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A team of experts detected what sounded like a campfire when they listened to an audio recording taken underwater off the coast of central Indonesia.
According to a study published last month by academics from British and Indonesian universities, it was a coral reef brimming with life, in which they utilized hundreds of such audio samples to teach a computer program to assess the health of a coral reef by listening to it.
Because of all the organisms that live on and in it, a healthy reef has a complex 'crackling, campfire-like' sound, whereas a deteriorated reef sounds more barren, according to life sciences specialist and team lead researcher Ben Williams.
According to the team's study published in the journal Ecological Indicators, the artificial intelligence (AI) system parses data points such as frequency and loudness of sound from audio samples and can detect whether the reef is healthy or damaged with at least 92 percent accuracy.
The researchers expect that by implementing this new AI system, conservation groups around the world will be able to track reef health more effectively.
Human-caused carbon emissions have warmed ocean surfaces by 0.13 degrees every decade and increased acidity by 30% since the industrial era, putting coral reefs under duress.
According to the Global Coral Reef Monitoring Network, almost 14% of the world's coral reefs were destroyed between 2009 and 2018, an area 2.5 times the size of the Grand Canyon National Park in the United States.
Coral reefs support more than 25% of marine species, including turtles, fish, and lobsters, although covering less than 1% of the ocean floor, making them ideal ground for global fishing businesses.
Syafyudin Yusuf, an Indonesian conservationist, and lecturer at Hasanuddin University's marine sciences division said the findings would aid in monitoring reef health in Indonesia.
In addition, the researchers plan to collect underwater recordings from reefs in Australia, Mexico, and the Virgin Islands to aid in the evaluation of coral restoration programs
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