A global dataset of spatiotemporal co-occurrence patterns of avian influenza virus-associated migratory birds

Migratory birds facilitate the cross-regional spread of pathogens such as avian influenza virus (AIV). Interspecies interactions among multiple migratory bird species within shared spatiotemporal habitats can substantially enhance pathogen transmission and evolution, thereby posing potential risks to public health and livestock safety. Recent advances in tracking technologies, such as GPS, combined with publicly accessible databases like Movebank, have enabled the reconstruction of avian migratory pathways. However, existing tracking data are largely collected from individual species, remain species-specific and are insufficient for characterizing interspecies contact during migration. By integrating available tracking data from 62 migratory bird species (comprising 3,944 individual records), this study constructed a co-occurrence dataset comprising 50 migratory bird species that exhibited spatial and temporal overlap at shared locations, with a daily temporal resolution and spatial resolution aligned with first-level administrative divisions. This dataset can facilitate the identification of potential hotspots for migratory bird-associated pathogen evolution, thereby providing data-driven support for the prevention and control of emerging infectious diseases.候鸟可介导禽流感病毒（AIV）等病原体的跨区域传播。在共享的时空生境中，多种候鸟物种间的种间相互作用可显著增强病原体的传播与演化进程，进而对公共卫生与畜禽安全构成潜在威胁。近年来，诸如全球定位系统（GPS）这类追踪技术的进步，结合Movebank等公开可访问数据库，使得重构鸟类迁徙路径成为可能。然而，现有的追踪数据大多针对单个物种收集，仍具有物种特异性，不足以刻画迁徙过程中的种间接触情况。本研究整合了62种候鸟的现有追踪数据（包含3944条个体记录），构建了一套共现数据集：该数据集涵盖50种候鸟，这些物种在共享栖息地存在时空重叠，其时间分辨率为每日，空间分辨率与一级行政区对齐。本数据集可助力识别候鸟相关病原体演化的潜在热点区域，从而为新发传染病的防控提供数据驱动的支撑。