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Article

Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching

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School of Natural & Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne NE1 7RU, UK
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Marine Spatial Ecology Lab, School of Biological Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
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Palau International Coral Reef Center, Koror 96940, Palau
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Coral Reef Watch, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA
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ReefSense Pty, Ltd., P.O. Box 343, Aitkenvale BC, QLD 4814, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Stuart Phinn
Remote Sens. 2021, 13(14), 2677; https://doi.org/10.3390/rs13142677
Received: 1 June 2021 / Revised: 2 July 2021 / Accepted: 5 July 2021 / Published: 7 July 2021
(This article belongs to the Section Coral Reefs Remote Sensing)
Increasingly intense marine heatwaves threaten the persistence of many marine ecosystems. Heat stress-mediated episodes of mass coral bleaching have led to catastrophic 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 5 km 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 the 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 the maximum of monthly means SST climatology (MMM) and accumulation windows of 4–8 weeks. This new configuration correctly predicted up to an additional 310 bleaching observations globally 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. View Full-Text
Keywords: marine heatwaves; sea surface temperature; mass coral bleaching; algorithm optimisation; spatiotemporal Bayesian modelling; R-INLA marine heatwaves; sea surface temperature; mass coral bleaching; algorithm optimisation; spatiotemporal Bayesian modelling; R-INLA
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MDPI and ACS Style

Lachs, L.; Bythell, J.C.; East, H.K.; Edwards, A.J.; Mumby, P.J.; Skirving, W.J.; Spady, B.L.; Guest, J.R. Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching. Remote Sens. 2021, 13, 2677. https://doi.org/10.3390/rs13142677

AMA Style

Lachs L, Bythell JC, East HK, Edwards AJ, Mumby PJ, Skirving WJ, Spady BL, Guest JR. Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching. Remote Sensing. 2021; 13(14):2677. https://doi.org/10.3390/rs13142677

Chicago/Turabian Style

Lachs, Liam, John C. Bythell, Holly K. East, Alasdair J. Edwards, Peter J. Mumby, William J. Skirving, Blake L. Spady, and James R. Guest. 2021. "Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching" Remote Sensing 13, no. 14: 2677. https://doi.org/10.3390/rs13142677

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