基于遥感和空间分析技术研究环境因子影响下的东亚候鸟时空迁徙规律

Bird-borne infectious diseases, such as the highly pathogenic avian influenza (HPAI) H5N1, have posed a significant risk to human and animal health. Migratory birds are not only the potential spreading agents but also the victims. Currently only crude data on flyways are available, a further understanding of bird migration laws and an accurate description of their spatiotemporal patterns are urgently required. This is the key for an efficient control of bird-borne infectious diseases. Plant phenology and temperature are considered to be the most important environmental factors in influencing bird migration activities. These effects have been investigated under experimental conditions but not in the wild using real migration data of individual birds. Remote sensing - derived environmental factors and satellite tracking data of migratory birds offer an opportunity to develop large-scale approaches to test the proposed hypotheses and previous findings. This study aims to investigate the effects of plant phenology and temperature on the migration schedule of Arctic breeding geese along the East Asian flyway. We investigate the bird migration process from a spatial-ecological perspective and provide solid basis for decision-making to control bird-borne infectious diseases, which is of important scientific significance and practical value.

鸟类携带流行病如高致病性禽流感给人类和动物健康造成了极大的威胁和隐患。迁徙鸟类在全球疾病大爆发中既是传播媒介又是受害者。现有的候鸟迁徙数据都相对粗糙，对其迁徙规律的进一步理解和动态格局的准确描述是有效监控鸟类携带流行病传播的关键，但当前仍然缺乏大尺度的实现方法。植被物候和温度是影响候鸟活动的关键环境因子，但其相关作用仅在实验条件下被深入研究过，却很少在大尺度的自然环境下或用个体候鸟实际迁徙数据验证。基于卫星影像提取环境特征和通过卫星跟踪器记录的候鸟迁徙信息为我们提供了验证这些生态假说和发现的机会。本研究运用环境遥感数据和候鸟卫星跟踪数据结合空间数据分析技术来探讨植被物候和地表温度对东亚迁徙路线上候鸟迁徙规律的影响。该研究顺应多学科交叉的发展趋势，将生态学科和遥感学科的概念方法充分结合起来加深对候鸟迁徙时空格局和决策机制的理解，并为鸟类携带流行病的监控提供决策依据，具有重要的科学和应用价值。

迁徙鸟类是指征生态系统平衡与健康的重要指标物种，也是鸟类携带流行病传播的重要媒介。对其迁徙规律和时空格局的研究和理解是维护生态系统平衡和实现对鸟类携带流行病有效监控的重要基础。但目前，尤其是在亚洲区，对候鸟迁徙格局及环境机制的了解还非常有限。春季迁徙的相关生态假说还未在大尺度下用实际的个体数据检验，对秋季迁徙规律的认知更是有限。基于高时空精度卫星影像提取环境特征和通过卫星跟踪记录的候鸟迁徙数据为我们提供了发现和验证相关生态假说的机会。本研究旨在利用高精度鸟类追踪数据和高时空精度的遥感数据所提取的环境信息，结合空间数据分析技术来提出、验证、扩展相关生态学假说、并深入探讨水鸟分布和移动的环境机制，以加深对水鸟季节性迁徙规律的进一步理解。该研究顺应多学科交叉的发展趋势，将生态学科和遥感学科的概念方法充分结合起来加深对候鸟迁徙时空格局和决策机制的理解，并为迁徙鸟类保护和湿地管理、鸟类携带流行病的监控以及应对全球环境变化影响提供决策依据，具有重要的科学和应用价值。