I am trying to estimate population sizes for 4 hedgehog populations in one city. All four locations were sampled within a month for 8 nights. I was recommended to use a Huggins model for closed populations.

As two of the populations seem to have very low hedgehog densities, I estimated detection probabilities by combining all four data sets and using a Mo model with p and c being the same values.

Now I would like to get N estimates for the four populations individually using my averaged value for p and c (being the same). I guess this must be the easiest part of the analysis, but I cannot figure out how to do this in MARK. If I run the model with fixed p and c values, I don't get derived estimates. Can anyone help me? Thank you very much in advance.

It's difficult to know exactly what you did given the limited details you provided, but you shouldn't need to fix p or c. If you haven't already, you should stratify your data into four groups (i.e., 4 sites). Then simply make all sites share the same intercept so that p=c and is constant among sites. You might also want to consider alternative models where there are 2 or 3 p parameters in a model and let AICc sort it out rather than enforcing constant p among all sites without assessing if this is a reasonable thing to do.

In the two p parameter model scenario, you could constrain one p to represent the two high density populations and constrain the other p to represent the two low population densities (i.e., 2 intercepts). In the 3 p parameter scenario, you could constrain p to be constant between the two low density populations and each of the high density populations could have their own estimate of p (i.e., 3 intercepts). I hope this helps!

Cheers,

Eric