| Popis: |
Additional file 1: Table S1. Fixed GLMM coefficients. β1, β2, β3 significance were calculated from t-value, while β3, β4, β5 from p-value. Table S2. Random GLMM coefficients. Table S3. GWR models coefficients. Table S3. Summary of mushroom, climate and physical data of the 98 sampled plots. All these variables were used to train the 15-variable machine learning models, while the 5-variable machine learning models and the statistical models only used those marked with *. The response variable is shown with **. Table S4. Tuned optimal hyperparameters using a k-fold CV. Table S5. Tuned optimal hyperparameters using an environmental CV. Figure S1. Study area, distribution of mushroom productivity monitoring plots (red points) and pine forest ecosystems represented by the sample plots (green area) Coordinates system: WGS 84 / UTM zone 31 N. Figure S2. GWR coefficient estimates according to geographical location. Coefficient of precipitation amount from August to October (A) and maximum temperature in August (B) in conditioned production model. Coefficient of precipitation amount from August to October (A) and maximum temperature in October (D) in occurrence model (C). Figure S3. Similarity in climatic conditions between the modeling data and the whole study area using five or 12 variables. Two-dimensional representation given by the two principal components, namely, PC1 and PC2, of a principal component analysis (PCA) of the modeling data (with a density map) and the environmental conditions over the whole study area (gray dots). |
