Modeling Cyanobacteria Ecology to Keep Harmful Algal Blooms at Bay
By: Betty Kreakie, Jeff Hollister, and Bryan Milstead
Despite a lengthy history of research on cyanobacteria, many important questions about this diverse group of aquatic, photosynthetic “blue-green algae” remain unanswered. For example, how can we more accurately predict cyanobacteria blooms in freshwater systems? Which lakes have elevated risks for such blooms? And what characteristics mark areas with high risks for cyanobacteria blooms?
These are important questions, and our ecological modeling work is moving us closer to finding some answers.
The gold standard for understanding cyanobacteria in lakes is direct measurements of certain water quality variables, such as levels of nutrients, chlorophyll a, and pigments. This of course requires the ability to take on site (“in situ”) samples, something that is not possible to do for every lake in the country. Our modeling work is focused on predicting cyanobacterial bloom risk for lakes that have not been directly sampled.
We are using remote sensing and geographic information systems (GIS) data to model bloom risk for all lakes in the continental United States. The work is also starting to shed light on some of the landscape factors that may contribute to elevated predicted bloom risk. For example, we know that different regions have different predictive risk. We are also learning about how lake depth and volume, as well as the surrounding land use impact cyanobacteria abundance.
In addition to our national modeling efforts, we are collaborating with others on smaller scale and more focused studies at regional and local scales. First, we are partnering with other EPA researchers to develop time-series models using data gathered frequently and over a long time by the U.S. Army Corp of Engineers. By using these data, we expect to tease apart information about annual timing and the intensity of blooms. We can also explore aspects of seasonal variability and frequency. Lastly, we are starting to explore ways to use approximately 25 years of data collected by Rhode Island citizen science as part of the University of Rhode Island’s Watershed Watch program. We hope to mine these data and uncover indicators of harmful algal bloom events.
With all this work, we and our partners are adding new chapters to the long history of cyanobacteria research in ways we hope will help communities better predict, reduce, and respond to harmful blooms.
About the Authors: EPA ecologists Betty Kreakie, Jeff Hollister, and Bryan Milstead are looking for ways to decrease the negative impacts of cyanobacteria and harmful algal blooms on human health and the environment.
NOTE: Join Betty Kreakie, Jeff Hollister, and Bryan Milstead for a Twitter chat today (June 26) at 2:00pm (eastern time zone) using the hashtag #greenwater. Please follow us @EPAresearch.
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