Diatom-based Weighted-averaging Transfer Functions for Great Lakes Coastal Water Quality: Relationships to Watershed Characteristics

• Published in: Journal of Great Lakes research Vol. 32; no. 2; pp. 321 - 347
• Main Authors: Reavie, Euan D., Axler, Richard P., Sgro, Gerald V., Danz, Nicholas P., Kingston, John C., Kireta, Amy R., Brown, Terry N., Hollenhorst, Thomas P., Ferguson, Michael J.
• Format: Journal Article
• Language: English
• Published: International Association for Great Lakes Research 01-01-2006; Elsevier B.V
• Subjects: Bacillariophyceae; coastlines; diatoms; Great Lakes environmental indicators; phosphorus; REGULAR ARTICLES; stressors; watershed; North America, Great Lakes; watershed; diatoms; stressors; Great Lakes environmental indicators; phosphorus; coastlines
• ISSN: 0380-1330; 0380-1330
• DOI: 10.3394/0380-1330(2006)32[321:DWTFFG]2.0.CO;2

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); r2jackknife = 0.65; RMSEP = 0.26 log (μg/L)). Diatom-based transfer functions for other water quality variables, such as total nitrogen, chloride, and chlorophyll α 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.