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.
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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.
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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.
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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.
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.
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).
a–j, 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. k–t, Smoothed LOESS lines (blue line) along with the standard error regions (shaded area) for relationships with continuous variables. Number of MPAs in parentheses.
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.
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]).
Extended Data Figure 8 Frequency distribution of MPA management, ecological and other key variables.
a–n, White bars indicate the distribution of scores from the latest available management assessments in 433 MPAs (a–j); 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.
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)
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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
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DOI: https://doi.org/10.1038/nature21708
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