Diatom-based models to infer nutrient concentrations are proven robust indicators, but evidence suggests that in the future these models will be little improved by using larger training sets. I present a simple means to summarize the water quality (WQ) data from a suite of coastal Great Lakes locations and develop a diatom-based WQ model using standard weighted-averaging methods. A one-dimensional WQ index was derived by summarizing measured environmental data (nutrients, pigments, solids) using dimension-reducing ordination and calculating the primary WQ gradient of interest. Evaluations of weighted-averaging diatom model predictions (WQ index model: r2 jackknife = 0.62, RMSEP = 1.32) indicate that the model has reconstructive power similar to a comparative model for total phosphorus concentrations (TP model: r2jackknife = 0.65, RMSEP = 0.26 logfpg/L + 1]), but that predictive bias was lower for the WQ model. Also, inferred WQ index data had a higher correlation to adjacent watershed characteristics than inferred TP data. We attribute this to the ability of an integrated WQ index to better characterize the overall quality of a site than a single nutrient variable such as phosphorus. The diatom-based WQ model may be advantageous for management where it is necessary to provide a summary inference of water quality condition at a coastal locale.
|Original language||English (US)|
|Number of pages||7|
|Journal||Journal of Great Lakes Research|
|Issue number||SPEC. ISS. 3|
|State||Published - 2007|
Bibliographical noteFunding Information:
Several scientists have been essential to the development of the Great Lakes coastal diatom assessment: J. Kingston, R. Axler, G. Sgro, N. Danz, A. Kireta, T. Brown, T. Hollenhorst, V. Brady, N. Andresen, M. Ferguson, E. Stoermer, J. Johansen, J. Henneck, J. Ameel, J. Reed, L. Anderson. This research was supported by a grant to Gerald Niemi from the U.S. Environmental Protection Agency’s Science to Achieve Results Estuarine and Great Lakes program through funding to the Great Lakes Environmental Indicators project, U.S. EPA Agreement EPA/R–8286750. This document has not been subjected to the agency’s required peer and policy review and therefore does not necessarily reflect the view of the agency, and no official endorsement should be inferred. This is contribution number 461 of the Center for Water and the Environment, Nat- ural Resources Research Institute, University of Minnesota Duluth.
- Great Lakes environmental indicators
- Water quality