
I have been running lmer models in R, looking at what effects reproductive success in Ground Hornbills (a South African Bird). My response variable is breeding success and is binomial (0-1) and my random effect is group ID. My response variables include rainfall, vegetation, group size, year, nests, and proportion of open woodland. 
 
I have run numerous models with success but I am confused about what the outputs are. When I run my first model with all my variables (all additive) then i get a low AIC value with only a few of the variables being significant. When i take out the varaibles that are not significant then my AIC becomes higher but I have more significant variables! When I keep taking out the unsignificant variables, I am left with a model that has nests, open woodland, and group size as being extremely significant BUT the AIC is high! Why is my AIC value increasing when I have fewer varaibles that are all significant and seem to be best explaining my data? Do i look at only the AIC when choosing the 'best' model or do I look at only the p-values? or both? The model with the lowest AIC at the moment has the most variables and most are not significant?
 
Please help. Any suggestions would be great!!
 		 	   		  
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