Under this approach, the capture histories for each animal are modeled in both forward and reverse direction. In forward direction, this is simple a CJS analysis: animals that are released at i and recaptured at some i+k occasion must have survived k intervals. In reverse direction, we are modeling entry or "seniority": an animal that was in the population at time i and had earlier been captured at i -k, must have entered the population k occasions (or earlier) ago. Putting these approaches together allows estimation of:
Phii - survival
fi - recruitment
pi - recapture
each of which may be modeled as time-specific, group-specific, or in relation to covariates.
The above is the "survival and recruitment" parameterization (Pradrec), but the Pradel model can be cast in terms of population growth rates (lambda) or seniority rates (the original Pradel parameter). There is also a 'recruitment only' version that is appropriate if mortality can be assumed nil.
The R script reads the capsid data, computes alternative Pradel models, conducts model averaging, and produces a table of real parameter estimates for the time specific model. Note that the Pradel approach does not estimate abundance; instead a per-capita recruitment rate f is directly produced. For comparison, the equivalent parameter from POPAN-JS would be
fi = Bi /Ni.
Comments on approaches
Either the POPAN or the Pradel approaches provide flexible modeling for CMR data for survival and recruitment estimation. Obviously, POPAN-JS provides additional information in the form of abundance estimates, but it must be kept in mind that these are very subject to biases if capture heterogeneity is an issue. Again, however, because both approaches depend ultimately on inclusion of unmarked animals, there is no avoiding sensitivity to the heterogeneity/ capture effect issue. To cut that knot we need more data: hence, the "Robust Design."
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