The field of spatiotemporal modeling and species distribution is witnessing significant developments, driven by the integration of multi-source datasets, advanced deep learning architectures, and innovative data preprocessing techniques. Researchers are exploring the potential of Transformer-based frameworks, Bayesian approaches, and multimodal learning to improve predictive accuracy and reliability in various applications, including avian disease surveillance, vehicle trajectory analysis, and biodiversity monitoring. These advancements are enabling more precise and scalable modeling of complex phenomena, with implications for conservation efforts, public health, and urban planning. Noteworthy papers include:

• Spatiotemporal Transformers for Predicting Avian Disease Risk from Migration Trajectories, which presents a novel framework for predicting disease risk with high accuracy.
• TrajMamba, an efficient and semantic-rich vehicle trajectory pre-training model that outperforms state-of-the-art baselines in both efficiency and accuracy.
• FrogDeepSDM, which enhances species distribution modeling for frogs using multimodal data and pseudo-absence imputation, achieving improved predictive accuracy.
• BATIS, a Bayesian framework for targeted improvement of species distribution models, which effectively combines fine-grained local insights with broader ecological patterns.
• Classical Feature Embeddings Help in BERT-Based Human Mobility Prediction, which proposes a semantic-temporal aware BERT model for human mobility forecasting, significantly improving prediction accuracy.