Thanks for the clarifications. So you have discrete sessions and I assume you are doing some observations' pooling (e.g. in fall and spring). It is important that your pooling periods are not too large compared to the intervals, two important papers on this topic are
Hargrove et al. 1994 and
O'Brien et al. 2005. If you have unequal time intervals between sessions, you have to let it know to MARK or E-SURGE or whatever you will use.
I think you first need to figure out how to better handle the information about translocation. I understood that a maximum of one single translocation could happen for each individual. This justified the idea of defining groups of non-translocated vs. translocated individuals. If you use, as I suggested, a grouping factor you would be pooling in the same "translocated" group individuals that have been translocated a different number of times. By doing that you would be allowed to test wether translocated individuals - no matter how many times - have different probabilities of apparent survival (and potentially recapture) compared to the non-translocated individuals. This can make little biological sense I guess. You better handle translocation as a state. The main difference between a group and a state in CMR analyses is that a grouping factor is a fixed individual trait (e.g. sex) whereas a state is a dynamic one (e.g. breeder/non-breeder, infected/non-infected, etc.). In your case "translocated" seems more like a state, am I right?
By the way, in CMR when you deal with states you need multistate models. However I see you have a further level of complexity in your data and this has to do with the translocation distance. With other animals you might have a situation where your data proceed from, say, three locations (A,B,C) and therefore the movements between sites (A-B,A-C,B-C and viceversa) already "include" the information about distance, however you do not have this framework and you need to figure out something else. A possiblity would be treating translocation as a different state depending on some ranging criterion of distances. For instance:
Non-translocated -> state A
< 100Km -> state B
> 100Km & <300Km -> state C
> 300Km - state D
The you may have encounter histories like this:
A00ABD00C0
00AA0A0B00
0C0D0B0000
000AC0C000
...
where the first individual has been captured in the first session of the study period, not captured in the 2nd and 3rd, recaptured in the fourth, recaptured in the 5th and translocated <100Km away, recaptured in the 6th and translocated >300Km away, not recaptured in the 7th and 8th, recaptured in 9th and translocated between 100Km and 300Km away, and not recaptured in the 10th. In E-SURGE you would need to replace letters with numbers. This would give you more flexibility than you had with the grouping factor approach and you would be able to test all the intermediate hypotheses you may think of (just to say one: only translocation have an effect, regardless of distance).
Now, regarding the censoring. For me, in general, censoring data is not a good idea. It is problematic for sure when the censored individuals have some characteristic(s) that make them different from the non-censored individuals. Sometimes this is obvious but sometimes it is not and may lead to biased estimates in a hard-to-predict way. Up to now the best way of handling incomplete or partial information on states is probably by using a multievent approach*, something that you can do in E-SURGE (a software that was created ad-hoc for this) and that at some time will be probably possible to do in MARK too (see
this answer of E. Cooch and the
Kendall et al. (2012)). Multievent modelling can be seen as a further generalization of the multistate models which in turn are a generalization of the classic Cormack-Jolly-Seber models. Conceptually, what a multievent approach does is mapping the events, which define the information you collect in the field (that may be incomplete or unsure), to (hidden) states which are the real object of interest.
In the example I made before you would then have:
Five states (dead; captured non-translocated; captured B-like translocated; captured C-like translocated; captured D-like translocated) and six events (0= not captured; 1= captured and non-translocated; 2= captured and B-like translocated; 3= captured and C-like translocated; 4= captured and D-like translocated; 5= captured and unknown if translocated).
Then you would need to set up a parameter that would be used to include the probability that a captured individual in one of the possible states has been indeed captured without information on translocation. If you decide to give it a try a good starting point is
Pradel (2005) and the
E-SURGE manual, also you may find a lot of papers using E-SURGE with a step-by-step explanation in the supplementary materials, and by the way, the phidot forum may be of great help too. Feel free to contact me in private if you need more specific help.