Hello,
Thanks for visiting my question. I have spent the past few weeks reading up on occupancy analysis, and looking through this forum has been very helpful.
I have presence/absence data for a single species at 63 sites recorded over 20 weekly surveys. The 20-week survey period includes a pre-hibernation, hibernation and post-hibernation stage – so generally speaking, ‘occupied sites’ tended to be used during the first few weeks, unused during mid-season, and then used again during the last few weeks. The individuals’ temporary unavailability during hibernation possibly violates the closure assumption but that’s another issue I’m trying to get my head around.
I am interested in assessing whether covariates had different impacts on occupancy/detection during the different stages in the survey period. For example, I’d like to look into whether a drop in site use was associated with temperature change, or whether a continuation in site use (through the supposed hibernation period) was associated with supplementary feeding.
To look at these different stages of the hibernation period, would it be best to just split the data into three separate datasets/projects (pre-hibernation, hibernation, and post-hibernation)?
My second question relates to rather high c hat values (>40) and tackling over-dispersion. Would a potential solution be pooling surveys so that 2 weeks (or more) of data become 1 survey? e.g. detection history 01 would become 1.
Thanks for your time.