to trust AIC (non-full model) or slope (full model)

The purpose to run regressions for butterfly richness again 5 environmental variables is to show the importance rank of the independent variables mainly by AIC.

In non-full models, they reveal that variable A tends to be more influential than the others by delta AIC.

However, in the full model, the regression coefficient of variable A is slightly second to that of variable B. (R-square of the full model is 1.43)

The conflicting outcomes (non-full model by AIC and full model by slope) seems to make it difficult to ascertain that variable A is the variable mostly weighted.

Please kindly suggest which criterion should be relied on for the specified purpose or any further test should be carried out.
Thank you.

Mean Joe

TS Contributor
Off the top of my head, I believe that the AIC should be used to ascertain the importance rank of the independent variables.

For one thing, I believe that the size of the regression coefficient is affected by the scale of the variable, eg if B is measured in inches then the coefficient will be smaller than if B was measured in feet. I believe this assumption can be evidenced by you, by dividing the values of B by 10, then comparing the coefficient results.