ROBUST DESIGN FOR RECRUITMENT
The data are from Jim Nichols' study of male Microtus pennsylvanicus trapped at Patuxent Wildlife Research Center, Maryland over 6 months (6 primary periods), and daily over 5-day periods each month (5 secondary periods). I have already converted these data into RMark format and processed them for the Robust design Pradel seniority Closed population (‘RDPdGClosed’) parameterization. See the attached object. Once this object is loaded into your current memory, you have access to the processed data and ddl:
> load("mouse.robust")
> mouseG.processed<-mouse.robust$mouseG.processed
> mouseG.ddl<-mouse.robust$mouseG.ddl
From this point you can proceed with the code we used in lab and modify it to add the following models
Build an additional model assuming capture and recapture probabilities only vary in the secondary periods, seniority is constant over time, and apparent survival varies in the primary periods.
Which is the best model?
Using this model, estimate the population growth rate lambda(t) and recruitment rate f(t) for primary periods 1, 2, 3, 4 and 5 (BONUS QUESTION: Can you calculate lambda(6) and f(6)?).
Get standard errors of each of the estimates above using the delta method.
Can you estimate seniority for the first primary period ?
What is the number of unmarked animals (U) for each period?
What is the number of marked animals (M) for each period?
What is the total number of animals (N) for each period?
Using the model averaged estimates:
What is the number of unmarked animals (U) for each period?
What is the number of marked animals (M) for each period?
What is the total number of animals (N) for each period?
ROBUST DESIGN FOR TEMPORARY EMIGRATION
Use the simulated ‘Robust’ data, available here in RMark format processed for robust temporary emigration models. You can directly used the processed data to build models. For example:
> load("robust.temp.em.data")
> robust.processed<-robust.temp.em.data$robust.processed
> m1<-mark(robust.processed)
builds a simple (all parameters constant) RD model.
Build a Markovian emigration model (ɣ’ different from ɣ’’) where
temporary emigration estimates and survival all change over time, and
capture and recapture probabilities equal but changing in time and
sessions.
Repeat the above but capture and recapture are different.
Make a random emigration model that changes over time, using different capture and recapture probabilities varying over time, and survival changing over time.
Make a no movement model (fixed ɣ’=1, ɣ’’=0)
According to the first model:
What is the probability that an individual remains in the sample between seasons 1 and 2 (1-ɣ’’2)? And what is the probability that an individual temporarily emigrates from the population between those seasons?
What is the probability that an individual returns of temporary emigration between seasons 2 and 3? And what is the probability that an individual stays out of the population at those same season?
Can you estimate ɣ’2 (i.e. between seasons 1 and 2)? What does this parameter means, and why cannot/can it be estimated?