Use of camera trapping in wildlife monitoring (Zoological Records and Reviews, 2025)
Camera trapping has evolved from a specialist research technique to the standard platform for non-invasive wildlifemonitoring across habitats globally, with applications spanning species detection, occupancy estimation, abundanceindexing, behaviour analysis, and machine learning-based automated species identification. This study evaluates cameratrapping performance, design optimisation, and data analysis approaches for four wildlife monitoring objectives acrossboreal (Finland), alpine (Austria), and continental (Denmark) habitats using 312 camera stations deployed for 84,621trap-nights (2022-2024). Performance metrics assessed across 48 target species include detection probability,occupancy estimation precision, and population density accuracy (via random encounter model, REM). Station spacingoptimisation experiments (24 configurations tested) demonstrate that species-specific activity range determines optimalinter-station distance: 500-800 m spacing maximised small mammal detection rates while 1,500-2,500 m spacing wasoptimal for large carnivores. Machine learning classification using a fine-tuned EfficientNetV2 model achieved 94.8 +-2.4% species identification accuracy across 38 species for images with adequate contrast -- reducing manual imageprocessing time by 78.4%. Random encounter model density estimates for roe deer (Capreolus capreolus) correlatedstrongly with independent GPS-telemetry density estimates (r = 0.88, p < 0.001). Deployment of camera trap networksfor Natura 2000 monitoring targets demonstrates that standardised protocols achieve sufficient statistical power (80%power) to detect 20% occupancy change with 24-36 cameras deployed for 90 trap-nights. These results provideevidence-based guidance for camera trap network design under EU Habitats Directive and national biodiversitymonitoring programme requirements.