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A forest loss report card for the world’s protected areas

Nature Ecology & Evolution volume 5, pages 520–529 (2021)Cite this article

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Abstract

Protected areas are a key tool in the conservation of global biodiversity and carbon stores. We conducted a global test of the degree to which more than 18,000 terrestrial protected areas (totalling 5,293,217 km2) reduce deforestation in relation to unprotected areas. We also derived indices that quantify how well countries’ forests are protected, both in terms of forested area protected and effectiveness of protected areas at reducing deforestation, in relation to vertebrate species richness, aboveground forest carbon biomass and background deforestation rates. Overall, protected areas did not eliminate deforestation, but reduced deforestation rates by 41%. Protected area deforestation rates were lowest in small reserves with low background deforestation rates. Critically, we found that after adjusting for effectiveness, only 6.5%—rather than 15.7%—of the world’s forests are protected, well below the Aichi Convention on Biological Diversity’s 2020 Target of 17%. We propose that global targets for protected areas should include quantitative goals for effectiveness in addition to spatial extent.

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Fig. 1: Locations of the 18,171 PAs in our main analysis and global deforestation rates.
Fig. 2: Deforestation rate distributions.
Fig. 3: Forest loss in and around PAs.
Fig. 4: Effects of control area deforestation rate and PA area on PA deforestation rates.
Fig. 5: Forest biodiversity threat index.
Fig. 6: Annual deforestation rate and total aboveground forest carbon versus adjusted forested area protected.

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Data availability

All data used are publicly available. Sources for the data are given in the Methods section.

Code availability

Analysis code is available at https://github.com/wolfch2/PA_matching.

References

  1. Ceballos, G. et al. Accelerated modern human-induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  2. De Groot, R. S., Alkemade, R., Braat, L., Hein, L. & Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272 (2010).

    Article  Google Scholar 

  3. Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Protected Planet Report 2016 (UNEP-WCMC and IUCN, 2016).

  5. Barnes, M. D., Glew, L., Wyborn, C. & Craigie, I. D. Prevent perverse outcomes from global protected area policy. Nat. Ecol. Evol. 2, 759–762 (2018).

    Article  PubMed  Google Scholar 

  6. Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).

    Article  Google Scholar 

  8. The State of the World’s Forests 2020 (FAO and UNEP, 2020).

  9. Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Coetzee, B. W., Gaston, K. J. & Chown, S. L. Local scale comparisons of biodiversity as a test for global protected area ecological performance: a meta-analysis. PLoS ONE 9, e105824 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Nolte, C., Agrawal, A., Silvius, K. M. & Soares-Filho, B. S. Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 110, 4956–4961 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. Spracklen, B., Kalamandeen, M., Galbraith, D., Gloor, E. & Spracklen, D. V. A global analysis of deforestation in moist tropical forest protected areas. PLoS ONE 10, e0143886 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Ewers, R. M. & Rodrigues, A. S. Estimates of reserve effectiveness are confounded by leakage. Trends Ecol. Evol. 23, 113–116 (2008).

    Article  PubMed  Google Scholar 

  19. Fuller, C., Ondei, S., Brook, B. W. & Buettel, J. C. First, do no harm: a systematic review of deforestation spillovers from protected areas. Glob. Ecol. Conserv. 18, e00591 (2019).

    Article  Google Scholar 

  20. Stolton, S. et al. in Protected Area Governance and Management (eds Worboys, G. L. et al.) 145–168 (ANU Press, 2015).

  21. Scharlemann, J. P. et al. Securing tropical forest carbon: the contribution of protected areas to REDD. Oryx 44, 352–357 (2010).

    Article  Google Scholar 

  22. Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).

    Article  Google Scholar 

  24. Amano, T. et al. Successful conservation of global waterbird populations depends on effective governance. Nature 553, 199–202 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Leader-Williams, N. & Albon, S. Allocation of resources for conservation. Nature 336, 533–535 (1988).

    Article  Google Scholar 

  26. Jachmann, H. Monitoring law-enforcement performance in nine protected areas in Ghana. Biol. Conserv. 141, 89–99 (2008).

    Article  Google Scholar 

  27. Critchlow, R. et al. Improving law-enforcement effectiveness and efficiency in protected areas using ranger-collected monitoring data. Conserv. Lett. 10, 572–580 (2017).

    Article  Google Scholar 

  28. Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).

    Article  Google Scholar 

  29. Waldron, A. et al. Targeting global conservation funding to limit immediate biodiversity declines. Proc. Natl Acad. Sci. USA 110, 12144–12148 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  31. Bruner, A. G., Gullison, R. E. & Balmford, A. Financial costs and shortfalls of managing and expanding protected-area systems in developing countries. BioScience 54, 1119–1126 (2004).

    Article  Google Scholar 

  32. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

  33. Report of the Conference of the Parties on its Sixteenth Session, held in Cancun from 29 November to 10 December 2010. Addendum. Part Two: Action Taken by the Conference of the Parties at its Sixteenth Session Report FCCC/CP/2010/7/Add.1 (UNFCCC, 2011).

  34. Fletcher, R., Dressler, W., Büscher, B. & Anderson, Z. R. Questioning REDD+ and the future of market-based conservation. Conserv. Biol. 30, 673–675 (2016).

    Article  PubMed  Google Scholar 

  35. Ministerio de Ambiente y Desarrollo Sostenible, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Política Nacional para la Gestión Integral de la Biodiversidad y Sus Servicios Ecosistémicos (MADS, 2012).

  36. Sims, K. R. E. & Alix-Garcia, J. M. Parks versus PES: evaluating direct and incentive-based land conservation in Mexico. J. Environ. Econ. Manag. 86, 8–28 (2017).

    Article  Google Scholar 

  37. James, A. N., Green, M. J. B. & Paine, J. R. A Global Review of Protected Area Budgets and Staff WCMC Biodiversity Series No.10 (World Conservation Press, 1999).

  38. Walker, S., Price, R., Rutledge, D., Stephens, R. T. & Lee, W. G. Recent loss of indigenous cover in New Zealand. New Zeal. J. Ecol. 30, 169–177 (2006).

    Google Scholar 

  39. Ewers, R. M. et al. Past and future trajectories of forest loss in New Zealand. Biol. Conserv. 133, 312–325 (2006).

    Article  Google Scholar 

  40. Sodhi, N. S. et al. The state and conservation of Southeast Asian biodiversity. Biodivers. Conserv. 19, 317–328 (2010).

    Article  Google Scholar 

  41. Grossman, G. M. & Krueger, A. B. Environmental Impacts of a North American Free Trade Agreement (National Bureau of Economic Research, 1991).

  42. Locke, H. et al. Three global conditions for biodiversity conservation and sustainable use: an implementation framework. Natl Sci. Rev. 6, 1080–1082 (2019).

    Article  Google Scholar 

  43. Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Walker, N., Patel, S., Davies, F., Milledge, S. & Hulse, J. Demand-Side Interventions to Reduce Deforestation and Forest Degradation (International Institute for Environment and Development, 2013).

  45. Marie-Vivien, D., Garcia, C. A., Kushalappa, C. G. & Vaast, P. Trademarks, geographical indications and environmental labelling to promote biodiversity: the case of agroforestry coffee in India. Dev. Policy Rev. 32, 379–398 (2014).

    Google Scholar 

  46. Symes, W. S., Rao, M., Mascia, M. B. & Carrasco, L. R. Why do we lose protected areas? Factors influencing protected area downgrading, downsizing and degazettement in the tropics and subtropics. Glob. Change Biol. 22, 656–665 (2016).

    Article  Google Scholar 

  47. Adams, W. M. et al. Biodiversity conservation and the eradication of poverty. Science 306, 1146–1149 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. Belle, E. et al. Protected Planet Report 2018 (UNEP-WCMC, IUCN and NGS, 2018).

  49. Geldmann, J. et al. Essential indicators for measuring area-based conservation effectiveness in the post-2020 global biodiversity framework. Preprint at https://doi.org/10.20944/preprints202003.0370.v1 (2020).

  50. Protected Areas Management Effectiveness Methodologies (Protected Planet 2020); http://go.nature.com/3ptIPHA

  51. Ervin, J. Rapid assessment of protected area management effectiveness in four countries. BioScience 53, 833–841 (2003).

    Article  Google Scholar 

  52. Conservancy, N. Conservation Action Planning: Developing Strategies, Taking Action, and Measuring Success at any Scale: Overview of Basic Practices (Nature Conservancy, 2007).

  53. Hockings, M. et al. The World Heritage Management Effectiveness Workbook: 2007 Edition: How to Build Monitoring, Assessment and Reporting Systems to Improve the Management Effectiveness of Natural World Heritage Sites 3rd draft (Univ. Queensland, 2007).

  54. Moomaw, W. R., Masino, S. A. & Faison, E. K. Intact forests in the United States: proforestation mitigates climate change and serves the greatest good. Front. For. Glob. Change 2, 27 (2019).

    Article  Google Scholar 

  55. Stolton, S., Hockings, M., Dudley, N., MacKinnon, K. & Whitten, T. Reporting Progress in Protected Areas: A Site-Level Management Effectiveness Tracking Tool (World Bank/WWF Alliance for Forest Conservation and Sustainable Use, 2003).

  56. Hockings, M. et al. The IUCN green list of protected and conserved areas: setting the standard for effective area-based conservation. Parks 25, 57–66 (2019).

    Google Scholar 

  57. Locke, H. Nature needs half: a necessary and hopeful new agenda for protected areas. Nat. N. South Wales 58, 7–17 (2014).

    Google Scholar 

  58. Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (WW Norton & Company, 2016).

  59. The World Database on Protected Areas (WDPA) (IUCN and UNEP-WCMC, accessed 1 January 2020); https://www.protectedplanet.net/

  60. Iacus, S. M., King, G. & Porro, G. Causal inference without balance checking: coarsened exact matching. Polit. Anal. 20, 1–24 (2012).

    Article  Google Scholar 

  61. Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538–549 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 (Columbia Univ. Center for International Earth Science Information Network, 2018).

  65. Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).

    Article  Google Scholar 

  66. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Bode, M., Tulloch, A. I., Mills, M., Venter, O. & Ando, W. A. A conservation planning approach to mitigate the impacts of leakage from protected area networks. Conserv. Biol. 29, 765–774 (2015).

    Article  PubMed  Google Scholar 

  68. Carranza, T., Balmford, A., Kapos, V. & Manica, A. Protected area effectiveness in reducing conversion in a rapidly vanishing ecosystem: the Brazilian Cerrado. Conserv. Lett. 7, 216–223 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  70. Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. B 278, 1633–1638 (2010).

    Article  PubMed  Google Scholar 

  71. Iacus, S. M., King, G. & Porro, G. CEM: software for coarsened exact matching. J. Stat. Softw. 30, 1–27 (2009).

    Article  Google Scholar 

  72. Rosenbaum, P. R. Sensitivity analysis for m-estimates, tests, and confidence intervals in matched observational studies. Biometrics 63, 456–464 (2007).

    Article  CAS  PubMed  Google Scholar 

  73. Keele, L. An Overview of rbounds: an R Package for Rosenbaum Bounds Sensitivity Analysis with Matched Data White Paper, Columbus 1–15 (2010); https://go.nature.com/2M5DKXM

  74. Keele, L. J. rbounds: Perform Rosenbaum Bounds Sensitivity Tests for Matched and Unmatched Data. R Package (2014); https://cran.r-project.org/package=rbounds

  75. World Development Indicators 2018 (World Bank, 2018).

  76. Conner, M. M., Saunders, W. C., Bouwes, N. & Jordan, C. Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration. Environ. Monit. Assess. 188, 555 (2016).

    Article  PubMed Central  Google Scholar 

  77. Murakami, D. spmoran (ver. 0.2.0): an R package for Moran eigenvector-based scalable spatial additive mixed modeling. Preprint at https://arxiv.org/abs/1703.04467v9 (2017).

  78. Murakami, D. & Griffith, D. A. Spatially varying coefficient modeling for large datasets: eliminating N from spatial regressions. Spat. Stat. 30, 39–64 (2019).

    Article  Google Scholar 

  79. Murakami, D. & Griffith, D. A. Balancing spatial and non-spatial variation in varying coefficient modeling: a remedy for spurious correlation. Preprint at https://arxiv.org/abs/2005.09981 (2020).

  80. Walker, W. et al. Forest carbon in Amazonia: the unrecognized contribution of Indigenous territories and protected natural areas. Carbon Manag. 5, 479–485 (2014).

    Article  CAS  Google Scholar 

  81. Robinson, E. J., Albers, H. J. & Busby, G. M. The impact of buffer zone size and management on illegal extraction, park protection, and enforcement. Ecol. Econ. 92, 96–103 (2013).

    Article  Google Scholar 

  82. Koop, G. & Tole, L. Is there an environmental Kuznets curve for deforestation? J. Dev. Econ. 58, 231–244 (1999).

    Article  Google Scholar 

  83. Barnes, M. D., Craigie, I. D., Dudley, N. & Hockings, M. Understanding local-scale drivers of biodiversity outcomes in terrestrial protected areas. Ann. NY Acad. Sci. 1399, 42–60 (2017).

    Article  PubMed  Google Scholar 

  84. Chamberlin, T. C. The method of multiple working hypotheses. Science 15, 92–96 (1890).

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA

    Christopher Wolf & Matthew G. Betts

  2. Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR, USA

    Taal Levi

  3. Global Trophic Cascades Program, Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA

    William J. Ripple

  4. Forest Biodiversity Research Network, Oregon State University, Corvallis, OR, USA

    Diego A. Zárrate-Charry & Matthew G. Betts

  5. Proyecto de Conservación de Aguas y Tierras, Bogotá, Colombia

    Diego A. Zárrate-Charry

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  1. Christopher Wolf
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Contributions

C.W. and M.G.B. conceived the project. C.W. conducted the data analysis and wrote the first draft with input from T.L., W.J.R., D.A.Z.-C. and M.G.B. All authors edited the manuscript.

Corresponding author

Correspondence to Christopher Wolf.

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The authors declare no competing interests.

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Peer review information Nature Ecology & Evolution thanks Jonas Geldmann 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

Extended Data Fig. 1 The distribution of IUCN categories for the 18,171 PAs in our primary spatial analysis.

Protected area categories are: Ia – ‘Strict Nature Reserve,’ Ib – ‘Wilderness Area,’ II – ‘National Park,’ III – ‘Natural Monument or Feature,’ IV – ‘Habitat/Species Management Area,’ V – ‘Protected Landscape/ Seascape,’ VI – ‘Protected area with sustainable use of natural resources.’ The protected areas were split into ‘Strict’ (categories I-IV), ‘Nonstrict’ (categories V-VI), and ‘Unknown’ (any other category).

Extended Data Fig. 2 Net annual forest loss rate within protected areas and in matched control areas.

In contrast to the forest loss results, net loss is not a true percentage since loss and gain are binary while cover is continuous (see SI Methods for details). Results are grouped by geographic region and PA category (IUCN category I-IV: ‘Strict,’ V-VI: ‘Nonstrict’). Points correspond to median (across PAs) percentage forest loss. Error bar end points are the 1st and 3rd quartiles for this variable. Forest loss within protected areas has generally been less than in nearby unprotected areas.

Extended Data Fig. 3 Change in the annual forest loss rate associated with the creation of PAs.

The change variable is the deforestation rate after minus before creation of a PA. Results are grouped by geographic region and PA category (IUCN category I-IV: ‘Strict,’ V-VI: ‘Nonstrict’). Points correspond to means, and error bars show standard errors.

Extended Data Fig. 4 Predictors of deforestation rates within protected areas.

Each row shows a different predictor variable, and the columns show coefficient estimates, standard errors, and FDR-adjusted p-values. Because a spatially varying coefficient model was used, estimates, etc. can all vary geographically. Travel time to nearest densely-populated area was also included as a predictor, but it was found to be non-significant, with no evidence of spatial variability. Only coefficients with associated p-value less than 0.05 are mapped.

Extended Data Fig. 5 Threatened and non-threatened forest vertebrate species richness.

We considered these spatial variables as predictors of deforestation within protected areas to explore relationships between PA effectiveness (with respect to limiting deforestation) and biodiversity.

Extended Data Fig. 6 Sensitivity analysis exploring the effect of stricter matching criteria.

Medians (center points) and 1st and 3rd quartiles (ranges) are shown. The first row is for our primary matching dataset (see Fig. 3) based on five classes per continuous matching covariate while the second row shows results based on 10 classes per covariate (only 9 were used for travel time – see Supplementary Methods). Overall, the use of stricter matching criteria did not appear to considerably alter our results.

Extended Data Fig. 7 Predictors of deforestation rates within protected areas for dataset using stricter matching criteria.

Travel time to nearest densely-populated area (p=0.20) was not spatially varying and is not shown in order to parallel our main results (Extended Data Fig. 4). Additionally, population density, PA age, and strict protection were all found to be constant spatially for this restricted dataset. Only coefficients with associated p-value less than 0.05 are mapped.

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Wolf, C., Levi, T., Ripple, W.J. et al. A forest loss report card for the world’s protected areas. Nat Ecol Evol 5, 520–529 (2021). https://doi.org/10.1038/s41559-021-01389-0

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