Inferring camera trap detection zones for rare species using species‐ and camera‐specific traits: a meta‐level analysis

Camera trapping is a vital tool for wildlife monitoring. Accurately estimating a camera's detection zone, the area where animals are detected, is essential, particularly for calculating population densities of unmarked species. However, obtaining enough detection events to estimate detection zones accurately remains difficult, particularly for rare species. Given that detection zones are influenced by species‐ and camera‐specific traits, it may be possible to infer detection zones from these traits when data are scarce. We conducted a meta‐level analysis to assess how the number of detection events, species traits and site‐specific variables influence the estimation of the effective camera trap detection distance and angle. We reviewed published studies on detection zones, performed a power analysis to estimate the sample sizes required for accurate and precise estimates and used mixed‐effects models to test whether detection zones can be predicted from biological and technical traits. Our results show that c. 50 detection events are needed to achieve error rates below 10%. The mixed‐effects models explained 81% and 85% of the variation in effective detection distance and angle, respectively. Key predictors of detection distance included body mass, right‐truncation distance and camera brand, while angle was predicted by camera brand and installation height. Importantly, we demonstrate that combining model‐based predictions with limited empirical data (fewer than 25 detections) can reduce estimation error to below 15% for rare species. This study highlights that detection zones can be predicted not only within, but also across, studies using shared traits and that the right‐truncation distance is a useful metric to account for habitat‐specific visibility. These findings enhance the utility of detection zones in ecological studies and support better study design, especially for rare or understudied species.