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School of Marine and Tropical Biology and the Australian Centre of Excellence for Coral Reef Studies, James Cook University

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    Climate change has emerged as the single greatest threat to coral reefs. The climate change threat will take many forms and includes projections that there will be higher abundances of coral diseases. Links have already been made between high temperatures and outbreaks of the disease ‘white syndrome’ in the Indo-Pacific but little is known about the disease due, in part, to not knowing where outbreaks will occur. We present results of a regression model that suggests the most severe outbreaks of white syndrome observed on the Great Barrier Reef, in late 2002, only occurred at sites that experienced high rates of temperature increase during summer months, rates not seen again in the GBR until 2009. We have produced an image for each summer since and including 2002 that colour-grades and maps white syndrome outbreak likelihood for northern Australia as high or low. The images are based on retrospective calculations of summer rates of temperature increase from high-resolution remotely sensed temperature data. The interactive tool produced from the images is the first like it for coral disease and forms the early warning system within a new coral disease outbreak response plan. The tool will help to target research and monitoring that can improve our understanding of white syndrome outbreaks and determine whether actions can be taken by managers to reduce the susceptibility of corals to such diseases (Maynard et al. in review). The data, presented as images, have no units. Pixels have been coloured red (~1 km resolution) that experienced heating rates at least as great as was experienced at sites where outbreaks of white syndromes occurred in the southern GBR late in 2002. This dataset was developed as part of the MTSRF program. Cite this dataset: Maynard J., Willis B. (2009) Predicting outbreaks of the coral disease white syndrome in northern Australia, eAtlas, https://eatlas.org.au/data/uuid/eaece897-3e9a-47ea-94cb-ee94195dac98