Harmful Algal Blooms (HABs)

Harmful algal blooms (HABs) are scientifically complex, economically damaging, and a
threat to ecosystem and human health.

Harmful algal blooms (HABs) are formed by dense populations of cyanobacteria or blue-green algae that can produce toxins, threaten public health, and contribute to economic losses that exceed $2 billion annually. The Toledo water crisis in summer 2014 illustrated this well, when a “do not drink” advisory was issued for unsafe levels of the harmful algal toxin microcystin in treated drinking water drawn from Lake Erie. With more than 400,000 people lacking drinking water for 3 days, the incident was reported worldwide and placed a spotlight on Lake Erie algal blooms as a threat to drinking water quality.

With our partners at NOAA GLERL, CIGLR is at the forefront of monitoring and forecasting HABs in Lake Erie, for the protection of human and ecosystem health. Our HAB research activities include:

1. Monitoring Harmful Algal Blooms

Water quality observations are essential for documenting the growth and spread of HABs. CIGLR and GLERL conduct weekly field sampling in western Lake Erie and Saginaw Bay of Lake Huron during the July-October bloom season. We sample and analyze temperature, Secchi disk transparency, algal parameters (chlorophyll, phycocyanin, phytoplankton abundance, toxin-producing cyanobacterial populations), algal toxins (microcystins and saxitoxins), and nutrients (phosphorus, nitrogen). We perform laboratory genetic tests to detect toxic populations of cyanobactera and determine how HABs are responding to water temperature and nutrients. Buoys and sensors deployed in western Lake Erie provide continuous water quality data during the bloom season. Data are available to the public for years 2009 to present (weekly monitoring) and 2014 to present (continuous systems).

In 2017, NOAA GLERL and CIGLR began field deployments of the first 2nd Generation Environmental Sample Processor (2G ESP) to be used in fresh water. The ESPniagara is an underwater robotic laboratory, or “lab in a can”, that collects and analyzes water for microcystin and communicates the results to data servers at GLERL in near-real time. Traditional lab techniques to determine algal toxin levels take days to complete. The ESPniagara provides drinking water managers with an earlier warning of algal blooms and their toxicity, enabling them to take action to protect public health sooner. The ESPniagara is deployed 5 miles west of the Toledo drinking water intake structure to get an early warning of elevated toxicity levels encroaching from Maumee Bay. The real-time toxin information that the ESPniagara provides has advanced two of NOAA’s important decision support tools — the Lake Erie HAB Bulletin and the Lake Erie HAB Tracker — that track and predict HAB density, size, and movement.

Despite these advances, 2G ESPniagara‘s fixed mooring fails to fully support the information needed to predict HAB toxicity and fulfill the needs of drinking water intake managers. To fill this gap, a project team consisting of CIGLR, NOAA GLERL, NOAA National Centers for Ocean Coastal Science (NCCOS), and Monterey Bay Aquarium Research Institute (MBARI) is developing new mobile HAB detection technology that will facilitate toxin forecasting and genomic observations at much greater frequency and in more areas of the lake. The team is miniaturizing the 2G ESP sampling system to fit into a long-range autonomous underwater vehicle (LRAUV). The new mobile 3G ESP will be deployed in 2018 and allow for weeks of flexible and targeted sampling, providing near real-time toxin and genomics measurements for integration into Lake Erie HAB toxicity forecasting products.

2. Predicting Future HABs

CIGLR and GLERL scientists use water quality data along with satellite data, weather forecasts, and water movement forecasts to predict future HABs in western Lake Erie. They have developed a specialized tool called the Lake Erie HAB Tracker that gives current bloom conditions and a 5-day forecast of algal bloom movement and size through 3D simulations and graphics. These predictions can provide stakeholders, such as drinking water intake managers, commercial fishing operators, and beach goers, with timely information for public health decision making. Our team is currently using observed data to improve 3D prediction capabilities and developing methods to incorporate toxin concentrations into the model.

We are developing a new model for Lake Erie to examine relationships between HABs and key environmental factors, like water temperature, river flows, nutrient loads, water column mixing, and sediment resuspension. Outcomes from the various loading-response scenarios will be used to develop relationships between nutrient loads and HABs in western Lake Erie.

We are also evaluating the contribution of biochemical processes to short-term (5-10 days) model projections of HABs. To do this, we are updating HAB biomass in the lake with data from satellite images and running the model with and without biochemical processes turned on until the next satellite image is available. The current forecast model relies on physical processes with no biochemical processes, and treats cyanobacterial cells as nonliving particles. By comparing model projections with and without biochemical processes, we are assessing whether they can significantly improve our short-term HAB forecasts.

3. Incorporating Human Dimensions

To meet the needs of coastal communities, public health officials, and local water quality managers and decision-makers, the research team includes social scientists that are addressing the human dimensions of HABs in the Great Lakes. The overall goals of this work are to: 1) identify and assess user needs related to water quality and human-health forecasting to guide the development and refinement of research project products and 2) disseminate NOAA/CIGLR research, tools, and technology to end users to educate and build a network of public health officials that utilize NOAA products and information for decision making.

The human dimensions team is conducting primary research that extends models of HABs and their impacts on water quality, with social dimensions that predict human behavioral responses to HABs or their impact on coastal communities. In addition to primary research, the team is leading outreach and stakeholder engagement efforts that facilitate the co‐design of research between scientists and stakeholder groups, such as local drinking water managers. They work with stakeholder groups to help translate research and model development into socially-useful forecasting tools, and disseminate HAB program scientific findings and monitoring data to key user groups (e.g., drinking water managers, beach managers, local officials) as well as the general public. They are also forging partnerships and networks with new stakeholder groups, including the recreational and charter fishing communities in the Great Lakes, through the use of human dimensions frameworks.

Stay up-to-date on the most recent news and scientific media generated from our HABs research here:



Berry, M.A., T.W. Davis, R.M. Cory, M.B. Duhaime, T.H. Johengen, G.W. Kling, J.A. Marino, P.A. Den Uyl, D. Gossiaux, G.J. Dick and V.J. Denef. 2016. Cyanobacterial harmful algal blooms are a biological disturbance to Western Lake Erie bacterial communities. Environmental Microbiology. 19:1149-1162. (DOI:10.1111/1462-2920.13640). Berry_etal.pdf

Berry, M.A., J.D. White, T.W. Davis, S. Jain, T.H. Johengen, G.J. Dick, O. Sarnelle and V.J. Denef. 2017. Are oligotypes meaningful ecological and phylogenetic units? A case study of Microcystis in freshwater lakes. Frontiers in Microbiology. 8(365). (DOI:10.3389/fmicb.2017.00365). Berry2_etal.pdf

Bertani, I., C.E. Steger, D.R. Obenour, G.L. Fahnenstiel, T.B. Bridgeman, T.H. Johengen, M.J. Sayers, R.A. Schuchman and D. Scavia. 2017. Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story? Science of The Total Environment. 575:294-308. (DOI:10.1016/j.scitotenv.2016.10.023). Bertani_etal.pdf

Cory, R.M., T.W. Davis, G.J. Dick, T.H. Johengen, V.J. Denef, M. Berry, S.E. Page, S.B. Watson, K. Yuhas and G.W. Kling. 2016. Seasonal dynamics in dissolved organic matter, hydrogen peroxide, and cyanobacterial blooms in Lake Erie. Frontiers in Marine Science. V3, Article 54. (DOI:10.3389/fmars.2016.00054). Cory_etal.pdf

Gobler, C.J., J.M. Burkholder, T.W. Davis, M.J. Harke, T. Johengen, C.A. Stow and D.B. Van de Waal. 2016. The dual role of nitrogen supply in controlling the growth and toxicity of cyanobacterial blooms. Harmful Algae. 54:87-97. (DOI:10.1016/j.hal.2016.01.010). Gobler_etal.pdf

Michalak, A.M., E.J. Anderson, D. Beletsky, S. Boland, N.S. Bosch, T.B. Bridgeman, J.D. Chaffin, K.H. Cho, R. Confesor, I. Daloglu, J.V. DePinto, M.A. Evans, G.L. Fahnenstiel, L. He, J.C. Ho, L. Jenkins, T.H. Johengen, K.C. Kuo, E. Laporte, X. Liu, M. McWilliams, M.R. Moore, D.J. Posselt, R.P. Richards, D. Scavia, A.L. Steiner, E. Vaerhamme, D.M. Wright and M.A. Zagorski. 2013. Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proceedings of the National Academy of Sciences. 110(16):6448-6452. (DOI:10.1073/pnas.1216006110). Michalak_etal.pdf

Millie, D.F., G.R. Weckman, G.L. Fahnenstiel, H.J. Carrick, E. Ardjmand, W.A. Young II, M. Sayers and R.A. Shuchman. 2014. Using artificial intelligence for CyanoHAB niche modeling: discovery and visualization of Microcystis-environmental associations within western Lake Erie. Canadian Journal of Fisheries and Aquatic Sciences. 71:1642-1654. (DOI:10.1139/cjfas-2013-0654). Millie_etal.pdf

Obenour, D.R., A.D. Gronewold, C.A. Stow and D. Scavia. 2014. Using a Bayesian hierarchical model to improve Lake Erie cyanobacterial bloom forecasts. Water Resources Research. 50(10):7847-7860. (DOI:10.1002/2014WR015616). Obenour_etal.pdf

Reavie, E.D., M. Cai, M.R. Twiss, H.J. Carrick, T.W. Davis, T.H. Johengen, D. Gossiaux, D.E. Smith, D. Palladino, A. Burtner and G.V. Sgro. Winter–spring diatom production in Lake Erie is an important driver of summer hypoxia. 2016. Journal of Great Lakes Research. 42(3):608-618. (DOI:10.1016/j.jglr.2016.02.013). Reavie_etal.pdf

Rowe, M.D., E.J. Anderson, T.T. Wynne, R.P. Stumpf, D.L. Fanslow, K. Kijanka, H.A. Vanderploeg, J.R. Strickler and T.W. Davis. 2016. Vertical distribution of buoyant Microcystis blooms in a Lagrangian particle tracking model for short-term forecasts in Lake Erie. Journal of Geophysical Research: Oceans. 121:5296-5314. (DOI:10.1002/2016JC011720). Rowe_etal.pdf

Scavia, D., J.D. Allan, K.K. Arend, S. Bartell, D. Beletsky, N.S. Bosch, S.B. Brandt, R.D. Briland, I. Daloglu, J.V. DePinto, D.M. Dolan, M.A. Evans, T.M. Farner, D. Goto, H. Han, T.O. Hook, R. Knight, S.A. Ludsin, D.M. Mason, A.M. Michalak, R.P. Richards, J.J. Roberts, D.K. Rucinski, E.S. Rutherford, D.J. Schwab, T. Sesterhenn, H. Zhang and Y. Zhou. 2014. Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia. Journal of Great Lakes Research. 40:226-246. (DOI:10.1016/j.jglr.2014.02.004). Scavia_etal.pdf

Steffen, M.M., T.W. Davis, R.M. McKay, G.S. Bullerjahn, L.E. Krausfeldt, J.M.A. Stough, M.L. Neitzey, N.E. Gilbert, G.L. Boyer, T.H. Johengen, D.C. Gossiaux, A.M. Burtner, D. Palladino, M.D. Rowe, G.J. Dick, K.A. Meyer, S. Levy, B.E. Boone, R.P. Stumpf, T.T. Wynne, P.V. Zimba, D. Gutierrez and S.W. Wilhelm. 2017. Ecophysiological Examination of the Lake Erie Microcystis Bloom in 2014: Linkages between Biology and the Water Supply Shutdown of Toledo, OH. Environmental Science and Technology. 51(12):6745-6755. (DOI:10.1021/acs.est.7b00856). Steffen_etal.pdf

Stumpf, R.P., T.W. Davis, T.T Wynne, J.L. Graham, K.A. Loftin, T.H. Johengen, D. Gossiaux, D. Palladino and A.M. Burtner. 2016. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae. 54:160-173. (DOI:10.1016/j.hal.2016.01.005). Stumpf_etal.pdf

Zhang, H., L. Boegman, D. Scavia, D.A. Culver. 2016. Spatial distributions of external and internal phosphorus loads in Lake Erie and their impacts on phytoplankton and water quality. Journal of Great Lakes Research. 42:1212-1227. (DOI:10.1016/j.jglr.2016.09.005). Zhang_etal.pdf

Research Themes


HABs Photo Gallery

Example HAB Tracker animation.