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<p>To restore degraded coral reef habitats, it is critical to ensure that the scleractinian broodstock utilized can withstand future heatwaves. However, reef coral resilience is normally assessed only after catastrophic stress events. By tapping into a rich, “molecules-to-satellites” dataset acquired during the Living Ocean Foundation’s research mission to the Republic of Palau, we trained an artificial intelligence to accurately predict pocilloporid coral thermotolerance from relatively cheap, easy-to-measure environmental and ecological survey parameters. Specifically, a neural network featuring 22 predictors, such as coral cover and colony size, could forecast the whereabouts and properties of climate-resilient colonies of <i>Pocillopora acuta</i> with ~ 90% accuracy. This machine-learning model enables practitioners to (1) estimate the climate resilience of local pocilloporid populations and (2) identify habitats characterized by high pocilloporid coral resilience.</p>

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Machine-learning algorithms for identifying climate-resilient corals in the Republic of Palau

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月27日

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  • Anderson B. Mayfield,
  • Alexandra C. Dempsey

摘要

To restore degraded coral reef habitats, it is critical to ensure that the scleractinian broodstock utilized can withstand future heatwaves. However, reef coral resilience is normally assessed only after catastrophic stress events. By tapping into a rich, “molecules-to-satellites” dataset acquired during the Living Ocean Foundation’s research mission to the Republic of Palau, we trained an artificial intelligence to accurately predict pocilloporid coral thermotolerance from relatively cheap, easy-to-measure environmental and ecological survey parameters. Specifically, a neural network featuring 22 predictors, such as coral cover and colony size, could forecast the whereabouts and properties of climate-resilient colonies of Pocillopora acuta with ~ 90% accuracy. This machine-learning model enables practitioners to (1) estimate the climate resilience of local pocilloporid populations and (2) identify habitats characterized by high pocilloporid coral resilience.