2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.

Lake water quality is known to be affected by local and regional drivers, including lake
physical characteristics, hydrology, landscape position, land cover, land use, geology, and
climate. Here, we demonstrate the utility of hypothesis testing within the landscape
limnology conceptual framework using a random forest algorithm on large, national-scale,
spatially explicit dataset, the United States Environmental Protection Agency 2007 National
Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers
across spatial scales, the importance of hydrologic connectivity in mediating water quality
drivers, and how the importance of both spatial scale and connectivity differ across
response variables for five important in-lake water quality metrics (total phosphorus, total
nitrogen, dissolved organic carbon, turbidity, and conductivity).