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August 1, 2025Global Ecology and BiogeographyOpen Access

Moving Beyond Temperature Metrics in Coral Bleaching Prediction Using Interpretable Machine Learning

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Authors

MCMandy W. M. CheungThe University of QueenslandMCMilani ChaloupkaThe University of QueenslandKHKarlo HockThe University of Queensland

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Overview

Interpretable machine learning predicts coral bleaching in response to multiple environmental drivers, indicating complexity in risk assessment.

Key Points

  • The model predicted coral bleaching intensities with 80% accuracy, showing the influence of multiple environmental factors.
  • Accumulated heat stress was the strongest predictor, but interactions with water flow and light also affected bleaching outcomes.
  • A spatially cross-validated ordinal random forest model was applied across three events on the Great Barrier Reef to analyze bleaching responses.
  • Integrating environmental heterogeneity into predictive models supports reef conservation and adaptation during climate change.

Cite This Study

Cheung et al. (2025) studied this question.

synapsesocial.com/papers/68af431bad7bf08b1ead1a87https://doi.org/10.1111/geb.70105
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