Aquatic ecosystem management requires knowledge of the links among landscape-level anthropogenic disturbances and aquatic ecosystem properties. With large catchment area to surface area ratios (CA:SA), reservoirs often receive substantial terrestrial subsidies and can be particularly sensitive to eutrophication. Reservoir numbers and attendant management problems are increasing, and tools are needed to categorize their eutrophication status. We analyzed a dataset of 109 reservoirs in Ohio (USA) in an effort to classify eutrophication status using landscape-level features and reservoir morphometry. These predictor variables were selected because they are relatively stable and easily measured. We employed regression tree analysis and used a composite eutrophication variable as our response variable. Our regression tree analysis accurately divided 67% of Ohio reservoirs into 4 eutrophication status groups using 3 predictor variables: percentage of catchment area composed of agriculture versus forest; maximum reservoir depth; and CA:SA. We can infer that reservoirs with catchments containing >71% forest will likely be oligotrophic to mesotrophic. For reservoirs with <71% catchment forest, trophic status is determined by the relative extent of catchment row crops and either CA:SA or maximum depth. We applied our regression tree to a subset of reservoirs in the Environmental Protection Agency's National Lakes Assessment (NLA; n = 339 reservoirs). With a few exceptions, we categorized NLA reservoirs by eutrophication status despite their broad geographical range across the contiguous USA. Our results show that a few easily measured, stable parameters can classify reservoir eutrophication status. Models like ours may be useful for broad-scale management decisions.
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© International Society of Limnology 2015.
- Land use