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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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A multivariate view of parallel evolution.

Stephen P De Lisle1, Daniel I Bolnick1

  • 1Department of Ecology & Evolutionary Biology, University of Connecticut, Storrs, Connecticut, 06269.

Evolution; International Journal of Organic Evolution
|June 10, 2020
PubMed
Summary
This summary is machine-generated.

New methods using eigen analysis reveal greater evolutionary parallelism than previously thought. Comparing covariance matrices helps identify major evolutionary axes and test hypotheses like genetic drift.

Keywords:
Convergent evolutiongasterosteus aculeatusmicroevolutionphenotypic vector analysisrandom matrix theory

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Area of Science:

  • Evolutionary Biology
  • Quantitative Genetics
  • Ecomorphology

Background:

  • Quantifying parallel evolution often involves analyzing angles and distances between lineage evolutionary vectors in multivariate trait space.
  • Current methods focus on element-by-element interpretation of distance matrices, typically testing against a null hypothesis of perfect parallelism.

Purpose of the Study:

  • To introduce complementary multivariate approaches, derived from evolutionary quantitative genetics, for analyzing parallel and convergent evolution.
  • To enable the identification of major axes of evolutionary change and define biologically relevant null hypotheses.

Main Methods:

  • Utilized eigen analysis and comparison of among-lineage covariance matrices.
  • Applied these methods to reanalyze a dataset of multivariate evolution in threespine stickleback across a lake/stream gradient.

Main Results:

  • The analysis revealed that a few major dimensions capture most of the variation in evolutionary change direction.
  • This indicates a higher degree of evolutionary parallelism than previously recognized.

Conclusions:

  • Multivariate approaches, particularly those involving covariance matrix comparisons, are essential for a comprehensive understanding of parallel and convergent evolution.
  • These methods allow for the identification of alternative adaptive solutions and the testing of null hypotheses such as genetic drift.