Occupancy and Detection:
The estimated sloth bear detection probability was 0.44 ± 0.1SE . The top model with lowest AIC value that influenced detection probability included termites, fruits and disturbance(Table 2) . Among them, termite was the most influential covariate that had significant effect on detection probability (Wi =0.81, β= 1.80 ± 0.53SE) of sloth bears. We used this top model for detectability in subsequent analyses to model occupancy probability. Occupancy results are presented in Table 3 . Among the set of candidate occupancy models, the model including termites (βT = 0.80 ± 0.40SE,Wi =0.12 ) and fruits ( βF = 0.58 ± 0.42SE,Wi =0.12) was the best occupancy model. Since, a single model did not fully explain the observed data and because of the inherent advantages of model averaging (Burnham & Anderson,1998) we obtained average occupancy estimates, and the associated standard errors from the most competitive models (ΔAIC<2). The model averaged occupancy estimate for our study area was 0.53 ± 0.04SE .