not constant predictor in lme part 2

[Bernd Puschner <puschner@psyres-stuttgart.de>]

for some reason, my mail is not delivered entirely. here the 2nd part,
sorry.

989002     142                 1.44444444        2.00000000
989002     519                 0.54444444        2.27272727
...

when modelling
> verlauf.abm2.lme <- lme(data = verlauf, random = ~IN.THER | CODE,
fixed = SC10M ~ ABM * IN.THER , na.action = na.omit) ### linear

i get

> summary(verlauf.abm2.lme)
Linear mixed-effects model fit by REML
 Data: verlauf
       AIC      BIC    logLik
  2010.786 2054.579 -997.3929

Random effects:
 Formula:  ~ IN.THER | CODE
 Structure: General positive-definite
                  StdDev   Corr
(Intercept) 0.4675282487 (Inter
    IN.THER 0.0003534833 -0.129
   Residual 0.2969792753

Fixed effects: SC10M ~ ABM * IN.THER
                Value  Std.Error   DF   t-value p-value
(Intercept)  1.180972 0.04409323 1249  26.78351  <.0001
        ABM -0.135917 0.02101214 1249  -6.46851  <.0001
    IN.THER -0.000287 0.00010049 1249  -2.85861  0.0043
ABM:IN.THER -0.000058 0.00005029 1249  -1.15339  0.2490
 Correlation:
            (Intr)    ABM INTHER
        ABM -0.853
    IN.THER -0.594  0.618
ABM:IN.THER  0.595 -0.692 -0.925

Standardized Within-Group Residuals:
       Min         Q1         Med        Q3      Max
 -3.942879 -0.4927709 -0.09348164 0.4112977 4.776529

Number of Observations: 1766
Number of Groups: 514

my question is: how do i interpret the significant effect of abm on
intercept (coefficent -0.135917) correctly?

any hints would be highly appreciated.

thanks.

bernd