Increasing climate change and habitat loss are driving unprecedented shifts in species distributions. Conservation professionals urgently need timely, high-resolution predictions of biodiversity risks, especially in ecologically diverse regions like Africa. We propose EcoCast, a spatio-temporal model designed for continual biodiversity and climate risk forecasting. Utilizing multisource satellite imagery, climate data, and citizen science occurrence records, EcoCast predicts near-term (monthly to seasonal) shifts in species distributions through sequence-based transformers that model spatio-temporal environmental dependencies. The architecture is designed with support for continual learning to enable future operational deployment with new data streams. Our pilot study in Africa shows promising improvements in forecasting distributions of selected bird species compared to a Random Forest baseline, highlighting EcoCast's potential to inform targeted conservation policies. By demonstrating an end-to-end pipeline from multi-modal data ingestion to operational forecasting, EcoCast bridges the gap between cutting-edge machine learning and biodiversity management, ultimately guiding data-driven strategies for climate resilience and ecosystem conservation throughout Africa.

翻译：日益加剧的气候变化与栖息地丧失正驱动物种分布发生前所未有的变化。保护从业者迫切需要及时、高分辨率的生物多样性风险预测，尤其是在非洲等生态多样性丰富的区域。我们提出EcoCast，一种专为持续生物多样性与气候风险预测设计的时空模型。该模型综合利用多源卫星影像、气候数据及公民科学观测记录，通过基于序列的Transformer建模时空环境依赖性，预测物种分布的近期（月度至季节性）变化。其架构设计支持持续学习，以便未来能结合新数据流进行业务化部署。我们在非洲的试点研究表明，相较于随机森林基线模型，EcoCast在预测选定鸟类物种分布方面展现出显著的改进潜力，凸显了其为针对性保护政策提供科学依据的能力。通过展示从多模态数据输入到业务化预测的端到端流程，EcoCast弥合了前沿机器学习与生物多样性管理之间的鸿沟，最终为非洲全境的气候适应性与生态系统保护提供数据驱动的决策指导。