Lachs, Liam, Bythell, John C., East, Holly K., Edwards, Alasdair J., Mumby, Peter J., Skirving, William J., Spady, Blake L., and Guest, James R. (2021) Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching. Remote Sensing, 13 (2677).

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Abstract

Increasingly intense marine heatwaves threaten the persistence of many marine ecosys- tems. 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 accumulat- ing 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 perfor- mance 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.

Item ID:	81014
Item Type:	Article (Research - C1)
ISSN:	2072-4292
Copyright Information:	© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
Date Deposited:	14 Nov 2023 00:41
FoR Codes:	37 EARTH SCIENCES > 3702 Climate change science > 370202 Climatology @ 50% 37 EARTH SCIENCES > 3708 Oceanography > 370803 Physical oceanography @ 50%
SEO Codes:	19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1905 Understanding climate change > 190501 Climate change models @ 50% 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1905 Understanding climate change > 190599 Understanding climate change not elsewhere classified @ 50%
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