Fig 1.
(A)-Study area showing matrices of major grid (each measuring 9.62 km2) spread across six conservation complexes in Nepal including red panda potential habitat along Nepal, India, and Bhutan. (B)-Distribution of the bamboo cover (BAM) from Maxent Modelling along with bamboo presence points (in black dots) along six conservation complexes in Nepal.
Table 1.
Effect of covariates on detection probability (p) of red panda across the mid-hills of Nepal.
Table 2.
β estimates and standard errors [in parentheses] from the logit link function based on the best and the univariate, single-species, single-season occupancy models for red panda detection probability (p) in mid-hills and high Himalayas in 2016.
Fig 2.
Relationships between highly influential covariates based on beta estimates (β) from univariate models and the probability of red panda detection (top) and occupancy (bottom).
Table 3.
Effect of covariates on occupancy (Ψ) of red panda across the mid-hills of Nepal.
Table 4.
β estimates from the logit link function based on best and univariate models for red panda occupancy probability (Psi).
Fig 3.
Site specific variation in red panda occupancy based on top model along the mid-hills and high mountains of Nepal.
Grid shading (with darker red color indicating higher probability of occupancy) shows site specific occupancy probabilities in the wet season of 2016 using single-species, single-season occupancy model. Grid with dark color shows area beyond the survey range.
Fig 4.
Variation (expressed in CV: Coefficient of variation, in %) in occupancy pattern based on estimated probabilities of occupancy in the wet season of 2016 using single-species, single-season occupancy model.
Table 5.
Comparison of red panda occupancy estimates (Ψ, SE(Ψ)) based on top model in six conservation complexes, protected areas, and outside protected area of Nepal [6].