Machine learning reveals cryptic dialects that explain mate choice in a songbird - Nature Communications
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The authors show that captive populations of zebra finches, which have been kept in isolation for up to 100 generations, have diverged in song dialect. When individuals singing different dialects are mixed, mating is assortative for song dialect.
  • @HaggunenonsOPM
    1•2 years ago

    Summary made by Quivr/GPT-4

    The academic article published in Nature Communications in 2022 discusses the use of machine learning to identify ‘cryptic song dialects’ in zebra finches. The study found that despite songs being highly individual-specific in zebra finches, machine learning was able to distinguish songs from multiple captive zebra finch populations with high precision. These ‘cryptic song dialects’ were found to predict strong assortative mating in the species.

    The researchers examined mating patterns across three generations using captive populations that have evolved in isolation for about 100 generations. They cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. The study found that female zebra finches preferentially pair with males whose song resembles that of the females’ adolescent peers.

    This research provides evidence that zebra finches, a model species for song learning, are sensitive to differences in song that have previously gone unnoticed by researchers.