Method to compare variable coefficient in two regression models I am regressing two butterfly richness variables (summer and winter) 
against a set of environmental variables separately. 
(variables with continuous numbers)
Environmental variables are identitcal in each model.
In the summer model,
the weight rank of coefficients is
temp > prec > ndvi.
The weight rank in winter is 
temp > ndvi > prec.
As it is almost implausible to compare the coefficients directly, 
pls advise any advanced method other than regression 
to discriminate such coefficient rank between seasons, 
such as canonical correlation analysis (unsure if it is suitable here)
The spatial info here
richness and environmental variables comprising 2000 grid (continuous distribution) spanning from 100 E to 130 E longitude, 18 to 25 N latitude.
 A: Here is my suggestion.  Rerun your model(s) using one single regression.  And, the Summer/Winter variable would be simply a single dummy variable (1,0).  This way you would have a coefficient for Summer to differentiate it from Winter.  And, the regression coefficients for your three other variables would be consistent with one single weight rank.   
A: One answer is to do a seemingly unrelated regression. Suppose that you only have a single predictor plus an intercept. Create a data set (or data matrix) like
wo 1 wp 0 0
so 0 0  1 sp

where 'wo' is the outcome in the winter season and 'wp' is the winter predictor/"X" value and 'so' is the summer outcome value and 'sp' is the summer predictor. The 1s represent the summer and winter intercept terms. Basically, you have two sets of variables: summer variables and winter variables. In the summer, all the winter variables are set to 0 and vice-versa in the summer.
After you run a regression on the full set of summer and winter variables using the data template above, you get a full covariance matrix that can be used to compare the coefficients for the winter months to the summer months using standard regression procedures.
This is one way of implementing @kwak's suggestion of having different slope coefficients for each season.
