首个面向欧洲生物多样性的多模态基础模型，可支持物种分布与环境变化预测。
BioAnalyst: A Foundation Model for Biodiversity
- 基于变压器架构，融合物种分布与遥感、气候等多源数据预训练
- 在500种植物分布建模中表现优异，可实现月度分辨率的年度预测
- 开源模型与流程，适合生态研究者开展跨区域生物多样性分析
多模态基础模型（FMs）为从异构生态数据中学习通用表征提供了路径，可轻松迁移至下游任务。然而，实际生物多样性建模仍呈碎片化，各数据集和目标需独立构建模型，限制了跨区域与类群的复用。为此，我们提出BioAnalyst，据我们所知，首个针对欧洲生物多样性分析与保护规划的多模态基础模型，空间分辨率达0.25°，适用于区域到国家级应用。BioAnalyst采用基于Transformer的架构，在大规模多模态数据上预训练，对齐物种出现记录与遥感指标、气候及环境变量。预训练后，通过轻量级滚动生成微调，适配多种下游任务，包括联合物种分布建模、生物多样性动态与种群趋势预测。在两个代表性下游任务上评估：(i) 500种维管植物的联合物种分布建模；(ii) 气候线性探查，使用逐月温度与降水数据。结果表明，BioAnalyst在生物与非生物任务中均能提供强大基线，具备年尺度预测能力与月度分辨率，首次将此类建模应用于生物多样性领域。模型权重、训练与微调流程已开源，以推动人工智能驱动的生态研究。
原文摘要 · Abstract (English)
Multimodal Foundation Models (FMs) offer a path to learn general-purpose representations from heterogeneous ecological data, easily transferable to downstream tasks. However, practical biodiversity modelling remains fragmented; separate pipelines and models are built for each dataset and objective, which limits reuse across regions and taxa. In response, we present BioAnalyst, to our knowledge the first multimodal Foundation Model tailored to biodiversity analysis and conservation planning in Europe at $0.25^{\circ}$ spatial resolution targeting regional to national-scale applications. BioAnalyst employs a transformer-based architecture, pre-trained on extensive multimodal datasets that align species occurrence records with remote sensing indicators, climate and environmental variables. Post pre-training, the model is adapted via lightweight roll-out fine-tuning to a range of downstream tasks, including joint species distribution modelling, biodiversity dynamics and population trend forecasting. The model is evaluated on two representative downstream use cases: (i) joint species distribution modelling and with 500 vascular plant species (ii) monthly climate linear probing with temperature and precipitation data. Our findings show that BioAnalyst can provide a strong baseline both for biotic and abiotic tasks, acting as a macroecological simulator with a yearly forecasting horizon and monthly resolution, offering the first application of this type of modelling in the biodiversity domain. We have open-sourced the model weights, training and fine-tuning pipelines to advance AI-driven ecological research.
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