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Leaf Morphology, Taxonomy and Geometric Morphometrics: A Simplified Protocol for Beginners

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  • Vincenzo Viscosi
  • Andrea Cardini

Abstract

Taxonomy relies greatly on morphology to discriminate groups. Computerized geometric morphometric methods for quantitative shape analysis measure, test and visualize differences in form in a highly effective, reproducible, accurate and statistically powerful way. Plant leaves are commonly used in taxonomic analyses and are particularly suitable to landmark based geometric morphometrics. However, botanists do not yet seem to have taken advantage of this set of methods in their studies as much as zoologists have done. Using free software and an example dataset from two geographical populations of sessile oak leaves, we describe in detailed but simple terms how to: a) compute size and shape variables using Procrustes methods; b) test measurement error and the main levels of variation (population and trees) using a hierachical design; c) estimate the accuracy of group discrimination; d) repeat this estimate after controlling for the effect of size differences on shape (i.e., allometry). Measurement error was completely negligible; individual variation in leaf morphology was large and differences between trees were generally bigger than within trees; differences between the two geographic populations were small in both size and shape; despite a weak allometric trend, controlling for the effect of size on shape slighly increased discrimination accuracy. Procrustes based methods for the analysis of landmarks were highly efficient in measuring the hierarchical structure of differences in leaves and in revealing very small-scale variation. In taxonomy and many other fields of botany and biology, the application of geometric morphometrics contributes to increase scientific rigour in the description of important aspects of the phenotypic dimension of biodiversity. Easy to follow but detailed step by step example studies can promote a more extensive use of these numerical methods, as they provide an introduction to the discipline which, for many biologists, is less intimidating than the often inaccessible specialistic literature.

Suggested Citation

  • Vincenzo Viscosi & Andrea Cardini, 2011. "Leaf Morphology, Taxonomy and Geometric Morphometrics: A Simplified Protocol for Beginners," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-20, October.
  • Handle: RePEc:plo:pone00:0025630
    DOI: 10.1371/journal.pone.0025630
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    References listed on IDEAS

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    1. Casper J Breuker & James S Patterson & Christian Peter Klingenberg, 2006. "A Single Basis for Developmental Buffering of Drosophila Wing Shape," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-7, December.
    2. Norman MacLeod & Mark Benfield & Phil Culverhouse, 2010. "Time to automate identification," Nature, Nature, vol. 467(7312), pages 154-155, September.
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    Cited by:

    1. Luis Gutiérrez & Ramsés H. Mena & Carlos Díaz-Avalos, 2020. "Linear models for statistical shape analysis based on parametrized closed curves," Statistical Papers, Springer, vol. 61(3), pages 1213-1229, June.
    2. Amro Daboul & Tatyana Ivanovska & Robin Bülow & Reiner Biffar & Andrea Cardini, 2018. "Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    3. Irene Epifanio & María Victoria Ibáñez & Amelia Simó, 2018. "Archetypal shapes based on landmarks and extension to handle missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 705-735, September.
    4. Olalekan Agbolade & Azree Nazri & Razali Yaakob & Abdul Azim Ghani & Yoke Kqueen Cheah, 2020. "Morphometric approach to 3D soft-tissue craniofacial analysis and classification of ethnicity, sex, and age," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-24, April.
    5. José Antonio Muñoz-Reyes & Marta Iglesias-Julios & Miguel Pita & Enrique Turiegano, 2015. "Facial Features: What Women Perceive as Attractive and What Men Consider Attractive," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    6. Vitale Nuzzo & Antonio Gatto & Giuseppe Montanaro, 2022. "Morphological Characterization of Some Local Varieties of Fig ( Ficus carica L.) Cultivated in Southern Italy," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    7. Andrew G Gardner & Jonathan N Fitz Gerald & John Menz & Kelly A Shepherd & Dianella G Howarth & Rachel S Jabaily, 2016. "Characterizing Floral Symmetry in the Core Goodeniaceae with Geometric Morphometrics," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-22, May.
    8. Aleix Alcacer & Irene Epifanio & M Victoria Ibáñez & Amelia Simó & Alfredo Ballester, 2020. "A data-driven classification of 3D foot types by archetypal shapes based on landmarks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.

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