Grantee Research Project Results
Final Report: Development of Environmental Indicators of Condition,Integrity, and Sustainability in the Coastal Regions of the US Great Lakes Basin
EPA Grant Number: R828675Center: Center for Air, Climate, and Energy Solutions
Center Director: Robinson, Allen
Title: Development of Environmental Indicators of Condition,Integrity, and Sustainability in the Coastal Regions of the US Great Lakes Basin
Investigators: Niemi, Gerald J. , Richards, Carl , Johnston, Carol A. , Zedler, Joy B. , Kingston, John C. , Reavie, Euan D. , Host, George E. , Stoermer, Eugene F. , Brady, Valerie J , Bedford, Barbara L. , Swackhamer, Deborah L. , Hanowski, JoAnn M. , Johansen, Jeffrey R. , Regal, Ronald R. , Sgro, Gerald V. , Howe, Robert W. , Smith, Charles , Ciborowski, Jan , Johnson, Lucinda , Simcik, Matthew , Axler, Richard , Hrabik, Thomas
Institution: University of Minnesota - Duluth , University of Minnesota
EPA Project Officer: Packard, Benjamin H
Project Period: January 11, 2001 through January 31, 2005 (Extended to January 9, 2006)
Project Amount: $6,000,000
RFA: Environmental Indicators in the Estuarine Environment Research Program (2000) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Water , Aquatic Ecosystems
Objective:
The goal of this research collaboration was to develop indicators that both estimate environmental condition and suggest plausible causes of ecosystem degradation in the coastal region of the U.S. Great Lakes. The collaboration consisted of eight broad components, each of which generated different types of environmental responses and characteristics of the coastal region. These indicators included biotic communities of amphibians, birds, diatoms, fish, macroinvertebrates, and wetland plants as well as indicators of polycyclic aromatic hydrocarbon (PAH) photo-induced toxicity and landscape characterization. These components are summarized below and discussed in more detail in the individual Final Reports for projects R828675C001–R828675C005.
The major question addressed was, “What environmental indicators can be developed to efficiently, economically, and effectively measure and monitor the condition, integrity, and long-term sustainability of the coastal region of the U.S. Great Lakes?”
Our specific objectives were to: (1) identify environmental indicators that are useful for defining the condition, integrity, and change of the ecosystems within the coastal region; (2) test indicators with a rigorous combination of existing data and field data to link stressors of the coastal region with environmental responses; and (3) recommend a suite of indicators to guide managers toward improved management decisions.
If implemented, the indicators can help managers: (1) communicate with the public on the condition and integrity of the coastal region; (2) guide development of monitoring programs to measure change in the coastal region; (3) identify areas in need of restoration or conservation strategies; and (4) provide input for modeling efforts to forecast future conditions of the coastal region.
Summary/Accomplishments (Outputs/Outcomes):
This reports summarizes a 5-year effort to test many of the proposed indicators for the coastal region. It includes revisions of some of the existing indicators and the development of new indicators to measure conditions and find potential causes of impairment within the U.S. Great Lakes coastal region.
Stress gradients within the U.S. Great Lakes coastal region were defined from 207 variables (e.g., agriculture, atmospheric deposition, land use/land cover, human populations, point source pollution, and shoreline modification) from 19 different data sources that were publicly available for the coastal region. Biotic communities along these gradients were sampled with a stratified, random design among representative ecosystems within the coastal zone. To achieve the sampling across this massive area, the coastal region was subdivided into 2 major ecological provinces and further subdivided into 762 segment sheds. Stress gradients were defined for the major categories of human-induced disturbance in the coastal region and an overall stress index was calculated, which represented a combination of all the stress gradients.
Investigators of this collaboration have had extensive interactions with the Great Lakes community. For instance, the Lake Erie Lakewide Management Plan (LaMP) has adopted many of the stressor measures as integral indicators of the condition of watersheds tributary to Lake Erie. Furthermore, the conceptual approach and applications for development of a generalized stressor gradient have been incorporated into a document defining the tiered aquatic life criteria for defining biological integrity of the nation’s waters.
A total of 14 indicators of the U.S. Great Lakes coastal region are presented for potential application. Each indicator is summarized with respect to its use, methodology, spatial context, and diagnosis capability. In general, the results indicate that stress related to agricultural activity and human population density/development had the largest impacts on the biotic community indicators. In contrast, the photoinduced PAH indicator was related primarily to industrial activity in the U.S. Great Lakes, and more than one-half of the sites sampled were potentially at risk of PAH toxicity to larval fish. One of the indicators developed for land use/land change was developed from Landsat imagery for the entire U.S. Great Lakes basin and for the period from 1992 to 2001. This indicator quantified the extensive conversions of both agricultural and forest land to residential area that has occurred during a short 9-year period.
Considerable variation in the responses was manifest at different spatial scales and many at surprisingly large scales. Significant advances were made with respect to development of methods for identifying and testing environmental indicators. In addition, many indicators and concepts developed from this project are being incorporated into management plans and U.S. Environmental Protection Agency methods documents. Further details, downloadable documents, and updates on these indicators can be found at the Great Lakes Environmental Indicators (GLEI) Project Web Site.
Study Area
The Great Lakes basin encompasses more than 760,000 km2 with the land area encompassing more than 515,000 km2. The U.S. portion of the land area includes more than 290,000 km2 with a total shoreline length of more than 7,800 km. The coastal region borders eight states and the Canadian province of Ontario. A boundary in climatic and physiographic features divides the basin into two broad regions of nearly equal size: the Laurentian Mixed Forest and the Eastern Broadleaf Forest provinces. General patterns of human activity and land use differ between provinces, with most agricultural activities occurring in the southern portion of the basin, whereas the northern portion of the basin remains largely forested. The southern portion contains deeper, more permeable, and highly buffered soils in comparison with the northern portion. Metropolitan areas are more common in the southern basin. We restricted this collaboration to the U.S. Great Lakes coastal region, primarily because of monetary and logistical limitations; however, most of the results are applicable to the entire Great Lakes ecosystem.
Sample Design
Our primary goal was to ensure that we distributed our sampling across the major axes of stress in the coastal region of the U.S. Great Lakes. In this way, our sampling represented a natural experiment in which biotic communities are examined across gradients of stress. We partitioned the entire U.S. Great Lakes coastline into 762 coastal watersheds, or “segment sheds.” Each segment shed consisted of the land area delineated by: (1) a segment of shoreline extending in both directions from the mouth of a second order or higher stream to one-half the distance to the adjacent streams, and (2) the associated drainage area. Segment shed area ranged widely, from 30 ha to 1.7 million ha. The number of segment sheds by lake is as follows: 102 in Lake Erie, 148 in Lake Huron, 157 in Lake Michigan, 90 in Lake Ontario, 12 in Lake St. Clair, and 236 in Lake Superior. An additional 17 segment sheds were found in connecting channels between the lakes.
Within each segment shed a total of 207 stress variables from 19 different data layers were compiled in a geographic information system (GIS). Each of the data layers was available from publicly available databases and in digital format. These data layers were classified into six major categories of stress within the Great Lakes including agricultural activity (21 variables), atmospheric deposition (11), land use/cover (23), human population and development (14), point source and non-point sources (79), and specific shoreline characteristics (6), plus one natural category, soil characteristics (53).
We employed a variety of multivariate statistical techniques including principal components analysis to reduce the dimensionality in these data and clustering techniques to identify groups of sites with similar stress profiles. A random stratified sampling design was used to select segment sheds from clusters with similar stress profiles while considering provinces and individual lakes. At the segment shed level, there were many sites that could be sampled. We also employed a random selection procedure as well as assessed access to select sites of various hydro-geomorphic types within a segment shed. Hydro-geomorphic types included the following: open-coast wetlands, riverine wetlands, protected wetlands, high energy shorelines, and embayments. Specific study site types were selected if they were relevant to a component. For instance, aquatic organisms were not studied in upland terrestrial areas nor were wetland plant communities studied in high energy shorelines. Final selection of study sites also was designed to maximize overlap, and thus integrate, across the different components of the study. For instance, because of the nature of the biological response, the contaminants component could sample the fewest sites, whereas the bird and amphibian subcomponent could sample hundreds of sites. Hence, most of the components sampled the contaminant sites and the bird and amphibian component sampled the most sites.
Analysis
Our basic approach to analysis was exploratory, in which the various environmental patterns (e.g., biological communities) were examined relative to the stress gradients. To reduce the number of potential relationships, each component identified stress gradients that were most relevant to the biota they sampled. The basic premise of these analyses was to explore whether there was a relationship between stress and biota. In most cases, each component also used a training set/test set approach in which the strength and predictability of the relationship was examined. These stress-biota response relationships also were examined over a wide variety of spatial scales to identify the appropriate scale in which the response could potentially be applied. For instance, because the stress variables were compiled in a GIS, calculations over many spatial scales were possible. The original selection of samples sites was based on stress variables calculated at the level of the segment shed, while most stress-response relationships for wetland complexes presented in the final analysis were based on stress variables calculated at the watershed level. However, we also explored stress-response relationships at various buffer distances from the specific sampling locations such as 500 m, 1000 m, or 5000 m buffers.
The stress gradients represent pressure indicators as defined by the State of the Lakes Ecosystem Conference. These gradients can be used individually to examine ecological changes associated with such activity as agriculture, human populations, or other stressors like atmospheric deposition. We explored each of these gradients in detail but also calculated an overall stress gradient in which all categories of stress were combined into one overall stress index. This stress index represents the integration of all 207 individual stress variables that were originally gathered and retains a high proportion of the variation in those original variables. We used this stress index to estimate the overall condition of the 762 segment sheds within the U.S. Great Lakes watershed. This stress index could be periodically evaluated to quantify the trend in condition of the U.S. Great Lakes coastal region. The stress index was evaluated by most of the components as a broad indicator of stress in the coastal region of the U.S. Great Lakes.
A final phase of the analysis included an integration phase in which we simultaneously analyzed the responses of amphibians, birds, diatoms, fish (sampled by electro-fishing and fyke nets), macroinvertebrates, and wetland vegetation in relation to biogeography (lake and province), hydro-geomorphic type, and the overall stress index. These considerations are critical for actual application of state (response) indicators because, in practice, one must know where to apply an indicator and whether there is a relationship to a potential stress. We used a hierarchical variance partitioning technique to identify the relative contribution of these major factors in an exploration of 66 individual state indicators. Furthermore, we also explored these same 66 indicators in an analysis of three major stressors (agriculture, human population/development, and point sources) with classification and regression trees. This analysis is in a preliminary status.
Results
A total of 341 wetland complexes, 122 high energy shorelines, 171 high energy/upland shorelines, and 26 embayments were sampled collectively across the U.S. Great Lakes coastal region (Figure 1). For wetland complexes, this represents more than 30 percent of the wetlands that currently occur in the study area. More than 25 wetland complexes were sampled by more than 4 components of this collaboration and more than 58 wetland complexes were sampled by 3 or more components. A summary of the sites visited by each of the components of the study includes the following: amphibians (214 wetland complexes), birds (224 wetland complexes, 171 high energy/upland shore areas), diatoms (98 wetland complexes, 68 high energy/near-shore areas, and 21 embayments), fish and macroinvertebrates (87 wetland complexes, 48 high energy/near-shore areas, and 20 embayments), photoinduced PAH, toxicity (48 sites), and wetland vegetation (90 wetland complexes).
Figure 1. General Locations of Study Sites Across the U.S. Great Lakes Coastal Region
The analysis of 66 indicator response variables for amphibians, birds, diatoms, electro-fish, fyke net fish, macroinvertebrates, and wetland vegetation by lake, province, hydro-geomorphic type, and the stress index revealed that lake was, on average, most important in explaining variation among the variables. This reveals that many of the indicators will need to be developed on a lake-by-lake basis. A surprising result was that hydro-geomorphic type was relatively unimportant for most of the variables, except for macroinvertebrates. Province also was not very important in explaining overall variation, but that was partially because lake and province are high related by common biogeography and lake had slightly better explanatory power over province. Indicators related to birds were among the best explained by the stress index. This indicates that birds may be among the better indicators that can be related with the stressors included in the overall stress index. In general, these results provide solid guidelines for examination of further relationships among the variables and for the refinement of indicators. These results are summarized more thoroughly in a manuscript that is submitted.
The second analysis used the same 66 indicator response variables, but focused on further evaluation of stressors and more than 5 spatial scales, including 100, 500, 1000, and 5000 m buffers and at the whole watershed scale. At each of these scales, stress was calculated by the proportion of row crop to represent agricultural stress; the proportional sum of low and high intensity urban, commercial/industrial, and road surface land to represent human development stress; and an index of point source and contaminant stress to represent pollution. The results indicated that the watershed scale was, in general, the best spatial scale for classifying the indicators. Row crop and human development were more related to the indicators than to the pollution variables, but we emphasize that the major contaminant responses were not included in this analysis. The biotic communities, however, were more highly related with the land use variables (agriculture and human population density/development) compared with pollution sources. These results also are summarized more thoroughly in a manuscript that is in review.
Indicators developed for the Great Lakes coastal regions are summarized in Table 1.
Table 1. Environmental Indicators Developed for the Great Lakes Coastal Region
| Indicator |
Measurement Method |
Description |
|
Amphibians of coastal wetlands |
Field surveys |
Species-based indicator using the responses of amphibians, especially the Spring Peeper, to stress gradients. |
|
Birds of coastal wetlands |
Field surveys |
Species-based indicator of ecological condition using counts of wetland birds across stress gradients. |
|
Birds of the coastal zone |
Field surveys |
Species-based indicator of ecological condition using counts of birds in the coastal zone across a reference gradient. |
|
PAH phototoxicity to larval fish |
Field, microscopy |
This indicator estimates the risk of photo-induced toxicity of PAHs to larval fish populations. |
|
Diatom-based chemical inference models |
Field, microscopy |
Diatom-based models were developed to infer a suite of important coastal parameters, including nutrients, water clarity and salinity variables. |
|
Diatom-based water quality condition model |
Field, microscopy |
This diatom-based model provides an overall inference of water quality at a coastal site. |
|
Multimetric diatom index of coastal habitat quality |
Field, microscopy |
This indicator uses broad taxonomic and functional characteristics of the diatom assemblage to rank a site within the range of habitat disturbance (low to high) in U.S. Great Lakes coastlines. |
|
Diatom deformities reflect pollution |
Field, microscopy |
Developmental deformities in the cell walls of diatoms appear to be related to contamination, and so deformity assessment is proposed as a possible indicator approach for the Great Lakes. |
|
Fish indicator indices in Typha-dominated wetlands |
Field surveys |
Typha-based index of biotic integrity were calibrated against stressor gradients to provide information about sources of impairment. |
|
Fish indicator indices in Scirpus (bullrush)-dominated wetlands |
Field surveys |
Scirpus (bullrush)-based index of biotic integrity were calibrated against stressor gradients to provide information about sources of impairment. |
|
Multitaxa wetland vegetation indices |
Field plots |
Indices that use a few selected plant species to evaluate wetland condition. |
|
Maximum canopy height |
Field measurements |
An index of plant biomass in which biomass increases with overall anthropogenic stress. |
|
Species dominance index |
Field measurements |
An index that indicates ecological integrity of wetlands from dominant plant species and their behavior. |
|
Land use/land cover change in the Great Lakes basin |
Landsat sensor data/GIS |
Detection of changes in land use and land cover over time in the Great Lakes basin. |
Journal Articles: 58 Displayed | Download in RIS Format
| Other center views: | All 279 publications | 67 publications in selected types | All 58 journal articles |
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| Type | Citation | ||
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Axler R, Henneck J, Kireta A, Sgro J, Kingston J. Surrogate water quality indicators for use in monitoring the Great Lakes coastal zone. Environmental Monitoring and Assessment (in preparation, 2004). |
R828675C001 (2003) |
not available |
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Bhagat Y, Ciborowski JJ, Johnson LB, Uzarski DG, Burton TM, Timmermans ST, Cooper MJ. Testing a fish index of biotic integrity for responses to different stressors in Great Lakes coastal wetlands. Journal of Great Lakes Research 2007;33(Suppl 3):224-235. |
R828675C003 (Final) |
Exit Exit Exit |
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Boers AM, Zedler JB. Stabilized water levels and Typha invasiveness. Wetlands 2009;28(3):676-685. |
R828675 (Final) |
Exit |
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Bourdaghs M, Johnston CA, Regal RR. Properties and performance of the Floristic Quality Index in Great Lakes coastal wetlands. Wetlands 2006;26(3):718-735. |
R828675C002 (Final) |
Exit |
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Bracey A, Kovalenko K, Niemi G, Giese E, Howe R, Grinde A. Effects of human land use on avian functional and taxonomic diversity within the upland coastal zone of the North American Great Lakes. AVIAN CONSERVATION AND ECOLOGYY 2022;17(2):6. |
R828675 (Final) |
Exit Exit |
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Brady VJ, Ciborowski JJH, Johnson LB, Danz NP, Holland JD, Breneman DH, Gathman JP. Optimizing fishing time: one vs. two-night fyke net sets in Great Lakes coastal systems. Journal of Great Lakes Research 2007;33(Suppl 3):236-244. |
R828675 (Final) R828675C003 (Final) |
Exit Exit Exit |
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Brazner JC, Danz NP, Niemi GJ, Regal RR, Trebitz AS, Howe RW, Hanowski JM, Johnson LB, Ciborowski JJH, Johnston CA, Reavie ED, Brady VJ, Sgro GV. Evaluation of geographic, geomorphic and human influences on Great Lakes wetland indicators:a multi-assemblage approach. Ecological Indicators 2007;7(3):610-635. |
R828675 (Final) R828675C001 (2004) |
Exit Exit Exit |
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Cade BS, Noon BR. A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment 2003;1(8):412-420. |
R828675C003 (Final) |
Exit |
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Crane JL, Richards C, Breneman D, Lozano S, Schuldt JA. Evaluating methods for assessing sediment quality in a Great Lakes embayment. Aquatic Ecosystem Heath & Management 2005;8(3):323-349. |
R828675 (Final) |
Exit |
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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. |
R828675C001 (2003) R828675C001 (2004) R828675C001 (Final) R828675C002 (2003) |
Exit Exit |
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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) |
Exit |
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Danz N, Reich P, Frelich L, Niemi G. Vegetation controls vary across space and spatial scale in a historic grassland-forest biome boundary. ECOGRAPHY 2011;34(3):402-414. |
R828675 (Final) |
Exit Exit |
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Frieswyk CB, Zedler JB. Vegetation change in Great Lakes coastal wetlands: deviation from the historical cycle. Journal of Great Lakes Research 2007;33(2):366-380. |
R828675 (Final) R828675C002 (Final) |
Exit Exit |
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Frieswyk CB, Johnston CA, Zedler JB. Identifying and characterizing dominant plants as an indicator of community condition. Journal of Great Lakes Research 2009;33(Suppl 3):125-135. |
R828675 (Final) |
Exit Exit |
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Ghioca-Robrecht DM, Johnston CA, Tulbure MG. Assessing the use of multiseason quickbird imagery for mapping invasive species in a Lake Erie coastal marsh. Wetlands 2009;28(4):1028-1039. |
R828675 (Final) |
Exit Exit |
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Grandmaison DD, Niemi GJ. Local and landscape influence on red-winged blackbird (Agelaius phoeniceus) nest success in Great Lakes coastal wetlands. Journal of Great Lakes Research 2007;33(Suppl 3):292-304. |
R828675 (2004) R829641 (Final) |
Exit Exit |
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Grigorovich IA, Mills EL, Richards CB, Breneman D, Ciborowski JJH. European valve snail Valvata piscinalis (Müller) in the Laurentian Great Lakes basin. Journal of Great Lakes Research 2005;31(2):135-143. |
R828675C003 (2004) R828675C003 (Final) R828777 (2003) |
Exit Exit Exit |
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Grigorovich IA, Kang M, Ciborowski JJH. Colonization of the Laurentian Great Lakes by the amphipod Gammarus tigrinus, a native of the North American Atlantic coast. Journal of Great Lakes Research 2005;31(3):333-342. |
R828675C003 (2004) R828675C003 (Final) |
Exit Exit |
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Hanowski J, Danz N, Howe R, Niemi G, Regal R. Consideration of geography and wetland geomorphic type in the development of Great Lakes coastal wetland bird indicators. EcoHealth 2007;4(2):194-205. |
R828675 (Final) R828675C004 (2004) |
Exit |
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Host GE, Schuldt J, Ciborowski JJH, Johnson LB, Hollenhorst T, Richards C. Use of GIS and remotely sensed data for a priori identification of reference areas for Great Lakes coastal ecosystems. International Journal of Remote Sensing 2005;26(23):5325-5342. |
R828675C003 (Final) R828777 (2003) |
Exit Exit |
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Howe RW, Regal RR, Niemi GJ, Danz NP, Hanowski JM. A probability-based indicator of ecological condition. Ecological Indicators 2007;7(4):793-806. |
R828675 (Final) |
Exit Exit Exit |
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Johnston C, Zedler J, Tulbure M, Frieswyk C, Bedford B, Vaccaro L, Brady V. A unifying approach for evaluating the condition of wetland plant communities and identifying related stressors. ECOLOGICAL APPLICATIONS 2009;19(7):1739-1757. |
R828675 (Final) |
Exit Exit |
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Johnston C, Zedler J, Tulbure M. Latitudinal gradient of floristic condition among Great Lakes coastal wetlands. JOURNAL OF GREAT LAKES RESEARCH 2010;36(4):772-779. |
R828675 (Final) |
Exit Exit |
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Johnston CA, Meysembourg P. Comparison of the Wisconsin and National Wetlands Inventories. Wetlands 2002;22(2):386-405. |
R828675C002 (2002) R828675C002 (2003) R828675C002 (Final) |
Exit Exit |
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Johnston CA. Shrub species as indicators of wetland sedimentation. Wetlands 2003;23(4):911-920. |
R828675C002 (2003) |
not available |
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Johnston CA, Bedford BL, Bourdaghs M, Brown T, Frieswyk C, Tulbure M, Vaccaro L, Zedler JB. Plant species indicators of physical environment in Great Lakes coastal wetlands. Journal of Great Lakes Research 2007;33(Suppl 3):106-124. |
R828675C002 (Final) |
Exit Exit |
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Johnston CA, Watson T, Wolter PT. Sixty-three years of land alteration in Erie Township. Journal of Great Lakes Research 2009;33(Suppl 3):253-268. |
R828675 (Final) |
Exit Exit |
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Kang M, Ciborowski JJH, Johnson LB. The influence of anthropogenic disturbance and environmental suitability on the distribution of the nonindigenous amphipod, Echinogammarus ischnus, at Laurentian Great Lakes coastal margins. Journal of Great Lakes Research 2007;33(Suppl 3):198-210. |
R828675C003 (Final) |
Exit Exit Exit |
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Kercher SM, Frieswyk CB, Zedler JB. Effects of sampling teams and estimation methods on the assessment of plant cover. Journal of Vegetation Science 2003;14(6):899-906. |
R828675C002 (2002) R828675C002 (2003) R828675C002 (2004) R828675C002 (Final) R828010 (Final) |
Exit Exit |
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Kovalenko K, Johnson L, Riseng C, Cooper M, Johnson K, Mason L, McKenna J, Sparks-Jackson B, Uzarski D. Great Lakes coastal fish habitat classification and assessment. JOURNAL OF GREAT LAKES RESEARCH 2018;44(5):1100-1109. |
R828675 (Final) |
Exit Exit |
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Kovalenko K, Brady V, Ciborowski J, Host G, Johnson J. Macroinvertebrate and Fish Community Metrics:Confounding Effects and Consistency over Time. WETLANDS 2020;40(5):1107-1116. |
R828675 (Final) |
Exit Exit |
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Niemi GJ, McDonald ME. Application of ecological indicators. Annual Review of Ecology, Evolution, and Systematics 2004;35:89-111. |
R828675 (2004) R828675C001 (Final) |
Exit |
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Niemi GJ, Brady VJ, Brown TN, Ciborowski JJH, Danz NP, Ghioca DM, Hanowski JM, Hollenhorst TP, Howe RW, Johnson LB, Johnston CA, Reavie ED. Development of ecological indicators for the U.S. Great Lakes coastal region - a summary of applications in Lake Huron. Aquatic Ecosystem Health & Management 2009;12(1):77-89. |
R828675 (Final) |
Exit Exit |
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Niemi G, Wardrop D, Brooks R, Anderson S, Brady V, Paerl H , Rakocinski C, Brouwer M, Levinson B, McDonald M. Rationale for a new generation of indicators for coastal waters. Environmental Health Perspectives 2004;112(9):979-986. |
R828675 (2004) R828675 (Final) R828677C001 (Final) R828684 (Final) R829458C003 (2003) R829458C008 (2003) R829458C008 (2004) |
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Olker J, Kovalenko K, Ciborowski J, Brady V, Johnson L. Watershed Land Use and Local Habitat:Implications for Habitat Assessment. WETLANDS 2016;36(2):311-321. |
R828675 (Final) |
Exit Exit |
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Panci H, Niemi G, Regal R, Tozer D, Gehring T, Howe R, Norment C. Influence of Local, Landscape, and Regional Variables on Sedge and Marsh Wren Occurrence in Great Lakes Coastal Wetlands. WETLANDS 2017;37(3):447-459. |
R828675 (Final) |
Exit Exit |
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Pengra BW, Johnston CA, Loveland TR. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sensing of Environment 2007;108(1):74-81. |
R828675 (Final) |
Exit Exit Exit |
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Peterson AC, Miemi GJ. Evaluation of the Ohio Rapid Assessment Method for Wetlands in the Western Great Lakes: An Analysis Using Bird Communities. Journal of Great Lakes Research 2007;33(3):280-291 |
R828675C004 (2003) R828675C004 (2004) |
Exit Exit |
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Price SJ, Marks DR, Howe RW, Hanowski J, Niemi GJ. The importance of spatial scale for conservation and assessment of anuran populations in coastal wetlands of the western Great Lakes. Landscape Ecology 2005;20(4):441-454. |
R828675C004 (2003) R828675C004 (Final) |
not available |
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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) |
not available |
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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) |
not available |
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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) |
Exit |
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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) |
Exit Exit |
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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) |
not available |
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Reavie E, Juggins S. Exploration of sample size and diatom-based indicator performance in three North American phosphorus training sets. AQUATIC ECOLOGY 2011;45(4):529-538. |
R828675 (Final) |
Exit Exit |
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Reavie E. Asymmetric, biraphid diatoms from the Laurentian Great Lakes. PEERJ 2023;11:1-70 |
R828675 (Final) |
Exit |
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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) |
not available |
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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) |
not available |
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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. |
R828675C001 (2004) R828675C001 (Final) |
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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) |
not available |
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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) |
not available |
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Trebitz AS, Brazner JC, Brady VJ, Axler R, Tanner DK. Turbidity tolerances of Great Lakes coastal wetland fishes. North American Journal of Fisheries Management 2007;27(2):619-633. |
R828675 (2004) |
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Tulbure M, Johnson C. Environmental Conditions Promoting Non-native Phragmites australis Expansion in Great Lakes Coastal Wetlands. WETLANDS 2011;30(3):577-587. |
R828675 (Final) |
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Uzarski DG, Burton TM, Cooper MJ, Ingram JW, Timmermans STA. Fish habitat use within and across wetland classes in coastal wetlands of the five Great Lakes: development of a fish-based index of biotic integrity. Journal of Great Lakes Research 2005;31(Suppl 1):171-187. |
R828675C003 (Final) |
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Vaccaro L, Bedford B, Johnston C. Litter Accumulation Promotes Dominance of Species of Cattails (Typha SPP.) in Lake Ontarior Wetlands. WETLANDS 2009;29(3):1036-1048. |
R828675 (Final) |
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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) |
not available |
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Wolter PT, Johnston CA, Niemi GJ. Mapping submerged aquatic vegetation in the U.S. Great Lakes using Quickbird satellite data. International Journal of Remote Sensing 2005;26(23):5255-5274. |
R828675C004 (Final) |
not available |
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Wolter PT, Johnston CA, Niemi GJ. Land use land cover change in the U.S. Great Lakes basin 1992 to 2001. Journal of Great Lakes Research 2009;32(3):607-628. |
R828675 (Final) |
Exit Exit |
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, Air, Geographic Area, Water, Ecosystem Protection/Environmental Exposure & Risk, Nutrients, Ecosystem/Assessment/Indicators, Ecosystem Protection, climate change, Ecological Effects - Environmental Exposure & Risk, Ecological Risk Assessment, Great Lakes, Ecological Indicators, coastal ecosystem, diatoms, ecological condition, aquatic ecosystem, hydrological stability, nutrient supply, nutrient transport, environmental monitoring, ecosystem assessment, hierarchically structured indicators, wetland vegetation, human activities, environmental stressor, hydrological, coastal environments, environmental consequences, hydrologic models, ecological assessment, ecosystem indicators, estuarine ecosystems, nutrient stress, aquatic ecosystems, toxic environmental contaminants, atmospheric pollutant loads, ecosystem impacts, environmental stressors, water quality, ecosystem stress, ecological response, climate variabilityRelevant Websites:
A Manager's Guide to Indicator Selection (PDF) (8 pp., 3.4MB)
New Index of Environmental Condition for Coastal Watersheds in the Great Lakes Basin (PDF) (2 pp., 281KB)
http://glei.nrri.umn.edu 
https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.files/fileID/7679 (PDF) (2 pp, 281K, about PDF)
Progress and Final Reports:
Original Abstract 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
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.