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Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

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Published on: November 28, 2019

Fluctuation domains in adaptive evolution.

Carl Boettiger1, Jonathan Dushoff, Joshua S Weitz

  • 1Center for Population Biology, University of California, Davis, United States. cboettig@ucdavis.edu

Theoretical Population Biology
|October 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces fluctuation domains to predict evolutionary rates. These domains, based on fitness landscape curvature, reveal predictable (dissipation) and variable (enhancement) evolutionary paths.

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Area of Science:

  • Evolutionary Biology
  • Theoretical Ecology
  • Quantitative Genetics

Background:

  • Predicting evolutionary trajectories is crucial in adaptive dynamics and quantitative genetics.
  • Understanding variation around the mean evolutionary path requires further theoretical development.

Purpose of the Study:

  • To derive an expression for variation between parallel evolutionary trajectories.
  • To introduce and define 'fluctuation domains' within the fitness landscape.
  • To explore how landscape curvature influences evolutionary predictability.

Main Methods:

  • Derivation of a novel mathematical expression for evolutionary trajectory variation.
  • Analysis of fitness landscape curvature (positive and negative).
  • Application to ecological scenarios involving competition for resources.

Main Results:

  • Identified 'fluctuation domains' characterized by fluctuation dissipation (negative curvature, adaptive peaks) and enhancement (positive curvature, adaptive valleys/branching points).
  • Demonstrated that landscape curvature dictates the predictability of evolutionary rates.
  • Showcased these dynamics in models of implicit and explicit resource competition.

Conclusions:

  • The derived expression provides a framework for understanding evolutionary unpredictability.
  • Fitness landscape curvature is a key determinant of evolutionary fluctuation domains.
  • This work offers new insights into the dynamics of phenotypic evolution under varying ecological pressures.