The Lake Erie HABs Grab: A binational collaboration to characterize the western basin cyanobacterial harmful algal blooms at an unprecedented high-resolution spatial scale

Monitoring of cyanobacterial bloom biomass in large lakes at high resolution is made possible by remote sensing. However, monitoring cyanobacterial toxins is only feasible with grab samples, which, with only sporadic sampling, results in uncertainties in the spatial distribution of toxins. To address this issue, we conducted two intensive "HABs Grabs" of microcystin (MC)-producing Microcystis blooms in the western basin of Lake Erie. These were one-day sampling events during August of 2018 and 2019 in which 100 and 172 grab samples were collected, respectively, within a six-hour window covering up to 2,270 km² and analyzed using consistent methods to estimate the total mass of MC. The samples were analyzed for 57 parameters, including toxins, nutrients, chlorophyll, and genomics. There were an estimated 11,513 kg and 30,691 kg of MCs in the western basin during the 2018 and 2019 HABs Grabs, respectively. The bloom boundary poses substantial issues for spatial assessments because MC concentration varied by nearly two orders of magnitude over very short distances. The MC to chlorophyll ratio (MC:chl) varied by a factor up to 5.3 throughout the basin, which creates challenges for using MC:chl to predict MC concentrations. Many of the biomass metrics strongly correlated (r > 0.70) with each other except chlorophyll fluorescence and phycocyanin concentration. While MC and chlorophyll correlated well with total phosphorus and nitrogen concentrations, MC:chl correlated with dissolved inorganic nitrogen. More frequent MC data collection can overcome these issues, and models need to account for the MC:chl spatial heterogeneity when forecasting MCs.

Exploring How Cyanobacterial Traits Affect Nutrient Loading Thresholds in Shallow Lakes: A Modelling Approach

Globally, many shallow lakes have shifted from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state due to eutrophication. Such shifts are often accompanied by toxic cyanobacterial blooms, with specialized traits including buoyancy regulation and nitrogen fixation. Previous work has focused on how these traits contribute to cyanobacterial competitiveness. Yet, little is known on how these traits affect the value of nutrient loading thresholds of shallow lakes. These thresholds are defined as the nutrient loading at which lakes shift water quality state. Here, we used a modelling approach to estimate the effects of traits on nutrient loading thresholds. We incorporated cyanobacterial traits in the process-based ecosystem model PCLake+, known for its ability to determine nutrient loading thresholds. Four scenarios were simulated, including cyanobacteria without traits, with buoyancy regulation, with nitrogen fixation, and with both traits. Nutrient loading thresholds were obtained under N-limited, P-limited, and colimited conditions. Results show that cyanobacterial traits can impede lake restoration actions aimed at removing cyanobacterial blooms via nutrient loading reduction. However, these traits hardly affect the nutrient loading thresholds for clear lakes experiencing eutrophication. Our results provide references for nutrient loading thresholds and draw attention to cyanobacterial traits during the remediation of eutrophic water bodies.

A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Harmful algal blooms (HABs) have been increasing in intensity worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll a data sampled during the HAB season (June-October) from 2008 to 2017 by five independent monitoring programs. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results demonstrate that space-time kriging explains over half of the variability in daily, location-specific chlorophyll observations, on average. Conditional simulations provide, for the first time, comprehensive estimates of overall bloom biomass (based on depth-integrated concentrations) and surface areal extent with quantified uncertainties. These new estimates are contrasted with previous Lake Erie HAB monitoring studies, and deviations among estimates are explored and discussed. Overall, results highlight the importance of maintaining sufficient monitoring coverage to capture bloom dynamics, as well as the benefits of the proposed approach for synthesizing data from multiple monitoring programs to improve estimation accuracy while reducing uncertainty.

Science meets policy: A framework for determining impairment designation criteria for large waterbodies affected by cyanobacterial harmful algal blooms

Toxic cyanobacterial harmful algal blooms (cyanoHABs) are one of the most significant threats to the security of Earth's surface freshwaters. In the United States, the Federal Water Pollution Control Act of 1972 (i.e., the Clean Water Act) requires that states report any waterbody that fails to meet applicable water quality standards. The problem is that for fresh waters impacted by cyanoHABs, no scientifically-based framework exists for making this designation. This study describes the development of a data-based framework using the Ohio waters of western Lake Erie as an exemplar for large lakes impacted by cyanoHABs. To address this designation for Ohio's open waters, the Ohio Environmental Protection Agency (EPA) assembled a group of academic, state and federal scientists to develop a framework that would determine the criteria for Ohio EPA to consider in deciding on a recreation use impairment designation due to cyanoHAB presence. Typically, the metrics are derived from on-lake monitoring programs, but for large, dynamic lakes such as Lake Erie, using criteria based on discrete samples is problematic. However, significant advances in remote sensing allows for the estimation of cyanoHAB biomass of an entire lake. Through multiple years of validation, we developed a framework to determine lake-specific criteria for designating a waterbody as impaired by cyanoHABs on an annual basis. While the criteria reported in this manuscript are specific to Ohio's open waters, the framework used to determine them can be applied to any large lake where long-term monitoring data and satellite imagery are available.

Establishing spatial and temporal patterns in Microcystis sediment seed stock viability and their relationship to subsequent bloom development in Western Lake Erie

This study assessed the distribution, abundance, and viability of pre- and post-overwintering Microcystis sediment seed stocks in Western Lake Erie and how these variables are potentially related to past and subsequent bloom formation. We conducted a two-year spatiotemporal survey of vegetative seed stocks in Western Lake Erie, the region where annual algal blooms generally develop. Sediment was collected from 16 sites covering an area of 375 km2 and water column depths ranging from 3-9 meters. Sample collection occurred in November 2014, April 2015, November 2015, and April 2016. The abundance of total and potentially-toxic Microcystis cell equivalents were determined using quantitative polymerase chain reaction. A series of laboratory experiments using lake sediment were conducted to assess the viability of Microcystis vegetative seed stocks. Across all sampling periods, the abundance of total Microcystis in the sediment ranged from 6.6 x 10(4) to 1.7 x 10(9) cell equivalents g-1, and potentially-toxic Microcystis ranged from 1.4 x 10(3) to 4.7 x 10(6) cell equivalents g-1. The percent potentially-toxic Microcystis in the sediment ranged from <1% to 68% across all samples. Total Microcystis abundance diminished significantly over winter with densities in spring nearly 10 times less than the previous fall. However, despite cell loss from fall to spring, lab experiments demonstrated that remaining non-toxic and potentially-toxic cells were viable after the overwintering period. Further, lab grow-out experiments indicate that potentially-toxic strains recruited at a slightly higher rate than non-toxic strains, and may in part, contribute to the pattern of higher relative toxicity during early stages of the blooms. The abundance and distribution of overwintering cells did not correlate strongly to areas in the lake where subsequent summer blooms were most persistent. However, numerical analysis suggests that recruitment of benthic overwintering populations could help explain a portion of the initial rapid increase in bloom biomass and the spatial extent of this bloom initiation, particularly when recruitment is paired with subsequent growth in appropriate water column conditions.

Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements

Microcystin (MCY)-producing harmful cyanobacterial blooms (cHABs) are an annual occurrence in Lake Erie, and buoys equipped with water quality sondes have been deployed to help researchers and resource managers track cHABs. The objective of this study was to determine how well water quality sondes attached to buoys measure total algae and cyanobacterial biomass and water turbidity. Water samples were collected next to two data buoys in western Lake Erie (near Gibraltar Island and in the Sandusky subbasin) throughout summers 2015, 2016, and 2017 to determine correlations between buoy sonde data and water sample data. MCY and nutrient concentrations were also measured. Significant (P < 0.001) linear relationships (R² > 0.75) occurred between cyanobacteria buoy and water sample data at the Gibraltar buoy, but not at the Sandusky buoy; however, the coefficients at the Gibraltar buoy differed significantly across years. There was a significant correlation between buoy and water sample total chlorophyll data at both buoys, but the coefficient varied considerably between buoys and among years. Total MCY concentrations at the Gibraltar buoy followed similar temporal patterns as buoy and water sample cyanobacterial biomass data, and the ratio of MCY to cyanobacteria-chlorophyll decreased with decreased ambient nitrate concentrations. These results suggest that buoy data are difficult to compare across time and space. Additionally, the inclusion of nitrate concentration data can lead to more robust predictions on the relative toxicity of blooms. Overall, deployed buoys with sondes that are routinely cleaned and calibrated can track relative cyanobacteria abundance and be used as an early warning system for potentially toxic blooms.

Interactions between nitrogen form, loading rate, and light intensity on Microcystis and Planktothrix growth and microcystin production

The toxin-producing, bloom-forming cyanobacterial genera Microcystis and Planktothrix require fixed nitrogen (N), such as nitrate, ammonium, or organic N (e.g., urea) for growth and production of microcystins (MC). Bioavailable N can enter lakes in pulses via tributary discharge and through in-lake recycling, which can maintain low N concentrations. Additionally, light intensity has been suggested to play a role in MC production. This study examined how three forms of N (nitrate, ammonium, and urea) interacted with N loading rate (one large pulse vs. many small pulses) and light intensity to stimulate Microcystis and Planktothrix growth and MC production using nutrient enrichment experiments. Enrichments of nitrate, ammonium, and urea resulted in greater cyanobacterial biovolumes and MC concentrations than phosphorus-only enrichments, and there was no difference between pulse (100 μmol/L) and press treatments (8.3 μmol/L every 4 h). Analysis of mcyD transcripts showed significant up-regulation within 4 h of ammonium and urea enrichment. High light intensities (300 μmol photons/m²/s) with N enrichment resulted in greater cyanobacterial biovolumes and MC concentrations than lower light intensities (30 and 3 μmol photons/m²/s). Overall, the results suggest Microcystis and Planktothrix can use many forms of N and that high light intensities enhance MC production during elevated N concentrations. Moreover, the results here further demonstrate the importance of considering N, as well as P, in management strategies aimed at mitigating cyanobacterial blooms.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments - since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins - especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.