• Alerstam, T. , A. Hedenström , and S. Åkesson , 2003: Long‐distance migration: Evolution and determinants. Oikos, 103, 247260, https://doi.org/10.1034/j.1600-0706.2003.12559.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ansari, S. , and Coauthors, 2018: Unlocking the potential of NEXRAD data through NOAA’s Big Data Partnership. Bull. Amer. Meteor. Soc., 99, 189204, https://doi.org/10.1175/BAMS-D-16-0021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Badr, H. S. , B. F. Zaitchik , and A. K. Dezfuli , 2015: A tool for hierarchical climate regionalization. Earth Sci. Inf., 8, 949958, https://doi.org/10.1007/s12145-015-0221-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Badr, H. S. , A. K. Dezfuli , B. F. Zaitchik , and C. D. Peters-Lidard , 2016: Regionalizing Africa: Patterns of precipitation variability in observations and global climate models. J. Climate, 29, 90279043, https://doi.org/10.1175/JCLI-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ballard, G. , G. R. Geupel , N. Nur , and T. Gardali , 2003: Long-term declines and decadal patterns in population trends of songbirds in western North America, 1979–1999. Condor, 105, 737755, https://doi.org/10.1093/condor/105.4.737.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamini, Y. , and Y. Hochberg , 1995: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc., 57B, 289300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

    • Search Google Scholar
    • Export Citation
  • Cohen, E. B. , F. R. Moore , and R. A. Fischer , 2012: Experimental evidence for the interplay of exogenous and endogenous factors on the movement ecology of a migrating songbird. PLOS ONE, 7, e41818, https://doi.org/10.1371/journal.pone.0041818.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comrie, A. C. , and E. C. Glenn , 1998: Principal components-based regionalization of precipitation regimes across the southwest United States and northern Mexico, with an application to monsoon precipitation variability. Climate Res., 10, 201215, https://doi.org/10.3354/cr010201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deppe, J. L. , and Coauthors, 2015: Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico. Proc. Natl. Acad. Sci. USA, 112, E6331E6338, https://doi.org/10.1073/pnas.1503381112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dezfuli, A. K. , 2011: Spatio-temporal variability of seasonal rainfall in western equatorial Africa. Theor. Appl. Climatol., 104, 5769, https://doi.org/10.1007/s00704-010-0321-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dezfuli, A. K. , and S. E. Nicholson , 2013: The relationship of rainfall variability in western equatorial Africa to the tropical oceans and atmospheric circulation. Part II: The boreal autumn. J. Climate, 26, 6684, https://doi.org/10.1175/JCLI-D-11-00686.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dokter, A. M. , and Coauthors, 2018: Seasonal abundance and survival of North America’s migratory avifauna determined by weather radar. Nat. Ecol. Evol., 2, 16031609, https://doi.org/10.1038/s41559-018-0666-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dokter, A. M. , and Coauthors, 2019: bioRad: Biological analysis and visualization of weather radar data. Ecography, 42, 852860, https://doi.org/10.1111/ecog.04028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fovell, R. G. , and M. Y. C. Fovell , 1993: Climate zones of the conterminous United States defined using cluster analysis. J. Climate, 6, 21032135, https://doi.org/10.1175/1520-0442(1993)006<2103:CZOTCU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gauthreaux, S. A. , 1971: A radar and direct visual study of passerine spring migration in southern Louisiana. The Auk, 88, 343365, https://doi.org/10.2307/4083884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R. , and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gordo, O. , 2007: Why are bird migration dates shifting? A review of weather and climate effects on avian migratory phenology. Climate Res., 35, 3758, https://doi.org/10.3354/cr00713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gwinner, E. , 1996: Circadian and circannual programmes in avian migration. J. Exp. Biol., 199, 3948, https://doi.org/10.1242/jeb.199.1.39.

  • Hawkins, A. S. , 1984: Flyways: Pioneering Waterfowl Management in North America. Fish and Wildlife Service, U.S. Department of the Interior, 517 pp.

    • Search Google Scholar
    • Export Citation
  • Holton, J. R. , J. A. Curry , and J. A. Pyle , 2003: Encyclopedia of Atmospheric Sciences. Academic Press, 2780 pp.

  • Horton, K. G. , and Coauthors, 2020: Phenology of nocturnal avian migration has shifted at the continental scale. Nat. Climate Change, 10, 6368, https://doi.org/10.1038/s41558-019-0648-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J. , and D. J. Karoly , 1981: The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci., 38, 11791196, https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • La Sorte, F. A. , D. Fink , W. M. Hochachka , J. P. DeLong , and S. Kelling , 2014a: Spring phenology of ecological productivity contributes to the use of looped migration strategies by birds. Proc. Roy. Soc., 281B, 20140984, https://doi.org/10.1098/rspb.2014.0984.

    • Search Google Scholar
    • Export Citation
  • La Sorte, F. A. , and Coauthors, 2014b: The role of atmospheric conditions in the seasonal dynamics of North American migration flyways. J. Biogeogr., 41, 16851696, https://doi.org/10.1111/jbi.12328.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lane, J. E. , L. E. Kruuk , A. Charmantier , J. O. Murie , and F. S. Dobson , 2012: Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature, 489, 554557, https://doi.org/10.1038/nature11335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, T. Y. , and Coauthors, 2019: MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks. Methods Ecol. Evol., 10, 19081922, https://doi.org/10.1111/2041-210X.13280.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lincoln, F. C. , 1935: The waterfowl flyways of North America. Circular 342, U.S. Department of Agriculture, 13 pp.

  • Lowery, G. H., Jr., 1945: Trans-Gulf spring migration of birds and the coastal hiatus. Wilson Bull., 57, 92121.

  • Mayor, S. J. , and Coauthors, 2017: Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep., 7, 1902, https://doi.org/10.1038/s41598-017-02045-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oliver, R. Y. , P. J. Mahoney , E. Gurarie , N. Krikun , B. C. Weeks , M. Hebblewhite , G. Liston , and N. Boelman , 2020: Behavioral responses to spring snow conditions contribute to long-term shift in migration phenology in American robins. Environ. Res. Lett., 15, 045003, https://doi.org/10.1088/1748-9326/ab71a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olsen, B. , Munster, V. J. , Wallensten, A. , Waldenström, J. , Osterhaus, A. D. and Fouchier, R. A. , 2006: Global patterns of influenza A virus in wild birds. Science, 312, 384388, https://doi.org/10.1126/science.1122438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenberg, K. V. , and Coauthors, 2019: Decline of the North American avifauna. Science, 366, 120124, https://doi.org/10.1126/science.aaw1313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schubert, S. , H. Wang , and M. Suarez , 2011: Warm season subseasonal variability and climate extremes in the Northern Hemisphere: The role of stationary Rossby waves. J. Climate, 24, 47734792, https://doi.org/10.1175/JCLI-D-10-05035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schulte, J. A. , and S. Lee , 2017: Strengthening North Pacific influences on United States temperature variability. Sci. Rep., 7, 124, https://doi.org/10.1038/s41598-017-00175-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schulte, J. A. , N. Georgas , V. Saba , and P. Howell , 2018: North Pacific influences on long island sound temperature variability. J. Climate, 31, 27452769, https://doi.org/10.1175/JCLI-D-17-0135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, D. , D. L. Swain , J. S. Mankin , D. E. Horton , L. N. Thomas , B. Rajaratnam , and N. S. Diffenbaugh , 2016: Recent amplification of the North American winter temperature dipole. J. Geophys. Res. Atmos., 121, 99119928, https://doi.org/10.1002/2016JD025116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A. , and J. L. Deppe , 2008: Simulating the effects of wetland loss and inter-annual variability on the fitness of migratory bird species. 2008 IEEE Int. Geoscience and Remote Sensing Symp., Boston, MA, IEEE, IV-838IV-841, https://doi.org/10.1109/IGARSS.2008.4779853.

    • Search Google Scholar
    • Export Citation
  • Somveille, M. , A. Manica , and A. S. Rodrigues , 2019: Where the wild birds go: Explaining the differences in migratory destinations across terrestrial bird species. Ecography, 42, 225236, https://doi.org/10.1111/ecog.03531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strong, C. , B. Zuckerberg , J. L. Betancourt , and W. D. Koenig , 2015: Climatic dipoles drive two principal modes of North American boreal bird irruption. Proc. Natl. Acad. Sci. USA, 112, E2795E2802, https://doi.org/10.1073/pnas.1418414112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Studds, C. E. , and P. P. Marra , 2011: Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. Roy. Soc., 278B, 34373443, https://doi.org/10.1098/rspb.2011.0332.

    • Search Google Scholar
    • Export Citation
  • Van Buskirk, J. , R. S. Mulvihill , and R. C. Leberman , 2009: Variable shifts in spring and autumn migration phenology in North American songbirds associated with climate change. Global Change Biol., 15, 760771, https://doi.org/10.1111/j.1365-2486.2008.01751.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Doren, B. M. , and K. G. Horton , 2018: A continental system for forecasting bird migration. Science, 361, 11151118, https://doi.org/10.1126/science.aat7526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vardanis, Y. , R. H. Klaassen , R. Strandberg , and T. Alerstam , 2011: Individuality in bird migration: Routes and timing. Biol. Lett., 7, 502505, https://doi.org/10.1098/rsbl.2010.1180.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waller, E. K. , T. M. Crimmins , J. J. Walker , E. E. Posthumus , and J. F. Weltzin , 2018: Differential changes in the onset of spring across US National Wildlife Refuges and North American migratory bird flyways. PLOS ONE, 13, e0202495, https://doi.org/10.1371/journal.pone.0202495.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S. Y. , L. Hipps , R. R. Gillies , and J. H. Yoon , 2014: Probable causes of the abnormal ridge accompanying the 2013–2014 California drought: ENSO precursor and anthropogenic warming footprint. Geophys. Res. Lett., 41, 32203226, https://doi.org/10.1002/2014GL059748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, A. B. , P. Kumar , and D. Tcheng , 2005: A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States. Remote Sens. Environ., 98, 120, https://doi.org/10.1016/j.rse.2005.05.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Youngflesh, C. , and Coauthors, 2021: Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol., 5, 987994, https://doi.org/10.1038/s41559-021-01442-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zuckerberg, B. , C. Strong , J. M. LaMontagne , S. S. George , J. L. Betancourt , and W. D. Koenig , 2020: Climate dipoles as continental drivers of plant and animal populations. Trends Ecol. Evol., 35, 440453, https://doi.org/10.1016/j.tree.2020.01.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Continental Patterns of Bird Migration Linked to Climate Variability

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  • 1 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Science Systems and Applications, Inc., Lanham, Maryland;
  • | 2 Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado;
  • | 3 Department of Forest and Wildlife Ecology, University of Wisconsin–Madison, Madison, Wisconsin;
  • | 4 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Science Systems and Applications, Inc., Lanham, Maryland;
  • | 5 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
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Abstract

For ∼100 years, the continental patterns of avian migration in North America have been described in the context of three or four primary flyways. This spatial compartmentalization often fails to adequately reflect a critical characterization of migration—phenology. This shortcoming has been partly due to the lack of reliable continental-scale data, a gap filled by our current study. Here, we leveraged unique radar-based data quantifying migration phenology and used an objective regionalization approach to introduce a new spatial framework that reflects interannual variability. Therefore, the resulting spatial classification is intrinsically different from the “flyway concept.” We identified two regions with distinct interannual variability of spring migration across the contiguous United States. This data-driven framework enabled us to explore the climatic cues affecting the interannual variability of migration phenology, “specific to each region” across North America. For example, our “two-region” approach allowed us to identify an east–west dipole pattern in migratory behavior linked to atmospheric Rossby waves. Also, we revealed that migration movements over the western United States were inversely related to interannual and low-frequency variability of regional temperature. A similar link, but weaker and only for interannual variability, was evident for the eastern region. However, this region was more strongly tied to climate teleconnections, particularly to the east Pacific–North Pacific (EP–NP) pattern. The results suggest that oceanic forcing in the tropical Pacific—through a chain of processes including Rossby wave trains—controls the climatic conditions, associated with bird migration over the eastern United States. Our spatial platform would facilitate better understanding of the mechanisms responsible for broadscale migration phenology and its potential future changes.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Amin Dezfuli, amin.dezfuli@nasa.gov

Abstract

For ∼100 years, the continental patterns of avian migration in North America have been described in the context of three or four primary flyways. This spatial compartmentalization often fails to adequately reflect a critical characterization of migration—phenology. This shortcoming has been partly due to the lack of reliable continental-scale data, a gap filled by our current study. Here, we leveraged unique radar-based data quantifying migration phenology and used an objective regionalization approach to introduce a new spatial framework that reflects interannual variability. Therefore, the resulting spatial classification is intrinsically different from the “flyway concept.” We identified two regions with distinct interannual variability of spring migration across the contiguous United States. This data-driven framework enabled us to explore the climatic cues affecting the interannual variability of migration phenology, “specific to each region” across North America. For example, our “two-region” approach allowed us to identify an east–west dipole pattern in migratory behavior linked to atmospheric Rossby waves. Also, we revealed that migration movements over the western United States were inversely related to interannual and low-frequency variability of regional temperature. A similar link, but weaker and only for interannual variability, was evident for the eastern region. However, this region was more strongly tied to climate teleconnections, particularly to the east Pacific–North Pacific (EP–NP) pattern. The results suggest that oceanic forcing in the tropical Pacific—through a chain of processes including Rossby wave trains—controls the climatic conditions, associated with bird migration over the eastern United States. Our spatial platform would facilitate better understanding of the mechanisms responsible for broadscale migration phenology and its potential future changes.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Amin Dezfuli, amin.dezfuli@nasa.gov
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