I'm analyzing the proportion of marked chicks vs. the number of chicks that were recaptured at one moth of age (not possible to use conventional capture-recapture analysis because we don't have a second recapture event). The idea is to find if there is a change over time in the season using the hatch date and year as explanatory variables and their quadratic terms (looking for nonlinearity) as well as interactions. I ran models using the different combinations of explanatory variables.
glm(cbind(recapt,mark-recaps)~date+year+year2+date:year+date:year2, family="x.quasibinomial", data=df)
So we decided to use proportion analysis. The data has a little overdispersion (c-hat=1.3), so I'm using a quasibinomial family as x.quasibinomial (see: https://cran.r-project.org/web/packages/bbmle/vignettes/quasi.pdf).
When I use model selection from
MuMIn package, the best model is the null model (QAIC=2572.0) as well as one model that includes an interaction (QAIC=2572.7, delta=0.68).
I already tried to change the control of my model as suggested here, but I think I don't have a convergence problem.
How can I interpret the output? How can I explain the presence of the null model at the top? Is it possible to use the second best model? what else can I change in the model setting?