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Responses of global waterbird populations to climate change vary with latitude

Nature Climate Change volume 10, pages 959–964 (2020)Cite this article

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Abstract

Most research on climate change impacts on global biodiversity lacks the resolution to detect changes in species abundance and is limited to temperate ecosystems. This limits our understanding of global responses in species abundance—a determinant of extinction risk and ecosystem function and services—to climate change, including in the highly biodiverse tropics. We address this knowledge gap by quantifying the abundance response of waterbirds, an indicator taxon of wetland biodiversity, to climate change at 6,822 sites between 55° S and 64° N. Using 1,303,651 count records of 390 species, we show that with temperature increase, the abundance of species and populations decreased at lower latitudes, particularly in the tropics, but increased at higher latitudes. These contrasting latitudinal responses indicate potential global-scale poleward shifts of species abundance under climate change. The negative responses to temperature increase in tropical species are of conservation concern, as they are often also threatened by other anthropogenic factors.

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Fig. 1: Latitudinal distribution of abundance responses to changes in temperature for each species.
Fig. 2: Mean abundance responses across 390 waterbird species to changes in temperature and precipitation in each 1° × 1° grid cell.
Fig. 3: Latitudinal patterns in waterbird abundance responses to temperature increases.
Fig. 4: Latitudinal patterns in waterbird abundance responses to precipitation increases.

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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 from Wetlands International at: http://iwc.wetlands.org/. The estimated abundance responses to temperature and precipitation as well as the importance of temperature and precipitation for each grid cell for each species are available as Supplementary Data 2. All the data on explanatory variables are freely available as specified in Extended Data Fig. 4.

Code availability

All the R codes used for the analyses are available as Supplementary Data 5–7.

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Acknowledgements

We thank the coordinators, thousands of volunteer counters and funders of the International Waterbird Census and Christmas Bird Count. T.A. was supported by the Grantham Foundation for the Protection of the Environment, the Kenneth Miller Trust, the Australian Research Council Future Fellowship (FT180100354) and the University of Queensland strategic funding. T.S. was funded by the Royal Society (Wolfson Merit Award WM170050, APEX APX/R1/191045), the Leverhulme Trust (RF/2/RFG/2005/0279, ID200660763) and the National Research, Development and Innovation Office of Hungary (ÉLVONAL KKP-126949, K-116310). H.S.W. was supported by the Cambridge Trust Cambridge-Australia Poynton Scholarship and the Cambridge Department of Zoology JS Gardiner Fellowship. W.J.S. is supported by Arcadia and The David and Claudia Harding Foundation. This work is also funded by EU Horizon 2020 BACI project (Grant Agreement 640176), 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. Thanks to M. Amano for all the support.

Author information

Authors and Affiliations

  1. School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia

    Tatsuya Amano

  2. Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Queensland, Australia

    Tatsuya Amano

  3. Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK

    Tatsuya Amano, Hannah S. Wauchope & William J. Sutherland

  4. Centre for the Study of Existential Risk, University of Cambridge, Cambridge, UK

    Tatsuya Amano

  5. Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK

    Tamás Székely

  6. Department of Evolutionary Zoology, University of Debrecen, Debrecen, Hungary

    Tamás Székely

  7. Department of Biology, Santa Clara University, Santa Clara, CA, USA

    Brody Sandel

  8. Wetlands International Global Office, Ede, The Netherlands

    Szabolcs Nagy, Taej Mundkur & Tom Langendoen

  9. Wetlands International Argentina, Buenos Aires, Argentina

    Daniel Blanco

  10. National Audubon Society, New York, NY, USA

    Nicole L. Michel

  11. BioRISC, St Catharine’s College, Cambridge, UK

    William J. Sutherland

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  1. Tatsuya Amano
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Contributions

T.A. designed the study. T.A., T.S., H.S.W., B.S., S.N., T.M., T.L., D.B. and N.L.M. 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.

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Correspondence to Tatsuya Amano.

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Extended data

Extended Data Fig. 1 Distribution of the 6,822 survey sites used in the analyses.

The area between pale pink lines represents the tropical region.

Extended Data Fig. 2 Annual rates of changes in January mean temperature and precipitation at the 6,822 survey sites used in the analyses.

The area between yellow lines represents the tropical region.

Extended Data Fig. 3 Hypotheses tested for explaining among- and within-species latitudinal variations in waterbird abundance responses to temperature and precipitation changes.

Supporting evidence includes information from refs. 65,66,67,68,69,70,71,72,73.

Extended Data Fig. 4 Additional hypotheses tested for explaining among-species variations in waterbird abundance responses to temperature and precipitation changes.

Information regarding expected patterns include refs. 26,27, 74,75,76; ref. 77 was used as a data source.

Extended Data Fig. 5 Effects of species-level predictors on waterbird abundance responses to temperature changes.

The estimated coefficients with 95% and 50% (thick lines) credible intervals of six explanatory variables for explaining among-species variations in the rate of abundance changes with increasing temperature (a) and the importance of temperature in explaining abundance changes (b). Filled circles indicate variables with 95% credible intervals not overlapping with zero. Only 213 species for which there were estimates at ten or more grid cells were analysed. Note that the estimated coefficients for Absolute latitude (linear) in both (a) and (b) and for Absolute latitude range in (b) are all positive.

Extended Data Fig. 6 Effects of species-level predictors on waterbird abundance responses to precipitation changes.

The estimated coefficients with 95% and 50% (thick lines) credible intervals of six explanatory variables for explaining among-species variations in the rate of abundance changes with increasing precipitation (a) and the importance of precipitation in explaining abundance changes (b). Filled circles indicate variables with 95% credible intervals not overlapping with zero. Only 213 species for which there were estimates at ten or more grid cells were analysed. Note that the estimated coefficient for Absolute latitude range in (b) is positive.

Extended Data Fig. 7 Sensitivity of the results on responses to temperatures to the choice of precipitation seasons.

Effects of species-level predictors on waterbird abundance responses to temperature changes when using precipitation during June, July and August in the model (see Statistical Analyses for more detail). The estimated coefficients with 95% and 50% (thick lines) credible intervals of six explanatory variables for explaining among-species variations in the rate of abundance changes with increasing temperature (a) and the importance of temperature in explaining abundance changes (b). Filled circles indicate variables with 95% credible intervals not overlapping with zero. Only 213 species for which there were estimates at ten or more grid cells were analysed. Note that the estimated coefficients for Absolute latitude (linear) in both (a) and (b) and for Absolute latitude range in (b) are positive while that for Absolute latitude (quadratic) in (b) is negative.

Extended Data Fig. 8 Sensitivity of the results on responses to precipitations to the choice of precipitation seasons.

Effects of species-level predictors on waterbird abundance responses to precipitation changes when using precipitation during June, July and August in the model (see Statistical Analyses for more detail). The estimated coefficients with 95% and 50% (thick lines) credible intervals of six explanatory variables for explaining among-species variations in the rate of abundance changes with increasing precipitation (a) and the importance of precipitation in explaining abundance changes (b). Filled circles indicate variables with 95% credible intervals not overlapping with zero. Only 213 species for which there were estimates at ten or more grid cells were analysed. Note that the estimated coefficient for Absolute latitude range in (b) is positive.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

Reporting Summary

Supplementary Data

Abstracts in Spanish, Portuguese, French, simplified Chinese and Japanese.

Supplementary Data 1

Species-level maps of distribution of estimated abundance responses to changes in temperature and precipitation in each 1° × 1° grid cell.

Supplementary Data 2

Estimated abundance responses to changes in temperature and precipitation in each 1° × 1° grid cell for each of the 390 species.

Supplementary Data 3

Estimated coefficients of population-level predictors for explaining within-species variations in the rate of waterbird abundance changes with increasing temperature, the importance of temperature in explaining abundance changes, the rate of abundance changes with increasing precipitation and the importance of precipitation in explaining abundance changes.

Supplementary Data 4

A list of the 390 waterbird species analysed in this study.

Supplementary Data 5

The R script for estimating abundance responses to temperature and precipitation changes and the importance of temperature and precipitation.

Supplementary Data 6

The R script for analysing among- and within-species latitudinal variations in abundance responses to temperature and precipitation changes.

Supplementary Data 7

The R script for analysing among- and within-species latitudinal variations in the importance of temperature and precipitation.

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Amano, T., Székely, T., Wauchope, H.S. et al. Responses of global waterbird populations to climate change vary with latitude. Nat. Clim. Chang. 10, 959–964 (2020). https://doi.org/10.1038/s41558-020-0872-3

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