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Tracking coral reef degradation and recovery using soundscapes

OPL team members involved

Ben Williams & David Curnick


University College London, University of Bristol & Mars Global





Project description

On thriving coral reefs fish, snapping shrimp and other organisms come together to produce a rich and diverse soundscape. However, on degraded reefs much of this acoustic complexity is lost. Recent advancements in technology have made passive acoustic monitoring (PAM) a cost-effective and low-effort means to study coral reefs, allowing the field of soundscape ecology on these habitats to begin establishing itself. However, due to its very recent emergence, many important questions remain unanswered and the full extent of its potential is still unknown.


Ben will spend the first half of his PhD on this project to help advance the use of PAM to support tropical reef restoration. This will be in collaboration with one of the world’s largest restoration projects led by Mars Global. Key goals of the project are:


  1. Identify optimal approaches to extract features from reef soundscape recordings which can be used in machine learning.

  2. Collect a database of recordings from healthy and degraded reef systems from several sites around the world using a standardised method.

  3. Use this database and machine learning methods developed in objective one to determine whether universal differences in the soundscape of healthy and degraded reefs are observed, or, whether these are specific to their region or context.

  4. Use outcomes from objective three to help track the progress of restoration projects local to each of the original sites targeted.


An important contributor to the information of interest held within a reefs soundscape is the abundance and diversity of fish sounds. Have a listen to some of these:

Coral reef fish sounds
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ben deploying hydrophone.jpeg

Ben deploys a hydrophone on a tropical reef where restoration began three years previously in South Sulawesi, Indonesia (Image courtesy of Tim Lamont).

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