Occupancy models are widely used to describe the distribution of rare and cryptic speciesâ€” those that occur on only a portion of the landscape and cannot be detected reliably during a single survey. However, occupancy models often provide inaccurate estimates of occupancy (Ïˆ Ì‚) and detection probabilities (p Ì‚) under these circumstances. We developed a new "conditional" occupancy design that more accurately estimates occupancy for rare species. Here we provide the full simulation dataset used to compare estimation properties of standard, removal and conditional designs. Data were simulated in R and analyzed using MCMC methods in package R2jags. See Specht et al. (in review) for description of methods. Please cite Specht et al. in further use of this data set.
|Date made available||2017|
|Publisher||Data Repository for the University of Minnesota|