Climatic associations
We used Partial Least-Squares Regression (PLSR) models to evaluate the
importance of ECV’s trends in describing the ΔCF variability. We first
estimated the optimal number of components required for the model using
10-fold cross-validation models repeated 100 times following (Kuhn and
Johnson, 2016). The optimal number of components was selected as the
lowest Root Mean Squared Error of Prediction (RMSEP). Knowing the
optimal number of components, we then developed a final iterative PLSR
model. This last model was the average of 5000 iterations, each using
50% of the data randomly selected to build the model and test it using
all the samples. We evaluated the model performance for each iteration
by examining the coefficient of determination (R2) and
the Root Mean Square Error (RMSE). We also assessed the importance of
each ECV in describing the ΔCF variability by estimating the Variable of
Importance of Prediction (VIP). The PLSR models were performed in R
using the pls package (Liland et al., 2021), whereas the VIP was
estimated using the plsVarSel package (Mehmood et al., 2012). We did not
split our data into training and testing datasets because our goal was
to disentangle the association between ECVs and ΔCF more than creating a
prediction model.