Linking fecal indicator bacteria concentrations in large mixed-use watersheds back to diffuse human sources, such as septic systems, has met limited success. In this study, 64 rivers that drain 84% of Michigan’s Lower Peninsula were sampled under baseflow conditions for Escherichia coli, Bacteroides thetaiotaomicron (a human source-tracking marker), landscape characteristics, and geochemical and hydrologic variables. E. coli and B. thetaiotaomicron were routinely detected in sampled rivers and an E. coli reference level was defined (1.4 log₁₀ most probable number⋅100 mL⁻¹). Using classification and regression tree analysis and demographic estimates of wastewater treatments per watershed, septic systems seem to be the primary driver of fecal bacteria levels. In particular, watersheds with more than 1,621 septic systems exhibited significantly higher concentrations of B. thetaiotaomicron. This information is vital for evaluating water quality and health implications, determining the impacts of septic systems on watersheds, and improving management decisions for locating, constructing, and maintaining on-site wastewater treatment systems. Significance New microbial source-tracking tools can be used to elucidate important nonpoint sources of water quality degradation and potential human health risks at large scales. Pollution arising from septic system discharges is likely more important than previously realized. Identifying these sources and providing reference levels for water quality provides a basis to assess water quality trends and ultimately remediate degraded areas.

To improve our understanding of lake landscape position, we compared four metrics based on different aspects of lake surface hydrologic connections: (1) “lake hydrology,” which is a general measure o...

Legacy effects of past land use and disturbance are increasingly recognized, yet consistent definitions of and criteria for defining them do not exis

Nutrient management is an essential part of watershed planning worldwide to protect water resources from both widespread landscape inputs of nutrients (N and P) and point source emissions. To provide information to regional watershed planners and better understand nutrient sources, we developed the Spatially Explicit Nutrient Source Estimate Map (SENSEmap) to quantify individual sources of N and P at their entry points in the landscape. We modeled seven sources of N and six sources of P across the U.S. Great Lakes Basin at 30‐m resolution: atmospheric deposition, septic systems, chemical nonagricultural fertilizer, chemical agricultural fertilizer, manure, nitrogen fixation, and point sources. By modeling these sources, we provide a more detailed view of nutrient inputs to the landscape beyond what would be possible from land use alone. We found that 71% and 88% of N and P, respectively, came from agricultural sources. The nature of agricultural nutrient inputs varied significantly across the basin, as relative contributions of chemical agricultural fertilizers, manure, and N fixation changed according to diverse land use practices regionally. We then applied k‐means cluster analysis and identified nine Nutrient Input Landscapes (NILs) with N and P source characteristics, grouped into intensive agricultural, urban, and rural landscapes. These NILs can offer insights into landscape variability that land use data alone cannot; within agricultural NILs, application of chemical fertilizer and manure varied greatly, but land uses were similar. These NILs can provide a framework for broadly categorizing watersheds that may prove useful to both ecological and management practices.