Region. Biological Reviews 73 : 413-448. Corlett, R. T. 2002. Frugivory and seed dispersal in degraded tropical East Asian landscapes. In: Seed Dispersal and Frugivory: Ecology, Evolution and Conservation, ed. Levey, D.J. Silva, W. R. and Galetti. M. CABI publishing, New York, pp. 451–466. Hernández, L., Parmenter, R. R., Dewitt, J. W., Lightfoot, D. C. and Laundré, J. W. 2002. Coyote diets in the Chihuahuan Desert, more evidence for optimal foraging. Journal of Arid Environments 51 : 613-624. Herrera, C. M. 1989. Frugivory and seed dispersal by carnivorous mammals and associated fruit characteristics in undisturbed Mediterranean habitats. Oikos 55 : 250–262. Jothish, P. S. 2011. Diet of the Common Palm Civet Paradoxurus hermaphroditus in a rural habitat in Kerala, India, and its possible role in seed dispersal. Small Carniv. Conserv. 45 : 14–17. Lekakul, B. and McNeely, J. 1977. Mammals of Thailand. Kurusaphra Press, Laprao, Thailand. Marino, J., Mitchell, R. and Johnson, P. J. 2009. Dietary specialization and climatic-linked variations in extant populations of Ethiopian wolves. African Journal of Ecology 48 : 517-525.

292 141

Nakashima, Y., Inoue, E., Inoue-Murayama, M. and Abd Sukor, J. R. 2010. Functional uniqueness of a small carnivore as seed dispersal agents: a case study of the common palm civet in the Tabin Wildlife Reserve, Sabah, Malaysia. Oecologia 164 : 721–730. Petdee, A. 2000. Feeding habits of the tiger (Panthera tigris Linnaeus) in Huai Kha Khaeng Wildlife Sanctuary by fecal analysis. MSc Thesis (In Thai), Kasetsart University, Bangkok, Thailand. (Unpublished). Rabinowitz, A. R. 1991. Behaviour and movements of sympatric civet species in Huai Kha Khaeng Wildlife Sanctuary, Thailand. Journal of Zoology 223 : 281-298. Rabinowitz, A. R. and Walker, S. R. 1991. The carnivore community in a dry tropical orst mosaic in Huai Kha Khaeng Wildlife Sanctuary, Thailand. Journal of Tropical Ecology 7 : 37-47. Simcharoen, S. 1990. Study on home range and daily activity of civets: family Viverridae. MSc thesis (In Thai), Kasetsart University, Bangkok, Thailand. (Unpublished). Simcharoen, S., Boontawee, P. and Phetdee, A. 1999. Home range size, habitat utilization and daily activities of Large Indian Civet (Viverra zibetha). In Wildlife Research Division, 1999. Research and progress report year. Department of National Park, Bangkok, Thailand (In Thai), pp. 43–64. Timmins, R. J., Duckworth, J. W., Chutipong, W., Ghimirey, Y., Willcox, D. H. A., Rahman, H., Long, B. and Choudhury, A. 2016. Viverra zibetha. The IUCN Red List of Threatened Species 2016: e.T41709A45220429. Retrieved from http://www. IUCN.org. Assessed on on 2 April 2019

293