Low resolution scans provide a sufficiently accurate, cost- and time-effective alternative to high resolution scans for interspecific 3D shape analyses
- Published
- Accepted
- Subject Areas
- Biodiversity, Evolutionary Studies, Zoology
- Keywords
- Geometric Morphometrics, Shape variation, Photogrammetry, Pseudomys delicatulus, geomorph, Systematic error, Random error, Generalized Procrustes analysis
- Copyright
- © 2018 Marcy et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Low resolution scans provide a sufficiently accurate, cost- and time-effective alternative to high resolution scans for interspecific 3D shape analyses. PeerJ Preprints 6:e26696v1 https://doi.org/10.7287/peerj.preprints.26696v1
Abstract
Background. Advances in three-dimensional (3D) shape capture technology have made powerful shape analyses, such as geometric morphometrics, more feasible. While the highly accurate micro-computed tomography (μCT) scanners have been the “gold standard,” recent improvements in 3D surface scanner resolution may make this technology a faster, more portable, and cost-effective alternative. Several studies have already compared the two scanning devices but all use relatively large specimens such as human crania. Here we perform shape analyses on Australia’s smallest rodent species to test whether a 3D surface scanner produces similar results to a μCT scanner.
Methods. We captured 19 delicate mouse crania with a μCT scanner and a 3D surface scanner for geometric morphometrics. We ran multiple Procrustes ANOVAs to understand how variation due to scan device compared to other sources of variation such as biologically relevant sources and operator error. We quantified operator error with morphological disparity and repeatability. Finally, we tested whether the different scan datasets could detect intra-specific variation using cross-validation classification. Shape patterns were visualized with Principal Component Analysis (PCA) plots.
Results. In all Procrustes ANOVAs, regardless of factors included, differences between individuals contributed the most to total variation. This is also reflected in the way individuals disperse on the PCA plots. Including only the symmetric component of shape increased the biological signal relative to variation due to device and due to error. 3D scans create a higher level of operator error as evidenced by a greater spread of their replicates on the PCA, a higher morphological disparity, and a lower repeatability score. However, in the test for small intra-specific differences, the 3D scan and μCT scan datasets performed identically.
Discussion. Compared to μCT scans, we find that even very low resolution 3D scans of very small specimens are sufficiently accurate to capture variation at the level of interspecific differences. We also make three recommendations for best use of low resolution data. First, we recommend analyzing the symmetric component of shape to decrease signal from operator error. Second, using 3D scans generates more random error due to increased landmarking difficulty, therefore be conservative in landmark choice and avoid multiple operators. Third, using 3D scans introduces a source of systematic error relative to μCT scans, therefore do not combine them when possible and especially in studies with little variation. Our findings support increased use of low resolution 3D images for most morphological studies; they are likely applicable to low resolution scans of large specimens made in a medical CT scanner, for example. As most vertebrates are relatively small, we anticipate our results to bolster more researchers designing affordable large scale studies on small specimens with 3D surface scanners.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Catalogue numbers and sex information for all specimens
Catalogue numbers are searchable in the Queensland Museum database, which also provided the sex information for the 19 delicate mouse (Pseudomys delicatulus) specimens we used in our study.
Supplementary methods for optimal use of an HD109 3D surface scanner for small biological specimens
This standard operating procedures outlines the best practices for 3D scanning, which we chose after extensive trial and error.
Landmark, semi-landmark curve, and patch point names and definitions
There are a total of 289 points used to capture crania shape. 58 are fixed landmarks (LM): the first 12 are centrally located and the remaining 46 come in right and left pairs (right is odd, left is even in the table numbering system). 145 points were placed along 39 curves as semi-landmarks: the first 7 curves are centrally located and the remaining 32 curves come in right and left pairs (right is even, left is odd). 86 points were placed on 12 surface patches, all of which come in right and left pairs (right is even, left is odd).
R script for analyses presented in this study
This file should be copied into R or Rstudio. All datasets required to run the analyses are contained in the supplementary information. Some analyses must be run in MorphoJ, an outside software freely available at: http://www.flywings.org.uk/morphoj_page.htm.
Raw landmark coordinates produced by digitizing 3D files in Viewbox
This is the raw format of our shape data produced by exporting the landmark coordinates once landmarking in Viewbox was finished.
Definitions of semilandmarks as required for sliding in geomorph during Procrustes superimposition
This table simply encodes the two neighboring points that a semi-landmark can slide between. Point numbers can be related to their position on the cranium using Fig. 3 or Table S2. This table is necessary to treat sliding semi-landmarks correcting during Procrustes alignment using geomorph's gpagen() function.
Table of digitized points with bilateral symmetry for symmetric shape analysis
This table only includes the points which have bilateral symmetry (i.e. a symmetric pair on the other left/right side of the skull). Point numbers can be related to their position on the cranium using Fig. 3 or Table S2. This table is necessary to isolate the symmetric component of shape in geomorph using the bilat.symmetry() function.
Sex identification for specimens in intra-specific analyses
This table is nearly identical to Table S1 except that the "Catalogue Number" column heading is shortened to "CatNum" to ease the merging datasets. This dataset is necessary to perform the intra-specific analyses presented here.