I’m working with a colleague who has some unusual mark-recapture data. What makes the data ‘unusual’ is that their data has missing sampling occasions on a per-individual basis. The sampling protocol wasn’t designed with a mark-recapture analysis in mind, but now they would like to get a survival estimate if at all possible, so I’m trying to determine if any modeling framework could accomplish this. More on the sampling protocol below.
My colleague is working with a species of tree frog in the Amazon. The biology of the frog in question is such that it is only found to occur on a single species of tree, and dispersal from one tree to another is extremely low. My colleague conducted field surveys over the course of 4 seasons (2018 dry, 2018 wet, 2019 dry, and 2019 wet). During each season, he surveyed individual trees for the frog species in question and collected samples of their toxic secretion. However, in each season, he surveyed a different number of trees:
2018 dry: 15 trees
2018 wet: 8 trees (no new trees surveyed, only returned to trees previously surveyed in 2018 dry)
2019 dry: 38 trees (returned to the 15 trees previously surveyed, plus 23 new trees surveyed)
2019 wet: 37 trees (returned to all but 1 tree surveyed in 2019 dry)
This sampling procedure resulted in individual frogs having missing encounter data depending on which tree they resided on. Below is a subset of the encounter histories:
- Code: Select all
1100
1110
..11
0.1.
0.10
Any thoughts on how to approach this analysis would be appreciated!
~Brandon