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The geometry of global protected lands

Nature Sustainability volume 7, pages 82–89 (2024)Cite this article

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Abstract

Expanding protected areas (PAs) is a worldwide endeavour aimed at addressing the biodiversity crisis and harnessing the benefits that natural ecosystems provide to humanity. However, conservation agendas often overlook the spatial geometry of PAs—shape and size—and its implications for critical issues such as agricultural encroachment, poaching, biological invasion or the contact between wildlife, domestic animals and humans. Here we show how the global PA expansion has added predominantly small and complex-shaped PAs. Consequently, one-third of protected lands are located within 2 km of unprotected areas, while only 0.6% extend beyond 100 km. Developed countries tend to have smaller, dispersed, perforated and fragmented PAs, while less-affluent nations host larger, more-compact units. Relatively smaller and less-compact PAs were also found within the world’s most critically endangered biomes. The results highlight overlooked threats for the long-term conservation of nature and global environmental sustainability. As countries continue to expand their PA networks, the consideration of the spatial geometry of protected lands becomes urgent.

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Fig. 1: Area under protection along a gradient of distance to the unprotected matrix.
Fig. 2: Relationship between the maximum depth and depth compactness of protected areas.
Fig. 3: Geographic patterns of the geometry of protected areas.
Fig. 4: Temporal trends of the geometry of protected areas.

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Data availability

All databases and datasets used in this study derive from published sources cited in the Methods section. Biome limits were obtained from Ecoregions2017 (available for download from https://ecoregions.appspot.com/). Terrestrial protected areas of the world were obtained from the March 2023 version of the UNEP–WCMC World Database on Protected Areas (available for download from https://www.protectedplanet.net/en). Shorelines were obtained from the World Vector Shoreline (WVS; available for download from https://shoreline.noaa.gov). Country boundaries were obtained from the high-spatial-resolution GADM 3.6 database (available for download from https://gadm.org/). In addition, Supplementary Data 1 and Supplementary Data 2 report the numerical results of the exposure level for each cultural and biogeographic region, as well as individual countries. Source data are provided with this paper.

Code availability

The codes for data processing and analyses are openly available through the Zenodo research data repository (https://doi.org/10.5281/zenodo.8347100). In addition, to enhance the interpretation of the exposure level calculations, we have included on the Zenodo data repository an analytical workflow and a ModelBuilder example for computing exposure levels in ArcGIS format.

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Acknowledgements

We thank A. Persia for her contribution in graphic design. In addition, we extend our thanks to P. Garay and G. Castellanos for their collaboration in the processing of the data used in this study. S.A.S. acknowledges funding from CONICET research grant PU-E, 22920160100037CO and from MINCyT research grant PICT, 2016-0972.

Author information

Authors and Affiliations

  1. Grupo de Estudios Ambientales, IMASL-UNSL/CONICET, San Luis, Argentina

    Santiago A. Schauman, Esteban G. Jobbágy & Germán Baldi

  2. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain

    Josep Peñuelas

  3. CREAF, Cerdanyola del Vallès, Catalonia, Spain

    Josep Peñuelas

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  1. Santiago A. Schauman
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  4. Germán Baldi
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Contributions

All authors made substantial contributions to the conception or design of the study. S.A.S., E.G.J. and G.B. designed the study with contributions from J.P. S.A.S. and G.B. obtained the data and performed the analysis. S.A.S., J.P., E.G.J. and G.B. discussed the results, contributed critically to the drafts and gave final approval for publication.

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Correspondence to Santiago A. Schauman.

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The authors declare no competing interests.

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Nature Sustainability thanks Dawn Wright and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Box plots of the exposure level to the unprotected matrix for each category of the IUCN.

Each sample corresponds to a 1 km spatial resolution cell (n = 15,459,063). Categories I-II represent strict objectives for conserving biodiversity, while categories III-VI represent less stringent objectives, allowing both non-consumptive and consumptive human activities. Box plots show the median (centre line), the first and third quartiles (box limits), and the 95% confidence intervals of the distribution (whiskers).

Source data

Extended Data Fig. 2 Box plots of the exposure level to the unprotected matrix for each cultural region.

Each sample corresponds to a 1 km spatial resolution cell (n = 15,459,063). Box plots show the median (centre line), the first and third quartiles (box limits), and the 95% confidence intervals of the distributions (whiskers).

Source data

Extended Data Fig. 3 Exposure level to the unprotected matrix for each biogeographic region.

(A) Box plots of the exposure level to the unprotected matrix for each biogeographic region. Each sample corresponds to a 1 km spatial resolution cell (n = 15,459,063). Box plots show the median (centre line), the first and third quartiles (box limits), and the 95% confidence intervals of the distributions (whiskers). (B) Cumulative growth curves of the total area protected for “distance to the unprotected matrix” for each biogeographic region. Dots indicate the contact zone, that is the protected cover fraction <2 km from PA edges.

Source data

Extended Data Fig. 4 Cumulative growth curves of the count of all PAs (n = 63,499) for each cultural region.

(A) For PA maximum depth (km). (B) For PA depth compactness (%).

Source data

Extended Data Fig. 5 Kendall’s correlation coefficients between the 13 metrics of spatial geometry and the two NMDS axes analysed (n = 63,499).

Colors represent the strength and sign of the correlation (from negative blue, to white, to positive red).

Source data

Extended Data Fig. 6 Examples of the relationships between global-level NMDS and four significant metrics of spatial geometry used in this study (convex hull, Polsby-Popper, size and depth compactness).

Dots indicate the PA samples (n = 63,499). Colors represent the values for each metric.

Source data

Extended Data Fig. 7 Location of countries within the global-level non-metric multidimensional scaling (NMDS).

(A) All countries analysed. Depicted countries contain at least 0.001% of the global area protected and provide polygonal data on the UNEP-WCMC World Database on Protected Areas (n = 158). (B) Regional detail of location of countries within the NMDS.

Source data

Extended Data Fig. 8 Violin plots of the distribution density kernel (count) for the PA relative size (log %) in each cultural region (n = 63,499).

Violins show the median (centre line), the first and third quartiles (box limits), the 95% confidence intervals of the distribution (whiskers), and outliers (dots).

Source data

Extended Data Fig. 9 Sensitivity to shorelines for the global-level NMDS.

(A) Global-level NMDS with the original dataset of PAs (n = 63,499), (B) global-level NMDS excluding PAs with their boundaries touching shorelines (n = 58,675), (C) regional-level NMDS patterns and (D) regional-level NMDS excluding PAs with their boundaries in contact with shorelines.

Source data

Extended Data Table 1 Metrics used to characterize the geometry of protected areas
Full size table

Supplementary information

Supplementary Information

Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1

Interactive cumulative growth curves of the total area protected for the distance to the unprotected matrix’ for each analysed country and cultural region.

Supplementary Data 2

Interactive cumulative growth curves of the total area protected for the distance to the unprotected matrix for each analysed biogeographic region.

Source data

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Schauman, S.A., Peñuelas, J., Jobbágy, E.G. et al. The geometry of global protected lands. Nat Sustain 7, 82–89 (2024). https://doi.org/10.1038/s41893-023-01243-0

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