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July / August 2003


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Seagrass Health

Photo courtesy of the National Oceanic and Atmospheric Administration


Seagrass Health
UMaine engineers develop neural networks to help biologists monitor declining underwater meadows

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Marine biologists from Maine to Australia are working with electrical and computer engineers to monitor an ecosystem – seagrass meadows – in retreat. Seagrass, which provides critical habitat for commercially important fish species, occupies about 10 percent of the world's coastal seas. Unlike seaweeds, these rooted underwater plants flower and drop their leaves like their land-based cousins.

In the 1930s, a disease decimated a type of seagrass known as eelgrass (Zostera marina) in Atlantic waters bordering North America and Europe. Although the cause is uncertain, it has been linked to warming water temperatures and microorganisms.

The beds eventually recovered, but today, eelgrass and other species of seagrasses appear to be in decline worldwide. Some losses have been severe. In the past 20 years, areas of the Indian River Lagoon on Florida's Atlantic coast have lost as much as 95 percent of their coverage. Maine's Taunton Bay has lost about 90 percent of its eelgrass in the last six years. Additional declines are being monitored in Australia and Europe.

A continuing decline could deal another blow to an already struggling global fishing industry.

The job for scientists is clear: understand what's causing the decline of these delicate habitats and develop ways to restore them. Researchers already know that reduced light transmittance through the water is a major factor. The problem usually starts at the deeper edges of the beds, where the light reaching the plants is only marginal, and progresses toward shallower regions as conditions deteriorate. Reduced light is often related to murky water conditions that result from land erosion.

To address this problem and even predict seagrass stress before more beds are lost, biologist Suzanne Fyfe at the University of Wollongong in Australia is using light reflected from seagrass leaves to develop an early warning system. Currently, satellite or aircraft remote sensing techniques can only detect deterioration in seagrass health after large-scale dieback has already occurred, she says.

To turn measurements of reflected light into a predictive tool, Fyfe has turned to University of Maine Assistant Professor Habtom Ressom, who leads a research team in the Intelligent Systems Laboratory (INTSYS) of the Department of Electrical and Computer Engineering. Ressom specializes in a computer software system known as an artificial neural network, or neural net.

In INTSYS are three faculty members, a research associate and more than a dozen graduate and undergraduate students. The seagrass project is one of several active studies in the lab. Others focus on DNA analysis, gene expression and industrial process control. Working on the seagrass project with Ressom are research associate Padma Natarajan, a 1999 UMaine graduate, and electrical engineering master's student Siva Srirangam.

The common thread running through the lab's research is the use of computational intelligence techniques to extract knowledge from data. Neural nets have been around for more than 40 years and today are widely used in industry and business. They improve voice transmission over telephone lines, teach machines to talk, recognize patterns and analyze financial markets. While they consist of sets of mathematical equations, neural networks are nevertheless inspired by nature. Individual parts of a neural net are viewed as nerve cells and the connections between them as the junctions that link cells.

"From an engineering perspective, our brains are essentially neural networks," says Ressom, who received his Ph.D. from the University of Kaiserslautern in Germany in 1999. "We can learn things from what we see. We can correct things. Initially, an artificial neural network has no idea about the relationships between inputs and outputs. As it runs, it will see its own errors and modify its own parameters."

To the casual observer, the neural net seems to perform statistical magic. It doesn't depend on knowledge of a specific system, but it does require quality data. Moreover, its ability to learn and adjust gives the neural net an advantage over conventional modeling approaches, especially in dealing with complex systems.

A seagrass ecosystem fits that model. A case in point is Fyfe's effort to predict seagrass stress on the basis of reflected light. Fyfe uses a device known as a spectroradiometer to identify the changes in the light reflected from seagrass leaves.

Ressom's neural network transforms the database of information into a mathematical tool. That tool can then be applied to remote sensing data to predict stress levels in sea-grass meadows before dieback occurs.

Running mathematical models is a bit like playing a game of darts in the dark. Scientists may know that their results are accurate within a certain range, but they don't know exactly how close they are to the bull's-eye. By improving model accuracy, neural nets turn the lights up a bit, letting scientists know that their results are closer to the mark.

Poor water quality, including murkiness or algae that keep out sunlight, is a threat to seagrass. In collaboration with scientists from St. Johns River Water Management District in Florida, Ressom and his team are studying how to use water quality data to achieve a better estimate of light reduction and, thus, seagrass health.

"The district monitors seagrass in the Indian River Lagoon in correlation with water quality parameters, such as nutrients, turbidity and clarity. They call seagrass ‘the barometer of the ecosystem,'" says Ressom.

In 2002, Ressom met with scientists from the district in a workshop on biodiversity and ecosystems for Indian River Lagoon. He co-organized it with Natarajan and other UMaine faculty members: Mohamad Musavi, George Markowsky, Thomas Wheeler, Anthony Stefanidis and Cristian Domnisoru.

The district already uses a model to correlate water quality parameters with light attenuation, says Ressom. Using the district's water quality data, the UMaine neural network came closer than the district's own model in predicting the relationship between water quality and light traveling through the water and the impact on seagrass health. "Neural networks try to correlate difficult-to-measure variables with easy-to-measure variables," says Ressom. "The advantage is that no prior information is necessary. That's why we are able to jump into these subjects. Our background is in electrical and computer engineering. I personally have no knowledge of the biological relationships."

In addition to their seagrass work, Ressom and his team are working with the National Aeronautics and Space Administration to apply a neural net to ocean data from satellites. Their goal is to estimate chlorophyll concentrations, an indication of algal growth and ocean vitality.

by Nick Houtman
July-August, 2003

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