Activity patterns of snow leopards (Panthera uncia) at their kill sites

Sammanfattning: The snow leopard (Panthera uncia) is an elusive felid, native to the mountains in central Asia. Basic knowledge about the snow leopards’ ecology has long been lacking but is advancing with the help of the GPS-technology. GPS cluster analysis can provide insight in the diet and prey selection of elusive predators, such as the snow leopard. Acceleration data from GPS collars can be used to study animal behavior but the two have never been combined to gain more detailed information of the feeding behavior of large carnivores. In this study reference values for activity loggers were derived by behavioral observations on a captive male snow leopard fitted with a GPS collar. These reference values were used to make interpretations on behaviors of free-ranging snow leopards at their kill sites. In the study a discriminant function analysis was used to (1) test the classification fit of the behaviors from the observed snow leopard with the acceleration data and (2) used the discriminant scores to predict behaviors of GPS-collared snow leopards in the wild using the software R (R 2.12.1 with package MASS). The result showed that behavior explained 83.5% and 94.2% of the variability of the activity data of the observed cat when behaviors where separated into seven and three behavioral categories, respectively. The three behavioral categories were high activity, medium activity and low activity. When predicting behaviors on wild snow leopards at kill sites I found that low activity was the dominating behavior at the kill sites with 85% and 88% for female and male snow leopards, followed by medium activity with 15% and 12%, respectively, and high activity with less than 1% for both sexes. This study suggests that cluster analysis in combination with acceleration data can be a used to get a better understanding of snow leopards behaviors at kill sites. Especially when looking at active and inactive behaviors, however by shortening the time interval for which the activity data is calculated this tool can probably also be used to gain a deeper understanding of the behavior of wild snow leopards.