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Global conservation outcomes depend on marine protected areas with five key features

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Abstract

In line with global targets agreed under the Convention on Biological Diversity, the number of marine protected areas (MPAs) is increasing rapidly, yet socio-economic benefits generated by MPAs remain difficult to predict and under debate1,2. MPAs often fail to reach their full potential as a consequence of factors such as illegal harvesting, regulations that legally allow detrimental harvesting, or emigration of animals outside boundaries because of continuous habitat or inadequate size of reserve3,4,5. Here we show that the conservation benefits of 87 MPAs investigated worldwide increase exponentially with the accumulation of five key features: no take, well enforced, old (>10 years), large (>100 km2), and isolated by deep water or sand. Using effective MPAs with four or five key features as an unfished standard, comparisons of underwater survey data from effective MPAs with predictions based on survey data from fished coasts indicate that total fish biomass has declined about two-thirds from historical baselines as a result of fishing. Effective MPAs also had twice as many large (>250 mm total length) fish species per transect, five times more large fish biomass, and fourteen times more shark biomass than fished areas. Most (59%) of the MPAs studied had only one or two key features and were not ecologically distinguishable from fished sites. Our results show that global conservation targets based on area alone will not optimize protection of marine biodiversity. More emphasis is needed on better MPA design, durable management and compliance to ensure that MPAs achieve their desired conservation value.

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Figure 1: Predicted global distribution of four community metrics for fishes associated with coral and rocky reefs outside of MPAs.
Figure 2: Mean response ratios for MPAs with different numbers of NEOLI (no take, enforced, old, large, isolated) features.
Figure 3: Mean response ratios with 95% confidence intervals for four community metrics and low, medium and high levels of five MPA features.

Change history

  • 12 February 2014

    Values denoting confidence limits off-scale have been added in Fig. 3.

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Acknowledgements

We thank the many Reef Life Survey (RLS) divers who contributed to data collection. Development of the RLS data set was supported by the former Commonwealth Environment Research Facilities Program, whereas analyses were supported by the Australian Research Council, a Fulbright Visiting Scholarship (to G.J.E.), the Institute for Marine and Antarctic Studies, and the Marine Biodiversity Hub, a collaborative partnership funded under the Australian Government’s National Environmental Research Program. Surveys were assisted by grants from the National Geographic Society, Conservation International, Wildlife Conservation Society, Winifred Violet Scott Trust, Tasmanian Parks and Wildlife Service, the Winston Churchill Memorial Trust, University of Tasmania, and ASSEMBLE Marine. We are grateful to the many park officers who assisted the study by providing permits and assisting with field activities, and to numerous marine institutions worldwide for hosting survey trips.

Author information

Authors and Affiliations

Authors

Contributions

G.J.E. and R.D.S.-S. conceived the project; G.J.E., R.D.S.-S., M.A.B., A.T.F.B., S.C.B., S.B., S.J.C., A.T.C., M.D., S.C.E., G.F., D.E.G., A.J.I., S.K., D.J.K., R.M., G.S., E.M.A.S. and many others collected the data; G.J.E., R.J.T., T.J.W., S.K. and S.C.E. prepared figures; G.J.E. drafted the initial manuscript; all authors contributed to analyses and interpretation.

Corresponding author

Correspondence to Graham J. Edgar.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Distribution of sites surveyed.

a, Number of NEOLI (no take, enforced, old, large and isolated) features at MPAs investigated (coloured circles). MPAs with most NEOLI features are overlaid on top; consequently numerous MPAs with one and two features are not visible. MPAs with five NEOLI features are (1) Cocos, (2) Kermadec Islands, (3) Malpelo, (4) Middleton Reef; MPAs with four NEOLI features are (5) Elizabeth Reef, (6) Poor Knights Islands, (7) Ship Rock, (8) Tortugas and (9) Tsitsikamma. b, All MPA and fished sites surveyed (black circles). Blue shading summarizes the number of sites surveyed within each ecoregion.

Extended Data Figure 2 Relative importance of the 14 covariates used in global prediction models developed with random forests.

Per cent change in accuracy for a given predictor variable is measured by the change between models that include or do not include that predictor variable, with accuracy assessed as the mean of the residuals squared. Residuals are based on a cross-validation technique to avoid bias, and the change in accuracy is divided by the standard error for a given tree then averaged across all trees.

Extended Data Figure 3 Predicted global distribution of fish biomass (kg per 250 m2) on fished coasts.

Predictions are from random forest models developed using data from 1,022 sites in fished locations worldwide. a, Sharks. b, Groupers. c, Jacks. d, Damselfishes. Note that scales in colour schemes differ among maps, and numbers represent predicted values represented by each colour after smoothing of log-transformed site-level data.

Extended Data Figure 4 Mean response ratios for MPAs with different number of NEOLI features.

Mean ratio values have been back transformed from logs and expressed as percentages with 95% confidence intervals. The number of NEOLI features varies from 0 at sites along fished coastlines to 5 for MPA sites with all NEOLI features. a, Plots calculated for sites where sharks, groupers, jacks and damselfishes were present and the subsets of MPAs with different numbers of NEOLI (no take, enforced, old, large, isolated) features. b, Mean response ratios for community metrics where each NEOLI feature was included within the set examined. 95% confidence limits that lie off-scale are shown by number. Sample sizes are shown in Extended Data Table 1.

Extended Data Figure 5 Mean response ratios for the subsets of sites at which sharks, groupers, jacks and damselfishes were observed.

Values have been back transformed to per cent, with 100% equivalent to fished coasts, and with 95% confidence intervals. The feature ‘regulations’ was analysed using data from 82 MPAs that are well enforced; the feature ‘enforcement’ was analysed using data from 75 MPAs that are no take; and the features ‘isolation’, ‘age’ and ‘area’ were analysed using data from 52 MPAs that are both no take and well enforced. Sharks were not observed in any no-take MPA with low enforcement, so the associated response ratio could not be calculated. 95% confidence limits that lie off-scale are shown by number. Sample sizes are shown in Extended Data Table 1.

Extended Data Table 1 Sample sizes applied in figures
Extended Data Table 2 Covariates used as predictor variables in global random forest models

Supplementary information

Supplementary Table 1

This table shows data associated with marine protected areas and ecoregions. Assessed levels for five key features for MPAs studied (l: low; m: medium; h: high), total number of NEOLI features, and observed and predicted species richness and biomass (per 250 m2 transect) for different ecological groups. (XLSX 66 kb)

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Edgar, G., Stuart-Smith, R., Willis, T. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014). https://doi.org/10.1038/nature13022

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