Co-variate Selection:
We selected a mix of six plausible remotely sensed and ground-based variables that reflected characteristics of landscape, condition of habitat, persistent anthropogenic pressures, and availability of major food resources based on the review of available literature. For small study area with few sample sites model loses power of explanation and unwanted errors increase as the number of variables are increased in a model. It is generally advised to use one variable per ten sites in occupancy model. Thus, following the principles of parsimony, we included three site co-variates and three sample covariates (See Table 1 ). We selected termites, fruits and disturbance as sample covariates and measured them in the field. Tree cover, terrain ruggedness index (TRI) and enhanced vegetation index (EVI) were site level covariates that were obtained from remotely sensed images. Termites and fruits were selected as variables as they represent the dominant food resource for sloth bears (Khanal & Thapa, 2014; Sukhadiya et al. 2013; Bargali et al. 2012; Joshi et al.1997). Studies in India have shown that sloth bears were positively associated with the topographic ruggedness (Puri et al. 2015; Srivathsa et al. 2017) and forest cover (Srivathsa et.al. 2017). Sloth bears have been reported to avoid human and livestock disturbance (Babu et al. 2015; Puri et al. 2015) but they have also been reported from human dominated landscapes with degraded habitats (Bargali et al. 2012). We combined human disturbance, livestock disturbance and fire in our search trails as a measure of disturbance. They are thought to prefer relatively dry habitats as indicated by the negative relationship between habitat occupancy and vegetation productivity (Srivathsa et al. 2017). We chose EVI rather than normalized difference vegetation index (NDVI) to measure vegetation productivity in our study as EVI has improved sensitivity. In Nepal, it was found that sloth bears move to grasslands during the dry season and prefer to remain in forests during the wet season (Joshi et al. 1995). We used the tree cover data prepared by Hansen et al (2013) as a proxy of habitat condition, higher cover indicating forested habitat and lower cover indicating grassland habitat. Covariates were first checked for collinearity (Figure 2), and then z transformed before running occupancy models (Kirshna et al. 2008; Panthi et al. 2017). We hypothesized that ceteris paribus 1) sloth bear occupancy will increase with increasing termites, fruits and heterogeneity in the terrain and, 2) sloth bear occupancy will decrease with increasing tree cover, EVI and disturbance.