I'm running 9 years of CMR data on bears and am investigating the effect of a flooding event on recruitment (f). I compared a model with constant phi and constant f except that I varied 1 year of f due to the effects of the flood event. This is a robust design Pradel full heterogeneity model with pi, c, p, and f0. When I run the model based on the recruitment model, I get low recruitment the year of the flood (0.02, normally about 0.22) but the beta for the effect includes zero. However, if I parameterize the model in terms of lambda, I get low lambda during the flood (0.87, normally 1.08) but the beta does NOT include zero.
Why the difference? The deviance is the same for both models and the derived estimate of lambda in the recruitment model is correct. I read in the manual that there are questions about constraining 2 parameters (phi and f, or phi and lambda), which I do not completely understand, so that may be part of it. My gut tells me that Mark is having a harder time estimating recruitment because it is a low number close to zero but I do not have any real evidence for that. I tried the same model with phi as time-varying and the flood effect was not significant (95% CI did not include zero) in either case. Can anyone offer any insight?
Thanks!!
Joe