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Area of Science:

  • Evolutionary biology
  • Quantitative genetics

Background:

  • Species interactions often feature exaggerated traits, leading to the coevolutionary arms race metaphor.
  • Rigorous evidence for reciprocal coevolution driving trait exaggeration is scarce due to challenges in quantifying coevolutionary selection intensity.

Purpose of the Study:

  • To develop a novel statistical method for estimating the intensity of coevolutionary selection.
  • To provide a tool for testing coevolutionary hypotheses in natural systems.

Main Methods:

  • Developed an approximate Bayesian computation (ABC) approach using population mean phenotypes.
  • The ABC method accommodates gene flow, variable environments, and strong selection, relaxing limitations of previous models.
  • Validated the method using simulated data with varying population sizes and parameter estimates.

Main Results:

  • The ABC method accurately infers coevolutionary selection strength with sufficient population sampling (≥10) and reliable parameter estimates.
  • Applied to *Camellia japonica* and *Curculio camelliae*, the method supports a coevolutionary hypothesis but does not rule out unilateral evolution.
  • A high correlation (0.941) between predicted and observed selection gradients on *Camellia* pericarp thickness validates the method's accuracy.

Conclusions:

  • The developed ABC approach offers a robust tool for quantifying coevolutionary selection.
  • This method advances our ability to test the role of reciprocal selection in shaping exaggerated traits.
  • The study provides strong empirical validation for the new quantitative method in an ecological context.