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

Publication ,  Journal Article
Vitek, NS; Manz, CL; Gao, T; Bloch, JI; Strait, SG; Boyer, DM
Published in: Ecology and evolution
July 2017

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.

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Published In

Ecology and evolution

DOI

EISSN

2045-7758

ISSN

2045-7758

Publication Date

July 2017

Volume

7

Issue

14

Start / End Page

5041 / 5055

Related Subject Headings

  • 4102 Ecological applications
  • 3104 Evolutionary biology
  • 3103 Ecology
  • 0603 Evolutionary Biology
  • 0602 Ecology
 

Citation

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Vitek, N. S., Manz, C. L., Gao, T., Bloch, J. I., Strait, S. G., & Boyer, D. M. (2017). Semi-supervised determination of pseudocryptic morphotypes using observer-free characterizations of anatomical alignment and shape. Ecology and Evolution, 7(14), 5041–5055. https://doi.org/10.1002/ece3.3058
Vitek, Natasha S., Carly L. Manz, Tingran Gao, Jonathan I. Bloch, Suzanne G. Strait, and Doug M. Boyer. “Semi-supervised determination of pseudocryptic morphotypes using observer-free characterizations of anatomical alignment and shape.Ecology and Evolution 7, no. 14 (July 2017): 5041–55. https://doi.org/10.1002/ece3.3058.
Vitek NS, Manz CL, Gao T, Bloch JI, Strait SG, Boyer DM. Semi-supervised determination of pseudocryptic morphotypes using observer-free characterizations of anatomical alignment and shape. Ecology and evolution. 2017 Jul;7(14):5041–55.
Vitek, Natasha S., et al. “Semi-supervised determination of pseudocryptic morphotypes using observer-free characterizations of anatomical alignment and shape.Ecology and Evolution, vol. 7, no. 14, July 2017, pp. 5041–55. Epmc, doi:10.1002/ece3.3058.
Vitek NS, Manz CL, Gao T, Bloch JI, Strait SG, Boyer DM. Semi-supervised determination of pseudocryptic morphotypes using observer-free characterizations of anatomical alignment and shape. Ecology and evolution. 2017 Jul;7(14):5041–5055.
Journal cover image

Published In

Ecology and evolution

DOI

EISSN

2045-7758

ISSN

2045-7758

Publication Date

July 2017

Volume

7

Issue

14

Start / End Page

5041 / 5055

Related Subject Headings

  • 4102 Ecological applications
  • 3104 Evolutionary biology
  • 3103 Ecology
  • 0603 Evolutionary Biology
  • 0602 Ecology