I apologize in advance for my question that can seem redundant, but I am still struggling to interpret the outcome of my lme model, as the other posts mainly deal with several categorical variables and I get really confused...

I have three continuous variables, `Delta` (my measurement), `Date`, and `Light` and two categorical variables, `Identity` (names) and `Treatment` applied to the subjects, which has 3 non ordered levels.

I understand how to interpret betas when the parameters are all categorical/all continuous - but the mix makes me perplex.

I use the lme model:

    model.lme = lme(Delta ~ Treatment + Light * Date, 
    random =~1|Identity, na.action = na.omit,
    control = lmeControl(optimum ="opt"))

and I get :

    Random effects:
     Formula: ~1 | ID
            (Intercept)  Residual
    StdDev:  0.08049669 0.3757045
    
    Fixed effects: Delta ~ Treatment + Light * Date 
                      Value  Std.Error     DF    t-value p-value
    (Intercept)  0.27590485 0.03358550 350400   8.214999  0.0000
    Treatment2  -0.06606907 0.04418992 350400  -1.495116  0.1349
    Treatment3  -0.01027265 0.04861505     16  -0.211306  0.8353
    Light        0.00088466 0.00009181 350400   9.635306  0.0000
    Date        -0.00083441 0.00004336 350400 -19.241967  0.0000
    Light:Date   0.00000134 0.00000050 350400   2.673734  0.0075
     Correlation: 
               (Intr) Trtmn2 Trtmn3 Light  DOY   
    Treatment2 -0.709                            
    Treatment3 -0.654  0.490                     
    Light      -0.109  0.000  0.000              
    DOY        -0.235  0.004  0.007  0.459       
    Light:DOY   0.109  0.000  0.000 -0.995 -0.462
    
    Standardized Within-Group Residuals:
            Min          Q1         Med          Q3         Max 
    -5.28753179 -0.23911026 -0.09499650  0.05150716 43.17908373 
    
    Number of Observations: 350422
    Number of Groups: 18

If I am right, Intercept is the base effect, the value of `Delta` when the others parameters = 0. But somehow in what I have read I understand that I should take intercept as `Treatment1` ? 

Does it mean that Treatment1 is taken as a reference to which other parameters are compared ? It happens that Treatment1 is a control condition, but I am not sure whether or not this is scientifically valid as I would like all treatments to be considered 'equals'.
If this is not right, what exactly is `Treatment1` here ?

Also, should I interpret the value as a linear relation, as the model assumes so ? eg. `Date` has a significant effect on `Delta`, and its value is negative. So the further we are in time the lower is `Delta` ? Is it correct to say that Delta decreases following a slope of 0,0008 ? Or is it more accurate to say that the slope is (0,2759 - 0,0008)?

I guess that if I have no proof that there is a linear relation (which is not very likely here I think) the only thing I can say with this model is 'Delta is significantly affected by Light and Date' ?

Many, many thanks in advance for any help on this,

Caspa