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Advanced general visual object tracking models have been drastically developed with the access of large annotated datasets and progressive network architectures. However, a general tracker always suffers domain shift when directly adopting to specific testing scenarios. In this paper, we dedicate to addressing the animal tracking problem by proposing a spatio-temporal inference module and a coarse-to-fine tracking strategy. In terms of tracking animals, non-rigid deformation is a typical challenge. Therefore, we particularly design a novel transformer-based inference structure where the changing animal state is transmitted across continuous frames. By explicitly transmitting the appearance variations, this spatio-temporal module enables adaptive target learning, boosting the animal tracking performance compared to the fixed template matching approaches. Besides, considering the altered contours of animals in …