Sample Size and Geometric Morphometrics Methodology Impact the Evaluation of Morphological Variation

Author:

Rummel A D1ORCID,Sheehy E T2,Schachner E R3ORCID,Hedrick B P4ORCID

Affiliation:

1. Department of BioSciences, Rice University , Houston, TX 77005 , USA

2. Department of Ecology and Evolutionary Biology, Tulane University , New Orleans, LA 70118 , USA

3. Department of Physiological Sciences, College of Veterinary Medicine, University of Florida , Gainesville, FL 32603 , USA

4. Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University , Ithaca, NY 14853 , USA

Abstract

Synopsis Geometric morphometrics has had a profound impact on our understanding of morphological evolution. However, factors such as sample size and the views and elements selected for two-dimensional geometric morphometric (2DGM) analyses, which are often dictated by specimen availability and time rather than study design, may affect the outcomes of those analyses. Leveraging large intraspecific sample sizes (n > 70) for two bat species, Lasiurus borealis and Nycticeius humeralis, we evaluate the impact of sample size on calculations of mean shape, shape variance, and centroid size. Additionally, we assessed the concordance of multiple skull 2D views with one another and characterized morphological variation in skull shape in L. borealis and N. humeralis, as well as a closely related species, Lasiurus seminolus. Given that L. seminolus is a morphologically cryptic species with L. borealis, we assessed whether differences in skull shape and in 2DGM approach would allow species discrimination. We found that reducing sample size impacted mean shape and increased shape variance, that shape differences were not consistent across views or skull elements, and that trends shown by the views and elements were not all strongly associated with one another. Further, we found that L. borealis and L. seminolus were statistically different in shape using 2DGM in all views and elements. These results underscore the importance of selecting appropriate sample sizes, 2D views, and elements based on the hypothesis being tested. While there is likely not a generalizable sample size or 2D view that can be employed given the wide variety of research questions and systems evaluated using 2DGM, a generalizable solution to issues with 2DGM presented here is to run preliminary analyses using multiple views, elements, and sample sizes, thus ensuring robust conclusions.

Funder

Louisiana State University Research Enhancement Program

Publisher

Oxford University Press (OUP)

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