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A new, fast method to search for morphological convergence with shape data.

Silvia Castiglione1, Carmela Serio1, Davide Tamagnini2

  • 1Dipartimento di Scienze della Terra, dell'Ambiente e delle Risorse, University of Naples Federico II, Napoli, Italy.

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|December 28, 2019
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Summary
This summary is machine-generated.

A new method, search.conv, efficiently detects morphological convergence between distantly related species using phylogenetic and trait data. This powerful tool accurately identifies convergent evolution with a low false positive rate.

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Area of Science:

  • Macroevolutionary biology
  • Phylogenetics
  • Quantitative morphology

Background:

  • Morphological convergence, the independent evolution of similar traits in distinct lineages, is a key area in macroevolutionary studies.
  • Existing methods for detecting convergence have limitations, often requiring specific data types or complex analyses.
  • There is a need for robust and efficient methods to test for morphological convergence, especially when incorporating paleontological data.

Purpose of the Study:

  • To introduce a novel statistical method, search.conv, for detecting morphological convergence.
  • To develop a method applicable to both ultrametric and non-ultrametric phylogenies with multivariate data.
  • To enable the incorporation of fossil data by allowing known phenotypes as ancestral states.

Main Methods:

  • The search.conv function within the R package RRphylo was developed to test if unrelated clades are more morphologically similar than predicted by their phylogenetic distance.
  • Simulations were used to assess the power and false positive rates of the search.conv method.
  • The method was applied to case studies including ungulate molars, sabertooth cat mandibles, and Caribbean anole ecomorphs.

Main Results:

  • The search.conv method demonstrated high power, correctly identifying simulated convergence in 95% of cases.
  • The Type I error rate (false positives) was found to be low, ranging from 4-6%.
  • search.conv is significantly faster, approximately three orders of magnitude, compared to a competing method.

Conclusions:

  • The search.conv method provides a powerful, accurate, and efficient new tool for studying morphological convergence.
  • Its ability to handle diverse phylogenetic data and incorporate fossil information makes it valuable for macroevolutionary research.
  • This method advances our ability to investigate evolutionary patterns of trait similarity across distantly related taxa.