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

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.

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

References

  1. McRae, L. et al. Arctic Species Trend Index 2010. Tracking Trends in Arctic Wildlife (CAFF International Secretariat, 2010).

  2. Lameris, T. K. et al. Potential for an Arctic-breeding migratory bird to adjust spring migration phenology to Arctic amplification. Glob. Change Biol. 23, 4058–4067 (2017).

    Google Scholar 

  3. Trautmann, S. in Bird Species (ed. Tietze, D. T.) 217–234 (Springer, 2018).

  4. Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Change 8, 992–996 (2018).

    ADS  Google Scholar 

  5. Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).

    ADS  CAS  PubMed  Google Scholar 

  6. White, C. M., Cade, T. J. & Enderson, J. H. Peregrine Falcons of the World (Lynx, 2013).

  7. Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).

    ADS  CAS  PubMed  Google Scholar 

  8. Otto-Bliesner, B. L., Marshall, S. J., Overpeck, J. T., Miller, G. H. & Hu, A. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science 311, 1751–1753 (2006).

    ADS  CAS  PubMed  Google Scholar 

  9. Brambilla, M., Rubolini, D. & Guidali, F. Factors affecting breeding habitat selection in a cliff-nesting peregrine Falco peregrinus population. J. Ornithol. 147, 428–435 (2006).

    Google Scholar 

  10. Hausdorff, F. Bemerkung über den Inhalt von Punktmengen. Math. Ann. 75, 428–433 (1914).

    MathSciNet  MATH  Google Scholar 

  11. Pulido, F. The genetics and evolution of avian migration. Bioscience 57, 165–174 (2007).

    Google Scholar 

  12. Perdeck, A. C. An experiment on the ending of autumn migration in starlings. Ardea 52, 133–139 (1964).

    Google Scholar 

  13. Delmore, K. E., Toews, D. P., Germain, R. R., Owens, G. L. & Irwin, D. E. The genetics of seasonal migration and plumage color. Curr. Biol. 26, 2167–2173 (2016).

    CAS  PubMed  Google Scholar 

  14. Impey, S. et al. Stimulation of cAMP response element (CRE)-mediated transcription during contextual learning. Nat. Neurosci. 1, 595–601 (1998).

    CAS  PubMed  Google Scholar 

  15. Bourtchuladze, R. et al. Deficient long-term memory in mice with a targeted mutation of the cAMP-responsive element-binding protein. Cell 79, 59–68 (1994).

    CAS  PubMed  Google Scholar 

  16. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Mayr, B. & Montminy, M. Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat. Rev. Mol. Cell Biol. 2, 599–609 (2001).

    CAS  PubMed  Google Scholar 

  18. Iguchi-Ariga, S. M. & Schaffner, W. CpG methylation of the cAMP-responsive enhancer/promoter sequence TGACGTCA abolishes specific factor binding as well as transcriptional activation. Genes Dev. 3, 612–619 (1989).

    CAS  PubMed  Google Scholar 

  19. Bartsch, D. et al. Aplysia CREB2 represses long-term facilitation: relief of repression converts transient facilitation into long-term functional and structural change. Cell 83, 979–992 (1995).

    CAS  PubMed  Google Scholar 

  20. Wieczorek, L. et al. Absence of Ca2+-stimulated adenylyl cyclases leads to reduced synaptic plasticity and impaired experience-dependent fear memory. Transl. Psychiatry 2, e126 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Rosenegger, D., Wright, C. & Lukowiak, K. A quantitative proteomic analysis of long-term memory. Mol. Brain 3, 9 (2010).

    PubMed  PubMed Central  Google Scholar 

  22. Ferguson, G. D. & Storm, D. R. Why calcium-stimulated adenylyl cyclases? Physiology (Bethesda) 19, 271–276 (2004).

    CAS  Google Scholar 

  23. Zhang, M. et al. Ca-stimulated type 8 adenylyl cyclase is required for rapid acquisition of novel spatial information and for working/episodic-like memory. J. Neurosci. 28, 4736–4744 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Yin, J. C. & Tully, T. CREB and the formation of long-term memory. Curr. Opin. Neurobiol. 6, 264–268 (1996).

    CAS  PubMed  Google Scholar 

  25. Wauchope, H. S. et al. Rapid climate-driven loss of breeding habitat for Arctic migratory birds. Glob. Change Biol. 23, 1085–1094 (2017).

    ADS  Google Scholar 

  26. Lok, T., Overdijk, O. & Piersma, T. The cost of migration: spoonbills suffer higher mortality during trans-Saharan spring migrations only. Biol. Lett. 11, 20140944 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. Brown, J. W. et al. Appraisal of the consequences of the DDT-induced bottleneck on the level and geographic distribution of neutral genetic variation in Canadian peregrine falcons, Falco peregrinus. Mol. Ecol. 16, 327–343 (2007).

    CAS  PubMed  Google Scholar 

  28. Wilcove, D. S. & Wikelski, M. Going, going, gone: is animal migration disappearing. PLoS Biol. 6, e188 (2008).

    PubMed  PubMed Central  Google Scholar 

  29. Mueller, J. C., Pulido, F. & Kempenaers, B. Identification of a gene associated with avian migratory behaviour. Proc. R. Soc. Lond. B 278, 2848–2856 (2011).

    CAS  Google Scholar 

  30. Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Res. 2, 115 (2013).

    ADS  PubMed  PubMed Central  Google Scholar 

  31. Douglas, D. C. et al. Moderating Argos location errors in animal tracking data. Methods Ecol. Evol. 3, 999–1007 (2012).

    Google Scholar 

  32. Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P. & Fagan, W. F. Social learning of migratory performance. Science 341, 999–1002 (2013).

    ADS  CAS  PubMed  Google Scholar 

  33. Trierweiler, C. et al. Migratory connectivity and population-specific migration routes in a long-distance migratory bird. Proc. R. Soc. Lond. B 281, 20132897 (2014).

    Google Scholar 

  34. Ambrosini, R., Møller, A. P. & Saino, N. A quantitative measure of migratory connectivity. J. Theor. Biol. 257, 203–211 (2009).

    ADS  MathSciNet  PubMed  Google Scholar 

  35. Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC, 2015).

  36. López-López, D. P., García-Ripollés, C. & Urios, V. Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors. Curr. Zool. 60, 642–652 (2014).

    Google Scholar 

  37. Benhamou, S. How to reliably estimate the tortuosity of an animal’s path: straightness, sinuosity, or fractal dimension? J. Theor. Biol. 229, 209–220 (2004).

    ADS  MathSciNet  PubMed  MATH  Google Scholar 

  38. Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).

    Google Scholar 

  39. Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Routledge Academic, 1988).

  40. Ganusevich, S. A. et al. Autumn migration and wintering areas of peregrine falcons Falco peregrinus nesting on the Kola Peninsula, northern Russia. Ibis 146, 291–297 (2004).

    Google Scholar 

  41. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhao, S. et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat. Genet. 45, 67–71 (2013).

    CAS  PubMed  Google Scholar 

  43. Damas, J. et al. Upgrading short-read animal genome assemblies to chromosome level using comparative genomics and a universal probe set. Genome Res. 27, 875–884 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhan, X. et al. Peregrine and saker falcon genome sequences provide insights into evolution of a predatory lifestyle. Nat. Genet. 45, 563–566 (2013).

    CAS  PubMed  Google Scholar 

  45. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Rodríguez-Ramilo, S. T. & Wang, J. The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol. Ecol. Resour. 12, 873–884 (2012).

    PubMed  Google Scholar 

  48. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    CAS  PubMed  Google Scholar 

  50. Tang, H. et al. Genetic structure, self-identified race/ethnicity, and confounding in case–control association studies. Am. J. Hum. Genet. 76, 268–275 (2005).

    CAS  PubMed  Google Scholar 

  51. Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).

    CAS  PubMed  Google Scholar 

  52. Staab, P. R., Zhu, S., Metzler, D. & Lunter, G. scrm: efficiently simulating long sequences using the approximated coalescent with recombination. Bioinformatics 31, 1680–1682 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pudlo, P. et al. Reliable ABC model choice via random forests. Bioinformatics 32, 859–866 (2016).

    CAS  PubMed  Google Scholar 

  54. Csilléry, K., François, O. & Blum, M. G. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3, 475–479 (2012).

    Google Scholar 

  55. Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modeling. R package version 1.3-3 https://cran.r-project.org/package=dismo (2020).

  56. Calenge, C. adhabitatHR: home range estimation. R package version 0.4.19 https://cran.r-project.org/package=adehabitatHR (2021).

  57. Fick, S. E. & Hijmans, R. J. WorldClim2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Google Scholar 

  58. Beyer, R. M., Krapp, M. & Manica, A. High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Sci. Data 7, 236 (2020).

    PubMed  PubMed Central  Google Scholar 

  59. Tarasov, P. E. et al. Last glacial maximum biomes reconstructed from pollen and plant macrofossil data from northern Eurasia. J. Biogeogr. 27, 609–620 (2000).

  60. Borchers, H. W. pracma: practical numerical math functions. R package version 2.3.3 https://cran.r-project.org/package=pracma (2021).

  61. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).

    PubMed  PubMed Central  Google Scholar 

  63. Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  64. Beissinger, T. M., Rosa, G. J., Kaeppler, S. M., Gianola, D. & de Leon, N. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genet. Sel. Evol. 47, 30 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. Szpiech, Z. A. & Hernandez, R. D. selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Zheng, G. X. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. François, O., Martins, H., Caye, K. & Schoville, S. D. Controlling false discoveries in genome scans for selection. Mol. Ecol. 25, 454–469 (2016).

    PubMed  Google Scholar 

  69. Fariello, M. I., Boitard, S., Naya, H., SanCristobal, M. & Servin, B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 193, 929–941 (2013).

    PubMed  PubMed Central  Google Scholar 

  70. Bonhomme, M. et al. Detecting selection in population trees: the Lewontin and Krakauer test extended. Genetics 186, 241–262 (2010).

    PubMed  PubMed Central  Google Scholar 

  71. Frichot, E. & François, O. LEA: an R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).

    Google Scholar 

  72. Pan, S. et al. Population transcriptomes reveal synergistic responses of DNA polymorphism and RNA expression to extreme environments on the Qinghai–Tibetan Plateau in a predatory bird. Mol. Ecol. 26, 2993–3010 (2017).

    CAS  PubMed  Google Scholar 

  73. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Yang, L. et al. TFBSshape: a motif database for DNA shape features of transcription factor binding sites. Nucleic Acids Res. 42, D148–D155 (2014).

    CAS  PubMed  Google Scholar 

  75. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21–29 (2015).

    PubMed  PubMed Central  Google Scholar 

  76. Li, R. et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Barbato, M., Orozco-terWengel, P., Tapio, M. & Bruford, M. W. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front. Genet. 6, 109 (2015).

    PubMed  PubMed Central  Google Scholar 

  78. Pitt, D. et al. Demography and rapid local adaptation shape Creole cattle genome diversity in the tropics. Evol. Appl. 12, 105–122 (2019).

    PubMed  Google Scholar 

  79. Carlzon, L., Karlsson, A., Falk, K., Liess, A. & Møller, S. Extreme weather affects peregrine falcon (Falco peregrinus tundrius) breeding success in South Greenland. Ornis Hungarica 26, 38–50 (2018).

    Google Scholar 

  80. Franke, A. et al. Status and trends of circumpolar peregrine falcon and gyrfalcon populations. Ambio 49, 762–783 (2020).

    PubMed  Google Scholar 

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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.

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Authors and Affiliations

Authors

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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The authors declare no competing interests.

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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.

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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).

Supplementary information

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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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