Wildlife Occupancy Estimation in Forests Using Occupancy Models

Reliable spatial data on wildlife occurrence are essential for ecological monitoring and environmental assessment, yet detection uncertainty often limits their interpretability. This study presents a camera-trap-based detection-non-detection dataset collected in a forested landscape, and provides grid-level occupancy estimates for water deer (Hydropotes inermis) and raccoon dog (Nyctereutes procyonoides) using an occupancy model framework. Repeated survey data were organized into detection histories, occupancy probability, and detection probability were estimated by incorporating observation variables and environmental variables. Both species exhibited higher occupancy probabilities in forest-dominated and less disturbed areas within the study extent. These results demonstrate the potential of occupancy models to quantitatively evaluate habitat suitability by accounting for detection probability, even with limited survey data, and provide baseline information for future environmental impact assessments and wildlife management.