Although the catchment has a long history of use as the fundamental scale linking land use to lake nutrient status, the predictive power of relationships defined at this scale is often low.  More spatially explicit studies suggest the optimal spatial scale where land use is most directly linked to characteristics of aquatic systems may be smaller than the catchment and differ depending on lake hydrology. Landscape position reflects the strength of connection between the lake and surface and subsurface hydrologic flow paths and, thus, may provide a key feature defining the optimal spatial scale linking land to water. In this study, we examined whether landscape position determines the optimal spatial scale defining the effects of land use on lake nutrients. We took a statistical model-building approach using multiple regression to predict lake nutrients from lake morphometry, natural hydrogeomorphic features such as elevation and geology, and land use/cover compiled at several spatial scales for approximately 300 lakes in Michigan, USA. We built separate models for three groups of lakes: all lakes, lakes high in the landscape (e.g. seepage and headwater), and lakes low in the landscape (flow through, drainage, etc.).

Results/Conclusions

For total phosphorus and total nitrogen, the optimal spatial scale for quantifying land use was either the entire catchment or the 500 m buffer around lakes, which represents the local catchment. We found that land use quantified for riparian (100 m) zones, or along inflowing streams was less related to lake nutrients.  We also found that the best hydrogeomorphic predictor variables differed by lakes of different landscape position. For example, total phosphorus in drainage lakes was best predicted by maximum depth, catchment:lake area ratio, geology, and agricultural and urban land use.  In contrast, total phosphorus in seepage lakes was best predicted by only mean depth and agriculture land use. These results suggest that lakes with different landscape position have fundamentally different relationships to both hydrogeomorphic and anthropogenic landscape features.