There is a lot going on beneath the surface of a coral reef — even if you don’t notice it when looking down at one from the top.
For years, scientists believed there was a great deal of underwater activity that could never be fully captured. Something has been happening continuously, layered and difficult to interpret.
It was only after recent advancements in technology that hidden patterns within that activity became visible.
With artificial intelligence, we can now analyze those signals far more effectively than in the past.
What did AI first allow scientists to discover about these ecosystems?
The challenge of monitoring complex marine ecosystems
A coral reef is considered one of the most biodiverse ecosystems on the planet. It supports a significant percentage of marine life.
Approximately a fourth of all species inhabit just one percent of the world’s oceans. Due to such a high number of organisms living in close proximity to each other, observing their activity has always proven challenging.
Researchers have used acoustic monitoring by installing equipment underwater to track reef environments over time.
This method captures an array of signals — both subtle and extreme — that marine species emit. However, processing this data has been time-consuming.
Often, scientists had to manually review recordings, identifying one pattern at a time.
Why traditional survey methods struggle to keep pace
As coral reefs face increasing pressure from climate change and human activity, this delay has become problematic.
The need for quicker observation techniques has increased as habitat loss continues due to rising temperatures and human activity.
Processing vast amounts of data makes tracking population changes an ongoing challenge.
Researchers now use artificial intelligence to analyze large volumes of data automatically.
Neural networks are used to categorize and interpret marine activity at a scale that would be impossible for humans alone. This allows researchers to process much more information in shorter periods of time.
As a result, reef conditions can now be analyzed much more broadly.
Rather than taking weeks, months, or years to determine changes, researchers can now identify those changes in much shorter periods. This is particularly beneficial for rapidly developing ecosystems.
Decoding the hidden acoustic language of coral reefs
Artificial intelligence is recognizing non-random patterns in signals generated by organisms living on coral reefs, which represent an audible soundscape.
Each organism produces a distinct type of activity depending on what it is doing, such as movement and feeding.
When combined, these interactions form recognizable patterns across this environment.
Where researchers began to make sense of reef sound patterns
Although these signals do not resemble human language, they contain meaningful information about which organisms are present and how active they are. Artificial intelligence allows this information to be interpreted more effectively.
As a result, the previously unknown “hidden language” of coral reefs has been identified.
Their soundscape consists of acoustic signals that indicate life throughout the ecosystem.
This new understanding is revolutionizing how researchers evaluate these ecosystems.
It moves beyond a reliance on visual observations to include audio‑based assessments.
Overall, healthier reefs produce rich and complex soundscapes, while damaged ones grow quieter over time.
These differences serve as quantifiable metrics for evaluating ecosystem health. This provides a clearer way to track ongoing changes within reef systems over time.
Researchers will no longer need to rely on constant visual surveys, allowing reef health to be monitored even in low‑visibility conditions.
If you want to learn more about this discovery, you can check the full study here: Seth McCammon, Nathan Formel, Sierra Jarriel, T. Aran Mooney; Rapid detection of fish calls within diverse coral reef soundscapes using a convolutional neural network. J. Acoust. Soc. Am. 1 March 2025; 157 (3): 1665–1683. https://doi.org/10.1121/10.0035829