Assessment of coral restoration's contribution to reef recovery using machine learning

Abstract

Coral restoration emerged globally as a form of life support for coral reefs, awaiting urgent mitigation of anthropogenic pressure. Yet its efficiency is difficult to assess, as ambitious transplantation programs handle hundreds of thousands of fragments, with survival rates inherently time-intensive to monitor. Due to limited available data, the influence of most environmental and methodological factors is still unknown. We therefore propose a new method which leverages machine learning to track each colonys individual health and growth on a large sample size. This is the first time artificial intelligence techniques were used to monitor coral at a colony scale, providing an unprecedented amount of data on coral health and growth. Here we show the influence of genus, depth and initial fragment size, alongside providing an outlook on coral restorations efficiency. We show that among 77,574 fragments, individual survival rate was 31% after 2 years (21% after 4 years), which is much lower than most reported results. In the absence of significant anthropogenic pressure, we showed that there was a depth limit below which Pocillopora fragments outperformed Acropora fragments, while the opposite was true past this threshold. During the mid-2019 heatwave, our research indicates that Pocillopora fragments were 37% more likely to survive than Acropora fragments. Overall, the total amount of live coral steadily increased over time, by more than 3,700 liters a year, as growth compensated for mortality. This supports the use of targeted coral restoration to accelerate reef recovery after mass bleaching events.