Balancing complexity and error in kinematic models fitting 2D and 3D four-bar linkage models to the opercular mechanism of largemouth bass (Micropterus salmoides)


Meeting Abstract

117-7  Sunday, Jan. 8 09:30 – 09:45  Balancing complexity and error in kinematic models: fitting 2D and 3D four-bar linkage models to the opercular mechanism of largemouth bass (Micropterus salmoides) OLSEN, AM*; CAMP, AL; BRAINERD, EL; Brown University aarolsen@gmail.com

The heads of ray-finned fishes contain numerous mobile skeletal elements, interconnected as mechanical chains that can transmit muscle forces to multiple and distant skeletal elements. A traditional model for these skeletal elements is the planar 4-bar linkage. While planar 4-bar models can accurately predict the motion of certain mechanisms in fish skulls, previous work found this model of the opercular mechanism overestimates jaw depression in largemouth bass by 50%. It is unclear what simplifying assumptions of the planar 4-bar contribute to this poor fit. In this study we combine in vivo XROMM data acquired during suction feeding in largemouth bass (Micropterus salmoides) with kinematic simulation using the R package ‘linkR’ to determine how assumptions of constant link lengths and planar motion affect model accuracy. We find changes in the link length of the 4-bar to be greatest in the interoperculum link, which lengthens by at least 5% at peak gape, due, in part, to stretching of the interopercular-mandibular ligament. We also find substantial deviations in the rotations of the operculum and lower jaw from a single, best-fit axis of rotation. In a comparison of 6 linkage models of varying complexity, ranging from 13 to 259 parameters, we find a distinct increase in error in transitioning from a 2D to 3D 4-bar model. A 2D 4-bar model has a mean error of 19 deg (1.9 mm) while a 3D 4-bar, requiring 6 additional parameters, has a mean error of 5.3 deg (0.8 mm). Thus, a 3D 4-bar linkage balances substantially improved accuracy with the addition of relatively few parameters, making it a useful model for understanding and comparing functional diversity in the skulls of fishes. This work was funded by NSF DBI-1612230.

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