COS 1-9
Examining the nutrient-color paradigm across macroscales: Multivariate spatial relationships among lake phosphorus, water color, and chlorophyll a

Monday, August 10, 2015: 4:20 PM
301, Baltimore Convention Center
C. Emi Fergus, Fisheries and Wildlife, Michigan State University, East Lansing, MI
Andrew O. Finley, Forestry and Geography, Michigan State University, East Lansing, MI
Tyler Wagner, U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, University Park, PA
Patricia Soranno, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI

The nutrient-color paradigm has been suggested to more fully characterize lake ecological properties and processes by integrating both nutrients and carbon measures compared to examining lake nutrients alone. Together lake phosphorus and colored dissolved organic carbon (water color) influence a number of important physical, chemical, and biological processes in lakes such as stratification, light attenuation, and primary production. Many studies have examined the relationships between lake total phosphorus (TP) and chlorophyll a (Chl) and demonstrated that there is a great deal of variation in these relationships across study systems and lake types. The inclusion of water color may clarify the relationships between nutrients and lake primary productivity. However, frequently lake TP and water color and their covariance are not explicitly studied together, and it is not well understood how these relationships may change across space. Our research objectives are to examine the nutrient-color paradigm at macroscales and to identify landscape variables that may account for variation in these relationships. We quantified spatial covariance among lake TP, water color, and Chl for >1000 inland lakes in 17 US states using hierarchical multivariate spatial models with a Bayesian framework.


Preliminary results show that there are regional differences in lake TP, Color, and Chl concentrations and that these variables exhibit a spatial covariance structure. Landscape variables account for some variation but there is remaining spatial variation in the residuals. By explicitly modeling multivariate, spatial relationships among these variables we reduce error in landscape models, improve model predictions, and advance our understanding of lake nutrient and carbon relationships with lake primary productivity at broad geographic extents.