In an effort to develop indicators for Great Lakes near-shore conditions, diatom-based transfer functions to infer water quality variables were developed from 155 samples collected from coastal Great Lakes wetlands, embayments and high-energy shoreline sites. Over 2,000 diatom taxa were identified, and 352 taxa were sufficiently abundant to include in transfer function development. Multivariate data exploration revealed strong responses of the diatom assemblages to stressor variables, including total phosphorus (TP). Spatial variables such as lake, latitude and longitude also had notable relationships with assemblage characteristics. A diatom inference transfer function for TP provided a robust reconstructive relationship (r2 = 0.67; RMSE = 0.28 log(μg/L); r2jackknife = 0.55; RMSEP = 0.33 log (μg/L)) that improved following the removal of 13 samples that had poor observed-inferred TP relationships (r2 = 0.75; RMSE = 0.22 log(μg/L); r 2jackknife = 0.65; RMSEP = 0.26 log (μg/L)). Diatom-based transfer functions for other water quality variables, such as total nitrogen, chloride, and chlorophyll a also performed well. Measured and diatom-inferred water quality data were regressed against watershed characteristics (including gradients of agriculture, atmospheric deposition, and industrial facilities) to determine the relative strength of measured and diatom-inferred data to identify watershed stressor influences. With the exception of pH, diatom-inferred water quality variables were better predicted by watershed characteristics than were measured water quality variables. Because diatom communities are subject to the prevailing water quality in the Great Lakes coastal environment, it appears they can better integrate water quality information than snapshot measurements. These results strongly support the use of diatoms in Great Lakes coastal monitoring programs.
|Original language||English (US)|
|Number of pages||27|
|Journal||Journal of Great Lakes Research|
|State||Published - 2006|
Bibliographical noteFunding Information:
We dedicate this paper to the memory of John C. Kingston. We are grateful to V. Brady for coordinating and assisting with the sampling design. Diatom identification and enumeration results were supported by N. Andresen, and diatom taxonomic support was provided by E. Stoermer and J. Jo-hansen. This work was made possible through the efforts of many other GLEI and EPA researchers who assisted in the field, laboratory, and with project logistics; J. Henneck was a lead individual in these efforts; J. Ameel analyzed much of the water chemistry; and J. Reed helped in the field and laboratory. We thank the EPA-MED laboratory and L. Anderson for analyzing DOC, and for constructive discussions with J. Thompson, J. Morrice, J. Kelly, and R. Kreis. This work also benefited from critical project review by E. Stoermer, R. Kreis, and S. Dixit. This research was supported by a grant to G. Niemi from the U.S. 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, 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 397 of the Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota Duluth.
- Great Lakes environmental indicators