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Recovery potential of the world's coral reef fishes

Nature volume 520, pages 341–344 (2015)Cite this article

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Abstract

Continuing degradation of coral reef ecosystems has generated substantial interest in how management can support reef resilience1,2. Fishing is the primary source of diminished reef function globally3,4,5, leading to widespread calls for additional marine reserves to recover fish biomass and restore key ecosystem functions6. Yet there are no established baselines for determining when these conservation objectives have been met or whether alternative management strategies provide similar ecosystem benefits. Here we establish empirical conservation benchmarks and fish biomass recovery timelines against which coral reefs can be assessed and managed by studying the recovery potential of more than 800 coral reefs along an exploitation gradient. We show that resident reef fish biomass in the absence of fishing (B0) averages ∼1,000 kg ha−1, and that the vast majority (83%) of fished reefs are missing more than half their expected biomass, with severe consequences for key ecosystem functions such as predation. Given protection from fishing, reef fish biomass has the potential to recover within 35 years on average and less than 60 years when heavily depleted. Notably, alternative fisheries restrictions are largely (64%) successful at maintaining biomass above 50% of B0, sustaining key functions such as herbivory. Our results demonstrate that crucial ecosystem functions can be maintained through a range of fisheries restrictions, allowing coral reef managers to develop recovery plans that meet conservation and livelihood objectives in areas where marine reserves are not socially or politically feasible solutions.

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Figure 1: Global reef fish biomass among management categories.
Figure 2: Coral reef fish responses across the spectrum of potential recovery.

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Acknowledgements

We thank M. Emslie, A. Cheal, J. Wetherall, C. Hutchery and K. Anthony for comments on early drafts of the manuscript. The Australian Institute of Marine Science, the ARC Centre of Excellence for Coral Reef Studies, and the John D. and Catherine T. MacArthur Foundation supported this research.

Author information

Authors and Affiliations

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

    M. Aaron MacNeil

  2. Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia B3H 3J5, Canada,

    M. Aaron MacNeil

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

    M. Aaron MacNeil, Nicholas A. J. Graham & Joshua E. Cinner

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

    Shaun K. Wilson

  5. Oceans Institute, University of Western Australia, Crawley, Western Australia 6009, Australia ,

    Shaun K. Wilson

  6. Coral Reef Ecosystems Division, NOAA Pacific Islands Fisheries Science Center, Honolulu, Hawaii 96818, USA,

    Ivor D. Williams

  7. Australian Research Council Centre of Excellence for Environmental Decisions (CEED), University of Queensland, Brisbane, St Lucia, Queensland 4074, Australia ,

    Joseph Maina

  8. Wildlife Conservation Society, Marine Programs, Bronx, New York 10460, USA ,

    Joseph Maina, Stacy Jupiter & Tim R. McClanahan

  9. School of Marine Science and Technology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

    Steven Newman & Nicholas V. C. Polunin

  10. Department of Biology, Fisheries Ecology Research Lab, University of Hawaii, Honolulu, Hawaii 96822, USA,

    Alan M. Friedlander

  11. Pristine Seas-National Geographic, Washington DC 20036, USA

    Alan M. Friedlander

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  1. M. Aaron MacNeil
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Contributions

M.A.M. conceived of the study with N.A.J.G., N.V.C.P., T.R.M., S.K.W. and J.E.C.; M.A.M. developed and implemented the analysis; M.A.M. led the manuscript with N.A.J.G., J.E.C. and S.K.W. All other authors contributed data and made substantive contributions to the text.

Corresponding author

Correspondence to M. Aaron MacNeil.

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The authors declare no competing financial interests.

Additional information

This is Social Ecological Research Frontiers (SERF) working group contribution number 10.

Extended data figures and tables

Extended Data Figure 1 Nuisance parameter posterior estimates for modelled recovery.

a, Joint Bayesian hierarchical recovery model. Prior (flat black line) and posterior (histograms) nuisance parameter densities (vertical dotted line at zero) for factors influencing total reef fish biomass (kg ha−1), including three parameters for a third order polynomial for hard coral cover (that is, hard coral (1), (2), (3)), an offset for atoll versus non-atoll, and three parameters for a third order polynomial for productivity (that is, productivity (1), (2), (3)). b, Estimated relationship between percentage hard coral cover and total biomass using posterior median values (blue line), with 99 samples from the posterior distribution of the parameters in a (thick grey lines) and marginal data (black dots; n = 832 reefs). c, Plot of observed depth and marginal total biomass given the full model (no depth effect present). d, Estimated relationship between atoll (1) versus non-atoll (0) and total biomass, with marginal data (boxplot and black squares). e, Plot of reserve size and marginal total fish biomass given the full model (no reserve size effect present). f, Estimated relationship between productivity and total biomass, with marginal data.

Extended Data Figure 2 Data provider random effect posteriors.

Bayesian hierarchical model posterior estimated effects of data provider identity, including 95% posterior densities (thin lines), 50% posterior densities (thick lines), and posterior median values (black circles). Results show no apparent bias among data providers, with little information present in provider identities.

Extended Data Figure 3 Bayesian P values for goodness of fit.

Discrepancy-based posterior predictive checks for Bayesian hierarchical model goodness of fit. Points represent Freeman–Tukey discrepancy measures between observed and expected values, D(yobs|θ), and simulated and expected values, D(ynew|θ). Plot shows high level of agreement between observed and simulated discrepancies (P = 0.521), indicating the model is not inconsistent with the observed data. Labelled clusters of distinct points reflect various components of the joint model.

Extended Data Figure 4 Posterior expected times to recovery among localities.

Bayesian hierarchical model posterior estimated times to recovery (0.9B0) for fished (green circles) and restricted (amber squares) localities around the world. Black lines are 50% highest posterior densities and symbols are posterior median values.

Extended Data Figure 5 Change in expected reserve age and potential effects under climate change.

a, Change in expected reserve age at recovery (contour lines; in years) given specified values for recovery (as a proportion of B0) and the 95% highest posterior density range for the rate of biomass growth (r0) estimated from a joint Bayesian hierarchical model of recovery. Expected recovery time from the most degraded locality (Ahus, PNG; posterior median: 94 kg ha−1) given r0 (posterior median: 0.054) is 59 years when recovery is defined at 0.9 B0 (blue dot). b, Response surface (contour lines) for potential change in B0 (kg ha−1) given a plausible range of decline in average primary productivity (from current 4.7 kg C ha−1 day−1) and coral cover (from current 26% average hard coral cover). Response surface based on model estimated effects of productivity and hard coral cover on B0 (Extended Data Fig. 1). Current conditions are in the top right (blue dot); a plausible scenario for 2040 given a 4% loss of primary productivity and a 2% annual loss of coral cover would lead to a 6% drop in expected B0, down to 953 kg ha−1 (dot-triangle).

Extended Data Figure 6 Average reef fish functional group across a biomass gradient.

a–i, Generalized additive model (GAM) fits to the relative proportion of excavators/scrapers (a), browsers (b), grazers (c), detritivores (d), planktivores (e), micro-invertivores (f), macro-invertivores (g), pisci-invertivores (h) and piscivores (i) in community log-biomass for 832 reef slope sites from around the world. Grey dots are reef-level observations; blue dots are a 0.1 log-kg interval moving average; GAM fits are represented by mean (solid black line) and 95% confidence intervals (dashed line) across the full data range. Mean model fits between initial reserve biomass and recovered log-biomass (vertical dotted lines) were scaled relative to their values at 0.1B0 to characterize reef fish functional responses in Fig. 2.

Extended Data Figure 7 GAM functional returns with uncertainty.

Average relative reef fish functional returns in log-biomass across the range from collapsed to recovered given the GAM fits in Fig. 2d; lines are GAM fits for log-biomass per functional group relative to their average biomasses at marine reserve age zero (estimated initial log-biomass) in Fig. 1; dashed lines are approximate 95% confidence intervals. Data include 832 individual reefs.

Extended Data Figure 8 Nuisance parameter residual error plots.

a–c, Joint Bayesian hierarchical recovery model nuisance parameter absolute residuals and residual histograms for percentage of hard coral cover (a), having been collected on an atoll (b) and average productivity in kg C ha−1 day−1(c). Dashed red lines indicate weak linear trends in absolute residuals showing no heteroscedasticity was present; blue solid lines show a normal probability distribution fit to the residuals, demonstrating appropriate normal sub-model fit.

Extended Data Table 1 DIC-based model selection for individual nuisance parameter sub-models
Full size table
Extended Data Table 2 DIC-based model selection for combined nuisance parameter sub-models
Full size table

Supplementary information

Supplementary Information

This file contains Data Provider Acknowledgements, a supplementary glossary of terms, PyMC (Python) code for the full Bayesian hierarchical model used to estimate recovery rates and unfished biomass from the global coral reef biomass dataset and Supplementary Table 1. (PDF 223 kb)

Supplementary Information

This file contains Supplementary Table 2, a complete reef fish list, including functional group categorization and inclusion criteria (where included=='1') for the recovery analysis. (XLS 190 kb)

Supplementary Information

This file contains Supplementary Table 3, posterior summary statistics for full Bayesian hierarchical analysis used to estimate recovery rates and unfished biomass from the global coral reef biomass dataset. (XLS 655 kb)

Supplementary Information

This file contains Supplementary Table 4, reef-site (reef) metadata including data provider, locality name, reef name, latitude, longitude, and year of collection. (XLS 126 kb)

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MacNeil, M., Graham, N., Cinner, J. et al. Recovery potential of the world's coral reef fishes. Nature 520, 341–344 (2015). https://doi.org/10.1038/nature14358

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