Studying Morphological Variation: Exploring the Shape Space in Evolutionary Anthropology
Shira Faigenbaum-Golovin, Ingrid Daubechies

TL;DR
This paper develops a mathematical framework for analyzing morphological variation in primate and mammal bones using differential geometry and machine learning, enabling robust evolutionary shape comparisons.
Contribution
It introduces a comprehensive mathematical approach to compare and analyze anatomical shapes across species, integrating multiple methods into a unified framework.
Findings
Robust shape comparison methods for anatomical surfaces.
Framework for cross-dataset landmark selection and shape classification.
Integration of differential geometry and machine learning for evolutionary morphology.
Abstract
We present results of a long-term team collaboration of mathematicians and biologists. We focus on building a mathematical framework for the shape space constituted by a collection of homologous bones or teeth from many species. The biological application is to quantitative morphological understanding of the evolutionary history of primates in particular, and mammals more generally. Similar to the practice of biologists, we leverage the power of the whole collection for results that are more robust than can be obtained by only pairwise comparisons, using tools from differential geometry and machine learning. This paper concentrates on the mathematical framework. We review methods for comparing anatomical surfaces, discuss the problem of registration and alignment, and address the computation of different distances. Next, we cover broader questions related to cross-dataset landmark…
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Taxonomy
TopicsPleistocene-Era Hominins and Archaeology · Language and cultural evolution
