I am analysing a CMR dataset on frogs (n = 10 repeat surveys each season, for 3 seasons) and I am interesting in comparing the super-population size N between each season to assess if there is a population increase or decrease per season. Therefore I am producing a POPAN model for each season and comparing N between the best models of each season. I am using sex as a group factor.
However, after using release.gof I found that one season is under-dispersed (~0.6). In the manual, underdispersion can be treated by either ignoring or using a c-hat adjustment as in the case of over-dispersion. I originally thought that not addressing under-dispersion would be crude so I tried adjusting N for underdispersion and I got really strange and small N estimates which are lower than Ni estimates (and also the amount of captures made).
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##Estimate of N without chat adjustment
Real Parameter N
1
Group:SexF [b]17.64930[/b]
Group:SexM [b]35.81409[/b]
##Estimate of N with chat under-dispersion adjustment (chat=0.6)
get.real(ms3,"N",se=T)
all.diff.index par.index estimate se lcl ucl
N gF a0 t1 57 12 [b]4.649306[/b] 2.353316 1.723978 12.53847
N gM a0 t1 58 13 [b]9.814086[/b] 3.669627 4.715932 20.42359
This does not appear to be correct so perhaps ignoring it is the better approach. However this is unfortunate as QAIC adjusted for under-dispersion appeared to differentiate close models better than AIC. Any ideas what the best approach is?
Additionally, I am using popan.derived to calculate the combination of N of the groups. I noticed that the number change depending on if chat is adjusted in the model or not, which is convenient. However in the output, the combination of N of the groups does not come with 95% CI's whereas the combination of the groups per survey period does. Perhaps I am missing it in the output or maybe this obtainable with the output using the Delta-method?
Any help or advice would be greatly appreciated. Many thanks in advanced.
Kind regards,
Chad