Semi-supervised determination of pseudocryptic morphotypes using observer-free characterizations of anatomical alignment and shape.

Published

Journal Article

Accurate, quantitative characterization of complex shapes is recognized as a key methodological challenge in biology. Recent development of automated three-dimensional geometric morphometric protocols (auto3dgm) provides a promising set of tools to help address this challenge. While auto3dgm has been shown to be useful in characterizing variation across clades of morphologically very distinct mammals, it has not been adequately tested in more problematic cases where pseudolandmark placement error potentially confounds interpretation of true shape variation. Here, we tested the sensitivity of auto3dgm to the degree of variation and various parameterization settings using a simulation and three microCT datasets that characterize mammal tooth crown morphology as biological examples. The microCT datasets vary in degree of apparent morphological differentiation, with two that include grossly similar morphospecies and one that includes two laboratory strains of a single species. Resulting alignments are highly sensitive to the number of pseudolandmarks used to quantify shapes. The degree to which the surfaces were downsampled and the apparent degree of morphological differentiation across the dataset also influenced alignment repeatability. We show that previous critiques of auto3dgm were based on poorly parameterized alignments and suggest that sample-specific sensitivity analyses should be added to any research protocol including auto3dgm. Auto3dgm is a useful tool for studying samples when pseudolandmark placement error is small relative to the true differences between specimens. This method therefore represents a promising avenue forward in morphometric studies at a wide range of scales, from samples that differ by a single genetic locus to samples that represent multiple phylogenetically diverse clades.

Full Text

Duke Authors

Cited Authors

  • Vitek, NS; Manz, CL; Gao, T; Bloch, JI; Strait, SG; Boyer, DM

Published Date

  • July 2017

Published In

Volume / Issue

  • 7 / 14

Start / End Page

  • 5041 - 5055

PubMed ID

  • 28770045

Pubmed Central ID

  • 28770045

Electronic International Standard Serial Number (EISSN)

  • 2045-7758

International Standard Serial Number (ISSN)

  • 2045-7758

Digital Object Identifier (DOI)

  • 10.1002/ece3.3058

Language

  • eng