Great Lakes National Program Office Research Articles

Revisiting zooplankton as indicators in the Great Lakes: Which indicators detect temporal changes in the zooplankton community composition?

Zooplankton are a vital component of the food web as the major conduit of energy from primary producers to planktivorous fishes. Therefore, changes in the structure of the zooplankton community affect how energy moves through aquatic ecosystems. Changes in the zooplankton community structure are typically documented through detailed community analysis, which can be difficult to interpret and communicate to non-experts. Alternatively, a few key summary indicators of community structure may better disseminate this information to a broader audience. In this study we analyzed zooplankton data from 1997 to 2019 from the US Environmental Protection Agency’s Great Lakes National Program Office to select indicators that summarized changes in the zooplankton community. Two indicators (percent of calanoids by biomass and areal density of herbivorous cladocerans) detected known changes in lakes Huron, Michigan, and Ontario, correlated with the crustacean zooplankton areal biomass above (herbivorous cladocerans) or in the hypolimnion (percent calanoids), and were related to the lake or basin-wide Carlson’s Trophic State Index based on chlorophyll (TSIchl) (percent calanoids by biomass). Our findings suggest that employing one or both selected indicators in addition to TSIchl and total crustacean zooplankton areal biomass, an already reported indicator in the Great Lakes that captures overall food availability for fish, would provide additional key information about zooplankton community structure in simple metrics that could be effectively communicated to stakeholders.

A comparison of methods for estimating euphotic depths in the Great Lakes from satellite observations

Because it defines the vertical extent of the zone of active photosynthesis, an estimate of the euphotic depth (Zeu), the depth in the water column at which the intensity of photosynthetically available radiation (PAR) is reduced to 1% of the intensity just below the surface, is a critical component of both depth resolved and vertically integrated numerical models of aquatic primary production. In recent years these models have become more detailed in both space and time and it is necessary to make estimates of Zeu at the same spatial and temporal scales. In large bodies of water such as the Great Lakes, satellite remote sensing is the only practical method available to provide such estimates at the needed resolution, but the algorithms used for estimating Zeu from satellite observations have not been rigorously tested in the Great Lakes. To assess these methods we used measurements of the vertical profile of PAR collected as part of the U.S. EPA’s, Great Lakes National Program Office (GLNPO) annual monitoring program to estimate Zeu. We compared the field measurements to satellite-derived estimates of Zeu obtained from a variety of algorithms ranging from simple empirical relationships to more complicated methods based on the inherent optical properties of the water. We found that an algorithm based on a quasi-analytic determination of absorption and backscatter coefficients that also incorporates a depth-dependent attenuation coefficient performed best.

Non-native Dreissena associated with increased native benthic community abundance with greater lake depth

Although the typical interaction between non-native invasive species and native species is considered to be negative, in some cases, non-native species may facilitate native species. Zebra and quagga mussels (Dreissena spp.) are aggressive invaders in freshwater systems, and they can alter energy flow by diverting nutrients from pelagic to benthic food-webs. In the last two decades, quagga mussels have largely replaced zebra mussels in shallow regions of the Laurentian Great Lakes and colonized deeper waters previously devoid of all dreissenids. Here, we aim to characterize potential positive effects of dreissenids in relation to depth on the benthic community in lakes Michigan and Huron. For this study, we used benthic survey data collected from Lake Michigan in 2015 and Lake Huron in 2017 and annual U.S. EPA Great Lakes National Program Office Long-term Biology Monitoring Program data for both lakes from 1998 to 2019. Benthic species richness and abundance (excluding dreissenids) in both lakes were almost three-fold higher in the nearshore (<70 m) compared to offshore (>70 m) communities. We found that, even though abundance of benthic invertebrates decreased with increased depth, total benthos density and biomass were higher in the presence than in the absence of quagga mussels in both lakes. Moreover, increased quagga mussel density and biomass with depth offset the lower benthos density and biomass at deeper depths, and samples with dreissenids had high densities of oligochaetes in both nearshore and offshore communities. These patterns are consistent with facilitative effects of quagga mussels on both shallow and deep-water benthic communities.

Benthic Data Collection in Lake Ontario: Continuation of a Long‐term Data Series

Photo 1. Marine technician Kathryn Johncock is collecting a benthic sample from Lake Ontario using Ponar bottom grab aboard of U.S. EPA Research Vessel Lake Guardian during 2018 benthic survey. Photo credit: A. Karatayev. Photo 2. Benthic samples are elutriated aboard and washed through a 500-μm net by Chris Korleski (U.S. EPA Great Lakes National Program Office, on left) and Alexander Karatayev (SUNY Buffalo State, on right). Photo credit: L. Burlakova. Photo 3. Exotic byssate bivalves quagga mussels were found in almost every benthic sample collected from Lake Ontario in 2018. Photo credit: A. Karatayev. Photo 4. Lake Ontario shoreline scattered with wind turbines. Photo credit: L. Burlakova. Photo 5. Lake Guardian is moving to the next station. Photo credit: L. Burlakova. These photographs illustrate the article “Density data for Lake Ontario benthic invertebrate assemblages from 1964 to 2018” by Burlakova, L. E., A. Y. Karatayev, A. R. Hrycik, S. E. Daniel, K. Mehler, L. G. Rudstam, J. M. Watkins, R. Dermott, J. Scharold, A. K. Elgin, T. F. Nalepa, E. K. Hinchey, and S. J. Lozano published in Ecology. https://doi.org/10.1002/ecy.3528.

Algorithmic Characterization of Lake Stratification and Deep Chlorophyll Layers From Depth Profiling Water Quality Data

AbstractWe develop and test algorithms for rapidly and consistently analyzing water quality profile data such as temperature and fluorescence that are used to identify lake thermostratification and deep chlorophyll layers (DCL). Currently, the processing of profile data and identification of key features are manual and subjective, and thus, the results are not comparable from one sampling event to another. In this study, we develop a method to approximate vertical temperature profiles with linear segments using a piecewise linear representation algorithm, from which stratification patterns can be extracted. We also propose an automated peak detection algorithm to identify the location and magnitude of DCL. The algorithms are applied to water quality profile data collected by the United States Environmental Protection Agency Great Lakes National Program Office, which conducts annual depth profiling using conductivity, temperature, depth profilers at fixed locations in the Great Lakes. The algorithms generate similar results to human judgments, with some outliers that show expert errors, algorithm limitations, and ambiguities in defining layers. We also show how the algorithms can analyze the shape of temperature and fluorescence profiles to detect unusual patterns. Lake Superior is used as a case study to reveal spatial and temporal trends of the thermocline, DCL, and the heat storage change from spring to summer. The results reveal that more heat was stored in the eastern basin of the lake. The methods proposed here will help take full advantage of historical depth profiling data and benefit future sampling processes by providing a consistent method.

Life after Dreissena: The decline of exotic suspension feeder may have significant impacts on lake ecosystems.

It is well documented that the introduction of dreissenid bivalves in eutrophic lakes is usually associated with decreases in turbidity and total phosphorus concentrations in the water column, concomitant increases in water clarity, as well as other physical changes to habitat that may have cascading effects on other species in the invaded waterbody. In contrast, there is a paucity of data on the ecological ramifications of the elimination or decline of dreissenids due to pollution, bottom hypoxia, or other mechanisms. Using data collected by the U.S. Environmental Protection Agency Great Lakes National Program Office's Long-Term Biology and Water Quality Monitoring Programs, we analyzed the impacts of the hypoxia-induced declines in Dreissena densities in the central basin of Lake Erie on major water chemistry and physical parameters. Our analysis revealed that the decline in Dreissena density in the central basin was concomitant with a decrease in spring dissolved silica concentrations and an increase in total phosphorus and near bottom turbidity not seen in the western or eastern basins. In contrast, opposite patterns in water quality were observed in the eastern basin, which was characterized by a high and relatively stable Dreissena population. We are the first to report that dreissenid-related shifts in water quality of invaded waterbodies are reversible by documenting that the sharp decline of Dreissena in the central basin of Lake Erie was concomitant with a shift from clear to turbid water.

U.S. EPA Great Lakes National Program Office monitoring of the Laurentian Great Lakes: Insights from 40 years of data collection

Abstract The U.S. EPA Great Lakes National Program Office (GLNPO) implements long-term monitoring programs to assess Great Lakes ecosystem status and trends for many interrelated ecosystem components, including offshore water quality as well as offshore phytoplankton, zooplankton and benthos; chemical contaminants in air, sediments, and predator fish; hypoxia in Lake Erie's central basin; and coastal wetland health. These programs are conducted in fulfillment of Clean Water Act mandates and Great Lakes Water Quality Agreement commitments. This special issue presents findings from GLNPO's Great Lakes Biology Monitoring Program, Great Lakes Water Quality Monitoring Program, Lake Erie Dissolved Oxygen Monitoring Program, Integrated Atmospheric Deposition Network, Great Lakes Fish Monitoring and Surveillance Program, and Great Lakes Sediment Surveillance Program. These GLNPO programs have generated temporal and spatial datasets for all five Great Lakes that form the basis for assessment of the state of these lakes, including trends in nutrients, key biological indicators, and contaminants in air, sediments and fish. These datasets are used by researchers and managers across the Great Lakes basin for investigating physical, chemical and biological drivers of ongoing ecosystem changes; some of these analyses are presented in this special issue, along with discussion of new methods and approaches for monitoring.

A brief history of the U.S. EPA Great Lakes National Program Office's water quality survey

Abstract The U.S. Environmental Protection Agency Great Lakes National Program Office (GLNPO) water quality survey (WQS) constitutes the longest-running, most extensive monitoring of water quality and the lower trophic level biota of the Laurentian Great Lakes, and has been instrumental in tracking shifts in nutrients and the lower food web over the past several decades. The initial impetus for regular monitoring of the Great Lakes was provided by the 1972 Great Lakes Water Quality Agreement (GLWQA) which asked the parties to develop monitoring and surveillance programs to ensure compliance with the goals of the agreement. The resulting monitoring plan, eventually known as the Great Lakes International Surveillance Plan (GLISP), envisioned a nine-year rotation of intensive surveys of the five lakes. A broadening of the scope of the GLWQA in 1978 and the completion of the first nine-year cycle of sampling, prompted reappraisals of the GLISP. During this pause, and using knowledge gained from GLISP, GLNPO initiated an annual WQS with the narrower focus of tracking water quality changes and plankton communities in the offshore waters of the lakes. Beginning in 1983 with lakes Erie, Huron, and Michigan, the WQS added Lake Ontario in 1986 and Lake Superior in 1992, evolving into its current form in which all five lakes are sampled twice a year. The WQS is unique in that all five lakes are sampled by one agency, using one vessel and one principal laboratory for each parameter group, and represents an invaluable resource for managing and understanding the Great Lakes.

Using Satellite Observations to Assess the Spatial Representativeness of the GLNPO Water Quality Monitoring Program.

The U.S. EPA's Great Lakes National Program Office (GLNPO) annual water quality survey (WQS) collects data at a relatively small number of stations in each lake. The survey was designed to measure conditions in the open-water regions of the lakes where an assumption of spatial homogeneity was thought likely to be met and the measured variables could be characterized by simple statistics. Here we use satellite observations to assess how well statistics based on samples collected in the GLNPO sampling network represent the lake-wide values of two variables, surface chlorophyll concentration and Secchi depth. We find strong linear relationships between the mean values calculated from the samples and the corresponding averages based on the subsets of the full satellite images. Although overall the means of the values from the sample locations agree well with means calculated from most of the non-coastal regions of the lakes, in terms of water depth, the GLNPO station averages best represent the regions of Lake Huron deeper than 30 m, of Lakes Michigan and Superior deeper than 90 m, and of Lake Ontario deeper than 60 m. When the lake regions are defined by distance offshore rather than by depth, the GLNPO station chlorophyll means in Lakes Huron, Ontario, and Superior are closest to the means for the area of the lakes > 10 km offshore. In Lake Michigan the closest correspondence is with the > 20 km offshore region. On a whole-lake basis in Lake Erie the GLNPO station chlorophyll averages are closest to the average calculated from the entire lake.

The benthic community of the Laurentian Great Lakes: analysis of spatial gradients and temporal trends from 1998-2014.

We used the results of seventeen years of Great Lakes benthic monitoring conducted by the U.S. EPA's Great Lakes National Program Office to describe the spatial and temporal patterns of benthic communities, assess their status, trends, and main drivers, and to infer the potential impact of these community changes on ecosystem functioning. Benthic abundance and diversity were higher at shallow (<70 m in depth) stations with chlorophyll concentrations above 3 μg/L than at deeper sites (<1 μg/L).We infer that lake productivity, measured by chlorophyll was likely the major driver of benthic abundance and diversity across lakes. Consequently, benthic diversity and abundance were the highest in the most productive Lake Erie, followed by lakes Ontario, Michigan, Huron, and Superior. Multivariate analysis distinguished three major communities shared among lakes (littoral, sublittoral, and profundal) that differed in species composition and abundance, functional group diversity, and tolerance to organic pollution. Analysis of temporal trends revealed that the largest changes occurred in profundal communities, apparent in significant shifts in dominant taxa across all lakes except Lake Superior. In lakes Michigan, Huron, and Ontario, the former dominant Diporeia was replaced with Dreissena and Oligochaeta. Profundal species, with the exception of dreissenids, became less abundant, and their depth distribution has shifted. In contrast, density and diversity of native littoral and sublittoral communities increased. The invasion of dreissenids was among the most important drivers of changes in benthic communities. Continued monitoring is critical for tracking unprecedented changes occurring in the Great Lakes ecosystem.

Trends in Mysis diluviana abundance in the Great Lakes, 2006–2016

Abstract With the large Diporeia declines in lakes Michigan, Huron, and Ontario, there is concern that a similar decline of Mysis diluviana related to oligotrophication and increased fish predation may occur. Mysis density and biomass were assessed from 2006 to 2016 using samples collected by the Great Lakes National Program Office's biomonitoring program in April and August in all five Great Lakes. Summer densities and biomasses were generally greater than spring values and both increased with bottom depth. There were no significant time trends during these 10–11 years in lakes Ontario, Michigan, or Huron, but there was a significant increase in Lake Superior. Density and biomass were highest in lakes Ontario and Superior, somewhat lower in Lake Michigan, and substantially lower in Lake Huron. A few Mysis were collected in eastern Lake Erie, indicating a small population in the deep basin of that lake. On average, mysids contributed 12–18% (spring-summer, Michigan), 18–14% (spring-summer, Superior), 30–13% (spring-summer, Ontario), and 3% (Huron) of the total open-water crustacean biomass. Size distributions consisted of two peaks, indicating a 2-year life cycle in all four of the deep lakes. Mysis were larger in Lake Ontario than in lakes Michigan, Superior, and Huron. Comparisons with available historic data indicated that mysid densities were higher in the 1960s–1990s (5 times higher in Huron, 2 times higher in Ontario, and around 40% higher in Michigan and Superior) than in 2006–2016.

Development of new indices of Great Lakes water quality based on profundal benthic communities

Abstract Benthic invertebrate biomonitoring has long been a tool of choice for assessing the impacts of anthropogenic stress in aquatic systems. The Oligochaete Trophic Index (OTI) is used by the U.S. EPA Great Lakes National Program Office to assess Great Lakes trophic status for State of the Great Lakes reporting under the Great Lakes Water Quality Agreement. OTI scores are based on pollution tolerances of ubiquitous profundal oligochaetes. OTI limitations include the fact that the index is based on a limited number of species belonging to a single oligochaete class, species assignment to trophic groups in the index were determined by best professional judgment and cannot be tested independently, and the index's correlation with lake productivity has not been evaluated. To address these concerns, we developed two new indices of Great Lakes water quality based on the OTI equation by: (1) expanding the number of oligochaete species included in the index and reassigning previous classifications of oligochaete species to trophic groups (improved OTI, or iOTI); and (2) adding non-oligochaete species to the OTI (modified Trophic Index, or mTI). Finally, we tested a modeling approach using Modern Analogue Technique (MAT) transfer functions based on species responses to a surface chlorophyll gradient to derive assessment of site trophic status and an independent assignment of species to trophic categories. We found that both iOTI and mTI had a stronger relationship with surface remote-sensed spring chlorophyll than did OTI, but MAT models had stronger correlations with chlorophyll than did any of the indices.

Announcement—guidance document for acquiring reliable data in ecological restoration projects

The Laurentian Great Lakes are undergoing intensive ecological restoration in Canada and the United States. In the United States, an interagency committee was formed to facilitate implementation of quality practices for federally funded restoration projects in the Great Lakes basin. The Committee's responsibilities include developing a guidance document that will provide a common approach to the application of quality assurance and quality control (QA/QC) practices for restoration projects. The document will serve as a “how‐to” guide for ensuring data quality during each aspect of ecological restoration projects. In addition, the document will provide suggestions on linking QA/QC data with the routine project data and hints on creating detailed supporting documentation. Finally, the document will advocate integrating all components of the project, including QA/QC applications, into an overarching decision‐support framework. The guidance document is expected to be released by the U.S. EPA Great Lakes National Program Office in 2017.

Great Lakes nearshore–offshore: Distinct water quality regions

We compared water quality of nearshore regions in the Laurentian Great Lakes to water quality in offshore regions. Sample sites for the nearshore region were from the US EPA National Coastal Condition Assessment and based on a criteria or sample-frame of within the 30-m depth contour or 5-km distance from the shoreline, whichever occurred first. The offshore sites were composed of US EPA Great Lakes National Program Office (GLNPO) annual monitoring sites. There was a contrast in both variability and mean values of water quality values between the nearshore and the offshore regions. Lake-by-lake the nearshore was more variable and had higher average parameter values than the offshore, except for NO2/3 which was lower in mean value. A subset of all sites was re-visited in supplemental years to explore temporal effects (57 nearshore sites in Lake Erie 2009 and for 67 nearshore sites in Lake Huron 2012). The operational sample-frame for nearshore water provided a reliable means for separating the lakes into two distinct and persistent water quality regions across years.

Verification of a simple band ratio algorithm for retrieving Great Lakes open water surface chlorophyll concentrations from satellite observations

Abstract We compared in situ surface chlorophyll concentration values measured between 2012 and 2015 as part of the U.S. Environmental Protection Agency's Great Lakes National Program Office (GLNPO) annual monitoring program with corresponding concentration estimates obtained by applying our previously published (Lesht et al., 2013) Great Lakes Fit (GLF) band ratio algorithm to data from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor. Coefficients used in the original GLF algorithm were derived from similarly matched GLNPO and satellite observations collected between 2002 and 2011. The Model II linear relationship between the original GLF-predicted log-transformed values and the new set (2012–2015) of field observations yielded intercept = 0.036, slope = 1.063, and r 2 = 0.830. Residuals for modeled chlorophyll concentrations below ~ 8.0 mg m − 3 were unbiased and normally distributed, but positively biased at higher modeled concentrations. When applied to the entire dataset (2002–2015), the linear relationship between the GLF-modeled and the observed values had intercept = 0.000, slope = 0.999, and r 2 = 0.820. New model coefficients derived from the entire (2002–2015) dataset were very similar to those obtained from the 2002–2011 data. Continual testing and assessment of any empirical model are desirable especially when the model is designed to be employed by a broad community. We conclude that this comparison of the GLF algorithm with the additional four years of independent data further validates its use for estimating surface chlorophyll concentrations from satellite observations of the open waters of the Great Lakes.

Lake Michigan: Nearshore variability and a nearshore–offshore distinction in water quality

We conducted a survey of the Lake Michigan nearshore using towed electronic instrumentation to collect data that were spatially highly resolved. The tow was 1049km in length along the 20-m depth contour and was supplemented with grab samples at 15 sites (on average every 75km). There was low frequency variability in the alongshore reach represented by macroscopic trends across large distance scales (over 100skm) with local variability or high frequency changes at much smaller distances (5–10km). We found that large scale spatial patterns in some water quality parameters were strongly correlated with adjacent landscape characterization. Landscape attributes most frequently retained in step-wise regression models for each water quality parameter were agricultural chemical factors, followed by shoreline modifications and point source attributes. Specific conductivity had the greatest amount of variability explained by landscape character (78%) with beam attenuation next (51%), and followed by chlorophyll and zooplankton (each ~30%). We combined our chemistry samples with data from the recent National Coastal Condition Assessment (2010 NCCA) and compared nearshore chemistry data with the Great Lakes National Program Office (GLNPO) offshore fixed monitoring sites. There was a significant distinction in both mean values and variability between the nearshore (<30m depth) and offshore waters. We also used historical data and found that the distinction between near shore and offshore waters has persisted and that the same long-term trends in parameter concentrations found in the offshore were also found in the near shore.

Remote-Sensing Applications for Environmental Health Research

More than 1,000 manmade satellites currently orbit our planet.1 Some are near the edge of the Earth’s atmosphere just a few hundred kilometers up. Others are tens of thousands of kilometers above us.2 They aid in communication, navigation, defense, and science. A small number3,4 play a critical and quickly expanding role: monitoring the Earth’s surface and atmosphere to track environmental conditions that are intimately tied to human health. A number of new Earth-observing missions are planned for the next decade, including Sentinel-5 aboard the European Space Agency’s MetOp Second Generation satellites (pictured).48 In the meantime researchers are finding new uses for the satellite ... Researchers and government agencies worldwide already use satellite data to monitor air pollutants, infectious disease epidemics, harmful algal blooms (HABs), climate change, and more. But as current research indicates, that’s only the beginning of what we can do with the technology, broadly referred to as “remote sensing.” In the coming years, new satellites will offer higher-resolution imagery in conjunction with more robust and precise algorithms to process the data they deliver. As a result, researchers expect to dramatically expand their ability to view and understand Earth’s land, water, and air, from its remotest ocean waters to its largest cities. The National Aeronautics and Space Administration (NASA) launched its first satellite in 1958,5 and TIROS-1, the country’s first meteorological satellite, came 2 years later.6 Within a few decades members of the epidemiological and public health communities began actively looking at satellite data, says John Haynes, program manager of the NASA Applied Sciences Health and Air Quality Applications Program. In recent years interest in remote-sensing data has soared, with newer avenues being developed and fine-tuned, including air-quality measurements and vector-borne disease projections. “There’s really been a paradigm shift in the use of remote sensing for public health issues,” Haynes says. “Every year there seems to be more and more interest.” Indeed, by March 2015 NASA will have launched 6 Earth-observing missions in 12 months,7 more than in any year in at least a decade.8 New launches include a “global precipitation observatory” that will make frequent global measurements of rain and snowfall, plus one satellite designed to measure soil moisture and another that will measure how carbon moves through the Earth’s atmosphere, land, and oceans. In addition, the International Space Station will receive three new instruments, one that will observe how winds behave around the world, one that will measure clouds and aerosols (particles suspended in the atmosphere)—two variables that remain difficult to predict in climate-change models—and one that will take global, long-term measurements of key components of the Earth’s atmosphere, including aerosols and ozone.9 The momentum will carry through at least the next 8 or so years, with NASA and other space agencies in Europe and Asia planning to launch new satellites that will provide even higher-resolution snapshots of the Earth. Along with technological and scientific advances, a third development is leading to new and improved applications of satellite data: NASA and the National Oceanic and Atmospheric Administration (NOAA) have made their satellite data available free of charge, Haynes says, while the European Space Agency (ESA) has reduced prices and promised to provide free access to data from its next generation of instruments. “More people use the data, and you get more out of it than when you try to restrict it,” says Raphael Kudela, an oceanographer at the University of California, Santa Cruz, who uses satellite imagery to study HABs. This free sharing of data has been instrumental in his field, allowing researchers at institutions around the world to study HABs from above and to improve systems to track and predict them.

A band-ratio algorithm for retrieving open-lake chlorophyll values from satellite observations of the Great Lakes

The U.S. Environmental Protection Agency's Great Lakes National Program Office (GLNPO) has collected water quality data from the five Great Lakes annually since 1993. We used the GLNPO observations made since 2002 along with coincident measurements made by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate-resolution Imaging Spectroradiometer (MODIS) to develop a new band-ratio algorithm for estimating chlorophyll concentrations in the Great Lakes from satellite observations. The new algorithm is based on a third-order polynomial model using the same maximum band ratios employed in the standard NASA algorithms (OC4 for SeaWiFS and OC3M for MODIS). The sensor-specific coefficients for the new algorithm were obtained by fitting the relationship to several hundred matched field and satellite observations. Although there are some seasonal variations in some lakes, the relationship between the observed chlorophyll values and those modeled using the new coefficients is fairly stable from lake to lake and across years. The accuracy of the satellite chlorophyll estimates derived from the new algorithm was improved substantially relative both to the standard NASA retrievals and to previously published algorithms tuned to individual lakes. Monte-Carlo fits to randomly selected subsets of the observations allowed us to estimate the uncertainty associated with the retrievals purely as a function of the satellite data. Our results provide, for the first time, a single simple band ratio method for retrieving chlorophyll concentrations in the offshore “open” waters of the Great Lakes from satellite observations.

Mercury temporal trends in top predator fish of the Laurentian Great Lakes

Mercury (Hg) contamination is widespread in the Laurentian Great Lakes region and is a serious environmental concern. In anaerobic environments such as lake sediments, mercury is transformed into methylmercury (MeHg) and can biomagnify up the food chain to toxic concentrations. The Great Lakes Fish Monitoring Program (GLFMP), administered by the US EPA Great Lakes National Program Office (GLNPO), aims to monitor temporal trends of mercury in the five Great Lakes using top predator fish as biomonitors. Total Hg (THg) concentrations were measured in Great Lake fish collected between 1999 and 2009. Single factor ANOVA determined that average fish THg concentrations over this time period in the five lakes were significantly different from one another in the order of Superior>Huron>Michigan>Ontario>Erie. By fitting the data to three different models (linear, quadratic, and two-segment piecewise), it was determined that Hg concentrations in top predator fish (lake trout, or walleye in Lake Erie) are currently increasing in Lake Erie and the Apostle Island sampling site in Lake Superior. Significant decreasing trends are evident in Lakes Michigan, Ontario, and the Rockport sampling site in Lake Huron, although all of the lakes exhibit elevated concentrations in fish compared to historic concentrations. As new Hg emission controls are implemented in the US, continued monitoring of Hg in Great Lakes fish will be needed to determine if they influence the current concentrations and trends.

Great Lakes monitoring results--comparison of probability based and deterministic sampling grids.

The Great Lakes may be viewed as a coastal environment, affected by the same meteorological and physical forces as the coastal ocean. The U.S. EPA, Great Lakes National Program Office (GLNPO) has monitored the open waters of the lakes, annually, since 1983. As part of the U.S. EPA Environmental Monitoring and Assessment Program (EMAP), a pilot study was performed in Lake Michigan to compare the existing GLNPO deterministic sampling grid with the EMAP probabilistic grid. Results of chemical analyses of trophic status indicators (total phosphorus and chlorophyll a) as well as nutrients and conventional limnological measurements, from spring and summer surveys in 1992 indicate little difference between the grids in the offshore region of the lake. The few statistically significant differences may be due to station distribution throughout the lake, or simple chance. This might be expected due to the well mixed nature of the open waters of Lake Michigan. The detection of a long-term trend for total phosphorus in Lake Michigan benefits from an annual program: viewing cumulative frequency distributions based on a four year EMAP interval does not convey information on the decrease in phosphorus in the lake. If the EMAP sampling grid were to be used in the Great Lakes, pilots in each of the lakes would be necessary for utilization of the existing long-term record as a basis for trend detection.