ganghis wrote:Hi Claudia,
Multiple sources of mortality can be modeled using the multistate framework. For an example having to do with tag recovery data, see
Schaub & Pradel ECOLOGY 85 (4): 930-938 APR 2004
As long as there is no censoring, you should be able to do this in MARK using the multistate data type, by fixing p=1, phi=1, and letting state transitions represent different survival and mortality parameters. You will need to code in several different states, such as 'alive' (call this state 'A'), 'dead because of cause B' (call this state B), 'dead because of cause C' (call this state C). For example, an individual that is known to be alive in sampling periods 1, 2, and 3, but dies of cause B between sampling periods 3 and 4 would receive the history AAABB in the case where there are 5 total sampling periods. The key here is that you need to fix some of the state transitions to 0 or 1 as well. For instance, the probability that an animal moves out of state B or C once they are dead is 0.
That is correct. In fact, Pradel and colleagues have shown in a variety of cases that a particular model is often just a special case of a more general multi-state model. A number of examples of this are given in the multi-state chapter in the book. As Paul notes, the 'trick' in most cases is to figure out which states are 'absorbing', or not, and what the detection rates are in various states, and fix parameters as needed.
I share Paul's uncertainty about censoring. As noted in the known fate chapter, censoring is a general problem - hopefully one you can avoid.