Fine-tuning heat stress algorithms to optimise global predictions of mass coral bleaching

Increasingly severe marine heatwaves under climate change threaten the persistence of many marine ecosystems. Mass coral bleaching events, caused by periods of anomalously warm sea surface temperatures (SST), have led to catastrophic levels of coral mortality globally. Remotely monitoring and forecasting such biotic responses to heat stress is key for effective marine ecosystem management. The Degree Heating Week (DHW) metric, designed to monitor coral bleaching risk, reflects the duration and intensity of heat stress events, and is computed by accumulating SST anomalies (HotSpot) relative to a stress threshold over a 12-week moving window. Despite significant improvements in the underlying SST datasets, corresponding revisions of the HotSpot threshold and accumulation window are still lacking. Here, we fine-tune the operational DHW algorithm to optimise coral bleaching predictions using the 5km satellite-based SSTs (CoralTemp v3.1) and a global coral bleaching dataset (37,871 observations, National Oceanic and Atmospheric Administration). After developing 234 test DHW algorithms with different combinations of HotSpot threshold and accumulation window, we compared their bleaching-prediction ability using spatiotemporal Bayesian hierarchical models and sensitivity-specificity analyses. Peak DHW performance was reached using HotSpot thresholds less than or equal to Maximum Monthly Mean SST and accumulation windows of 4 - 8 weeks. This new configuration correctly predicted up to an additional 310 bleaching observations compared to the operational DHW algorithm, an improved hit rate of 7.9 %. Given the detrimental impacts of marine heatwaves across ecosystems, heat stress algorithms could also be fine-tuned for other biological systems, improving scientific accuracy, and enabling ecosystem governance.