U.S. coastal wetlands of the Laurentian Great Lakes span a north-to-south gradient (latitude 47-41° N) of increasing human population and agricultural intensity that alters their water chemistry and vegetation. We related field-measured water chemistry to vegetation condition and composition using data from 48 freshwater coastal wetlands along this sub-continental gradient, building upon previous findings that GIS-derived landscape descriptors could adequately predict vegetation condition in Great Lakes coastal wetlands. Our aim was to determine (1) whether plant communities could be differentiated by their surface water chemistry, and (2) if water chemistry could better predict wetland vegetation condition than GIS-derived variables. Seven distinct plant communities were identified by agglomerative hierarchical clustering and non-metric multidimensional scaling of vegetation cover data: Sphagnum-carpeted poor fens, Sparganium eurycarpum marshes, Calamagrostis canadensis wetlands, Schoenoplectus pungens marshes, Phragmites australis marshes, and two floristically distinct Typha-dominated marshes. There were significant differences (ANOVA) among the seven plant assemblages identified for most water chemistry metrics (Cl-, chlorophyll a, conductivity, NO3-N, pH, total N, total P, total suspended solids), but dissolved oxygen, dissolved organic carbon, and NH4-N did not vary significantly across the assemblages. The two different Typha-dominated plant communities were chemically distinct from each other in chlorophyll a, conductivity, NO3-N, pH and total suspended solids concentrations, and we recommend that they be separated into distinct associations: Typha spp. - Thelypteris palustris - Hydrocharis morsus-ranae and Typha spp. - C. canadensis - Leersia oryzoides. Plant communities tended to be geographically clustered, but wetlands that were geographic outliers of their floristic type were chemically similar to other wetlands in their plant community grouping despite being on different lakes. When offered both GIS-derived and field-measured potential predictor variables, a regression tree model of wetland condition chose only GIS-derived variables. However, a classification tree model derived solely from field-measured water chemistry variables correctly classified 79% of the sites into four plant community groupings based on total N, conductivity, and pH. Grouping wetlands by plant communities could provide a scientifically-defensible basis for stricter water quality standards to protect sensitive wetland types.
Bibliographical noteFunding Information:
Anett Trebitz and John Morrice of the U.S. Environmental Protection Agency Mid-Continent Ecology Division and Richard Axler of the Natural Resources Research Institute of the University of Minnesota-Duluth were responsible for water sampling and analysis. Nicholas Danz managed the chemistry data. M. Aho, A. Boers, K. Bailey Boomer, M. Bourdaghs, K. Cappillino, R. Clark, S. Cronk, A. Freeman, C. Frieswyk, D. James, C. Johnson, L. Ladwig, A. Marsh, M. Tittler, L. Vaccaro, and C. Williams collected vegetation field data. This research was partially supported (2001–2006) by a grant from the United States Environmental Protection Agency's Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through funding to the Great Lakes Environmental Indicators (GLEI) Project, US EPA Agreement EPA/R-828675. Although the research described in this article has been funded wholly or in part by the U.S. Environmental Protection Agency , it has not been subjected to the Agency's required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.
- Great Lakes coast
- Plant community
- Water chemistry