Hello all,
I am trying to run an analysis for a summer's worth of data. However, sites were sampled relatively sporadically throughout the period of time. This is to say that some sites were sampled up to 5 times within a week, whereas other sites were sampled twice, and still other sites sampled about once per month.
As a result, I have a dataset like this (https://ibb.co/zF9V18L), with a great number of missing values.
I have a few questions:
1. I have the capacity to sample in a more dedicated fashion this spring (i.e. sample all sites 3 times within a 2-week period), so are these data even salvageable?
2. Given how sparsely populated the data are, does it make sense to split these up into closed windows, and then use a multi-season model? Is there a way to intuit results given that both occupancy status as well as detection may very well vary as a result of the specific ponds being sampled, rather than the time-step (i.e. there would be some primary periods where there are a greater proportion of known occupied ponds)?
3. If I go that route, does it make sense to keep sites where I only sampled once in a given primary period?
Any feedback is appreciated, even the obvious 'why didn't you think of these things prior to last summer'.