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Density estimation of Asian using photographic capture– recapture sampling based on chest marks

Dusit Ngoprasert1,5, David H. Reed2, Robert Steinmetz3, and George A. Gale4

1Conservation Ecology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, 10150, Thailand 2Department of Biology, The University of Louisville, Louisville, KY 40292, USA 3World Wide Fund for Nature–Thailand Office, Bangkok, 10900, Thailand 4Conservation Ecology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, 10150, Thailand

Abstract: Assessing the of of concern is greatly aided by unbiased estimates of population size. Population size is one of the primary parameters determining urgency of conservation action, and it provides baseline data against which to measure progress toward recovery. Asiatic bears ( thibetanus) and sun bears (Helarctos malayanus) are vulnerable to extinction, but no statistically rigorous population density estimates exist for wild bears of either species. We used a camera-based approach to estimate density of these sympatric species. First, we tested a technique to photograph bear chest marks using 3 camera traps mounted on trees facing each other in a triangular arrangement with bait in the center. Second, we developed criteria to identify individual sun bears and black bears based on chest-mark patterns and tested the level of congruence among 5 independent observers using a set of 234 photographs. Finally, we camera-trapped wild bears at 2 study areas (Khlong E-Tow, 33 km2, and Khlong Samor-Pun, 40 km2) in Khao Yai National Park, Thailand, and used chest marks to identify individual bears and thereby derive capture histories for bears of each species. Average congruence among observers’ identifications of individual bears was 78.4% for black bear and 92.9% for across sites. At Khlong E-Tow, we recorded 13 black bears (8 M, 4 F, 1 unknown sex) and 8 sun bears (1 M, 5 F, 2 unknown sex). At Khlong Samor-Pun, we recorded 10 black bears (6 M, 4 F) and 6 sun bears (4 M, 2 F). We used a spatially explicit capture–recapture method, resulting in density estimates of 8.0 (SE 5 3.04) and 5.8 (SE 5 2.31) black bears per 100 km2 and 5.9 (SE 5 3.07) and 4.3 (SE 5 2.32) sun bears per 100 km2 for each study area, respectively. Our camera trap design and chest-mark identification criteria can be used to estimate density of sun bears and black bears, enhancing knowledge of the conservation status of these threatened and little-known bear species.

Key words: Asiatic black bear, camera trapping, density estimation, Helarctos malayanus, Khao Yai National Park, spatially-explicit capture–recapture, sun bear, Thailand, Ursus thibetanus

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Estimates of population size provide important either species (International Union for Conservation information about extinction risk and can be used to of Nature 2010). indicate the effectiveness of management actions Capture–recapture techniques offer a powerful over time (O’Grady et al. 2004, Skalski et al. 2005). method for estimating abundance and have Asiatic black bears (Ursus thibetanus) and sun bears been frequently applied to bears. For example, (Helarctos malayanus) are sympatric in many areas population estimates of bears have been derived of Southeast Asia (Steinmetz 2011) and are similarly from resighting of radiocollared (Garshelis vulnerable to extinction, yet reliable abundance et al. 1999) and from camera-trapping to resight estimates do not exist anywhere in this region for bears marked with ear tags (Mace et al. 1994). In addition to these methods, which require initial [email protected] physical capture of bears for marking, noninvasive

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Fig. 1. Camera trap locations in Khlong E-Tow (KET) and Khlong Samor-Pun (KSP) study areas, Khao Yai National Park, Thailand, Dec 2009–May 2010. approaches such as hair-snaring to obtain DNA Study area from hair are increasingly used to estimate bear We conducted this study in Khao Yai National population size and density (Mowat and Strobeck Park (hereafter Khao Yai), which is the third largest 2000, Gardner et al. 2010, Proctor et al. 2010). national park in Thailand; it was designated a World Capture–recapture sampling using remote cameras Heritage site in 2005. Khao Yai (2,168 km2)isin is an effective technique to estimate abundance of central Thailand (N 14u 059–E 101u 509), with cryptic that have uniquely identifiable coat elevations from 100 to 1,350 m above sea level. patterns (Karanth 1995) and has been used to Annual precipitation ranges from 2,000 to 3,000 mm; estimate abundance of ( tigris; Kar- the dry period (Dec–Jan) averages ,15 mm of rain anth and Nichols 1998), snow (Uncia uncia; per month. From 1993 to 2002, average monthly Jackson et al. 2006), (Panthera onca; Silveira temperatures ranged from 21uC (Dec–Jan) to 32uC et al. 2009), ( pardalis; Trolle and (Apr–May), and -round average humidity was Kery 2003), and ( rufus; Heilbrun et al. 78.6% (range: 52.1–89.6%; Kitamura et al. 2005, 2003). Similarly, facial and pelage patterns of Andean Savini et al. 2008). The dominant vegetation was bears (Tremarctos ornatus) have recently been used to seasonal evergreen forest (also known as semi- identify individuals and thereby estimate abundance evergreen forest). (Rios-Uzeda et al. 2007). Asiatic black bears and sun We established 2 study areas, Khlong E-Tow bears have distinctive chest marks that may be useful (KET) and Khlong Samor-Pun (KSP), both in the for individual identification, but the efficacy and western portion of the park (Fig. 1). Elevations of reliability of such a method has not been explored. In camera locations ranged from 700 to 860 m (x¯ 5 this paper we (1) describe a camera trap technique to 771 m) in KET and from 373 to 561 m (x¯5 439 m) in obtain photos of bear chest marks, (2) develop criteria KSP. Khlong E-Tow was within old-growth season- to identify individual black bears and sun bears by al evergreen forest. Khlong Samor-Pun was a mosaic their chest marks, and (3) apply these techniques to of seasonal evergreen forest, regenerating forest, and estimate density of each species in a tropical forest. .

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Methods whether our small sample of captive bears (15 black Camera system bears and 11 sun bears) would reflect variation Chest marks of Asiatic black bears and sun bears among larger wild populations. Instead, we used the are only visible when bears are standing on their photographs resulting from our actual capture– hind legs (Fig. 2); chest marks are not visible when recapture field surveys to devise identification bears are walking or standing on all 4 legs. criteria. Therefore, we designed a baited, 3-camera arrange- We obtained numerous photos of the same bear in ment that enticed bears to stand on 2 legs while the wild because of our camera setup (cameras set on facing a camera. Prior to field study, we tested the burst mode) and because bears often spent consid- design at the Banglamung Wildlife Breeding Center erable time under the bait. This provided us with a in Thailand, which houses captive bears of both large dataset to determine which bear postures and species in semi-natural, large enclosures with trees. photo angles permitted unambiguous visibility of We used digital cameras with a white incandescent chest marks; where, on the chest mark, unique flash and a passive infrared trigger (Stealth Cam characters tended to occur; and how to focus our STC-I590, Stealth Cam, Grand Prairie, , USA) attention when examining photos. Three of the to photograph bears. We mounted 3 cameras on authors (D.N., R.S., and G.G.) together examined trees approximately 3–4 m apart and facing each all photographs and jointly agreed on individual other in a triangular arrangement (Fig. 3). We baited identifications from which a final capture history camera stations with 6 kg of meat, hung 2 m above was derived. the ground at the central position among the Upon examining a photograph, we first focused cameras. on major distinctions between marks, particularly Cameras were set on burst mode to take multiple the overall shape. For black bears the overall shape exposures of 6 sequential still pictures, with a 1- varied in terms of (1) thickness (i.e., vertical width) minute delay between triggering. Multiple exposures of chest marks (Fig. 2a versus 2b), (2) the degree of increased the likelihood that at least one photo tapering from the central portion towards the upper would capture the bear standing and facing one of tips of the mark (Fig. 2b versus 2i), (3) lengths of left the cameras. These initial trials showed that both and right upper tips of the mark (Fig. 2g versus 2j), bear species would indeed stand to attempt to obtain (4) arrangement of the 2 upper tips of the mark (i.e., the bait and that photos of their chest marks could straight, curving; Fig. 2b versus 2i), and (5) degree of thus be obtained. pointedness of the central portion of the mark After testing we used the baited, 3-camera (Fig. 2b versus 2i). For sun bears, the overall shape arrangement in the field. Camera traps were active varied in terms of (1) thickness of the entire chest 24 hours a day. We changed the bait, batteries, and mark (Fig. 2l versus 2r), (2) presence of a jagged memory card every 3 weeks, and used these 3-week (Fig. 2p) or smooth edge (Fig. 2s), and (3) the periods as sampling occasions for capture–recapture arrangement of the 2 tips of the U-shape mark analysis. When a camera was not functional for a (Fig. 2k versus 2o). Sun bears had additional major portion of each 3-week sampling period, we used the pattern variations not found in black bears last day the camera was working to calculate the (Figs. 2k–t): chest mark not contiguous, but sepa- number of trap-nights. Our intention was to make rated by black in the middle at the throat (Fig. 4a), the bait inaccessible but in many cases bears were or chest mark in a U-shape (Fig. 4b) or O-shape able to obtain the bait by pulling on suspension (Fig. 4c). ropes or climbing adjacent trees. After examining major features, we used more subtle distinctions; these tended to be plentiful but Individual identification from chest marks required more intensive scrutiny than major features. We familiarized ourselves with chest-mark varia- We visually divided the chest mark into 5 subsec- tion among bears using photos of individual captive tions, roughly equal in area: upper left tip, upper bears from the camera trap trials mentioned right tip, central bottom portion, and the 2 middle previously. Although we noticed substantial varia- sections on either side. We then looked for the tion in chest-mark patterns among captive bears, we following distinctive features in each subsection: did not attempt to develop a priori criteria for convexity, concavity, bumps and projections along distinguishing wild individuals, as we were uncertain mark edges (Figs. 4d and e); notches along mark

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Fig. 2. Examples of chest-mark patterns of 10 wild Asiatic black bears (a–j) and 10 wild sun bears (k–t) photographed Dec 2009–Feb 2010 and Mar–May 2010 in Khao Yai National Park, Thailand, using baited stations each with 3 camera traps for chest-mark detection.

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Fig. 3. Baited camera trap station configuration for chest-mark detection. edges (Fig. 4b); and black spots or blotches that without distinctive or unique patterns, we classified contrasted with the overall white or orange chest the photo as a non-capture and discarded it from mark (Fig. 4b). To examine these subtle features we further use (Fig. 5). enlarged photographs on a large computer screen. To assess the repeatability of individual bear Individual sun bear chest marks were easier to identifications, we assessed congruence of our photo differentiate than chest marks of black bears because identifications with those of 4 inexperienced and they were more variable. Black bears often required independent observers. D.N. briefly explained to greater enlargement of photos to see subtle differ- these observers the above methods to distinguish ences between otherwise similar-looking individuals bears; each observer then conducted identifications (Fig. 4d versus e). With practice even these subtle independently. There were 10,399 photos of bears differences became readily apparent in most cases, obtained from the field cameras, so it was imprac- resulting in agreement among us. tical to use the entire set of photos; 234 photographs The bear’s posture and angle relative to the (printed photographs and their corresponding digital camera affected the appearance of chest marks, images) of bears from the full data set were used to particularly which parts of the marks were visible. conduct the test. Photos were chosen for the The ideal position for identification was when the congruence test based on the criteria outlined above, bear faced the camera (180u), with front legs held at with bears standing in a suitable posture and facing a waist level (Fig. 2). Additionally, for sun bears, it camera. Each photo was given a unique identifica- was preferable to have the animal looking up, tion code so that they could be compared among because sun bear chest marks occur further up on observers. We asked observers to pool individual the neck or throat than in black bears. We used the photos of bears they believed to be the same following rule to reduce misidentification of bears: if individual into unique folders, such that each the angle or posture obscured all 5 subsections of the presumed individual bear had its own folder. chest mark, or if the visible subsections were those Observers were also instructed to place photos they

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Fig. 4. Close-up photos showing details of bear chest-mark pattern variation among Asiatic black bears and sun bears used for individual identification. Circle highlight unique features and the arrow notes the relative length of individual marks. did not feel confident in ascribing to a particular curred when each observer thought a photo be- individual into a separate folder. We refer to these longed to a different bear (Scenarios H, Table 1), or photos as uncertain. in combination with observer classification of photo Degree of congruence was calculated by compar- as uncertain (scenarios I–J, Table 1). Following ing the number of exact matches across observers for Kelly et al. (2008), we would not have used a photo a given photo (Table 1). Full agreement (100%) for assessing congruence if all observers were occurred when all 5 observers (the authors’ identi- uncertain about reliably ascribing a photo to an fication being considered as one observer) concluded individual bear (Scenario K); however, this did not that a photo belonged to the same individual bear occur during the actual congruence test. Each photo (Scenario A, Table 1) and partial agreement oc- was thus assessed for its percent congruence among curred when 2–4 observers agreed a photo belonged observers. We averaged these values to derive an to the same bear (scenarios B–G, Table 1). A overall percent congruence for each bear species at complete lack of congruence (0% agreement) oc- each study site.

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Fig. 5. Photograph in which the angle and posture of the bear obscured the chest mark or only revealed a partial view of a mark subsection that lacked distinctive characters. Such photos were considered non- captures and were not used for individual identification.

Density estimation modeled as a function of distance from the capture At each study site, we set camera stations in a locations to the animal’s activity center. The square grid, 1.5 to 2 km apart. We used this distance detection function has 2 parameters: g0 is the so that even bears with small home ranges (e.g., probability of detection at the activity center of an annual of sun bear is as small as 6 km2 animal, and sigma (s) is the spatial scale of in Borneo [Wong et al. 2004]) had access to multiple movement, which is derived from distances among stations. Nineteen baited camera trap stations were recapture locations of individual animals (Efford et set in KET during December 2009–February 2010 al. 2009). We tested if the detection function of bears (except occasion 1, where we only used 15 stations). followed a half-normal or negative exponential A polygon encompassing the outermost camera distribution. locations covered 33 km2. We set 18 camera-trap Detection models for each species were estimated stations in KSP during March–May 2010; the from pooled data of individual capture histories trapping polygon covered 40 km2. There were 3 from both study areas. We fit each detection model sampling occasions at each study site, each with a by maximizing the conditional likelihood (Borchers duration of 3 weeks. and Efford 2008). For each model, we tested a We used spatially explicit capture–recapture mod- different buffer radius (mask) around the trap grid eling (SECR) to estimate density of sun bears and following the suggestions of the ‘‘mask.check’’ tool. black bears using the ‘‘secr’’ package version 2.3.2 We compared 9 models for each species. We first (Efford 2011) in R software (R Development Core fit a null detection model with no covariates. We Team 2011). SECR fits a spatial model of detection then compared this null model with 8 models and a spatial model for the distribution of activity representing different sources of variation potential- centers. The detection probability in SECR is ly affecting detection probabilities: 2 models with

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Table 1. Demonstration of the calculation of percent agreement among observers identifying bears by their chest marks. The table shows 11 possible scenarios in which there were various degrees of congruence among 5 independent observers identifying the number of bear individuals in 11 bear photos. Letters (B1–B6) refer to presumed individual bears and ‘‘uncertain’’ means that the observer could not confidently assign a photo to an individual bear. Observer 1 classified photos 1–10 as belonging to 2 different bears, whereas observers 2–4 classified the photos to 3 individual bears, and observer 5 to 4 bears. Observers 1–4 classified photos 1 and 2 as representing bear B1, whereas all observers except observer 3 indicated photo 3 also belonged to B1, and so on. Photos categorized as uncertain by all observers were not used to assess agreement (photo 11). Observer Scenario Photo ID 123 4 5% Agreement A 1 B1 B1 B1 B1 B1 100 B 2 B1 B1 B1 B1 B2 80 C 3 B1 B1 uncertain B1 B1 80 D 4 B1 uncertain B1 B1 uncertain 60 E 5 B1 B1 B1 B2 B2 60 F 6 B2 B2 uncertain uncertain uncertain 40 G 7 B2 B2 B3 uncertain uncertain 40 H 8 B2 B3 B4 B5 B6 0 I 9 B2 uncertain uncertain uncertain B3 0 J 10 B2 uncertain uncertain uncertain uncertain 0 K 11 uncertain uncertain uncertain uncertain uncertain not used

study site (site; KET, KSP) as a covariate, 3 models when candidate models had a DAICc , 2. In these with behavioral responses after first detection cases the average effective sampling areas and (learned response [b], learned trap [bk] and avoid- average standard errors of effective sampling areas ance [Bk]), a model in which capture probabilities were calculated using the delta method (Efford varied with capture occasion (t), a model with 2011). We compared the precision of density individual capture heterogeneity, and a model estimates among species and study areas using the representing possible heterogeneity because of cam- coefficient of variation (CV). era failure. The learned response model (b, g0,b, s,1) is equivalent to model Mb in the classical capture–recapture approach and assumes an overall, Simulation study non-spatial response to traps (i.e., capture probabil- We conducted a simulation study to evaluate the ity changes after the first encounter). In SECR, efficacy of our baited camera-trap capture–recapture spatial locations of traps are considered as well, method for monitoring bear populations. We de- which allows station-specific behavioral responses. If fined a coefficient of variation ,20% as a threshold bears were more likely to return to particular camera for assessing whether resulting data were reliable stations after first detection, we considered this a for monitoring purposes (Pollock et al. 1990). We learned trap response (bk). Likewise, if bears were simulated a trap array of 36 trap stations (6 x 6 trap less likely to return to particular baited stations after stations) with 3 levels of spacing between traps that first detection, we considered this trap avoidance were representative of the study areas: 2,500 m, (Bk). Cameras sometimes malfunctioned or the 3,000 m, and 4,000 m. The initial parameters (g0 and memory card filled-up, and we examined the effect s) were determined from our pooled data using of camera malfunction using a post-hoc model. For individual capture histories from both species of bear this final model, we treated the total number of trap and both of our study areas combined to maximize nights at a bait station as a site-specific covariate sample size. Species-specific and site-specific detec- (trap nights model). tion functions for black bear and sun bear were not We used Akaike’s Information Criterion corrected supported compared with detection based on pooled for small samples (AICc) to identify the most capture histories (AICc weight had approximately supported models (Akaike 1973). We used the best 7.6 times more support than models for separate model to estimate density for each species at each species and sites). Capture probability (g0)is site. We used model averaging to estimate density recommended to be .0.2 to ensure reliable estimates

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was 19.5 (SE 5 0.34, n 5 321 [stations x cameras x occasions]). Thus, most cameras worked for most of each 3-week occasion.

Individual identification from chest marks Only 234 of 10,399 photos were usable for analysis (2.3%), including 127 photos of black bears and 35 photos of sun bears in KET, and 36 photos of black bears and 36 photos of sun bears in KSP. These photos were used as the basis for the congruence test among independent observers. The number of black bears identified by the authors and independent observers ranged from 12 to 14 individuals in KET Fig. 6. The average percent congruence among 5 and from 10 to 11 in KSP. Identified sun bears observers based on photographs (n = 234 photos) of ranged from 7 to 10 individuals at KET; all wild bears at Khlong E-Tow (KET; Dec 2009–Feb observers identified 6 individuals at KSP. The 2010) and Khlong Samor-Pun (KSP; Mar–May 2010) in Khao Yai National Park, Thailand. average congruence among observers based on individual photos was 85.6% across the 2 sites and the 2 species. Congruence in observers’ identifica- (Boulanger et al. 2004). We therefore restricted our tions was 10–20% higher for sun bears than black simulation to g0 values of 0.25, 0.30 and 0.35. We bears (KET: black bear 5 78.4%, sun bear 5 88.0%; used a s value of 2,240 m from our pooled individual KSP: black bear 5 78.3%, and sun bear 5 97.8%; capture histories as a fixed parameter. We limited the Fig. 6). The average percent of uncertain photos number of sample occasions to 3, 4, or 5 based on across all observers, sites, and species was 9.9% what we believed to be realistic cost and time (Table 2). We observed a greater classification constraints. We used 3 levels of bear density as uncertainty for black bears (15.5% of all photos) starting parameters, based on our estimates of 3 than for sun bears (4%; Table 2). bears/100 km2, 5 bears/100 km2, and 8 bears/100 km2. Simulations were fitted using half-normal detection Density estimation functions with assumed Poisson distributions of We identified 13 adult black bears (8 M, 4 F, 1 camera traps. We used CV and standard errors of unknown sex) and 8 adult sun bears (1 M, 5 F, 2 CV to summarize simulation results based on 100 unknown sex) at KET. Five black bears and 5 sun replicates. bears were recaptured at least once. The maximum number of stations at which each species was detected at KET was 6 for black bears (2 individuals) Results and 4 for sun bears (1 individual). At KSP we We obtained 4,246 photos of black bears and 349 identified 10 adult black bears (6 M, 4 F) and 6 adult photos of sun bears in KET, and 5,234 photos of sun bears (4 M, 2 F). Four black bears and 2 sun black bears and 570 photos of sun bears in KSP. The bears were recaptured at least once. The number of majority of photos were of bears walking into and recaptured bears in KSP was constant after the around the baited area and rolling on the ground second occasion (Table 3). The maximum number of underneath the bait or rubbing on the bait. At KET stations at which each species was captured at KSP we photographed black bears at 16 stations (84% of was 3 for black bear (1 individual) and 3 for sun bear the KET stations) and sun bears at 9 stations (47%). (1 individual). The number of bears captured on the At KSP black bears were photographed at 8 stations third occasion increased for both species compared (44%) and sun bears at 9 stations (50%). Camera to the first and second occasions (Table 3). Com- malfunction was uncommon during the study: 8.1% bining both study sites, 61% of all black bears and of cameras malfunctioned during the first capture 43% of sun bears were never recaptured. occasion, 9.0% during the second occasion, and For black bears, a half-normal detection function 9.0% during the third occasion. The average number had 1.12 times more support than a negative of trap nights of operating cameras at each station exponential function. Learned trap (g0,bk) and

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Table 2. Percent of photos (n = 234 photos) that observers could not confidently assign to an individual bear (‘‘uncertain’’ photos), from bears in the wild in Khlong E-Tow (KET; Dec 2009–Feb 2010) and Khlong Samor- Pun (KSP; Mar–May 2010) in Khao Yai National Park, Thailand. Percentage of photos which were uncertain Asiatic black bear Sun bear Observers KET KSP KET KSP Avg % 1 8.7 5.6 5.7 0.0 5.0 2 7.1 2.8 0.0 0.0 2.5 3 19.7 19.4 5.7 2.8 11.9 4 31.5 25.0 8.6 5.6 17.7 5 15.7 19.4 11.4 2.8 12.3 Avg % 16.5 14.4 6.3 2.2 9.9

avoidance models (g0,Bk) were equally supported covariate had little support (Table 4). We model- (DAICc 5 1.11), suggesting a behavioral response to averaged results from the behavior response models our traps; however, we had too few capture (bk and Bk) to estimate the density of black bears. occasions to determine the direction of the response. We could not obtain estimates based on the The site model (g0,Site, s,1) had 62 times less individual heterogeneity model because it failed to support (DAICc 5 7.93). Capture probability under converge. the g0,bk model was 0.18 (95% CI 5 0.09–0.31), For sun bears, the half-normal detection function and estimated scale of movement (s) was 2,603 m was better supported (1.84 times) than the negative (95% CI 5 1,810–3,744 m). Capture probability exponential function. The learned trap (g0,bk) under the trap-specific avoidance model (g0,Bk) model had 4.3 times more support than the next- was 0.19 (95% CI 5 0.10–0.32), and estimated s was best model (null model). Capture probability for the 2,567 m (95% CI 5 1,801–3,661 m). The time- learned response model was 0.10 (95% CI 5 0.03– varying model and the model with trap-nights as a 0.25) and estimated scale of movement was 2,745 m

Table 3. Capture–recapture statistics for Asiatic black bear and sun bear obtained from chest marks in the Khlong E-Tow (KET; Dec 2009–Feb 2010) and Khlong Samor-Pun (KSP; Mar–May 2010) sections of Khao Yai National Park, Thailand. n = individuals detected on each occasion; u = individuals detected for the first time on each occasion; f = individuals detected exactly f times; M(t+1) cumulative number of individuals detected; detections = number of detections, including within-occasion recaptures; bait stations visited = stations at which at least one detection was recorded. nt, ut, and M(t+1) are always equivalent on Occasion 1. Khlong E-Tow Khlong Samor-Pun Occasion Occasion Numbers of individual bears 1231 2 3 Asiatic black bear n 4798 6 1 u 4458 2 0 f 8326 3 1 M(t+1) 4 8 13 8 10 10 Detections 8 12 17 9 7 2 Bait stations visited 8 9 12 5 6 2 Bait stations used 15 19 19 18 18 18 Sun bear n 3382 6 1 u 3232 4 0 f 3413 3 0 M(t+1) 3582 6 6 Detections 4482 8 2 Bait stations visited 4362 8 2 Bait stations available 15 19 19 18 18 18

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Table 4. Models from spatially explicit capture–recapture methods for Asiatic black bear and sun bear in Khlong E-Tow (KET; Dec 2009–Feb 2010) and Khlong Samor-Pun (KSP; Mar–May 2010) in Khao Yai National Park, Thailand. DAICc is the absolute difference in the Akaike’s information criterion (AIC) value adjusted for small sample sizes (AICc) between the best-fit model and the model under consideration, wi is the Akaike weight, which provides a measure of relative support for each model. g0 = Probability of detection at the activity center of an animal, s = spatial scale of movement following Efford et al. (2009).

Species Model name Model Number of parameters D AICc wi

Asiatic black bear Learned trap (bk) g0,bk, s,1 3 0.00 0.62 Avoidance (Bk) g0,Bk, s,1 3 1.11 0.36 Site variation1 g0,site, s,1 3 7.93 0.01 Null model g0,1, s,1 2 8.44 0.01 Site variation 2 g0,site, s,site 4 10.14 0.00 Trap nights g0,trap, s,1 3 10.74 0.00 Learned response (b) g0,b, s,1 3 10.86 0.00 Time factor (t) g0,t, s,1 4 14.01 0.00 Sun bear Learned trap (bk) g0,bk, s,1 3 0.00 0.60 Null model g0,1, s,1 2 2.90 0.14 Learned response (b) g0,b, s,1 3 2.96 0.14 Avoidance (Bk) g0,Bk, s,1 3 4.72 0.06 Site variation1 g0,site, s,1 3 6.06 0.03 Trap nights g0,trap, s,1 3 6.09 0.03 Time factor (t) g0,t, s,1 4 8.68 0.01 Site variation 2 g0,site, s,site 4 9.51 0.01

(95% CI 5 1,519–4,962 m). Capture probability Discussion under the null model was 0.19 (95% CI 5 0.08–0.38), We found that Asiatic black bear and sun bear can and estimated scale of movement was 2,270 m (95% be individually identified from chest marks with CI 5 1,506–3,424 m). reasonable accuracy. Our baited camera trap design Density estimates for KET were 8.0 black bears/ effectively attracted bears and encouraged animals 2 100 km (95% CI 5 3.9–16.5) and 5.9 sun bears/ to stand upright and expose their chest marks, 2 100 km (95% CI 5 2.3–15.4). Density estimates for allowing for capture–recapture density estimation. 2 KSP were 5.8 black bears/100 km (95% CI 5 2.7– Importantly, we were able to obtain data on 2 12.3) and 4.3 sun bears/100 km (95% CI 5 1.6– sympatric sun bears and black bears in the same 11.6). CVs of the density estimates for KET were study, providing the first density estimates for either similar to KSP (KET: black bear 5 0.38, sun bear 5 species in Southeast Asia. 0.52; KSP: black bear 5 0.40, sun bear 5 0.54). Our inter-observer congruence test showed that most photographs obtained from a sample of wild Simulation bears were classified similarly by different observers. Under the low-density assumptions (3.0 bears/ Although we do not know the true number of bears 100 km2), no models had an estimated CV , 0.20 in our sample, the high congruence obtained, even (Table 5). At intermediate densities (5 bears/100 km2) by inexperienced observers trained to recognize the and g0 5 0.30, at least 4 sample occasions were criteria we developed, suggests a high degree of needed to reach a CV , 0.20 and 5 sample occasions repeatability of this method. Black bears were more were needed when g0 , 0.30. Our best scenario for difficult to distinguish in our sample because they monitoring at intermediate densities was with 4 had more subtle variation in their chest marks than occasions, g0 5 0.35, and a spacing among bait sun bears and because there were apparently more stations of 4,000 m. Only 3 occasions were required individuals. Percent congruence among observers 2 when bear density was high (8 bears/100 km )atg0 was 14.6% lower for black bears than with sun bears. 5 0.25 and station spacing of 3,000 or 4,000 m, but Additionally, the percent of photos considered the CV estimates just exceeded our monitoring uncertain among observers was nearly 4 times benchmark (CV 5 0.21). The best scenario for greater for black bears (Table 2), reflecting observ- monitoring at high density was for g0 . 0.30 with a ers’ decreased confidence in identifying black bears. station spacing of 4,000 m, when only 3 sample Full frontal photos of chest marks were best for occasions were needed to achieve the target CV. identifying individuals. We learned to be cautious

Ursus 23(2):117–133 (2012) 128 DENSITY ESTIMATION OF ASIATIC BLACK BEAR AND SUN BEAR N Ngoprasert et al.

Table 5. Coefficient of variation (CV) and standard error (SE) of CV for simulations of spatially explicit capture–recapture models based on 100 replicates for Asiatic black bear and sun bear in Khlong E-Tow (KET; Dec 2009–Feb 2010) and Khlong Samor-Pun (KSP; Mar–May 2010) in Khao Yai National Park, Thailand. g0 is the probability of detection at the activity center of an animal. Note that a CV ,0.20 is considered adequate for long-term monitoring. Occasions 345 Density/ Camera 2 100 km g0 spacing (m) CV SE CV SE CV SE 3 0.25 2500 0.41 0.012 0.43 0.070 0.36 0.010 3000 0.37 0.008 0.32 0.005 0.30 0.006 4000 0.41 0.036 0.30 0.007 0.26 0.004 0.30 2500 0.40 0.012 0.34 0.006 0.33 0.006 3000 0.34 0.006 0.31 0.006 0.29 0.004 4000 0.34 0.016 0.27 0.005 0.24 0.003 0.35 2500 0.39 0.018 0.34 0.007 0.33 0.005 3000 0.33 0.006 0.29 0.005 0.28 0.004 4000 0.29 0.005 0.25 0.004 0.23 0.003 5 0.25 2500 0.31 0.005 0.27 0.003 0.26 0.003 3000 0.28 0.005 0.24 0.003 0.22 0.002 4000 0.28 0.006 0.22 0.004 0.20 0.002 0.30 2500 0.29 0.005 0.26 0.004 0.25 0.004 3000 0.25 0.003 0.23 0.003 0.22 0.002 4000 0.23 0.003 0.20 0.002 0.18 0.002 0.35 2500 0.29 0.005 0.26 0.003 0.24 0.004 3000 0.25 0.003 0.23 0.002 0.22 0.003 4000 0.22 0.003 0.19 0.002 0.18 0.002 8 0.25 2500 0.23 0.003 0.21 0.002 0.20 0.002 3000 0.21 0.002 0.19 0.002 0.18 0.002 4000 0.21 0.003 0.17 0.001 0.15 0.001 0.30 2500 0.22 0.003 0.20 0.002 0.19 0.002 3000 0.20 0.002 0.18 0.002 0.17 0.002 4000 0.18 0.002 0.16 0.001 0.14 0.001 0.35 2500 0.22 0.002 0.20 0.002 0.19 0.002 3000 0.19 0.002 0.18 0.002 0.17 0.002 4000 0.17 0.001 0.15 0.001 0.14 0.001 with photos that captured chest marks from even a not used for initial captures). One bear had slight angle, because photos of the same individual distinctive scarring on the top of the head which from different angles could initially appear to be did not fade during the short survey period. The different animals. We reduced this source of bias by other bear was missing its right front paw. However, only using photos that revealed all, or at least the use of scars may not be appropriate for longer- distinctive portions, of bear chest marks, while also term open population studies, as scars can heal and portraying the bear in a posture that did not distort fade. Likewise, missing limbs may not be reliable as a the shape of marks (with forelegs down at the sides). distinguishing characteristic in many places in Asia Consequently, we considered only a small percent of because snaring is common and bears that have lost available photos to be usable. Ideally, the repeat- limbs are commonly observed in the wild (Hwang et ability of the criteria we used to identify individual al. 2010; G. Fredriksson, Institute of Biodiversity bears should be verified through comparison with and Dynamics, University of Amster- another, independent method. For example, Ma- dam, Netherlands, personal communication, 2011). goun et al. (2011) compared (Gulo gulo) Our hanging baits were often obtainable by bears; photos with DNA samples of the same individual such reward bait may induce a learned response to animals. the trap (Tredick and Vaughan 2009, Proctor et al. Two black bear individuals had unique marks in 2010). Indeed, for both bear species, models that addition to their chest marks, which were helpful for incorporated behavioral responses (trap-specific assigning recaptures (these secondary marks were learned response or trap-specific avoidance) were

Ursus 23(2):117–133 (2012) DENSITY ESTIMATION OF ASIATIC BLACK BEAR AND SUN BEAR N Ngoprasert et al. 129 among the top models (Table 4). However, we had Camera trap malfunction occurred infrequently too few occasions to determine the nature of the (,9% per occasion) and at consistent rates across behavioral response (attraction or avoidance). In- occasions. Accordingly, the trap malfunction model deed, capture histories of black bears at KET was not supported by our data. Thus, camera failure suggested that bears were more likely to return to probably did not reduce capture probabilities. In our particular baited stations after first detection, with study, logistical constraints prevented us from re- increasing n over time (Table 3), whereas at KSP baiting frequently, which may have limited initial bears had the opposite response, with n declining captures of new animals and reduced recapture rates. over time. Additional capture occasions would Indeed, about half of black bears and sun bears were provide a better understanding of behavioral effects never recaptured. More frequent baiting may in- on capture probability and density estimates. crease recapture rates and thereby improve preci- Capture heterogeneity among individual bears in sion. our population was likely, as it is ubiquitous in bear To our knowledge, this study provides the first populations but can be difficult to detect with small density estimates based on a capture–recapture samples and low capture probabilities (Proctor et al. framework for either bear species in Asia, so there 2010). Indeed, our heterogeneity model failed to are no existing studies for comparison. Estimated converge. Capture heterogeneity likely biased our densities of both species in Khao Yai were ,8/ density estimates low (Noyce et al. 2001). Another 100 km2. Compared with other bear species, this potential source of heterogeneity in our study may density is extremely low: it is comparable to the have arisen from sampling areas that were small lowest densities recorded for American black bears relative to bear home ranges (Harmsen et al. 2011). (Ursus americanus; 6/100 km2, LeCount 1987; 9/ Although geographic closure is not assumed under 100 km2, Miller et al. 1987) and is 3 times lower than the spatially explicit framework, lack of geographic the median density for that species in closure would reduce recapture rates, introduce (25/100 km2; Garshelis 2011). Asiatic black bears capture heterogeneity, and reduce precision of density and sun bears have been poached heavily in Asia for estimates (Proctor et al. 2010). However, even if the their commercially valuable body parts (Bhiksu et al. size of the study area introduced heterogeneity into 2002, Shepherd and Nijman 2008, Foley et al. 2011), our black bear density estimation, we expected this to so their population densities are reduced from be less of a concern for the sun bear estimates because natural or historical levels. Even so, based on our our trapping area was 2–3 times larger than the observations of the abundance of bear sign and average home range of sun bears documented in frequency of direct sightings in numerous protected Borneo (Wong et al. 2004). Other causes of hetero- areas in mainland Southeast Asia, we believe that geneity could be due to social and densities of sun bears and Asiatic black bears in intraspecific avoidance. We note that many bears Khao Yai are relatively high compared with many rolled at baited sites, thus probably leaving scent; this other areas. Estimated densities of Asiatic black bear may have attracted or repelled other individuals were about 1.4 times greater than sun bear at both depending on their social status (Clapham et al. 2012). study sites, though these differences were not Time of year and natural food availability statistically different. A park-wide sign survey in influences animal visitation rates at bait stations 2008 in Khao Yai found black bear sign abundance (Koerth and Kroll 2000, Clark et al. 2005). Our field was 5 times greater than sun bear (Ngoprasert et al. work at both sites coincided with a natural period of 2011), and a 4-year park-wide camera trap study low food availability in Khao Yai (Brockelman et al. (2003–07) detected black bears 1.9 times more 2011, Ngoprasert et al. 2011). More work is needed frequently than sun bears (Jenks et al. 2011). to assess the attractiveness of baits in relation to Although these 3 studies indicate the same trend, natural food availability to determine how season we currently do not have sufficient evidence to affects detection probability and resulting popula- conclude that black bear occur at higher densities tion estimates. We found no indication that bear than sun bear. detections varied with time (Table 4), but we do not We were able to identify the sex of 92% of the know the effect of the natural food supply on individually identified bears in our sample, because detection rates because our study did not encompass identifiable bears by definition were standing and periods with high and low levels of food availability. facing a camera, which exposed their genitals. Also,

Ursus 23(2):117–133 (2012) 130 DENSITY ESTIMATION OF ASIATIC BLACK BEAR AND SUN BEAR N Ngoprasert et al. for both species, rubbing behavior on the ground to determine spacing among camera stations because below baits aided in sex determination because the bear movement patterns can vary greatly among genital area was often exposed. This allowed us to areas (Mowat et al. 2005, Obbard et al. 2010, estimate the sex ratio, which is useful for assessing Proctor et al. 2010). Also, appropriate spacing is the representativeness of bear population data critical to obtaining recaptures at multiple trap (Garshelis 2011). The sex ratio of black bears in stations (Dillon and Kelly 2007), which is critical our combined sample was skewed towards males (14 for determining the scale of movement and activity M:8 F), whereas for sun bears this ratio was slightly centers that SECR uses to estimate density. Expect- female dominated (5 M:7 F). Although admittedly edly, our simulations showed increasing precision for based on a small samples size, the skewed sex ratio the same design as density increased. for black bears may suggest (1) uneven sex ratios in Our camera trapping method could provide useful the living population, (2) unequal capture probabil- information on reproduction. We obtained photos of ities, or (3) wider-ranging movements by the more 2 female black bears with 2 cubs each and 1 female frequently captured sex (Garshelis 2011). The latter 2 sun bear with 1 cub. The baited stations induced possibilities are likely in our study. Female Asiatic these bear families to remain in front of cameras, black bears sometimes avoid adult males (Huygens allowing observation of minimum litter size. The and Hayashi 2001, Hwang et al. 2010). Also, males proportion of adult females with cubs recorded by tend to have larger home ranges and range more camera traps also has the potential to be used as an widely, resulting in males encountering cameras indicator of population health (Keating et al. 2002). more frequently than females. Our trap array Our study indicates the baited camera-trap station probably covered an area smaller than typical home we designed can be used to obtain density estimates ranges of male Asiatic black bears. This lack of of coexisting bear species in a tropical forest. With geographic closure for the wider-ranging sex would larger sample sizes and sample areas to improve lead to a male-biased sex ratio (Garshelis 2011). precision, this method may be useful for monitoring Future studies could increase success of sex identi- trends in bear abundance or density. The method fication, perhaps with design innovations such as could also be used to monitor movements of used for (Magoun et al. 2011). Adding individual bears between patches in a population. sex as an individual covariate may also address This information would greatly enhance our knowl- additional sources of capture heterogeneity (Gardner edge of the conservation status, ecology, and et al. 2010). We did not use sex as a covariate demography of these threatened and little-known because our sample sizes were too small (M. Efford, bear species. University of Otago, Dunedin, New Zealand, personal communication, 2011). Our estimates had low precision and currently Acknowledgments have limited value for monitoring purposes; howev- This paper is dedicated to the memory of David er, our simulation did suggest several methods for H. Reed. Special thanks to H. Lee, Director of improving the precision of future studies. The Conservation Genome Resource Bank for Korean simulation indicated that areas of low bear density Wildlife, who provided the initial financial support would present significant challenges for long-term for this research. This work was also supported by monitoring. For such areas, increases in capture King Mongkut’s University of Technology Thon- probability will be key to improving the precision of buri, the National Research Council of Thailand, density estimation (Proctor et al. 2010, Harmsen the International Association for Bear Research and et al. 2011), particularly when the number of sample Management (IBA Research and Conservation occasions cannot be increased because of cost Grants), Office of the Thai Higher Education constraints (Sharma et al. 2010) or violation of Commission (Strategic Scholarships Fellowships population closure assumptions (Proctor et al. 2010). Frontier Research Networks), and Thailand Re- Our simulations also indicated our baited camera search Fund and National Center for Genetic stations were not sufficiently spaced. A spacing of Engineering and Biotechnology (TRF/BIOTEC Spe- 4,000 m with a minimum of 4 sample occasions cial Program for Biodiversity Research and Training would have produced more precise density estimates. grant BRT R351138). The authors thank the We suggest researchers conduct a preliminary survey following individuals and institutions for facilitating

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