by jhines » Mon Oct 21, 2019 7:45 am
I think there may be some confusion about the definition of "conditional" occupancy. In Presence, the "conditional" occupancy estimates are estimates of occupancy, given the detection history for each site. In a single-season model, any site with at least one detection will have a "conditional" occupancy estimate of 1.0 and sites with no detections will have a value < 1.0. These estimates can be computed for any/all models in the model-set, so you could use model-averaging on these just as you would for the "unconditional" estimates.
Estimates from any model are conditional on the model, so if you have a few models which are equally (nearly) likely, model-averaging the estimates allows you to present estimates which also include model uncertainty.
So, your question should really be 2 questions: 1) Should I use "conditional" occupancy estimates or "unconditional" estimates? 2) Should I use estimates from the top model or model-averaged estimates? The answer to the first question depends on what you want to convey. If it's information only about surveyed sites (eg., perhaps a map of occupancy of an area), then the conditional estimates may be more appropriate. If it's about any site with characteristics similar to covariates in the model, then the unconditional estimates are more appropriate. The answer to the 2nd question also depends on what you want to say. If it's about the relationship of occupancy and some covariate(s), you might give the beta estimates for the effect of that covariate. If it's about occupancy in general on a site-by-site basis, the model-averaged estimates may be best.