Final Report: Development and Assessment of Environmental Indicators Based on Birds and Amphibians in the Great Lakes Basin

EPA Grant Number: R828675C004
Subproject: this is subproject number 004 , 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: Development and Assessment of Environmental Indicators Based on Birds and Amphibians in the Great Lakes Basin
Investigators: Howe, Robert W. , Hanowski, JoAnn M. , Niemi, Gerald J. , Smith, Charles
Institution: University of Wisconsin - Green Bay , Cornell University , University of Minnesota
EPA Project Officer: Hiscock, Michael
Project Period: January 10, 2001 through January 9, 2005 (Extended to January 9, 2006)
RFA: Environmental Indicators in the Estuarine Environment Research Program (2000) RFA Text |  Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Water , Ecosystems


Birds and amphibians have been used as indicators of the condition of the Great Lakes, especially wetland ecosystems, for several years (Environment Canada and the U.S. Environmental Protection Agency [EPA], 2003; Weeber and Vallianatos, 2001). Moreover, birds have been used as ecological indicators in a variety of contexts in many parts of the United States and Canada (Morrison 1986).

The objectives of this research project were to: (1) develop a suite of scientifically robust, cost-effective indices of bird and amphibian assemblages that reflect the ecological condition of the Great Lakes; (2) quantify the extent to which these indices are related to environmental pressure indicators such as land use characteristics, water quality, presence of exotic species, and hydrological modifications; (3) derive predictive models based on statistical relationship between pressure indicators and indices of bird/amphibian diversity and abundance; (4) use these models to infer ecological conditions at local and regional scales and to establish or improve the baseline for environmental monitoring programs; (5) develop a quality assurance/quality control infrastructure for future assessments of bird and amphibian communities; and ultimately (6) provide scientific recommendations for improving and monitoring the ecological health of the Great Lakes basin.

Summary/Accomplishments (Outputs/Outcomes):

Experimental Approach

We evaluated both coastal wetlands and uplands within 1 km of the Great Lakes shoreline using standardized methods that are already in place for the Marsh Monitoring Program (coastal wetlands) or general studies of upland birds (Howe, et al., 1997). Most sites were sampled during only a single year; our approach was to include an extensive sample including many sites rather than an intensive sample of fewer sites. Approximately 10 percent of the sites were sampled during both years to provide some indication of annual variation, and a pilot study during 2001 explored alternative sampling approaches. Specifically, we sampled a larger number of points per wetland and additional sampling methods such as timed searches and tadpole traps.

Data collected over the 2-year period provided a basis for multivariate analyses of species’ associations and environmental correlates. These analyses were used to develop probability-based indicators of ecological condition that explicitly incorporate species’ responses to an independently measured reference gradient of environmental stress. Our approach represents not only a new method for calculating ecological indicators based on birds and amphibians, but an entirely new method for the development of indicators in general.

Bird Survey Methods

We used a standard protocol established by Ribic, et al. (1999) to conduct wetland breeding bird surveys during June through early July 2000, 2001, and 2002. Surveys were conducted by trained observers (Hanowski and Niemi, 1995) between 5:00 a.m. and 9:30 a.m. CDT and on mornings with no precipitation and winds below 18 kph. From 1 to 3 half-circle sample points at least 200 m apart were placed in each coastal wetland depending on the area of the wetland complex. Each point was sampled 1 time with an initial 5-minute passive count, followed by a tape playback of several cryptic species, followed by an additional 5-minute passive listening.

Upland birds were sampled in roadside transects within approximately 1 km of the Great Lakes shoreline (Figure 1). A single transect consisted of 15 points at least 500 m apart (Figure 2). At each point, located and documented by a GPS reading, a trained observer conducted a 10-minute, unlimited-radius bird count following the standard protocol of Ralph, et al. (1995) and Howe, et al. (1997), a method that is straightforward and easily repeated by trained observers. All counts were conducted between approximately sunrise and 9:00 a.m.

Figure 1. Locations (Dots) of Coastal Wetland Study Sites for Birds and Amphibians

Figure 2. Upland Bird Survey Route Near Ashland, WI, Consisting of 15 Points at Least 500 m Apart. Bird survey points (black dots) are encircled by 500 m GIS buffer, within which we evaluated the proportion of land cover types. Colored pixels represent land cover classes developed by Wolter, et al. (2005).

Amphibian Survey Methods

We followed guidelines outlined by the Marsh Monitoring Program (MMP) for conducting amphibian calling surveys at the same points that were sampled for wetland breeding birds (Weeber and Vallianatos, 2001). Three calling surveys were conducted at each site following MMP temperature guidelines. Surveys started one-half hour after sunset and were completed before midnight. Each survey was 3 minutes in length and conducted in good weather conditions (light winds and no precipitation). We assigned calling level codes to the detection of each species. Data from all analyses were entered into a Microsoft Access database and then double-entered to assure accuracy.


The overall distribution of sites sampled for birds and amphibians spanned the entire U.S. Great Lakes coastal region (Figure 1). We sampled a total of 220 wetland complexes for amphibians from 2002 to 2003, which included a total of 331 individual points (Table 1). Similarly, a total of 224 wetland complexes were sampled for birds, including 338 individual points sampled. In contrast, 171 upland coastline segments were sampled, with a total of 2,544 individual points (15 points per segment). Overall, this represented a sample of approximately one-third of the available segment sheds and more than 30 percent of the wetland complexes within the U.S. Great Lakes coastal region.

Table 1. Distribution of Study Sites Among Taxonomic Groups and Lakes During 2002-2003



Upland Birds

Wetland Birds


















































Cost Effectiveness

In the 2001 pilot study we tracked the labor and travel costs to complete a sample for amphibians, wetland birds, and upland birds. We found that, on average, a sample of 15 upland points costs approximately four times as much to complete compared to a wetland bird survey, and that an amphibian sample was approximately three times more costly than a wetland bird sample. The pilot study also indicated that 3-point samples were optimal from a cost-benefit perspective for sampling larger coastal wetlands.


We recorded at least 12 species of frogs and toads, 3 of which were observed at fewer than 5 and another (Mink Frog, Rana septentrionalis) at only 11 of the 361 point counts (Figure 3). The most commonly reported species was Spring Peeper, followed by Green Frog (Rana clamitans), Gray Treefrog (Hyla versicolor and Hyla chrysoscelis), American Toad (Bufo americanus), Northern Leopard Frog (Rana pipiens), Chorus Frog (Pseudacris maculata and P. triseriata), Bullfrog (Rana catesbeiana), and Wood Frog (Rana sylvatica). Distributions of most species showed clear geographic variation between the northern Laurentian Mixed Forest Ecological Province and the southern Eastern Deciduous Forest Ecological Province (see below). A typical coastal wetland point yielded three to five anuran species, with somewhat higher numbers in Lakes Ontario and Huron and lower numbers in Lakes Erie and Superior (Figure 4).

Figure 3. Amphibian Species Observed in Coastal Wetland Study Sites. Width of bar is proportionate to the number of points (of 361) at which the species was recorded. Copes Gray Treefrog (Hyla chrysoscelis) and Eastern Gray Treefrog (Hyla versicolor) were combined as Gray Treefrog.

Figure 4. Distribution of Species Richness Among Amphibian Point Counts in Different Lakes. Values for each count combine all 3 survey dates.

Documented relationships between species and independent measures of environmental stress, critical for developing meaningful environmental indicators, were variable both among anuran species and among geographic areas for the same species. We used a multivariate analysis of remote-sensed land cover variables (Wolter, et al., in press) and other measures associated with human environmental impacts (Danz, et al., 2005; see report for Grant No. R828675C001) to develop an index of environmental stress or “reference condition” ranging from 0 (most degraded) to 10 (least degraded). Sites were grouped into categories of similar reference condition (e.g., 0-0.5, 0.5-1.0, 1.0-1.5, etc.), and the frequency of each anuran species was plotted against the 0-10 environmental gradient. Results reflected the north-south variation in abundance within species (Figure 5) as well as the sensitivity of different species to environmental stress.

Strongest (positive) response to the reference gradient was exhibited by Spring Peeper (Figure 5), which was the only species to show a consistent relationship to environmental stress in both the northern and southern ecological provinces. Bullfrog showed a strong negative relationship with condition in the northern ecological province but showed little or perhaps a slightly positive relationship in the southern province. Likewise, American Toad showed a positive relationship with condition in the northern province (like Spring Peeper) but the opposite relationship in the southern part of the Great Lakes.

Figure 5. Relationship Between Frequency of Occurrence (= Probability) of Spring Peeper in Coastal Wetlands Characterized by Different Degrees of Human Impact. Sites with low values of condition, measured by multivariate analysis of land cover, pollution emissions, agricultural intensity, and other environmental impacts are characterized by high levels of environmental stress or degradation, whereas sites with high values of condition are characterized by low levels of environmental stress.

These anomalies warn against the application of anuran-based indicators across the entire Great Lakes basin. The fact that some species show positive response to condition, whereas others show a negative response, also suggests that anuran species richness is a poor indicator of environmental condition in the Great Lakes coastal zone, at least with respect to the reference gradient used in our analysis. A direct analysis of species richness (Figure 6) confirms this advice.

Figure 6. Relationship Between Reference Condition (Based on Multivariate Analysis of Land Cover and Other Variables) and Anuran Species Richness. Sites from the northern (N = Laurentian Mixed Forest) and southern (S = Eastern Deciduous Forest) ecological provinces are plotted separately.

Application of these species/environmental stress relationships to the development of anuran-based indicators is in progress. Our general approach to indicator development is described in the section on birds of coastal wetlands. Based on the data presented here, the abundance or frequency of Spring Peepers is the simplest and most reliable indicator across the U.S. portion of the Great Lakes coastal zone.

In a more intensive investigation of anurans in Lakes Michigan and Huron, Steven Price led an evaluation of the relationships between anuran frequencies and a broader range of land cover variables, including measurements collected directly at the wetland survey points. Like our general analysis, Price’s analysis (Price, et al., 2005) also used remote sensing data from Landsat 5 and Landsat 7 imagery. We found that most (but not all) anuran species were most sensitive to land cover variables measured at rather large geographic scales (3 km radius). For nearly every species, variables associated with urbanization (residential development, road density, etc.) showed a negative relationship with anuran frequency of occurrence. These results suggest that the reference gradient used in our general analysis might include variables that have confounding effects on anuran/environment relationships.

Wetland Birds

Wetland birds were sampled at 371 points in 215 wetland complexes, nearly all of which also were the part of the amphibian survey. The most frequently recorded species, Red-Winged Blackbird, was more than three times more abundant than the second most commonly recorded species (European Starling). Other common birds in the coastal wetland samples included (in decreasing order of abundance) Canada Goose, Herring Gull, Ring-Billed Gull, Yellow Warbler, Common Grackle, Common Yellowthroat, Tree Swallow, and Song Sparrow. Because these species are so ubiquitous, they provide little information about the environmental condition of a given wetland.

The majority of the 155 bird species recorded in coastal wetlands were much less common than these 10 abundant species. A typical 10-minute census using the standard marsh monitoring protocol (Ribic, et al., 1999) yielded between 11-18 species, often more than 20. The richness and familiarity coastal wetland bird species therefore provide outstanding opportunities for developing indicators of ecological condition in the Great Lakes coastal zone.

Figure 7. Numbers of Bird Species Recorded in Standard Point Counts in Great Lakes Coastal Wetlands During 2002-03. “# of counts = the number of point counts (of 371) with the given number of bird species (0-2, 3-4, 5-6, 7-8, etc.).

We again used the multivariate-derived “reference gradient” of wetland complexes to identify species that exhibit consistent responses (positive or negative) to environmental stress. This reference gradient was established through a principal components analysis (PCA) of 39 environmental variables, including previously derived PCA scores from the analysis of Danz, et al., 2005 (see report for Grant No. R828675C001) and proportion of land cover in six classes (natural non-wetland, wetland, residential, commercial/industrial, agricultural, and roads) within different radii from the center of the complex (100 m, 500 m, 1 km, and 5 km). Variables were chosen because they could be ordered on a scale from most-impacted to least-impacted by human activities or from highest proportion wetland area to lowest proportionate wetland area. Results (Figures 8 and 9) yielded 5 interpretable axes that explained 68 percent of the variation in the original variables. Scores on each of the axes, ordered from most-impacted by humans to least-impacted by humans (or lowest proportion wetland to highest proportion wetland), were weighted by the proportion of variance explained and combined to form a single gradient of ecological condition ranging from 0 (highly degraded non-wetland) to 10 (minimally degraded wetland).

Figure 8. Axes 1 and 2 From PCA of Coastal Wetland Complexes based on Surrounding Land Cover and Human-related Variables Associated With the Coastal Segment Watershed (Danz, et al., 2005; R828675C001). Size of triangles is proportional to % natural nonwetland vegetation within 500 m of the center for the wetland complex. Annotations (natural vegetation, agricultural, urban/industrial) indicate predominant attributes of sites in different regions of the ordination map.

Figure 9. Axes 1 and 3 From PCA of Coastal Wetland Complexes Based on Surrounding Land Cover and Human-related Variables Associated With the Coastal Segment Watershed (Danz, et al., 2005; R828675C001). Size of triangles is proportional to % wetland vegetation within 100 m of the center for the wetland complex. Annotations indicate predominant attributes of sites in regions of the ordination map.

Given the reference gradient, we plotted frequencies of occurrence of bird species in different categories of sites (condition = 0-1, 1-2, 2-3, etc.). The results, which we call Species-Specific Sensitivity-Detectability (SSD) functions, can be modeled by a 4 parameter mathematical expression describing the probability of observing the species when condition is equal to 0, the probability of observing the species when condition is equal to 10, the value of condition where the probability of observing the species is halfway between the minimum and maximum probabilities, and the steepness of the nonlinear relationship. The SSD functions take into account both the sensitivity of the species to environmental stress as well as probability of observing the species even in optimal conditions. We used an iterative procedure in Microsoft Excel to estimate best-fit parameters for species that were observed in at least 10 of the 371 point counts. From 41 species that showed significantly significant relationships with the nonlinear SSD model (r > 0.433, p < 0.05) we selected 25 wetland or open country species calculating site-specific indicator of ecological condition. We excluded forest species, colonial nesters, and most species of open upland habitats, although several birds with broad habitat preferences (e.g., American Crow, American Goldfinch) were included in the list of 25 species. Sandhill Crane, American Bittern, Sedge Wren, Common Yellowthroat, and Yellow Warbler showed strongest positive associations with the reference gradient, while Mallard, Common Grackle (Figure 10b), and European Starling showed strongest negative relationships. The shapes of the best-fit SSD functions varied according to the ecology and overall abundance of different species. Bald Eagle, for example, showed a rather low probability of occurrence even at optimal condition, whereas the more abundant Swamp Sparrow, which also exhibited a positive relationship with reference condition, is somewhat likely to occur even in rather degraded sites. Armed with parameter estimates for the SSD functions of 25 species, we calculated bird-derived values of ecological condition for 20 sites that had been excluded from the analysis used to calculate the SSD functions. Our new, probability-based ecological indicator (Cobs) can be derived from presence/absence data for the 25 target species at a given site. Rather than use the standard method of adding or multiplying weightings to produce an index, however, our method “works backward” from the observed data, using an approach pioneered by Hilborn and Mangel (1997). In other words, we use computer iteration (e.g., the solver function in Microsoft Excel) to ask: “What is the value of Cobs, ranging from 0 to 10 that best fits the observed presence/absence data and the previously derived SSD functions?” Results have proven to be remarkably robust and provide insights beyond the information inherent in the reference gradient. A plot of reference condition (Cref) calculated from environmental variables against ecological condition (Cobs) based on bird occurrences (Figure 11) shows that the two measures can be significantly different. For example, values of ecological condition (Cobs) for sites with moderately low environmental condition (Cref = 1-5) generally were much lower than the corresponding values of Cref, perhaps reflecting a threshold of environmental condition, below which bird species occur less frequently than expected based on environmental variables alone.

Figure 10. Species-Specific Sensitivity/Detectability (SSD) Functions for Alder Flycatcher, a Sensitive Species Whose Presence Indicates High Values of Ecological Condition and Common Grackle, a Tolerant Species Whose Absence Indicates High Values of Condition.

Figure 11. Comparison of Reference Condition (Cref)Based on Multivariate Analysis of Land Cover and Other Environmental VariablesWith Ecological Condition (Cobs) Based on Presence/Absence of 25 Selected BirdSpecies.

Upland Birds

We identified 187 bird species in the survey of 171 coastal segments, each sampled with a route of 15 standard ten-minute point counts. To assess annual variation in species composition, 23 of the routes were sampled during both 2002 and 2003. In total, this phase of the project evaluated 2544 separate point counts. Although we refer to the census results as upland bird assemblages, the species included birds of wetlands, forests, urban areas, and all habitat types located within approximately 1 km of the shoreline. Like most biotic assemblages, birds of the Great Lakes coastal zone followed a log-normal distribution of relative abundance (Figure 12), with relatively few abundant species and many species with moderate to low relative abundance.

Figure 12. Rank Abundance Diagram of Bird Species Observed in 2544 Roadside Bird Counts Within Approximately 1 km of the Great Lakes Shoreline. Representative species are shown with arrows pointing toward the corresponding bar. Number of individuals refers to the total number of birds reported in all point counts combined.

The most abundant species (Ring-billed Gull, European Starling, Herring Gull, American Crow, House Sparrow, American Robin) were familiar birds of urban and suburban environments in both the northern Laurentian Mixed Forest Ecological Province and the southern Eastern Deciduous Forest Ecological Province. Other species differed substantially between the two geographic provinces, however (Figure 13), warranting a separate analysis of ecological indicators for each region.

Figure 13. Ordination (Nonmetric Multidimensional Scaling) of Coastal Segments Based on Bird Species Composition in Standardized Survey Routes

Like our analysis of coastal wetlands, we calculated “reference condition” for sites based on environmental attributes, in this case the proportional area in six general land cover classes within 100 m, 500 m, 1 km, 3 km, and 5 km of the 15 bird survey points. PCA was used to generate a single gradient ranging from 0 (maximally impacted by human activities) to 10 (minimally impacted by human activities).

We plotted the proportion of points (maximum = 15) at which the specieswas recorded against the reference condition for each route, excluding 20 routesfor later validation of the model. These relationships (e.g., Figure 14) wereused to estimate 4-parameter SSD functions (Howe, et al., in preparation). Statisticallysignificant SSD functions (p < 0.05) were derived for 72 bird species in thenorthern (Laurentian Mixed Forest) ecological province and for 50 bird speciesin the southern (Eastern Deciduous Forest) ecological province.

Figure 14. SSD Functions for (a) Canada Werbler, (b) Rock Pigeon, (c) Nashville Warbler, and (d) American Robin in the Laurentian Mixed Forest Ecological Province. Solid lines illustrate the best-fit, four parameter mathematical function described in Howe, et al. (in preparation). Probability is the proportion of points (max. = 15) at which the species was observed. Condition represents a reference gradient based on land cover, ranging from (maximally impacted by humans) to 10 (minimally impacted).

Once parameters of SSD functions were established, the ecological condition of new sites could be calculated through iteration (Hilborn and Mangel, 1997). In this case, we derived the value of condition (Cobs) that yielded the closest fit between observed species frequencies (among the 15 bird census points) and the predicted frequencies given the species’ SSD functions. We applied this method to the 20 sites withheld from the derivation of SSD functions. Results again illustrated a close fit between reference condition and bird-based condition (Figure 15), but biologically meaningful deviations were evident.

Figure 15. Correlation Between Ecological Condition Based on Bird Species Composition (y axis) and Reference Condition Based on Land Cover Variables (x axis) in Great Lakes Coastal Segments. The 20 sites were not included in calculations of SSD functions.

Summary and Recommendations

Our analysis provides not only robust and flexible ecological indicators for the Great Lakes coastal zone, but we propose a framework for developing biotic indicators anywhere and for any group of species. Details of our approach, including parameter estimates for applying the method to Great Lakes coastal wetlands and coastal segments are contained in two manuscripts, one submitted for publication and the other soon to be submitted.

The recipe for applying this method is very simple, although the preliminary work of deriving SSD functions (which we have done for birds and amphibians) requires a large-scale data set for the region of interest. To calculate ecological condition for specific sites, a manager or researcher only needs to provide a list of species observed in 1 or more counts like the ones used to develop the SSD functions (in this case, standard point counts). If the site can be sampled multiple times (e.g., at different points or at different times), then probabilities of occurrence can be provided for each species, ideal input for the calculation of ecological condition. The solver function in Microsoft Excel can be used to derive values of condition. Eventually, we hope to provide a Web-based utility that will enable any user with field data to generate estimates of condition for sites of interest. Selection of species for deriving the estimate might be expanded or limited based on the nature of the field work or the range of habitats sampled. In all cases, however, the estimates of condition will be developed in the framework of a standard scale from 0 to 10 and in the context of an explicit reference gradient.


Hanowski JM, Niemi GJ. Experimental design considerations for establishing an off-road, habitat-specific bird monitoring program using point-counts. USDA Forest Service Gen. Tech. Rep. PSW-GTR-149, 1995;145-150.

Hilborn R, Mangel M. The ecological detective: confronting models with data monographs of population biology 28. New Jersey: Princeton University Press, 1997.

Howe RW, Niemi GJ, Lewis SJ, Welsh DA. A standard method for monitoring songbird populations in the Great Lakes region. Passenger Pigeon 1997;59:183-194.

Morrison ML. Bird populations as indicators of environmental change. Current Ornithology 1986;3:429 451.

Ralph CJ, Sauer JR, Droege S. Monitoring bird populations by point counts. Gen. Tech. Rep. PSW-GTR-149. Pacific Southwest Research Station, USDA Forest Service, Albany, CA, 1995, 187 pp.

Ribic CA, Lewis SJ, Melvin S, Bart J, Peterjohn B. Proceedings of the marsh bird monitoring workshop. U.S. Fish and Wildlife Service, U.S. Geological Survey, 1999.

Weeber RC, Vallianatos M. The Marsh Monitoring Program 1995-1999. Monitoring Great Lakes Wetlands and their Amphibians and Bird Inhabitants. Published by Bird Studies Canada in cooperation with Environment Canada and U.S. Environmental Protection Agency, 2000.

Journal Articles on this Report : 2 Displayed | Download in RIS Format

Other subproject views: All 23 publications 4 publications in selected types All 3 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 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. R828675 (Final)
R828675C004 (2003)
R828675C004 (Final)
not available
Journal Article 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

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, ECOSYSTEMS, Ecosystem Protection/Environmental Exposure & Risk, Ecosystem/Assessment/Indicators, Ecosystem Protection, exploratory research environmental biology, Monitoring/Modeling, Ecological Effects - Environmental Exposure & Risk, Ecological Monitoring, Environmental Monitoring, Ecological Risk Assessment, Great Lakes, Ecological Indicators, Risk Assessment, amphian population model, coastal ecosystem, anthropogenic stress, ecological condition, biodiversity, environmental measurement, ecosystem assessment, coastal environments, ecological assessment, ecosystem indicators, aquatic ecosystems, environmental stress, birds, water quality, ecological models, land use

Relevant Websites: Exit

Progress and Final Reports:

Original Abstract
  • 2001
  • 2002
  • 2003 Progress Report
  • 2004 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