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ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal

Publication ,  Journal Article
Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I
Published in: Methods in Ecology and Evolution
April 1, 2019

Shape characterizers are metrics that quantify aspects of the overall geometry of a three-dimensional (3D) digital surface. When computed for biological objects, the values of a shape characterizer are largely independent of homology interpretations and often contain a strong ecological and functional signal. Thus, shape characterizers are useful for understanding evolutionary processes. Dirichlet normal energy (DNE) is a widely used shape characterizer in morphological studies. Recent studies found that DNE is sensitive to various procedures for preparing 3D mesh from raw scan data, raising concerns regarding comparability and objectivity when utilizing DNE in morphological research. We provide a robustly implemented algorithm for computing the Dirichlet energy of the normal (ariaDNE) on 3D meshes. We show through simulation that the effects of preparation-related mesh surface attributes, such as triangle count, mesh representation, noise, smoothing and boundary triangles, are much more limited on ariaDNE than DNE. Furthermore, ariaDNE retains the potential of DNE for biological studies, illustrated by its effectiveness in differentiating species by dietary preferences. Use of ariaDNE can dramatically enhance the assessment of the ecological aspects of morphological variation by its stability under different 3D model acquisition methods and preparation procedure. Towards this goal, we provide scripts for computing ariaDNE and ariaDNE values for specimens used in previously published DNE analyses.

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Published In

Methods in Ecology and Evolution

DOI

EISSN

2041-210X

Publication Date

April 1, 2019

Volume

10

Issue

4

Start / End Page

541 / 552

Related Subject Headings

  • 4104 Environmental management
  • 3109 Zoology
  • 3103 Ecology
  • 0603 Evolutionary Biology
  • 0602 Ecology
  • 0502 Environmental Science and Management
 

Citation

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Shan, S., Kovalsky, S. Z., Winchester, J. M., Boyer, D. M., & Daubechies, I. (2019). ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal. Methods in Ecology and Evolution, 10(4), 541–552. https://doi.org/10.1111/2041-210X.13148
Shan, S., S. Z. Kovalsky, J. M. Winchester, D. M. Boyer, and I. Daubechies. “ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal.” Methods in Ecology and Evolution 10, no. 4 (April 1, 2019): 541–52. https://doi.org/10.1111/2041-210X.13148.
Shan S, Kovalsky SZ, Winchester JM, Boyer DM, Daubechies I. ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal. Methods in Ecology and Evolution. 2019 Apr 1;10(4):541–52.
Shan, S., et al. “ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal.” Methods in Ecology and Evolution, vol. 10, no. 4, Apr. 2019, pp. 541–52. Scopus, doi:10.1111/2041-210X.13148.
Shan S, Kovalsky SZ, Winchester JM, Boyer DM, Daubechies I. ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal. Methods in Ecology and Evolution. 2019 Apr 1;10(4):541–552.
Journal cover image

Published In

Methods in Ecology and Evolution

DOI

EISSN

2041-210X

Publication Date

April 1, 2019

Volume

10

Issue

4

Start / End Page

541 / 552

Related Subject Headings

  • 4104 Environmental management
  • 3109 Zoology
  • 3103 Ecology
  • 0603 Evolutionary Biology
  • 0602 Ecology
  • 0502 Environmental Science and Management