bioRxiv preprint doi: https://doi.org/10.1101/2020.07.23.218289; this version posted January 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. F. R. O’Keefe 1 Shape Dimensionality Metrics for Landmark Data 2 3 F. Robin O'Keefe1* 4 5 Marshall University, Biological Sciences, Huntington, WV 6 7 *corresponding author: F. Robin O’Keefe, Professor, Marshall University, College of 8 Science 265, One John Marshall Drive, Hunington, WV 25755. Phone: +1 304 696 2427. 9 Email:
[email protected] 10 11 Running Head: Whole Shape Integration Metrics 12 13 Keywords: Dire wolf, Canis dirus, geometric morphometrics, modularity and integration, 14 information entropy, effective rank, effective dispersion, latent dispersion. 15 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.23.218289; this version posted January 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. WHOLE SHAPE INTEGRATION METRICS 16 ABSTRACT 17 The primary goal of this paper is to examine and rationalize different integration metrics 18 used in geometric morphometrics, in an attempt to arrive at a common basis for the 19 characterization of phenotypic covariance in landmark data. We begin with a model 20 system; two populations of Pleistocene dire wolves from Rancho La Brea that we 21 examine from a data-analytic perspective to produce candidate models of integration.