Marine researchers in Florida have developed a novel approach to tackle the decline of coral reefs by using underwater surveillance technology. This innovative system, akin to a doorbell, has helped scientists identify fish species that are detrimental to coral restoration efforts.
The study revealed that three types of fish—redband parrotfish, foureye butterflyfish, and stoplight parrotfish—were responsible for consuming over 97% of the coral used as bait in experiments conducted near Miami. These insights are crucial for guiding coral reef restoration, especially after a dramatic 90% reduction in Florida's coral cover since the 1970s, largely due to climate change and unprecedented ocean temperatures in recent years.
Diego Lirman, a leading figure in coral restoration and an associate professor at the University of Miami, emphasized the importance of understanding which fish species prey on newly planted corals. This knowledge allows conservationists to choose reef sites with fewer of these fish and select appropriate coral species for planting.
The research team, supported by the Fish & Wildlife Foundation of Florida, created several recording devices using GoPro cameras in waterproof casings mounted on PVC frames. After initial challenges, such as overheating batteries and water leaks, the devices were successfully anchored to the seabed at Paradise Reef near Key Biscayne.
These coral-baited remote underwater video systems, or C-Bruvs, captured time-lapse footage over several weeks. Despite some setbacks, including the theft of one device, the project was deemed successful. Analysis showed that redband parrotfish were the most aggressive, responsible for 56.3% of coral bites, followed by foureye butterflyfish at 36.9%, and stoplight parrotfish at 4%.
The findings, presented by UM marine scientist Erin Weisman at a conservation symposium, highlight the potential to reduce predation by selecting optimal sites and coral species before large-scale restoration efforts.
Looking ahead, Lirman suggested that incorporating artificial intelligence could streamline the analysis of video footage, which is currently labor-intensive. AI could potentially automate the identification of fish and their behaviors, enhancing the efficiency of future research.