Back to the bones: do muscle area assessment techniques predict functional evolution across a macroevolutionary radiation?
Back to the bones: do muscle area assessment techniques predict functional evolution across a macroevolutionary radiation?
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DOI：
10.1098/rsif.2021.0324
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发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Cox PG
中科院分区：
文献类型：
--
作者：
Bates KT;Wang L;Dempsey M;Broyde S;Fagan MJ;Cox PG
测量骨骼上的附着或调节区域是快速生成用于大规模宏观进化研究的肌肉比例和功能性能估计的流行手段。在这里，我们提供了第一次评估的准确性，这些肌肉面积评估（MAA）技术估计肌肉比例，力输出和骨负荷在一个比较macroevolutionary背景下使用啮齿动物咀嚼系统作为一个案例研究。我们发现，MAA方法表现不佳，在肌肉特性，咬合力，特别是骨应力产生大的绝对误差。也许更根本的是，这些方法经常无法正确地捕捉啮齿动物形态之间的许多定性差异，特别是在有限元模型中的应力模式。我们的研究结果对这些方法的有效性表示怀疑，这些方法可以为生物力学模型提供输入数据，用于了解化石记录中的功能转变，甚至可能在分类丰富的统计模型中检查大规模的宏观进化模式。我们建议，未来的工作应该回到骨骼测试，如果附着面积和肌肉大小之间的相关性在大量的物种同源肌肉产生强大的预测关系，可用于提供更准确的预测宏观进化和功能研究。
Measures of attachment or accommodation area on the skeleton are a popular means of rapidly generating estimates of muscle proportions and functional performance for use in large-scale macroevolutionary studies. Herein, we provide the first evaluation of the accuracy of these muscle area assessment (MAA) techniques for estimating muscle proportions, force outputs and bone loading in a comparative macroevolutionary context using the rodent masticatory system as a case study. We find that MAA approaches perform poorly, yielding large absolute errors in muscle properties, bite force and particularly bone stress. Perhaps more fundamentally, these methods regularly fail to correctly capture many qualitative differences between rodent morphotypes, particularly in stress patterns in finite-element models. Our findings cast doubts on the validity of these approaches as means to provide input data for biomechanical models applied to understand functional transitions in the fossil record, and perhaps even in taxon-rich statistical models that examine broad-scale macroevolutionary patterns. We suggest that future work should go back to the bones to test if correlations between attachment area and muscle size within homologous muscles across a large number of species yield strong predictive relationships that could be used to deliver more accurate predictions for macroevolutionary and functional studies.
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