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Abstract Modern computational and imaging methods are revolutionizing the fields of comparative morphology, biomechanics, and ecomorphology. In particular, imaging tools such as X-ray micro computed tomography (µCT) and diffusible iodine-based contrast enhanced CT allow observing and measuring small and/or otherwise inaccessible anatomical structures, and creating highly accurate three-dimensional (3D) renditions that can be used in biomechanical modeling and tests of functional or evolutionary hypotheses. But, do the larger datasets generated through 3D digitization always confer greater power to uncover functional or evolutionary patterns, when compared with more traditional methodologies? And, if so, why? Here, we contrast the advantages and challenges of using data generated via (3D) CT methods versus more traditional (2D) approaches in the study of skull macroevolution and feeding functional morphology in bats. First, we test for the effect of dimensionality and landmark number on inferences of adaptive shifts during cranial evolution by contrasting results from 3D versus 2D geometric morphometric datasets of bat crania. We find sharp differences between results generated from the 3D versus some of the 2D datasets (xy, yz, ventral, and frontal), which appear to be primarily driven by the loss of critical dimensions of morphological variation rather than number of landmarks. Second, we examine differences in accuracy and precision among 2D and 3D predictive models of bite force by comparing three skull lever models that differ in the sources of skull and muscle anatomical data. We find that a 3D model that relies on skull µCT scans and muscle data partly derived from diceCT is slightly more accurate than models based on skull photographs or skull µCT and muscle data fully derived from dissections. However, the benefit of using the diceCT-informed model is modest given the effort it currently takes to virtually dissect muscles from CT scans. By contrasting traditional and modern tools, we illustrate when and why 3D datasets may be preferable over 2D data, and vice versa, and how different methodologies can complement each other in comparative analyses of morphological function and evolution.
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GoodFibes: An R Package for The Detection of Muscle Fibers from diceCT Scans
Synopsis Contrast enhanced computed-tomography imaging like diffusible iodine-based contrast-enhanced computed tomography (diceCT) can provide detailed information on muscle architecture important to comparative analyses of functional morphology, using non-destructive approaches. However, manual segmentation of muscle fascicles/fibers is time-consuming, and automated approaches are at times inaccessible and unaffordable. Here, we introduce GoodFibes, an R package for reconstructing muscle architecture in 3D from diceCT image stacks. GoodFibes uses textural analysis of image grayscale values to track straight or curved fiber paths through a muscle image stack. Accessory functions provide quality checking, fiber merging, and 3D visualization and export capabilities. We demonstrate the utility and effectiveness of GoodFibes using two datasets, from an ant and bat diceCT scans. In both cases, GoodFibes provides reliable measurements of mean fiber length compared to traditional approaches, and is as effective as currently available software packages. This open-source, free to use software package will help to improve access to tools in the analysis of muscle fiber anatomy using diceCT scans. The flexible and transparent R-language environment allows other users to build on the functions described here and permits direct statistical analysis of the resulting fiber metrics. We hope that this will increase the number of comparative and evolutionary studies incorporating these rich and functionally important datasets.
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- Award ID(s):
- 2135927
- PAR ID:
- 10451884
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Integrative Organismal Biology
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2517-4843
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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