One Size Does Not Fit All—Ignoring Spatial Variation in Occupancy and Detectability Underestimates the Required Number of Camera Trap Sites

Occupancy models applied to camera trap data are widely used in wildlife monitoring, yet recommendations on the number of survey sites often assume homogeneous conditions and simple model structures. These assumptions are rarely met, and violations can yield biased or imprecise estimates. We used a simulation‐based power analysis to evaluate site number requirements for accurate occupancy estimation under three scenarios of spatial variation in occupancy (𝜓) and detection ( p ): (i) no variation (benchmark), (ii) simple variation (one covariate per process) and (iii) complex variation (multiple covariates, interactions and categorical predictors). Across combinations of baseline occupancy (𝜓 = 0.20, 0.50, 0.80), cumulative detection probability ( p * = 0.65, 0.80, 0.95) and varying degrees of spatial variability, we fitted single‐season occupancy models to simulated datasets of 10–1000 sites. We then identified the minimum number of sites required to achieve root‐mean‐squared error (RMSE) ≤ 0.10 in occupancy estimates. Sample size requirements varied widely across scenarios. Cumulative detection probability strongly influenced required sample sizes, while occupancy mainly mattered when detection was low. With no variation, accurate estimates required 10–40 sites. With simple variation, spatial requirements ranged from 20–120 sites regardless of the degree of spatial variation. Complex variation imposed the highest costs: Models with 2, 3, 4 and 8 parameters per component (𝜓 and p ) required 40–600 sites. Our results highlight that there is no one‐size‐fits‐all number of sites, as requirements differ by an order of magnitude across realistic scenarios. When occupancy and detection vary across space, especially with multiple or categorical predictors, more sites are needed, and sampling effort scales with model complexity. Logistic constraints may force a trade‐off between complex models whose inference is unreliable due to insufficient numbers of sites and simpler, potentially unrealistic models. We recommend simulation‐based power analyses to navigate this trade‐off.