Multiscale Class-Attention Transformer for Bird Migration State Prediction

Bird migration plays a pivotal role in maintaining ecological equilibrium globally. Yet, the significant annual mortality during migratory journeys and the potential dissemination of zoonotic pathogens through avian movement pose critical challenges. This study delves into the migration patterns of North American vultures, foreseeing their future migration trends. To achieve this, we constructed a new dataset based on publicly available data on MoveBank. Leveraging the concept of multiscale feature hierarchies and class token with the transformer framework, the novel McaT prediction model was introduced. This model, characterized by channel-resolution scale stages, outperformed prior models, exhibiting heightened accuracy in forecasting bird migration states across diverse time scales. By enhancing our ability to foresee migration trends, this study contributes significantly to wildlife conservation and public health, ensuring the preservation of avian diversity and preempting potential zoonotic outbreaks on a global scale.