Seasonal changes in light and physicochemical conditions have strong impacts on cyanobacteria, but how they affect community structure, metabolism, and biogeochemistry of cyanobacterial mats remains unclear. Light may be particularly influential for cyanobacterial mats exposed to sulphide by altering the balance of oxygenic photosynthesis and sulphide-driven anoxygenic photosynthesis. We studied temporal shifts in irradiance, water chemistry, and community structure and function of microbial mats in the Middle Island Sinkhole (MIS), where anoxic and sulphate-rich groundwater provides habitat for cyanobacteria that conduct both oxygenic and anoxygenic photosynthesis. Seasonal changes in light and groundwater chemistry were accompanied by shifts in bacterial community composition, with a succession of dominant cyanobacteria from Phormidium to Planktothrix, and an increase in diatoms, sulphur-oxidizing bacteria, and sulphate-reducing bacteria from summer to autumn. Differential abundance of cyanobacterial light-harvesting proteins likely reflects a physiological response of cyanobacteria to light level. Beggiatoa sulphur oxidation proteins were more abundant in autumn. Correlated abundances of taxa through time suggest interactions between sulphur oxidizers and sulphate reducers, sulphate reducers and heterotrophs, and cyanobacteria and heterotrophs. These results support the conclusion that seasonal change, including light availability, has a strong influence on community composition and biogeochemical cycling of sulphur and O2 in cyanobacterial mats.

Abstract. The western basin of Lake Erie has a history of recurrent cyanobacterial harmful algal blooms (HABs) despite decades of efforts by the United States and Canada to limit phosphorus loading, a major driver of the blooms. In response, the National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs in western Lake Erie (WLE). Here we describe the data collected from this monitoring program from 2012 to 2021. This dataset includes observations on physicochemical properties, major nutrient fractions, phytoplankton pigments, microcystins, and optical properties for western Lake Erie. This dataset is particularly relevant for creating models, verifying and calibrating remote sensing algorithms, and informing experimental research to further understand the water quality dynamics that influence HABs in this internationally significant body of freshwater. The dataset can be freely accessed from NOAA National Centers for Environmental Information (NCEI) at https://doi.org/10.25921/11da-3x54 (Cooperative Institute for Great Lakes Research, University of Michigan and NOAA Great Lakes Environmental Research Laboratory, 2019).

This Master’s Project focused on improving the understanding of harmful algal blooms (HABs) within Lake Erie and Saginaw Bay as well as characterizing needs of water utility managers for information products. This research addressed four different topics: 1) In Chapter 2: Quantifying the impact of nutrient availability and form on the growth of HABs in Lake Erie and Saginaw Bay, we found that phosphorus was the main limiting nutrient based on nutrient addition treatments, but with nutrient reduction treatments, nitrogen appeared to be co-limiting, suggesting reductions in phosphorus alone may not eliminate blooms. 2) In Chapter 3: Mechanistic Assessment of Dreissenid Mussel Grazing Impacts on Phytoplankton Growth Rates, we found that the synergistic effect of nutrient excretion and dilution through grazing had the largest impact on total phytoplankton growth, followed by nutrient excretion and dilution through grazing, respectively. The overall impact of grazing by Dreissenid mussels on cyanobacteria growth rates was limited, and mussel-mediated nutrient recycling was the strongest explanatory mechanism. 3) In Chapter 4: Comparative Analysis of Microcystis Colony Buoyant Velocities in Western Lake Erie and Saginaw Bay of Lake Huron: Effects of Colony Size and Light Intensity, we show that Western Lake Erie and Saginaw Bay differ in the way that Microcystis buoyancy responds to light and may therefore ultimately affect how the bloom is transported throughout each ecosystem. Understanding the effects of light on buoyancy will help explain observed differences in vertical distribution and movement of Microcystis in the two lakes. 4) In Chapter 5: Characterization of Public Water System Needs and Attitudes Related to HAB Toxicity in Saginaw Bay and Western Lake Erie, we describe a focus group and survey of water system managers in Saginaw Bay about how HABs impacted them and if a HAB forecast model would be useful. Our results show that managers believed the forecast would be useful, and that they would be willing to use it. It also suggested that managers feel unprepared in the event of a HAB beyond what has occurred recently in the Bay, so efforts should be taken to increase their knowledge and preparedness.

Diatoms are among the few eukaryotes known to store nitrate (NO3 - ) and to use it as an electron acceptor for respiration in the absence of light and O2 . Using microscopy and 15 N stable isotope incubations, we studied the relationship between dissimilatory nitrate/nitrite reduction to ammonium (DNRA) and diel vertical migration of diatoms in phototrophic microbial mats and the underlying sediment of a sinkhole in Lake Huron (USA). We found that the diatoms rapidly accumulated NO3 - at the mat-water interface in the afternoon and 40% of the population migrated deep into the sediment, where they were exposed to dark and anoxic conditions for ~75% of the day. The vertical distribution of DNRA rates and diatom abundance maxima coincided, suggesting that DNRA was the main energy generating metabolism of the diatom population. We conclude that the illuminated redox-dynamic ecosystem selects for migratory diatoms that can store nitrate for respiration in the absence of light. A major implication of this study is that the dominance of DNRA over denitrification is not explained by kinetics or thermodynamics. Rather, the dynamic conditions select for migratory diatoms that perform DNRA and can outcompete sessile denitrifiers.

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatiotemporal variability in cyanobacteria vertical structure. Cyanobacteria were generally homogenously distributed during the growing season except under certain conditions. As water depth increased and high surface layer concentrations were observed, cyanobacteria were found to be more vertically stratified and the assumption of homogeneity was less supported. Cyanobacteria vertical distribution was related to wind speed and wave height, with increased stratification at low wind speeds (<4.9 m/s) and wave heights (<027 m). Once wind speed and wave height exceeded these thresholds the assumption of vertically uniform cyanobacteria populations was justified. These findings suggest that remote sensing can provide adequate estimates of water column cyanoHAB biomass in most conditions; however, the incorporation of bathymetry and environmental conditions could lead to improved biomass estimates. Additionally, cyanobacteria contributions to total chlorophyll-a were shown to change throughout the season and across depth, suggesting the need for remote sensing algorithms to specifically identify cyanobacteria. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

NOAA GLERL has routinely flown a hyperspectral imager to detect cyanobacteria harmful algal blooms (cyanoHABs) over the Great Lakes since 2015. Three consecutive years of hyperspectral imagery over the Great Lakes warn drinking water intake managers of the presence of cyanoHABs. Western basin imagery of Lake Erie contributes to a weekly report to the Ohio Environmental Protection Agency using the cyanobacteria index (CI) as an indicator of the presence of cyanoHABs. The CI is also used for the weekly NOAA NCCOS cyanoHAB Lake Erie bulletin applied to satellite data. To date, there has not been a sensor comparison to look at the variability between the satellite and hyperspectral imagery on a pixel-by-pixel basis, as well as a time scale comparison between measurements from buoys and shipboard surveys. The spatial scale is a measure of size of a cyanobacteria bloom on a scale of meters to kilometers. The change in the spatial scale or spatial variability has been quantified from satellite and airborne imagery using a decorrelation scale analysis to find the point at which the values are not changing or are not correlated with each other. The decorrelation scales were also applied to the buoy and shipboard survey data to look at temporal scales or changes in time on hourly to daytime scales for blue-green algae, chlorophyll and temperature. These scales are valuable for ecosystem modelers and for those initiating sampling efforts to optimize sampling plans and to infer a potential mechanism in an observational study from a synoptic viewpoint.

Lake Erie has experienced dramatic changes in water quality over the past several decades requiring extensive monitoring to assess effectiveness of adaptive management strategies. Remote sensing offers a unique potential to provide synoptic monitoring at daily time scales complementing in-situ sampling activities occurring in Lake Erie. Bio-optical remote sensing algorithms require knowledge about the inherent optical properties (IOPs) of the water for parameterization to produce robust water quality products. This study reports new IOP and apparent optical property (AOP) datasets for western Lake Erie that encapsulate the May–October period for 2015 and 2016 at weekly sampling intervals. Previously reported IOP and AOP observations have been temporally limited and have not assessed statistical differences between IOPs over spatial and temporal gradients. The objective of this study is to assess trends in IOPs over variable spatial and temporal scales. Large spatio-temporal variability in IOPs was observed between 2015 and 2016 likely due to the difference in the extent and duration of mid-summer cyanobacteria blooms. Differences in the seasonal trends of the specific phytoplankton absorption coefficient between 2015 and 2016 suggest differing algal assemblages between the years. Other IOP variables, including chromophoric, dissolved organic matter (CDOM) and beam attenuation spectral slopes, suggest variability is influenced by river discharge and sediment re-suspension. The datasets presented in this study show how these IOPs and AOPs change over a season and between years, and are useful in advancing the applicability and robustness of remote sensing methods to retrieve water quality information in western Lake Erie.

Abstract Models of primary production in aquatic systems must include a means to estimate subsurface light. Such models often use the Beer–Lambert law, assuming exponential attenuation of light with depth. It is further assumed that the diffuse attenuation coefficient may be estimated as a summation of scattering/absorbing constituent concentrations multiplied by their respective specific attenuation coefficients. While theoretical deviations from these assumptions have been documented, it is useful to consider the empirical performance of this common approach. Photosynthetically active radiation (PAR) levels and water quality conditions were recorded weekly from six to eight monitoring stations in western Lake Erie between 2012 and 2016. Exponential PAR extinction models yielded a mean attenuation coefficient of 1.55 m (interquartile range = 0.74–1.90 m). While more complex light attenuation models are available, analysis of residuals indicated that the simple Beer–Lambert model is adequate for shallow, eutrophic waters similar to western Lake Erie (R2 > 0.9 for 96% of samples). Three groups of water quality variables were predictive of PAR attenuation: total and nonvolatile suspended particles, dissolved organic substances (dissolved organic carbon and chromophoric dissolved organic matter), and organic solids (volatile suspended solids and chlorophyll). Multiple regression models using these variables predicted 3–90% of the variability in PAR attenuation, with a median adjusted R2 = 0.86. Explanatory variables within these groups may substitute for each other while maintaining similar model performance, indicating that various combinations of water quality variables may be useful to predict PAR attenuation, depending on availability within a model framework or monitoring program.

Harmful algal blooms (HABs) in Lake Erie have been prominent in recent years. The bloom in 2014 reached a severe level causing the State of Ohio to declare a state of emergency. At that time NASA Glenn Research Center was requested by stakeholders to help monitor the blooms in Lake Erie. Glenn conducted flights twice a week in August and September and assembled and distributed the HAB information to the shoreline water resource managers using its hyperspectral imaging sensor (in development since 2006), the S􀂱3 Viking aircraft, and funding resources from the NASA Headquarters Earth Science Division. Since then, the State of Ohio, National Oceanic and Atmospheric Administration (NOAA), and U.S. Environmental Protection Agency (EPA) have elevated their funding and activities for observing, monitoring, and addressing the root cause of HABs. Also, the communities and stakeholders have persistently requested NASA Glenn􀂶s participation in HAB observation. Abundant field campaigns and sample analyses have been funded by Ohio and NOAA, which provided a great opportunity for NASA to advance science and airborne hyperspectral remote sensing economically. Capitalizing on this opportunity to advance the science of algal blooms and remote sensing, NASA Glenn conducted the Airborne Hyperspectral Observation of harmful algal blooms campaign in 2015 that was, in many respects, twice as large as the 2014 campaign. Focusing mostly on Lake Erie, but also including other small inland lakes and the Ohio River, the campaign was conducted in partnership with a large number of partners specializing in marine science and remote sensing. Airborne hyperspectral observation of HABs holds promise to distinguish potential HABs from nuisance blooms, determine their concentrations, and delineate their movement in an augmented spatial and temporal resolution and under clouds􀂲all of which are excellent complements to satellite observations. Working with collaborators at several Ohio and Michigan institutions as well as one in South Dakota and one in Alabama, this effort was able to provide next-day georeferenced estimates of cyanobacteria and scum concentrations. Very prompt processing and analysis of the hyperspectral imagery is necessary for the information to be acted upon. For example, a next-day report of an overflight over the Ohio River indicated that a bloom could be present as far downstream as the Cincinnati intake, but the Ohio EPA had not received visual reports of a bloom that far downstream. Water samples were obtained at the Cincinnati water intake, based on the flight data, and detected microcystins in the source water. The flight data helped State and municipal authorities realize the potential extent of that bloom, and triggered response sampling, before the visual river-wide scums started forming. The present document describes the process that was utilized to take raw remote sensing data and create information products; this includes system calibration and validation, efforts to correct atmospheric effects, and algorithms that produce the data products. Furthermore, successful research into improved algorithms for expanding the capability to delineate in water constituents is included. Finally, comparisons that show expected relationships between ground-based measurements and hyperspectral imager version 2 (HSI2) data results are presented, giving confidence in the remote sensing products.