A DNA test to predict which parts of the globe may be hit by harmful algal blooms has been developed by University of Connecticut researchers.
Senjie Lin, an Associate Professor of Molecular Ecology in the Department of Marine Sciences, says that being able to predict the spread of algae may be very helpful in reducing the economic impact on fisheries, recreational activities, and aquaculture sites.
AdvertisementThe approach may also decrease the outbreaks of food poisoning due to contamination of seafood by the toxins some of these algae produce, according to the researcher.
Lin points out that the geographic extent, frequency, intensity, and economic impact of harmful algal blooms have increased dramatically in recent decades throughout the coastlines of the world.
He says that climate change and increasing levels of pollution are to blame for algal bloom occurrences.
He also blames aquaculture operations for the problem, due to the large mass of concentrated waste products from cultured animals.
Algae include cyanobacteria, dinoflagellates, diatoms, raphidophytes, haptophytes, and various other species many of which produce potent toxins.
Some, however, are hazardous simply because of the unusually high biomass they produce along a coastline, lake, or other body of water.
"To minimize economic and environmental impacts, an early warning detection system is needed," says Lin.
He has reviewed the two molecular biology techniques that are most commonly used to detect harmful algae, with the putatively toxic dinoflagellate Pfiesteria piscicida as a case study.
In his study paper, Lin provides practical information on the technical aspects of using biological markers, DNA or RNA, to detect the algae quickly and easily without the need for highly sophisticated methods or equipment.
He says that a portable device that could be used on board research vessels or fishing vessels could lead to a huge success.
A research article describing the test has been published in the International Journal of Environment and Pollution.
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