Bright spots among the world’s coral reefs

Journal name:
Nature
Volume:
535,
Pages:
416–419
Date published:
DOI:
doi:10.1038/nature18607
Received
Accepted
Published online

Ongoing declines in the structure and function of the world’s coral reefs1, 2 require novel approaches to sustain these ecosystems and the millions of people who depend on them3. A presently unexplored approach that draws on theory and practice in human health and rural development4, 5 is to systematically identify and learn from the ‘outliers’—places where ecosystems are substantially better (‘bright spots’) or worse (‘dark spots’) than expected, given the environmental conditions and socioeconomic drivers they are exposed to. Here we compile data from more than 2,500 reefs worldwide and develop a Bayesian hierarchical model to generate expectations of how standing stocks of reef fish biomass are related to 18 socioeconomic drivers and environmental conditions. We identify 15 bright spots and 35 dark spots among our global survey of coral reefs, defined as sites that have biomass levels more than two standard deviations from expectations. Importantly, bright spots are not simply comprised of remote areas with low fishing pressure; they include localities where human populations and use of ecosystem resources is high, potentially providing insights into how communities have successfully confronted strong drivers of change. Conversely, dark spots are not necessarily the sites with the lowest absolute biomass and even include some remote, uninhabited locations often considered near pristine6. We surveyed local experts about social, institutional, and environmental conditions at these sites to reveal that bright spots are characterized by strong sociocultural institutions such as customary taboos and marine tenure, high levels of local engagement in management, high dependence on marine resources, and beneficial environmental conditions such as deep-water refuges. Alternatively, dark spots are characterized by intensive capture and storage technology and a recent history of environmental shocks. Our results suggest that investments in strengthening fisheries governance, particularly aspects such as participation and property rights, could facilitate innovative conservation actions that help communities defy expectations of global reef degradation.

At a glance

Figures

  1. Global patterns and drivers of reef fish biomass.
    Figure 1: Global patterns and drivers of reef fish biomass.

    a, Reef fish biomass among 918 study sites. Points vary in size and colour proportional to the amount of fish biomass. bd, Standardized effect size of local-scale social drivers, nation/state-scale social drivers, and environmental covariates, respectively. Parameter estimates are Bayesian posterior median values, 95% uncertainty intervals (UI; thin lines), and 50% UI (thick lines). Black dots indicate that the 95% UI does not overlap 0; grey closed circles indicates that 75% of the posterior distribution lies to one side of 0; and grey open circles indicate that the 50% UI overlaps 0.

  2. Bright and dark spots among the world’s coral reefs.
    Figure 2: Bright and dark spots among the world’s coral reefs.

    a, Each site’s deviation from expected biomass (y axis) along a gradient of nation/state mean biomass (x axis). The 50 sites with biomass values >2 standard deviations above or below expected values were considered bright (yellow) and dark (black) spots, respectively. Each grey vertical line represents a nation/state; those with bright or dark spots are labelled and numbered. There can be multiple bright or dark spots in each nation/state. b, Map highlighting bright and dark spots with large circles, and other sites in small circles. Numbers correspond to panel a.

  3. Differences in key social and environmental conditions between bright spots, dark spots, and ‘average’ sites. a, Social and institutional conditions; b, external- or donor-driven projects; c, technologies; d, environmental conditions.
    Figure 3: Differences in key social and environmental conditions between bright spots, dark spots, and ‘average’ sites. a, Social and institutional conditions; b, external- or donor-driven projects; c, technologies; d, environmental conditions.

    P values are determined using Fisher’s exact test. Intensive netting includes beach seine nets, surround gill nets, and muro-ami.

  4. Marginal relationships between reef fish biomass and social drivers.
    Extended Data Fig. 1: Marginal relationships between reef fish biomass and social drivers.

    a, Local population growth; b, market gravity; c, nearest settlement gravity; d, tourism; e, nation/state population size; f, Human Development Index; g, high compliance marine reserve (0 is fished baseline); h, restricted fishing (0 is fished baseline); i, low-compliance marine reserve (0 is fished baseline); j, voice and accountability; k, reef fish landings; l, ocean productivity; m, depth (−1 = 0–4 m, 0 = 4–10 m, 1 = >10 m); n, reef flat (0 is reef slope baseline); o, reef crest flat (0 is reef slope baseline); p, lagoon/back reef flat (0 is reef slope baseline). All variables displayed on the x axis are standardized. Red lines are the marginal trend line for each parameter as estimated by the full model. Grey lines are 100 simulations of the marginal trend line sampled from the posterior distributions of the intercept and parameter slope, analogous to conventional confidence intervals. Two asterisks indicate that 95% of the posterior density is in either a positive or negative direction (Fig. 1b–d); a single asterisk indicates that 75% of the posterior density is in either a positive or negative direction.

  5. Correlation plot of candidate continuous covariates before accounting for collinearity (Extended Data Table 4).
    Extended Data Fig. 2: Correlation plot of candidate continuous covariates before accounting for collinearity (Extended Data Table 4).

    Collinearity between continuous and categorical covariates (including biogeographic region, habitat, protection status, and depth) were analysed using box plots.

  6. Model fit statistics.
    Extended Data Fig. 3: Model fit statistics.

    Top, Bayesian P values (BpV) for the full model indicating goodness of fit, based on posterior discrepancy. Points are Freeman–Tukey differences between observed and expected values, and simulated and expected values within the MCMC scheme (n = 10,000). Plot shows no evidence for lack of fit between the model and the data. Bottom, Posterior distribution for the degrees of freedom parameter (ν) in our supplementary analysis of candidate distributions. The highest posterior density of 3.46, with 97.5% of the total posterior density below 4 provides strong evidence in favour of a non-central t distribution relative to a normal distribution and supports the use of 3.5 for ν.

  7. Box plot of deviation from expected as a function of the presence or absence of key social and environmental conditions expected to produce bright spots.
    Extended Data Fig. 4: Box plot of deviation from expected as a function of the presence or absence of key social and environmental conditions expected to produce bright spots.

    Boxes range from the first to third quartile and whiskers extend to the highest value that is within 1.5× the inter-quartile range (that is, distance between the first and third quartiles). Data beyond the end of the whiskers are outliers, which are plotted as points.

Tables

  1. Summary of social and environmental covariates
    Extended Data Table 1: Summary of social and environmental covariates
  2. List of nations/states covered in study and their respective average biomass (kg ha−1 ± standard error)
    Extended Data Table 2: List of nations/states covered in study and their respective average biomass (kg ha−1 ± standard error)
  3. Model selection of potential gravity indicators and components
    Extended Data Table 3: Model selection of potential gravity indicators and components
  4. Variance inflation factor (VIF) scores for continuous data before and after removing variables due to collinearity
    Extended Data Table 4: Variance inflation factor (VIF) scores for continuous data before and after removing variables due to collinearity
  5. List of bright and dark spot locations, population status, and protection status
    Extended Data Table 5: List of bright and dark spot locations, population status, and protection status

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Author information

Affiliations

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

    • Joshua E. Cinner,
    • Cindy Huchery,
    • M. Aaron MacNeil,
    • Nicholas A.J. Graham,
    • Eva Maire,
    • Christina C. Hicks,
    • Andrew Hoey &
    • David Mouillot
  2. Australian Institute of Marine Science, PMB 3 Townsville MC, Townsville, Queensland 4810, Australia

    • M. Aaron MacNeil
  3. Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia B3H 3J5 Canada

    • M. Aaron MacNeil
  4. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK

    • Nicholas A.J. Graham &
    • Christina C. Hicks
  5. Wildlife Conservation Society, Global Marine Program, Bronx, New York 10460, USA

    • Tim R. McClanahan,
    • Joseph Maina,
    • Stephanie D’Agata,
    • Stuart J. Campbell &
    • Katherine E. Holmes
  6. Australian Research Council Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane St Lucia, Queensland 4074, Australia

    • Joseph Maina &
    • Maria Beger
  7. Department of Environmental Sciences, Macquarie University, North Ryde, New South Wales 2109, Australia

    • Joseph Maina &
    • Stephanie D’Agata
  8. MARBEC, UMR 9190, IRD-CNRS-UM-IFREMER, Université Montpellier, 34095 Montpellier Cedex, France

    • Eva Maire &
    • David Mouillot
  9. Center for Ocean Solutions, Stanford University, California 94305, USA

    • John N. Kittinger,
    • Christina C. Hicks &
    • Larry Crowder
  10. Conservation International Hawaii, Betty and Gordon Moore Center for Science and Oceans, 7192 Kalaniana‘ole Hwy, Suite G230, Honolulu, Hawaii 96825, USA

    • John N. Kittinger
  11. Department of Geography, University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA

    • Camilo Mora
  12. School of Marine and Environmental Affairs, University of Washington, Seattle, Washington 98102 USA

    • Edward H. Allison
  13. Institut de Recherche pour le Développement, UMR IRD-UR-CNRS ENTROPIE, Laboratoire d’Excellence LABEX CORAIL, BP A5, 98848 Nouméa Cedex, New Caledonia

    • Stephanie D’Agata &
    • Laurent Vigliola
  14. Ecology & Evolution Group, School of Life Sciences, University Park, University of Nottingham, Nottingham NG7 2RD, UK

    • David A. Feary
  15. Coral Reef Ecosystems Division, NOAA Pacific Islands Fisheries Science Center, Honolulu, Hawaii 96818, USA

    • Ivor D. Williams
  16. UMR Entropie, Labex Corail, –IRD, Université de Perpignan, 66000 Perpignan, France

    • Michel Kulbicki
  17. EA4243 LIVE, University of New Caledonia, BPR4 98851 Nouméa Cedex, New Caledonia

    • Laurent Wantiez
  18. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania 7001, Australia

    • Graham Edgar &
    • Rick D. Stuart-Smith
  19. Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, USA

    • Stuart A. Sandin
  20. The Nature Conservancy, Brisbane, Queensland 4101, Australia

    • Alison L. Green
  21. Future of Fish, 7315 Wisconsin Ave, Suite 1000W, Bethesda, Maryland 20814, USA

    • Marah J. Hardt
  22. Fisheries Ecology Research Lab, Department of Biology, University of Hawaii, Honolulu, Hawaii 96822, USA

    • Alan Friedlander
  23. National Geographic Society, Pristine Seas Program, 1145 17th Street NW, Washington DC 20036-4688, USA

    • Alan Friedlander
  24. Department of Parks and Wildlife, Kensington, Perth, Western Australia 6151, Australia

    • Shaun K. Wilson
  25. Oceans Institute, University of Western Australia, Crawley, Western Australia 6009, Australia

    • Shaun K. Wilson
  26. The Israeli Society of Ecology and Environmental Sciences, Kehilat New York 19 Tel Aviv, Israel

    • Eran Brokovich
  27. Marine Science Institute, University of California, Santa Barbara, California 93106-6150, USA

    • Andrew J. Brooks
  28. Departamento de Ciencias Marinas., Recinto Universitario de Mayaguez, Universidad de Puerto Rico, San Juan 00680, Puerto Rico

    • Juan J. Cruz-Motta
  29. School of Life Sciences, University of Technology, Sydney, New South Wales 2007, Australia

    • David J. Booth
  30. UMR ENTROPIE, Laboratoire d’Excellence LABEX CORAIL, Institut de Recherche pour le Développement, CS 41095, 97495 Sainte Clotilde, La Réunion

    • Pascale Chabanet
  31. Blue Ventures Conservation, 39-41 North Road, London N7 9DP, UK

    • Charlie Gough
  32. Coastal Resources Association, St. Joseph St., Brgy. Nonoc, Surigao City, Surigao del Norte 8400, Philippines

    • Mark Tupper
  33. Leibniz Centre for Tropical Marine Ecology (ZMT), Fahrenheitstrasse 6, D-28359 Bremen, Germany

    • Sebastian C. A. Ferse
  34. Fisheries Economics Research Unit, University of British Columbia, 2202 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada

    • U. Rashid Sumaila

Contributions

J.E.C. conceived of the study with support from M.A.M., N.A.J.G., T.R.M., J.K., C.Hu., D.M., C.M., E.H.A., and C.C.Hi.; C.Hu. managed the database; M.A.M., J.E.C., and D.M. developed and implemented the analyses; J.E.C. led the manuscript with M.A.M. and N.A.J.G. All other authors contributed data and made substantive contributions to the text.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

This is the Social-Ecological Research Frontiers (SERF) working group contribution no. 11.

Reviewer Information Nature thanks S. Qian, B. Walker and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Marginal relationships between reef fish biomass and social drivers. (288 KB)

    a, Local population growth; b, market gravity; c, nearest settlement gravity; d, tourism; e, nation/state population size; f, Human Development Index; g, high compliance marine reserve (0 is fished baseline); h, restricted fishing (0 is fished baseline); i, low-compliance marine reserve (0 is fished baseline); j, voice and accountability; k, reef fish landings; l, ocean productivity; m, depth (−1 = 0–4 m, 0 = 4–10 m, 1 = >10 m); n, reef flat (0 is reef slope baseline); o, reef crest flat (0 is reef slope baseline); p, lagoon/back reef flat (0 is reef slope baseline). All variables displayed on the x axis are standardized. Red lines are the marginal trend line for each parameter as estimated by the full model. Grey lines are 100 simulations of the marginal trend line sampled from the posterior distributions of the intercept and parameter slope, analogous to conventional confidence intervals. Two asterisks indicate that 95% of the posterior density is in either a positive or negative direction (Fig. 1b–d); a single asterisk indicates that 75% of the posterior density is in either a positive or negative direction.

  2. Extended Data Figure 2: Correlation plot of candidate continuous covariates before accounting for collinearity (Extended Data Table 4). (345 KB)

    Collinearity between continuous and categorical covariates (including biogeographic region, habitat, protection status, and depth) were analysed using box plots.

  3. Extended Data Figure 3: Model fit statistics. (154 KB)

    Top, Bayesian P values (BpV) for the full model indicating goodness of fit, based on posterior discrepancy. Points are Freeman–Tukey differences between observed and expected values, and simulated and expected values within the MCMC scheme (n = 10,000). Plot shows no evidence for lack of fit between the model and the data. Bottom, Posterior distribution for the degrees of freedom parameter (ν) in our supplementary analysis of candidate distributions. The highest posterior density of 3.46, with 97.5% of the total posterior density below 4 provides strong evidence in favour of a non-central t distribution relative to a normal distribution and supports the use of 3.5 for ν.

  4. Extended Data Figure 4: Box plot of deviation from expected as a function of the presence or absence of key social and environmental conditions expected to produce bright spots. (134 KB)

    Boxes range from the first to third quartile and whiskers extend to the highest value that is within 1.5× the inter-quartile range (that is, distance between the first and third quartiles). Data beyond the end of the whiskers are outliers, which are plotted as points.

Extended Data Tables

  1. Extended Data Table 1: Summary of social and environmental covariates (204 KB)
  2. Extended Data Table 2: List of nations/states covered in study and their respective average biomass (kg ha−1 ± standard error) (408 KB)
  3. Extended Data Table 3: Model selection of potential gravity indicators and components (159 KB)
  4. Extended Data Table 4: Variance inflation factor (VIF) scores for continuous data before and after removing variables due to collinearity (101 KB)
  5. Extended Data Table 5: List of bright and dark spot locations, population status, and protection status (356 KB)

Supplementary information

PDF files

  1. Supplementary Information (511 KB)

    This file contains Supplementary Text and Data and an additional reference.

Additional data