Hi,
I'm hoping to get some clarification on the following issue. I have 16-year CMR data set of birds marked as young (L) and adults (SY and ASY). I'm trying to estimate population sizes of each age class for each year of the study using a POPAN model. I tested the GOF of the most general model and found significant transience among young birds (not surprising since many die after fledging or disperse). Even after manually removing young that were never recaptured, I still have a transience issue. I also tried suppressing the first encounter, which took care of the transience issue but led to a trap-dependence issue.
I know building time since marking (TSM) models (i.e., Φa2*t, pt) is recommended in this instance, but I'm wondering if incorporating the above age classes in each model is enough to take care of it. I created my tsm variable based off this post (viewtopic.php?f=21&t=2591), and since I know age has an effect on survival, I've already incorporated age class into several models. Right now the Phi models I'm working with are
Phi.dot=list(formula=~1)
Phi.time=list(formula=~time*tsm)
Phi.Time=list(formula=~Time*tsm)
Phi.age=list(formula=~age_class*tsm)
Phi.int.time=list(formula=~time*age_class*tsm)
Phi.int.Time=list(formula=~tsm*Time*age_class)
Phi.add.time=list(formula=~tsm*time+tsm*age_class)
Phi.add.Time=list(formula=~tsm*Time+tsm*age_class)
which I'm thinking are overly complicated (and maybe redundant)?
Simply put, do I need to include the tsm variable in all my models, or is including the age classes in each model adequate to deal with transience? Any clarification or suggestions would be greatly appreciated.
Thank you.