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A meta-analysis reveals edge effects within marine protected areas


Marine protected areas (MPAs) play a leading role in conserving and restoring marine environments. MPAs can benefit both marine populations within their boundaries and external populations owing to a net export of organisms (spillover). However, little is known about variation in performance within MPAs. For example, edge effects may degrade populations within MPAs close to their boundaries. Here we synthesize empirical estimates of 72 taxa of fish and invertebrates to explore spatial patterns across the borders of 27 no-take MPAs. We show that there is a prominent and consistent edge effect that extends approximately 1 km within the MPA, in which population sizes on the border are 60% smaller than those in the core area. Our analysis of cross-boundary population trends suggests that, globally, the smallest 64% of no-take MPAs (those of less than 10 km2 in area) may hold only about half (45–56%) of the population size that is implied by their area. MPAs with buffer zones did not display edge effects, suggesting that extending no-take areas beyond the target habitats and managing fishing activities around MPA borders are critical for boosting MPA performance.

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Fig. 1: Hypotheses for spatial patterns of marine populations across MPA borders.
Fig. 2: Map of 27 no-take MPAs for which size estimates of marine population across borders were available and suitable for analysis.
Fig. 3: Spatial patterns of population sizes with distance from MPA borders.
Fig. 4: The global impact of edge effect on no-take MPAs by size categories.
Fig. 5: Spatial patterns of population sizes across MPA borders as a function of MPA and taxa characteristics.

Data availability

All sources of data analysed in this meta-analysis are reported in Supplementary Table 1. The dataset generated during the current study is available at

Code availability

The code generated during the current study and all relevant datasets for analysis are available in the GitHub repository (


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We are grateful to M. Harmelin-Vivien for assisting with access to the BIOMEX-Spillover project database49, to B. Pike from MPAtlas21 for providing access to their database and to D. Shapiro-Goldberg for revising the manuscript. S.O. was partially supported by the Israeli Nature and Parks Authority.

Author information




S.O. conceived the study, formulated the research question, conducted the literature review, collected the data, performed the meta-analyses and wrote the early draft and final version of the manuscript; I.G. provided guidance on the meta-analyses, performed complementary analyses and contributed to the writing of the manuscript; J.B. conceived the study, participated in the formulation of the research question, revised the manuscript and supervised the work. All authors gave final approval for publication and agree to be held accountable for the work performed herein.

Corresponding author

Correspondence to Sarah Ohayon.

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The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Tessa Mazor, Nils Krueck, Kristian Metcalfe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 PRISMA flow diagram describing the review process for studies suitable for the meta-analysis.

The diagram provides details on the steps we took to select studies for inclusion in the meta analysis. We initially identified 974 records. The final number of studies included after screening and eligibility assessment was 24.

Extended Data Fig. 2 Spatial pattern of population sizes with distance from MPA borders (with data points superimposed on the plots).

a, Spatial pattern across MPA borders for the entire dataset (22 km inside the MPAs to 27 km in fished areas; n = 1,619, ΔAICc = 257.9, DE = 45.8%), b, spatial pattern across MPA borders zoomed in to distances between −3 km to 6 km, c, spatial pattern with analysis restricted to within the MPAs for the entire available range (n = 594, ΔAICc = 53.9, DE = 44.9%), d, spatial pattern with analysis restricted to within the MPAs, zoomed in to distances between −3 km to 0 (MPA border). The decline in population size starts approximately 1.0–1.5 km inside the MPAs, indicating a strong edge effect. The y axes are effect size (calculated as log of each data point relative to the innermost value reported within the MPA, for each sample) and the x axes are distance (m) from MPA border. The blue line represents the MPAs border, with negative numbers indicating distances within the MPAs and positive numbers distances in fished areas. The red dashed line represents the edge-effect distance, where the estimated effect size drops below zero, that is, biomass/density/CPUE begin to decrease relative to the innermost value). The tics along the x axes represent data point locations. Grey shaded area around the curve denotes 95% confidence interval.

Extended Data Fig. 3 Sensitivity analysis for the spatial pattern of population sizes across MPA borders.

a, GAM model for distance subset data,−3km inside MPAs to 6 km in fished areas (n = 1,484, k = 39, ΔAICc = 378.15, DE = 58.5% when using distance from MPA border compared to a reduced model excluding distance), b, analysis displaying underlying data points, c, GAM model with k = 20 (ΔAICc =366.39, DE = 57.9%), d, GAM model with k = 50 (ΔAICc =379.13, DE = 58.5%), (c) GAM model with different smooth term for distance: bs = ’tp’ (ΔAICc =360.95, DE = 57.8%), e, GAM model with no weighting parameter (ΔAICc =325.49, DE = 57.2%). Under all these analyses a clear edge effect is still noticeable.

Extended Data Fig. 4 Validation tests for the spatial pattern of population sizes across MPA borders.

a, A model with original population size estimates (density/biomass/CPUE, log-transformed) serving as the response variables, b, a model with relative X axis, where distance from the MPA border is standardized to the innermost location for each unique sample. Under both analyses a clear edge effect is still noticeable.

Extended Data Fig. 5 Spatial pattern of population sizes across MPA borders as a function of habitat variables.

The separate effect of (a) habitat type (coral reef: 12 MPAs, 107 samples, n = 1,024; temperate rocky reef: 14 MPAs, 39 samples, n = 439; ΔAICc = 13.45, DE = 59.4%), b, habitat continuity (habitat is continuous: 25 MPAs, 135 samples, n = 1,387; habitat is non-continuous: 2 MPAs, 12 samples, n = 97; ΔAICc = 3.36, DE = 59.4%), c, quantitative control for habitat quality (quantitative control performed: 13 studies, 19 MPAs, 78 samples, n = 727; no quantitative control: 11 studies, 11 MPAs, 69 samples, n = 757; ΔAICc = 29.17, DE = 59.8%).

Extended Data Fig. 6 Spatial pattern of population sizes with distance from MPA borders as a function of MPA and taxa characteristics (with data points superimposed on the plots).

The separate effect of (a) commercial value (commercial taxa: 124 samples, n = 1,248; non-commercial taxa: 23 samples, n = 236), b, taxa (fish: 125 samples, n = 1,190; invertebrates: 22 samples, n = 294) (c) protection level (low: 54 samples, n = 483; high: 93 samples, n = 1,001), and d, buffer zone (without buffer zone: 106 samples, n = 1,220; with buffer zone: 41 samples, n = 264), on the spatial patterns of marine population sizes across MPA borders. The y axes are effect size (calculated as log of each data point relative to the innermost value reported within the MPA, for each sampling set) and the x axes are distance (m) from MPA border. Blue vertical lines represent the MPA borders with negative numbers indicating distances within the MPAs and positive numbers distances within fished areas. Analysis was performed on a reduced dataset, where the majority of data was concentrated (6 km outside the MPA border and 3 km inside the MPA, resulting in n = 1,484) in order to improve model performance. Shaded area around the model curves denotes 95% confidence intervals.

Extended Data Fig. 7 Spatial pattern of population sizes across MPA borders as a function of their mobility level.

a, Sessile taxa (Mollusca; 8 samples, n = 76) display a limited edge effect of ~ 150 m, b, sedentary taxa (lobsters and low-mobility fish such as Serranidae, Pomacentridae, Chaetodontidae; 16 samples, n = 257) display a deeper edge effect of ~ 400 m, c, mobile taxa display a large edge effect reaching ~1,500 m into the MPA (mobile fish; 100 samples, n = 915). The limited sample size for sessile taxa produces high uncertainty in the estimates. The analysis is restricted to the commercial taxa dataset (124 samples, n = 1,248, ΔAICc = 53.83, DE = 63.8%) to remove the masking created by the spatial patterns associated with the non-commercial taxa.

Extended Data Fig. 8 Spatial pattern of commercial fish populations across MPA borders as a function of MPA age or size.

The separate effect of a, MPA age (young: age = < 10 years, 52 samples, n = 399; old: age >10 years, 56 samples, n = 594; ΔAICc = 7.7, DE = 57.7%), b, MPA size (small: size < 3 km2, 65 samples, n = 735; large: size > 3 km2, 43 samples, n = 258; ΔAICc = 5.9, DE = 57%). Young MPAs present shorter edge-effect distance compared to old MPAs, but with similar magnitude of population size reduction. Similarly, small MPAs present shorter edge-effect distance compared to large MPAs with similar magnitude of population size reduction. The analysis is restricted to the commercial fish dataset (containing 108 samples, n = 993) to avoid the confounding effect stemming from the spatial patterns associated with taxa identity and commercial value.

Extended Data Fig. 9 The relationship between the magnitude of the edge effect and MPA size or age.

a, MPA size (estimate: 0.17, t-value: 0.47), and b, MPA age (estimate: 1.7, t-value: 30.19). The response variable (Y axis) is the linear slope of the relationship between the effect size and distance. When the magnitude of the effect size (total reduction in population size) is similar, a larger edge effect will be manifested as a shallower slope. Data was restricted to (1) within the MPA and until -3,000 m, (2) sets that have at least 3 data points, (3) at least one of the data points for each set is located >0.5 km inside the MPA, (4) fish with commercial value. Analyses are based on linear models weighted by the inverse of the estimated variance of the slope. In all cases, MPA name was added as a random effect.

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Ohayon, S., Granot, I. & Belmaker, J. A meta-analysis reveals edge effects within marine protected areas. Nat Ecol Evol 5, 1301–1308 (2021).

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