Lake Classification: A classification system is often used to reduce the number of different ecosystem types that governmental agencies are charged with monitoring and managing. We compare the ability of several different hydrogeomorphic (HGM) classifications to group lakes for water chemistry/clarity. We ask three questions: (1) Which approach to lake classification (regionalization, landscape position, lake-specific, or some combination) is most successful at classifying lakes for similar water chemistry/clarity? (2) Which HGM features are most strongly related to the lake classes? and, (3) Can a single classification successfully classify lakes for all of the water chemistry/clarity variables examined? We use classification and regression tree (CART) analysis of HGM features to classify six water chemistry/clarity variables from 151 minimally disturbed lakes in Michigan USA. We developed two CART models for each water chemistry/clarity variable: HGM characteristics alone and HGM characteristics combined with regionalizations and landscape position. The combined CART models had the highest strength of evidence (ωi range 0.92-1.00) and maximized within class homogeneity (ICC range 36-66%) for all water chemistry/clarity variables except water color and chlorophyll a. The most successful single classification in our study was on average 20% less successful in classifying other water chemistry/clarity variables. Thus, our results show that no single classification maximizes success for all lake variables examined. Therefore, we suggest that the most successful classification is (1) specific to one response variable, and (2) capable of incorporating information at multiple spatial scales and from a variety of different sources (regionalization and local HGM variables). Land use legacies: The recognition of legacy effects from historical land use/land cover (LULC) is a conceptual advance that has clarified the relationship between LULC and ecosystem responses. Legacy effects can be defined as effects which perpetuate beyond an expected or perceived endpoint in time. The goal of our research was to investigate LULC legacy effects on lake water chemistry. Water chemistry and five time steps of LULC data were collected from 35 lakes in the Huron River Watershed, Michigan. We took both a correlational and mechanistic approach to represent how temporal changes in LULC influence lake water chemistry. We used principal components of LULC over time to build hierarchical regression models linking to water chemistry. We also created a mechanistic groundwater flow model to estimate spatially-explicit groundwater travel times. The groundwater travel time was used to create a legacy LULC map for subsequent regression modeling. Our correlative models show that some water chemistry characteristics show a stronger link to legacy LULC than others and may be explained by the solubility and reactivity of the chemical. Our mechanistic models offer insights about how groundwater interacts with LULC change to create legacy effects and show how naturally occurring conservative tracers can provide a basis for comparison against nutrient relationships to the landscape. By categorizing the chemistry variables by their key characteristics of solubility and reactivity, we are better equipped to explore other mechanisms that are important for the physical transport and biogeochemical transformations of these chemicals.
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