"One of the things that happens in seafood supply chains is that as soon as the vessels can kind of get out of sight, it's just impossible to know what they're doing," said Girón-Nava.
To change that, Girón-Nava and his collaborators at the Stanford Center for Ocean Solutions are turning to powerful tools, big data and artificial intelligence.
First, it helps to understand that large commercial vessels are often fitted with automatic identification systems - sometimes known as AIS - that generate information about their location. Many commercial fishing fleets also voluntarily contribute to databases, known as proactive vessel registers, that can provide transparency about where they are and what they're catching. Pairing all that information with satellite and other records begins to paint a picture.
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"We have all of these machine learning AI algorithms to say, 'What were they doing at a given point? Were they fishing? Are they just moving? Were they meeting with another vessel that they have?'" he said.
The Stanford team is working with collaborators, including Global Fishing Watch, which developed the satellite algorithms to not only analyze all that commercial fishing data but essentially make it more widely accessible.
This allows companies up and down the seafood supply chain to eliminate illegally caught products and assures consumers that what they're buying was caught in a sustainable way.
Lindsay Jennings is project director with another collaborator, the Santa Cruz-based group FishWise.
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"And a lot of that comes down as a consumer with purchasing power, of where we want to put our dollars," said Jennings.
The data tracking project is still in development, but Girón-Nava and his team are hoping that it can eventually have an important impact on both illegal fishing and help responsible companies to better protect our ocean environments.
"Because, of course, what we are trying to do is to empower companies to do it themselves," he said.
Learn more about the project here.