The Great Lakes watersheds have an important influence on the water quality of the nearshore environment, therefore, watershed characteristics can be used to predict what will be observed in the streams. We used novel landscape information describing the forest cover change, along with forest census data and established land cover data to predict total phosphorus and turbidity in Great Lakes streams. In Lake Superior, we modeled increased phosphorus as a function of the increase in the proportion of persisting forest, forest disturbed during 2000-2009, and agricultural land, and we modeled increased turbidity as a function of the increase in the proportion of persisting forest, forest disturbed during 2000-2009, agricultural land, and urban land. In Lake Michigan, we modeled increased phosphorus as a function of ecoregion, decrease in the proportion of forest disturbed during 1984-1999 and watershed storage, and increase in the proportion of urban land, and we modeled increased turbidity as a function of ecoregion, increase in the proportion of forest disturbed during 2000-2009, and decrease in the proportion softwood forest. We used these relationships to identify priority areas for restoration in the Lake Superior basin in the southwestern watersheds, and in west central and southwest watersheds of the Lake Michigan basin. We then used the models to estimate water quality in watersheds without observed instream data to prioritize those areas for management. Prioritizing watersheds will aid effective management of the Great Lakes watershed and result in efficient use of restoration funds, which will lead to improved nearshore water quality.
Mixed effects model
Seilheimer, Titus S.; Zimmerman, Patrick L.; Stueve, Kirk M.; Perry, Charles H. 2013. Landscape-scale modeling of water quality in Lake Superior and Lake Michigan watersheds: How useful are forest-based indicators Journal of Great Lakes Research. 39(2): 211-223.