Artificial Intelligence in Conservation Research
Science depends on observations, and much of what we have discovered about species has simply been through monitoring. Scientists dedicate countless hours toward observation in an effort to discover intricacies of a species’ behavior, life history, populations, and health. Though essential, this endeavor often entails prolonged periods of surveillance, where noteworthy instances occur in fleeting moments of countless hours spent observation.
Scientists at FWC’s Fish and Wildlife Research Institute are actively integrating artificial intelligence into the observation and recording process. Tools that allow scientists to spend less time manually scanning footage, images, or sound files are available and ready to use. AI can help scientists identify species, conduct population assessments, understand reproduction and behavior, and more through video, photo, and audio samples. The AI used in this new project is not the same kind publicized in the news; it is not generative in the way some AI tools create entirely new images and content. Rather, conservation AI tools focus on object detection and pattern recognition to aid scientific research. Specifically, researchers train models to look for certain patterns in images, videos, or sound files by providing sample images of target criteria.
Scientists, in particular marine biologists, have spent decades sifting through vast repositories of video, sound, and image data to identify target species and behaviors. As they sort, they assign labels to highlight important events and record different species. AI can use these labels to identify similar patterns across expansive, unlabeled datasets. While scientists will retain responsibility for their work to promote conservation, AI will be able to facilitate. FWRI’s Dr. David Kochan, Dr. Lucas McEachron, Nick Alcaraz, and Lauren Gentry have dedicated years to these efforts. “We have the technology, now we just need to help people use it,” Dr. McEachron says.
One place this technology has been successfully implemented is the Florida Aquarium, as a monitor for coral species. In a concerted conservation effort to combat stony coral tissue loss disease, researchers collected various species of coral from the Florida Reef Tract and now house them in aquaria. Many of these species have never been in aquaria before, and we now have the opportunity to research them through intense observation. This new collection requires near constant attention to appropriately monitor the corals’ activity. Rather than continue with human observation, which is primarily reliant on volunteers, the aquarium has implemented a camera to observe corals. The camera streams video of the corals and records anomaly events, such as movement in the tank or other activity of the corals. Importantly, the camera can detect spawning events, a notoriously challenging event to capture and observe, and alert scientists to the activity. The team is advancing this program through work to engineer an in-situ monitoring system to observe corals in the wild. The system would allow for more real-time coral and reef habitat research without the limitations of current scuba based monitoring or traditional camera traps. This program’s success is a model for the transformative potential of Artificial Intelligence in other areas of conservation science.
Our Information Science and Management team is developing a gateway, or collaborative space, in conjunction with the Southeast Coastal Ocean Observing Association (SECOORA) where different teams can share the AI tools they have implemented. The daunting task of coding and designing models to fit specific projects often dissuades research teams from embracing AI, but many AI applications are transferable. For example, a model used to detect bird sounds can be modified to detect boat sounds, and the AI gateway allows users to identify similar applications and modify as appropriate. Establishing a dedicated collaborative platform will utilize widespread use of expertise and resources through crowdsourcing, expediting the development and deployment of AI tools tailored to specific conservation challenges.
Dr. McEachron says that research teams from across the country will be able to share worked examples from previous and ongoing projects, labeled and unlabeled videos, sound, and images for model training and testing; data standards; and more. The gateway provides a shared space to store and learn from other conservation AI applications and promotes a culture of interdisciplinary collaboration. The gateways is currently undergoing beta testing and is slated for a full launch later this year. Please visit secoora.org for updates and access to many other related ocean observing tools.