Distance-based population density estimates from camera traps: Strong impact of technical choices in automated pipelines

Camera Trap Distance Sampling (CTDS) enables population density estimation of unmarked species from camera trap data by incorporating animal-to-camera distances. In this study, we systematically evaluate the sensitivity of CTDS-derived density estimates to variation in key pipeline parameters, using a high-resolution camera trap dataset from Central Europe. Over a 12-month period, 110 cameras were deployed across a forested area of 94 km 2 . Animal-to-camera distances were estimated via monocular depth estimation. To reduce manual annotation effort, we introduce an automated masking approach for calibration images based on the Segformer model, saving an estimated minimum of two minutes per image. Using red deer ( Cervus elaphus ) as model species, a full-factorial sensitivity study (n = 135 parameter configurations) was conducted, varying MegaDetector confidence threshold, depth sampling method, mean trigger intervals and right-hand distance truncation schemes. Results show that population density estimates are highly sensitive to the choice of depth sampling method, MegaDetector confidence threshold and mean trigger interval. Parameter-induced variation led to differences of up to 200% in population estimates relative to the lowest estimate, twice as much as the variation observed between spatial subsets of the study area differing in vegetation characteristics. However, relative differences between the two sub-areas remained largely consistent across configurations. These results highlight the critical impact of technical choices within CTDS workflows and emphasize the need for transparent reporting and standardization of key pipeline parameters. All CTDS analyses using automated pipelines should therefore be accompanied by dataset-specific sensitivity analyses to account for parameter uncertainty and validation challenges. Overall, our study demonstrates both the methodological challenges and practical applicability of automated CTDS for large-scale wildlife monitoring. • Automated masking reduces manual effort in depth calibration by ∼ 2 min per image. • CTDS parameter variation caused 200% difference, twice our spatial density variation. • Depth sampling, confidence thresholds and trigger intervals affect density estimates. • Automated CTDS requires standardization and dataset-specific sensitivity analyses.