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Challenges and advances in measuring phenotypic convergence.

David M Grossnickle1, William H Brightly2, Lucas N Weaver3

  • 1Natural Sciences Department, Oregon Institute of Technology, Klamath Falls, OR, United States.

Evolution; International Journal of Organic Evolution
|May 21, 2024
PubMed
Summary
This summary is machine-generated.

New Ct measures accurately identify phenotypic convergence by calculating lineage distances over time. Existing C-measures can misidentify divergent taxa as convergent, highlighting the need for improved evolutionary studies.

Keywords:
Ornstein–Uhlenbeck modelsadaptive evolutionconvergent evolutionevolutionary modelsphylomorphospace

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Area of Science:

  • Evolutionary Biology
  • Quantitative Phylogenetics
  • Adaptive Evolution

Background:

  • Phenotypic convergence studies provide evidence for adaptive evolution.
  • Quantitative methods like C-measures and Ornstein-Uhlenbeck (OU) models are popular for studying convergence.
  • Existing C-measures may misidentify divergent lineages as convergent.

Purpose of the Study:

  • To test the performance of existing convergence measures (C-measures, OU models) under various evolutionary scenarios.
  • To develop novel, more accurate measures for identifying phenotypic convergence.
  • To address the limitations of current methods in distinguishing true convergence from divergence.

Main Methods:

  • Development and application of novel Ct1-Ct4 measures calculating time-specific lineage distances.
  • Testing C-measures and Ct-measures under diverse evolutionary scenarios.
  • Mimicking Ornstein-Uhlenbeck model-fitting analyses with a priori regime assignments.

Main Results:

  • C-measures frequently misidentify divergent lineages as convergent.
  • Novel Ct-measures minimize misidentification of divergent taxa by calculating time-specific distances.
  • All convergence measures are influenced by phenotypic space outliers.
  • Multiple-regime OU models may show support even with divergent lineages, not always indicating true convergence.

Conclusions:

  • The new Ct1-Ct4 measures offer an improved tool for assessing phenotypic convergence.
  • Existing convergence measures have limitations and can be unreliable.
  • Researchers should use multiple lines of evidence and acknowledge method limitations when testing convergence hypotheses.