Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Phenology of nocturnal avian migration has shifted at the continental scale

Nature Climate Change volume 10, pages 63–68 (2020)Cite this article

Subjects

Abstract

Climate change induced phenological shifts in primary productivity result in trophic mismatches for many organisms1,2,3,4, with broad implications for ecosystem structure and function. For birds that have a synchronized timing of migration with resource availability, the likelihood that trophic mismatches may generate a phenological response in migration timing increases with climate change5. Despite the importance of a holistic understanding of such systems at large spatial and temporal scales, particularly given a rapidly changing climate, analyses are few, primarily because of limitations in the access to appropriate data. Here we use 24 years of remotely sensed data collected by weather surveillance radar to quantify the response of a nocturnal avian migration system within the contiguous United States to changes in temperature. The average peak migration timing advanced in spring and autumn, and these changes were generally more rapid at higher latitudes. During spring and autumn, warmer seasons were predictive of earlier peak migration dates. Decadal changes in surface temperatures predicted spring changes in migratory timing, with greater warming related to earlier arrivals. This study represents one of the first system-wide examinations during two seasons and comprises measures from hundreds of species that describe migratory timing across a continent. Our findings provide evidence of spatially dynamic phenological shifts that result from climate change.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: WSR locations and phenological time series.
Fig. 2: Predicted seasonal phenological change.
Fig. 3: Change in spring and fall migration phenology and temperature across 143 WSR stations.
Fig. 4: Anomaly and decadal change comparisons of migration phenology and annual temperature.

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available at https://doi.org/10.6084/m9.figshare.10062239.v1.

Code availability

Radar processing code and algorithms can be found at https://zenodo.org/record/3352264#.XXesby2ZPRY

References

  1. Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).

    CAS  Google Scholar 

  2. Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).

    CAS  Google Scholar 

  3. Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).

    CAS  Google Scholar 

  4. Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).

    Google Scholar 

  5. Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298 (2001).

    CAS  Google Scholar 

  6. Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Google Scholar 

  7. Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).

    Google Scholar 

  8. Saino, N. et al. Climate warming, ecological mismatch at arrival and population decline in migratory birds. Proc. R. Soc. B 278, 835–842 (2011).

    Google Scholar 

  9. Jones, T. & Cresswell, W. The phenology mismatch hypothesis: are declines of migrant birds linked to uneven global climate change? J. Anim. Ecol. 79, 98–108 (2010).

    Google Scholar 

  10. Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).

    CAS  Google Scholar 

  11. Chmura, H. E. et al. The mechanisms of phenology: the patterns and processes of phenological shifts. Ecol. Monogr. 89, e01337 (2019).

    Google Scholar 

  12. Berthold, P. Control of Bird Migration (Chapman and Hall, 1996).

  13. Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).

    CAS  Google Scholar 

  14. Butler, C. J. The disproportionate effect of global warming on the arrival dates of short-distance migratory birds in North America. Ibis 145, 484–495 (2003).

    Google Scholar 

  15. Møller, A. P., Rubolini, D. & Lehikoinen, E. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc. Natl Acad. Sci. USA 105, 16195–16200 (2008).

    Google Scholar 

  16. Kelly, J. F. & Horton, K. G. Toward a predictive macrosystems framework for migration ecology. Glob. Ecol. Biogeogr. 25, 1159–1165 (2016).

    Google Scholar 

  17. Mayor, S. J. et al. Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7, 1–10 (2017).

    Google Scholar 

  18. Jonzén, N. et al. Rapid advance of spring arrival dates in long-distance migratory birds. Science 312, 1959–1961 (2006).

    Google Scholar 

  19. Balbontín, J. et al. Individual responses in spring arrival date to ecological conditions during winter and migration in a migratory bird. J. Anim. Ecol. 78, 981–989 (2009).

    Google Scholar 

  20. Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).

    Google Scholar 

  21. Horton, K. G. et al. Holding steady: little change in intensity or timing of bird migration over the Gulf of Mexico. Glob. Change Biol. 25, 1106–1118 (2019).

    Google Scholar 

  22. Lin, T. et al. MISTNET: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks. Methods Ecol. Evol. 10, 1908–1922 (2019).

    Google Scholar 

  23. La Sorte, F. A. et al. The role of atmospheric conditions in the seasonal dynamics of North American migration flyways. J. Biogeogr. 41, 1685–1696 (2014).

    Google Scholar 

  24. Marra, P. P., Francis, C. M., Mulvihill, R. S. & Moore, F. R. The influence of climate on the timing and rate of spring bird migration. Oecologia 142, 307–315 (2005).

    Google Scholar 

  25. Post, E., Steinman, B. A. & Mann, M. E. Acceleration of phenological advance and warming with latitude over the past century. Sci. Rep. 8, 3927 (2018).

    Google Scholar 

  26. Bauer, S. et al. The grand challenges of migration ecology that radar aeroecology can help answer. Ecography 42, 861–875 (2019).

    Google Scholar 

  27. Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).

    Google Scholar 

  28. Miller, R. A., Carlisle, J. D., Paprocki, N., Kaltenecker, G. S. & Heath, J. A. in Phenological Synchrony and Bird Migration: Changing Climate and Seasonal Resources in North America Vol. 47 (eds Wood, E. M. & Kellermann, J. L.) 177–191 (CRC, 2015).

  29. Jarjour, C., Frei, B. & Elliott, K. H. Associations between sex, age and species-specific climate sensitivity in migration. Anim. Migr. 4, 23–36 (2017).

    Google Scholar 

  30. Lameris, T. K. et al. Arctic geese tune migration to a warming climate but still suffer from a phenological mismatch. Curr. Biol. 28, 2467–2473.e4 (2018).

    CAS  Google Scholar 

  31. Rakhimberdiev, E. et al. Fuelling conditions at staging sites can mitigate Arctic warming effects in a migratory bird. Nat. Commun. 9, 1–10 (2018).

    Google Scholar 

  32. Carlisle, J. D. et al. Landbird migration in the American West: recent progress and future research directions. Condor 111, 211–225 (2009).

    Google Scholar 

  33. Gill, J. A. et al. Why is timing of bird migration advancing when individuals are not? Proc. R. Soc. B 281, 20132161 (2014).

    Google Scholar 

  34. Horton, K. G. et al. Bright lights in the big cities: migratory birds’ exposure to artificial light. Front. Ecol. Environ. 17, 209–214 (2019).

    Google Scholar 

  35. Crum, T. D., Alberty, R. L. & Burgess, D. W. Recording, archiving, and using WSR-88D data. Bull. Am. Meteorol. Soc. 74, 645–653 (1993).

    Google Scholar 

  36. Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).

    CAS  Google Scholar 

  37. Richardson, W. J. Timing and amount of bird migration in relation to weather: a review. Oikos 30, 224–272 (1978).

    Google Scholar 

  38. Richardson, W. J. in Bird Migration (ed. Gwinner, E.) 78–101 (Springer, 1990).

  39. Long, J., Darrell, T. & Shelhamer, E. Fully convolutional networks for semantic segmentation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3431–3440 (IEEE, 2015).

  40. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2015).

  41. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Google Scholar 

  42. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT, 2016).

  43. Farnsworth, A. et al. A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern US. Ecol. Appl. 26, 752–770 (2016).

    Google Scholar 

  44. Browning, K. A. & Wexler, R. The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor. 7, 105–113 (1968).

    Google Scholar 

  45. Buler, J. J. & Diehl, R. H. Quantifying bird density during migratory stopover using weather surveillance radar. IEEE Trans. Geosci. Remote Sens. 47, 2741–2751 (2009).

    Google Scholar 

  46. Horton, K. G., Van Doren, B. M., Stepanian, P. M., Farnsworth, A. & Kelly, J. F. Where in the air? Aerial habitat use of nocturnally migrating birds. Biol. Lett. 12, 20160591 (2016).

    Google Scholar 

  47. Sheldon, D. et al. Approximate Bayesian inference for reconstructing velocities of migrating birds from weather radar. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence 1334–1340 (AAAI, 2013).

  48. Sheldon, D. WSRLIB: MATLAB Toolbox for Weather Surveillance Radar (Bitbucket, 2015); https://bitbucket.org/dsheldon/wsrlib

  49. Dokter, A. M. et al. Bird migration flight altitudes studied by a network of operational weather radars. J. R. Soc. Interface 8, 30–43 (2011).

    Google Scholar 

  50. Horton, K. G., Van Doren, B. M., Stepanian, P. M., Farnsworth, A. & Kelly, J. F. Seasonal differences in landbird migration strategies. Auk 133, 761–769 (2016).

    Google Scholar 

  51. Chilson, P. B. et al. Estimating animal densities in the aerosphere using weather radar: to Z or not to Z? Ecosphere 3, art72 (2012).

    Google Scholar 

  52. Sullivan, B. L. et al. The eBird enterprise: an integrated approach to development and application of citizen science. Biol. Conserv. 169, 31–40 (2014).

    Google Scholar 

  53. Wood, S. mgcv: mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation. R package version 1.8-28 (2015).

  54. Mesinger, F. et al. North American regional reanalysis. Bull. Am. Meteorol. Soc. 87, 343–360 (2006).

    Google Scholar 

Download references

Acknowledgements

NSF Advances in Biological Informatics (ABI-1661259), Division of Information and Intelligent Systems (IIS-1633206) and Integrative and Collaborative Education and Research (1927743) programmes, as well as a Leon Levy Foundation and an Edward W. Rose Postdoctoral Fellowship supported this research.

Author information

Authors and Affiliations

  1. Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA

    Kyle G. Horton

  2. Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA

    Frank A. La Sorte, Wesley M. Hochachka & Andrew Farnsworth

  3. College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA

    Daniel Sheldon, Tsung-Yu Lin, Kevin Winner, Garrett Bernstein & Subhransu Maji

  4. Department of Computer Science, Mount Holyoke College, South Hadley, MA, USA

    Daniel Sheldon

Authors
  1. Kyle G. Horton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank A. La Sorte
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Sheldon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tsung-Yu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kevin Winner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Garrett Bernstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Subhransu Maji
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wesley M. Hochachka
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Andrew Farnsworth
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors worked to conceive and design this study. K.G.H., F.A.L., D.S. and A.F. drafted the manuscript. T.-Y.L., K.W., G.B., S.M., K.G.H. and D.S. designed the radar algorithms, and processed and summarized the radar data. K.G.H. generated the figures and D.S., W.M.H. and K.G.H. designed the analyses. All the authors provided editorial advice, approved the final version of this manuscript and are in agreement to be accountable for all aspects of the work.

Corresponding author

Correspondence to Kyle G. Horton.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Horton, K.G., La Sorte, F.A., Sheldon, D. et al. Phenology of nocturnal avian migration has shifted at the continental scale. Nat. Clim. Chang. 10, 63–68 (2020). https://doi.org/10.1038/s41558-019-0648-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-019-0648-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing