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Occupancy models involve both the probability a site is occupied and theprobability occupancy is detected. The homogeneous occupancy model, where theoccupancy and detection probabilities are the same at each site, admits anorthogonal parameter transformation that yields a two-stage process tocalculate the maximum likelihood estimates so that it is not necessary tosimultaneously estimate the occupancy and detection probabilities. Thetwo-stage approach is examined here for the heterogeneous occupancy model wherethe occupancy and detection probabilities now depend on covariates that mayvary between sites and over time. There is no longer an orthogonaltransformation but this approach effectively reduces the parameter space andallows fuller use of the R functionality. This permits use of existing vectorgeneralised linear models methods to fit models for detection and allows thedevelopment of an iterative weighted least squares approach to fit models foroccupancy. Efficiency is examined in a simulation study and the full maximumlikelihood and two-stage approaches are compared on several data sets.

Two-stage approaches to the analysis of occupancy data II. The heterogeneous model and conditional likelihood