I am a novice in Generalized Additive Models (GAMs) and I would need some advice on these models. From capture data, I would like to assess the effect of longitudinal changes in proportion of forests on abundance of skunks. To test this, I built this GAM where the dependent variable is the number of unique skunks and the independent variables are the X coordinates of the centroids of trapping sites (called "X" in the GAM) and the proportion of forests within the trapping sites (called "prop_forest" in the GAM):

mod <- gam(nb_unique ~ s(x,prop_forest), offset=log_trap_eff, family=nb(theta=NULL, link="log"), data=succ_capt_skunk, method = "REML", select = TRUE) 

Family: Negative Binomial(13.446) 
Link function: log 

nb_unique ~ s(x, prop_forest) 

Parametric coefficients: 
            Estimate Std. Error z value Pr(>|z|) 
(Intercept) -2.02095    0.03896  -51.87   <2e-16 *** 
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Approximate significance of smooth terms: 
                   edf Ref.df Chi.sq  p-value 
s(x,prop_forest) 3.182     29  17.76 0.000102 *** 
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

R-sq.(adj) =   0.37   Deviance explained =   49% 
-REML = 268.61  Scale est. = 1         n = 58
Should I include the simple effects of independent variables "X" and "prop_forest" into the GAM when the interaction is significant? I ask this question because the longitude and latitude are often included as an interaction term in a GAM (i.e., s(X,Y)) without the simple effects (however, I tested for the simple effects and they were not significant in my case).

Is it correct to include the interaction between X and proportion of forests when my objective is to test longitudinal changes in proportion of forests?

Thanks a lot for your time.
Have a nice day.