Hello all,
I am looking for some advice about appropriate models for some PIT tag data. I am new to Mark and I have begun working my way through the “Gentle Guide”. So far I am kind of stumped. Our objectives are to estimate survival and abundance of an arboreal rodent. We began live trapping in the study area and implanting individuals with PIT tags.
This trapping has been almost continuous throughout the last three years and we can treat the entire population as marked. Following our initial captures we began monitoring individuals at communal nesting sites (cavities) with our PIT-tag recorders. To find these cavities we followed radio collared individuals until the radio collars died or were removed. It is this PIT-tag data that we wish to use as our resighting data
My problem is with how to treat the non-random resight probabilities per individual (initially we were interested in the social behaviour of the species) as at different times different groups of squirrels were followed. Each individual does not have an equal probability of being resighted. The Poisson-log normal mark-resight model seemed appropriate assuming I separate the data into primary and secondary periods. However, the non-random resight probabilities complicate things. Any thoughts or suggestions about how I could go about estimating survival and abundance given non-random resight probability would be much appreciated? Or account for it in modeling?
Thanks,
Aaron