Identifying Regions for Conservation of Sloth Bears Through Occupancy Modelling in North-Eastern Karnataka, India

Identifying regions for conservation of sloth bears through occupancy modelling in north-eastern Karnataka, India

Sayantan Das1, Saurav Dutta2, Sharmi Sen3, Jijumon A. S.2, S. Babu4, Honnavalli Nagraj Kumara4, and Mewa Singh1,5,6

1Biopsychology Laboratory and Institution of Excellence, University of Mysore, Manasagangotri, Mysore-570006, Karnataka, India 2Indian Institute of Science Education and Research-Kolkata, Mohanpur Campus, Post: BCKV Main Office, Mohanpur, Nadia-741252, West Bengal, India 3Indian Institute of Science Education and Research-Mohali, Knowledge City, Sector 81, SAS Nagar, Manauli-140306, Punjab, India 4Sa´lim Ali Centre for Ornithology and Natural History, Anaikatty, Coimbatore-641108, Tamil Nadu, India 5Organismal Biology Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, Karnataka, India

Abstract: In the absence of information on species in decline with contracting ranges, management should emphasize remaining populations and protection of their habitats. Threatened by anthropogenic pressure including habitat degradation and loss, sloth bears (Melursus ursinus) in India have become limited in range, habitat, and population size. We identified ecological and anthropogenic determinants of occurrence within an occupancy framework to evaluate habitat suitability of non-protected regions (with sloth bears) in northeastern Karnataka, India. We employed a systematic sampling methodology to yield presence–absence data to examine a priori hypotheses of determinants that affected occupancy. These covariates were broadly classified as habitat or anthropogenic factors. Mean number of termite mounds and trees positively influenced sloth bear occupancy, and grazing pressure expounded by mean number of livestock dung affected it negatively. Also, mean percentage of shrub coverage had no impact on bear inhabitance. The best fitting model further predicted habitats in Bukkasagara, Agoli, and Benakal reserved forests to have 38%,75%, and 88%, respectively, of their sampled grid cells with high occupancies (.0.70) albeit little or no legal protection. We recommend a conservation strategy that includes protection of vegetation stand- structure, maintenance of soil moisture, and enrichment of habitat for the long-term welfare of this species.

Key words: conservation, distribution, ecology, India, Karnataka, Melursus ursinus, occupancy modelling, sloth bears

DOI: 10.2192/URSUS-D-14-00008.1 Ursus 25(2):111–120 (2014)

Conservation and management policies and prac- et al. 2007, Krishna et al. 2008, Boyd and Foody tices directed at animal species should ideally be 2011, Nandy et al. 2012). However, occurrence or developed from complete information on demo- abundance of species might not be a veritable output graphic characteristics, behavioral patterns, life- of the relationship between a species and its habitat, history traits, and habitat use (Margules and Pressey because occurrence and abundance can be an 2000). Habitat use by a species, and the factors that imperfect surrogate for demographic performance influence habitat use, should in turn be evaluated (Van Horne 1983, Hobbs and Hanley 1990, Tyre using rigorous statistical and modelling techniques. et al. 2001). Therefore, patterns of occupancy along With recent advances in geo-spatial tools and with occupancy correlates can only determine modelling techniques, predicting species occurrence, habitat suitability and its capacity to support distribution, and abundance has become common populations. This functional link between a species’ (e.g., Scott et al. 2001, Pearce and Boyce 2006, Best occupancy and the environmental factors influencing it is critical for understanding habitat processes that 6email: [email protected] can be used to guide management (Franklin et al.

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2000, Breininger and Carter 2003). This exercise is of the species with respect to feeding, hiding, and even more important for generalist species that occur other requisites. This information is crucial to primarily outside protected areas and are involved in identify suitable habitats for conservation and conflicts with humans, including human casualties. development of management plans. Our objective The sloth bear (Melursus ursinus) in India serves as was to estimate occupancy of sloth bears and assess a model species under these criteria. Because of the potential effects of environmental and anthro- resource extraction, habitat fragmentation, habitat pogenic determinants such that we can establish loss, and high conflict across their range in drier potential suitability of habitats and demarcate them regions in central (Johnsingh 1986, Servheen 1990, for long-term conservation of the species. Chauhan et al. 1999, Rajpurohit and Krausman 2000, Bargali 2004), western, and southeastern (Krishna Raju et al. 1987) India, Garshelis et al. Study area (1999b:234–235, 2008) appealed for a concerted We conducted the study in 6 sites in the revenue action to ‘‘expand and update’’ distribution maps districts of Hospet and Koppal, specifically Daroji of the species in ‘‘relation to forest cover and Wildlife Sanctuary (WLS; 15u169N, 76u369E; 5,083 ha), boundaries of protected areas.’’ Such an action is Bukkasagara Reserve Forest (RF; 15u199N, 76u339E; imperative ‘‘to delineate discrete population units’’ 3,169 ha), Daroji RF (15u149N, 76u409E; 465 ha), coupled with information on ‘‘land use and land Toranagallu RF (15u129N, 76u419E; 547 ha), Agoli conditions’’ in regions outside the protected area RF (15u289N, 76u239E; 2,796 ha), and Benakal RF network to assess their potential for supporting (15u269N, 76u239E; 3,864 ha; Fig. 1). We selected these viable populations (Garshelis et al. 2008). areas based on their close proximity to each other The sloth bear is endemic to the Indian subcon- and unpublished reports of sloth bear presence and tinent (Erdbrink 1953, Sathyakumar et al. 2012), incidences of conflicts with humans. Only Daroji WLS with a historical distribution from the foothills of the and a non-demarcated 2,685.50 ha of Bukkasagara Himalayas in northern India to the dry slopes of the RF is ‘‘legally protected’’ for wildlife, whereas the Western Ghats in the south (Bargali et al. 2004). others are reserved forests located close to villages and However, sloth bear populations are currently are open to resource extraction, including timber limited to 5 regions in India: northern, northeastern, harvest, sand-mining, and livestock grazing (Kiran central, southeastern, and southwestern populations 2011). Daroji WLS is separated from Bukkasagara (Garshelis at al. 1999b, Johnsingh 2003, Yoganand RF by a road and village settlements along its et al. 2006, Sathyakumar et al. 2012). This drastic northern boundary but is contiguous with Daroji range contraction has rendered the species Vulner- RF and Toranagallu RF along its southeastern able to Extinction (IUCN 2013) and led to its border. Six kilometers northwest of Bukkasagara RF inclusion in Schedule I of the Indian Wildlife is Agoli RF and Benakal RF, which are divided by a (Protection) Act as amended in 2003 (GOI 1972, state highway. The habitat is largely rocky and 2003). Currently, only about 10% of the species’ bouldery; dry deciduous scrub and southern thorn current distribution in India contains high-quality forests (Champion and Seth 1968) are the only forest habitat (Yoganand et al. 2006). Techniques used to types, with Acacia spp. as the dominant species. The assess sloth bear–habitat relationships included as- vegetation in this region exists in a degraded state, sessment of home-range and habitat-selection analy- though afforestation programs have ensured promi- sis using radiotelemetry (Joshi et al. 1995, 1999; nent vegetation in certain pockets. The study area has a Ratnayeke et al. 2007), den-site examination, and semi-arid climate characterized by hot summers (34u– indirect bear signs such as droppings, tracks, and claw 45uC) during April–June and low rainfall (571.92 mm) marks (Akhtar et al. 2004, Chauhan et al. 2004). from June to November. These studies have thus revealed only qualitative habitat characteristics essential for the species. Sloth bears appear to use different habitats to Methods meet their life requisites (e.g., foraging, cover; Survey design Wrangham and Rubenstein 1986). Therefore, eco- We determined sampling units by overlaying the logical studies should characterize each habitat to study sites with 2-km2 grid cells as per the Forest develop a thorough understanding of requirements Department’s survey estimates of the existence of 1

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Fig. 1. The location of the study sites, Daroji Wildlife Sanctuary, Bukkasagara Reserve Forest (RF), Daroji RF, Toranagallu RF, Agoli RF, and Benakal RF, and pictorial depiction of survey design for estimation of sloth bear occupancy are shown. Map showing (a) the location of study region in India, (b) the six study sites with their approximate geographical spacing overlaid with a 2-km2 grid, and (c) sampling units (strips) represented as ‘‘boxes’’ along the diagonals of each grid cell. The study was carried out in two phases, firstly during June– July 2011 and secondly, June–July 2012. bear/1.5 km2 in Daroji WLS (Kiran 2011). Diagonal and 30 grid cells, respectively (Fig. 1 and Table 1). of a square grid cell was designated as ‘‘1’’ if it ran Additionally, we selected grid cells with a minimum from the direction of north-west to the south-east spatial replicate of 3 for modelling because the and ‘‘2’’ if otherwise. We inscribed spatial replicates detection probability of indirect signs of bear was in the form of strips of 500-m length and 20-m width ‘‘low’’ otherwise. along the 2 diagonals of the square grid at intervals of 150 m (Fig. 1). To avoid duplication of data, we Identification of covariates ascribed the covariates to the third strip of diagonal We selected measurable habitat covariates repre- 1 when strip 3 of diagonal 1 coincided with strip 3 of sentative of food, vegetation structure, and covari- diagonal 2. Limitation of manpower and logistics, ates indicative of past and present anthropogenic and crepuscular activity cycle of the species, justified disturbances. Sloth bears are myrmecophagous (Po- the usage of spatial replicates instead of temporal cock 1933, Erdbrink 1953, Gopal 1991, Sacco and replicates in a single-season survey (Krishna et al. Valkenburgh 2006), predating especially on ants 2008, Saracco et al. 2011). Thus, Daroji WLS, (Formicidae) and termites (Isoptera; Laurie and Bukkasagara RF, Daroji RF, Toranagallu RF, Seidensticker 1977, Gopal 1991, Joshi et al. 1997) Agoli RF, and Benakal RF bore 44, 30, 9, 9, 37, along with other animal matter (Bargali et al. 2004)

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Table 1. Outline of grid-cell–level information included in the analysis and details of sloth bear droppings and/ or tracks recovered from each survey site of Daroji Wildlife Sanctuary (WLS), Bukkasagara Reserve Forest (RF), Daroji RF, and Toranagallu RF (India) during June–July 2011, and Agoli RF and Benakal RF (India) during June–July 2012. Percent of grid cells included Total no. of No. of grid cells with animal Study area in occupancy analysis (%) grid cells signs Daroji WLS 66 44 18 Bukkasagara RF 43 30 08 Daroji RF 00 09 00 Toranagallu RF 00 09 00 Agoli RF 65 37 21 Benakal RF 57 30 17 and berries (Gopal 1991). Therefore, food-resource missing data points or observations and were determinants included number of termite mounds accommodated within the model likelihood frame- and feeding trees, and habitat factors included work proposed by Mackenzie et al. (2002). number of trees and percentage of shrub coverage. The sloth bear is largely nocturnal (Laurie and We visually estimated shrub coverage based on the Seidensticker 1977, Joshi et al. 1995, Gopalaswamy proportion of area covered by shrubs in a single 2006) or crepuscular throughout the study regions strip. Record of feeding (fruiting or non-fruiting) because the vegetation cover is sparse and daytime trees was combined with record of non-feeding trees temperature remains high throughout the year to form the covariate ‘‘trees’’ (with diam at breast (Akhtar et al. 2004). Therefore, presence of the ht [DBH] .5 cm), which represented both food species had to be established by indirect means (i.e., resources and habitat characteristics. Anthropogenic through droppings or tracks; Garshelis et al. 1999a). pressure in the form of grazing and timber harvest A minimum of 2 observers surveyed strips on foot was also quantified. We quantified grazing by using a hand-held Global Positioning System device calculating the number of livestock (i.e., cattle and (Garmin eTrex30 and Garmin HCx Vista, Kansas sheep) -dung present, and extraction of firewood City, Kansas, USA) and recorded bear droppings or through enumeration of stumps and loppings tracks, number of termite mounds (Te), percentage (chopped branches and twigs) from trees of DBH shrub coverage (SC), number of trees (T), number of .5 cm, within the strips. This resulted in the stumps and/or logs (SL), and number of livestock identification of 5 site covariates presumed to dung (LD) within each strip. Droppings of bears influence occupancy of sloth bears. We used shrub- were identified by their characteristic cylindrical coverage, the only covariate that could influence shape with tapering ends and ascertained through detection of indirect signs, to model detection close examination of content, which remained domi- probability. nated by either insects or seeds (Joshi et al. 1997, Sreekumar and Balakrishnan 2002, Bargali et al. Field methods 2004, Ramesh et al. 2009). Sloth bear tracks were We conducted single surveys in Daroji WLS, identified based on its characteristic specular print Bukkasagara RF, Daroji RF, and Toranagallu RF with the larger toe pointing outward and prominent during June–July 2011 and in Agoli RF and Benakal claw marks (Karanth et al. 2002). Consequently, a RF during June–July 2012. We selected cells in grids site (grid cell) was deemed occupied if a bear for sampling that included .25% of the cell area dropping or track was detected in .1 of the 8 spatial because grid cells at the periphery usually enclosed replicates (naı¨ve estimate of occupancy). These smaller area. In cases where undisturbed habitat detection–non-detection data in each strip formed occurred beyond forest boundaries within a grid cell, the basis for construction of detection histories for we extended the sampling effort to strips within the each grid cell. For strips that could not be sampled, grid cell but beyond the study sites. However, we treated the replicate as a missing observation sampling was limited by inaccessibility to strips (MacKenzie et al. 2002). Overall, 60% of the grid owing to their location on steep ridges or on cells were sampled. We first averaged each covariate sprawling boulders. Such strips were treated as over 8 strips and secondly, z-transformed it to

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Table 2. Summary of model-selection procedure for parameters, were executed as per the framework of factors affecting detection probability of bear Burnham and Anderson (1998). We tabulated models droppings when data from the study regions of in ascending order of their relative quality based on Daroji Wildlife Sanctuary, Bukkasagara Reserve Akaike Information Criterion values adjusted for Forest (RF), Agoli RF, and Benakal RFs (India) were cumulatively evaluated. The study regions were sample size (AICc; Burnham and Anderson 1998). We surveyed in two phases, firstly from June to July report the estimates of both detection probability and 2011 and secondly from June to July 2012. the occupancy as mean 6 standard error.

a b c d e f Model p^ AICc wi K b1 y(.),p(SC) 0.456 709.79 0.916 3 0.25 Results y(.),p(.) 0.465 714.57 0.084 2 0.00 We did not detect sloth bear sign at Daroji RF and aSC—shrub coverage of the plot. bp^—estimated species detection probability. Toranagallu RF and excluded these sites from c AICc—AIC corrected for small-sample bias. analyses. Therefore, only Daroji WLS, Bukkasagara d wi—AICc model wt. RF, Agoli RF, and Benakal RF were considered, e K—no. of parameters estimated by the model. which contributed 29, 13, 24, and 17 grid cells, fb —beta co-efficient of the sampling covariate. 1 respectively, to the occupancy model. In the 83 grid cells where sloth bear sign was observed, the number rescale and normalize the data, prior to occupancy of detections varied from 1/strip to 27/strip. The analysis. highest number of cumulative bear signs per grid cell were recorded from Benakal RF (8.4 signs/cell), Occupancy estimation closely followed by Agoli RF (6.35 signs/cell) while We constructed detection histories and estimated Daroji WLS (1.59 signs/cell) and Bukkasagara RF the probability that a grid cell was occupied (yy^), and (0.67 signs/cell) had lower recovery of animal signs the detection probability ( ^p), using maximum likeli- per grid cell (Table 1). hood functions (MacKenzie et al. 2002) in Program The estimated average detection probability ( ^p)of PRESENCE (ver. 3.0; MacKenzie et al. 2006). Data bears in a grid cell was 0.47 6 0.003. Contrary to from all the study regions were pooled into a single expectation, detection probability was positively occupancy analysis because of the small area of each influenced by shrub coverage (b1 5 0.25; Table 2); study region (,5,100 ha). We employed logistic however, this being a logical improbability, we models with logit link and binomial error to evaluate ignored it. As a result, detection probability p was the effect of covariates on model parameters, occu- held constant over all spatial replicates (plots) in pancy (yy^) and detection probability (^p). Based subsequent models. The model-averaged occupancy on current knowledge of sloth bear ecology, we estimate was yy^ 5 0.79 6 0.007. The number of speculated that covariates indexing food and vegeta- termite mounds (Te) and trees (T) together [y(T + tion structure would positively affect occupancy, Te)] had the greatest predictive power of site whereas covariates indexing anthropogenic pressures occupancy (Table 3). However, each of these covar- would negatively affect it. We used a step-wise iates performed poorly when modelled individually. approach to first model effects of covariates on The second-best model also included percentage detection (p), and then modelled y. Among the shrub cover; however, the DAICc value (1.65) covariates we considered, only undergrowth–shrub- suggests that percentage shrub cover did not have coverage could affect the detection of bear droppings a substantial effect on occupancy. The covariates, or tracks and was therefore used to model p[y(.), trees (b1 5 3.17, CI 5 1.93–4.41) and termite p(SC)] (Table 2). Both spatial and numerical pair-wise mounds (b1 5 2.65, CI 5 1.27–4.02) had the greatest correlation analyses using Pearson correlation analy- effect on determining bear occupancy (TSwi 5 0.994, sis did not identify autocorrelation (21 , r , 1, p . TeSwi 5 0.985). Among the anthropogenic determi- 0.05) among covariates, and thus all model selections nants, sloth bear occurrence was negatively corre- were uncorrelated. Subsequently, we formulated a lated to livestock dung (b1 5 20.53, CI 5 20.98 to candidate set of 8 a priori models (involving 1 of the 5 20.08) but positively correlated to stumps and covariates) to investigate the influence of covariates loppings (b1 5 0.73, CI 5 0.54–1.41) albeit at low on occurrence. Selection of models, succeeded by relative values of importance (LDSwi 5 0.244, SLSwi computation of model weights and averaging of 5 0.244), signifying a weak effect on occupancy.

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Table 3. Summary of model-selection procedure for all the factors and/or covariates influencing occupancy of sloth bear when the study regions of Daroji Wildlife Sanctuary, Bukkasagara Reserve Forest (RF), Agoli RF, and Benakal RFs (India) were cumulatively assessed. The study regions were surveyed in two phases, firstly from June to July 2011, and secondly from June to July 2012.

^ a ^ b c d e f g Model yy (95% CI) p^ (SE) DAICc wi K T + Te 0.782 0.730–0.832 0.471 0.024 0.00 0.512 4 T + Te + SC 0.782 0.731–0.834 0.471 0.024 1.65 0.224 5 T + Te + LD + SL 0.780 0.727–0.833 0.472 0.024 2.31 0.161 6 T + Te + SC + LD + SL 0.781 0.726–0.836 0.471 0.024 3.64 0.083 7 T 0.786 0.747–0.826 0.469 0.024 7.22 0.014 3 Te 0.790 0.752–0.827 0.468 0.024 9.27 0.005 3 SC 0.799 0.759–0.839 0.463 0.024 11.71 0.002 3 LD + SL 0.791 0.762–0.819 0.467 0.024 17.82 0.000 4 y(.),p(.) 0.798 0.689–0.876 0.465 0.025 22.74 0.000 2 aT—Trees; Te—Termite; SC—Shrub coverage; LD—Livestock dung; SL—Stumps and loppings. byy^—estimated occupancy parameter corrected for ‘detection probability’. c^ p^—estimated species detection probability. dSE—associated standard error of occupancy estimate. e AICc—AIC corrected for small-sample bias; DAICc—difference in AICc values between each model and the model with the lowest AICc. f wi—AICc model wt. gK—no. of parameters estimated by the model.

Overall, 4.8%, 28.9%, and 66.27% of the sampled effectiveness of bear management strategies across grid cells (83 grid cells) were classified as low (yy^ 5 sites and times. 0.01–0.30), medium (yy^ 5 0.31–0.70), and high (yy^ 5 0.71–1.00), respectively, based on their occupancy Occupancy estimates (Fig. 2). The study region of Benakal RF Information on recent range, abundance, and had the greatest percentage (88%) of sampled grid distribution of sloth bears was determined through cells designated as highly occupied, followed by the compilation of previous literatures, unpublished Agoli RF (75%), Daroji WLS (59%), and finally information, and state-wide questionnaires of field Bukkasagara RF (39%). managers (Sathyakumar et al. 2012). The National Bear Conservation and Welfare Action Plan (2012) projected the extent of the national distribution at Discussion 400,000 km2. Furthermore, almost 50% of subsisting We provide the first occupancy estimate of sloth populations reside outside protected areas (Yoga- bears in their dry range in Hospet and Koppal nand et al. 2006) and these populations have been districts of Karnataka, India. Sloth bears occurred steadily declining (Garshelis et al. 1999b). Estimate over a large expanse of sites in reserved forests available under an occupancy framework designed outside the protected region, emphasizing the need to specifically study tigers (Panthera tigris), reported for their protection and uninterrupted management. a figure of 16,582 km2 of sloth bear distribution in We identified portions of Agoli RF, Benakal RF and Karnataka in 2010 (a declination of 19% from 2006; Bukkasagara RF as highly used and occupied by the Jhala et al. 2011). The only attempt to generate species because these consisted of a greater propor- occupancy-level information of sloth bears in India tion of grid cells with high degree of occupancy than by Ramesh et al. (2012) yielded an occupancy of yy^ 5 were found in Daroji WLS. Despite earlier evidenc- 0.83 6 0.01SE in Mudumalai Tiger Reserve that was es, bears were absent in Daroji RF and Toranagallu beyond the moderate range of 0.20–0.80 obtained in RF due to possible extirpation; Daroji RF is site-occupancy studies (Linkie et al. 2007, Mortelliti partially submerged by Daroji reservoir (owing to and Boitani 2008, Sarmento et al. 2011). This heavy sewage discharge by industries), and large- anomalous estimate was based on a detection scale industries (e.g., steel factories) operate adjoin- probability of p 5 0.23 6 0.07SE, lower than the ing to these 2 regions. We additionally propose threshold of 0.30 recommended for an unbiased the usage of ‘‘occupancy’’ as an index to evaluate estimate of occupancy (for no. of sites 5 40, Spatial–

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Fig. 2. Estimated sloth bear occupancy data generated for each grid, which were extracted from the best- fitting model, [y(T + Te)] when all the study sites were cumulatively modelled. Un-sampled grid cells comprised those that were partially sampled or not sampled. Daroji Wildlife Sanctuary, Bukkasagara Reserve Forest (RF), Daroji RF, and Toranagallu RF (India) were surveyed during June–July 2011; and Agoli RF and Benakal RF were sampled during June–July 2012.

Temporal replicate . 5) as outlined by Mackenzie this low-density species (e.g., Yoganand et al. 2006). et al. (2003). This was primarily due to larger land- This is possibly due to either intensive search by scape and heterogeneous vegetation composition. observers, and/or bears preferably defecating in Previous efforts to evaluate sloth bear distribution available open spaces. Alternatively, unlike habitat in India have also ignored the issue of detectability. In of the southwestern population of bears, these this study, bear sign was recorded in 83 out of 139 patches are isolated and are smaller (,321 km2; sampled grid cells, generating a naı¨ve occupancy of Ramesh et al. 2012) in size, which could have led to 0.60. Through incorporation of imperfect detection of high detection probability. An occupancy study in animals into occupancy models, the estimated pro- Mudumalai (Ramesh et al. 2012) showed weak portion of area occupied by sloth bears substantially influences of termite mounds (AICtermites . AICnull) increased to 0.79, a 24% increase over the naı¨ve and fruiting trees (AICfruiting trees . AICnull) in the estimate. occupancy models, undermining their importance. This can be explained by diverse availability of food Covariates influencing detection probability resources in Mudumalai (Ramesh et al. 2009). On and occurrence the contrary, the current study regions remained The estimated detection probability of sloth bear resource-limited during the surveyed season, result- droppings was 0.47, which is considerably high for ing in enhanced dependence on the only available

Ursus 25(2):111–120 (2014) 118 CONSERVATION OF SLOTH BEARS N Das et al. resources—termite mounds and possibly, feeding of the factors identified, and public education– trees. Therefore, the only covariates that co-affected awareness can help secure the future of the species. inhabitation–occupancy by the species, as predicted by the a priori hypothesis were (1) Tree and (2) Termite mounds. Trees, although not distinguished Acknowledgments between feeding and non-feeding, could be a strong We thank the Karnataka Forest Department for indicator of both hiding shelters and food resources according permission and assistance during field- in general. Conclusively, bears used only those areas work. We acknowledge the support of Mr. D. of the forests that were secure in terms of food Sharma, Chief Wildlife Warden, Karnataka. Our resources and safety. This finding mirrors reports of gratitude also to Mr. G. Hanumanthaiyya, Range sloth bears using patches of Lantana shrub in Panna Forest Officer, Gangawati Circle, for accommoda- National Park in India (Yoganand et al. 2013) and tions and logistic support. Mr. Maulabhussain, areas with thick vegetation cover in Royal Chitwan Forest Guard, Gangawati Circle, is gratefully National Park in Nepal (Laurie and Seidensticker acknowledged for his support and assistance. SD, 1977). In congruence to our a priori hypothesis SD, SS and JAS thank the Indian Academy of and the findings of Ratnayeke et al. (2007), grazing Sciences, Bangalore, for their fellowship awards. pressure had a negative influence on occupancy, but lopping influenced it positively (though weakly). Usage of lopped sites by bears is explained by the Literature cited severe limitation of space because most of these AKHTAR, N., H.S. BARGALI, AND N.P.S. CHAUHAN. 2004. habitats are small and exist as disconnected islands. Sloth bear habitat use in disturbed and unprotected However, occupancy analysis was limited by our areas of Madhya Pradesh, India. Ursus 15:203–211. inability to include or record ‘‘presence–distance BARGALI, H.S. 2004. The ecology of the problematic sloth bear (Melurusus ursinus) and mitigation of human–bear from den–cave’’ as a possible covariate; we feel that conflict in Bilaspur forest division, Madhya Pradesh. foraging explorations and hence site selection– Thesis, Wildlife Institute of India, Dehradun, India. settlement depend on proximity to den site, as ———, N. AKHTAR, AND N.P.S. CHAUHAN. 2004. Feeding predicted by Multiple Central Place Foraging ecology of sloth bears in a disturbed area in central Theory (Chapman et al. 1989) and/or the energy- India. Ursus 15:212–217. maximizing strategy hypothesis (Hall 1962, Schoener BEST, B.D., P.N. HALPIN,E.FUJIOKA, A.J. READ, S.S. 1971). Similar studies on sloth bears in the future QIAN, L.J. HAZEN, AND R.S. SCHICK. 2007. Geospatial should explore the role of presence–absence of ‘‘den web services within a scientific workflow: Predicting site’’ on site occupancy. marine mammal habitats in a dynamic environment. Ecological Informatics 2(3):210–223. Recommendation for conservation of BOYD, D.S., AND G.M. FOODY. 2011. An overview of recent remote sensing and GIS based research in ecological the species informatics. Ecological Informatics 6(1):25–36. Management intervention for the species’ protec- BREININGER, D.R., AND G.M. CARTER. 2003. Territory tion and conservation should include the following: quality transitions and source–sink dynamics in a Firstly, Agoli RF, Benakal RF, and Bukkasagara RF Florida scrub-jay population. Ecological Application require immediate management plans that ensure 13:516–529. their strict protection. Secondly, removal of trees, BURNHAM, K.P., AND D.R. ANDERSON. 1998. Model including the ones not fed on by bears, should be selection and inference: A practical information-theo- curtailed immediately. Thirdly, because the sloth bear retic approach. Springer-Verlag, New York, New York, is the umbrella species in the region, habitats in USA. Bukkasagara RF, Agoli RF, and Benakal RF could CHAMPION, S.H., AND S.K. SETH. 1968. A revised survey of be enriched through planting of native tree species the forest types of India. Manager of Publications, Government of India, New Delhi, India. (fruit-bearing or otherwise), which can serve as both CHAPMAN, C.A., L.J. CHAPMAN, AND R.L. MCLAUGHLIN. hiding and/or escape areas and food resources. 1989. Multiple central place foraging by spider mon- Fourthly, moisture content of the soil is to be keys: Travel consequences of using many sleeping sites. maintained within a range that facilitates nesting Oecologia 79:506–511. by termites (Wood 1988). Finally, only long-term CHAUHAN, N.P.S., H.S. BARGALI, AND N. AKHTAR. 1999. monitoring of the population, stochasticity–dynamics Human–sloth bear conflicts in the state of Madhya

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