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Adult male birds advance spring migratory phenology faster than females and juveniles across North America.

Montague H C Neate-CleggMorgan W Tingley
Published in: Global change biology (2022)
Advances in spring migratory phenology comprise some of the most well-documented evidence for the impacts of climate change on birds. Nevertheless, surprisingly little research has investigated whether birds are shifting their migratory phenology equally across sex and age classes-a question critical to understanding the potential for trophic mismatch. We used 60 years of bird banding data across North America-comprising over 4 million captures in total-to investigate both spring and fall migratory phenology for a total of 98 bird species across sex and age classes, with the exact numbers of species for each analysis depending on season-specific data availability. Consistent with protandry, in spring (n = 89 species), adult males were the first to arrive and immature females were the last to arrive. In fall (n = 98), there was little difference between sexes, but adults tended to depart earlier than juveniles. Over 60 years, adult males advanced their phenology the fastest (-0.84 days per decade, 95 CrI = -1.22 to -0.47, n = 36), while adult and immature females advanced at a slower pace, causing the gap in male and female arrival times to widen over time. In the fall, there was no overall trend in phenology by age or sex (n = 57), driven in part by high interspecific variation related to breeding and molt strategies. Our results indicate consistent and predictable age- and sex-based differences in the rates at which species' springtime phenology is shifting. The growing gap between male and female migratory arrival indicates sex-based plasticity in adaptation to climate change that has strong potential to negatively impact current and future population trends.
Keyphrases
  • climate change
  • human health
  • electronic health record
  • big data
  • genetic diversity
  • childhood cancer
  • machine learning
  • risk assessment
  • high resolution
  • artificial intelligence
  • deep learning
  • current status