Newswise — ITHACA, N.Y – A combination of ecological field methods and cutting-edge artificial intelligence has helped an interdisciplinary research group detect eelgrass wasting disease at nearly three dozen sites along a 1,700-mile stretch of the West Coast, from San Diego to southern Alaska.
The key finding: Eelgrass wasting is associated with warmer-than-normal water temperatures, particularly in early summer, regardless of the region. Eelgrass is a vital coastal species of seagrass for fish habitat, biodiversity, shoreline protection and carbon sequestration.
The Cornell University research team – led by Carla Gomes, professor of computing and information science, and Drew Harvell, professor emeritus of ecology and evolutionary biology – reported their findings in Limnology and Oceanography. Co-lead authors include Brendan Rappazzo, doctoral student in computer science, and Lillian Aoki, a former postdoctoral researcher in Harvell’s lab who is now a research scientist at the University of Oregon.
Gomes and Rappazzo led the development of the Eelgrass Lesion Image Segmentation Application (EeLISA, pronounced eel-EYE-zah), an AI system that, when properly trained, can quickly analyze thousands images of seagrass leaves and distinguished diseased from healthy tissue.
How quickly does EeLISA work? According to the researchers, it works 5,000 times faster than human experts, with comparable accuracy. And as the application gets fed more information, it gets “smarter” and produces more consistent results.
“That’s really a key component,” said Rappazzo. “If you give the same eelgrass scan to four different people to label, they’ll all give variable measurements of disease. You have all this variation, but with EeLISA, it’s not only faster but it’s consistently labeled.”
“In traditional machine learning, you need large amounts of labeled data up front,” Gomes said. “But with EeLISA, we’re getting feedback from the scientists providing the images, and the system improves very rapidly. So, in the end, it doesn’t require that many labeled examples.”
This project involved a network of 32 field sites along the Pacific coast, stretching across 23 degrees of latitude. This diversity of regions allowed for the study of seagrass wasting disease in different climates and environments.
The AI-enabled research revealed that warm-water anomalies – regardless of what normal temperatures were for a particular region – were the key driver of disease. This told the researchers that studying the relationship between disease and climate change is necessary for all conditions, and not just in seagrass meadows in warm locations.
“We have invested a decade developing the disease recognition tools to monitor these outbreaks at a large spatial scale,” Harvell said, “because our early studies suggested eelgrass could be sensitive to warming-induced outbreaks. Eelgrass is an essential marine habitat, and a critical link in the chain of survival for fishes such as salmon and herring.”
For additional information, see this Cornell Chronicle story.
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