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.

  • Analysis
  • Published:

The geometry of global protected lands

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

Subjects

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.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

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.

Similar content being viewed by others

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.

References

  1. Dudley, N., Stolton, S. & Shadie, P. Guidelines for Applying Protected Area Management Categories (IUCN, 2008).

  2. Decision X/2: The Strategic Plan for Biodiversity 2011–2020 and the Aichi Biodiversity Targets (UNEP/CBD/COP, 2011).

  3. Baldi, G. Nature protection across countries: do size and power matter? J. Nat. Conserv. 56, 125860 (2020).

    Article  Google Scholar 

  4. Kunming–Montreal Global Biodiversity Framework (GBF) (UNEP/CBD, 2021).

  5. Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).

    Article  CAS  Google Scholar 

  6. Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).

    Article  CAS  Google Scholar 

  7. Geldmann, J., Manica, A., Burgess, N., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 201908221 (2019).

    Article  Google Scholar 

  8. Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 6390 (2018).

    Article  Google Scholar 

  9. Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S. Accelerated human population growth at protected area edges. Science 321, 123–126 (2008).

    Article  CAS  Google Scholar 

  10. Defries, R., Hansen, A., Newton, A. C. & Hansen, M. C. Increasing isolation of protected areas in tropical forests over the past twenty years. Ecol. Appl. 15, 19–26 (2005).

    Article  Google Scholar 

  11. Joppa, L. N., Loarie, S. R. & Pimm, S. L. On the protection of “protected areas”. Proc. Natl Acad. Sci. USA 105, 6673–6678 (2008).

    Article  CAS  Google Scholar 

  12. Buxton, R. T. et al. Noise pollution is pervasive in US protected areas. Science 356, 531 (2017).

    Article  CAS  Google Scholar 

  13. Fuente, B. D. L. et al. Built-up areas within and around protected areas: global patterns and 40-year trends. Glob. Ecol. Conserv. 24, e01291 (2020).

    Google Scholar 

  14. Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).

    Article  CAS  Google Scholar 

  15. Hansen, A. & Defries, R. Ecological mechanisms linking protected areas to surrounding lands. Ecol. Appl. 17, 974–988 (2007).

    Article  Google Scholar 

  16. Liu, X. et al. Animal invaders threaten protected areas worldwide. Nat. Commun. 11, 2892 (2020).

    Article  CAS  Google Scholar 

  17. Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti–Mara ecosystem. Science 363, 1424–1428 (2019).

    Article  CAS  Google Scholar 

  18. Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).

    Article  CAS  Google Scholar 

  19. Güneralp, B. & Seto, K. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ. Res. Lett. 8, 014025 (2013).

    Article  Google Scholar 

  20. Laurance, W. F. Do edge effects occur over large spatial scales? Trends Ecol. Evol. 15, 134–135 (2000).

    Article  CAS  Google Scholar 

  21. Ries, L., Murphy, S. M., Wimp, G. M. & Fletcher, R. J. Closing persistent gaps in knowledge about edge ecology. Curr. Landsc. Ecol. Rep. 2, 30–41 (2017).

    Article  Google Scholar 

  22. Faust, C. L. et al. Pathogen spillover during land conversion. Ecol. Lett. 21, 471–483 (2018).

    Article  Google Scholar 

  23. Gibb, R. et al. Zoonotic host diversity increases in human-dominated ecosystems. Nature 584, 398–402 (2020).

    Article  CAS  Google Scholar 

  24. Wilkinson, D. A., Marshall, J. C., French, N. P. & Hayman, D. T. S. Habitat fragmentation, biodiversity loss and the risk of novel infectious disease emergence. J. R. Soc. Interface 15, 20180403 (2018).

    Article  Google Scholar 

  25. van Velden, J. L. et al. Bushmeat hunting and consumption is a pervasive issue in African savannahs: insights from four protected areas in Malawi. Biodivers. Conserv. 29, 1443–1464 (2020).

    Article  Google Scholar 

  26. Curran, L. M. et al. Lowland forest loss in protected areas of Indonesian Borneo. Science 303, 1000–1003 (2004).

    Article  CAS  Google Scholar 

  27. Ward, M. et al. Just ten percent of the global terrestrial protected area network is structurally connected via intact land. Nat. Commun. 11, 4563 (2020).

    Article  CAS  Google Scholar 

  28. Hillman, J. R., Lundquist, C. J. & Thrush, S. F. The challenges associated with connectivity in ecosystem processes. Front. Mar. Sci. 5, 364 (2018).

    Article  Google Scholar 

  29. Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376, 1101–1104 (2022).

    Article  CAS  Google Scholar 

  30. Brondízio, E., Settele, J., Diaz, S. & Ngo, H. T. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2021).

  31. Sherbinin, A. & Freudenberger, M. Migration to protected areas and buffer zones; can we stem the tide? Parks 8, 38–53 (1998).

    Google Scholar 

  32. Scholte, P. Immigration: a potential time bomb under the integration of conservation and development. Ambio 32, 58–64 (2003).

    Article  Google Scholar 

  33. Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).

    Article  CAS  Google Scholar 

  34. Wauchope, H. S. et al. Protected areas have a mixed impact on waterbirds, but management helps. Nature 605, 103–107 (2022).

    Article  CAS  Google Scholar 

  35. Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).

    Article  CAS  Google Scholar 

  36. Gatiso, T. T. et al. Effectiveness of protected areas influenced by socio-economic context. Nat. Sustain. 5, 861–868 (2022).

    Article  Google Scholar 

  37. Ebregt, A. & De Greve, P. Buffer Zones and Their Management (National Reference Centre for Nature Management, 2000).

  38. Wells, M. & McShane, T. Integrating protected area management with local needs and aspirations. Ambio 33, 513–519 (2005).

    Article  Google Scholar 

  39. User Manual for the World Database on Protected Areas and World Database on Other Effective Area-Based Conservation Measures: 1.6 (UNEP and WCMC, 2019).

  40. Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).

    Article  Google Scholar 

  41. Golden Kroner, R. E. et al. The uncertain future of protected lands and waters. Science 364, 881–886 (2019).

    Article  CAS  Google Scholar 

  42. Kashwan, P. Inequality, democracy, and the environment: a cross-national analysis. Ecol. Econ. 131, 139–151 (2017).

    Article  Google Scholar 

  43. Ramutsindela, M., Guyot, S., Boillat, S., Giraut, F. & Bottazzi, P. The geopolitics of protected areas. Geopolitics 25, 240–266 (2020).

    Article  Google Scholar 

  44. Marinaro, S., Grau, R. H. & Aráoz, E. Extent and originality in the creation of national parks in relation to government and economical changes in Argentina. Ecol. Austral 22, 1–10 (2012).

  45. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    Article  Google Scholar 

  46. Hoekstra, J. M., Boucher, T. M., Ricketts, T. H. & Roberts, C. Confronting a biome crisis: global disparities of habitat loss and protection. Ecol. Lett. 8, 23–29 (2005).

    Article  Google Scholar 

  47. 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  Google Scholar 

  48. Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. https://doi.org/10.1126/sciadv.aaw2869 (2019).

  49. Allan James, R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Science 376, 1094–1101 (2022).

    Article  CAS  Google Scholar 

  50. Raskin, P. et al. The Great Transition: The Promise and Lure of Times Ahead (Stockholm Environment Institute, 2002).

  51. World Database on Protected Areas (UNEP and WCMC, 2019); https://www.protectedplanet.net/en

  52. Long, G. Conceptions générales sur la cartographie biogeographique intégreé de la vegetation et de son écologie (Centre d’etudes phytosociologiques et ecologiques, 1968).

  53. Jaeger, J. A. G. Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landsc. Ecol. 15, 115–130 (2000).

    Article  Google Scholar 

  54. Legendre, P. & Legendre, L. F. J. Numerical Ecology (Elsevier Science, 1998).

  55. Oksanen, J. et al. vegan: Community ecology package. R package version 2.6-2 https://cran.r-project.org/web/packages/vegan/ (2022).

  56. Sievert, C. et al. plotly: Create interactive web graphics via “plotly.js”. R package version 4.10.0 https://cran.r-project.org/web/packages/plotly/ (2021).

  57. Wei, T. et al. corrplot: Visualization of a correlation matrix. R package version 0.92 https://cran.r-project.org/web/packages/corrplot/ (2021).

  58. Wilson, J. P. et al. A five-star guide for achieving replicability and reproducibility when working with GIS software and algorithms. Ann. Am. Assoc. Geogr. 111, 1311–1317 (2021).

    Google Scholar 

Download references

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

Authors
  1. Santiago A. Schauman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Josep Peñuelas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Esteban G. Jobbágy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Germán Baldi
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Santiago A. Schauman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

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

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-023-01243-0

This article is cited by

Search

Advanced 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