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.
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The ARC Centre of Excellence for Coral Reef Studies, Stanford University, and University of Montpellier funded working group meetings. This work was supported by J.E.C.’s Pew Fellowship in Marine Conservation and ARC Australian Research Fellowship. Thanks to M. Barnes for constructive comments.
The authors declare no competing financial interests.
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.
Extended data figures and tables
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.
Extended Data Figure 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.
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.
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.
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.
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Cinner, J., Huchery, C., MacNeil, M. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016). https://doi.org/10.1038/nature18607
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