Global conservation outcomes depend on marine protected areas with five key features (original) (raw)

Change history

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

  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, 6000, Western Australia, 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

Authors

  1. Graham J. Edgar
  2. Rick D. Stuart-Smith
  3. Trevor J. Willis
  4. Stuart Kininmonth
  5. Susan C. Baker
  6. Stuart Banks
  7. Neville S. Barrett
  8. Mikel A. Becerro
  9. Anthony T. F. Bernard
  10. Just Berkhout
  11. Colin D. Buxton
  12. Stuart J. Campbell
  13. Antonia T. Cooper
  14. Marlene Davey
  15. Sophie C. Edgar
  16. Günter Försterra
  17. David E. Galván
  18. Alejo J. Irigoyen
  19. David J. Kushner
  20. Rodrigo Moura
  21. P. Ed Parnell
  22. Nick T. Shears
  23. German Soler
  24. Elisabeth M. A. Strain
  25. Russell J. Thomson

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 toGraham J. Edgar.

Ethics declarations

Competing interests

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

Full size table

Extended Data Table 2 Covariates used as predictor variables in global random forest models

Full size table

Supplementary information

Supplementary Table 1 (download XLSX )

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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