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Climate-driven flyway changes and memory-based long-distance migration

Nature volume 591, pages 259–264 (2021)Cite this article

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An Author Correction to this article was published on 02 August 2021

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Abstract

Millions of migratory birds occupy seasonally favourable breeding grounds in the Arctic1, but we know little about the formation, maintenance and future of the migration routes of Arctic birds and the genetic determinants of migratory distance. Here we established a continental-scale migration system that used satellite tracking to follow 56 peregrine falcons (Falco peregrinus) from 6 populations that breed in the Eurasian Arctic, and resequenced 35 genomes from 4 of these populations. The breeding populations used five migration routes across Eurasia, which were probably formed by longitudinal and latitudinal shifts in their breeding grounds during the transition from the Last Glacial Maximum to the Holocene epoch. Contemporary environmental divergence between the routes appears to maintain their distinctiveness. We found that the gene ADCY8 is associated with population-level differences in migratory distance. We investigated the regulatory mechanism of this gene, and found that long-term memory was the most likely selective agent for divergence in ADCY8 among the peregrine populations. Global warming is predicted to influence migration strategies and diminish the breeding ranges of peregrine populations of the Eurasian Arctic. Harnessing ecological interactions and evolutionary processes to study climate-driven changes in migration can facilitate the conservation of migratory birds.

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Fig. 1: Migration system.
Fig. 2: Past formation and present maintenance of migration routes, and genetic basis for differences in migration distance.
Fig. 3: Shortened migration route and population decline in European populations owing to global warming.

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Data availability

All of the sequenced genome data have been deposited in the GenBank under accession number PRJNA686418. The tracking data are included in the Arctic Animal Movement Archive and in Movebank under the identifiers 103426553 and 934079034. Source data are provided with this paper.

Change history

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Acknowledgements

This study was supported by National Natural Science Foundation of China (31821001, 31930013, 31911530186 and 91740201), Strategic Priority Program of Chinese Academy of Sciences (XDB31000000), the National Key Program of Research and Development, Ministry of Science and Technology (2016YFC0503200), Youth Innovation Promotion Association of Chinese Academy of Sciences (2020086) to S.P., Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (2019QZKK0501), Water Ecological Security Assessment, the Major Research Strategy for Middle and Lower Yangtze River (ZDRW-ZS-2017-3), Biodiversity Survey, Monitoring and Assessment Project (2019–2023) of Ministry of Ecology and Environment, China, the Royal Society to X.Z. and M.W.B., and the CAS President’s International Fellowship Initiative for Visiting Scientists to M.W.B. Funding for satellite tracking, sampling and partial genome resequencing was provided by the Environment Agency-Abu Dhabi. Additional tracking and sampling from Kolguev and Kola was funded by the Max Planck Institute of Animal Behavior to I.P. and Earthspan (www.earthspan.foundation). We thank J. M. Graves for her suggestions; M. Al Bowardi and M. Al Mansouri for their support; X. Li, Q. Dai, X. Liu, Y. Chen, Y. Lin, J. Jiao, G. Wang, X. Guang, W. He and M. Barbato for their help with data analysis; W. Wu, X. Hou, Y. Wang and Y. Zhan for their advice on figure drawing; and O. Kulikova, V. Pozdnyakov, S. Troyev, L. Zhou, Y. Shang, C. Dai, Y. Yin, C. Li and N. C. Fox for assistance during fieldwork.

Author information

Author notes

  1. These authors contributed equally: Zhongru Gu, Shengkai Pan, Zhenzhen Lin

Authors and Affiliations

  1. Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Zhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long & Xiangjiang Zhan

  2. Cardiff University–Institute of Zoology Joint Laboratory for Biocomplexity Research, Chinese Academy of Sciences, Beijing, China

    Zhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long, Michael W. Bruford, Andrew Dixon & Xiangjiang Zhan

  3. University of the Chinese Academy of Sciences, Beijing, China

    Zhongru Gu, Li Hu, Han Su, Juan Long, Mengru Sun & Xiangjiang Zhan

  4. School of Biological Sciences, University of Bristol, Bristol, UK

    Xiaoyang Dai

  5. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China

    Jiang Chang

  6. Key Laboratory of RNA Biology, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Yuanchao Xue & Mengru Sun

  7. Wild Animal Rescue Centre, Moscow, Russia

    Sergey Ganusevich

  8. Institute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Ekaterinburg, Russia

    Vasiliy Sokolov

  9. Arctic Research Station of the Institute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Labytnangi, Russia

    Aleksandr Sokolov & Ivan Pokrovsky

  10. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany

    Ivan Pokrovsky

  11. Laboratory of Ornithology, Institute of Biological Problems of the North FEB RAS, Magadan, Russia

    Ivan Pokrovsky

  12. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Fen Ji

  13. School of Biosciences and Sustainable Places Institute, Cardiff University, Cardiff, UK

    Michael W. Bruford

  14. Emirates Falconers’ Club, Abu Dhabi, United Arab Emirates

    Andrew Dixon

  15. Reneco International Wildlife Consultants, Abu Dhabi, United Arab Emirates

    Andrew Dixon

  16. International Wildlife Consultants, Carmarthen, UK

    Andrew Dixon

  17. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Xiangjiang Zhan

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  1. Zhongru Gu
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  2. Shengkai Pan
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  3. Zhenzhen Lin
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  4. Li Hu
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  5. Xiaoyang Dai
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  6. Jiang Chang
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  8. Han Su
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  9. Juan Long
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  10. Mengru Sun
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  11. Sergey Ganusevich
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  15. Fen Ji
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  18. Xiangjiang Zhan
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Contributions

X.Z. led the project. X.Z. and A.D. conceived and designed the study. A.D., S.G., V.S., A.S., I.P., J.L. and Z.L. conducted the fieldwork and sample collection. X.Z. and A.D. examined migration paths, migration connectivity and genetic structure of peregrines across Eurasia. X.Z. and M.W.B. supervised the population genomic research. Z.G., S.P., L.H., J.C. and X.D. performed the data analyses. Z.L., Y.X., M.S., H.S. and F.J. conducted the molecular experiments. X.Z. and Z.G. wrote the manuscript, with contributions from M.W.B., S.K. and A.D.

Corresponding author

Correspondence to Xiangjiang Zhan.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Simon G. Sprecher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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 Sampling sites for tracking peregrines in the Arctic.

The sample size, visit years for each place and the peregrines equipped with Argos satellite transmitters are shown.

Extended Data Fig. 2 The broad-front migration pattern of peregrines.

a, Four main wintering regions identified in the cluster analysis. b, Migration paths with the centroids of breeding and wintering MCP for each bird, and the MCP of wintering ranges for all birds (dashed line), are shown. c, G function results in the point pattern analysis, showing a broad-front wintering distribution. The solid and dashed line denote the observed and theoretical value of G, respectively. The 95% confidence interval of theoretical G value is shadowed. The P value was calculated for the statistic of maximum absolute deviation using Monte Carlo simulations (n = 100). d, The distance from each winter centroid to its nearest neighbour centroid (nearest neighbour distance) is shown (n = 40).

Extended Data Fig. 3 Comparison of migration strategy between short-distance and long-distance groups.

a, Variable importance estimated by random forest modelling. b, Comparisons of migratory strategy between the short-distance (SD) and long-distance (LD) groups. Significance was determined by a two-sided t-test. Sample size (n) for each comparison is shown. In the box plots, the centre line represents the median, whiskers represent maximum and minimum values, and box boundaries represent 75th and 25th percentiles.

Extended Data Fig. 4 ABC simulation and parameter inference.

a, Linear discriminant summary statistics values of the simulated datasets and the observations given the four ABC candidate models. On the basis of the three statistics (LD1, LD2 and LD3), model 1 is best-supported, because the targets (dark) fit simulated data (shadow) well. b, Distribution of divergence times estimated using the chunks supporting model 1. One column represents one chunk; only 100 chunks are shown. The density bar denotes the posterior distribution of inferred divergence time in each chunk.

Extended Data Fig. 5 Maintenance mechanisms of present migration routes.

a, Route cluster analysis on the basis of Hd. b, χ2 testing results of climate zones between adjacent migration routes at the whole-route level. c, Schematic of environment comparisons between neighbouring geographical bands. Each route was divided into geographical bands parallel to the main migration direction. Grids at regular intervals were chosen from neighbouring bands for comparison. d, Environmental boundaries coinciding with migration route boundaries. The Eurasian continent was divided into geographical bands (at 2° longitude). The P values of paired t-tests between compared bands are shown, and the dashed line equals 0.05 (top). The bar is scaled to the number of spaces between two targeted bands in a paired comparison. The MCPs (90%) of five migration routes are shaded (bottom). Arrows point to the coincidence between environmental and migration route boundaries. Distinct environment difference within the Popigai route may result from the inclusion of large ‘barrier islands’ of unsuitable region in the comparison. e, Illustration of the model simulating the least-cost migration path. For a typical migration route, we simulated the potential migration path (dashed lines) along which a peregrine departs from its actual breeding site (for example, B1 in route 1) and flies along a least-cost path, but then winters in a wintering site of the neighbouring route (for example, W2 in route 2). B1, B2 and B3 denotes breeding areas; W1, W2 and W3 denote wintering areas. Solid lines are the actual tracked migration path. f, Comparison of migration costs between within-route and across-route paths (P = 0.01, t = −2.58, degrees of freedom = 101.68). Significance was calculated using a two-sided t-test (n = 45 and 64 for within- and cross-route, respectively). In the box plots, the centre line represents the median, whiskers represent maximum and minimum values, and box boundaries represent 75th and 25th percentiles.

Extended Data Fig. 6 Differences in breeding and wintering areas between present and future (2070).

Predicted changes in breeding (top) and wintering (bottom) area under the RCP 8.5 scenario (left), and zoomed-in Kola and Europe (right).

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Gu, Z., Pan, S., Lin, Z. et al. Climate-driven flyway changes and memory-based long-distance migration. Nature 591, 259–264 (2021). https://doi.org/10.1038/s41586-021-03265-0

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