Data_Amazon_forest_regeneration

Data and maps produced for the study "Simple Ecological Indicators Benchmark Regeneration Success of Amazonian Forests" data_plot.csv → Data on forest attributes and predictor variables used to analyze the drivers of regeneration. data.figure2.csv → GLMM model results and effect sizes used to generate Figure 2. raster.bdod_1km_mask.tif and raster.clay_1km_mask.tif → Raster data of soil bulk density and soil clay at 1 km resolution, used to generate predicted regeneration curves for the entire Amazon (Figure 3). agb1mask_20_250m.tif → Map of Aboveground Biomass values along the optimal successional trajectory across the Amazon, predicted using GLMM models. ba_250m_year_20.tif → Map of Basal Area values along the optimal successional trajectory across the Amazon, predicted using GLMM models. div_hill_250m_year_20.tif → Map of Species Diversity values along the optimal successional trajectory across the Amazon, predicted using GLMM models. max_dbh_250m_year_20.tif → Map of Maximum DBH values along the optimal successional trajectory across the Amazon, predicted using GLMM models. sh_250m_year_20.tif → Map of Structural Heterogeneity (SH) values along the optimal successional trajectory across the Amazon, predicted using GLMM models. spc_rich_250m_year_20.tif → Map of Species Richness values along the optimal successional trajectory across the Amazon, predicted using GLMM models. Drivers_forest_Regeneration_Fig2.R → R script for GLMM model analysis, which loads the dataset Regenera_all_plot_data_2022_05_16d.csv and generates Figure 2 using data.figure2.csv. Predict_Amazon_Curves_Figure_3.R → R script to generate predicted regeneration curves across the Amazon based on GLMM results, loading raster.clay_1km_mask.tif and raster.bdod_1km_mask.tif.GEE_code_Predict_Maps.txt → Google Earth Engine code to generate predicted maps of ecological integrity at 250 m resolution for the entire Amazon.