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Spying on Stingrays: First-ever Tags Show Elusive Behaviors, Habitats

A whitespotted eagle ray swims with the multi-sensor tag.

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Biologging – an innovative, non-invasive method of tracking animals in the wild – is transforming how scientists study movement, behavior and social interactions. Using compact electronic devices that can remain attached for hours or even months, researchers can now gather detailed data with minimal disruption to the animals’ natural lives.

Although biologging has been widely applied to marine mammals such as turtles and sharks, skates and stingrays (batoids) have been overlooked. This oversight is concerning, as many batoid species are increasingly at risk of extinction yet play critical roles in marine food webs. However, their behavior remains poorly understood – largely because studying them in the wild has been so challenging. For example, one major challenge is their unique body shape: unlike sharks, rays lack a prominent dorsal fin, and some species have ultra-smooth skin, making it difficult to attach tracking devices securely.

Researchers from ӣ����Ƶ’s Harbor Branch Oceanographic Institute are the first to successfully develop and field-test a multi-sensor biologging tag on the elusive whitespotted eagle ray (Aetobatus narinari), a species found in tropical and subtropical coastal waters.

Feeding primarily on hard-shelled prey such as clams and conch, this large, powerful predator can grow more than 2 meters (6.5 feet) in wingspan and weigh several hundred kilograms. Although known for long-distance migrations, they often linger in coastal habitats and lagoons, making them an ideal candidate to test the new biologging technology.

The study findings, published in the journal , demonstrate that the tag’s innovative design enabled it to remain securely attached even in strong currents. This resulted in the longest documented attachment times for external tags on pelagic rays – lasting up to 60 hours. This is the first time a biologging system like this has been successfully used on a stingray species that feeds on hard-shelled prey.

“These animals are powerful, fast-moving and live in dynamic, high-energy environments, which makes tagging them a real challenge,” said Matt Ajemian, Ph.D., senior author, an associate research professor and director of the  (FEC) at ӣ����Ƶ Harbor Branch. “Our goal was to create a system that could be applied in seconds, stay on during natural behaviors, and collect rich, multi-dimensional data. We’re now able to observe not just where these rays go, but how they feed, how they move through their habitats, and how they interact with other species – insights that were virtually impossible to capture before.”

The custom-built tag integrates a motion sensor, video camera, underwater microphone, satellite transmitter and acoustic tracker – all within a compact, lightweight design. These tags are engineered to capture detailed feeding behaviors, with particular attention to interactions involving armored prey.

A key innovation is the fast, minimally-invasive attachment system, which uses silicone suction cups and specially designed straps secured near the ray’s spiracles (the small openings just behind the eyes). The design and implementation of the spiracle straps significantly increased retention and is likely applicable to other similar species of rays.  

“Our work marks a turning point in how we study elusive marine species like pelagic rays,” said Cecilia M. Hampton, corresponding author and a Ph.D. student in the FEC lab at ӣ����Ƶ Harbor Branch. “We’ve shown that complex behaviors – like the crunching of clams – can be identified using sound and movement data alone, even without video. This opens up exciting possibilities for long-term ecological monitoring using simpler, more efficient tags. It’s not just about observing feeding – we’re beginning to map out entire behavioral landscapes, from foraging strategies to social dynamics. These insights are vital for understanding how rays respond to environmental change and how best to protect them.”

To pinpoint key movement signals that predict these behaviors, researchers used a supervised machine learning method called a Random Forest model. They trained the model on data from one tagged ray, with two reviewers first labeling behaviors – “swimming,” “browsing” and “digging” – by reviewing video footage. The model predicted foraging behavior well, which paves the way for more accessible and smaller tags to be used in the future for the same performance. More data from varied locations will help further develop these relationships.

Looking ahead, the researchers say their tagging system could be adapted to other ray species, with slight modifications to account for differences in body size and spiracle shape.

“As biologging technologies advance, combining data streams like movement, sound and video – and applying machine learning for behavior classification – could turn rays into mobile surveyors of ocean health and benthic habitats,” Ajemian said.

Study co-authors are Breanna C. DeGroot, State College of Florida; Lauran Brewster, Ph.D., School for Marine Science & Technology, University of Massachusetts, Dartmouth; Kim Bassos-Hull, Ph.D., Sharks & Rays Conservation Program, Mote Marine Laboratory; Benjamin A. Metzger, ӣ����Ƶ Harbor Branch; and T. Aran Mooney, Biology Department, Woods Hole Oceanographic Institute. 

This work was supported by the National Science Foundation and Harbor Branch Oceanographic Institute Foundation.

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