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Introducing multiple factor analysis (MFA) as a diagnostic taxonomic tool complementing principal component analysis

L Lee Grismer1,2

  • 1Herpetology Laboratory, Department of Biology, La Sierra University, 4500 Riverwalk Parkway, Riverside, California 92505, USA La Sierra University Riverside United States of America.

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|August 14, 2025
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Summary
This summary is machine-generated.

Multiple factor analysis (MFA) effectively integrates diverse data types for taxonomic diagnosis, outperforming principal component analysis (PCA) in comprehensive morphological assessments. MFA provides a statistically robust method for analyzing both numeric and categorical traits in species differentiation.

Keywords:
Diagnosisherpetologymeristic datamorphometric datamultivariate statisticsstatistical defensibilitytaxonomy

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Area of Science:

  • Taxonomy and Systematics
  • Quantitative Biology
  • Herpetology

Background:

  • Traditional taxonomic diagnoses often omit or anecdotally treat categorical characters due to variability.
  • Principal Component Analysis (PCA) is effective for single numeric data types but can be biased when analyzing multiple types.
  • Operational Taxonomic Units (OTUs) require robust statistical methods for assessing differentiation.

Purpose of the Study:

  • Introduce Multiple Factor Analysis (MFA) as a superior diagnostic tool for taxonomy.
  • Compare and contrast MFA with Principal Component Analysis (PCA).
  • Highlight the utility of integrating diverse character types for a total-evidence morphological output.

Main Methods:

  • Multiple Factor Analysis (MFA) for integrating numeric (meristic, morphometric) and categorical characters.
  • Principal Component Analysis (PCA) for analyzing single numeric data types.
  • Non-parametric permutation of analysis of variance (PERMANOVA) for statistical significance testing of OTU positions.

Main Results:

  • MFA enables a comprehensive, total-evidence morphological analysis by integrating diverse character types.
  • PCA is best suited for single numeric data types; using multiple types can bias results.
  • PERMANOVA offers a statistically defensible method for assessing OTU differentiation significance.

Conclusions:

  • MFA is a powerful tool for taxonomic diagnosis, particularly for integrating varied morphological data.
  • MFA offers a statistically sound approach to utilizing categorical characters in taxonomy.
  • Robust statistical methods like PERMANOVA are essential for validating taxonomic analyses.