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Updated: May 10, 2025

Super-Resolution Microscopy of the Synaptonemal Complex Within the Caenorhabditis elegans Germline
Published on: September 13, 2022
Topological and variational modularity: A case study using the pectoral girdle across the Chrosomus eos-neogaeus
Kevin K Duclos1, Thomas Grünbaum2, Bernard Angers3
1Cumming School of Medicine, University of Calgary, Calgary, Canada.
Abstract:
Modularity and integration are key developmental properties and have remained central in evo-devo research because of how they relate to evolvability. While modularity and integration have commonly been assessed with landmark-based geometric morphometrics (GMM), other methods such as anatomical network analysis (AnNA) are increasingly being explored. Nonetheless, AnNA has seldom been used to assess variability within taxa, and there have been no attempts to verify whether its results are commensurate with GMM. We used the pectoral girdle of members of the Chrosomus eos-neogaeus hybridization complex as a case study system to assess differences between AnNA and GMM-based approaches and discuss how they should be best used. The general anatomy and composition of the pectoral girdle do not vary much within the complex; however, bones within the pectoral girdle show significant diversity in morphology and in the presence of sutures. Indeed, C. neogaeus displays characteristically enlarged coracoids and radials, and bone fusion between the cleithra, coracoids, and radials, while C. eos displays a gracile and unfused pectoral girdle. Hybrids display morphologies that are distinct from both parental species. AnNA detected multiple potential patterns of modularity, and GMM supported similar patterns of modularity across the complex but suggested different trajectories of morphological variation. Altogether, AnNA can be a valuable tool to formulate novel hypotheses in understudied taxa, which may then be tested using GMM, but careful morphological descriptions of skeletal systems are a valuable addition to the interpretation of both AnNA and GMM approaches.
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