I've fitted a linear model using:
m2 <- lm(GPP ~ rainfall + summer.temp + parcel.size + soil.nutrients, data=gpp)
As seen from the partial relationship plots below, the linear model for parcel.size and summer.temp is not entirely appropriate and does not capture the pattern in the data very well.
I want to know how I can capture the these non-linear relationships (i.e. parcel.size and summer.temp) better (while still using a linear model [lm] if possible). I've tried polynomial regression for these explanatory variables as well as log transforming the response (GPP). Both methods does not work. Removing outliers / influential points from the data is not allowed. Any advice would be appreciated!