Final Report: Great Lakes Diatom and Water Quality Indicators

EPA Grant Number: R828675C001
Subproject: this is subproject number 001 , established and managed by the Center Director under grant R828675
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: EAGLES - Great Lakes Environmental Indicators Project
Center Director: Niemi, Gerald J.
Title: Great Lakes Diatom and Water Quality Indicators
Investigators: Reavie, Euan D. , Andresen, Norman A. , Axler, Richard , Ferguson, Michael J. , Johansen, Jeffrey R. , Kingston, John C. , Kireta, Amy R. , Sgro, Gerald V. , Stoermer, Eugene F.
Institution: University of Minnesota , John Carroll University , University of Michigan
EPA Project Officer: Packard, Benjamin H
Project Period: January 10, 2001 through January 9, 2005
RFA: Environmental Indicators in the Estuarine Environment Research Program (2000) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Water , Ecosystems

Objective:

The overall objective of this research project was to quantify the extent to which pressure indicators influence diatom community structure in nearshore wetlands, estuaries, and reaches of the Laurentian Great Lakes. Developing effective indicators of ecological condition requires that indicators be calibrated to identify their responses to important environmental stressors (Karr and Chu, 1999; Seegert, 2001; Niemi and McDonald, 2004). The main goals of calibration are to identify environmental optima and tolerances of indicator taxa and to define systems with similar biota that respond similarly to anthropogenic stresses (e.g., Radar and Shiozawa, 2001). Calibrated bioindicators are needed to monitor the impacts of human activities that increase the nutrient supply to water bodies, giving rise to cultural eutrophication, a human-driven process that has numerous adverse effects (Carpenter, et al., 1998a; 1998b). Phosphorus and nitrogen compounds from agricultural and urban activities are universally recognized to be the major causes of cultural eutrophication. Of the large suite of potential bioindicators, diatom algae are popular because the taxa have definable optima along gradients of environmental conditions. In addition, the diatoms are taxonomically distinct, abundant in almost all aquatic environments, respond rapidly to changing conditions, and are well preserved in sediment deposits (Hall and Smol, 1999). Hence, researchers can use changes in community composition (expressed as percent abundance of each taxon) to classify and quantify long-term environmental changes that result from anthropogenic activities.

Summary/Accomplishments (Outputs/Outcomes):

Methods

Coastal sample locations were selected, and associated segment sheds were characterized, as described in detail by Danz, et al. (2005, 2006). Water quality sampling methods are described by Reavie, et al. (2006) and additional variable-specific details are provided in articles that are cited therein. The sampling, preparation, and assessment (identification, enumeration and statistical evaluation) of diatom materials are also presented by Reavie, et al. (2006).

To date, four diatom-based indicators of Great Lakes coastal quality have been developed. With selection of an indicator based on need and/or logistic constraints, the following indicators can be used to infer past and present information about coastal habitat quality. These indicators are listed in order of decreasing complexity of application.

Diatom-Based Inference Models for Water Quality Variables

Modern datasets, also known as training sets, provide the basis for development of indicator transfer functions by relating contemporary assemblages with their corresponding environmental measurements (e.g., water quality stressors). Algal assemblages are proven robust indicators of stressors such as nutrients (e.g., Tibby, 2004; Meriläinen, et al., 2003; Ramstack, et al., 2003), water clarity (Dixit and Smol, 1994) and acidification (e.g., Siver, et al., 2003), as well as a suite of other water quality problems in freshwater ecosystems (Smol, 2002). A diatom transfer function is derived by relating diatom taxa assemblages in a training set of samples (e.g., from lakes, river reaches, coastal locales) to an environmental variable of interest (e.g., total phosphorus or nitrogen, pH, chloride, suspended solids) from a particular region (Charles, 1990). The transfer function consists of taxa coefficients (environmental optima and tolerances) that can be used to infer quantitative information about the variable of interest, based on the abundance of each taxon in a sample assemblage. Transfer function evaluation and testing involves the comparison of diatom-inferred water quality to measured water quality to evaluate function robustness that is characterized by a coefficient of determination (r2) and an “error of prediction.” Although measured water quality variables are suitable for comparison to diatom-inferred water quality, contemporary measurements are often based on single (“snapshot”) measurements from the epilimnetic environment at each site in the training set. This is not surprising because multiple time-integrated measurements can be costly and are often not logistically feasible in monitoring programs. Several studies have shown that, because of short-term fluctuations in freshwater parameters, snapshot measurements of water quality variables such as nutrients can misrepresent the prevailing water quality (e.g., Bradshaw, et al., 2002; Detenbeck, et al., 1996). Assemblages of algae, that are physiologically subject to water chemistry, have the potential to provide time-integrated inferences of limnological conditions.

In an effort to develop indicators for Great Lakes near-shore conditions, diatom-based transfer functions to infer water quality variables were developed from the Great Lakes coastal samples. Transfer functions for 17 site-level water quality variables (Tables 1 and 2 in Reavie, et al., 2006) were developed using weighted averaging (WA) regression with inverse deshrinking and jackknife (leave-one-out) cross-validation for error estimation and lognormal taxa transformation. Diatom-inferred estimates of water quality variables for each sample were calculated by taking the optimum of each taxon to that variable, weighting it by its abundance in that sample, and calculating the average of the combined weighted taxa optima. The strength of the transfer functions were evaluated by calculating the squared correlation coefficient (r2) and the root mean square error, between measured values and transfer function estimates of those values for all samples. Jackknifing (WAjackknife) was used in transfer function validation to provide a more realistic error estimate the root mean square error of prediction (RMSEP) because the same data were used to both generate and test the WA transfer function.

More than 2000 diatom taxa were identified, and 352 taxa were sufficiently abundant to include in transfer function development (Table 3 in Reavie, et al., 2006). Multivariate data exploration revealed strong responses of the diatom assemblages to stressor variables, including total phosphorus (TP). A diatom inference transfer function for TP provided a robust reconstructive relationship (r2 = 0.65; RMSEP = 0.26 log (µg/L)).

Relationships between diatom-inferred water quality variables and watershed characteristics, such as urban and agricultural land use, have provided an important link between bioindicators and anthropogenic influences in the watershed (Dixit and Smol, 1994). Such comparisons of diatom-inferred water quality to watershed stressors may reflect the strength of these transfer functions, particularly regarding their ability to infer more holistic stressor impacts beyond the water quality parameters that directly influence the indicator assemblages. For each modeled water quality variable, both measured and diatom-inferred values were regressed against the set of watershed-level predictors using multiple linear regression and evaluated using the coefficient of determination. The regressions tested the relationship between watershed properties and water quality in the adjacent coastal system and were used to determine whether diatom-inferred or measured water quality is more closely related to watershed characteristics such as agricultural and urban development. Measured and diatom-inferred water quality data from the Great Lakes coastlines 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. The diatom community at a site is subject to its prevailing water quality condition, and so diatom-inferred water quality data should also reflect this condition. Diatoms are likely to integrate water quality conditions over longer temporal periods (e.g., past days to weeks) compared to water chemistry measurements (e.g., past hours).

Diatom-Based Integrative Water Quality Model

Training sets like that described above have become the mainstay of modeling methods to calibrate diatom indicators to water quality variables of interest. The present-day distributions of diatoms (or any of several indicator organisms) are calibrated across the gradient of a selected environmental parameter. If we are to continue refining these models, there is a greater need to focus on better characterization of species-environmental relationships. The transfer function approach described in the previous section deals with single water quality variables, each individually used to build a diatom-based model. These models are useful because managers often are interested in particular variables, such as phosphorus or chloride, to make water quality diagnoses. However, weaknesses in these models are associated with a loss of ecological information, resulting from the inability of a single water quality variable to account for the primary gradient of environmentally explainable variation in the diatom assemblage data.

In the comparison of observed to diatom-inferred data there is a tendency for diatom-inferred values to overestimate quantitative variables at the low end of the environmental gradient and underestimate at the high end (e.g., Ryves, et al., 2002; Yang and Duthie, 1995; Leland, et al., 2001; Reavie and Smol, 2001; Werner and Smol, 2005). These errors associated with model application are likely to be a result of unexplained noise in the data that is expected when one deals with highly complex biological data such as diatom assemblages. Another likely cause of model error is that the selected variable of interest captures only a fraction of the variation in the diatom data that may be explained by other measured variables. For instance, it is well known that the measured total phosphorus gradient from a set of sites typically is correlated with nitrogen, water clarity (e.g., Secchi and color), organic compounds and suspended solids, all of which can have water quality implications. Because of intercorrelation among water quality variables, teasing out the independent responses of the diatoms to these variables can be difficult. This second indicator approach presents an evaluation of a model based on a water quality gradient, derived by integrating a series of measured chemical variables.

This evaluation used the same set of chemical and diatom data used by Reavie, et al., (2006) to derive an integrated water quality model. The diatom-based water quality index was created to form a comprehensive measurement to use as a general indicator of water quality and to examine how well the diatoms responded along this gradient, versus specific variables such as TP. The first step summarized the measured chemical data into the comprehensive water quality index. A principal components analysis of all chemical variables identified the major environmental gradient that would be considered ranging from “high” (i.e., low-nutrient, clear-water sites) to “low” (i.e., high-nutrient, high Cl, turbid sites) water quality.

The water quality variable was used to calibrate diatom species coefficients using standard methods (Reavie, et al., 2006), based on the diatom responses across the water quality gradient. WA calculations with jackknife cross-validation were used to provide diatom-inferred water quality values. As for the previous indicator, adjacent watershed data were regressed against measured and diatom-inferred data using multiple linear regression.

Comparisons of observed to diatom-inferred data indicated good predictive ability for the water quality model (r2jackknife = 0.62, RMSEP = 1.32). The relative power of these models also was illustrated by comparing measured and diatom-inferred data to watershed characteristics. For both models, diatom-inferred parameters are better correlated to watershed characteristics than measured parameters. The combined water quality variable is better correlated to watershed characteristics than measured TP. This is likely a result of water quality being derived from several chemical parameters and, hence, should better reflect overall chemical condition than a single nutrient. Combination of these variables into a single water quality variable offers a notable advantage to characterizing watershed-measured water quality relationships, and an increase in r2 occurs for diatom-inferred-water quality over diatom-inferred-TP.

Summarizing water chemistry variables into a comprehensive index of water quality appears to be suited to WA approaches and proffers some advantages over using specific environmental variables. Mainly, this approach appears to better characterize diatom-environmental relationships by merging several variables that simultaneously influence species assemblages. Disadvantages of using a water quality index may include some loss of information for water quality managers who may be interested in specific variables, such as phosphorus. However, given that we are unlikely to see significant improvements in training set nutrient models using standard WA approaches, an integrated measure of water quality is a useful next step to building more informative models.

Diatom-Based Multimetric Index of Disturbance

A diatom-based multimetric index to infer coastline disturbance was developed for Great Lakes coastal wetlands, embayments and high-energy sites. Unlike the previous two indicators, the multimetric index was derived and tested using a fundamentally different approach. Clearly the previous two indicators will be of interest to managers and paleoecologists, but they have some logistic constraints (e.g., time and monetary dedication, taxonomic expertise, specialized software, and steep learning curve) that may limit their choice by managers who consider “algae” as an environmental quality indicator. Index approaches provide a means to evaluate environmental quality at a locale based on the diatom assemblage, and can be flexible enough to suit a greater user audience. Furthermore, algal indices can simultaneously include several characteristics of the assemblage at a locale, and can potentially provide an integrated picture of impacts at a site by not being limited to inferring water quality parameters.

We developed 38 diatom-based metrics from taxonomic (e.g., proportion of a particular genus) and functional (e.g., proportion with a particular adaptive strategy, such as planktonic existence or the ability to assimilate atmospheric nitrogen) characteristics of the assemblage.

The following approach was used to identify metrics for multimetric development from the complete list of candidate metrics. (1) The suitability of each candidate metric was evaluated using stepwise regression to the stressor principal components described by Danz, et al. (2005, 2006). In other words, metrics were compared to watershed characteristics such as agricultural intensity and urban development to identify the power of each metric to reflect anthropogenic stress. (2) Similarly, each candidate metric was related to natural gradients using stepwise regression to identify metrics that were being largely determined by natural factors. (3) Covariance among candidate metrics was investigated to identify redundancies. Within groups of covarying candidate metrics, metrics were selected that, simultaneously, best tracked stress and least tracked natural gradients.

Many of the proposed metrics were redundant (highly correlated) or had no apparent relationship to stressors and so were not considered further. Important and independent metrics were identified as the best candidates for inclusion in development of a multimetric index to infer environmental quality.

The multimetric index was developed based on the sum of the selected metrics, with each metric weighted based on the strength of its relationship to the stressor gradient. In this way, metrics with weak, but still significant, relationships to anthropogenic stressors would play less of a role in multimetric calculations. Fifteen candidate metrics met the criteria of our selection process. To create an approach that was adaptable to the limitations of a user audience, two variations of the multimetric index were developed. (1) The full, 15-metric compilation. Most of the metrics included in this index are proportions of particular genera. Also included are the proportions of monoraphid taxa (taxa with a raphe structure on only one valve of the cell wall), biraphid taxa (taxa with a raphe on both valves), and the complex of taxa comprising Achnanthidium minutissimum, a common species with several forms, subspecies and varieties. The Shannon-Weaver index of diversity and diatom-inferred chloride concentration (derived using the WA model, above) are also included. (2) A simpler, 13-metric compilation that excluded the Shannon-Weaver diversity value, and the diatom-inferred Cl value. These two metrics require detailed taxonomic identification of the diatom assemblage at a site, and so they were removed to suit the constraints of a user group with less time, funds and/or taxonomic knowledge. As expected, this simplified metric was shown to be less robust than the full, 15-metric approach, but was still robust enough to characterize known impacted locales from less impacted ones.

Diatom Valve Deformities as Indicators of Pollution

The occurrence of morphological abnormalities in birds (Ludwig, et al., 1996), fish (Smith, et al., 1994), and invertebrates (Diggins and Stewart, 1993), particularly in the coastal region of Lake Erie and its tributaries, is well known. These effects usually are attributed to toxic organic compounds. Reports of similar effects on benthic diatoms are not as widely reported but not unknown (Dickman, 1998).

Morphologically abnormal diatoms from the genus Tabularia were collected at a coastal site in Lake Erie, near Cleveland, Ohio, an area with a legacy of severe environmental problems. The locality of collection is locally known as Whiskey Island, a peninsula found at the mouth of the Cuyahoga River. The island area has been an industrial site, a ship graveyard, and a waste disposal area. It currently is the site of a salt mine and has recently been developed as a large marina. The collection site is subject to numerous discharges, including industrial contaminants. There are number of potential causes of morphological abnormalities in diatom frustules. Nonlethal mechanical damage may produce clones of cells that have structural defects. Based on our observations, it appears that frustular abnormalities are common in diatom communities that undergo toxic stress. It probably is safe to say that diatoms growing in unstable habitats are particularly susceptible to abnormal frustule formation, and the abnormalities present near Cleveland likely are related to these factors.

Observations showed an extreme variety of atypical shapes (Figure 4 in Stoermer and Andresen, 2006); frustules were bent, asymmetric, had irregular striae patterns, irregular margins, or any combination of these characters. Abnormalities in diatom valve structure have been noted and reported virtually since the group was first studied. In general, deformities have been associated with pollution or eutrophication (Antoine and Evans, 1986; Klee and Schmidt, 1987). More specific chemical causes include silica limitation (Booth and Harrison, 1979) and increased salinity in freshwater habitats (Tuchman, et al., 1984). Of the possible causes, suggested by Barber and Carter (1981), their first category, “chemical abnormalities in the habitat” seems most likely in the populations we studied.

Presence of orphological abnormalities of the diatoms was not anticipated to be one of the Great Lakes Environmental Indicators Project indicators, and so to date we have only assessed these site-specific data near Cleveland. However, the presence of benthic diatoms that are atypical, in terms of both distribution and morphology, in the Great Lakes may offer valuable insights into toxic effects. Although the present state of knowledge does not permit firm conclusions concerning the populations described here, investigation of benthic diatom populations in the Great Lakes is a neglected topic that deserves more research attention.

Conclusions:

These results to date strongly support the use of diatoms in Great Lakes coastal monitoring programs to track the impacts of anthropogenic stressors. Because the diatoms clearly respond to anthropogenic stressor influences from the watershed, integrating the diatom indicators with upland indicators (e.g., vegetation, birds) should provide a strong holistic view of overall disturbance, and a powerful management tool for Great Lakes decision makers. There also is considerable value in these indicators for retrospective assessments. Because long-term measured water quality data can be sparse or unreliable, and pre-European settlement data are unavailable, diatom-based paleoecological studies in the Great Lakes have been valuable in describing background conditions and anthropogenic impacts (Stoermer, et al., 1993). To date, these studies have focused on sediment cores collected from deep, open water areas, and so provide integrated assessments of long-term water quality from a large coastal region or lake. The diatom-environmental relationships in this report can also provide a tool for near-shore paleoecological studies and the assessment of more localized impacts, such as the paleolimnology of wetlands that have been impacted by cultural eutrophication.

References:

Antoine SE, Evans KB. Teratological variations in the River Wye diatom flora, Wales, U.K. In: Ricard M, ed. Proceedings of the Eighth International Diatom Symposium Koeltz, Koenigstein, 1986, pp. 375-384.

Barber HG, Carter JR. Observations on some deformities in British diatoms. Microscopy 1981;34:214-226.

Booth B, Harrison PJ. Effect of silicate limitation on valve morphology in Thalassiosira and Coscinodiscus (Bacillariophyceae). Journal of Phycology 1979;15:326-329.

Bradshaw EG, Anderson NJ, Jensen JP, Jeppesen E. Phosphorus dynamics in Danish lakes and the implications for diatom ecology and palaeoecology. Freshwater Biology 2002;47:1963-1975.

Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications 1998a;8:559-568.

Carpenter SR, Cole JJ, Kitchell JF, Pace ML. Impact of dissolved organic carbon, phosphorus and grazing on phytoplankton biomass and production in experimental lakes. Limnology and Oceanography 1998b;43:73–80.

Charles DF. A checklist for describing and documenting diatom and chrysophyte calibration data sets and equations for inferring water chemistry. Journal of Paleolimnology 1990;3:175-178.

Detenbeck NE, Taylor DL, Lima A, Hagley C. Temporal and spatial variability in water quality of wetlands in the Minneapolis/St. Paul, MN metropolitan area: implications for monitoring strategies and designs. Environmental Monitoring and Assessment 1996;40:11-40.

Dickman M. Diatom stratigraphy in acid-stressed lakes in the Netherlands, Canada, and China. In: Suba Rao C, ed. Acid Stress and Aquatic Microbial Interactions. CRC Press, 1998, pp. 115-143.

Diggins TP, Stewart KM. Deformities of aquatic larval midges (Chironomidae: Diptera) in the sediments of the Buffalo River, New York. Journal of Great Lakes Research 1993;19:648-659.

Dixit SS, Smol JP. Diatoms as indicators in the environmental monitoring and assessment program–surface waters. Environmental Monitoring and Assessment 1994;31:275-306.

Hall RI, Smol JP. Diatoms as indicators of lake eutrophication. In: Stoermer EF, Smol JP, eds. The Diatoms: Applications for the Environmental and Earth Sciences. Cambridge, UK: Cambridge University Press, 1999, pp. 128-168.

Karr JR, Chu EW. Restoring life in running waters: better biological monitoring. Washington, DC: Island Press, 1999.

Klee R, Schmidt R. Eutrophication of the Mondsee (Upper Austria) as indicated by the diatom stratigraphy of a sediment core. Diatom Research 1987;2:55-76.

Leland HV, Brown LR, Meuller DK. Distribution of algae in the San Joaquin River, California, in relation to nutrient supply, salinity and other environmental factors. Freshwater Biology 2001;46:1139-1167.

Ludwig JP, Kurita-Matsaba H, Auman HJ, Ludwig ME, Summer CL, Giesy JP, Tillitt DE, Jones PD. Deformities, PCBs, and TCDD-equivalents in double-crested cormorants (Phalacrocorax auritus) and Caspian terns (Hydroprogne caspia) of the upper Great Lakes 1986-1991: testing a cause-effect hypothesis. Journal of Great Lakes Research 1996;22:172-197.

Meriläinen JJ, Hynynen J, Palomäki A, Mäntykoski K, Witick A. Environmental history of an urban lake: a palaeolimnological study of Lake Jyväsjärvi, Finland. Journal of Paleolimnology 2003;30:387-406.


Journal Articles on this Report : 14 Displayed | Download in RIS Format

Other subproject views: All 35 publications 19 publications in selected types All 16 journal articles
Other center views: All 268 publications 54 publications in selected types All 45 journal articles
Type Citation Sub Project Document Sources
Journal Article Danz NP, Regal RR, Niemi GJ, Brady VJ, Hollenhorst T, Johnson LB, Host GE, Hanowski JM, Johnston CA, Brown T, Kingston J, Kelly JR. Environmentally stratified sampling design for the development of Great Lakes environmental indicators. Environmental Monitoring and Assessment 2005;102(1-3):41-65. R828675 (Final)
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  • Abstract from PubMed
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  • Abstract: Springer
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  • Journal Article Danz NP, Niemi GJ, Regal RR, Hollenhorst T, Johnson LB, Hanowski JM, Axler RP, Ciborowski JJH, Hrabik T, Brady VJ, Kelly JR, Morrice JA, Brazner JC, Howe RW, Johnston CA, Host GE. Integrated measures of anthropogenic stress in the U.S. Great Lakes Basin. Environmental Management 2007;39(5):631-647. R828675 (Final)
    R828675C001 (Final)
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  • Journal Article Niemi GJ, McDonald ME. Application of ecological indicators. Annual Review of Ecology, Evolution, and Systematics 2004;35:89-111. R828675 (2004)
    R828675C001 (Final)
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  • Journal Article Ramstack JM, Fritz SC, Engstrom DR, Heiskary SA. The application of a diatom-based transfer function to evaluate regional water–quality trends in Minnesota since 1970. Journal of Paleolimnology 2003;29:79-94. R828675C001 (Final)
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    Journal Article Reavie ED, Axler RP, Sgro GV, Danz NP, Kingston JC, Kireta AR, Brown TN, Hollenhorst TP, Ferguson MJ. Diatom-based weighted-averaging transfer functions for Great Lakes coastal water quality: relationships to watershed characteristics. Journal of Great Lakes Research 2006;32(2):321-347. R828675C001 (Final)
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    Journal Article Reavie ED. A diatom-based water quality model for Great Lakes coastline. Journal of Great Lakes Research 2007;33(Suppl 3):86-92. R828675C001 (Final)
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  • Journal Article Reavie ED, Kireta AR, Kingston JC, Sgro GV, Danz NP, Axler RP, Hollenhorst TP. Comparison of simple and multimetric diatom-based indices for Great Lakes coastline disturbance. Journal of Phycology 2008;44(3):787-802. R828675C001 (Final)
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  • Journal Article Reavie E, Smol J. Diatom-environmental relationships in 64 alkaline southeastern Ontario (Canada) lakes: a diatom-based model for water quality reconstructions. Journal of Paleolimnology 2001;25(1):25-42. R828675C001 (Final)
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    Journal Article Ryves DB, McGowan S, Anderson NJ. Development and evaluation of a diatom-conductivity model from lakes in West Greenland. Freshwater Biology 2002;47:995-1014. R828675C001 (Final)
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    Journal Article Seegert G. The development, use, and misuse of biocriteria with and emphasis on index of biotic integrity. Environmental Science and Pollution Research 2001;3:51-58. R828675C001 (Final)
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    Journal Article Sgro GV, Ketterer ME, Johansen JR. Ecology and assessment of the benthic diatom communities of four Lake Erie estuaries using Lange-Bertalot tolerance values. Hydrobiologia 2006;561(1):239-249. R828675 (Final)
    R828675C001 (2004)
    R828675C001 (Final)
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  • Journal Article Siver PA, Ricard R, Goodwin R, Giblin AE. Estimating historical in-lake alkalinity generation from sulfate reduction and its relationship to lake chemistry as inferred from algal microfossils. Journal of Paleolimnology 2003;29:179-197. R828675C001 (Final)
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    Journal Article Tibby J. Development of a diatom–based model for inferring total phosphorus in southeastern Australian water storages. Journal of Paleolimnology 2004;31:23-36. R828675C001 (Final)
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    Journal Article Werner P, Smol JP. Diatom–environmental relationships and nutrient transfer functions from contrasting shallow and deep limestone lakes in Ontario, Canada. Hydrobiologia 2005;533:145-173. R828675C001 (Final)
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    Supplemental Keywords:

    Great Lakes, monitoring, indicators, risk assessment, stressor, ecological effects, animal, plant, diatoms, toxics, aquatic ecosystem, aquatic ecosystems, atmospheric pollutant loads, climate variability, coastal ecosystem, coastal environments, diatoms, ecological assessment, ecological condition, ecological response, ecosystem assessment, ecosystem impacts, ecosystem indicators, ecosystem stress, environmental consequences, environmental stressor, environmental stressors, estuarine ecosystems, hierarchically structured indicators, human activities, hydrologic models, hydrological, hydrological stability, nutrient stress, nutrient supply, nutrient transport, toxic environmental contaminants,, RFA, Scientific Discipline, ENVIRONMENTAL MANAGEMENT, Geographic Area, Water, ECOSYSTEMS, Ecosystem Protection/Environmental Exposure & Risk, Nutrients, Ecosystem/Assessment/Indicators, Ecosystem Protection, Ecological Effects - Environmental Exposure & Risk, Air Deposition, Ecological Monitoring, Ecological Risk Assessment, Ecology and Ecosystems, Great Lakes, Ecological Indicators, Risk Assessment, coastal ecosystem, diatoms, ecological condition, aquatic ecosystem, hydrological stability, nutrient supply, nutrient transport, ecosystem assessment, hierarchically structured indicators, wetland vegetation, environmental stressor, hydrological, coastal environments, environmental consequences, ecological assessment, ecosystem indicators, estuarine ecosystems, nutrient stress, aquatic ecosystems, toxic environmental contaminants, water quality, ecosystem stress, ecological response

    Relevant Websites:

    http://glei.nrri.umn.edu Exit

    Progress and Final Reports:

    Original Abstract
  • 2001
  • 2002 Progress Report
  • 2003 Progress Report

  • Main Center Abstract and Reports:

    R828675    EAGLES - Great Lakes Environmental Indicators Project

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R828675C001 Great Lakes Diatom and Water Quality Indicators
    R828675C002 Vegetative Indicators of Condition, Integrity, and Sustainability of Great Lakes Coastal Wetlands
    R828675C003 Testing Indicators of Coastal Ecosystem Integrity Using Fish and Macroinvertebrates
    R828675C004 Development and Assessment of Environmental Indicators Based on Birds and Amphibians in the Great Lakes Basin
    R828675C005 Development and Evaluation of Chemical Indicators for Monitoring Ecological Risk