Quantitative genetic study of the adaptive process

R. G. Shaw, F. H. Shaw

Research output: Contribution to journalReview articlepeer-review

30 Scopus citations

Abstract

The additive genetic variance with respect to absolute fitness, V A (W), divided by mean absolute fitness sets the rate of ongoing adaptation. Fisher's key insight yielding this quantitative prediction of adaptive evolution, known as the Fundamental Theorem of Natural Selection, is well appreciated by evolutionists. Nevertheless, extremely scant information about V A (W) is available for natural populations. Consequently, the capacity for fitness increase via natural selection is unknown. Particularly in the current context of rapid environmental change, which is likely to reduce fitness directly and, consequently, the size and persistence of populations, the urgency of advancing understanding of immediate adaptive capacity is extreme. We here explore reasons for the dearth of empirical information about V A (W), despite its theoretical renown and critical evolutionary role. Of these reasons, we suggest that expectations that V A (W) is negligible, in general, together with severe statistical challenges of estimating it, may largely account for the limited empirical emphasis on it. To develop insight into the dynamics of V A (W) in a changing environment, we have conducted individual-based genetically explicit simulations. We show that, as optimizing selection on a trait changes steadily over generations, V A (W) can grow considerably, supporting more rapid adaptation than would the V A (W) of the base population. We call for direct evaluation of V A (W) and in support of prediction of rates adaptive evolution, and we advocate for the use of aster modeling as a rigorous basis for achieving this goal.

Original languageEnglish (US)
Pages (from-to)13-20
Number of pages8
JournalHeredity
Volume112
Issue number1
DOIs
StatePublished - Jan 2014

Keywords

  • Adaptation
  • Aster models
  • Breeding value
  • Fitness
  • Quantitative genetics
  • Stabilizing selection

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