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.

Protected areas have a mixed impact on waterbirds, but management helps

Abstract

International policy is focused on increasing the proportion of the Earth’s surface that is protected for nature1,2. Although studies show that protected areas prevent habitat loss3,4,5,6, there is a lack of evidence for their effect on species’ populations: existing studies are at local scale or use simple designs that lack appropriate controls7,8,9,10,11,12,13. Here we explore how 1,506 protected areas have affected the trajectories of 27,055 waterbird populations across the globe using a robust before–after control–intervention study design, which compares protected and unprotected populations in the years before and after protection. We show that the simpler study designs typically used to assess protected area effectiveness (before–after or control–intervention) incorrectly estimate effects for 37–50% of populations—for instance misclassifying positively impacted populations as negatively impacted, and vice versa. Using our robust study design, we find that protected areas have a mixed impact on waterbirds, with a strong signal that areas managed for waterbirds or their habitat are more likely to benefit populations, and a weak signal that larger areas are more beneficial than smaller ones. Calls to conserve 30% of the Earth’s surface by 2030 are gathering pace14, but we show that protection alone does not guarantee good biodiversity outcomes. As countries gather to agree the new Global Biodiversity Framework, targets must focus on creating and supporting well-managed protected and conserved areas that measurably benefit populations.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Map of study sites.
Fig. 2: Changes in estimates of protected area impact under different study designs.
Fig. 3: Estimates of protected area impact under a BACI study design.
Fig. 4: Predictors of protected area impact.

Data availability

The waterbird count data used in this study are collated and managed by Wetlands International and the National Audubon Society, and are available on request (http://iwc.wetlands.org/index.php/ and http://netapp.audubon.org/cbcobservation/, respectively). We requested all data from both providers for the years 1900–2018, for all waterbird families (see Supplementary Information 2), and for sites in all available countries (though data from Russia was excluded as permissions were not given). All the data that pertain to explanatory variables are freely available, as specified in Extended Data Tables 2, 3.

Code availability

The code used to produce all analysis and figures are archived on Zenodo at https://doi.org/10.5281/zenodo.5794511. Code are also available on GitHub at https://github.com/hannahwauchope/PAImpact; this is the recommended mode of access as it will contain any updates or clarifications.

References

  1. High Ambition Coalition for Nature and People. 50 Countries Announce Bold Commitment to Protect at Least 30% of the World’s Land and Ocean by 2030 (Campaign for Nature, 2021).

  2. Waldron A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).

  3. 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, 23209–23223 (2019).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  4. Nelson, A. & Chomitz, K. M. Protected Area Effectiveness in Reducing Tropical Deforestation (The World Bank, 2009).

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

    Article  Google Scholar 

  6. Feng, Y. et al. Assessing the effectiveness of global protected areas based on the difference in differences model. Ecol. Indic. 130, 108078 (2021).

    Article  Google Scholar 

  7. Laurance, W. F. et al. The fate of Amazonian forest fragments: A 32-year investigation. Biol. Conserv. 144, 56–67 (2011).

    Article  Google Scholar 

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

    Article  CAS  PubMed  ADS  Google Scholar 

  9. Terraube, J., Van doninck, J., Helle, P. & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  10. 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  ADS  Google Scholar 

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

    Article  CAS  PubMed  ADS  Google Scholar 

  12. Kleijn, D., Cherkaoui, I., Goedhart, P. W., van der Hout, J. & Lammertsma, D. Waterbirds increase more rapidly in Ramsar-designated wetlands than in unprotected wetlands. J. Appl. Ecol. 51, 289–298 (2014).

    Article  Google Scholar 

  13. Reyes-Arriagada, R. et al. Population trends of a mixed-species colony of Humboldt and Magellanic Penguins in Southern Chile after establishing a protected area. Avian Conserv. Ecol. 8, 13 (2013).

    Google Scholar 

  14. Bukart, K. Motion 101 passes at IUCN, calls for protecting 50% of Earth’s lands and seas. One Earth https://www.oneearth.org/motion-101-passes-at-iucn-calls-for-protecting-50-of-earths-lands-and-seas/ (2021).

  15. Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021).

  16. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

  17. 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  Google Scholar 

  18. Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592 (2007).

    Article  PubMed  Google Scholar 

  19. 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 

  20. Rodrigues, A. S. L. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  21. Redford, K. H. The empty forest. BioScience 42, 412–422 (1992).

    Article  Google Scholar 

  22. Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. N. Direct. Eval. 2009, 75–84 (2009).

    Article  Google Scholar 

  23. Adams, V. M., Barnes, M. & Pressey, R. L. Shortfalls in conservation evidence: moving from ecological effects of interventions to policy evaluation. One Earth 1, 62–75 (2019).

    Article  ADS  Google Scholar 

  24. Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).

    Article  PubMed  Google Scholar 

  25. Kingsford, R. T., Roshier, D. A. & Porter, J. L. Australian waterbirds time and space travellers in dynamic desert landscapes. Mar. Freshw. Res. 61, 875–884 (2010).

    Article  CAS  Google Scholar 

  26. The Ramsar Convention Secretariat. Managing Ramsar Sites. ramsar.org https://www.ramsar.org/sites-countries/managing-ramsar-sites (2014).

  27. European Commission. The Birds Directive. https://ec.europa.eu/environment/nature/legislation/birdsdirective/index_en.htm (accessed 3 April 2022).

  28. Zhang, W., Sheldon, B. C., Grenyer, R. & Gaston, K. J. Habitat change and biased sampling influence estimation of diversity trends. Curr. Biol. 31, 3656–3662.e3 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Bruner, A. G., Gullison, R. E., Rice, R. E. & da Fonseca, G. A. B. Effectiveness of parks in protecting tropical biodiversity. Science 291, 125–128 (2001).

    Article  CAS  PubMed  ADS  Google Scholar 

  30. 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 

  31. Rabinowitz, D. In The Biological Aspects of Rare Plant Conservation (ed. Synge, H.) 205–217 (John Wiley & Sons, 1981).

  32. Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  33. Hettiarachchi, M., Morrison, T. H. & McAlpine, C. Forty-three years of Ramsar and urban wetlands. Glob. Environ. Change 32, 57–66 (2015).

    Article  Google Scholar 

  34. Munishi, P., Chuwa, J., Kilungu, H., Moe, S. & Temu, R. Management effectiveness and conservation initiatives in the Kilombero Valley Flood Plains Ramsar Site, Tanzania. Tanzania J. For. Nat. Conserv. 81, 1–10 (2012).

    Google Scholar 

  35. Fahrig, L. Why do several small patches hold more species than few large patches? Glob. Ecol. Biogeogr. 29, 615–628 (2020).

    Article  Google Scholar 

  36. Newmark, W. D. Extinction of mammal populations in western North American National Parks. Conserv. Biol. 9, 512–526 (1995).

    Article  Google Scholar 

  37. 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 

  38. Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42 (2017).

    Article  Google Scholar 

  39. Wetlands International. Asian Waterbird Census. https://south-asia.wetlands.org/our-approach/healthy-wetland-nature/asian-waterbird-census/ (accessed 3 April 2022).

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

    Article  CAS  PubMed  ADS  Google Scholar 

  41. 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 

  42. Kingsford, R. T., Bino, G. & Porter, J. L. Continental impacts of water development on waterbirds, contrasting two Australian river basins: global implications for sustainable water use. Glob. Change Biol. 23, 4958–4969 (2017).

    Article  ADS  Google Scholar 

  43. Jia, Q., Wang, X., Zhang, Y., Cao, L. & Fox, A. D. Drivers of waterbird communities and their declines on Yangtze River floodplain lakes. Biol. Conserv. 218, 240–246 (2018).

    Article  Google Scholar 

  44. Lehikoinen, A., Rintala, J., Lammi, E. & Pöysä, H. Habitat-specific population trajectories in boreal waterbirds: alarming trends and bioindicators for wetlands. Animal Conserv. 19, 88–95 (2016).

    Article  Google Scholar 

  45. Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).

    Article  Google Scholar 

  46. Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).

    Article  Google Scholar 

  47. Wauchope, H. et al. Quantifying the impact of protected areas on near-global waterbird population trends, a pre-analysis plan. Preprint at https://doi.org/10.7287/peerj.preprints.27741v2 (2019).

  48. Nosek, B. A., Ebersole, C. R., DeHaven, A. C. & Mellor, D. T. The preregistration revolution. Proc. Natl Acad. Sci. USA 115, 2600–2606 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

  50. QGIS Geographic Information System (QGIS, 2021).

  51. Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).

  52. Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).

  53. The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME) www.protectedplanet.net (UNEP-WCMC and IUCN, 2019).

  54. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (NOAA, 2017).

  55. Coetzer, K. L., Witkowski, E. T. F. & Erasmus, B. F. N. Reviewing Biosphere Reserves globally: effective conservation action or bureaucratic label? Biol. Rev. 89, 82–104 (2014).

    Article  PubMed  Google Scholar 

  56. Ament, J. M. & Cumming, G. S. Scale dependency in effectiveness, isolation, and social-ecological spillover of protected areas. Conserv. Biol. 30, 846–855 (2016).

    Article  PubMed  Google Scholar 

  57. Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).

    Article  Google Scholar 

  58. Salmerón Gómez, R., García, Pérez, J., López Martín, M. D. M. & García, C. G. Collinearity diagnostic applied in ridge estimation through the variance inflation factor. J. Appl. Stat. 43, 1831–1849 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  59. Gu, X. S. & Rosenbaum, P. R. Comparison of multivariate matching methods: structures, distances, and algorithms. J. Comput. Graph. Stat. 2, 405–420 (1993).

    Google Scholar 

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

    Article  MathSciNet  PubMed  PubMed Central  MATH  Google Scholar 

  61. King, G. & Nielsen, R. Why propensity scores should not be used for matching. Pol. Anal. 27, 435–454 (2019).

    Article  Google Scholar 

  62. Rosenbaum, P. R. DOS: design of observational studies. https://cran.r-project.org/web/packages/DOS/index.html (2018).

  63. Linden, A. A matching framework to improve causal inference in interrupted time-series analysis. J. Eval. Clin. Pract. 24, 408–415 (2018).

    Article  PubMed  Google Scholar 

  64. Simmons, B. I., Hoeppke, C. & Sutherland, W. J. Beware greedy algorithms. J. Anim. Ecol. 88, 804–807 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Austin, P. C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28, 3083–3107 (2009).

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  66. Rubin, D. B. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv. Outcomes Res. Methodol. 2, 169–188 (2001).

    Article  Google Scholar 

  67. Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).

  68. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/index.html (2021).

  69. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).

    Google Scholar 

  70. Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).

    Article  Google Scholar 

  71. Christensen, R. Ordinal–regression models for ordinal data. https://cran.r-project.org/web/packages/ordinal/index.html (2019).

  72. Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Op. Source Softw. 3, 772 (2018).

    Article  ADS  Google Scholar 

  73. McKay, M. D., Beckman, R. J. & Conover, W. J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245 (1979).

    MathSciNet  MATH  Google Scholar 

  74. Carnell, R. lhs: latin hypercube samples. https://cran.r-project.org/web/packages/lhs/index.html (2020).

  75. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  76. Lu, C. & Tian, H. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: Shifted hot spots and nutrient imbalance. Earth Syst. Sci. Data 9, 181–192 (2017).

    Article  ADS  Google Scholar 

  77. Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  78. Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).

    Article  ADS  Google Scholar 

  79. Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 17001 (2017).

    Article  Google Scholar 

  80. Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).

    Article  CAS  PubMed  ADS  Google Scholar 

  81. Sandvik, B. World Borders Dataset. Thematic Mapping http://thematicmapping.org/downloads/world_borders.php (2009).

  82. BirdLife International. Species Distribution Data Download http://www.birdlife.org/datazone/info/spcdownload (accessed 25 February 2020).

  83. Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).

    Article  Google Scholar 

  84. WWF International. Management Effectiveness Tracking Tool https://wwfeu.awsassets.panda.org/downloads/mett2_final_version_july_2007.pdf (2007).

Download references

Acknowledgements

We thank the coordinators, thousands of volunteer counters, and funders of the International Waterbird Census. This data collection effort is funded by the Ministry of the Environment of Japan, Environment Canada, AEWA Secretariat, EU LIFE+ NGO Operational Grant, MAVA Foundation, Swiss Federal Office for Environment and Nature, French Ministry of Environment and Sustainable Development, UK Department of Food and Rural Affairs, Norwegian Nature Directorate, Dutch Ministry of Economics, Agriculture and Innovation, DOB Ecology and Wetlands International members. CBC Data is provided by National Audubon Society and through the generous efforts of Bird Studies Canada and countless volunteers across the western hemisphere. H.S.W. was funded by a Cambridge–Australia Poynton Scholarship, Cambridge Department of Zoology J. S. Gardiner Studentship and Cambridge Philosophical Society Grant. H.S.W. and B.I.S. are funded by the Royal Commission for the Exhibition of 1851. W.J.S. was funded by Arcadia, The David and Claudia Harding Foundation and MAVA. J.P.G.J. was supported by a visiting fellowship to Fitzwilliam College Cambridge. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). Finally, the authors would like to acknowledge the use of the University of Exeter High-Performance Computing (HPC) facility in carrying out this work.

Author information

Authors and Affiliations

Authors

Contributions

H.S.W., J.P.G.J., J.G., B.I.S., T.A., R.A.F., A.J. and W.J.S. conceived the study. D.E.B., T.L., T.M. and S.N. provided waterbird count data, which H.S.W. and T.A. collated. H.S.W. performed analysis, produced figures and wrote the text with advice from all authors, especially J.P.G.J., J.G., B.I.S., T.A., A.J. and W.J.S. All authors contributed to the review of the manuscript before submission for publication and approved the final version.

Corresponding authors

Correspondence to Hannah S. Wauchope or Taej Mundkur.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Gergana Daskalova and the other, anonymous reviewers for their contribution to the peer review of this work. Peer review reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Changes in estimates of protected area impact under different study designs, for all analyses.

Proportion of Before-After (BA) or Control-Intervention (CI) populations that changed outcome when analysed under a BACI framework, by each analysis (n = 21; 20 full parameter, plus one focal analysis). Shown for all populations (a), then the proportion of positive (b), no (c) or negative impact populations (d) that changed in outcome. Each point is an analysis, with boxplots showing distribution (box bounded by 25th and 75th percentiles, centre shows 50th percentile, whiskers extend to 1.5*IQR above 75th percentile, for maxima, or below 25th percentile, for minima). Large points show focal analysis estimates.

Extended Data Fig. 2 Estimates of protected area impact under a BACI study design, for all analyses.

Percentage of populations that have been positively, negatively or not impacted by protected areas, by each analysis (n = 21; 20 full parameter analyses, plus one focal analysis). Each point is an analysis, with boxplots showing distribution (box bounded by 25th and 75th percentiles, centre shows 50th percentile, whiskers extend to 1.5*IQR above 75th percentile, for maxima, or below 25th percentile, for minima). Large points show estimates from focal analysis. Panels show estimates under BACI (a), Before-After (b) or Control-Intervention (c) frameworks.

Extended Data Fig. 3 Estimates of protected area impact under a BA study design.

Proportion of populations (n = 6263) showing various responses to protection, per site (a; n = 860) and species (b; n = 66), when response to protection is calculated in a BA framework. Each species/site is one bar, with the proportion of their populations in each category shown on the y axis. Bar width is scaled to the number of populations of that species/site in the dataset, log scaled in the case of species, with a wider bar meaning the species/site has more populations. Each colour represents a different way a population can respond to protection, and an example of each is shown at the bottom. This figure is based on our focal analysis; Extended Data Fig. 2b shows the proportion of populations within each broad outcome category across all full parameter analyses.

Extended Data Fig. 4 Estimates of protected area impact under a CI study design.

Proportion of populations (n = 3783) showing various responses to protection, per site (a; n = 698) and per species (b; n = 32), when response to protection is calculated in a CI framework. Each species/site is one bar, with the proportion of their populations in each category shown on the y axis. Bar width is scaled to the number of populations of that species/site in the dataset, log scaled in the case of species, with a wider bar meaning the species/site has more populations. Each colour represents a different way a population can respond to protection, and an example of each is shown at the bottom. This figure is based on our focal analysis; Extended Data Fig. 2c shows the proportion of populations within each broad outcome category across all full parameter analyses.

Extended Data Fig. 5 Predictors of protected area impact, with odds ratios and confidence intervals.

Odds ratios for covariates predicting protected area (PA) effectiveness under a BACI framework. Estimated using cumulative link mixed models, points show model estimates, tails show 95% confidence intervals, and significance is indicated by bold colours (P < 0.05). Dashed line given at an odds ratio of one (ratios above one indicate a positive relationship, and below one a negative relationship). Y axis shows all analyses (20 full parameter analyses, plus one focal analysis, with the focal analysis given in the first row). Colours show covariate grouping. Orders are measured relative to Anseriformes, and Anthromes relative to Urban. Note that we expect continuous variables (PA Area, Body Size, Governance) to have smaller coefficients as they express odds ratios per unit increment.

Extended Data Fig. 6 Schematic demonstrating matching procedure.

Example of the matching procedure for one species, using a toy dataset of 6 protected sites (A to F) and 3 unprotected sites (X, Y and Z), with three dummy example covariates, climate (cloud), land use (wheat) and human population (person). See methods, ‘Matching’ for a more detailed step by step walk through of this process.

Extended Data Table 1 Parameter estimates and sample sizes across analyses
Extended Data Table 2 Covariates used to perform site matching
Extended Data Table 3 Covariates used to assess what factors affect protected area impact

Supplementary information

Supplementary Information

This file contains Supplementary Sections 1–14, including Supplementary Figs. 1–11, Tables 1–3 and references.

Reporting Summary

Peer Review File

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wauchope, H.S., Jones, J.P.G., Geldmann, J. et al. Protected areas have a mixed impact on waterbirds, but management helps. Nature 605, 103–107 (2022). https://doi.org/10.1038/s41586-022-04617-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-04617-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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