672 R. Pal et al.


the estimated probability of obtaining an image of an animal that is within θ degrees (angle covered by the camera’s field of view), K is the total number of camera-trap locations and w (truncation distance) in front of the camera at a snapshot of the moment. The effort at a point k was measured as ek = θ Tk/2 πt where θ/2π describes the fraction of a circle covered by a camera, Tk is the period of camera deployment (in seconds), and t is the unit of time used to determine a finite set of snapshot moments within Tk (also in seconds). Wedefined the period of camera deployment as the time the target species was expected to be active during the sampling period. For the bharal, this was a 12-hour period per day (6.00–18.00) in both seasons and for the Himalyan musk deer a 12-hour period per day (18.00–6.00) in summer and a 14-hour period per day (6.00–20.00) in winter. 1/A is the availability correction factor. Seven camera traps malfunctioned because of technical


errors and were not included in the final analysis. For the analysis in Distance, we modelled the detection from using the same functions as Howe et al. (2017): half normal with 0, 1 or 2 Hermite polynomial adjustment terms; hazard rate with 0, 1,or 2 cosine adjustments; uniform with 1 or 2 cosine adjustments. As model selection methods based on Akaike’s information criterion (AIC) tend to favour overly complex models because of overdispersion in the data, we selected models using a recently proposed two-step procedure (Howe et al., 2019): (1) Firstly, the best model is selected on the basis of AIC adjusted for overdispersion (QAIC) within each key function, where the overdispersion param- eter (Ĉ) is calculated from the ratio between the χ2 statistics of the most parameterized model for each key function and its degrees of freedom (χ2/df). (2) Secondly, the best model is selected with the smallest values of the χ2 goodness-of-fit statistic divided by its degrees of freedom (across QAIC- selected models, one from each key function). We used the point transect distance sampling method in Distance (Thomas et al., 2010) for all analyses.


Results


Sampling bias test In case of the Himalayan musk deer, the elevation, rugged- ness and slope of sampled camera-trap locations were not biased: the mean values of sampled locations were not sig- nificantly different from the mean elevation, ruggedness and slope of 100 random points in both seasons (P.0.05 in each case, Mann–Whitney U test; Supplementary Fig. 1). Bonferroni confidence intervals indicated no par- ticular aspect category was preferred for sampling in winter or summer (Supplementary Fig. 1). Similarly, in the case of the bharal, the ruggedness, elevation and slope of the sam- pled camera locations were not different from the mean ruggedness, elevation, slope and aspect of the random


points (Supplementary Fig. 2). Encounter rates were highly variable amongst locations and did not show any spatial autocorrelation for the bharal (Moran’sI P: 0.6 in summer, 0.9 in winter) or the musk deer (Moran’sI P: 0.8 in summer, 0.6 in winter).


Density estimates


The bharal was photo-captured by 17 out of 21 camera traps deployed in summer, and 14 out of 24 in winter. We obtained 1,059 snapshots in 104 videos in summer and 949 snapshots in 61 videos in winter. In summer, one of the cameras contributed a large number of captures (c. 60% of the total dataset). This particular camera was placed on a steep slope with cliffs on both sides, and close (10–15 m) to an intensively used bharal trail along a stream. Consequently, a large number of observations by this camera were within 9–12 m as most of the bharals fol- lowed the path to move up or down the slope. Because of this bias, the initially estimated density of 0.15 ± SE 0.31 individuals/km2 had a high CV (207.35). We removed this camera from the final analysis to get an estimate with reduced bias. Amongst the summer captures, we found an excess of distances close to the camera (Fig. 3). The hazard-rate model is more sensitive than the half- normal model to this excess, resulting in an implausible rapid fall-off in the detection probability. Therefore, we used the second-best model (Table 1), the half-normal model, for estimating bharal density in summer (0.51 ± SE 0.1 individuals/km2,CV = 0.31). In winter, the best model was the hazard-rate model, and the second-best half- normal model resulted in the same density estimates (0.64 ± SE 0.2 individuals/km2,CV = 0.37; Table 1). Himalayan musk deer were captured by 11 out of 28


cameras in summer and 6 out of 25 cameras in winter. We obtained 564 snapshots in 102 videos in summer and 166 snapshots in 31 videos in winter. Himalayan musk deer data did not show the heterogeneity in capture probabil- ities amongst cameras that we observed for the bharal, nor any evidence of bias in terms of distances (Fig. 3). The best model was the hazard-rate model with cosine adjust- ment in both seasons (Table 1), and estimated density was 0.4 ± SE 0.1 individuals/km2 (CV = 0.34) in summer and 0.1 ± SE 0.05 individuals/km2 (CV = 0.48) in winter (Table 1).


Discussion


Our estimates of bharal density in summer (0.5 ± SE 0.1 individuals/km2) and winter (0.6 ± SE 0.2 individuals/km2) were similar. Estimates of bharal densities from three differ- ent locations in Spiti, the nearest trans-Himalayan landscape (using standardized double observer method; Suryawanshi et al., 2012) were 1.60, 1.49 and 3.19/km2. Estimates of bharal


Oryx, 2021, 55(5), 668–676 © The Author(s), 2021. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S003060532000071X


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