Hi All,
I am attempting to tease apart the influence of human harvest rates and annual food productivity on grizzly bear demographics using 8 years of DNA-based mark-recapture data.
I have been using the Robust Design Huggins Closed Population models but have not yet settled on the Recruitment (RDPdfHuggins) or Lambda (RDPdLHuggins) version.
I began with the Lambda version, where I used a step approach where I tested the fit of a few models to p, while keeping Phi and Lambda constant (~1). Following this I retained the best model for p, and tested models for Phi, while leaving Lambda constant, and finally retained the top Phi model and then modelled Lambda.
When I used the exact same approach outlined above for the Recruitment version, my top model for p was identical, as expected, but my top model for Phi was different when using (RDPdfHuggins vs RDPdLHuggins).
My top Phi model for RDPdfHuggins, while leaving f constant (~1) was also a constant survival (phi~1), but when I did the same thing RDPdLHuggins, human harvest came out as my top survival parameter.
Am I just not understanding how the model derivation is conducted, as the Phi, Lambda and Recruitment parameters are obviously not mutually exclusive? Once I get this small issue figured, out, I'm also wondering whether the Phi and f model is a better approach than the Phi and Lambda model given that Phi and f are different processes, while Lambda is simply Phi+f, which makes modelling somewhat difficult as we are trying to fit a model to multiple processes.
Any input on this issue?
Cheers,
Clayton