Coral reefs have a complex soundscape – and even experts have to conduct painstaking analysis to measure reef health based on sound recordings.
In the new study, University of Exeter scientists trained a computer algorithm using multiple recordings of healthy and degraded reefs, allowing the machine to learn the difference.
The computer then analysed a host of new recordings, and successfully identified reef health 92% of the time.
The team used this to track the progress of reef restoration projects.
“Coral reefs are facing multiple threats including climate change, so monitoring their health and the success of conservation projects is vital,” said lead author Ben Williams.
“One major difficulty is that visual and acoustic surveys of reefs usually rely on labour-intensive methods.
“Visual surveys are also limited by the fact that many reef creatures conceal themselves, or are active at night, while the complexity of reef sounds has made it difficult to identify reef health using individual recordings.
“Our approach to that problem was to use machine learning – to see whether a computer could learn the song of the reef.
“Our findings show that a computer can pick up patterns that are undetectable to the human ear. It can tell us faster, and more accurately, how the reef is doing.”
The fish and other creatures living on coral reefs make a vast range of sounds.
The meaning of many of these calls remains unknown, but the new AI method can distinguish between the overall sounds of healthy and unhealthy reefs.
The recordings used in the study were taken at the Mars Coral Reef Restoration Project, which is restoring heavily damaged reefs in Indonesia.
Co-author Dr Tim Lamont, from Lancaster University, said the AI method creates major opportunities to improve coral reef monitoring.
“This is a really exciting development. Sound recorders and AI could be used around the world to monitor the health of reefs, and discover whether attempts to protect and restore them are working,” Dr Lamont said.
“In many cases it’s easier and cheaper to deploy an underwater hydrophone on a reef and leave it there than to have expert divers visiting the reef repeatedly to survey it – especially in remote locations.”
The study was funded by the Natural Environment Research Council and the Swiss National Science Foundation.
The paper, published in the journal Ecological Indicators, is entitled: “Enhancing automated analysis of marine soundscapes using machine learning to combine ecoacoustic indices.”