Beech Leaf Disease Risk Mapping Data

All analyses were conducted in ArcMap and R using a combination of spatial, statistical, and ecological modeling packages. The terra and raster packages were used for reading, aligning, resampling, masking, and processing all raster datasets to a common projection, resolution, and extent. The dismo package was used to generate 19 bioclimatic variables (BIO1–BIO19) from monthly AdaptWest data and to perform MESS analysis for detecting environmental novelty under future climates. The ecospat package was employed for spatial autocorrelation and Mantel correlogram analysis, while usdm and corrplot were used to calculate variance inflation factors (VIF) and visualize correlation matrices to control for multicollinearity among predictors. Species distribution and risk modeling were performed using biomod2, which supported model calibration, evaluation, ensemble construction, and projection. The modeling workflow implemented multiple algorithms (e.g., GLM, GBM, RF, CTA, MAXNET) with nested cross-validation and model selection based on TSS, ROC, and Boyce Index metrics. The dplyr, tidyr, and tibble packages were used for data cleaning, transformation, and summarization of model outputs and range-size metrics. The ggplot2 and scales packages were applied to visualize model evaluations, variable importance, and response curves. Finally, ensemble projections and range shift analyses were performed through BIOMOD_EnsembleForecasting() and BIOMOD_RangeSize(), while all spatial summaries, correlation plots, and MESS maps were exported as high-resolution figures for reproducibility and interpretation.