Stanford GES 205 - Recurrences Coral Mortality Indian Ocean

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Science 289, 1703–1705 (2000).22. Falkowski, P. G. et al. in Coccolithophores: Molecular Processes to Global Impact (eds Thierstein, H. &Young, J.) (Springer, Berlin, in the press).23. Price, N. M. et al. Preparation and chemistry of the artificial algal culture medium Aquil. Biol.Oceanogr. 6, 443–461 (1988/89).24. Ho, T.-Y. et al. The elemental composition of some marine phytoplankton. J. Phycol. (submitted).25. Cullen, J. T., Field, T. S. & Sherrell, R. M. The determination of trace elements in filtered suspendedmarine particulate material by sector field HR-ICP-MS. J. Anal. At. Spectrom. 16, 1307–1312(2001).26. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approachto multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).27. Ihaka, R. & Gentleman, R. R. Language for data analysis and graphics. J. Comp. Graph. Stat. 5, 299–314(1996).Supplementary Information accompanies the paper on www.nature.com/nature.Acknowledgements We thank K. Wyman, C. Fuller, P. Field and R. Sherrell for assisting us withthe elemental analysis, and L. Hedin, R. Sherrell and J. Raven for comments. This work wassupported by the National Science Foundation ‘Evolution and Radiation of EukaryoticPhytoplankton Taxa’ (EREUPT) Biocomplexity Program (Rutgers University) and the Centre forEnvironmental Bioinorganic Chemistry at the Princeton Environmental Institute (PrincetonUniversity).Competing interests statement The authors declare that they have no competing financialinterests.Correspondence and requests for materials should be addressed to A.Q.([email protected]) or P.G.F. ([email protected])...............................................................Predicted recurrences of mass coralmortality in the Indian OceanCharles R. C. SheppardDepartment of Biological Sciences, University of Warwick, Coventry CV4 7AL, UK.............................................................................................................................................................................In 1998, more than 90% of shallow corals were killed on mostIndian Ocean reefs1. High sea surface temperature (SST) was aprimary cause2,3, acting directly or by interacting with otherfactors3–7. Mean SSTs have been forecast to rise above the 1998values in a few decades2,3; however, forecast SSTs rarely flowseamlessly from historical data, or may show erroneous seasonaloscillations, precluding an accurate prediction of when lethalSSTs will recur. Differential acclimation by corals in differentplaces complicates this further3,7,8. Here I scale forecast SSTs at 33Indian Ocean sites where most shallow corals died in 1998 (ref. 1)to identify geographical patterns in the timing of probable repeatoccurrences. Reefs located 10–158 south will be affected every 5years by 2010–2025. North and south from this, dates recede in apattern not directly related to present SSTs; paradoxically, someof the warmest sites may be affected last. Temperatures lethal tocorals vary in this region by 6 8C, and acclimation of a modest2 8C by corals could prolong their survival by nearly 100 years.Timing of recovery from the 1998 massive coral mortality onIndian Ocean reefs (refs 1, 2, 6 and Fig. 1) and how frequently risingSSTs will cause repeat mortalities are issues of practical urgency formany countries because of the high value of reefs to shorelineprotection, biodiversity, protein supply and tourism6,9,10.Raw,modelled SSTs cross supposed thresholds of coral bleaching in afew decades2, but scaling problems in forecast data, coral acclim-ation3,7,8and different absolute SSTs and rates of SST rise varymarkedly between sites, which greatly affects estimates of whenrising temperatures will reach values that proved lethal before. Exactdates remain unattainable, but a probability approach proves veryrevealing in terms of both timing and geographical pattern. Usingthe Indian Ocean, whose reefs were worst affected in the warm SSTsof 1998, I have ‘blended’ forecast SSTs seamlessly onto historical SSTdata, with appropriately scaled forecast seasonal cycles, for 33 sites.Historical SST data from 1871–1999 from the HadISST1 dataset11,12were combined with surface (‘skin’) temperature from 1950–2099 from the HadCM3 model for each site; the latter equates withSST (see http://www.cru.uea.ac.uk/cru/info/modelcc/). Both seriesare monthly; HadISST1 cells are 18 latitude and longitude, whereasthe HadCM3 cells used


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