{"id":324,"date":"2025-07-23T13:47:48","date_gmt":"2025-07-23T13:47:48","guid":{"rendered":"https:\/\/scienceblog.com\/wildscience\/?p=324"},"modified":"2025-07-23T13:47:48","modified_gmt":"2025-07-23T13:47:48","slug":"tiny-tags-unlock-hidden-lives-of-elusive-stingrays","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/wildscience\/2025\/07\/23\/tiny-tags-unlock-hidden-lives-of-elusive-stingrays\/","title":{"rendered":"Tiny Tags Unlock Hidden Lives of Elusive Stingrays"},"content":{"rendered":"

Biologging has cracked open a new window into the hidden world of stingrays. In a pioneering study, researchers at Florida Atlantic University have successfully deployed the first multi-sensor tags on whitespotted eagle rays (Aetobatus narinari), revealing their feeding strategies, habitat use, and behavioral patterns in unprecedented detail. These findings are crucial for understanding the ecological roles of these elusive, shell-crushing predators\u2014and for protecting them in a changing ocean.<\/p>\n

Cracking Clams and Breaking Barriers<\/h2>\n

Unlike sharks or turtles, stingrays have been largely left out of biologging research. Their smooth skin and lack of dorsal fins make attaching equipment notoriously difficult. But FAU\u2019s custom tag\u2014equipped with a video camera, motion sensors, microphone, satellite tracker, and acoustic beacon\u2014solves that problem with a clever attachment system using suction cups and soft spiracle straps.<\/p>\n

The tags remained secure even in strong currents, with some lasting up to 60 hours\u2014the longest known retention for external tags on pelagic rays. And what they captured was astonishing.<\/p>\n

From Buried Clams to Behavioral Maps<\/h2>\n

In one deployment, a wild ray was recorded digging into sediment, crunching armored prey, and swimming through multiple habitats, including reefs and seagrass meadows. These behaviors were detected not just by video, but by motion and sound\u2014feeding events left distinctive spikes in acceleration data and audio signatures as the ray crushed clam shells.<\/p>\n

Using machine learning, the team trained a model to recognize behaviors like \u201cswimming,\u201d \u201cbrowsing,\u201d and \u201cdigging\u201d based on sensor data alone. The model achieved an overall accuracy of 80.6%, suggesting simpler tags may eventually detect key behaviors without video.<\/p>\n

Notable Findings<\/h2>\n