Temperature patterns and mechanisms influencing coral bleaching during the 2016 El Niño

Article metrics

Abstract

Under extreme heat stress, corals expel their symbiotic algae and colour (that is, ‘bleaching’), which often leads to widespread mortality. Predicting the large-scale environmental conditions that reinforce or mitigate coral bleaching remains unresolved and limits strategic conservation actions1,2. Here we assessed coral bleaching at 226 sites and 26 environmental variables that represent different mechanisms of stress responses from East Africa to Fiji through a coordinated effort to evaluate the coral response to the 2014–2016 El Niño/Southern Oscillation thermal anomaly. We applied common time-series methods to study the temporal patterning of acute thermal stress and evaluated the effectiveness of conventional and new sea surface temperature metrics and mechanisms in predicting bleaching severity. The best models indicated the importance of peak hot temperatures, the duration of cool temperatures and temperature bimodality, which explained ~50% of the variance, compared to the common degree-heating week temperature index that explained only 9%. Our findings suggest that the threshold concept as a mechanism to explain bleaching alone was not as powerful as the multidimensional interactions of stresses, which include the duration and temporal patterning of hot and cold temperature extremes relative to average local conditions.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Indo-Pacific scale and severity of coral bleaching during the 2016 El Niño/Southern Oscillation event.
Fig. 2: Effect of environmental variables on coral bleaching.

Data availability

Data are available at the Knowledge Network for Biocomplexity (https://knb.ecoinformatics.org) via https://doi.org/10.5063/F1WQ024C.

Code availability

R code is available on GitHub: https://github.com/WCS-Marine/2016-bleaching-patterns

References

  1. 1.

    Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).

  2. 2.

    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).

  3. 3.

    Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: a case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).

  4. 4.

    McClanahan, T. R. et al. Western Indian Ocean coral communities: bleaching responses and susceptibility to extinction. Mar. Ecol. Prog. Ser. 337, 1–13 (2007).

  5. 5.

    Littman, R., Willis, B. L. & Bourne, D. G. Metagenomic analysis of the coral holobiont during a natural bleaching event on the Great Barrier Reef. Environ. Microbiol. Rep. 3, 651–660 (2011).

  6. 6.

    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).

  7. 7.

    Ainsworth, T. D. A. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338–342 (2016).

  8. 8.

    Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Change Biol. 20, 3823–3833 (2014).

  9. 9.

    McClanahan, T. R., Weil, E., Cortes, J., Baird, A. H. & Ateweberhan, M. in Coral Bleaching (eds van Oppen, M. J. H. & Lough, J. M.) 121–138 (Ecological Studies Vol. 205, Springer, 2009).

  10. 10.

    Frieler, K. et al. Limiting global warming to 2 °C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2013).

  11. 11.

    Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).

  12. 12.

    Kleypas, J. A., Danabasoglu, G. & Lough, J. M. Potential role of the ocean thermostat in determining regional differences in coral reef bleaching events. Geophys. Res. Lett. 35, L03613 (2008).

  13. 13.

    Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).

  14. 14.

    Brainard, R. E. et al. Ecological impacts of the 2015/16 El Niño in the central equatorial Pacific. Bull. Am. Mineral. Soc. 99, S21–S26 (2018).

  15. 15.

    Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, e33353 (2012).

  16. 16.

    McClanahan, T. R. Changes in coral sensitivity to thermal anomalies. Mar. Ecol. Prog. Ser. 570, 71–85 (2017).

  17. 17.

    Heron, S. F. et al. Validation of reef-scale thermal stress satellite products for coral bleaching monitoring. Remote Sens. 8, 59 (2016).

  18. 18.

    Kayanne, H. Validation of degree heating weeks as a coral bleaching index in the northwestern Pacific. Coral Reefs 36, 63–70 (2017).

  19. 19.

    Maina, J., McClanahan, T. R., Venus, V., Ateweberhan, M. & Madin, J. Global gradients of coral exposure to environmental stresses and implications for local management. PLoS ONE 6, e23064 (2011).

  20. 20.

    Beyer, H. L. et al. Risk‐sensitive planning for conserving coral reefs under rapid climate change. Conserv. Lett. 11, e12587 (2018).

  21. 21.

    Edmunds, P. J. et al. Persistence and change in community composition of reef corals through present, past, and future climates. PLoS ONE 9, e107525 (2014).

  22. 22.

    Zhang, N., Feng, M., Hendon, H. H., Hobday, A. J. & Zinke, J. Opposite polarities of ENSO drive distinct patterns of coral bleaching potentials in the southeast Indian Ocean. Sci. Rep. 7, 2443 (2017).

  23. 23.

    Skirving, W. J. et al. The relentless march of mass coral bleaching: a global perspective of changing heat stress. Coral Reefs 38, 547–557 (2019).

  24. 24.

    Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933 (2003).

  25. 25.

    Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999).

  26. 26.

    Thompson, D. M. & van Woesik, R. Corals escape bleaching in regions that recently and historically experienced frequent thermal stress. Proc. R. Soc. B 276, 2893–2901 (2009).

  27. 27.

    West, J. M. & Salm, R. V. Resistance and resilience to coral bleaching: implications for coral reef conservation and management. Conserv. Biol. 17, 956–967 (2003).

  28. 28.

    McClanahan, T. R., Muthiga, N. A. & Mangi, S. Coral and algal changes after the 1998 coral bleaching: interaction with reef management and herbivores on Kenyan reefs. Coral Reefs 19, 380–391 (2001).

  29. 29.

    Graham, N. A., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).

  30. 30.

    McClanahan, T. R. & Maina, J. Response of coral assemblages to the interaction between natural temperature variation and rare warm-water events. Ecosystems 6, 551–563 (2003).

  31. 31.

    Diaz-Pulido, G. & McCook, L. J. The fate of bleached corals: patterns and dynamics of algal recruitment. Mar. Ecol. Prog. Ser. 232, 115–128 (2002).

  32. 32.

    Veron, J. Corals of the World Vols 1–3 (Australian Institute of Marine Science, 2000).

  33. 33.

    NOAA Coral Reef Watch (NOAA Satellite and Information Service, accessed 20 January 2017); http://coralreefwatch.noaa.gov/satellite/hdf/index.php

  34. 34.

    Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh-Gulberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Glob. Change Biol. 11, 2251–2265 (2005).

  35. 35.

    Giorgino, T. Computing and visualizing dynamic time warping alignments in R: The dtw package. J. Stat. Softw. 31, 1–24 (2009).

  36. 36.

    Bond, N. Hydrostats: Hydrologic indices for daily time series data. R package version 0.2.7 (2019); https://github.com/nickbond/hydrostats

  37. 37.

    Freeman, J. B. & Dale, R. Assessing bimodality to detect the presence of a dual cognitive process. Behav. Res. Methods 45, 83–97 (2013).

  38. 38.

    Deevi, S. modes: Find the modes and assess the modality of complex and mixture distributions, especially with big datasets. R package version 0.7.0 (2016); https://CRAN.R-project.org/package=modes

  39. 39.

    Graham, M. H. Confronting multicollinearity in ecological multilple regression. Ecology 84, 2809–2815 (2003).

  40. 40.

    Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs at Risk Revisited (World Resources Institute, 2011).

  41. 41.

    Hijmans, R. K., Phillips, S., Leathwick, J. & Elith, J. dismo: Species distribution modeling. R package version 1.1-4 (2017); https://CRAN.R-project.org/package=dismo

  42. 42.

    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

  43. 43.

    Zimprich, D. Modeling change in skewed variables using mixed beta regression models. Res. Hum. Dev. 7, 9–26 (2010).

  44. 44.

    Skaug, H., Fournier, D., Nielsen, A., Magnusson, A. & Bolker, B. glmmADMB: Generalized linear mixed models using AD model builder. R package version 0.7 7 (2019); https://rdrr.io/rforge/glmmADMB/

  45. 45.

    Gelman, A. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27, 2865–2873 (2008).

  46. 46.

    Burnham, K. P. and Anderson, D. R. Model Selection and Inference: A Practical Information–Theoretic Approach 2nd edn (Springer, 2002).

  47. 47.

    Long, J. A. jtools: Analysis and presentation of social scientific data. R package version 0.9.0 (2017); https://mran.microsoft.com/snapshot/2017-12-11/web/packages/jtools/index.html

  48. 48.

    Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. Biometrics 65, 992–993 (2009).

  49. 49.

    Mantel, N. Ranking procedures for arbitrarily restricted observation. Int. Biom. Soc. 23, 65–78 (1967).

  50. 50.

    Legendre, P. & Fortin, M. J. Spatial pattern and ecological analysis. Vegetatio 80, 107–138 (1989).

  51. 51.

    Bjørnstad, O. N. ncf: Spatial nonparametric covariance functions. R package version 1.1–5 (2013); https://CRAN.R-project.org/package=ncf

  52. 52.

    Canty, A. & Ripley, B. boot: Bootstrap R (S-plus) functions. R package version 1.3-22 (2019); https://cran.r-project.org/web/packages/boot/boot.pdf

  53. 53.

    R Core Team R : A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018); www.R-project.org/

Download references

Acknowledgements

T.R.M. and N.A.M. received support from the John D. and Catherine T. MacArthur Foundation and the Sustainable Poverty Alleviation from Coastal Ecosystem Services (SPACES) NE-K010484-1 project. E.S.D. was supported by a Banting Postdoctoral Fellowship from the Natural Sciences and Engineering Research Council of Canada and the John D. and Catherine T. MacArthur Foundation. Data collection in the Solomon Islands was supported by the Wallace Research Foundation, and the Waitt Foundation supported data collection in Fiji. Maldives data collection was supported by IUCN and USAID. M.M.M.G. received support from the French National Research Agency under the STORISK project (no. ANR-15-CE03-0003). Data collection in Zanzibar was partly supported by the NORHED project coordinated by the State University of Zanzibar (SUZA). The Tiffany & Co. Foundation and the John D. and Catherine T. MacArthur Foundation supported data collection in some Western Indian Ocean reefs. V.J.P. received support from the DST-INSPIRE Faculty Programme, and Z. Tyabji and S. Chandrasekhar assisted V.J.P. with data collection in the Andaman Islands. R.A. received funding support from the Pew Marine Fellowship and an Intramural Project from the Spanish National Research Council (CSIC-201330E062). S.A.K. was funded by the VILLUM Foundation (no. 10114). A.T.H. received funding from the Carnegie Corporation of New York. Indonesia data collection was supported by the John D. and Catherine T. MacArthur Foundation and Margaret A. Cargill Philanthropies. We thank the following people for assistance with data collection: A. Baird, A. Halford, R. J.-L. Komeno, C. Miternique, A. dan Muhidin, E. Muttaqin, E. Montocchio, C. Muhando, S. Pardede, N. Summers and S. Yadav.

Author information

T.R.M., E.S.D. and J.M.M conceived the study with the support of all the authors. T.R.M led the manuscript writing, with the help of E.S.D. and J.M.M. E.S.D., J.M.M., S.D. and T.R.M. conducted all the analyses. All the other authors contributed data, edited and approved the manuscript.

Correspondence to Tim R. McClanahan.

Additional information

Peer review information Nature Climate Change thanks Mathieu Pernice and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Tables 1–3.

Reporting summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark