I would like to use the simulation module to compare effectiveness of different study designs for robust occupancy. The study designs involve different permutations of running 80 site-visits per year. In a given year, some sites will be visited multiple times; others only once. To accommodate other work we want to do, we are considering whether we have to focus on a limited number of sites over the whole study, or whether we can afford to keep many more sites in the target group, without visiting all sites each year.
No matter what we do, and in common with published studies using robust occupancy designs, all designs we are considering include lots of missing data, representing site-date combinations that were not surveyed. Is there a way to have MARK simulate data with missing values? Since this is a property of the data/design, not of the occupancy/detection parameters, I tried generating the ‘true’ data assuming all sites were visited on all dates. Then for my estimation models, I treated each site as a group, and set p(detection)=0 for dates when I wanted missing data. Assuming this would compromise parameter estimates, I thought it would still give a reasonable ranking of different study designs. However, in my test runs, MARK usually shuts down.
Any suggestions?
Linda