Abstract
Correlations between morphology and lifestyle of extant taxa are useful for predicting lifestyles of extinct relatives. Here, we infer the locomotor behaviour of Palaeosciurus goti from the middle Oligocene and Palaeosciurus feignouxi from the lower Miocene of France using their femoral morphology and different machine learning methods. We used two ways to operationalize morphology, in the form of a geometric morphometric shape dataset and a multivariate dataset of 11 femoral traits. The predictive models were built and tested using more than half (180) of the extant species of squirrel relatives. Both traditional models such as linear discriminant analysis and more sophisticated models like neural networks had the greatest predictive power. However, the predictive power also depended on the operationalization and the femoral traits used to build the model. We also found that predictive power tended to improve with increasing body size. Contrary to previous suggestions, the older species, P. goti, was most likely arboreal, whereas P. feignouxi was more likely terrestrial. This provides further evidence that arboreality was already the most common locomotor ecology among the earliest squirrels, while a predominantly terrestrial locomotor behaviour evolved shortly afterwards, before the vast establishment of grasslands in Europe.