Team of UH Hilo students, faculty publish work on coral health and disease
Date: Friday, May 8, 2020
Contact: Alyson Kakugawa-Leong, (808) 932-7669
For Immediate Release
Students and faculty in the `Ike Wai Research Experience in Data Science Program at the University of Hawaiʻi at Hilo had their work on coral health and disease published in the May 6 edition of Frontiers Marine Science. The publication is the result of research done in Summer 2019.
“A Comparison of the Diagnostic Accuracy of in-situ and Digital Image-Based Assessments of Coral Health and Disease” addresses a pressing issue in the field of marine science, which is the capability of digital images to be used for detecting disease in marine environments. The students conducted underwater conventional coral health surveys and subsequently collected imagery for high-resolution 3D models of the same study plots. This unique approach allowed them to compare coral health assessments from visual surveys (human eyes on the reef) and digital analysis (computer-based).
The findings showed that the human visual assessment is more sensitive for detecting disease than the digital approach. The digital approach was comparable, however, and thus can be a useful tool when human divers cannot safely access reef habitats for visual surveys. As agencies around the globe are adopting digital imaging methods for monitoring reefs, this paper provides useful clarity of the pros and cons of using new technologies versus conventional techniques.
The students involved in the publication include Sofia Ferreira (Marine Science), Drew Gotshalk (Computer Science), Chad Kinoshita (Computer Science), Micah Marshall (Mathematics), Nicholas Del Moral (Computer Science), Shane Murphy (Marine Science), Kailey Pascoe (Tropical Conversation Biology and Environmental Science), Alexandra Runyan (Marine Science), Alexander Spengler (Marine Science), Brittany Wells (Marine Science), and Danielle Wilde (Marine Science). Faculty members are Drs. John Burns (Marine Science), Grady Weyenberg (Mathematics), and Travis Mandel (Computer Science).
“This project was a close collaboration between students and faculty in Marine Science, Computer Science, and Mathematics,” said Dr. Grady Weyenberg, assistant professor of mathematics and co-author of the publication. “Everybody got to learn a bit about how marine scientists conduct coral surveys, the CS problems involved in building 3D models from photographs, and the math modeling and computation that goes into more advanced Bayesian statistical models.
“The research is quite novel for Marine Science as there have been very few studies comparing the accuracy of the two methods of diagnosing coral reef health. From a statistical point of view, the problem is interesting because when the two methods disagree, we have no ‘gold-standard’ available to tell us which method is correct and which is wrong, so we must build that uncertainty into our models when comparing the methods,” he explained.
“This was a really great example of advancing science in an interdisciplinary manner,” added Dr. John Burns, assistant professor of marine science and co-author of the publication. “The students did an amazing job on all aspects of the project. They all brought varying levels of expertise that enabled us to quickly collect a large dataset from Hawaiian reefs and analyze it using various technological tools.
“By the end of six weeks they had completed all analyses and prepared the first draft of the manuscript as well as put together some exceptional presentations. To me, this project and resulting paper really capture one of the main goals of the UH Hilo Data Science program, which is bringing together students from multiple disciplines to conduct exciting applied research,” he noted.
The UH Hilo `Ike Wai Research Experience in Data Science Program is funded through UH Hilo’s participation in the $20 million `Ike Wai project awarded to the state in 2016 by the National Science Foundation’s Experimental Program to Stimulate Competitive Research (EPSCoR).
The full paper is available at: https://www.frontiersin.org/articles/10.3389/fmars.2020.00304/full.
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