The subject of a multi-state Barker joint live dead model was discussed on this forum a while ago (“Resightings in multi-state models?” in 2011 and “Resightings in multi-state models?”in 2017), but since then not a lot has been said about this specific model.
At the moment, I am working with a dataset with ringing data, resightings and recoveries of barnacle geese. These geese are ringed or recaptured in both summer and winter and can be resighted and recovered all year round. After reading the manual and the literature, a barker joint live dead model seems to be the best fit here.
The geese are caught in the summer in three different breeding areas. In winter, there is only one sampling area. Because we are interested in the transition between the different breeding areas, I would like to extend the barker model to a multi-state barker model. Because we are not able to directly assign geese ringed in the winter to a specific breeding area, state uncertainty must be taken into account.
Since this model was last discussed on this forum, it has been implemented into Mark.
Because the explanation of this model is not yet included in the manual, I have read the help file supplied by MARK and any articles on multi-state models with state uncertainty I could found. The explanation in the help file is fairly concise and there are a number of parameters that I do not understand completely.
It concerns the parameters rho, rho’, b and pi.
If I understand correctly, delta would say something about the probability of giving the animal the correct state?
It is a very complicated model, so I am a bit anxious about the analyses. Any help would be greatly appreciated.
Thanks!