Data from: 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-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 approximately 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.