Jolly-Seber spatial mark-resight samplers marginalizing out latent individual IDs and integrated telemetry survival
This model is an SMR version of the Jolly-Seber model here (JS-SCR-Dcov):
https://github.com/benaug/Jolly-Seber-N-Prior-DA
It also has allows integrated telemetry survival data, see here for an SCR version:
https://github.com/benaug/Spatial-IPM-Telemetry
The SMR model comes from the marginal SMR models here:
https://github.com/benaug/Spatial-Mark-Resight-Marginal
This model considers sample types: marked with ID, marked with no ID, unmarked, unknown marked status. To speed up computation, it uses the approach of Herliansyah et al. (2024, section 4.3) in the custom N/z and activity center updates.
https://link.springer.com/article/10.1007/s13253-023-00598-3
Currently, I assume 1) the mark status does not change within a primary occasion (no interspersed marking and resighting), and 2) the mark status of all marked individuals is known in all years. This likely limits us to marks that are telemetry (GPS) collars. Marked individuals can carry their marks across years, but I currently assume mark loss/censoring is uninformative.
The model can be modified for some unknown mark status scenarios. For example, a telemetry collar can die, so you wouldn't know that individual is still in the population, but you may resight it and know it must be one of the marked individuals. So individuals with dead collars would produce all "marked with no ID" sample types.
The model currently assumes there is a marking and resighting process in every primary occasion. You can modify the effort for each, but currently you must include at least one occasion of each process in each year. The next thing I will do is to set it up to allow arbitrary combinations of marking and resighting like is done in the Sapatial-IPM-Telemetry repo for 2 SCR processes.