674 R. Pal et al.


TABLE 1 Details of the top three models used to estimate the densities of the bharal Pseudois nayaur and the Himalayan musk deer Moschus leucogaster in summer and winter in the Upper Bhagirathi basin, Uttarakhand, India, showing key functions (defining parametric shapes for the detection function), adjustment types (to allow for departures fromthe parametric shape), the number of adjustment terms selected (order), overdispersion factor (Ĉ), Akaike’s information criterion adjusted for overdispersion (QAIC), and density estimates with standard error (SE) and coefficient of variance (CV).


Key function


Bharal (summer) Hazard


Half normal Uniform


Bharal (winter) Hazard


Half normal Uniform


Musk deer (summer) Hazard


Half normal Uniform


Musk deer (winter) Hazard


Half normal Uniform


Hermite polynomial Cosine


Cosine


Hermite polynomial Cosine


Cosine Cosine Cosine Cosine


Adjustment type Order


0 2 1


1 2 2


1 0 1


1 0 1


Ĉ 2.93 29.23


123.98 5.51


8.71


31.24 6.30


8.12 9.63


4.28 4.54 4.67


QAIC


1,083.87 118.05 33.06


626.89 400.63 118.95


287.50 381.97 279.80


172.03 119.78 142.58


Estimate ± SE


1.61 ± 0.60 0.51 ± 0.10 0.16 ± 0.05


0.64 ± 0.20 0.64 ± 0.20 0.35 ± 0.10


0.42 ± 0.10 0.29 ± 0.10 0.26 ± 0.10


0.10 ± 0.05 0.10 ± 0.05 0.08 ± 0.03


overestimated musk deer densities as the drive count meth- od is known for overestimating the density of animals (Takeshita et al., 2016). In addition, they were carried out in a small portion (c. 2.5 km2) of a protected area; small study areas combined with a bias towards good habitat qual- ity can result in highly overestimated densities (Suryawanshi et al., 2019). Our density estimates are associated with high coefficients


of variation. This high variability is probably caused by land- scape topography and species biology. The fit of the model for the solitary Himalayan musk deer was better than for the group-living bharal. Here, we discuss some of the issues we faced using sampling with camera traps, andmake sugges- tions as to how these can be addressed in future studies. For the bharal, the main problem that caused bias in the


distances at which individuals were captured was the inad- equate camera view because of slopes. The ruggedness of the landscape also influences the approach angle and the dis- tance covered by the cameras: those on hilltops or at the base of a hill covered distances of 10–20 m, whereas cameras on hill slopes covered distances of 6–10 m(dependingonthe slope). Topographic variability probably also influenced detection probability and the estimated angle of the cam- era view. Future studies in similar landscapes could use statistical tests to examine the effects of these parameters more thoroughly. Another issue encountered with the group-living bharal


was that animals grazing close to the camera blocked the view of animals that were further away. This can make it impossible to calculate the distance from the camera for individuals in the background, leading to a bias towards


CV


0.38 0.31 0.30


0.37 0.37 0.36


0.34 0.34 0.34


0.48 0.47 0.46


individuals recorded at shorter distances. However, such incidents were relatively rare in our study (six occasions). Herd behaviour also affects captures, as bharals tend to follow the first individual when moving together. Because we analysed individual distances from the camera, this can cause heaping in the distances recorded (Fig. 3). Distance sampling with camera traps requires setting the


cameras in burst or video mode. Our effort to implement this method in the Greater Himalayan alpine habitats failed because cameras were continuously triggered by grass movements in the field of view (RP, pers. obs., 2017). We had to discard data from four camera traps in this study for the same reason. Mounting cameras higher off the ground could help minimize this problem. In addition, the imprecise (high CV) estimates suggest that more sam- pling locations are required to improve precision (Howe et al., 2017; Cappelle et al., 2019). The ability of camera sensors to detect moving animals


may vary depending on camera type and placement, tem- perature, and humidity (Hofmeester et al., 2017). Different camera models can be tested at a site to assess the ability to detect animals. There could be inconsistencies between the theoretical and actual angle of view θ, which can lead to biased estimates. This can result in underestimates if sensors are less sensitive to movements near the edges of the camera’s field of view (i.e. the effective angle can be smaller than the assumed angle). This can be addressed with field tests to estimate the effective angle θ,which canthenbeac- counted for in the analysis. Imprecise measures of distance should not be an issue if they are appropriately binned in distances for the analysis (Buckland et al., 2015). However,


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


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136  |  Page 137  |  Page 138  |  Page 139  |  Page 140  |  Page 141  |  Page 142  |  Page 143  |  Page 144  |  Page 145  |  Page 146  |  Page 147  |  Page 148  |  Page 149  |  Page 150  |  Page 151  |  Page 152  |  Page 153  |  Page 154  |  Page 155  |  Page 156  |  Page 157  |  Page 158  |  Page 159  |  Page 160  |  Page 161  |  Page 162  |  Page 163  |  Page 164