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Simple predictors of bite force in bats: the good, the better and the better still

机译:蝙蝠咬合力的简单预测指标:好,更好,更好

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Bite forces of 39 species from six families of New World bats with a variety of diets are quantified with a force meter under field conditions. Using regression approaches we search for a model that is a good morphological predictor of these bite forces. Body mass, an index that ignores differences in skull morphology, has a statistically significant relationship with bite force (R2=0.76) but is a relatively poor predictor compared with our best model (R2=0.94). The two best models of the eight we examine are one based on an estimate of strength of dentary, which is really simple beam theory; and the other based on muscle mass and jaw mechanics of input and output arms. Both models explain about 90% of the variation in bite force. However, the combination of these variables together in multiple regression works even better, explaining about 94% of the variation. Our model derived from beam theory relies on bony characteristics, which are readily available from museum specimens. This model will be of particular use to students of fossils or ecomorphology for inferring bite force. We also test Freeman's earlier predictions about bite forces of bats with gracile versus robust skulls. These predictions can be only partially confirmed. For species we measured, bats with gracile skulls did have weak bites; however, bats designated by Freeman as having robust skulls did not have particularly strong bites.
机译:在田间条件下用力计对来自新世界蝙蝠的六个科的39种不同饮食的叮咬力进行定量。使用回归方法,我们搜索的模型可以很好地预测这些咬合力。体重是忽略头骨形态差异的指标,与咬合力具有统计学上的显着关系(R2 = 0.76),但与我们的最佳模型(R2 = 0.94)相比,预测指标相对较差。我们研究的八种最佳模型中的两种是基于对牙齿强度的估计的模型,这实际上是简单的束理论。另一个基于输入输出臂的肌肉质量和下颌力学。两种模型都可以解释咬合力变化的90%。但是,这些变量在多重回归中的组合效果更好,可以解释大约94%的变化。我们基于光束理论的模型依赖于骨质特征,这些特征可以从博物馆标本中轻松获得。该模型将特别适用于化石或生态形态学的学生以推断咬力。我们还测试了Freeman先前关于具有脆弱头骨和坚固头骨的蝙蝠咬合力的预测。这些预测只能得到部分确认。对于我们测得的物种,具颅骨的蝙蝠的咬合力确实很弱。但是,弗里曼指定具有坚硬头骨的蝙蝠没有特别强的咬合能力。

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