Unsupervised learning reveals rapid gait adaption after leg loss and regrowth in spiders

Abstract Many arthropods and some vertebrates can voluntarily lose (autotomize) limbs during antagonistic encounters, and some can regenerate functional replacements. Spiders in particular frequently autotomize one or more legs. In this study, we investigated the time course of locomotor recovery after leg loss and regeneration in juvenile tarantulas (Arachnida: Araneae) with no prior experience of autotomy. We recorded high-speed video of spiders running with all legs intact, then immediately after, and again one day after, they autotomized two legs. The legs were allowed to regenerate, and the same sequence of experiments repeated. Running performance, posture, and path tortuosity were measured from video tracking. Spiders were found to resume their pre-autotomy speed and stride frequency after leg regeneration and in ≤1 day after both autotomies; furthermore, path tortuosity was unaffected by these treatments. They adjusted their posture to compensate for missing legs, spreading their remaining legs and running with their bodies rotated 11-15 deg from their velocity. To analyze gaits, we applied unsupervised machine learning for the first time to measured kinematic data in combination with gait space metrics. Spiders were found to robustly adopt new gait patterns immediately after losing legs, with no evidence of learning. This novel clustering approach both demonstrated concordance with previously-hypothesized gaits and revealed transitions between and variations within these patterns. More generally, clustering in gait space enables the identification of patterns of leg motions in large datasets that correspond to either known gaits or undiscovered behaviors. Competing Interest Statement The authors have declared no competing interest.