Understanding global patterns of biodiversity change is crucial for conservation research, policies and practices. However, for most ecosystems, the lack of systematically collected data at a global level limits our understanding of biodiversity changes and their local-scale drivers. Here we address this challenge by focusing on wetlands, which are among the most biodiverse and productive of any environments1,2 and which provide essential ecosystem services3,4, but are also amongst the most seriously threatened ecosystems3,5. Using birds as an indicator taxon of wetland biodiversity, we model time-series abundance data for 461 waterbird species at 25,769 survey sites across the globe. We show that the strongest predictor of changes in waterbird abundance, and of conservation efforts having beneficial effects, is the effective governance of a country. In areas in which governance is on average less effective, such as western and central Asia, sub-Saharan Africa and South America, waterbird declines are particularly pronounced; a higher protected area coverage of wetland environments facilitates waterbird increases, but only in countries with more effective governance. Our findings highlight that sociopolitical instability can lead to biodiversity loss and undermine the benefit of existing conservation efforts, such as the expansion of protected area coverage. Furthermore, data deficiencies in areas with less effective governance could lead to underestimations of the extent of the current biodiversity crisis.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The Ramsar Convention on Wetlands. Wetland Ecosystem Services. http://archive.ramsar.org/cda/en/ramsar-pubs-info-ecosystem-services/main/ramsar/1-30-103%5E24258_4000_0__ (2011)
Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. Camb. Philos. Soc. 81, 163–182 (2006)
Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Wetlands and Water Synthesis (World Resources Institute, 2005)
United Nations General Assembly. Transforming Our World: the 2030 Agenda for Sustainable Development. Resolution Adopted by the General Assembly on 25 September 2015 (United Nations, 2015)
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, causes, and consequences of Anthropocene defaunation. Annu. Rev. Ecol. Evol. Syst. 47, 333–358 (2016)
Balmford, A., Green, R. E. & Jenkins, M. Measuring the changing state of nature. Trends Ecol. Evol. 18, 326–330 (2003)
Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature 405, 243–253 (2000)
Convention on Biological Diversity. Decision X/2. The Strategic Plan for Biodiversity 2011–2020 and the Aichi Biodiversity Targets (Secretariat of the Convention on Biological Diversity, 2010)
Intergovernmental Platform on Biodiversity and Ecosystem Services. Generic Scoping Report for the Regional and Subregional Assessments of Biodiversity and Ecosystem Services (Intergovernmental Platform on Biodiversity and Ecosystem Services, 2015)
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014)
WWF. Living Planet Report 2016. Risk and Resilience in a New Era (WWF International, 2016)
Bowler, D. E. et al., Cross-realm assessment of climate change impacts on species’ abundance trends. Nat. Ecol. Evol. 1, 0067 (2017)
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)
The Ramsar Convention on Wetlands. Classification System for Wetland Type http://archive.ramsar.org/cda/en/ramsar-documents-guidelines-strategic-framework-and/main/ramsar/1-31-105%5E20823_4000_0__#B (2012)
Boere, G. C., Galbraith, C. A. & Stroud, D. A. eds. Waterbirds Around the World (The Stationery Office, 2006)
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)
Smith, R. J., Muir, R. D. J., Walpole, M. J., Balmford, A. & Leader-Williams, N. Governance and the loss of biodiversity. Nature 426, 67–70 (2003)
Umemiya, C., Rametsteiner, E. & Kraxner, F. Quantifying the impacts of the quality of governance on deforestation. Environ. Sci. Policy 13, 695–701 (2010)
Ceddia, M. G., Bardsley, N. O., Gomez-y-Paloma, S. & Sedlacek, S. Governance, agricultural intensification, and land sparing in tropical South America. Proc. Natl Acad. Sci. USA 111, 7242–7247 (2014)
Harring, N. Understanding the effects of corruption and political trust on willingness to make economic sacrifices for environmental protection in a cross-national perspective. Soc. Sci. Q. 94, 660–671 (2013)
Sundström, A. Covenants with broken swords: corruption and law enforcement in governance of the commons. Glob. Environ. Change 31, 253–262 (2015)
Miller, D. C., Agrawal, A. & Timmons Roberts, J. Biodiversity, governance, and the allocation of international aid for conservation. Conserv. Lett. 6, 12–20 (2013)
Nourani, E., Kaboli, M. & Collen, B. An assessment of threats to Anatidae in Iran. Bird Conserv. Int. 25, 242–257 (2015)
Brandolin, P. G. & Blendinger, P. G. Effect of habitat and landscape structure on waterbird abundance in wetlands of central Argentina. Wetl. Ecol. Manag. 24, 93–105 (2016)
Brochet, A.-L. et al. Preliminary assessment of the scope and scale of illegal killing and taking of birds in the Mediterranean. Bird Conserv. Int. 26, 1–28 (2016)
Morrison, R. I. G. et al. Dramatic declines of semipalmated sandpipers on their major wintering areas in the Guianas, Northern South America. Waterbirds 35, 120–134 (2012)
Lockwood, M. Good governance for terrestrial protected areas: A framework, principles and performance outcomes. J. Environ. Manage. 91, 754–766 (2010)
Kirby, J. S. et al. Key conservation issues for migratory land- and waterbird species on the world’s major flyways. Bird Conserv. Int. 18, S49–S73 (2008)
Delany, S. Guidance on Waterbird Monitoring Methodology: Field Protocol for Waterbird Counting (Wetlands International, 2010)
Dunn, E. H. et al. Enhancing the scientific value of the Christmas Bird Count. Auk 122, 338–346 (2005)
van Roomen, M . et al. Waterbird and Site Monitoring along the Atlantic Coast of Africa: Strategy and Manual (BirdLife International, Common Wadden Sea Secretariat and Wetlands International, 2014)
LeBaron, G. S. The 115th Christmas Bird Count (National Audubon Society, 2015)
van Roomen, M ., van Winden, E & van Turnhout, C. Analyzing Population Trends at the Flyway Level for Bird Populations Covered by the African Eurasian Waterbird Agreement: Details of a Methodology (SOVON Dutch Centre for Field Ornithology, 2011)
Kaufmann, D ., Kraay, A & Mastruzzi, M. The Worldwide Governance Indicators: Methodology and Analytical Issues (September 2010) https://ssrn.com/abstract=1682130 (2010)
Hsu, A. et al. 2016 Environmental Performance Index http://epi.yale.edu/reports/2016-report (2016)
BirdLife International. The BirdLife Checklist of the Birds of the World: Version 7 http://www.birdlife.org/datazone/userfiles/file/Species/Taxonomy/BirdLife_Checklist_Version_70.zip (2014)
BirdLife International and NatureServe. Bird Species Distribution Maps of the World (BirdLife International and NatureServe, 2014)
Gill, F. & Donsker, D. (eds) IOC World Bird List (v 5.1) http://www.worldbirdnames.org/DOI-5/master_ioc_list_v5.1.xls (2015)
Amano, T., Okamura, H., Carrizo, S. F. & Sutherland, W. J. Hierarchical models for smoothed population indices: the importance of considering variations in trends of count data among sites. Ecol. Indic. 13, 243–252 (2012)
Amano, T., Székely, T., Koyama, K., Amano, H. & Sutherland, W. J. A framework for monitoring the status of populations: an example from wader populations in the East Asian–Australasian flyway. Biol. Conserv. 143, 2238–2247 (2010)
Link, W. A. & Sauer, J. R. Seasonal components of avian population change: joint analysis of two large-scale monitoring programs. Ecology 88, 49–55 (2007)
Lunn, D. J., Thomas, A., Best, N. & Spiegelhalter, D. WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10, 325–337 (2000)
Sturtz, S., Ligges, U. & Gelman, A. R2WinBUGS: a package for running WinBUGS from R. J. Stat. Softw. 12, 1–16 (2005)
R Core Team. R: a Language and Environment for Statistical Computing http://www.R-project.org/ (R Foundation for Statistical Computing, 2016)
Link, W. A., Sauer, J. R. & Niven, D. K. A hierarchical model for regional analysis of population change using Christmas Bird Count data, with application to the American Black Duck. Condor 108, 13–24 (2006)
Gelman, A ., Carlin, J ., Stern, H . & Rubin, D. Bayesian Data Analysis 2nd edn (Chapman & Hall and CRC, 2003)
Pearce-Higgins, J. W . et al. Geographical variation in species’ population responses to changes in temperature and precipitation. Proc. R. Soc. Lond. Ser. B 282, 20151561 (2015)
Bare, M., Kauffman, C. & Miller, D. C. Assessing the impact of international conservation aid on deforestation in sub-Saharan Africa. Environ. Res. Lett. 10, 125010 (2015)
Thomas, A., Best, N., Lunn, D., Arnold, R. & Spiegelhalter, D. GeoBUGS User Manualhttp://www.mrc-bsu.cam.ac.uk/software/bugs/ (2004)
van de Pol, M. & Wright, J. A simple method for distinguishing within- versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009)
de Villemereuil, P., Wells, J. A., Edwards, R. D. & Blomberg, S. P. Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evol. Biol. 12, 102 (2012)
Abadi, F. et al. Importance of accounting for phylogenetic dependence in multi-species mark–recapture studies. Ecol. Modell. 273, 236–241 (2014)
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999)
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: a test and review of evidence. Am. Nat. 160, 712–726 (2002)
Donoghue, M. J. & Ackerly, D. D. Phylogenetic uncertainties and sensitivity analyses in comparative biology. Philos. Trans. R. Soc. Lond. B 351, 1241–1249 (1996)
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012)
Grossman, G. M. & Krueger, A. B. Economic growth and the environment. Q. J. Econ. 110, 353–377 (1995)
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, e197 (2004)
McKee, J., Chambers, E. & Guseman, J. Human population density and growth validated as extinction threats to mammal and bird species. Hum. Ecol. 41, 773–778 (2013)
Center for International Earth Science Information Network - CIESIN - Columbia University, and Centro Internacional de Agricultura Tropical - CIAT. Gridded Population of the World, Version 3 (GPWv3): Population Density Gridhttp://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density (2005)
Green, R. E., Cornell, S. J., Scharlemann, J. P. W. & Balmford, A. Farming and the fate of wild nature. Science 307, 550–555 (2005)
Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010)
Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016)
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)
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)
Pavón-Jordán, D. et al. Climate-driven changes in winter abundance of a migratory waterbird in relation to EU protected areas. Divers. Distrib. 21, 571–582 (2015)
UNEP-WCMC and IUCN. Protected Planet: The World Database on Protected Areas (WDPA)www.protectedplanet.net (2015)
Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008)
Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & van Bommel, F. P. J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 131, 93–105 (2006)
Robbins, C. S., Sauer, J. R., Greenberg, R. S. & Droege, S. Population declines in North American birds that migrate to the neotropics. Proc. Natl Acad. Sci. USA 86, 7658–7662 (1989)
Pocock, M. J. O. Can traits predict species’ vulnerability? A test with farmland passerines in two continents. Proc. R. Soc. Lond. Ser. B 278, 1532–1538 (2011)
Owens, I. P. F. & Bennett, P. M. Ecological basis of extinction risk in birds: habitat loss versus human persecution and introduced predators. Proc. Natl Acad. Sci. USA 97, 12144–12148 (2000)
Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014)
We thank the coordinators, thousands of volunteer counters and funders of the International Waterbird Census and Christmas Bird Count (see Supplementary Notes for information on funders); D. Unterkofler for preparing the NWC data, H. Okamura for statistical advice, J. P. González-Varo for his comments on an earlier draft and M. Amano for long-standing support.
The authors declare no competing financial interests.
Reviewer Information Nature thanks R. Fuller 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 figures and tables
Sites from the International Waterbird Census are shown in yellow (African–Eurasian Waterbird Census), pink (Asian Waterbird Census) and green (Neotropical Waterbird Census). Christmas Bird Count shown in cyan.
a, b, Mean annual changes in abundance for 373 migratory (a) and 88 non-migratory (b) waterbird species (that is, community-level changes). The migratory status of each species was assigned using the BirdLife Data Zone (see Methods).
Extended Data Figure 3 Relationships between community-level changes in abundance and protected areas or surface water.
a, Relationship between community-level changes in abundance and the proportion of sites covered by protected areas. b, Relationship between community-level changes in abundance and surface water change. Regression lines are based on the estimated coefficients in Fig. 3a; values and regression lines for grid cells in areas with more (in blue) and less (in red) effective governance in a. n = 2,079 grid cells.
Extended Data Figure 4 Effects of six hypothesized predictors on population-level changes in abundance.
a–f, Medians and 95% credible intervals of the estimated coefficients for 293 species are shown in order of decreasing positive effect size from the left (those with 95% credible intervals not overlapping with zero shown in red). The numbers of species with significant positive and negative coefficients are also shown, with the number of non-migratory species in parentheses. See Extended Data Table 1 for more detail regarding predictors.
Extended Data Figure 5 Sensitivity of results to the correlation between governance and GDP per capita and designation years of protected areas.
a, b, Estimated coefficients in the multivariate analysis of community-level (n = 2,079 grid cells) (a) and species-level (on the basis of 293 species; see Supplementary Data 2 for the number of grid cells for each species) (b) changes in abundance, in which governance was replaced with linear and quadratic terms of GDP per capita. c, d, Estimated coefficients in the multivariate analysis of community-level (n = 2,079 grid cells) (c) and species-level (on the basis of 293 species; see Supplementary Data 2 for the number of grid cells in each species) (d) changes in abundance, in which only protected areas known to have been designated before 1990 (the first survey year in our dataset) were used (most conservative approach). Posterior medians with 95% and 50% (thick lines) credible intervals are shown. Coefficients with 95% credible intervals not overlapping with zero are shown in red.
a, Global distribution of mean annual changes in abundance across 447 waterbird species, excluding the 14 seabird species, between 1990 and 2013. b, c, Estimated coefficients in the multivariate analysis of community-level (n = 2,079 grid cells) (b) and species-level (on the basis of 447 species; see Supplementary Data 2 for the number of grid cells in each species) (c) changes in abundance, in which the 14 seabird species were excluded. Posterior medians with 95% and 50% (thick lines) credible intervals are shown. Coefficients with 95% credible intervals not overlapping with zero are shown in red.
Extended Data Figure 7 Sensitivity of the results to the choice of CBC survey sites for the analyses.
a, Global distribution of mean annual changes in abundance across 461 waterbird species between 1990 and 2013, after excluding 41 CBC grid cells that contained neither landscape-scale wetland areas nor local-scale surface water occurrences within 1km of all the survey sites included. b, c, Estimated coefficients in the multivariate analysis of community-level (n = 2,038 grid cells) (b) and species-level (on the basis of 293 species) (c) changes in abundance, in which 41 CBC grid cells that contained neither landscape-scale wetland areas nor local-scale surface water occurrences within 1km of all the survey sites were excluded. d, Global distribution of mean annual changes in abundance across 461 waterbird species between 1990 and 2013, after excluding eight CBC grid cells in which the proportion of urban areas was over 0.3. e, f, Estimated coefficients in the multivariate analysis of community-level (n = 2,071 grid cells) (e) and species-level (on the basis of 293 species) (f) changes in abundance, in which eight CBC grid cells with a proportion of urban areas of over 0.3 were excluded. Posterior medians with 95% and 50% (thick lines) credible intervals are shown. Coefficients with 95% credible intervals not overlapping with zero are shown in red.
Extended Data Figure 8 Relationships between the proportion of sites covered by protected areas and governance or GDP per capita.
a, b, The relationship between governance (a) or GDP per capita (b) and the proportion of sites covered by protected areas. Colours indicate regions: blue, North America; green, South America; navy, Europe; orange, Africa; red, western and central Asia; yellow, south and southeast Asia; cyan, east Asia and Russia; and dark green, Oceania.
This file contains a Supplementary Discussion, Supplementary References, Supplementary Notes and full legends for Supplementary Data sets 1 and 2. (PDF 305 kb)
This zipped file contains the abstract translated into Arabic, Chinese, French, Japanese, Persian, Portuguese and Spanish. (ZIP 1059 kb)
About this article
Cite this article
Amano, T., Székely, T., Sandel, B. et al. Successful conservation of global waterbird populations depends on effective governance. Nature 553, 199–202 (2018). https://doi.org/10.1038/nature25139
Improving waterbird monitoring and conservation in the Sahel using remote sensing: a case study with the International Waterbird Census in Sudan
Winter bird abundance, species richness and functional guild composition at Delhi’s ponds: does time of day and wetland extent matter?
Journal of Urban Ecology (2021)
The Science of Nature (2021)
Conservation Biology (2021)
Multiscale effects of habitat and surrounding matrices on waterbird diversity in the Yangtze River Floodplain
Landscape Ecology (2021)