Occupancy estimation and modelling the effects of covariates:
Studies have demonstrated that spatial replication can serve as a good surrogate for temporal replication in occupancy studies of sloth bears (Srivathsa et.al.2017). For our grids we considered 1 km search trail as one spatial replicate. We employed an equal search effort of 4 km in each grid, thus maintaining four spatial replicates in each grid. Detection histories were constructed for each grid, where ‘1’ indicates detection of species and ‘0’ indicates non-detection and ‘.‘ indicates a missing observation. For example a detection history of ‘010-‘ indicates that the sloth bear or its sign was not detected in first and third search trail, detected in the second search trail and the sampling was not done in the fourth search trail. For each covariate, data recorded in each segment along the search trail were pooled to obtain an single average covariate score. We ran single-species single season occupancy analysis using a maximum likelihood-based approach in the PRESENCE software 2.12.31to derive calculated occupancy (Hines, 2006). We followed a two-step process to estimate probability of detection and probability of bear occurrence. First, we modelled detection (p) keeping constant structure for occupancy model as ψ (.). We hypothesized that three ground based covariates 1) Termite 2) Fruit and 3) Disturbance Index would affect our probability of detecting sloth bears and its signs along the search trail, so we used them in the first step for modelling detection probability. We hypothesized that sloth bear signs detection will be higher in areas with termite mounds and fruits and they will be lower in areas with high disturbance. We modelled different combinations of the detectability (p) covariates and selected the best model based on minimum AIC, keeping the ψ fixed.
In the second step, we modelled probability of occupancy (ψ) keeping the top detection model from step one as a constant structure for detection model (Srivathsa et al. 2017; Panthi et al. 2017; Doherty et al. 2012). We constructed a set of 27 priori candidate models, each representing a different ecological hypothesis. These models included either single or additive effects of two or more covariates to investigate the influence of covariates on occurrence. Model fit was assessed using the parametric bootstrap procedure (MacKenzie & Bailey, 2004). The covariate models were compared and ranked using an information theoretic approach, relying on Akaike Information Criterion (AIC) for testing relative model fits. Models with ΔAIC of <2 were strongly supported by the data. Due to inherent advantage of model averaging (Burnham & Anderson, 1998), the final occupancy estimates and associated standard error were averaged across the model set. To infer relative influence of covariates on occurrence, we summed the computed model weights of all the model containing the particular covariate. Additionally, we used the estimated β-coefficients to assess the strength of association of each covariate with occupancy probability.