When linkages deviate from planarity A new 3D computational linkage model applied to the cranial linkages of birds and fishes


Meeting Abstract

95.3  Tuesday, Jan. 6 14:00  When linkages deviate from planarity: A new 3D computational linkage model applied to the cranial linkages of birds and fishes OLSEN, AM*; WESTNEAT, MW; University of Chicago, IL; University of Chicago, IL aolsen@uchicago.edu

Skeletal linkages, or closed loops of inter-jointed, rigid elements, have evolved multiple times independently in the feeding apparatus of several groups of vertebrates, including fishes, snakes and birds. Previous research has shown that the geometry of a linkage is often sufficient to accurately predict its dynamic properties in vivo. Thus, linkage geometry can be used to better understand musculoskeletal function in single organisms, the functional diversity of species in an ecosystem or the evolution of musculoskeletal systems in a comparative context. Predictions of linkage properties have largely been made using 2D linkage models. However, 2D models are less suitable for modeling linkages that deviate substantially from planarity, such as the cranial linkages of birds and some fishes. More generally, we lack models for analyzing and comparing linkage geometries (2D and 3D) within the same theoretical framework. We present a new computational model, written in the R language, that predicts force and torque distribution, range of motion and kinematic transmission from four- and five-bar linkages of two and three dimensions. We use this model to develop a set of minimum parameters sufficient to represent all possible 3D four-bar linkage configurations, a subset of which includes all possible 2D configurations. Lastly, we apply this model to a dataset of 3D landmarks collected from a diversity of bird and fish skulls to ask whether and how deviations from planarity affect linkage properties. This new model unifies 2D and 3D linkages into a single theoretical framework, providing more realistic representations and accurate predictions of biological linkages. This work was funded by NSF grants DGE-0903637 and IOS-142549.

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