Successful conservation of global waterbird populations depends on effective governance

Abstract

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.

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Figure 1: Population-level changes in waterbird abundance in each 1° × 1° grid cell between 1990 and 2013.
Figure 2: Mean changes in abundance across 461 waterbird species (community-level changes) between 1990 and 2013.
Figure 3: Effects of predictors on community-level changes in waterbird abundance.
Figure 4: Effects of predictors on species-level abundance changes in 293 waterbird species that were recorded in at least ten grid cells.

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Acknowledgements

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.

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Contributions

T.A., T.S. and W.J.S. designed the study. T.A., T.S., B.S., S.N., T.M., T.L., D.B. and C.U.S. collected and prepared data for the analyses. T.A. analysed the data and wrote the paper. All authors discussed the results and commented on the manuscript at all stages.

Corresponding author

Correspondence to Tatsuya Amano.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks R. Fuller and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Distribution of the 25,769 survey sites used in the analyses.

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.

Extended Data Figure 2 Global distribution of mean annual changes in abundance.

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.

af, 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.

Extended Data Figure 6 Sensitivity of the results to the inclusion of seabird species.

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.

Extended Data Table 1 Hypotheses and explanatory variables tested for explaining the patterns in waterbird abundance changes over space and species
Extended Data Table 2 Correlation matrix (Spearman’s rank correlation) of nine potential predictors of waterbird abundance changes (n = 2,079 grid cells)

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, Supplementary References, Supplementary Notes and full legends for Supplementary Data sets 1 and 2. (PDF 305 kb)

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Supplementary Information

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Supplementary Data

This file contains Supplementary Data 2, see the Supplementary Information document for a full description. Supplementary Data set 1 is available at https://doi.org/10.6084/m9.figshare.5669827 (XLSX 65 kb)

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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

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