Résumé
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 P.
feignouxi from the lower Miocene of France using their femoral morphology
and different machine learning methods. We used two ways to operationalise
morphology, in the form of a geometric morphometric shape dataset and a
multivariate dataset of eleven femoral traits. The predictive models were
built and tested using more than half (180) of the extant species of
squirrel relatives. The neural network model had the greatest predictive
power, sometimes outperforming more traditional methods such as linear
discriminant analysis. However, the predictive power also depended on the
operationalisation and the femoral traits used to build the model. We also
found that predictive power tended to slightly 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.