Measurement error in μCT-based three-dimensional geometric morphometrics introduced by surface generation and landmark data acquisition.

Computed-tomography-derived (CT-derived) polymesh surfaces are widely used in geometric morphometric studies. This approach is inevitably associated with decisions on scanning parameters, resolution, and segmentation strategies. Although the underlying processing steps have been shown to potentially contribute artefactual variance to three-dimensional landmark coordinates, their effects on measurement error have rarely been assessed systematically in CT-based geometric morphometric studies. The present study systematically assessed artefactual variance in landmark data introduced by the use of different voxel sizes, segmentation strategies, surface simplification degrees, and by inter- and intra-observer differences, and compared their magnitude to true biological variation. Multiple CT-derived surface variants of the anuran (Amphibia: Anura) pectoral girdle were generated by systematic changes in the factors that potentially influence the surface geometries. Twenty-four landmarks were repeatedly acquired by different observers. The contribution of all factors to the total variance in the landmark data was assessed using random-factor nested permanovas. Selected sets of Euclidean distances between landmark sets served further to compare the variance among factor levels. Landmark precision was assessed by landmark standard deviation and compared among observers and days. Results showed that all factors, except for voxel size, significantly contributed to measurement error in at least some of the analyses performed. In total, 6.75% of the variance in landmark data that mimicked a realistic biological study was caused by measurement error. In this landmark dataset, intra-observer error was the major source of artefactual variance followed by inter-observer error; the factor segmentation contributed < 1% and slight surface simplification had no significant effect. Inter-observer error clearly exceeded intra-observer error in a different landmark dataset acquired by six partly inexperienced observers. The results suggest that intra-observer error can potentially be reduced by including a training period prior to the actual landmark acquisition task and by acquiring landmarks in as few sessions as possible. Additionally, the application of moderate and careful surface simplification and, potentially, also the use of case-specific optimal combinations of automatic local thresholding algorithms and parameters for segmentation can help reduce intra-observer error. If landmark data are to be acquired by several observers, it is important to ensure that all observers are consistent in landmark identification. Despite the significant amount of artefactual variance, we have shown that landmark data acquired from microCT-derived surfaces are precise enough to study the shape of anuran pectoral girdles. Yet, a systematic assessment of measurement error is advisable for all geometric morphometric studies.