Mammal density estimation using camera traps: new perspectives

Reliable estimates of wildlife abundance are fundamental to ecological research and conservation, yet remain difficult to obtain for many species. Over the past decades, camera traps have become a central tool for wildlife monitoring because they provide non-invasive observations across broad spatial and temporal scales. However, camera-trap data do not directly reflect animal density. Instead, observed capture rates are shaped by detection processes that depend on biotic and abiotic factors including sensor properties, deployment geometry, animal movement, body size, and environmental conditions. As a result, variation in detectability represents a key limitation for inference from camera-trap data. This thesis addresses how wildlife density estimation from camera-trap data can be improved by explicitly accounting for detection processes and detectability, with a focus on unmarked terrestrial mammals that cannot be individually identified. Using the Random Encounter Model (REM) as a analytical framework, the thesis examines how deployment decisions, detection geometry, and data structure jointly shape what can be inferred from camera-trap encounters.