Armed with AI, Study Identifies Prey from Predator Crunching Sounds

A whitespotted eagle ray feeds on hard-shelled mollusks in its natural habitat. The distinctive crunching sounds produced during feeding are helping FAU researchers develop AI-powered tools to monitor predator-prey interactions beneath the ocean surface. (Photo credit: Cat Nickell)

Interactions between hard-shelled marine mollusks such as clams and snails and their predators play a critical but largely unseen role in shaping coastal ecosystems. These organisms help stabilize shorelines, filter water and support biodiversity, making them foundational to coastal health. Yet they are increasingly threatened by ocean acidification and expanding populations of mobile shell-crushing predators.

What makes these interactions especially difficult to study is not just where they occur, but how quickly they unfold. Many predators, including highly mobile rays, forage in subtidal environments where direct observation is limited. As a result, a key ecological process - mollusk consumption by predators - has remained difficult to quantify in natural systems, despite its importance being recognized for decades.

Fortunately, these interactions are not silent. Every crushed clam or shattered snail shell produces a distinct acoustic signature - a brief but information-rich sound that can be recorded underwater. Passive acoustic monitoring and autonomous recording systems enable researchers to "listen" to these feeding events as they occur in real time. However, the challenge is how to reliably extract it from vast and noisy underwater recordings.

Florida Atlantic University researchers have developed a machine learning framework that improves the detection and classification of shell-crushing sounds in underwater recordings. Using controlled tank experiments with whitespotted eagle rays (Aetobatus narinari) - highly mobile predators known for cracking hard shells - the team trained the system to identify these feeding events more accurately amid ocean noise.

Rather than relying on a single method, the system uses a multi-step approach. It first scans large datasets to flag potential shell-crushing sounds based on their acoustic patterns, then applies a second layer of machine learning to reduce false detections by separating real feeding events from background noise.

Once validated, the system goes further by classifying the type of prey being consumed using both traditional and deep learning methods, including random forests, long short-term memory networks, and convolutional neural networks (CNNs), each trained to recognize subtle patterns in acoustic structure.

A key finding of the study, published in Ecological Informatics, was that highly complex AI models were not always necessary for strong performance. Simpler methods using gammatone-based features were nearly as effective as advanced deep learning systems at detecting shell-crushing sounds, while requiring far less computing power. The results suggest these streamlined approaches could make long-term underwater monitoring more practical, scalable and cost-effective in real marine environments.

"Shell-crushing sounds contain a surprising amount of ecological information about predator-prey interactions and feeding behavior," said Laurent Chérubin, Ph.D., corresponding author and a research professor at FAU's Harbor Branch Oceanographic Institute. "This work shows how passive acoustic monitoring can be used not only to detect these events, but also to better understand how marine predators interact with their environment in places that are otherwise difficult to observe."

By detecting and classifying the sounds predators make while consuming different types of prey, the approach brings scientists closer to remotely measuring shellfish predation rates in natural marine environments.

"From an ecological perspective, this technology opens the door to quantifying predator impacts in a way we've never been able to do before," said Matt Ajemian, Ph.D., senior author, an associate research professor and director of the Fisheries Ecology and Conservation Lab (FEC) at FAU Harbor Branch. "Being able to remotely detect and classify feeding events means we can begin measuring predation pressure on mollusk populations at ecosystem scales, not just in isolated observations. That represents a major step forward for coastal ecology and conservation."

Importantly, the system was not only effective in controlled tank conditions but also demonstrated strong performance in field settings using both animal-borne acoustic tags and fixed underwater recorders. Even when trained exclusively on tank data, the model successfully detected feeding events and identified associated prey types in natural environments with high reliability.

"Beyond simple detection, our approach also provides insight into predator behavior itself," said Ajemian. "Acoustic patterns reflected not only prey type, but also handling strategies and processing time, raising the possibility that researchers may eventually be able to distinguish individual feeding behaviors and even prey size classes based on these sounds."

As shellfish aquaculture and coastal restoration expand, understanding predator interactions with mollusk populations is increasingly important for conservation and management. Because the prey examined ranged from buried filter feeders to more mobile species, the system could help track mollusk mortality across a wide range of coastal habitats.

"Our findings point to a clear path for scalable, real-time acoustic monitoring of marine ecosystems," said first author Ali Ibrahim, Ph.D., an assistant professor of teaching in FAU's College of Engineering and Computer Science. "The computational efficiency of GTCC-based models makes them especially well-suited for autonomous underwater platforms with limited power and processing capacity, enabling long-term monitoring in remote marine environments where high-performance computing is not practical."

Study co-authors are Cecilia M. Hampton, a Ph.D. student in the FEC lab at FAU Harbor Branch; Breanna C. DeGroot, State College of Florida; and Hanqi Zhaung, Ph.D., associate dean and professor in FAU's Department of Electrical Engineering and Computer Science.

This research was supported by the Specialty License Plate fund administered by the Harbor Branch Oceanographic Institute Foundation and a National Science Foundation grant.

Whitespotted eagle rays crunch clams in the lab and in the wild. These sounds helped FAU researchers develop an AI-powered system that can detect and classify shell-crushing feeding events underwater. (Video credit: FAU Harbor Branch, Cat Nickell and Conrad Pfalzgraf)

A whitespotted eagle ray crunches on clams.

Close-up of a whitespotted eagle ray crunching on clams.

A whitespotted eagle ray swims in its natural habitat (Photo credit: Cat Nickell)

-FAU-