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Predicting climate-driven regime shifts versus rebound potential in coral reefs

Nature volume 518, pages 94–97 (2015)Cite this article

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Abstract

Climate-induced coral bleaching is among the greatest current threats to coral reefs, causing widespread loss of live coral cover1. Conditions under which reefs bounce back from bleaching events or shift from coral to algal dominance are unknown, making it difficult to predict and plan for differing reef responses under climate change2. Here we document and predict long-term reef responses to a major climate-induced coral bleaching event that caused unprecedented region-wide mortality of Indo-Pacific corals. Following loss of >90% live coral cover, 12 of 21 reefs recovered towards pre-disturbance live coral states, while nine reefs underwent regime shifts to fleshy macroalgae. Functional diversity of associated reef fish communities shifted substantially following bleaching, returning towards pre-disturbance structure on recovering reefs, while becoming progressively altered on regime shifting reefs. We identified threshold values for a range of factors that accurately predicted ecosystem response to the bleaching event. Recovery was favoured when reefs were structurally complex and in deeper water, when density of juvenile corals and herbivorous fishes was relatively high and when nutrient loads were low. Whether reefs were inside no-take marine reserves had no bearing on ecosystem trajectory. Although conditions governing regime shift or recovery dynamics were diverse, pre-disturbance quantification of simple factors such as structural complexity and water depth accurately predicted ecosystem trajectories. These findings foreshadow the likely divergent but predictable outcomes for reef ecosystems in response to climate change, thus guiding improved management and adaptation.

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Figure 1: Recovery and regime shift dynamics on Seychelles coral reefs.
Figure 2: Trajectories in the functional structure of fish assemblages.
Figure 3: Bayesian hierarchical logistic regression of predictor variables for the probability of a regime shift.
Figure 4: Contour biplot of the probability of regime shift (red shading) or recovery (blue shading) based on initial structural complexity and water depth.

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Acknowledgements

This research was principally supported by the Australian Research Council (DP1094932, DE130101705), the Leverhulme Trust (F/00 125/M), and the Western Indian Ocean Marine Science Association. The Natural Environment Research Council (GR3/1154) funded work in Fiji. We thank the Seychelles Fishing Authority, Seychelles Marine Parks Authority, Nature Seychelles, and Seychelles National Meteorological Services for technical and logistical assistance. Many thanks to N. Polunin for support early in the project, to N. Cariglia for collecting the sea urchin data, to K. Chong-Seng for collecting the juvenile coral data, to C. Huchery for helping develop the wave exposure model, to J. Turner for photos a and b in Extended Data Fig. 1, and T. McClanahan and N. Dulvy for sharing data used in Extended Data Table 3 and Extended Data Fig. 5. J. Cinner, C. Hicks, K. Nash, and three anonymous referees provided useful comments on the manuscript.

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Authors and Affiliations

  1. Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, 4811, Queensland, Australia

    Nicholas A. J. Graham, M. Aaron MacNeil & David Mouillot

  2. Centre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft NR33 OHT, UK,

    Simon Jennings

  3. School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK,

    Simon Jennings

  4. Australian Institute of Marine Science, PMB 3 Townsville MC, Townsville, 4810, Queensland, Australia

    M. Aaron MacNeil

  5. ECOSYM, UMR CNRS-UM2 5119, Université Montpellier 2, 34095 Montpellier Cedex, France,

    David Mouillot

  6. Department of Parks and Wildlife, Kensington, Perth, 6151, Western Australia, Australia

    Shaun K. Wilson

  7. School of Plant Biology, Oceans Institute, University of Western Australia, Crawley, 6009, Western Australia, Australia

    Shaun K. Wilson

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  1. Nicholas A. J. Graham
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  4. David Mouillot
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Contributions

N.A.J.G. conceived of the study with S.K.W. and M.A.M.; N.A.J.G. S.J., and S.K.W. collected the data; N.A.J.G., M.A.M., and D.M. developed and implemented the analyses; N.A.J.G. led the manuscript with S.J., M.A.M., D.M., and S.K.W.

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Correspondence to Nicholas A. J. Graham.

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Extended data figures and tables

Extended Data Figure 1 Changing condition of Seychelles coral reefs.

a, b, Coral reefs of the inner Seychelles were typified by high coral cover and low macroalgal cover in 1994. c, d, The 1998 coral bleaching event caused widespread coral loss, but some reefs maintained their structural complexity (c), while others collapsed (d) by 2005. e, f, In 2011, many reefs had recovered to high live coral cover (e), while others had undergone a regime shift to abundant macroalgal cover (f).

Extended Data Figure 2 Principal components analysis of benthic composition on 21 reefs across the inner Seychelles 1994–2011.

Reefs coloured blue are tracking back to pre-disturbance benthic composition in 2005–2011 following the 1998 bleaching event, whereas reefs coloured red are shifting to alternate benthic compositions, dominated by macroalgae (n = 84).

Extended Data Figure 3 Distance from pre-disturbance benthic community composition.

Euclidian distance in multivariate space, plotted against percent cover of dominant biotic benthic organisms (live coral in blue, macroalgae in red) (n = 63). a, 2005 data. b, 2008 data. c, 2011 data. Shading represents 95% confidence bounds for the mean trend lines of each habitat type.

Extended Data Figure 4 Changing coral and macroalgal cover is relation to pre-disturbance values.

a, Data for recovering reefs, where the change in coral cover compared to 1994 was reducing through time, whereas change in macroalgae remained stable (n = 42). b, Data for regime shifting reefs where the decline in coral cover persisted through time, and changes in macroalgae increased through time (n = 42).

Extended Data Figure 5 Comparison of effect size posterior density distributions for depth and initial structural complexity in predicting coral versus macroalgae outcomes post disturbance in Seychelles versus 6 other countries across the Indo-Pacific.

a, Depth effect size plot, dark blue posterior distribution for Seychelles, grey for other countries (n = 51). b, Initial structural complexity effect size plot, dark blue distribution for Seychelles, green for other countries (n = 14). Depth and structural complexity variables were standardized in both analyses before estimation and all posterior distributions have more than 95% of their density below zero.

Extended Data Figure 6 Collinearity matrix of the eleven predictor variables.

Extended Data Figure 7 Map of study sites around the inner Seychelles.

Sites in blue are recovering from the 1998 mass bleaching event, whereas sites in red have undergone a regime shift to macroalgal cover.

Extended Data Table 1 Coral cover and macroalgal cover at recovering and regime shifting sites in 2011, with the change (based on the model slope) in coral cover and macroalgae estimates from sites between 2005–2011
Full size table
Extended Data Table 2 Rationale for predictor variables included in models determining different post-disturbance reef trajectories on Seychelles reefs
Full size table
Extended Data Table 3 Mean values with 95% confidence intervals for predictor covariates in Seychelles compared to other coral reef locations where similar data for the covariates were available
Full size table

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Graham, N., Jennings, S., MacNeil, M. et al. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015). https://doi.org/10.1038/nature14140

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