I am doing a multiple regression
My response variable is monthly  insect abundance 
my predictors are 2 climate variables (monthly accumulated precipitation and monthly temperature)
I want to verify which of these climate variables is best in predicting insect abundance. So far it is just a simple multiple regression, but I also want to verify if delayed climate response is better predictor of insect abundance, meaning that I will regress insect abundance on month T vs. climate variable on month T-1.
1) Should I do two multiple regressions, delayed and non delayed, and just choose the one that has the lower AIC?
2) Is there a way to add all these variables in a single model. I know i would have collinearity if  have both  delayed and non delayed variables in  one model, and I don't know how to deal with that .  

Any feedback would very much appreciated indeed.
Thanks



Humberto Dutra

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 	'Discipline - Success doesn't just happen. You have to be intentional about it, and that takes discipline.' - John Maxwell
       
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