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).
Waldron A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).
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).
Google Scholar
Nelson, A. & Chomitz, K. M. Protected Area Effectiveness in Reducing Tropical Deforestation (The World Bank, 2009).
Scharlemann, J. P. W. et al. Securing tropical forest carbon: the contribution of protected areas to REDD. Oryx 44, 352–357 (2010).
Google Scholar
Feng, Y. et al. Assessing the effectiveness of global protected areas based on the difference in differences model. Ecol. Indic. 130, 108078 (2021).
Google Scholar
Laurance, W. F. et al. The fate of Amazonian forest fragments: A 32-year investigation. Biol. Conserv. 144, 56–67 (2011).
Google Scholar
Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Amano, T. et al. Successful conservation of global waterbird populations depends on effective governance. Nature 553, 199–202 (2018).
Google Scholar
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).
Google Scholar
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).
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).
Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021).
Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).
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).
Google Scholar
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).
Google Scholar
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
Google Scholar
Rodrigues, A. S. L. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).
Google Scholar
Redford, K. H. The empty forest. BioScience 42, 412–422 (1992).
Google Scholar
Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. N. Direct. Eval. 2009, 75–84 (2009).
Google Scholar
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).
Google Scholar
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).
Google Scholar
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).
Google Scholar
The Ramsar Convention Secretariat. Managing Ramsar Sites. ramsar.org https://www.ramsar.org/sites-countries/managing-ramsar-sites (2014).
European Commission. The Birds Directive. https://ec.europa.eu/environment/nature/legislation/birdsdirective/index_en.htm (accessed 3 April 2022).
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Rabinowitz, D. In The Biological Aspects of Rare Plant Conservation (ed. Synge, H.) 205–217 (John Wiley & Sons, 1981).
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).
Google Scholar
Hettiarachchi, M., Morrison, T. H. & McAlpine, C. Forty-three years of Ramsar and urban wetlands. Glob. Environ. Change 32, 57–66 (2015).
Google Scholar
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).
Fahrig, L. Why do several small patches hold more species than few large patches? Glob. Ecol. Biogeogr. 29, 615–628 (2020).
Google Scholar
Newmark, W. D. Extinction of mammal populations in western North American National Parks. Conserv. Biol. 9, 512–526 (1995).
Google Scholar
Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).
Google Scholar
Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42 (2017).
Google Scholar
Wetlands International. Asian Waterbird Census. https://south-asia.wetlands.org/our-approach/healthy-wetland-nature/asian-waterbird-census/ (accessed 3 April 2022).
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).
Google Scholar
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).
Google Scholar
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).
Google Scholar
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).
Nosek, B. A., Ebersole, C. R., DeHaven, A. C. & Mellor, D. T. The preregistration revolution. Proc. Natl Acad. Sci. USA 115, 2600–2606 (2018).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
QGIS Geographic Information System (QGIS, 2021).
Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).
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).
Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (NOAA, 2017).
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).
Google Scholar
Ament, J. M. & Cumming, G. S. Scale dependency in effectiveness, isolation, and social-ecological spillover of protected areas. Conserv. Biol. 30, 846–855 (2016).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Gu, X. S. & Rosenbaum, P. R. Comparison of multivariate matching methods: structures, distances, and algorithms. J. Comput. Graph. Stat. 2, 405–420 (1993).
Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010).
Google Scholar
King, G. & Nielsen, R. Why propensity scores should not be used for matching. Pol. Anal. 27, 435–454 (2019).
Google Scholar
Rosenbaum, P. R. DOS: design of observational studies. https://cran.r-project.org/web/packages/DOS/index.html (2018).
Linden, A. A matching framework to improve causal inference in interrupted time-series analysis. J. Eval. Clin. Pract. 24, 408–415 (2018).
Google Scholar
Simmons, B. I., Hoeppke, C. & Sutherland, W. J. Beware greedy algorithms. J. Anim. Ecol. 88, 804–807 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/index.html (2021).
Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).
Google Scholar
Christensen, R. Ordinal–regression models for ordinal data. https://cran.r-project.org/web/packages/ordinal/index.html (2019).
Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Op. Source Softw. 3, 772 (2018).
Google Scholar
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).
Google Scholar
Carnell, R. lhs: latin hypercube samples. https://cran.r-project.org/web/packages/lhs/index.html (2020).
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).
Google Scholar
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).
Google Scholar
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
Google Scholar
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).
Google Scholar
Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 17001 (2017).
Google Scholar
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).
Google Scholar
Sandvik, B. World Borders Dataset. Thematic Mapping http://thematicmapping.org/downloads/world_borders.php (2009).
BirdLife International. Species Distribution Data Download http://www.birdlife.org/datazone/info/spcdownload (accessed 25 February 2020).
Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).
Google Scholar
WWF International. Management Effectiveness Tracking Tool https://wwfeu.awsassets.panda.org/downloads/mett2_final_version_july_2007.pdf (2007).
Source: Ecology - nature.com
