I am strangling to find the right modeling method for my data. Short explain of the dataset : I have a variable called alpha diversity ( Alpha diversity refers to the average species diversity in a habitat or specific area. Alpha diversity is a local measure) and we want to see the effect of environmental( n=9) variables on it

Here is the scatter plots of each variables x Alpha diversity(Shannon) . Because of the complexity of the data I decided yo use GAM in order to catch linear, non-linear and no-relationships within the dtaset
In the following plot you can see the scatter plots of each variables blue line linear red loess ( by using geom_smooth)

GAMs 1. first I ran the GAM by using smooth functions for all the variables as follows

GAM1 <- gam(Shannon ~ s(Distance_from_city_centre, bs = 'cr', k = 5)+
          s(Light_complete_100m, bs = 'cr', k = 5)+
          s(Temperature_Celsius, bs = 'cr', k = 5)+
          s(Human_presence, bs = 'cr', k = 5)+
          s(NDVI, bs = 'cr', k = 5)+
          s(Sound_dbC, bs = 'cr', k = 5)+
          s(Closest_Road_m, bs = 'cr', k = 5)+
          s(Closest_Path_m, bs = 'cr', k = 5)+
          s(Tree_cover, bs = 'cr', k = 5),
        data=data_stats_model,method = "REML")

and the results :

From the edf I see that many of the variables are close to linear ( edf=1) So for these variables I don't use a smoother and I run again the model as follows

GAM4 <- gam(Shannon ~ s(Distance_from_city_centre, bs = 'cr', k = 20) +
          Light_complete_100m +
          Temperature_Celsius +
          s(Human_presence, bs = 'cr', k = 25)+
          NDVI+
          Sound_dbC+
          s(Closest_Road_m, bs = 'cr', k = 5)+
          s(Closest_Path_m, bs = 'cr', k = 5)+
          Tree_cover,
        data=data_stats_model, method = "REML")

and the results are :

So my questions are :

1. Is this the right logic
2. If yes, how to handle the multicollinearity? When I ran LMM for the same data I used the VIF strategy

I would appreciate any help, Anna