NON-INVASIVE TECHNIQUES FOR MONITORING SNOW LEOPARD (PANTHERA UNCIA) POPULATIONS IN PAKISTAN

Abstract

A rare and endangered carnivore, the snow leopard (Panthera uncial) is frequently regarded as a flagship species for biodiversity conservation and a gauge of ecosystem health. But the main challenge in its monitoring is its relatively low population, huge home space, and isolated habitat. For a better understanding of its existence, this particular study employed non-invasive techniques for collecting data from Khunjerab National Park (KNP) (2010–2011) by sign surveys and camera trapping to assess detection probability (p) and occupancy (ψ), accompanied by camera trap records from Chitral Gol National Park (CGNP) (2009). Sign survey data was analyzed using PRESENCE 2.1 is used to analyze the sign survey data while camera trapping data was analyzed by logistic regression. The detection probability for all fresh signs was 0.646 (SE = 0.041), while for fresh scrapes (less than 7 days), it was 0.600 (SE = 0.100). Overall, the occupancy estimates for scrapes were 0.855 (SE = 0.043), whereas the values for all new signs were 0.849 (SE = 0.100). Snow leopards made up 606 (64%) of the 934 photos taken in KNP, with a trap success rate of 0.051 for every 100 trap nights. Because there was no snow leopards were photographed in CGNP, 25 pictures of other animals yielded a small insufficient 0.00053 success rate out of 100 trap nights. Scent lures successfully drew canid species, but not all predators, according to regression analysis. The results indicate the effectiveness of use of sign surveys along with camera trapping for snow leopard monitoring, although larger sample sizes are required for accurate statistical analysis. Furthermore, lure treatments can improve the identification of carnivores, especially canids, which supports their application in upcoming conservation initiatives.