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Predicting evolutionary potential: A numerical test of evolvability measures.

Thomas F Hansen1, Thomas M Solvin1,2, Mihaela Pavlicev1,3

  • 1Department of Biology, University of Oslo, Oslo, Norway.

Evolution; International Journal of Organic Evolution
|February 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces quantitative measures for evolutionary potential and constraints, successfully predicting microevolutionary responses. Conditional evolvability accurately forecasts evolutionary rates under selective constraints.

Keywords:
Conditional evolvabilityevolvabilitypleiotropyrate of evolutionselection responseselective constraint

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Area of Science:

  • Evolutionary biology
  • Quantitative genetics

Background:

  • Microevolutionary theory often lacks quantitative predictive power.
  • Existing measures of selection and evolutionary potential are not always operationally linked to theory.

Purpose of the Study:

  • To assess operational measures of evolvability and constraint in predicting short-term selection responses.
  • To investigate the impact of selective constraints on trait evolution.
  • To evaluate conditional evolvability as a measure of evolutionary potential.

Main Methods:

  • Utilized individual-based simulations to generate selection responses.
  • Employed a set of operational measures for evolvability and constraint.
  • Focused on scenarios with stabilizing selection on other traits impeding response in one trait.

Main Results:

  • Conditional evolvability successfully predicted evolutionary rates in equilibrium situations.
  • Characteristic times to reach equilibria were inversely proportional to fitness load from constraining characters.
  • Evolvabilities and conditional evolvabilities bracketed selection responses.

Conclusions:

  • Operational measures, particularly conditional evolvability, can quantitatively predict microevolutionary responses.
  • These measures are valuable for quantifying evolutionary potential on relevant timescales.
  • Selective constraints significantly influence evolutionary trajectories and rates.