[R-sig-eco] mixed model for repeat obs

[CL Pressland]

I have a data set that is unbalanced and consists of:

67 SITEs measured over several YEARs every WEEK for butterflies (LEPS per 
m). I'm interested in the MANagement code assigned to each site, but I have 
also data on TEMPerature, average SUN and WIND. My guess is that a linear 
mixed model would be most appropriate and have constructed this code:

model1<-lme(LEPS~MAN,random=~YEAR/WEEK|SITE)

The output gives me:
--------------------------------------------------------------------
Linear mixed-effects model fit by REML
 Data: NULL
        AIC       BIC   logLik
  -37631.24 -37566.48 18824.62

Random effects:
 Formula: ~YEAR/WEEK| SITE
 Structure: General positive-definite, Log-Cholesky parametrization
             	 StdDev       Corr
(Intercept)   	5.875102e-03 (Intr) YEAR
YEAR          	1.392439e-06 -0.164
YEAR:WEEK 		5.068196e-07  0.531  0.301
Residual      	3.532589e-02

Fixed effects: LEPS ~ MAN
                  Value   Std.Error   DF  t-value p-value
(Intercept) 0.009866718 0.001428957 9793 6.904841    0.00
MAN     	0.000028304 0.001127429   65 0.025105    0.98
 Correlation:
        (Intr)
MAN -0.685

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-2.70566579 -0.40089121 -0.18073723  0.05900735 19.16411466

Number of Observations: 9860
Number of Groups: 67
--------------------------------------------------------------------

This output confuses me greatly! I figure that this clearly means that 
management has no effect on butterflies but how can I figure out what 
effect SITE, YEAR and WEEK have on the data? Would I have to also include 
them in the fixed effects side of the formula (I'm unsure if this is 
allowed)? Also, how could I include my weather variables? Would they just 
be placed on the fixed effect side of the formula? If they are correlated 
(I expect the weather variables are) would I have to place them in an 
interaction rather than separately?

Any help that would be given would be gratefully received!

Kate