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Testing Adaptive Hypotheses of Convergence with Functional Landscapes: A Case Study of Bone-Cracking Hypercarnivores

Overview of attention for article published in PLOS ONE, May 2013
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Title
Testing Adaptive Hypotheses of Convergence with Functional Landscapes: A Case Study of Bone-Cracking Hypercarnivores
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0065305
Pubmed ID
Authors

Zhijie Jack Tseng

Abstract

Morphological convergence is a well documented phenomenon in mammals, and adaptive explanations are commonly employed to infer similar functions for convergent characteristics. I present a study that adopts aspects of theoretical morphology and engineering optimization to test hypotheses about adaptive convergent evolution. Bone-cracking ecomorphologies in Carnivora were used as a case study. Previous research has shown that skull deepening and widening are major evolutionary patterns in convergent bone-cracking canids and hyaenids. A simple two-dimensional design space, with skull width-to-length and depth-to-length ratios as variables, was used to examine optimized shapes for two functional properties: mechanical advantage (MA) and strain energy (SE). Functionality of theoretical skull shapes was studied using finite element analysis (FEA) and visualized as functional landscapes. The distribution of actual skull shapes in the landscape showed a convergent trend of plesiomorphically low-MA and moderate-SE skulls evolving towards higher-MA and moderate-SE skulls; this is corroborated by FEA of 13 actual specimens. Nevertheless, regions exist in the landscape where high-MA and lower-SE shapes are not represented by existing species; their vacancy is observed even at higher taxonomic levels. Results highlight the interaction of biomechanical and non-biomechanical factors in constraining general skull dimensions to localized functional optima through evolution.

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Geographical breakdown

Country Count As %
United States 2 2%
Unknown 112 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 23%
Student > Master 18 16%
Researcher 14 12%
Student > Bachelor 8 7%
Student > Doctoral Student 7 6%
Other 19 17%
Unknown 22 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 44%
Earth and Planetary Sciences 18 16%
Environmental Science 6 5%
Biochemistry, Genetics and Molecular Biology 3 3%
Engineering 3 3%
Other 7 6%
Unknown 27 24%