Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania

The random encounter model (REM) is a novel method for estimating animal density fromcamera trap data without the need for individual recognition. It has never been used to estimate the density oflarge carnivore species, despite these being the focus of most camera trap studies worldwide. In this context,we applied the REM to estimate the density of female lions (Panthera leo) from camera traps implemented inSerengeti National Park, Tanzania, comparing estimates to reference values derived from pride census data.More specifically, we attempted to account for bias resulting from non-random camera placement at lionresting sites under isolated trees by comparing estimates derived from night versus day photographs, betweendry and wet seasons, and between habitats that differ in their amount of tree cover. Overall, we recorded 169and 163 independent photographic events of female lions from 7,608 and 12,137 camera trap days carried outin the dry season of 2010 and the wet season of 2011, respectively. Although all REM models consideredover-estimated female lion density, models that considered only night-time events resulted in estimates thatwere much less biased relative to those based on all photographic events. We conclude that restricting REMestimation to periods and habitats in which animal movement is more likely to be random with respect tocameras can help reduce bias in estimates of density for female Serengeti lions. We highlight that accurateREM estimates will nonetheless be dependent on reliable measures of average speed of animal movement andcamera detection zone dimensions.