This repository contains supporting data products to enable the soundscape mapping outlined in the associated publication (DOI forthcoming). Data were used to extract acoustic recording location environmental data for training random forest models to spatially predict 2021 ecoacoustic metrics. The accompanying code will be linked to the GitHub repository. Files include: Data: clustered_fold_k10.rsd: indices of the model data used if geoCV approach extracted_predictors_vif3.csv: site-specific predictor values extracted from predictors_annual_20230223.tif final_predictors_vif3.csv: a two column table summarizing the VIF selected predictors final_sites_2017-2021.csv: the list of 1,195 potential sites predictor_sprmn_corr.csv: correlation matrix for predictors in model data predictors_annual_20230223.tif: all predictors response_df_200623.csv: site level ecoacoustic metrics Results: map_correlations.tar: pairwise response map correlations pdps.tar: partial dependence plot data performance.tar: model performance summaries predictions_maps.tar: final median and IQR model prediction surfaces variable_importance.tar: summaries for variable importance analyses Contact Colin Quinn at cq73@nau.edu for questions related to this repository or the underlying work. Original wav recordings are expected to be made publicly available on the NASA DAACs in the near future.