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

Journal name:
Nature
Volume:
506,
Pages:
216–220
Date published:
DOI:
doi:10.1038/nature13022
Received
Accepted
Published online
Corrected online

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 (>100km2), 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 (>250mm 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.

At a glance

Figures

  1. Predicted global distribution of four community metrics for fishes associated with coral and rocky reefs outside of MPAs.
    Figure 1: Predicted global distribution of four community metrics for fishes associated with coral and rocky reefs outside of MPAs.

    Predictions are from random forest models developed using data from 1,022 sites in fished locations worldwide. a, Species richness of all fishes (number of species sighted per 250m2). b, Species richness of large (>250mm total length) fishes (per 250m2). c, Total biomass of all fishes (kg per 250m2). d, Total biomass of large fishes (kg per 250m2).

  2. Mean response ratios for MPAs with different numbers of NEOLI (no take, enforced, old, large, isolated) features.
    Figure 2: Mean response ratios for MPAs with different numbers of NEOLI (no take, enforced, old, large, isolated) features.

    Mean ratio values have been back transformed from logs and expressed as percentages with 95% confidence intervals, with 100% equivalent to fished coasts. Sites on fished coasts have 0 NEOLI features. a, Mean response ratios for four community metrics. b, Mean response ratios for community metrics where each NEOLI feature was included within the set examined. The ‘no-take’ plot with two features, for example, depicts the mean response for no-take MPAs with a single other NEOLI feature. 95% confidence limits that lie off-scale are shown by number. Samples sizes are shown in Extended Data Table 1.

  3. Mean response ratios with 95% confidence intervals for four community metrics and low, medium and high levels of five MPA features.
    Figure 3: Mean response ratios with 95% confidence intervals for four community metrics and low, medium and high levels of five MPA features.

    Values have been back transformed to per cent scale, with 100% equivalent to fished coasts. The feature ‘regulations’ was analysed using data from 82 MPAs with high enforcement; 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. 95% confidence limits that lie off-scale are shown by number. Samples sizes are shown in Extended Data Table 1.

  4. Distribution of sites surveyed.
    Extended Data Fig. 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.

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

  6. Predicted global distribution of fish biomass (kg per 250[thinsp]m2) on fished coasts.
    Extended Data Fig. 3: Predicted global distribution of fish biomass (kg per 250m2) 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.

  7. Mean response ratios for MPAs with different number of NEOLI features.
    Extended Data Fig. 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.

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

Tables

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

Change history

Corrected online 12 February 2014
Values denoting confidence limits off-scale have been added in Fig. 3.

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Author information

Affiliations

  1. Institute for Marine and Antarctic Studies, University of Tasmania, GPO Box 252-49, Hobart, Tasmania 7001, Australia

    • Graham J. Edgar,
    • Rick D. Stuart-Smith,
    • Stuart Kininmonth,
    • Neville S. Barrett,
    • Just Berkhout,
    • Colin D. Buxton,
    • Antonia T. Cooper,
    • Marlene Davey,
    • German Soler &
    • Russell J. Thomson
  2. Institute of Marine Sciences, School of Biological Sciences, University of Portsmouth, Ferry Road, Portsmouth PO4 9LY, UK

    • Trevor J. Willis
  3. Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, SE-106 91 Stockholm, Sweden

    • Stuart Kininmonth
  4. School of Plant Science, University of Tasmania, GPO Box 252, Hobart, Tasmania 7001, Australia

    • Susan C. Baker
  5. Charles Darwin Foundation, Puerto Ayora, Galapagos, Ecuador

    • Stuart Banks
  6. The Bites Lab, Natural Products and Agrobiology Institute (IPNA-CSIC), 38206 La Laguna, Tenerife, Spain

    • Mikel A. Becerro
  7. Elwandle Node, South African Environmental Observation network, Private Bag 1015, Grahamstown 6140, South Africa

    • Anthony T. F. Bernard
  8. Wildlife Conservation Society, Indonesia Marine Program, Jalan Atletik No. 8, Bogor Jawa Barat 16151, Indonesia

    • Stuart J. Campbell
  9. Department of Water, Perth, Western Australia 6000, Australia

    • Sophie C. Edgar
  10. Facultad de Recursos Naturales, Escuela de Ciencias del Mar, Pontificia Universidad Catolica de Valparaıso, Valparaıso, Chile

    • Günter Försterra
  11. Centro Nacional Patagonico, Consejo Nacional de Investigaciones Cientificas y Tecnicas, Bvd Brown 2915, 9120 Puerto Madryn, Argentina

    • David E. Galván &
    • Alejo J. Irigoyen
  12. Channel Islands National Park, United States National Park Service, 1901 Spinnaker Dr., Ventura, California 93001, USA

    • David J. Kushner
  13. Instituto de Biologia, Universidade Federal do Rio de Janeiro, Av. Carlos Chagas Filho 373, Rio de Janeiro 21941-902, Brazil

    • Rodrigo Moura
  14. Scripps Institution of Oceanography, UC San Diego, Mail Code 0227, 9500 Gilman Dr., La Jolla, California 92093-0227, USA

    • P. Ed Parnell
  15. Leigh Marine Laboratory, University of Auckland, 160 Goat Island Road, Leigh 0985, New Zealand

    • Nick T. Shears
  16. Dipartimento di Scienze Biologiche, Geologiche ed Ambientali, Università di Bologna, Via San Alberto, Ravenna 163-48123, Italy

    • Elisabeth M. A. Strain

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Distribution of sites surveyed. (214 KB)

    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.

  2. Extended Data Figure 2: Relative importance of the 14 covariates used in global prediction models developed with random forests. (126 KB)

    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.

  3. Extended Data Figure 3: Predicted global distribution of fish biomass (kg per 250m2) on fished coasts. (554 KB)

    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.

  4. Extended Data Figure 4: Mean response ratios for MPAs with different number of NEOLI features. (169 KB)

    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.

  5. Extended Data Figure 5: Mean response ratios for the subsets of sites at which sharks, groupers, jacks and damselfishes were observed. (185 KB)

    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 Tables

  1. Extended Data Table 1: Sample sizes applied in figures (141 KB)
  2. Extended Data Table 2: Covariates used as predictor variables in global random forest models (89 KB)

Supplementary information

Excel files

  1. Supplementary Table 1 (66 KB)

    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.

Additional data