The primary obstacle in studying these underwater predation events lies not only in their elusive locations but also in the fleeting nature of the encounters. Predators such as the whitespotted eagle rays (Aetobatus narinari) forage silently in subtidal zones where direct visual observation is hindered by light availability and water clarity. Consequently, the critical ecological process of mollusk consumption remains difficult to quantify in natural settings, leaving a significant knowledge gap in coastal marine ecology.

Unexpectedly, these predation events broadcast distinct acoustic signatures through the water. The fracturing and crushing of clam and snail shells generate unique sounds—transient acoustic signals rich with ecological information. Employing passive acoustic monitoring techniques coupled with autonomous recording devices, researchers can now “listen in” on these feeding behaviors as they happen in situ, capturing data inaccessible through visual surveys alone. Nonetheless, the challenge remains to reliably isolate these faint shell-crunching sounds amid the cacophony of underwater noise.

Addressing this, a team from Florida Atlantic University (FAU) has created an innovative machine learning framework designed to enhance the detection and classification of these subtle shell-crushing acoustic events. Through controlled aquarium trials featuring whitespotted eagle rays—a species renowned for their shell-cracking feeding strategy—the researchers built and trained an AI system adept at distinguishing feeding sounds from ambient oceanic noise, vastly advancing the capability to monitor predator-prey interactions acoustically.

This framework employs a sophisticated, multi-tiered approach. Initially, it processes extensive underwater audio recordings to identify potential predation events via acoustic pattern recognition. Subsequent analytical layers refine these detections by using machine learning classifiers to minimize false positives, thereby filtering actual shell-crushing events from environmental background sounds with high precision.

Beyond mere detection, the system also categorizes the type of mollusk prey consumed during these events. This is achieved by integrating traditional classification algorithms such as random forests with advanced deep learning architectures, including long short-term memory networks (LSTMs) and convolutional neural networks (CNNs). Each method is fine-tuned to recognize nuanced features in the acoustic structure of shell-crushing sounds, enabling detailed insights into prey identity.

Significantly, the study, recently published in the journal Ecological Informatics, demonstrates that complex AI architectures are not always essential for robust performance. Simplified models leveraging gammatone feature cepstral coefficients (GTCCs)—a biologically inspired auditory filterbank approach—proved nearly as effective as deep learning models in detecting shell-crushing sounds, while demanding significantly less computational power. This finding holds promise for scalable, long-duration deployment in challenging marine environments where energy and processing capacity are constrained.

As Laurent Chérubin, Ph.D., a research professor at FAU’s Harbor Branch Oceanographic Institute and lead author, emphasizes, these acoustic signals reveal substantial ecological information beyond mere occurrence. Passive acoustic monitoring represents a transformative tool, offering unprecedented access to predator-prey dynamics in otherwise inaccessible ocean habitats, enhancing our understanding of marine ecosystem functionality.

The implications for coastal ecosystem management are profound. By remotely detecting and classifying predation events, the new technology enables quantification of predator impacts on mollusk populations at ecosystem-wide scales—a methodological leap beyond fragmented, location-specific observations. This ability not only enriches basic ecological knowledge but also equips managers with actionable insights into shellfish populations vital for habitat restoration and commercial aquaculture.

The system’s effectiveness extends beyond controlled laboratory settings. Tested in real-world conditions, including data from animal-borne acoustic tags and fixed underwater sensors, the AI framework reliably identified feeding events and prey types in natural habitats. Its resilience when trained exclusively on tank data yet performing accurately in the field demonstrates robust generalizability, critical for widespread application.

Further intriguing is the framework’s capacity to elucidate predator behavior. According to Dr. Matt Ajemian, senior author and director of the Fisheries Ecology and Conservation Lab at FAU Harbor Branch, the acoustic signatures not only reflect prey species but also reveal handling techniques and processing durations. This opens potential avenues for scientists to distinguish between individual feeding strategies and even estimate prey size categories from subtle variations in shell-crushing sounds.

As global investments in shellfish aquaculture and coastal restoration intensify, tools that effectively monitor predator-prey interactions grow increasingly vital. Considering the diverse prey types analyzed range from buried filter feeders to agile mobile shellfish, this AI-powered acoustic monitoring system emerges as a versatile instrument for tracking mollusk mortalities and ecosystem health across heterogeneous coastal environments.

Finally, the computational efficiency of GTCC-based detection models is especially advantageous for deployment on autonomous underwater platforms constrained by limited power and processing resources. This capability supports extensive, real-time ecological monitoring in remote marine areas where traditional sensor networks are impractical, heralding a new era in marine ecology research.

The research represents a collaborative effort among scientists at Florida Atlantic University, including Ph.D. candidates and faculty from the College of Engineering and Computer Science, highlighting the power of interdisciplinary approaches to address complex ecological challenges with innovative technological solutions. Funded partially by the National Science Foundation and institutional grants, this work exemplifies how AI and acoustic technologies can transform environmental conservation, providing a vital toolkit for safeguarding marine ecosystems under increasing anthropogenic pressure.

Subject of Research: Animals

Article Title: Evaluation of a signal processing and machine learning framework to detect and classify shell-crushing predation events

News Publication Date: 7-May-2026

Web References:

• Florida Atlantic University: https://www.fau.edu/
• Ecological Informatics Journal: https://www.sciencedirect.com/science/article/pii/S1574954126002013

References:

• DOI: 10.1016/j.ecoinf.2026.103795

Image Credits: FAU Harbor Branch, Cat Nickell and Conrad Pfalzgraf

Keywords

Artificial intelligence, aquatic animals, natural resources conservation, sustainability, wildlife management, engineering, technology, acoustics, sound, underwater acoustics, wildlife, predators, marine conservation, ecological restoration, ecosystem management

The research leverages an impressive methodological approach, deploying 631 underwater cameras to systematically monitor shark movements and quantify the biomass of prey fish across various reef habitats. This comprehensive data collection enabled the researchers to construct robust statistical models that elucidate shark habitat preferences, specifically highlighting their affinity for small, densely populated reef areas teeming with prey. Unlike previously held assumptions that general prey abundance suffices for predator presence, this study emphasizes the spatial concentration of prey as a critical factor enhancing feeding efficiency and survival.

Caribbean reef sharks exhibit a clear preference for confined reef regions where prey fish biomass is not only abundant but densely aggregated, reducing the energetic costs associated with foraging. This spatial selectivity likely enhances the sharks’ ability to secure meals with minimal effort, which is especially vital given the energy demands of their predatory lifestyle. Furthermore, these strategic hunting grounds may offer additional benefits by granting sharks easier access to adjacent habitats and reducing their own vulnerability to larger predatory sharks, thereby shaping the intricate predator-prey dynamics within reef ecosystems.

The findings underscore a nuanced ecological interplay: while overfishing and hunting remain prominent threats to shark populations, the depletion of prey species could indirectly undermine shark survival by diminishing critical foraging grounds. This indirect impact of overfishing on predator populations introduces a challenging layer for conservationists, who must now consider prey fish biomass management as an integral component of effective shark protection protocols.

Moreover, the study highlights the distinct behavioral patterns of Caribbean reef sharks, which tend to frequent steep reef walls and deeper reef zones. These habitat preferences align with established knowledge about their ecological niches but also provide fresh insight into their spatial ecology relative to prey availability. Such detailed behavioral data refine our understanding of shark movement ecology and can inform more targeted conservation measures.

An additional dimension to this research is the apparent avoidance behavior Caribbean reef sharks exhibit towards larger shark species, which typically inhabit bigger reef areas rich in prey. This interspecific interaction likely influences the smaller reef sharks’ habitat choice, adding complexity to ecosystem management efforts that must account for multiple predator hierarchies.

The implications of this multi-faceted research are profound. Conservation policies that focus exclusively on banning shark fishing may fall short if they neglect the health and structure of reef ecosystems that support prey density. Protecting these reef habitats to maintain a complex structure that offers shelter for prey fish is essential to sustain robust predator populations. Reef complexity thus emerges as a cornerstone for both sustainable fisheries and shark conservation.

Equally important is the revelation that prey conservation, an often overlooked aspect, essentially supports apex predator sustainability. Overfishing of prey species can create trophic cascades that ultimately threaten shark populations, emphasizing the need for integrated management approaches that address the entire marine food web.

This FIU-led study calls for a paradigm shift in marine conservation tactics. It advocates holistic ecosystem-based management that balances predator protection with the stewardship of prey species and habitat integrity. By doing so, it may halt or even reverse the decline of critical shark populations while preserving marine biodiversity.

Importantly, the use of computational simulation and statistical modeling in this research provides a powerful tool for marine ecologists, enabling the prediction of shark presence based on prey biomass and habitat characteristics. Such analytical advancements bolster the scientific rigor underpinning conservation strategies, facilitating evidence-based policymaking.

In conclusion, this study contributes a vital scientific perspective that may redefine how marine conservationists and policymakers approach shark population recovery. By explicitly linking prey abundance and spatial distribution to shark habitat preferences, it champions a comprehensive conservation framework that transcends traditional species-centric approaches and embraces the intricate interconnectedness of marine ecosystems.

The study’s findings resonate beyond the Caribbean reefs, offering insights applicable to global shark conservation. As marine ecosystems worldwide grapple with the combined pressures of overfishing, habitat degradation, and climate change, this research provides a timely reminder of the intricate ecological balances underpinning apex predator survival and the critical need to manage all trophic levels effectively.

Subject of Research: Animals
Article Title: The Need to Manage Prey Fish Biomass to Support Shark Conservation
News Publication Date: 6-May-2026
Web References: DOI: 10.1111/acv.70067
Image Credits: Andy Mann
Keywords: Marine life, Marine biology, Marine ecology, Marine conservation, Marine ecosystems, Marine food webs, Oceans