Predicting eutrophication status in reservoirs at large spatial scales using landscape and morphometric variables
| Autor: | Elisabeth J. Hagenbuch, María J. González, Michael J. Vanni, William H. Renwick, R. Scott Hale, Lesley B. Knoll, Martin H. H. Stevens, Jonathan C. Sieber Denlinger |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Inland Waters. 5:203-214 |
| ISSN: | 2044-205X 2044-2041 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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