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The incubation environment does not explain significant variation in heart rate plasticity among avian embryos.

Alexandra G ConesEve R SchneiderDavid F Westneat
Published in: The Journal of experimental biology (2024)
The conditions an organism experiences during development can modify how they plastically respond to short-term changes in their environment later in life. This can be adaptive because the optimal average trait value and the optimal plastic change in trait value in response to the environment may differ across different environments. For example, early developmental temperatures can adaptively modify how reptiles, fish and invertebrates metabolically respond to temperature. However, whether individuals within populations respond differently (a prerequisite to adaptive evolution), and whether this occurs in birds, which are only ectothermic for part of their life cycle, is not known. We experimentally tested these possibilities by artificially incubating the embryos of Pekin ducks (Anas platyrhynchos domesticus) at constant or variable temperatures. We measured their consequent heart rate reaction norms to short-term changes in egg temperature and tracked their growth. Contrary to expectations, the early thermal environment did not modify heart rate reaction norms, but regardless, these reaction norms differed among individuals. Embryos with higher average heart rates were smaller upon hatching, but heart rate reaction norms did not predict subsequent growth. Our data also suggests that the thermal environment may affect both the variance in heart rate reaction norms and their covariance with growth. Thus, individual avian embryos can vary in their plasticity to temperature, and in contrast to fully ectothermic taxa, the early thermal environment does not explain this variance. Because among-individual variation is one precondition to adaptive evolution, the factors that do contribute to such variability may be important.
Keyphrases
  • heart rate
  • heart rate variability
  • blood pressure
  • magnetic resonance
  • life cycle
  • heart failure
  • gene expression
  • atrial fibrillation
  • machine learning
  • deep learning
  • disease virus