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A Practical Guide to Phylogenetics for Nonexperts
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Phyletic groups on networks.

Ward C Wheeler1

  • 1Division of Invertebrate Zoology, American Museum of Natural History, Central Park West @ 79th Street, New York, NY, 10024-5192, USA.

Cladistics : the International Journal of the Willi Hennig Society
|November 19, 2021
PubMed
Summary
This summary is machine-generated.

New phyletic group types, periphyletic, epiphyletic, and anaphyletic, are defined for phylogenetic networks. These terms clarify evolutionary relationships beyond traditional classifications, especially in systems with non-vertical inheritance.

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Computational biology

Background:

  • Traditional phylogenetic classifications (monophyletic, paraphyletic, polyphyletic) are based on tree structures.
  • Phylogenetic networks, which include reticulate (networked) edges, better represent complex evolutionary histories like hybridization and horizontal gene transfer.
  • Existing definitions struggle to precisely describe groups within networks that appear monophyletic on a tree but have complex origins.

Discussion:

  • Introduces three new phyletic group types: periphyletic, epiphyletic, and anaphyletic.
  • These terms generalize Farris's algorithmic definitions to phylogenetic networks.
  • They describe groups that are monophyletic on a tree but involve gains or losses of members through network edges, accounting for reticulate evolution.

Key Insights:

  • Periphyletic, epiphyletic, and anaphyletic designations provide a nuanced vocabulary for evolutionary patterns in networks.
  • These classifications are crucial for understanding non-vertical inheritance mechanisms.
  • The definitions are algorithmically grounded, allowing for computational application.

Outlook:

  • Facilitates clearer discussions of evolution in systems with recombination (viruses), horizontal exchange (bacteria), hybridization (plants, animals), and linguistic evolution.
  • Provides a framework for analyzing complex evolutionary histories not adequately captured by tree-only models.
  • Potential applications in comparative genomics, population genetics, and historical linguistics.