Estimating elephant density using motion‐sensitive cameras: challenges, opportunities, and parameters for consideration - Morrison, Jacqueline; Omengo, Fred; Jones, Martin; Symeonakis, Elias; Walker, Susan L.; Cain, Bradley (The Journal of wildlife management, 2022)
With extinction rates far exceeding the natural background rate, reliable monitoring of wildlife populations has become crucial for adaptive management and conservation. Robust monitoring is often labor intensive with high economic costs, particularly in the case of those species that are subject to illegal poaching, such as elephants, which require frequent and accurate population estimates over large spatial scales. Dung counting methods are commonly employed to estimate the density of elephants; however, in the absence of a full survey calibration, these can be unreliable in heterogeneous habitats where dung decay rates may be highly variable. We explored whether motion‐sensitive cameras offer a simple, lower cost, and reliable alternative for monitoring in challenging forest environments. We estimated the density of African savanna elephants (Loxodanta africana) in a montane forest using the random encounter model and assessed the importance of surveying parameters for future survey design. We deployed motion‐sensitive cameras in 65 locations in the Aberdare Conservation Area in Kenya during June to August in 2015 to 2017, for a survey effort of 967 days, and a mean encounter rate of 0.09 ± 0.29 (SD) images/day. Elephants were captured in 16 locations. Density estimates varied between vegetation types, with estimates ranging from 6.27/km2 in shrub, 1.1/km2 in forest, 0.53/km2 in bamboo (Yushania alpine), and 0.44/km2 in the moorlands. The average speed of animal movement and the camera detection zone had the strongest linear associations with density estimates (R = −0.97). The random encounter model has the potential to offer an alternative, or complementary method within the active management framework for monitoring elephant populations in forests at a relatively low cost. Obtaining reliable estimates of elephant densities in forest environments is challenging, often expensive, and resource intensive. We demonstrated a reliable, low‐cost method for deriving elephant densities using random encounter models (REM) and images from motion‐sensitive cameras. This study highlights the importance of an appropriate stratification of sampling effort, and the accurate calibration of the REM parameters, particularly speed of daily animal movement, and camera detection distance.