Department of Life Sciences, University of Lincoln, Lincoln, United Kingdom.
PeerJ. 2022 Jul 12;10:e13731. doi: 10.7717/peerj.13731. eCollection 2022.
I present a Bayesian phylogenetic predictive modelling (PPM) framework that allows the prediction of muscle parameters (physiological cross-sectional area, ) in extinct archosaurs from skull width ( ) and phylogeny. This approach is robust to phylogenetic uncertainty and highly versatile given its ability to base predictions on simple, readily available predictor variables. The PPM presented here has high prediction accuracy (up to 95%), with downstream biomechanical modelling yielding bite force estimates that are in line with previous estimates based on muscle parameters from reconstructed muscles. This approach does not replace muscle reconstructions but one that provides a powerful means to predict from skull geometry and phylogeny to the same level of accuracy as that measured from reconstructed muscles in species for which soft tissue data are unavailable or difficult to obtain.
我提出了一个贝叶斯系统发育预测建模 (PPM) 框架,该框架允许根据头骨宽度 ( ) 和系统发育来预测已灭绝的恐龙的肌肉参数(生理横截面积, )。该方法对系统发育不确定性具有很强的稳健性,并且由于其能够基于简单、易于获得的预测变量进行预测,因此具有高度的通用性。这里提出的 PPM 具有很高的预测准确性(高达 95%),下游生物力学模型产生的咬合力估计与之前基于重建肌肉的肌肉参数的估计值一致。这种方法不是替代肌肉重建,而是提供了一种强大的方法,可以根据头骨几何形状和系统发育来预测 ,其准确性与从软组织数据不可用或难以获得的物种的重建肌肉中测量到的肌肉参数一样高。