Hi,
I'm running a single-season single-species occupancy model and am struggling to understand why my top model estimate of occupancy (psi = 0.24) is less than my naive occupancy estimate (psi = 0.38). I detected individuals at 16/42 sites with no false positives, so I'm not sure how it's possible that my top model is reporting an occupancy of 0.24, when the null model has a much worse AIC score and reports an occupancy estimate of 0.42 in MARK.
I have been working in both PRESENCE and MARK. My models in both programs reflect the same AIC scores and beta estimates, so I know they're both running the same between programs. Oddly enough, when I calculate the overall psi estimate manually for my top model in PRESENCE by summing the psi-conditional and dividing it by the total number of sites I get psi = 0.42. Since I know the top model in MARK and PRESENCE are identical, why would I be getting a difference of psi = 0.24 in MARK and psi = 0.42 in PRESENCE? Am I looking for overall occupancy in the wrong place in MARK? I was using the estimate for psi that is found under 'real estimates.'
I did read a post in a different thread that said "You shouldn't be getting an occupancy estimate from the standard single season that is lower than the naive estimate. Or are you talking about a model that has covariates in it? If that case the individual estimate for some sites might be lower," but it was unclear what it meant. Does that mean this can sometimes occur when covariates are added? My top model has one covariate for psi and two for p.
Thanks for any insight!
~Holly