Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

Francy, Donna S.; Graham, Jennifer L.; Stelzer, Erin A.; Ecker, Christopher D.; Brady, Amie M.G.; Struffolino, Pam; Loftin, Keith A.

doi:10.3133/sir20155120

Cited by 30 publications

(25 citation statements)

References 5 publications

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“…The phytoplankton phyla composition observed in this study was also found in other investigated Ohio lakes such as lake Erie [20] and other smaller lakes [6] [21]. The dominance of Cyanobacteria, which was also observed in a number of Ohio lakes [6], could be typical for summer algal blooms.…”

Section: Discussionsupporting

confidence: 83%

“…The dominance of Cyanobacteria, which was also observed in a number of Ohio lakes [6], could be typical for summer algal blooms. However, how a nontoxic-algal-bloom community shifted to a toxic algal bloom could be a key point for signaling cyanobacterial blooms, and has not been further investigated in the previous studies.…”

Section: Discussionmentioning

confidence: 73%

“…Many HAB species present a risk to the health of humans or animals and other animals by their toxins and other bioactive compounds [5]. Over the past 10 years, HABs have been increasing in Ohio inland lake waters [6]. Cyanotoxins including microcystin and saxitoxin were detected from 74% of the samples collected from Ohio source and recreational surface waters [7].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, HABs and associated toxins are a major water-quality issue not only in Lake Erie, but also in the other inland lakes in Ohio. Therefore, monitoring of HABs in this lake has also been carried out [6] [7]. However, the pattern of phytoplanktonic community successions and toxin producing species during blooms has not been documented.…”

Section: Introductionmentioning

confidence: 99%

“…Phytoplankton are an important basal resource to heterotrophic organisms in lakes, and their growth, succession and community structures determine the potential productivity of the ecosystem [11] as well as the status of the ecosystem and water quality. There are increasing concerns about the impacts on water quality, especially visual appearance, tastes and odors associated with Cyanobacteria blooms on the lake [12]. Phytoplankton are often considered to be indicators of water quality [13].…”

Section: Introductionmentioning

confidence: 99%

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Community Structures of Phytoplankton with Emphasis on Toxic Cyanobacteria in an Ohio Inland Lake during Bloom Season

Chen

Allen

2017

JWARP

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The community structures of phytoplankton are important factors and indicators of lake water quality. Harmful algal blooms severely impact water supply, recreational activities and wildlife habitat. This study aimed to examine the phytoplankton composition and variations using microscopy, and identify harmful Cyanobacteria in weekly samples taken from four sites at Harsha Lake in southwest Ohio. Over the course of the summer in 2015, the phytoplankton of Harsha Lake consisted mainly of 13 taxa belonging to Bacillariophyta, Chlorophyta, Cryptophyta, Cyanobacteria, Dinophyta and Euglenophyta. Their significant successions started with Bacillariophyta and/or Chlorophyta, then bloomed with Cyanobacteria and ended with Chlorophyta and/or Dinophyta. Cyanobacteria members: Microcystis, Planktothrix, Dolichospermum, Aphanizomenon, Cylindrospermopsis, and Oscillatoria from the Cyanophyceae were identified to be dominant genera. These organisms varied spatially and temporally in similar patterns along with the variations of nutrients and formed the summer bloom with the total biomasses ranging from 0.01 to 114.89 mg L−1 with mean of 22.88 mg L−1. M. aeruginosa and P. rubescens were revealed as the microcystin producers, while A. circinalis and Aphanizomenon sp. were identified as a saxitoxin producer through cloning and sequencing PCR products of mcyA, mcyE and sxtA genes. The biomasses of phytoplankton, Cyanobacteria and Microcystis were positively correlated to nutrients, especially to total nitrogen. The total ELISA measurement for microcystin positively correlated with Cyanobacteria (R2 = 0.66, P < 0.0001), Microcystis (R2 = 0.64, P < 0.0001) and phytoplankton (R2 = 0.59, P < 0.0001). The basic information on the occurrence and biomasses of Cyanobacteria and total phytoplankton, and the analysis for toxic species, which were the first report for the inland water in Ohio, USA, will document the succession patterns of phytoplankton and toxin production over a season and provide data to predict risk occurrence to both human and ecological factors.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Community Structures of Phytoplankton with Emphasis on Toxic Cyanobacteria in an Ohio Inland Lake during Bloom Season

Chen

Allen

2017

JWARP

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Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander,

Thomas,

Moore

et al. 2024

Ecosphere

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Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data.

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Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio

Francy

Brady

Stelzer

et al. 2020

Environ Monit Assess

Self Cite

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Cyanobacterial harmful algal blooms and the toxins they produce are a global water-quality problem. Monitoring and prediction tools are needed to quickly predict cyanotoxin action-level exceedances in recreational and drinking waters used by the public. To address this need, data were collected at eight locations in Ohio, USA, to identify factors significantly related to observed concentrations of microcystins (a freshwater cyanotoxin) that could be used in two types of sitespecific regression models. Real-time models include easily or continuously-measured factors that do not require that a sample be collected; comprehensive models use a combination of discrete sample-based measurements and real-time factors. The study sites included two recreational sites and six water treatment plant sites. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including waterquality data compiled from continuous monitors. Comprehensive models were useful at some sites with lagged variables for cyanobacterial toxin genes, dissolved nutrients, and (or) nitrogen to phosphorus ratios. Because models can be used for management decisions, important measures of model performance were sensitivity, specificity, and accuracy of estimates above or below the microcystin concentration threshold standard or action level. Sensitivity is how well the predictive tool correctly predicts exceedance of a threshold, an important measure for water-resource managers. Sensitivities > 90% at four Lake Erie water treatment plants indicated that models with continuous monitor data were especially promising. The planned next steps are to collect more data to build larger site-specific datasets and validate models before they can be used for management decisions.

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Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

Cited by 30 publications

References 5 publications

Community Structures of Phytoplankton with Emphasis on Toxic Cyanobacteria in an Ohio Inland Lake during Bloom Season

Community Structures of Phytoplankton with Emphasis on Toxic Cyanobacteria in an Ohio Inland Lake during Bloom Season

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio

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