Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019

The datasets are to accompany a manuscript describing the prediction of submersed aquatic vegetation presence and its potential vulnerability and recovery potential. The data and accompanying analysis scripts allow users to run the final random forests predictive model and reproduce the figures reported in the manuscript. Files from several data sources (aqa_2010_lvl3_pct_oute_joined_VEG_BARCODE.csv, eco_states_near_SAV.csv, ltrm_vegsrs_thru2019_GEOMORPHIC_METRICS_final.csv, vegetation_data.csv, and water_full.csv) were combined into a single .csv file (analysis_data_for_SAV_RandomForest.csv) used as the input for the random forest model. When intersecting points with geomorphic metrics some sites were moved slightly...

Categories: Data; Tags: Aquatic Biology, Ecology, Mississippi River, Pool 13, Pool 4, All tags... Pool 8, USGS Science Data Catalog (SDC), Water Resources, biota, ecosystem state, machine learning, macrophyte, predictive model, random forest, resilience, submergent plant, submersed plant, upper Mississippi River, vulnerability, Fewer tags