CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1

Skirving, William; Marsh, Benjamin L; Cour, Jacqueline L. De La; Liu, Gang; Harris, Anthony M.; Maturi, Eileen; Geiger, Erick; Eakin, C. Mark

doi:10.3390/rs12233856

Cited by 108 publications

(147 citation statements)

References 18 publications

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“…The DHW metric is a measure of accumulated heat stress widely used to predict mass coral bleaching caused by anomalous temperatures above typical summertime conditions (Heron et al 2016;Safaie et al 2018;Skirving et al 2019;Sully et al 2019). The remotesensed SST products underpinning the operational NOAA DHW metric have improved stepwise over the last two decades (Wellington et al 2001;Liu et al 2003;Liu et al 2013;Skirving et al 2020), however, there has not yet been a corresponding revision of the HotSpot threshold and accumulation window used in this algorithm. Here, we developed 234 different DHW algorithm variants each with a different HotSpot threshold and accumulation window.…”

Section: Discussionmentioning

confidence: 99%

“…Heat stress metrics were derived from a combination of CoralTemp v3.1 (Skirving et al 2020), a gapfree global 5km daily SST dataset from 1985 until present, and corresponding 5km MMM climatology (Skirving et al 2020). At each spatially referenced survey record, environmental data were extracted from the 5km grid cell encompassing that coordinate.…”

Section: Temperature Datamentioning

confidence: 99%

“…The DHWop value for April 1st is the summation of 1.4 and 2.8⁰C each divided by seven, which is 0.6⁰C-weeks. The 0.5⁰C HotSpot value was not included in the summation as it was below 1⁰C (Skirving et al 2020).…”

Section: Temperature Datamentioning

confidence: 99%

“…Specifically, the Degree Heating Week (DHW) metric is used as an indicator of heat stress levels sufficient to induce coral bleaching. DHW is computed as the accumulation of positive temperature anomalies (HotSpot) above a hypothesised coral bleaching stress temperature (i.e., 1⁰C above the Maximum of Monthly Means SST climatology -MMM) over the previous 12 weeks (Liu et al 2003;Skirving et al 2020). The DHW algorithm was designed in the 1990s, and the HotSpot threshold of 1⁰C above MMM and accumulation window of 12 weeks were chosen based on field and experimental evidence from Panama and the Caribbean (Glynn and D'Croz 1990;Jokiel and Coles 1990).…”

Section: Introductionmentioning

confidence: 99%

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

Lachs

Bythell

East

et al. 2021

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Temperature Datamentioning

confidence: 99%

Section: Temperature Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lachs

Bythell

East

et al. 2021

Preprint

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“…Percent bleaching was calculated as the number of points with bleached cover divided by the total number of points (bleached +healthy). Climatology data for each survey site were obtained from NOAA's Coral Reef Watch Product Suite Version 3.1[32,33] and include the average minimum and maximum sea surface temperatures (SSTs) over…”

mentioning

confidence: 99%

A comparative baseline of coral disease across the central Red Sea

Aeby

Shore

Jensen

et al. 2021

Preprint

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The Red Sea is a unique environment for corals with a strong environmental gradient characterized by temperature extremes and high salinities, but minimal terrestrial runoff or riverine input and their associated pollution. Disease surveys were conducted along 22 reefs in the central Red Sea along the Saudi Arabian coast in October 2015, which coincided with a bleaching event. Our objectives were to 1) document types, prevalence, and distribution of coral diseases in a region with minimal terrestrial input, 2) compare regional differences in diseases and bleaching along a latitudinal gradient of environmental conditions, and 3) use histopathology to characterize disease lesions at the cellular level. Coral reefs of the central Red Sea had a widespread but a surprisingly low prevalence of disease (<0.5%), based on the examination of >75,750 colonies. Twenty diseases were recorded affecting 16 coral taxa and included black band disease, white syndromes, endolithic hypermycosis, skeletal eroding band, growth anomalies and focal bleached patches. The three most common diseases were Acropora white syndrome (59.1% of the survey sites), Porites growth anomalies (40.9%), and Porites white syndrome (31.8%). Over half of the coral genera within transects had lesions and corals from the genera Acropora, Millepora and Lobophyllia were the most commonly affected. Cell-associated microbial aggregates were found in four coral genera resembling patterns found in the Indo-Pacific. Differences in disease prevalence, coral cover, amount of heat stress as measured by degree heating weeks (DHW) and extent of bleaching was evident among sites. Disease prevalence was not explained by coral cover or DHW, and a negative relationship between coral bleaching and disease prevalence was found. The northern-most sites off the coast of Yanbu had the highest average DHW values but absence of bleaching and the highest average disease prevalence was recorded. Our study provides a foundation and baseline data for coral disease prevalence in the Red Sea, which is projected to increase as a consequence of increased frequency and severity of ocean warming.

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Multi‐Factor Coral Disease Risk: A new product for early warning and management

Caldwell,

Liu,

Geiger

et al. 2024

Ecological Applications

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Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near‐real‐time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi‐Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid‐ to high‐disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co‐developed with end‐user groups to improve use and understanding of ecological forecasts. The models provide near‐real‐time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real‐world complexities and, in doing so, better supports near‐term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.

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CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1

Cited by 108 publications

References 18 publications

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

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

A comparative baseline of coral disease across the central Red Sea

Multi‐Factor Coral Disease Risk: A new product for early warning and management

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