Several sustainable development goals, such as life on land, sustainable cities and communities, as well as good health and well-being, are heavily dependent on the migration of avian species from one end of the Earth to another. Machine learning tasks in this space rely on a supervised approach where a categorical output corresponding to a certain movement phenomenon is estimated leveraging geospatial and weather covariates. Although these covariates are sufficient for some tasks, they have limitations for some other tasks. We argue that some of these tasks can benefit from more expressive data, such as those generated by Large Language Models (LLMs). In this work therefore, we consider the task of augmenting spatio-temporal geospatial data with the output from an LLM for improved animal mobility prediction tasks. More specifically, first, we prompt an LLM and show it can be used to generate text data that can be used on its own for predicting animal movement phenomena, surpassing the performance of spatio-temporal geospatial data on some experiments. Second, we propose an algorithm to eliminate redundancies in LLM queries and reduce carbon footprints by finding coordinates in a mobility dataset that maximizes entropy. Third, we show that when these two data modalities are used together, the performance on the aforementioned prediction tasks is significantly higher than when each modality is used on its own. Finally, we propose a novel compositional attention framework to combine the two data modalities and select relevant features by alternating between geospatial covariates and LLM embeddings. Experiments on two tasks for reducing biodiversity loss via the forecast of migration states and another task for managing future global health risks via the one-health paradigm by estimating stop-over duration show that the proposed approach outperforms competing baselines.