Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Capacity shortfalls hinder the performance of marine protected areas globally

Abstract

Marine protected areas (MPAs) are increasingly being used globally to conserve marine resources. However, whether many MPAs are being effectively and equitably managed, and how MPA management influences substantive outcomes remain unknown. We developed a global database of management and fish population data (433 and 218 MPAs, respectively) to assess: MPA management processes; the effects of MPAs on fish populations; and relationships between management processes and ecological effects. Here we report that many MPAs failed to meet thresholds for effective and equitable management processes, with widespread shortfalls in staff and financial resources. Although 71% of MPAs positively influenced fish populations, these conservation impacts were highly variable. Staff and budget capacity were the strongest predictors of conservation impact: MPAs with adequate staff capacity had ecological effects 2.9 times greater than MPAs with inadequate capacity. Thus, continued global expansion of MPAs without adequate investment in human and financial capacity is likely to lead to sub-optimal conservation outcomes.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Per cent of MPAs exceeding or falling below threshold values for indicators of effective and equitable management processes.
Figure 2: MPA effects on fish populations (biomass).
Figure 3: Relationship between MPA management processes and ecological impact.
Figure 4: Reported level of MPA staff capacity.

Similar content being viewed by others

References

  1. Lubchenco, J. & Grorud-Colvert, K. Making waves: The science and politics of ocean protection. Science 350, 382–383 (2015)

    Article  CAS  ADS  Google Scholar 

  2. UNEP-WCMC & IUCN. Protected Planet Report 2016 (United Nations Environment Programme (UNEP) World Conservation Monitoring Centre (UNEP-WCMC) and International Union for Conservation of Nature (IUCN), 2016)

  3. Secretariat of the CBD. Aichi Target 11. Decision X/2. Convention on Biological Diversity (2011)

  4. UN. United Nations Sustainable Development Goal 14: Conserve and sustainably use the oceans, seas and marine resources. http://www.un.org/sustainabledevelopment/oceans/ (2016)

  5. Lester, S. E. et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009)

    Article  ADS  Google Scholar 

  6. Mascia, M. B., Claus, C. A. & Naidoo, R. Impacts of marine protected areas on fishing communities. Conserv. Biol. 24, 1424–1429 (2010)

    Article  Google Scholar 

  7. Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014)

    Article  CAS  ADS  Google Scholar 

  8. Pollnac, R. B., Crawford, B. R. & Gorospe, M. L. G. Discovering factors that influence the success of community-based marine protected areas in the Visayas, Philippines. Ocean Coast. Manage. 44, 683–710 (2001)

    Google Scholar 

  9. Basurto, X., Blanco, E., Nenadovic, M. & Vollan, B. Integrating simultaneous prosocial and antisocial behavior into theories of collective action. Sci. Adv. 2, e1501220 (2016)

    Article  ADS  Google Scholar 

  10. Mascia, M. B. in Marine Reserves: A Guide to Science, Design, and Use (eds Sobel, J. & Dahlgren, C. ) 164–186 (Island Press, 2004)

  11. Pollnac, R . et al. Marine reserves as linked social-ecological systems. Proc. Natl Acad. Sci. USA 107, 18262–18265 (2010)

    Article  CAS  ADS  Google Scholar 

  12. Fox, H. E. et al. How Are Our MPAs Doing? Challenges in assessing global patterns in marine protected area performance. Coast. Manage. 42, 207–226 (2014)

    Google Scholar 

  13. Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. New Dir. Eval. 2009, 75–84 (2009)

    Article  Google Scholar 

  14. Ahmadia, G. N. et al. Integrating impact evaluation in the design and implementation of monitoring marine protected areas. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140275 (2015)

    Article  Google Scholar 

  15. Ostrom, E. Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge Univ. Press, 1990)

  16. Scianna, C., Niccolini, F., Gaines, S. D. & Guidetti, P. ‘Organization Science’: A new prospective to assess marine protected areas effectiveness. Ocean Coast. Manage. 116, 443–448 (2015)

    Google Scholar 

  17. Hockings, M . et al. Evaluating Effectiveness: A Framework for Assessing Management Effectiveness of Protected Areas 2nd edn (International Union for Conservation of Nature (IUCN), 2006)

  18. Coad, L. et al. Measuring impact of protected area management interventions: current and future use of the Global Database of Protected Area Management Effectiveness. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140281 (2015)

    Article  Google Scholar 

  19. Claudet, J. et al. Marine reserves: size and age do matter. Ecol. Lett. 11, 481–489 (2008)

    Article  Google Scholar 

  20. Lester, S. & Halpern, B. Biological responses in marine no-take reserves versus partially protected areas. Mar. Ecol. Prog. Ser. 367, 49–56 (2008)

    Article  ADS  Google Scholar 

  21. McClanahan, T. R., Marnane, M. J., Cinner, J. E. & Kiene, W. E. A comparison of marine protected areas and alternative approaches to coral-reef management. Curr. Biol. 16, 1408–1413 (2006)

    Article  CAS  Google Scholar 

  22. Cinner, J. E . et al. Comanagement of coral reef social-ecological systems. Proc. Natl Acad. Sci. USA 109, 5219–5222 (2012)

    Article  CAS  ADS  Google Scholar 

  23. Nolte, C. & Agrawal, A. Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the Amazon rainforest. Conserv. Biol. 27, 155–165 (2013)

    Article  Google Scholar 

  24. Williams, I. D. et al. Human, oceanographic and habitat drivers of central and western Pacific coral reef fish assemblages. PLoS One 10, e0120516 (2015)

    Article  Google Scholar 

  25. Wenger, A. S. et al. Effects of reduced water quality on coral reefs in and out of no-take marine reserves. Conserv. Biol. 30, 142–153 (2016)

    Article  Google Scholar 

  26. Advani, S., Rix, L. N., Aherne, D. M., Alwany, M. A. & Bailey, D. M. Distance from a fishing community explains fish abundance in a no-take zone with weak compliance. PLoS One 10, e0126098 (2015)

    Article  Google Scholar 

  27. Ferraro, P. J. & Pressey, R. L. Measuring the difference made by conservation initiatives: protected areas and their environmental and social impacts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140270 (2015)

    Article  Google Scholar 

  28. Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014)

    Article  CAS  ADS  Google Scholar 

  29. Sandvik, B. World Borders Dataset. http://thematicmapping.org/downloads/world_borders.php (2016)

  30. IUCN and UNEP-WCMC. The World Database on Protected Areas (WDPA). (United Nations Environment Programme (UNEP) World Conservation Monitoring Centre (UNEP-WCMC) and International Union for Conservation of Nature (IUCN) http://www.protectedplanet.net (2015)

  31. Stolton, S. et al. Management Effectiveness Tracking Tool: Reporting progress in Protected areas sites; second edition (World Bank/WWF Forest Alliance and WWF, 2007)

  32. Staub, F . & Hatziolos, M. E. Score Card to Assess Progress in Achieving Management effectiveness goals for Marine Protected Areas (Prepared for the World Bank, 2004)

  33. NOAA. NOAA Coral Reef Conservation Program MPA Management Assessment Checklist (National Oceanic and Atmospheric Administration (NOAA), 2010)

  34. Mora, C. & Sale, P. Ongoing global biodiversity loss and the need to move beyond protected areas: a review of the technical and practical shortcomings of protected areas on land and sea. Mar. Ecol. Prog. Ser. 434, 251–266 (2011)

    Article  ADS  Google Scholar 

  35. Rosenbaum, P. R. Design sensitivity and efficiency in observational studies. J. Am. Stat. Assoc. 105, 692–702 (2010)

    Article  CAS  MathSciNet  Google Scholar 

  36. Sekhon, J. S. Multivariate and propensity score matching. J. Stat. Softw. 42, 1–52 (2011)

    Article  Google Scholar 

  37. Alerstam, T., Hedenstrom, A. & Akesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003)

    Article  Google Scholar 

  38. Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. Camb. Philos. Soc. 90, 1215–1247 (2015)

    Article  Google Scholar 

  39. Halpern, B. S., Lester, S. E. & Kellner, J. B. Spillover from marine reserves and the replenishment of fished stocks. Environ. Conserv. 36, 268–276 (2010)

    Google Scholar 

  40. Russ, G. R. et al. Rapid increase in fish numbers follows creation of World’ s largest marine reserve network. Curr. Biol. 18, 514–515 (2006)

    Article  Google Scholar 

  41. Halpern, B. S. & Warner, R. R. Marine reserves have rapid and lasting effects. Ecol. Lett. 5, 361–366 (2002)

    Article  Google Scholar 

  42. Gell, F. R. & Roberts, C. M. Benefits beyond boundaries: the fishery effects of marine reserves. Trends Ecol. Evol. 18, 448–455 (2003)

    Article  Google Scholar 

  43. R Development Core Team. R: a language and environment for statistical computing, version 3.2.3. (2015)

  44. Caliendo, M. & Kopeinig, S. Some practical guidance for the implementation of propensity score matching. J. Econ. Surv. 22, 31–72 (2008)

    Article  Google Scholar 

  45. Keele, L. An overview of rbounds: an R package for Rosenbaum bounds sensitivity analysis with matched data. (2010)

  46. Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: A conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006)

    Article  MathSciNet  Google Scholar 

  47. Strobl, C., Malley, J. & Tutz, G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14, 323–348 (2009)

    Article  Google Scholar 

  48. Hapfelmeier, A., Hothorn, T., Ulm, K. & Strobl, C. A new variable importance measure for random forests with missing data. Stat. Comput. 24, 21–34 (2012)

    Article  MathSciNet  Google Scholar 

  49. Hothorn, T., Hornik, K., Strobl, C. & Zeileis, A. Package ‘Party’: A Laboratory for Recursive Partytioning. R package version 3.1-128. (2015)

  50. Pinheiro, J. C. & Bates, D. M. Linear and nonlinear mixed-effects models, R package version 3.1-128. http://cran.r-project.org/web/packages/nlme/index.html (2016)

Download references

Acknowledgements

This research was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1052875, as part of the working group: Solving the Mystery of Marine Protected Area (MPA) Performance: Linking Governance, Conservation, Ecosystem Services and Human Well Being. D.A.G. was jointly supported by postdoctoral fellowships from the Luc Hoffmann Institute and SESYNC. We thank the following data providers: Atlantic Gulf Rapid Reef Assessment (AGRRA) contributors and data managers, Conservation International, Healthy Reefs Initiative, I. Williams (NOAA Coral Reef Ecosystem Program), NOAA Coral Reef Conservation Program, K. Knights (Global Database for Protected Area Management Effectiveness), G. Edgar and R. Stuart-Smith (Reef Life Surveys), The Nature Conservancy, Wildlife Conservation Society, and the World Conservation Monitoring Centre. We also thank other members of the SESYNC MPA Pursuit team: A. Agrawal, G. Cid, A. Henshaw, I. Nur Hidayat, W. Liang, P. McConney, M. Nenadovic, J. E. Parks, B. Pomeroy, C. Strasser and M. Webster, and P. Marchand of SESYNC for scientific support. We acknowledge GEF, USAID, and the many other funders who supported authors’ time and data collection. This is contribution no. 9 of the research initiative Solving the Mystery of MPA Performance.

Author information

Authors and Affiliations

Authors

Contributions

H.E.F. and M.B.M. conceived the study. D.A.G. led the analysis and data compilation with the assistance of H.E.F., M.B.M., G.N.A., L.G., S.E.L., M.B., I.C., E.S.D., C.M.F., J.G., S.H., O.P.J., L.C., G.G., P.J.M, H.T., S.W. and S.W. C.M.F. prepared the maps. D.A.G., H.E.F., M.B.M., G.N.A., L.G. and S.E.L. wrote the manuscript with the input of all the other authors.

Corresponding author

Correspondence to David A. Gill.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks A. Rosenberg, B. Worm and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Key domains and illustrative indicators for assessing management efficacy and equity.

Indicators with asterisks are those that were used in this study. Details on indicator descriptions, sources and citations are located in Supplementary Table 1.

Extended Data Figure 2 Sources and major steps in the data compilation and analysis.

See Supplementary Table 2 for more details on data sources. CRCP, Coral Reef Conservation Program.

Extended Data Figure 3 Per cent of MPAs by managing authority exceeding or falling below threshold values for indicators of effective and equitable management processes.

Details on indicators, scores and threshold values in Supplementary Tables 1 and 3. Dark blue bars (right) indicate the proportion of MPAs with scores at or above the threshold value, light blue bars (left) indicate the proportion below the threshold. Scores are from the latest assessment year where data were available from 433 MPAs.

Source data

Extended Data Figure 4 Mean fish biomass response ratios (lnRR) by fishing regulations.

Mean (dot) and 95% confidence intervals (error bars) for areas where fishing is prohibited (dark blue) and multi-use MPA areas (light blue) in 254 zones in 218 MPAs.

Source data

Extended Data Figure 5 Relationship between mean fish biomass response ratios (lnRR) and key predictor variables used in the analysis of the relationship between MPA management processes and ecological impact (n ≤ 62 MPAs).

aj, Mean (dot) and 95% confidence intervals (error bars) of the response ratios for each management score and indicator. Details on threshold levels and score descriptions in Supplementary Table 3. kt, Smoothed LOESS lines (blue line) along with the standard error regions (shaded area) for relationships with continuous variables. Number of MPAs in parentheses.

Source data

Extended Data Figure 6 Spearman rank correlations amongst management indicators, national variables and other key variables (n = 433 MPAs).

Variables ordered using hierarchical clustering, displaying values for significant correlations only (P < 0.05). Circle size and colour indicate the correlative strength and direction, respectively (blue, positive; red, negative). Most of the management indicators for procedural efficacy were significantly correlated with each other (for example, correlation coefficient for monitoring and management plan = 0.49). National level variables (GDP, HDI) were poorly correlated with management indicators and were not included in this study. ENF, acceptable enforcement capacity; BGT, acceptable budget capacity; REG, appropriate MPA regulations; MON, monitoring informing management activities; MPL, implementing existing management plan; BND, clearly defined boundaries; LEG, legally gazetted; STF, adequate staff capacity/presence; DEV, non-state/shared management; IDM, inclusive decision-making; SIZ, MPA size (ln[km2]); AGE, MPA age (ln[years]); HDI, Human Development Index 2010; GDP, gross domestic product per capita (ln[US$ PPP]) 2013.

Source data

Extended Data Figure 7 Spearman rank correlations amongst fish metrics, management indicators, and other key variables for the 62 MPAs used in the management and ecological data analysis.

Circle size and colour indicate the correlative strength and direction, respectively (blue, positive; red, negative). Variables ordered by type (that is, ecological, management, and so on) and not hierarchical clusters, displaying values for significant correlations only (P < 0.05). BIO, lnRR; DEN, natural logarithm of fish density response ratio; FSZ, natural logarithm of fish mean size response ratio; RCH, natural logarithm of fish species richness response ratio; DEV, non-state/shared management; IDM, inclusive decision-making; LEG, legally gazetted; REG, appropriate MPA regulations; BND, clearly defined boundaries; ENF, acceptable enforcement capacity; MON, monitoring informing management activities; MPL, implementing existing management plan; STF, adequate staff capacity/presence; BGT, acceptable budget capacity; NTZ, proportion of survey sites for an MPA sampled from within a prohibited-fishing (no-take) zone; SIZ, MPA size (ln[km2]); AGE, MPA age (ln[years]); CHO, chlorophyll a concentration (ln[mg m−3]); SHR, distance from shore (ln[km]).

Source data

Extended Data Figure 8 Frequency distribution of MPA management, ecological and other key variables.

an, White bars indicate the distribution of scores from the latest available management assessments in 433 MPAs (aj); MPAs where fish biomass data were available (n ≤ 218 MPAs) (k–n). Grey bars indicate MPAs used in the analysis modelling the relationship between management processes and ecological impact (n ≤ 62 MPAs). Indicators for inclusive decision-making (b) and enforcement (g) have a maximum score of 2. Non-integer values were reported scores by few managers, or represent the median value of multiple assessments in the latest year. k, Mean (MPA-level) response ratios (natural log scale) for fish biomass. l, Proportion of survey sites for an MPA sampled from within a prohibited-fishing (no-take) zone (0, all multi-use area; 1, all no-take/prohibited fishing area). m, MPA age (years between establishment and fish survey). n, MPA size (thousand km2). MPA age and size were transformed to the log scale for the analysis.

Extended Data Figure 9 Random forest variable importance plots.

Random forest variable importance measures for management (blue bars) and other (non-management; grey bars) variables as they relate to ecological impact in 62 MPAs. a, b, Results from models with all management indicators (as shown in Fig. 3a in the main text) (a) and management indicators with few missing data and not highly correlated with other predictors (that is, excluding legal status, acceptable budget, management plan, country and ecoregion) (b). Only values greater than the red dashed line are considered to have non-random importance scores. c, d, Predicted and observed response ratio values from the random forest models in a and b respectively, along with the linear fitted line (dashed blue line) and a smoothed LOESS line along with the standard error region (grey line and shaded area). R2 values for the linear fit are also shown.

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Tables 1-10 and additional references.This file was replaced on 29 March 2017 to add a reference. (PDF 702 kb)

Supplementary Data 1

This file contains the MPA management assessment data, a subset of which was used in the analysis. (CSV 59 kb)

Supplementary Data 2

This file contains the MPA fish biomass response ratio data, a subset of which was used in the analysis. (CSV 24 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gill, D., Mascia, M., Ahmadia, G. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017). https://doi.org/10.1038/nature21708

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature21708

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing