Literature DB >> 12653309

The promise of geometric morphometrics.

Joan T Richtsmeier1, Valerie Burke DeLeon, Subhash R Lele.   

Abstract

Nontraditional or geometric morphometric methods have found wide application in the biological sciences, especially in anthropology, a field with a strong history of measurement of biological form. Controversy has arisen over which method is the "best" for quantifying the morphological difference between forms and for making proper statistical statements about the detected differences. This paper explains that many of these arguments are superfluous to the real issues that need to be understood by those wishing to apply morphometric methods to biological data. Validity, the ability of a method to find the correct answer, is rarely discussed and often ignored. We explain why demonstration of validity is a necessary step in the evaluation of methods used in morphometrics. Focusing specifically on landmark data, we discuss the concepts of size and shape, and reiterate that since no unique definition of size exists, shape can only be recognized with reference to a chosen surrogate for size. We explain why only a limited class of information related to the morphology of an object can be known when landmark data are used. This observation has genuine consequences, as certain morphometric methods are based on models that require specific assumptions, some of which exceed what can be known from landmark data. We show that orientation of an object with reference to other objects in a sample can never be known, because this information is not included in landmark data. Consequently, a descriptor of form difference that contains information on orientation is flawed because that information does not arise from evidence within the data, but instead is a product of a chosen orientation scheme. To illustrate these points, we apply superimposition, deformation, and linear distance-based morphometric methods to the analysis of a simulated data set for which the true differences are known. This analysis demonstrates the relative efficacy of various methods to reveal the true difference between forms. Our discussion is intended to be fair, but it will be obvious to the reader that we favor a particular approach. Our bias comes from the realization that morphometric methods should operate with a definition of form and form difference consistent with the limited class of information that can be known from landmark data. Answers based on information that can be known from the data are of more use to biological inquiry than those based on unjustifiable assumptions.

Mesh:

Year:  2002        PMID: 12653309     DOI: 10.1002/ajpa.10174

Source DB:  PubMed          Journal:  Am J Phys Anthropol        ISSN: 0002-9483            Impact factor:   2.868


  44 in total

1.  Precision and error of three-dimensional phenotypic measures acquired from 3dMD photogrammetric images.

Authors:  Kristina Aldridge; Simeon A Boyadjiev; George T Capone; Valerie B DeLeon; Joan T Richtsmeier
Journal:  Am J Med Genet A       Date:  2005-10-15       Impact factor: 2.802

2.  Morphometric analysis of facial landmark data to characterize the facial phenotype associated with fetal alcohol syndrome.

Authors:  Tinashe Mutsvangwa; Tania S Douglas
Journal:  J Anat       Date:  2007-02       Impact factor: 2.610

3.  Illustrating ontogenetic change in the dentition of the Nile monitor lizard, Varanus niloticus: a case study in the application of geometric morphometric methods for the quantification of shape-size heterodonty.

Authors:  Domenic C D'Amore
Journal:  J Anat       Date:  2015-05-04       Impact factor: 2.610

4.  An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization.

Authors:  Peter Claes; Jonas Reijniers; Mark D Shriver; Jonatan Snyders; Paul Suetens; Joachim Nielandt; Guy De Tré; Dirk Vandermeulen
Journal:  J Anat       Date:  2014-11-09       Impact factor: 2.610

5.  Delimitation of a continuous morphological character with unknown prior membership: application of a finite mixture model to classify scapular setae of Abacarus panticis.

Authors:  Tsung-Jen Shen; Chi-Chien Kuo; Chin-Fah Wang; Kun-Wei Huang
Journal:  Exp Appl Acarol       Date:  2014-03-20       Impact factor: 2.132

6.  Facial surface morphology predicts variation in internal skeletal shape.

Authors:  Nathan M Young; Krunal Sherathiya; Luis Gutierrez; Emerald Nguyen; Sona Bekmezian; John C Huang; Benedikt Hallgrímsson; Janice S Lee; Ralph S Marcucio
Journal:  Am J Orthod Dentofacial Orthop       Date:  2016-04       Impact factor: 2.650

Review 7.  Large-scale objective phenotyping of 3D facial morphology.

Authors:  Peter Hammond; Michael Suttie
Journal:  Hum Mutat       Date:  2012-03-20       Impact factor: 4.878

8.  Postnatal brain and skull growth in an Apert syndrome mouse model.

Authors:  Cheryl A Hill; Neus Martínez-Abadías; Susan M Motch; Jordan R Austin; Yingli Wang; Ethylin Wang Jabs; Joan T Richtsmeier; Kristina Aldridge
Journal:  Am J Med Genet A       Date:  2013-03-12       Impact factor: 2.802

9.  Shape-Based Classification of 3D Head Data.

Authors:  Linda Shapiro; Katarzyna Wilamowska; Indriyati Atmosukarto; Jia Wu; Carrie Heike; Matthew Speltz; Michael Cunningham
Journal:  Proc Int Conf Image Anal Process       Date:  2009

10.  The effects of muscular dystrophy on the craniofacial shape of Mus musculus.

Authors:  Donna Carlson Jones; Miriam L Zelditch; Paula Lightfoot Peake; Rebecca Z German
Journal:  J Anat       Date:  2007-04-25       Impact factor: 2.610

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