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<p>Accurate, site-specific prediction of coral bleaching is critical for managing reef resilience under accelerating climate change. Conventional thermal stress metrics such as Degree Heating Weeks (DHW) provide valuable global alerts but often misrepresent local bleaching risk in ecologically heterogeneous reef systems. Here, we develop and validate a site-adaptive Bayesian hierarchical framework that integrates NOAA Coral Reef Watch thermal indices (DHW, HotSpot, Maximum Monthly Mean anomaly, rate of onset, and consecutive hot days) with ecological and oceanographic modulators including turbidity (Kd490), wind speed, and depth to predict both bleaching probability and severity across 10,571 reef observations (1994–2022) in the Coral Triangle and South China Sea. The hierarchical model, implemented in PyMC using the No-U-Turn Sampler, improved predictive performance substantially (AUC = 0.87; ordinal accuracy = 74%) compared with fixed-threshold DHW approaches (AUC = 0.72; accuracy = 61%). Posterior site-level offsets from the canonical DHW = 4&#xa0;°C-weeks ranged from −1.8 to + 2.3&#xa0;°C-weeks, revealing systematic ecological modulation of bleaching tolerance. Turbidity consistently reduced bleaching odds (<i>β</i> = −0.39), and its interaction with DHW (<i>β</i> = −0.27) indicated intensified buffering under extreme heat stress. This study represents the first regional application of a site-adaptive Bayesian hierarchical framework for coral bleaching prediction. By translating global satellite-derived thermal stress indices into reef-specific risk forecasts, it provides a robust, probabilistic foundation for climate-smart coral reef management and adaptive early-warning systems across the Indo-Pacific.</p>

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A site-adaptive Bayesian hierarchical framework for predicting coral bleaching across the coral triangle and South China Sea

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Idham Khalil,
  • Mohammad Shawkat Hossain,
  • Rudiyanto,
  • Nurul Hidayah Mat Zaki,
  • Aidy M. Muslim

摘要

Accurate, site-specific prediction of coral bleaching is critical for managing reef resilience under accelerating climate change. Conventional thermal stress metrics such as Degree Heating Weeks (DHW) provide valuable global alerts but often misrepresent local bleaching risk in ecologically heterogeneous reef systems. Here, we develop and validate a site-adaptive Bayesian hierarchical framework that integrates NOAA Coral Reef Watch thermal indices (DHW, HotSpot, Maximum Monthly Mean anomaly, rate of onset, and consecutive hot days) with ecological and oceanographic modulators including turbidity (Kd490), wind speed, and depth to predict both bleaching probability and severity across 10,571 reef observations (1994–2022) in the Coral Triangle and South China Sea. The hierarchical model, implemented in PyMC using the No-U-Turn Sampler, improved predictive performance substantially (AUC = 0.87; ordinal accuracy = 74%) compared with fixed-threshold DHW approaches (AUC = 0.72; accuracy = 61%). Posterior site-level offsets from the canonical DHW = 4 °C-weeks ranged from −1.8 to + 2.3 °C-weeks, revealing systematic ecological modulation of bleaching tolerance. Turbidity consistently reduced bleaching odds (β = −0.39), and its interaction with DHW (β = −0.27) indicated intensified buffering under extreme heat stress. This study represents the first regional application of a site-adaptive Bayesian hierarchical framework for coral bleaching prediction. By translating global satellite-derived thermal stress indices into reef-specific risk forecasts, it provides a robust, probabilistic foundation for climate-smart coral reef management and adaptive early-warning systems across the Indo-Pacific.