[R] How do I compare 47 GLM models with 1 to 5 interactions and unique combinations?

[Jhope]

Hi R-listers,

I have developed 47 GLM models with different combinations of interactions
from 1 variable to 5 variables. I have manually made each model separately
and put them into individual tables (organized by the number of variables)
showing the AIC score. I want to compare all of these models. 

1) What is the best way to compare various models with unique combinations
and different number of variables? 
2) I am trying to develop the most simplest model ideally. Even though
adding another variable would lower the AIC, how do I interpret it is worth
it to include another variable in the model? How do I know when to stop? 

Definitions of Variables:
HTL - distance to high tide line (continuous)
Veg - distance to vegetation 
Aeventexhumed - Event of exhumation
Sector - number measurements along the beach
Rayos - major sections of beach (grouped sectors)
TotalEggs - nest egg density

Example of how all models were created: 
Model2.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ Aeventexhumed,
data=data.to.analyze, family=binomial)
Model7.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ HTL:Veg, family =
binomial, data.to.analyze)
Model21.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ HTL:Veg:TotalEggs,
data.to.analyze, family = binomial)
Model37.glm <- glm(cbind(Shells, TotalEggs-Shells) ~
HTL:Veg:TotalEggs:Aeventexhumed, data.to.analyze, family=binomial)

Please advise, thanks! 
J


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