Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • BCO-DMO Processing Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Project Information
• Funding

Updates are planned for this dataset to extend the time range into 2027 as part of this project.

Day trips aboard a small fieldwork boat in Harrington Sound, Bermuda, from the Bermuda Aquarium Museum and Zoo dock, during October, 2022 and 2023 (depth 0-20 m).

Whitespotted eagle rays (Aetobatus narinari, LSID urn:lsid:marinespecies.org:taxname:217426) were spotted visually from a moving vessel and collected via a knotted seine net by encircling the animal and entangling it. Rays were brought aboard for measurements, photographs and tagging (Passive Integrated Transponder and CATS-CAM) and kept in a small plastic pool with ambient seawater. Once procedures were complete, rays were released on site, or brought to the Bermuda Aquarium for further examination. Techniques described in the following Ajemian et al. (2012) and Bassos-Hull et al. (2014).  

Version 1 (version date 2025-06-10):
* adjusted parameter names to comply with database requirements (no spaces)
* added pictures as supplemental file to the dataset
* converted dates to ISO format

Version 2:
* Dataset updated with additional data from 2023.
* Metadata for 2023 ray photos added to data table 964473_v1_rays.csv to become attached Data File 964473_v2_rays.csv
* photo zip file "Ray Photos (2022).zip" renamed "Ray_Photos_2022.zip" No changes to the contents of this zip compared to the version 1 of the dataset.
* Additional 2023 photos added to newly attached file Ray_Photos_2023.zip (.DS_Store) files removed before zipping the photos.

NSF Award Abstract:
Shellfish (mollusks, crustaceans, etc.) are facing unprecedented pressures under global climate change, which is threatening the variety of ecosystem services these animals provide to coastal communities. While much research has been dedicated to understanding how changing ocean conditions can influence shellfish development, far less has explored the potential impacts from increasing populations of large, shell-crushing predators (i.e., rays, turtles, etc.) that are experiencing poleward expansions of their ranges. This knowledge gap is likely due to the challenges of working with these mobile species, which require novel technology to track their dynamic distribution and thus foraging effects on shellfish communities. This project will build fundamental knowledge on marine habitats susceptible to predation from large mobile predators in order to ensure a sustainable future for shellfish species. Further, the work will provide guidance to costly shellfish restoration programs that are otherwise “flying blind” with respect to predation risk. The project will have local, regional, and global educational dimensions. Firstly, this project will strengthen FAU’s graduate programs by supporting a graduate student and providing a platform for the PI to develop a new graduate course, which will be offered and evaluated twice throughout the award period. Additionally, numerous undergraduate summer interns and middle-high school students will be recruited to interact with the PI via immersive, hands-on field excursions. Lastly, the fascination of the general public and students with these charismatic animals and the project’s tangible technological components will facilitate developing an interactive “Audio Waves” exhibit at a local outreach center, which will be evaluated several times during the project and slated for permanent display.

Our scientific understanding of the ecological role of large mobile durophages (i.e., shell-crushing predators) is limited due to challenges presented by the elusive nature of these species. These shortcomings hinder our scientific understanding of their role in benthic community dynamics. Filling such knowledge gaps requires novel approaches that can detect and classify predator-prey interactions in situ. Using multiple large predator models (rays, sea turtles, fish, and crabs), the project will: 1) capture and characterize predator feeding (shell-crushing) sounds and shell fragmentation patterns, 2) understand in situ detection constraints of the predation signal within the context of natural underwater noise using simulations, and 3) quantify the distribution of predator foraging impacts across two model seascapes in Bermuda and Florida via integration of habitat- and individual-based (animal tags) passive acoustics. Detection and classification (by both predator and prey) will be completed using novel application of machine-learning techniques, which will be used to automate predation event extraction from extensive data archives. Recording equipment will be strategically distributed across seascapes to permit a multi-scale understanding of durophagy and testing of theoretical models of predation (e.g., optimal/central place foraging). Long-term monitoring will also provide an opportunity to assess the role of environmental/oceanographic variables in driving these interactions. Consequently, this work will fill a large knowledge gap in the dynamics of marine food webs.