12-12-11fish
Matthew Kenrick

Over the past few years, the FDA has been compiling a fish DNA library to help combat seafood fraud. But despite its best efforts, many sushi eaters and other seafood diners are still chowing down on mislabeled and unsustainable fish species on the regular.

Now a Canadian team has gone a step further, compiling a DNA barcoding library of tens of thousands of Atlantic ocean fishes, and making much of it available directly to other research scientists and the public. You can thank Canadian biologist Paul Bentzen and his colleagues at Dalhousie University. Yes, despite the funny name, this is a real university. From Phys.org:

According to Paul Bentzen, Professor in the Department of Biology, “With growing pressures from fisheries, climate change and invasive species, it is more important than ever to monitor and understand biodiversity in the sea, and how it is changing. Our database provides a new tool for species identification that will help us monitor biodiversity. The availability of ever easier to use DNA sequencing technology can make almost anyone ‘expert’ at identifying species — and all it takes is a scrap of tissue.”

He continued, “There can be many steps in the supply chain between when the fish leaves the water and when it appears on a plate. With many desirable species becoming ever more scarce and expensive, there will always be temptation to substitute a cheaper fish (or an illegally harvested one) for a legal, more expensive one. We know it happens. DNA data never lie, unlike some seafood labels and restaurant menus. With the DNA database, it will be easier to detect seafood fraud when it happens.”

The database aims to fight fraud with readily available public information. The problem: It’s only searchable by wonky scientific names and jargon. That means it’s useful to other scientists, but not so useful to regular people. Until this kind of info gets funneled into an easily parse-able application, consumers and policy-makers will likely just feel like they’re drowning in data.